基于粒子滤波的微弱雷达目标检测方法
|
摘要
机动微弱目标检测是雷达信号处理领域面临的严峻挑战之一。当目标回波信噪比过低，基于单帧数据的相干或非相干累积方法无法保证可靠检测时，可采用检测前跟踪技术。检测前跟踪技术是一种长时间信号累积方法，通过联合处理多帧观测数据同时实现目标检测和跟踪。但早期的基于动态规划、Hough变换以及最大似然估计的检测前跟踪算法仅适合处理近似直线运动的目标。粒子滤波器(算法)解决统计特性已知的非线性、非高斯问题具有现有算法无可比拟的优势，而代价参考粒子滤波器(算法)具有处理统计特性未知的非线性问题的优势。上述粒子滤波器和代价参考粒子滤波器可有效实现雷达机动微弱目标长时间累积检测和跟踪。因此，研究和设计基于粒子滤波器和代价参考粒子滤波器的检测前跟踪算法对于检测和跟踪低信噪比机动目标具有重要的理论意义和应用价值。
本论文的主要工作是研究和设计检测前跟踪算法。提出的方法包括基于辅助粒子滤波器的似然比检验，基于代价参考粒子滤波器的广义似然比检验，基于代价参考粒子滤波器的存在概率检验，和基于前向-后向代价参考粒子滤波器的具有全变差惩罚的广义似然比检验。这些方法能够实现低信噪比条件下机动微弱目标的有效检测和跟踪。
本论文内容可概括为以下四个部分：
1.粒子滤波算法。通过介绍和分析粒子滤波算法和代价参考粒子滤波算法，提出了两种改进的代价参考粒子滤波算法。粒子滤波算法，如序贯重要性重采样(sequential importance resampling, SIR)和辅助粒子滤波算法(auxiliary particle filter,APF)等，利用大量带有权值的随机样本近似目标状态的后验概率密度函数，基于近似后验概率密度函数可实现多种准则下的目标状态估计。然而粒子率滤波算法要求动态系统的统计特性已知，实际情况往往无法满足。代价参考粒子滤波算法针对系统统计特性未知情况下的状态估计问题，采用用户自定义的风险和代价函数代替粒子滤波中的预测和更新后验概率密度函数实现重采样和更新过程。通过重新定义改进的代价和风险函数，提出了两种改良的代价参考粒子滤波器，提高了统计特性未知时的状态估计性能。
2.基于粒子滤波的似然比检测方法。通过分析基于SIR的似然比检测方法的不足，提出了基于APF的似然比检测方法和基于代价参考粒子滤波算法的广义似然比检测方法。采用APF重采样前的未归一化权值构造近似似然比，本文提出了基于APF的似然比检测方法。该方法性能优于基于SIR的似然比检测方法，可有效检测和跟踪统计特性已知时的机动微弱目标。然而，基于SIR和APF的似然比检测方法只适合处理统计特性已知的问题。对于统计特性未知的机动微弱目标检测问题，本文提出了基于代价参考粒子滤波的广义似然比检侧方法。该方法利用代价参考粒子滤波器输出的估计状态序列构造广义似然比，无需系统的统计信息。
3.基于粒子滤波的存在概率检测方法。针对统计特性未知时的机动微弱目标检测和跟踪问题，提出了基于代价参考粒子滤波器的存在概率检测方法。在长观测时间情况下，目标常常在观测期间进入和离开一个雷达分辨单元。因此，有时除了要求检测器给出一个分辨单元内是否存在目标外，还要求其报告目标进入和离开分辨单元的时刻。在动态系统的状态向量中引入一个模拟目标存在和消失的存在变量，可得到各个时刻的存在概率。各个时刻的存在概率决定目标是否出现在某一分辨单元内，也可确定目标进入和离开分辨单元的时刻。对于统计特性已知的动态系统，基于SIR的存在概率检验能够很好地实现机动微弱目标检测和跟踪。对于统计特性未知的动态系统，通过引入存在变量和相关系数提高存在概率的估计准确性，本文提出了基于代价参考粒子滤波的存在概率检验方法并用于统计特性未知的机动微弱目标检测和跟踪。另外，基于所有时刻的存在概率构建一个二元判决的检验统计量，通过该统计量可以给出一个分辨单元目标存在的二元判决。
4.基于前后-向代价参考粒子滤波的包含全变差惩罚的广义似然比检测。针对噪声背景下未知非线性调频信号检测问题，提出了一种基于前向-后向代价参考粒子滤波的包含全变差惩罚的广义似然比检测方法。上述方法将非线性调频信号建模为分段线性调频信号。每个线性调频片段的中心频率、调频率以及调频率的变化率形成当前时刻的状态向量。将信号和观测随时间的演化建模为统计特性未知的非线性动态系统。通过定义新的代价和风险函数，提出了前向-后向代价参考粒子滤波算法估计信号的状态序列和瞬时频率曲线。基于估计状态序列可构造广义似然比检测统计量，基于估计瞬时频率曲线可构造全变差检测统计量。广义似然比和全变差是检验非线性调频信号存在与否的重要特征。因此，将上述特征融合，提出了具有全变差惩罚的广义似然比检测器。与两种经典的检测方法相比，基于前向-后向代价参考粒子滤波的含有全变差惩罚的广义似然比检测方法明显改善了对未知非线性频率调制信号的检测能力。
Weak maneuvering target detection is one of the most important tasks in radar signalprocessing systems. Under the condition of very low signal to noise ratio (SNR), thecoherent or incoherent integrations based on the single frame data are difficult to givereliable detection results. The track-before-detect (TBD) technique is considered to bean effective approach to realize weak maneuvering target detection and tracking. TheTBD technique is a method to implement long time signal integration, which canprocess multiple frames of measurements together and simultaneously perform targetdetection and tracking. However, the early TBD technique is implemented by the Houghtransform, dynamic programming, and maximal likelihood estimation, which prohibit orpenalize deviations from straight line motion. Compared with the now availablemethods, particle filter (PF) has outstanding performance in dealing with thenonlinear/non-Gaussian problem when the statistical information is given. And thecost-reference particle filter (CRPF) performs well in dealing with the nonlinearproblem when the statistical information is unknown. The PF and CRPF are effectivemethods of detection and tracking of weak targets in long integration time. Therefore, itis important to investigate and design the TBD algorithms based upon PF and CRPF forweak maneuvering target detection and tracking.
The major works of this dissertation are to investigate and design TBD methods.The proposed methods include the likelihood ratio test based upon the auxiliary particlefilter, the generalized likelihood ratio test based upon the cost-reference particle filter,the existence probability test based on the cost-reference particle filter, and thegeneralized likelihood ratio test with total variation penalty using the forward-backwardcost-reference particle filter. These methods can implement effective detection andtracking of weak maneuvering targets in the case of low SNR.The content of the dissertation is organized as follows:
1. Review particle filtering algorithms. By introducing and analyzing the PF andCRPF, two modified versions of the CRPF are presented to improve the capability ofthe state estimation in the case of unknown statistics. In the PFs, such as the sequentialimportance resampling particle filter (SIR) and the auxiliary PF (APF), the posteriorprobability density functions of the states of a dynamic system are approximated by alarge number of weighted particles, based on which the states can be estimated bymeans of different rules. However, the PFs require known statistics of the dynamicsystem including the system noise and measurement noise, which is barely encountered in applications. The CRPF was developed for dynamic systems of unknown statistics,where the user-defined cost and risk functions are used to replace the predicted andupdated posterior probability densities in the PFs for the resampling and updateprocedures. By redefining the improved cost and risk functions, two modified CRPFsare proposed for enhancing the capability of state estimation in dynamic systems ofunknown statistics.
2. Likelihood ratio detectors based upon particle filters. By analyzing thedefects of the likelihood ratio detector based on the SIR, the likelihood ratio detectorbased upon the APF and the generalized likelihood ratio detector based upon the CRPFare proposed. Using the unnormalized weights of the APF before resampling toapproximate the likelihood ratio of observation, the APF based likelihood ratio test isproposed, which has better capability in weak maneuvering target detection and trackingthan the SIR based likelihood ratio test. However, the SIR and APF based likelihoodratio test require known statistics of a dynamic system. A CRPF based generalizedlikelihood ratio test is proposed for weak maneuvering target detection and trackingunder the condition of unknown statistics, where the generalized likelihood ratio isconstructed from the estimated state sequence provided by the CRPF.
3. Existence probability test based upon particle filter. A CRPF based existenceprobability test is proposed for detection and tracking of weak maneuvering target whenthe dynamic system has unknown statistics. Under the condition of long oberservationtime, a target often enters and leaves a radar resolution cell during the observation. Thus,a detector is sometimes required to report the time instants for a target to enter and leaveexcept for its presence. By adding an existence variable into the state vector to modeltarget presence and absence, the existence probability at each time instant can beestimated, which can be used to measure the target presence or absence in a resolutioncell at each time instant. Moreover, the existence probabilities can show the timeinstants for target to enter and leave the resolution cell. For the dynamic systems ofknown statistics, the existence probability test based upon the SIR performs well inweak maneuvering target detection and tracking. For the dynamic systems of unknownstatistics, by introducing an existence variable and a correlative coefficient to improvethe estimation of the existence probabilities, the existence probability test based uponthe CRPF is proposed for weak target detection and tracking. Moreover, the existenceprobabilities at all the time instant are combined to form a binary test statistic to give abinary decision whether a target is present or absent in a resolution cell.
4. Generalized likelihood ratio detector with total variation penalty via the forward-backward CRPF. Aiming at the detection problem of unknown nonlinear FMsignals in noise background, a generalized likelihood ratio test method with the totalvariation penalty based upon the forward-backward CRPF is proposed. In the proposedmethod, a nonlinear FM signal is modeled into piecewise linear FM signals. The centralfrequency, chirp rate, and its change rate of each signal segment form the state vector ofthe signal at this time instant. The evolution of the signal and measurements aremodeled as a nonlinear dynamic system of unknown statistics. By defining new cost andrisk functions, a forward-backward CRPF is proposed to estimate the states and theinstantaneous frequency (IF) curve of a signal. The generalized likelihood ratio teststatistic based upon the estimated states and the total variation of the estimated IF curveare important features to decide whether a nonlinear FM signal exists or not. Therefore,the two features are fused to construct the GLRT detector with total variation penalty.Compared with the two state-of-the-art detectors, the proposed detector providessignificant improvement in detection of unknown nonlinear FM signals.
本论文的主要工作是研究和设计检测前跟踪算法。提出的方法包括基于辅助粒子滤波器的似然比检验，基于代价参考粒子滤波器的广义似然比检验，基于代价参考粒子滤波器的存在概率检验，和基于前向-后向代价参考粒子滤波器的具有全变差惩罚的广义似然比检验。这些方法能够实现低信噪比条件下机动微弱目标的有效检测和跟踪。
本论文内容可概括为以下四个部分：
1.粒子滤波算法。通过介绍和分析粒子滤波算法和代价参考粒子滤波算法，提出了两种改进的代价参考粒子滤波算法。粒子滤波算法，如序贯重要性重采样(sequential importance resampling, SIR)和辅助粒子滤波算法(auxiliary particle filter,APF)等，利用大量带有权值的随机样本近似目标状态的后验概率密度函数，基于近似后验概率密度函数可实现多种准则下的目标状态估计。然而粒子率滤波算法要求动态系统的统计特性已知，实际情况往往无法满足。代价参考粒子滤波算法针对系统统计特性未知情况下的状态估计问题，采用用户自定义的风险和代价函数代替粒子滤波中的预测和更新后验概率密度函数实现重采样和更新过程。通过重新定义改进的代价和风险函数，提出了两种改良的代价参考粒子滤波器，提高了统计特性未知时的状态估计性能。
2.基于粒子滤波的似然比检测方法。通过分析基于SIR的似然比检测方法的不足，提出了基于APF的似然比检测方法和基于代价参考粒子滤波算法的广义似然比检测方法。采用APF重采样前的未归一化权值构造近似似然比，本文提出了基于APF的似然比检测方法。该方法性能优于基于SIR的似然比检测方法，可有效检测和跟踪统计特性已知时的机动微弱目标。然而，基于SIR和APF的似然比检测方法只适合处理统计特性已知的问题。对于统计特性未知的机动微弱目标检测问题，本文提出了基于代价参考粒子滤波的广义似然比检侧方法。该方法利用代价参考粒子滤波器输出的估计状态序列构造广义似然比，无需系统的统计信息。
3.基于粒子滤波的存在概率检测方法。针对统计特性未知时的机动微弱目标检测和跟踪问题，提出了基于代价参考粒子滤波器的存在概率检测方法。在长观测时间情况下，目标常常在观测期间进入和离开一个雷达分辨单元。因此，有时除了要求检测器给出一个分辨单元内是否存在目标外，还要求其报告目标进入和离开分辨单元的时刻。在动态系统的状态向量中引入一个模拟目标存在和消失的存在变量，可得到各个时刻的存在概率。各个时刻的存在概率决定目标是否出现在某一分辨单元内，也可确定目标进入和离开分辨单元的时刻。对于统计特性已知的动态系统，基于SIR的存在概率检验能够很好地实现机动微弱目标检测和跟踪。对于统计特性未知的动态系统，通过引入存在变量和相关系数提高存在概率的估计准确性，本文提出了基于代价参考粒子滤波的存在概率检验方法并用于统计特性未知的机动微弱目标检测和跟踪。另外，基于所有时刻的存在概率构建一个二元判决的检验统计量，通过该统计量可以给出一个分辨单元目标存在的二元判决。
4.基于前后-向代价参考粒子滤波的包含全变差惩罚的广义似然比检测。针对噪声背景下未知非线性调频信号检测问题，提出了一种基于前向-后向代价参考粒子滤波的包含全变差惩罚的广义似然比检测方法。上述方法将非线性调频信号建模为分段线性调频信号。每个线性调频片段的中心频率、调频率以及调频率的变化率形成当前时刻的状态向量。将信号和观测随时间的演化建模为统计特性未知的非线性动态系统。通过定义新的代价和风险函数，提出了前向-后向代价参考粒子滤波算法估计信号的状态序列和瞬时频率曲线。基于估计状态序列可构造广义似然比检测统计量，基于估计瞬时频率曲线可构造全变差检测统计量。广义似然比和全变差是检验非线性调频信号存在与否的重要特征。因此，将上述特征融合，提出了具有全变差惩罚的广义似然比检测器。与两种经典的检测方法相比，基于前向-后向代价参考粒子滤波的含有全变差惩罚的广义似然比检测方法明显改善了对未知非线性频率调制信号的检测能力。
Weak maneuvering target detection is one of the most important tasks in radar signalprocessing systems. Under the condition of very low signal to noise ratio (SNR), thecoherent or incoherent integrations based on the single frame data are difficult to givereliable detection results. The track-before-detect (TBD) technique is considered to bean effective approach to realize weak maneuvering target detection and tracking. TheTBD technique is a method to implement long time signal integration, which canprocess multiple frames of measurements together and simultaneously perform targetdetection and tracking. However, the early TBD technique is implemented by the Houghtransform, dynamic programming, and maximal likelihood estimation, which prohibit orpenalize deviations from straight line motion. Compared with the now availablemethods, particle filter (PF) has outstanding performance in dealing with thenonlinear/non-Gaussian problem when the statistical information is given. And thecost-reference particle filter (CRPF) performs well in dealing with the nonlinearproblem when the statistical information is unknown. The PF and CRPF are effectivemethods of detection and tracking of weak targets in long integration time. Therefore, itis important to investigate and design the TBD algorithms based upon PF and CRPF forweak maneuvering target detection and tracking.
The major works of this dissertation are to investigate and design TBD methods.The proposed methods include the likelihood ratio test based upon the auxiliary particlefilter, the generalized likelihood ratio test based upon the cost-reference particle filter,the existence probability test based on the cost-reference particle filter, and thegeneralized likelihood ratio test with total variation penalty using the forward-backwardcost-reference particle filter. These methods can implement effective detection andtracking of weak maneuvering targets in the case of low SNR.The content of the dissertation is organized as follows:
1. Review particle filtering algorithms. By introducing and analyzing the PF andCRPF, two modified versions of the CRPF are presented to improve the capability ofthe state estimation in the case of unknown statistics. In the PFs, such as the sequentialimportance resampling particle filter (SIR) and the auxiliary PF (APF), the posteriorprobability density functions of the states of a dynamic system are approximated by alarge number of weighted particles, based on which the states can be estimated bymeans of different rules. However, the PFs require known statistics of the dynamicsystem including the system noise and measurement noise, which is barely encountered in applications. The CRPF was developed for dynamic systems of unknown statistics,where the user-defined cost and risk functions are used to replace the predicted andupdated posterior probability densities in the PFs for the resampling and updateprocedures. By redefining the improved cost and risk functions, two modified CRPFsare proposed for enhancing the capability of state estimation in dynamic systems ofunknown statistics.
2. Likelihood ratio detectors based upon particle filters. By analyzing thedefects of the likelihood ratio detector based on the SIR, the likelihood ratio detectorbased upon the APF and the generalized likelihood ratio detector based upon the CRPFare proposed. Using the unnormalized weights of the APF before resampling toapproximate the likelihood ratio of observation, the APF based likelihood ratio test isproposed, which has better capability in weak maneuvering target detection and trackingthan the SIR based likelihood ratio test. However, the SIR and APF based likelihoodratio test require known statistics of a dynamic system. A CRPF based generalizedlikelihood ratio test is proposed for weak maneuvering target detection and trackingunder the condition of unknown statistics, where the generalized likelihood ratio isconstructed from the estimated state sequence provided by the CRPF.
3. Existence probability test based upon particle filter. A CRPF based existenceprobability test is proposed for detection and tracking of weak maneuvering target whenthe dynamic system has unknown statistics. Under the condition of long oberservationtime, a target often enters and leaves a radar resolution cell during the observation. Thus,a detector is sometimes required to report the time instants for a target to enter and leaveexcept for its presence. By adding an existence variable into the state vector to modeltarget presence and absence, the existence probability at each time instant can beestimated, which can be used to measure the target presence or absence in a resolutioncell at each time instant. Moreover, the existence probabilities can show the timeinstants for target to enter and leave the resolution cell. For the dynamic systems ofknown statistics, the existence probability test based upon the SIR performs well inweak maneuvering target detection and tracking. For the dynamic systems of unknownstatistics, by introducing an existence variable and a correlative coefficient to improvethe estimation of the existence probabilities, the existence probability test based uponthe CRPF is proposed for weak target detection and tracking. Moreover, the existenceprobabilities at all the time instant are combined to form a binary test statistic to give abinary decision whether a target is present or absent in a resolution cell.
4. Generalized likelihood ratio detector with total variation penalty via the forward-backward CRPF. Aiming at the detection problem of unknown nonlinear FMsignals in noise background, a generalized likelihood ratio test method with the totalvariation penalty based upon the forward-backward CRPF is proposed. In the proposedmethod, a nonlinear FM signal is modeled into piecewise linear FM signals. The centralfrequency, chirp rate, and its change rate of each signal segment form the state vector ofthe signal at this time instant. The evolution of the signal and measurements aremodeled as a nonlinear dynamic system of unknown statistics. By defining new cost andrisk functions, a forward-backward CRPF is proposed to estimate the states and theinstantaneous frequency (IF) curve of a signal. The generalized likelihood ratio teststatistic based upon the estimated states and the total variation of the estimated IF curveare important features to decide whether a nonlinear FM signal exists or not. Therefore,the two features are fused to construct the GLRT detector with total variation penalty.Compared with the two state-of-the-art detectors, the proposed detector providessignificant improvement in detection of unknown nonlinear FM signals.
引文
[1]吴顺君，梅晓春.雷达信号处理和数据处理技术[M].北京：电子工业出版社,2008.
[2]吴兆平.雷达信号长时间处理算法研究[D].西安：西安电子科技大学，雷达信号处理重点实验室，2012.
[3] M. I Skolnik著，左群声等译.雷达系统导论（第三版）[M].北京：电子工业出版社,2010.
[4]丁鹭飞，耿富录.雷达原理（第三版）[M].西安：西安电子科技大学，2002.
[5]周文瑜，焦培南.超视距雷达技术[M].北京：电子工业出版社，2008.
[6] S. Blackman and R. Popoli. Design and Analysis of Modern Tracking System [M].Norwood, MA: Artech House,1999.
[7] L. A. Johnston and V. Krishnamurthy. Performance analysis of dynamicprogramming track before detect algorithm [J]. IEEE Trans. Aerosp. Electron. Syst.,2002,38(1):228-242.
[8] S. Buzzi, M. Lops, L. Venturino and M. Ferri. Track-before-detect procedures in amulti-target environment [J]. IEEE Trans. Aerosp. Electron. Syst.,2008,44(3):1135-1150.
[9] S. J. Davey, M. G. Rutten and B. Cheung. A comparison of detection performancefor several track-before-detect algorithms [C].11thInternational Conference onInformation Fusion, Cologone, German,2008.
[10]W. R. Wallace. The use of track-before-detect in pulse-Doppler radar [C]. IEERadar conference,2002, pp.315-319.
[11]S. M. Tonissen and R. J. Evans. Performance of dynamic programming techniquesfor track-before-detect [J]. IEEE Trans. Aerosp. Electron. Syst.,1996,32(4):1440-1451.
[12]J. Arnold, S. Shaw and H. Pasternack. Efficient target tracking using dynamicprogramming [J]. IEEE Trans. Aerosp. Electron. Syst,1993,29(1):44-56.
[13]强勇，焦李成，保铮.一种有效的用于雷达弱目标检测的算法[J].电子学报，2003,32(3):440-443.
[14]曲长文，黄勇，苏峰.基于动态规划的多目标检测前跟踪算法[J].电子学报，2006,34(12):2138-2141.
[15]李涛，吴嗣亮，曾海滨，侯舒娟.基于动态规划的雷达目标检测前跟踪新算法[J].电子学报,2008,36(9):1824-1828.
[16]W. Paul, Z. James and W. Ku. Analysis of multiframe target detection using pixelstatistics [J]. IEEE Trans. Aerosp. Electron. Syst.,1995,31(1):238-247.
[17]Y. Boers and J. N. Driessen. Multitarget particle filter track before detectapplication [J]. IEE Proc.-Radar Sonar Navig.,2004,151(6):351-357.
[18]S. Buzzi, M. Lops and L. Venturino. Track-before-detect procedures for earlydetection of moving target from airborne radars [J]. IEEE Trans. Aerosp. Electron.Syst.,2005,41(3):937-954.
[19]S. M. Tonissen, Y. Bar-Shalon. Maximum likelihood track-before-detect withfluctuating target amplitude [J]. IEEE Trans. Aerosp. Electron. Syst.,1998,34(3):796-809.
[20]G. A. Richards. Application of Hough transform as a track-before-detect method [R].IEE, London WCIRBL, UK,1996.
[21]B. D. Carlson, E. D. Evans and S. L. Wilson. Search radar detection and track withthe Hough transform Part I: System concept [J]. IEEE Trans. Aerosp. Electron. Syst.,1994,30(1):102-108.
[22]B. D. Carlson, E. D. Evans and S. L. Wilson. Search radar detection and track withthe Hough transform Part II: Detection statistics [J]. IEEE Trans. Aerosp. Electron.Syst.,1994,30(1):109-115.
[23]B. D. Carlson, E. D. Evans and S. L. Wilson. Search radar detection and track withthe Hough transform Part III: Detection performance with binary integration [J].IEEE Trans. Aerosp. Electron. Syst.,1994,30(1):116-125.
[24]D. A. Wynn, D. C. Cooper. Coherent track-before-detection processing in aship-based multifunction radar [C]. Radar Conference2002,206-209.
[25]J. Darid. Track-before-detect processing for an airborne type radar [C]. Record ofthe IEEE1990International Radar Conference, pp.422-427, Arlington, VA,1990.
[26]D. J. Salmond and H. Birch. A particle filter for track-before-detect [C]. Proceedingof the American Control Conference, Arlington,2001.
[27]Y. Boers and H. Driessen. A particle-filter-based detection scheme [J]. IEEE SignalProcess. Letter,2003,10(10):300-302.
[28]M. F. Fallon, S. Godsill. Acoustic source localization and tracking using trackbefore detect [J]. IEEE Trans. Audio, Speech and Language process.,2010,18(6):1228-1242.
[29]H.-T. Su, T.-P. Wu, H.-W. Liu and Z. Bao. Rao-blackwellised particle filter basedtrack-before-detect algorithm [J]. IET Signal Process.,2008,2(2):169-176.
[30]D. Orlando, G. Ricci and Y. Bar-Shalom. Track-before-detect algorithms for targetswith kinematic constraints [J]. IEEE Trans. Aerosp. Electron. Syst.,2011,47(3):1837-1840.
[31]M. G. Rutten, B. Ristic and N. J. Gordon. A comparison of particle filters forrecursive track-before-detect [C].8th International Conference on InformationFusion,2005, vol.1.
[32]J. Lu, P.-L. Shui and H.-T. Su. Track-before-detect method based on cost-referenceparticle filter in nonlinear dynamic systems with unknown statistics [J]. IET SignalProcess,2014,8(1):85-94.
[33]卢锦，苏洪涛，水鹏朗.辅助粒子滤波实现天波超视距雷达弱目标似然比检测[J].西安交通大学学报,2013,47(8):93-97.
[34]J. M. Headrick and M. I. Skolnic. Over-the-horizon radar in the HF band [J].Proceedings of the IEEE,1974,62(6):665-673.
[35]R. E. Kalman. A new approach to linear filtering and prediction problems [J]. Trans.ASME, Journal of Basic Engineering,1960,82:35-45.
[36]陆光华，彭学愚，张林让，毛用才.随机信号处理[M].西安：西安电子科技大学出版社,2002.
[37]Y. Bar-Shalon, X. R. Li and T. Kirubarajan. Estimation with applications to trackingand navigation [M]. New York: John Wiley&Sons,2001.
[38]B. Ristic, S. Arulampalam and N. Gordon. Beyond the Kalman filter: particle filtersfor tracking applications [M]. Boston·London: Artech House,2004.
[39]M. S. Arulampalam, S. Maskell, N. Gordon and T. Clapp. A tutorial on particlefilters for online nonlinear/non-Gaussian Bayesian tracking [J]. IEEE Trans. SignalProcess.,2002,50(2):174-188.
[40]B. D. O. Anderson and J. B. Moore. Optimal filtering [M]. Englewood Cliffs, NJ:Prentice-Hall,1979.
[41]H. W. Sorenson, D. L. Alspach. Recursive Bayesian estimation using Gaussiansums [J]. Automatica,1971,7:465-479.
[42]S. Julier, J. Uhlmann and H. F. Durrant-White. A new method for nonlineartransformation of means and covariances in filters and estimators [J]. IEEE Trans.Automatic Control,2000,45:477-482.
[43]S. Julier, J. Uhlmann.“Data fusion in nonlinear systems”, in Handbook ofmultisensory data fusion, Boca Raton, London, New York, Washington DC: CRCPress LLC,2001.
[44]W. A. Wan and R. Van der Merwe. The unscented Kalman filter for nonlinearestimation [C]. Proc. IEEE Symp. Adaptive Systems for Signal Proc., Lake Louise,Alberta, Canada,2000.
[45]H. W. Sorenson. On the development of practical nonlinear filters [J]. InformationSciences,1974,7:253-270.
[46]S. C. Kramer and H. W. Sorenson. Recursive Bayesian estimation using piece-wiseconstant approximations [J]. Automatica,1988,24(6):789-801.
[47]K. Kastella. Finite difference methods for nonlinear filtering and automatic targetrecognition, in Multigarget-multisensory tracking (Y. Bar-Shalom and W. D. Blair,eds.), vol. III, ch.5, Norwood, MA: Artech House,2000.
[48]L. D. Stone. A Bayesian approach to multiple-target tracking, in Handbook ofmultisensory data fusion (D. L. Hall and J. Linas, eds.), ch.10, Boca Raton, FL:CRPFC Press,2001.
[49]F. Martinerie and P. Forster. Data association and tracking using hidden Markovmodels and dynamic programming [C]. Proc. Conf. ICASSP92,1992.
[50]L. R. Rabiner. A tutorial on hidden Markov models and selected applications inspeech recognition [C]. Proc. Of the IEEE,1989,77:257-285.
[51]J. M. Hammersley, K. W. Morton. Poor man s Monte Carlo [J]. J. Royal StatisticalSociety,1954,16:23-28.
[52]J. E. Handschin, D. Q. Mayne. Monte Carlo techniques to estimate the conditionalexpectation in multi-state non-linear filtering [J]. Intern. Journal of Control,1969,9(5):547-559.
[53]H. Akashi, H. Kumamoto. Random sampling approach to state estimation inswitching environments [J]. Automatica,1977,13:429-434.
[54]B. P. Carlin, N. G. Polson and D. S. Stoffer. A Monte Carlo approach to non-normaland non-linear state space modeling [J]. J. Amer. Statistical Assoc.,1992,87:493-500.
[55]M. West. Modelling with mixtures [M]. Bayesian statistics4, Oxford UniversityPress,1992.
[56]P. Muller. Monte Carlo integration in general dynamic models [J]. Contemp. Math.,1991,115:145-163.
[57]N. J. Gordon, D. J. Salmond and A. F. M. Smith. Novel approach tononlinear/non-Gaussian Bayesian state estimation [J]. IEE Proc.-F,1993,140(2):107-113.
[58]A. H. Jaxwinski. Stochastic processes and filtering theory [M]. New York:Academic Press,1970.
[59]A. Doucet, J. de Freitas and N. J. Gordon, eds., Sequential Monte Carlo methods inpractice [M]. New York: Spring,2001.
[60]A. Doucet, S. Godsill and C. Andriea. On sequential Monte Carlo samplingmethods for Bayesian filtering [J]. Statistics and Computing,2000,10(3):197-208.
[61]G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear statespace models [J]. Journal of Computational and Graphical Statistics,1996,5(1):1-25.
[62]J. Carpenter, P. Clifford and P. Fearnhead. Improved particle filter for non-linearproblems [J]. IEE Proc.-F: Radar and Sonar Navigation,1999,146, pp.2-7.
[63]J. S. Liu and R. Chen. Sequential Monte Carlo methods for dynamic systems [J]. J.Amer. Statist. Assoc.,1994,89(425):278-288.
[64]R. van der Merwe, A. Doucet, N. de Freitas and E. Wan. The unscented particlefilter [R]. Tech. Rep. CUED/F-INFENG/TR380, Cambridge UniversityEngineering Department,2000.
[65]M. Pitt and N. Shephard. Filtering via simulation: Auxiliary particle filters [J].Journal of the American Statistical Association,1999,94(446):590-599.
[66]W. R. Gilks and C. Berzuini. Following a moving target–Monte Carlo inferencefor dynamic Baysian models [J]. Journal of the Royal Statistical Society, B,63, pp.127-146,2001.
[67]G. Storvik. Particle filters for state-space models with the presence of unknownstate parameters [J]. IEEE Trans. Signal Process.,2002,50(2):281-289.
[68]S. McGinnity and G. W. Irwin. Multiple model bootstrap filter for maneuveringtarget tracking [J]. IEEE Trans. Aerosp. Electron. Syst.,2000,36(3):1006-1012.
[69]P. M. Djuri, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo and J.Míguez. Particle filtering [J]. IEEE Signal Processing Magazine, Sept.2003,19-38.
[70]T. Ghirmai, M. F. Bugallo, J. Míguez and P. M. Djuri. A sequential Monte Carlomethod for adaptive blind timing estimation and data detection [J]. IEEE Trans.Signal Process.,2005,53(8):2855-2865.
[71]C. S. Maíz, E. M. Molanes-López, J. Míguez and P. M. Djuri. A particle filteringscheme for processing time series corrupted by outliers [J]. IEEE Trans. SignalProcess.,2012,60(9):4611-4627.
[72]S. Yang. Particle filtering based estimation of consistent motion and disparity withreduced search points [J]. IEEE Trans. Circuits and Systems for Video Technology,2012,22(1):91-104.
[73]J. MacCormick and A. Blake. A probabilistic exclusion principle for trackingmultiple objects [C]. Proc. Int. Conf. Computer Vision, pp.572-578,1999.
[74]P. D. Moral. Measure valued processes and interacting particle system: Applicationto nonlinear filtering problems [J]. Annals of Applied Probability,1998,8(2):438-495.
[75]K. Kanazawa, D. Koller and S. J. Russell. Stochastic simulation algorithms fordynamic probabilistic networks [C]. Proceedings of the Eleventh AnnualConference on Uncertainty inAI (UAI95),99.346-351,1995.
[76]A. Doucet. On sequential Monte Carlo methods for Bayesian filtering [R]. Dept.Eng., Univ. Cambridge, UK. Tech. Rep.,1998.
[77]N. Bergman. Recursive Bayesian estimation: Navigation and tracking applications
[D]. Ph. D. dissertation, Link ping Univ., Link ping, Sweden,1999.
[78]A. Doucet, N. Gordon and V. Krishnamurthy. Particle filters for state estimation ofjump Markov linear systems [J]. IEEE Trans. Signal Processing,2001,49:613-624.
[79]B. Rupley. Stochastic simulation [M]. New York: Wiley,1987.
[80]C. Musso, N. Oudjane, and F. LeGland. Improving regularized particle filters. InSequential Monte Carlo Methods in Practice (A. Doucet, N. De Freitas, and N. J.Gordon, eds.), New York: Springer,2001.
[81]J. Míguez, M. F. Bugallo, and P. M. Djuri. A new class of particle filters forrandom dynamic systems with unknown statistics [J]. EURASIP J. Appl. SignalProcess.,2004,2004(15):2278-2294.
[82]J. Míguez. Analysis of selection methods for cost-reference particle filtering withapplications to maneuvering target tracking and dynamic optimization [J]. Digit.Signal Process.,2007,17(4):787-807.
[83]J. Míguez. Analysis of parallelizable resampling algorithm for particle filtering [J].Signal Process.,2007,87(12):3155-3174.
[84]R. N. Bracewell. The Fourier transform and its application,2nded [M]. New York:McGraw-Hill,1978.
[85]J. C. Wood and D. T. Barry. Linear signal synthesis using the Radon-Wingertransform [J]. IEEE Trans. Signal Process.,1994,42(8):2105-2111.
[86]J. C. Wood and D. T. Barry. Radon transformation of time-frequency distributionsfor analysis of multicomponent signals [J]. IEEE Trans. Signal Process.,1994,42(11):3166-3177.
[87]M.-S. Wang, A. K. Chan, and C. K. Chui. Linear frequency-modulated signaldetection using Radon-ambiguity transform [J]. IEEE Trans. Signal Process.,1998,46(3):571-586.
[88]S. Mann and S. Haykin. The chirplet transform: Physical considerations [J]. IEEETrans. Signal Process.,1995,43(11):2745-2761.
[89]A. Bultan. A four-parameter atomic decomposition of chirplets [J]. IEEE Trans.Signal Process.,1999,47(3):731-745.
[90]G. Wang, X.-G. Xia, B. T. Root, V. C. Chen. Y. Zhang, and M. Amin. Maneuveringtarget detection in over-the-horizon radar using adaptive clutter rejection andadaptive chirplet transform [C]. IEE Proc. Radar Sonar Navig.,2003,150(4):292-298.
[91]G. Warren and R. Balasubramanian. Time-frequency detection of gravitationalwaves [J]. Phys. Rev. D.,1999,60(10):102001.
[92]E. Chassande-Mottin and A Pai. Best chirplet chain: Near-optimal detection ofgravita-tional wave chirps [J]. Phys. Rev. D.,2007,73(4):042003.
[93]E. J. Candes, P. R. Charlton, and H. Helgason. Detecting highly oscillatory signalsby chirplet path pursuit [J]. Applied and Computational Harmonic Analysis,2008,24(1):14-40.
[94]C. Steger. An unbiased detector of curvilinear structures [J]. IEEE Trans. PatternAnal. Machine Intell.,1998,20(2):113-125.
[95]P.-L. Shui, Z. Bao, and H.-T. Su. Nonparametric detection of FM signals usingtime-frequency ridge energy [J]. IEEE Trans. Signal Process.,2008,56(5):1749-1760.
[96]Y. Barniv. Dynamic programming solution for detecting dim moving targets [J].IEEE Trans. Aero. Electron. Syst.,1985,21:144-156.
[97]Y. Barniv and O. Kella. Dynamic programming solution for detecting dim movingtargets Part II: Analysis [J]. IEEE Trans. Aero. Electron. Syst.,1987,23:776-788.
[98]S. M. Tonissen and R. J. Evans. Target tracking using dynamic programming:Algorithm and performance [C]. Proceedings of the34th IEEE Conference onDecision and Control,3:2741-2746, New Orleans, LA,1995.
[99]J. D. R. Kramer and W. S. Reid. Track-before-detect processing for an airborne typeradar [C]. IEEE International Radar Conference1990, pp.422-427.
[100] A. Moqiseh, and M. M. Nayebi.3-D Hough transform for surveillance radartarget detection [C]. IEEE Radar Conference2008, pp.1-5, Rome,2008.
[1] R. E. Kalman. A new approach to linear filtering and prediction problems [J]. Trans.ASME, Journal of Basic Engineering,1960,82(D):35-45.
[2] B. Ristic, S. Arulampalam and N. Gordon. Beyond the Kalman filter: particle filtersfor tracking applications [M]. Boston·London: Artech House, Chapter1, pp.1-17,2004.
[3]赵树杰,赵建勋.信号检测与估计理论[M].北京：清华大学出版社，2005:380-449.
[4]陆光华，彭学愚，张林让，毛用才。随机信号处理[M]。西安：西安电子科技大学出版社，2002:48-58.
[5] M. West and J. Harrison. Bayesian forecasting and dynamic models [M]. New York:Springer-Verlag,1997.
[6] N. Bergman. Recursive Bayesian estimation [M]. Sweden: Linus&Linea AB,1999.
[7] N. J. Gordon, D. J. Salmond and A. F. M. Smith. Novel approach tononlinear/non-Gaussian Bayesian state estimation [J]. IEE proceedings-F,1993,140(2):107-113.
[8] B. Ristic, S. A. Arulampalam and N. Gordon. A tutorial on particle filters for onlinenonlinear/non-Gaussian Bayesian tracking [J]. IEEE Trans. Signal Process.,2002,50(2):174-188.
[9] A. Doucet. On sequential simulation-based methods for Bayesian filtering [R].Technical report CUED/F-INFENG/TR.310,1998.
[10]V. E. Benes. Exact finite-dimensional filters with certain diffusion nonlinear drift[J]. Stochastics,1981,5:65-92.
[11]F. E. Daum. Exact finite dimensional nonlinear filters [J]. IEEE Trans. AutomaticControl,1986,31(7):616-622.
[12]F. E. Daum. Beyond Kalman filters: practical design of nonlinear filters [J]. Proc.SPIE,1995,2561:252-262.
[13]B. D. O. Anderson and J. B. Moore. Optimal Filtering [M]. Englewood Cliffs, NJ:Prentice-Hall,1979.
[14]Y. Bar-Shalon and X. R. Li and T. Kirubarajan. Estimation with applications totracking and navigation [M]. New York: John Wiley&Sons,2001.
[15]S. Julier, J. Uhlmann and H. F. Durrant-White. A new method for nonlineartransformation of means and covariances in filters and estimators [J]. IEEE Trans.Automat Contr,2000,45(3):477-482.
[16]S. Julier and J. Uhlmann. Data fusion in nonlinear systems [M]. Handbook ofmultisensor data fusion (D. L. Hall and J. Linas, eds.), ch.13, Bova Raton, FL:CRPFC,2001.
[17]E. A. Wan and R. van der Merwe. The unscented Kalman filter for nonlinearestimation [C]. Proc. Symp. Adaptive System Signal Processing, Communication,Control, Lake Louise, AB, Canada,2000.
[18]J. M. Hammersley and K. W. Morton. Poor man s Monte Carlo [J]. Journal of theRoyal Statistical Society B,1954,16:23-38.
[19]J. E. Handschin and D. Q. Mayne. Monte Carlo techniques to estimate theconditional expectation in multi-stage non-linear filtering [J]. Intern. Journal ofControl,1969,9(5):547-559.
[20]H. Akashi and H. Kumamoto. Random sampling approach to state estimation inswitching environments. Automatica,1977,13:429-434.
[21]A. Smith. Sequential Monte Carlo Methods in practice [M]. New York:Spring-Verlag,2001.
[22]P. M. Djuri, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo and J.Míguez. Particle filtering [J]. IEEE Signal Processing Magazine,2003,20(5):19-38.
[23]T. Ghirmai, M. F. Bugallo, J. Míguez and P. M. Djuri. A sequential Monte Carlomethod for adaptive blind timing estimation and data detection [J]. IEEE Trans.Signal Process.,2005,53(8):2855-2865.
[24]G. S. Maíz, E. M. Monlanes-López, J. Míguez and P. M. Djuri. A particle filteringscheme for processing time series corrupted by outliers [J]. IEEE Trans. SignalProcess.,2012,60(9):4611-4627.
[25]S. Yang. Particle filtering based estimation of consistent motion and disparity withreduced search points [J]. IEEE Trans. Circuits and Syst. Video Tech.,2011,22(1):91-104.
[26]P. M. Djuri, M. Khan and D. E. Johnston. Particle filtering of stochastic volatilitymodeled with leverage [J]. IEEE Journal of selected topics in signal processing,2012,6(4):327-336.
[27]周文瑜，焦培南等.超视距雷达技术[M].北京：电子工业出版社,2008.
[28]J. M. headrick and M. I. Skolnik. Over-the-horizon radar in the HF band [J].Proceedings of the IEEE,1974,62(6):665-673.
[29]J. Míguez, M. F. Bugallo and P. M. Djuri. A new class of particle filters forrandom dynamic systems with unknown statistics [J]. EURASIP J. Appl. SignalProcess.,2004,2004(15):2278-2294.
[30]J. Míguez. Analysis of selection methods for cost-reference particle filtering withapplications to maneuvering target tracking and dynamic optimization [J]. DigitalSignal Process.,2007,17(4):787-807.
[31]J. Míguez. Analysis of parallelizable resampling algorithm for particle filtering,Signal Process.,2007,87(12):3155-3174.
[32]Y. C. Ho and R. C. K. Lee. A Bayesian approach to problems in stochasticestimation and control [J]. IEEE Trans. Automat. Contr.,1964, AC-9:333–339.
[33]S. Julier. A skewed approach to filtering [J]. Proc. SPIE,1998,3373:271–282.
[34]E. A. Wan and R. Van der Merwe. The unscented Kalman filter for nonlinearestimation [C]. Proc. Symp. Adaptive Syst. Signal Process., Commun. Contr., LakeLouise, AB, Canada,2000.
[35]E. A. Wan and R. van der Merwe.“The unscented Kalman filter,” in KalmanFiltering and Neural Netwarks (S. Haykin). New York: John Wiley&Sons,2001.
[36]F. Martinerie and P. Forster. Data association and tracking using hidden Markovmodels and dynamic programming [C].1992IEEE Internaltion Conference onAcostics, Speech, and Signal Processig (ICASSP-92), San Francisco, CA,1992.
[37]L. R. Rabiner. A tutorial on hidden Markov models and selected applications inspeech recognition [J]. Proc. IEEE,1989,77(2):257–285.
[38]L. R. Rabiner and B. H. Juang. An introduction to hidden Markov models [J]. IEEEAcoust., Speech, Signal Processing Mag.,1986,3(1):4–16.
[39]R. L. Streit and R. F. Barrett. Frequency line tracking using hidden Markov models[J]. IEEE Trans. Acoust., Speech, Signal Process.,1990,38(4):586–598.
[40]A. Kong, J. S. Liu and W. H. Wong. Sequential imputations and Bayesian missingdata problem [J]. Journal of the American Statistical Association,1994,89:278-288.
[41]A. Doucet, S. Godsill and C. Andrieu. On sequential Monte Carlo samplingmethods for Bayesian filtering [J]. Statistics and Computing,2000,10:197-208.
[42]A. Doucet, N. Gordon and V. Krishnamurthy. Particle filters for state estimation ofjump Markov linear systems. IEEE Trans. Signal Process.,2001,49(3):613-624.
[43]卢锦,苏洪涛,水鹏朗.代价参考粒子滤波实现天波雷达目标状态估计[J].西安电子科技大学学报,2013,40(5):20-25+59.
[44]H. W. Sorenson. On the development of practical nonlinear filters [J]. InformationSciences,1974,7:253-270.
[45]S. C. Kramer and H. W. Sorenson. Recursive Bayesian estimation using piece-wiseconstant approximations [J]. Automatica,1988,24(6):789-801.
[46]K. Kastella. Finite difference methods for nonlinear filtering and automatic targetrecognition, in Multigarget-multisensory tracking (Y. Bar-Shalom and W. D. Blair,eds.), vol. III, ch.5, Norwood, MA: Artech House,2000.
[47]L. D. Stone. A Bayesian approach to multiple-target tracking, in Handbook ofmultisensory data fusion (D. L. Hall and J. Linas, eds.), ch.10, Boca Raton, FL:CRPFC Press,2001.
[48]L. R. Rabiner. A tutorial on hidden Markov models and selected applications inspeech recognition [C]. Proc. Of the IEEE,1989,77(2):257-285.
[1]吴顺君，梅晓春.雷达信号处理和数据处理技术[M].北京：电子工业出版社，2008.
[2]赵树杰,赵建勋.信号检测与估计理论[M].北京：清华大学出版社，2005.
[3] S. M. Kay著,罗鹏菲等译.统计信号处理基础——估计与检测理论[M].北京：电子工业出版社，2011.
[4] M. I. Skolnik著，左群声等译.雷达系统导论（第三版）[M].北京：电子工业出版社，2010.
[5] H. T. Su, T. P. Wu, H. W. Liu and Z. Bao. Rao-Blackwellised particle filter basedtrack-before-detect algorithm [J]. IET signal processing,2008,2(2):169-176.
[6] Y. Barniv. Dynamic programming solution for detecting dim moving targets [J].IEEE Trans. Aeros. Electron. Syst.,1985,21(1):144-156.
[7] S. M. Tonissen, R. J. Evans. Performance of dynamic programming techniques fortrack-before-detect [J]. IEEE Trans. Aerosp. Electron. Syst.,1996,32(4):1440-1451.
[8] L. Johnston, V. Krishnamurthy. Performance analysis of a dynamic programmingtrack before detect algorithm [J]. IEEE Trans. Aerosp. Electron. Syst.,2002,38(1):228-242.
[9] J. Arnold, S. Shaw and H. Pasternack. Efficient target tracking using dynamicprogramming [J]. IEEE Trans. Aerosp. Electron. Syst.,1993,29(1):44-56.
[10]S. M. Tonissen, Y. Bar-Shalom. Maximum likelihood track-before-detect withfluctuating target amplitude [J]. IEEE Trans. Aerosp. Electron. Syst.,1998,43(3):796-809.
[11]P.-L. Shui, Z. Bao and H.-T. Su. Nonparametric detection of FM signals usingtime-frequency ridge energy [J]. IEEE Trans. Signal Process.,2008,56(5):1749-1760.
[12]G. Warren and R. Balasubramanian. Time-frequency detection of gravitationalwaves [J]. Phys. Rev. D.,1999,60(10):102001.
[13] J. C. Wood and D. T. Barry. Linear signal syntheses using the Radon-Wignertransform [J]. IEEE Trans. Signal Process.,1994,42(8):2105–2111.
[14]M.-S. Wang, A. K. Chan, and C. K. Chui. Linear frequency-modulated signaldetection using Radon-ambiguity transform [J]. IEEE Trans. Signal Process.,1998,46(3):571–586.
[15]S. Mann and S. Haykin. The chirplet transform: Physical considerations [J]. IEEETrans. Signal Process.,1995,43(11):2745–2761.
[16]B. D. Carlson. E. D. Evans and S. L. Wilson. Search radar detection and track withthe Hough transform, part I: System concept [J]. IEEE Trans. Aerosp. Electron.Syst.,1994,30:102-108.
[17]B. D. Carlson, E. D. Evans, S. L. Wilson. Search Radar Detection and Track withthe Hough Transform. Part II: Detection Statistics [J]. IEEE Trans Aerosp.Electron.Syst.,1994,30(1):109-115.
[18]B. D. Carlson, E. D. Evans, S. L. Wilson. Search Radar Detection and Track withthe Hough Transform. Part III: Detection Performance with Binary Integration [J].IEEE Trans Aerosp. Electron. Syst.,1994,30(1): l16-125.
[19]Y. Boers and H. Driessen. A particle-filter-based detection scheme [J]. IEEE SignalProcess. letter,2003,10(10):300-302.
[20]胡洪涛，胡敬忠，胡士强.基于辅助粒子滤波的红外小目标检测前跟踪算法[J].控制与决策,2005,20(11):1208-1211.
[21]卢锦，苏洪涛，水鹏朗.辅助粒子滤波实现天波超视距雷达弱目标似然比检测[J].西安交通大学学报,2013,47(8):90-95.
[22]丁露飞,耿富录.雷达原理[M].西安：西安电子科技大学，2006.
[23]T. Thayaparan, S. Kennedy. Detection of maneuvering air target in sea-clutter usingjoint time–frequency analysis techniques [J]. IEE Proc. Radar Sonar Navig.,2004,151(1):19–30.
[24]J. Míguez, M. F. Bugallo and P. M. Djuri. A new class of particle filters forrandom dynamic systems with unknown statistics [J]. EURASIP J. Appl. SignalProcess.,2004,2004(15):2278-2294.
[25]J. Míguez. Analysis of selection methods for cost-reference particle filtering withapplications to maneuvering target tracking and dynamic optimization [J]. DigitalSignal Process.,2007,17(4):787-807.
[26]J. Míguez. Analysis of parallelizable resampling algorithm for particle filtering,Signal Process.,2007,87(12):3155-3174.
[27]J. Lu, P.-L. Shui and H.-T. Su. Track-before-detect method based on cost-referenceparticle filter in non-linear dynamic systems with unknown statistics [J]. IET SignalProcess.,2014,8(1):85-94.
[28]M. Novey, T. Adali and A. Roy. A complex generalized Gaussiandistribution-characterization, generation, and estimation [J]. IEEE Trans. SignalProcess.,2010,58(3):1427-1433.
[29]J. M. Headrick, J. F. Thomason. Application of high-frequency radar [J]. Radio Sci.,1998,33(4):1045-1054.
[30]周文瑜,焦培南.超视距雷达技术[M].北京:电子工业出版社,2008.
[31]L. R. Wyatt. Progress in the interpretation of HF sea echo: HF radar as a remotesensing tool [J]. IEE Proc., Radar Sonar Navig.,1990,137(2):139-147.
[32]S. J. Anderson. Adaptive remote sensing with HF skywave radar [J]. IEE Proc. F,Radar Signal Process.,1992,139(2):182-192.
[33]M. Six, J. Parent, A. Bourdillon and J. Delloue. A new multi-beam receivingequipment for the valensole skywave HF radar: description and applications [J].IEEE Trans. Geosci. Remote Sens.,1996,34(3):708-719.
[34]G. A. Fabrizio, A. B. Gershmanand M. D. Turley. Robust adaptive beamformingfor HF surface wave over-the-horizon radar [J]. IEEE Trans. Aerosp. Electron. Syst.,2004,40(2):510-525.
[35]G. Wang, X.-G. Xia, B. T. Root, etc. Maneuvering target detection inover-the-horizon radar by using adaptive chirplet transform with subspace clutterrejection [J]. IEE Proc., Radar Sonar Navig.,2003,150(4):292-198.
[36]Y. Zhang, M. G. Amin and G. J. Frazer. High-resolution time-frequencydistribution for maneuvering target detection in over-the-horizon radars [J]. IEEProc., Radar Sonar Navig.,2003,150(4):299-304.
[37]张卓奎，陈慧婵.随机过程[M].西安:西安电子科技大学出版社，2003.
[38]B. Ristic, S. Arulampalam and N. Gordon. Beyond the Kalman filter: particle filtersfor tracking applications [M]. Boston·London: Artech House, chpter11, pp.239,2004.
[1] D. Musicki, R. Evans, and S. Stankovic. Integrated probabilistic data association [J].IEEE Trans. Automatic Control,1994,39(6):1237-1241.
[2] S. B. Colegrove, A. W. Davis, and J. K. Ayliffe. Track initiation and nearestneighbors incorporated into probabilistic data association [J]. Journal of electricaland electronics engineers,1986,6:191-198.
[3] Y. Bar-Shalom, K. C. Chang and H. A. P. Blom. Automatic track formation inclutter with a recursive algorithm. Multi-target-Multi-sensor tracking [M].Norwood, MA: Artech House,1992.
[4] C. Musso, N. Oudjane, and F. LeGland. Improving regularized particle filters [M].Sequential Monte Carlo Methods in Practice (A. Doucet, N. de Freitas, and N. J.Gordon, eds.), New York: Spring,2001.
[5] S. McGinnity and G. E. Irwin. Multiple model bootstrap filter for maneuveringtarget tracking [J]. IEEE Trans. Aerosp. Electron. Syst.,2000,36(3):1006-1012
[6] D. S. Angelova, T. A. Semerdjiev, V. P. Jilkov, and E. A. Semerdjiev. Application ofMonte Carlo method for tracking maneuvering target in clutter [J]. Mathematicsand Computers in Simulation,2001,55:15-23.
[7] Y. Boers and H. Driessen. Hybrid state estimation: a target tracking application [J].Automatica,2002,38:2153-2158.
[8] C. Hue, J. L. Cadre, and P. Pérez. Sequential Monte Carlo methods for multipletarget tracking and data fusion [J]. IEEE Trans. Signal Process.,2002,50:309-325.
[9] B. Ristic, S. A.rulampalam, N. Gordon. Beyond the Kalman filter: particle filtersfor tracking application [M]. Boston London: Artech House,2004.
[10]D. J. Salmond and H. Birch. A particle filter for track-before-detect[C]. Proceedingsof American Control Conference, Arlington, VA,2001.
[11]H.-T. Su, T.-P. Wu, H.-W. Liu and Z. Bao. Rao-Blackwellised particle filter basedtrack-before-detect algorithm [J]. IET Signal Process.,2008,2(2):169-176,2008.
[12]张卓奎，陈慧婵.随机过程[M].西安:西安电子科技大学,2003.
[13]施仁杰.马尔科夫链基础及其应用[M].西安：西安电子科技大学,1992.
[14]王梓坤.生灭过程和马尔科夫链[M].北京:科学出版社,1980.
[15]S. M. Tonissen, Y. Bar-Shalom. Maximum likelihood track-before-detect withfluctuating target amplitude [J]. IEEE Trans. Aeros. Electron. Syst.,1998,34(3):796-809.
[16]吴兆平.雷达信号长时间处理算法研究[D].西安：西安电子科技大学，2012.
[17]M. Rollason and D. Salmond. A particle filter for track-before-detect of a targetwith unknown amplitude [C]. IEE Int Seminar Target Tracking: Algorithms andApplications, Enscheda, Netherlands,2001
[18]D. J. allantyne, H, Y. Chan and M. A. Kouritzin. Novel branching particle methodfor tracking [C]. Proc. SPIE, Signal and Data Processing of Small targets,vol.4048,2000.
[19]J. Liang, X.-Y. Peng, and Y.-T. Ma. Particle estimation algorithm using correlationof observation for nonlinear system state [J]. Electronics Letters,2008,44(8):553-554.
[20]J. Liang, and X.-Y. Peng. Improved particle filter for nonlinear system state [J].Electronics Letters,2008,44(21):1275-1277.
[21]M. Novey, T. Adali, and A. Roy. A complex generalized Gaussiandistribution-characterization, generation, and estimation [J]. IEEE Trans. SignalProcess.,2010,58(3):1427-1433.
[22]P.-L. Shui, Z. Bao, and H.-T. Su. Nonparametric detection of FM signals using
time-frequency ridge energy [J]. IEEE Trans. Signal Process.,2008,56(5):
1479-1460.
[1] P.-L. Shui, Z. Bao, and H.-T. Su. Nonparametric detection of FM signals usingtime-frequency ridge energy [J]. IEEE Trans. Signal Process.,2008,56(5):1749-1760.
[2] S. M. Kay. Fundamentals of statistical signal processing, Volume II: detectiontheory [M]. Englewood Cliffs, NJ: Prentice-Hall,1998.
[3] M-S Wang A. K. Chan, and C. K. Chui. Linear frequency-modulated signaldetection using Radon-ambiguity transform [J]. IEEE Trans. Signal Process.,1998,46(3):571-586.
[4] G. Wang, X.-G. Xia, B. T. Root, V. C. Chen, Y. Zhang and M. Amin. Manoeuvringtarget detection in over-the-horizon radar by using adaptive chirplet transformwith subspace clutter rejection [J]. IEE Proc., Radar Sonar Navig.,2004,40(2):510-525.
[5] Y. Zhang, M. Amin and G. Frazer. High-resolution time-frequency distribution formanoeuvring target detection in over-the-horizon radars [J]. IEE Proc., RadarSonar Navig.,2003,150(4):299-304.
[6] A. Yasotharan and T. Thayaparan. Time-frequency method for detecting anaccelerating target in sea clutter [J]. IEEE Trans. Aerosp. Electron. Syst.,2006,42(4):1289-1310.
[7] R. A. Carmaoa, W. L. Huang, and B. Torresani. Multiridge detection andtime-frequency reconstruction [J]. IEEE Trans. Signal Process.,1999,47(2):480-492.
[8] M. Morvidone, B. Torresani. Time scale approach for chirp detection [C]. Int. J.Wavelets Multireslut. Inf. Process.,2003,1(1):19-49,2003.
[9] P-L Shui, H-Y Shang, and Y-B Zhao. Instantaneous frequency estimation based ondirectionally smoothed pseudo-Wigner-Ville distribution bank [J]. IET Radar,Sonar and Navigation,2007,1(4):317-325.
[10]E. Chassande-Mottin and A. Pai.Best chirplet chain: Near-optimal detection ofgravita-tional wave chirps [J]. Phys. Rev. D.,2007,73(4):042003.
[11]E. J. Candes, P. R. Charlton, and H. Helgason. Detecting highly oscillatory signalsby chirplet path pursuit [C]. Applied and Computational Harmonic Analysis,2008,24(1):14-40.
[12]M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particlefilters for nonlinear/non-Gaussian Bayesian tracking [J]. IEEE Trans. SignalProcess.,2002,50(2):174-188.
[13]B. Ristic, M. Arulampalam and N. Gordon. Beyond the Kalman filter: particlefilters for tracking application. Boston·London: Artech House,2004.
[14]M. Pitt and N. Shephard. Filtering via simulation: auxiliary particle filters [J]. J.Amer. Statist. Assoc.,1999,94(446):590-599.
[15]G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear statespace models [J]. J. Comput. Graph. Statist.,1996,5(1):1-25.
[16]S. M. Tonissen and Y. Bar-Shalom. Maximum likelihood track-before-detect withfluctuating target amplitude [J]. IEEE Trans. Aerosp. Electron. Syst.,1998,34(3):796-809.
[17]Y. Boers, H.Driessen. A particle-filter-based detection scheme [J]. IEEE SignalProcess. Lett.,2003,10(10):300-302.
[18]H. T. Su, P. L. Shui, H. W. Liu, and Z. Bao. Rao-Blackwellised particle filterbased track-before-detect algorithm [J]. IET Signal Process.,2008,2(2):169-176.
[19]J. Míguez, M.Bugallo, and P.Djuri. A new class of particle filters for randomdynamic systems with unknown statistics [J]. EURASIP J. Appl. Signal Process.,2004,15(2004):2278-2294.
[20]J.Míguez. Analysis of selection methods for cost-reference particle filtering withapplications to manoeuvring target tracking and dynamic optimization [J]. DigitalSignal Process.,2007,17(4):787-807.
[21]J.Míguez. Analysis of parallelizable resampling algorithm for particle filtering [J].Signal Process.,2007,87(12):3155-3174.
[22]M. Boli, P. M. Djuri, and S. Hong. Resampling algorithms and architectures fordistributed particle filters [J]. IEEE Trans. Signal Process.,2005,53(7):2442-2450.
[23]Yang S. Particle filtering based estimation of consistent motion and disparity withreduced search points [J]. IEEE Trans. Circ. Syst. Video Tech.,2012,22(1):91-104.
[24]W. Rudin. Real and complex analysis [M]. McGraw-Hill series in HigherMathematics, New York: McGraw-Hill,1966.
[25]R. L. Burden and J. D. Faires. Numerical analysis [M]. Thomson Learning, Inc.,(Seventh edition),2001.
[26]J. P. Marques de Sa. Pattern recognition [M]. Springer-Verlag, Berlin Heidelberg,2001.
[2]吴兆平.雷达信号长时间处理算法研究[D].西安：西安电子科技大学，雷达信号处理重点实验室，2012.
[3] M. I Skolnik著，左群声等译.雷达系统导论（第三版）[M].北京：电子工业出版社,2010.
[4]丁鹭飞，耿富录.雷达原理（第三版）[M].西安：西安电子科技大学，2002.
[5]周文瑜，焦培南.超视距雷达技术[M].北京：电子工业出版社，2008.
[6] S. Blackman and R. Popoli. Design and Analysis of Modern Tracking System [M].Norwood, MA: Artech House,1999.
[7] L. A. Johnston and V. Krishnamurthy. Performance analysis of dynamicprogramming track before detect algorithm [J]. IEEE Trans. Aerosp. Electron. Syst.,2002,38(1):228-242.
[8] S. Buzzi, M. Lops, L. Venturino and M. Ferri. Track-before-detect procedures in amulti-target environment [J]. IEEE Trans. Aerosp. Electron. Syst.,2008,44(3):1135-1150.
[9] S. J. Davey, M. G. Rutten and B. Cheung. A comparison of detection performancefor several track-before-detect algorithms [C].11thInternational Conference onInformation Fusion, Cologone, German,2008.
[10]W. R. Wallace. The use of track-before-detect in pulse-Doppler radar [C]. IEERadar conference,2002, pp.315-319.
[11]S. M. Tonissen and R. J. Evans. Performance of dynamic programming techniquesfor track-before-detect [J]. IEEE Trans. Aerosp. Electron. Syst.,1996,32(4):1440-1451.
[12]J. Arnold, S. Shaw and H. Pasternack. Efficient target tracking using dynamicprogramming [J]. IEEE Trans. Aerosp. Electron. Syst,1993,29(1):44-56.
[13]强勇，焦李成，保铮.一种有效的用于雷达弱目标检测的算法[J].电子学报，2003,32(3):440-443.
[14]曲长文，黄勇，苏峰.基于动态规划的多目标检测前跟踪算法[J].电子学报，2006,34(12):2138-2141.
[15]李涛，吴嗣亮，曾海滨，侯舒娟.基于动态规划的雷达目标检测前跟踪新算法[J].电子学报,2008,36(9):1824-1828.
[16]W. Paul, Z. James and W. Ku. Analysis of multiframe target detection using pixelstatistics [J]. IEEE Trans. Aerosp. Electron. Syst.,1995,31(1):238-247.
[17]Y. Boers and J. N. Driessen. Multitarget particle filter track before detectapplication [J]. IEE Proc.-Radar Sonar Navig.,2004,151(6):351-357.
[18]S. Buzzi, M. Lops and L. Venturino. Track-before-detect procedures for earlydetection of moving target from airborne radars [J]. IEEE Trans. Aerosp. Electron.Syst.,2005,41(3):937-954.
[19]S. M. Tonissen, Y. Bar-Shalon. Maximum likelihood track-before-detect withfluctuating target amplitude [J]. IEEE Trans. Aerosp. Electron. Syst.,1998,34(3):796-809.
[20]G. A. Richards. Application of Hough transform as a track-before-detect method [R].IEE, London WCIRBL, UK,1996.
[21]B. D. Carlson, E. D. Evans and S. L. Wilson. Search radar detection and track withthe Hough transform Part I: System concept [J]. IEEE Trans. Aerosp. Electron. Syst.,1994,30(1):102-108.
[22]B. D. Carlson, E. D. Evans and S. L. Wilson. Search radar detection and track withthe Hough transform Part II: Detection statistics [J]. IEEE Trans. Aerosp. Electron.Syst.,1994,30(1):109-115.
[23]B. D. Carlson, E. D. Evans and S. L. Wilson. Search radar detection and track withthe Hough transform Part III: Detection performance with binary integration [J].IEEE Trans. Aerosp. Electron. Syst.,1994,30(1):116-125.
[24]D. A. Wynn, D. C. Cooper. Coherent track-before-detection processing in aship-based multifunction radar [C]. Radar Conference2002,206-209.
[25]J. Darid. Track-before-detect processing for an airborne type radar [C]. Record ofthe IEEE1990International Radar Conference, pp.422-427, Arlington, VA,1990.
[26]D. J. Salmond and H. Birch. A particle filter for track-before-detect [C]. Proceedingof the American Control Conference, Arlington,2001.
[27]Y. Boers and H. Driessen. A particle-filter-based detection scheme [J]. IEEE SignalProcess. Letter,2003,10(10):300-302.
[28]M. F. Fallon, S. Godsill. Acoustic source localization and tracking using trackbefore detect [J]. IEEE Trans. Audio, Speech and Language process.,2010,18(6):1228-1242.
[29]H.-T. Su, T.-P. Wu, H.-W. Liu and Z. Bao. Rao-blackwellised particle filter basedtrack-before-detect algorithm [J]. IET Signal Process.,2008,2(2):169-176.
[30]D. Orlando, G. Ricci and Y. Bar-Shalom. Track-before-detect algorithms for targetswith kinematic constraints [J]. IEEE Trans. Aerosp. Electron. Syst.,2011,47(3):1837-1840.
[31]M. G. Rutten, B. Ristic and N. J. Gordon. A comparison of particle filters forrecursive track-before-detect [C].8th International Conference on InformationFusion,2005, vol.1.
[32]J. Lu, P.-L. Shui and H.-T. Su. Track-before-detect method based on cost-referenceparticle filter in nonlinear dynamic systems with unknown statistics [J]. IET SignalProcess,2014,8(1):85-94.
[33]卢锦，苏洪涛，水鹏朗.辅助粒子滤波实现天波超视距雷达弱目标似然比检测[J].西安交通大学学报,2013,47(8):93-97.
[34]J. M. Headrick and M. I. Skolnic. Over-the-horizon radar in the HF band [J].Proceedings of the IEEE,1974,62(6):665-673.
[35]R. E. Kalman. A new approach to linear filtering and prediction problems [J]. Trans.ASME, Journal of Basic Engineering,1960,82:35-45.
[36]陆光华，彭学愚，张林让，毛用才.随机信号处理[M].西安：西安电子科技大学出版社,2002.
[37]Y. Bar-Shalon, X. R. Li and T. Kirubarajan. Estimation with applications to trackingand navigation [M]. New York: John Wiley&Sons,2001.
[38]B. Ristic, S. Arulampalam and N. Gordon. Beyond the Kalman filter: particle filtersfor tracking applications [M]. Boston·London: Artech House,2004.
[39]M. S. Arulampalam, S. Maskell, N. Gordon and T. Clapp. A tutorial on particlefilters for online nonlinear/non-Gaussian Bayesian tracking [J]. IEEE Trans. SignalProcess.,2002,50(2):174-188.
[40]B. D. O. Anderson and J. B. Moore. Optimal filtering [M]. Englewood Cliffs, NJ:Prentice-Hall,1979.
[41]H. W. Sorenson, D. L. Alspach. Recursive Bayesian estimation using Gaussiansums [J]. Automatica,1971,7:465-479.
[42]S. Julier, J. Uhlmann and H. F. Durrant-White. A new method for nonlineartransformation of means and covariances in filters and estimators [J]. IEEE Trans.Automatic Control,2000,45:477-482.
[43]S. Julier, J. Uhlmann.“Data fusion in nonlinear systems”, in Handbook ofmultisensory data fusion, Boca Raton, London, New York, Washington DC: CRCPress LLC,2001.
[44]W. A. Wan and R. Van der Merwe. The unscented Kalman filter for nonlinearestimation [C]. Proc. IEEE Symp. Adaptive Systems for Signal Proc., Lake Louise,Alberta, Canada,2000.
[45]H. W. Sorenson. On the development of practical nonlinear filters [J]. InformationSciences,1974,7:253-270.
[46]S. C. Kramer and H. W. Sorenson. Recursive Bayesian estimation using piece-wiseconstant approximations [J]. Automatica,1988,24(6):789-801.
[47]K. Kastella. Finite difference methods for nonlinear filtering and automatic targetrecognition, in Multigarget-multisensory tracking (Y. Bar-Shalom and W. D. Blair,eds.), vol. III, ch.5, Norwood, MA: Artech House,2000.
[48]L. D. Stone. A Bayesian approach to multiple-target tracking, in Handbook ofmultisensory data fusion (D. L. Hall and J. Linas, eds.), ch.10, Boca Raton, FL:CRPFC Press,2001.
[49]F. Martinerie and P. Forster. Data association and tracking using hidden Markovmodels and dynamic programming [C]. Proc. Conf. ICASSP92,1992.
[50]L. R. Rabiner. A tutorial on hidden Markov models and selected applications inspeech recognition [C]. Proc. Of the IEEE,1989,77:257-285.
[51]J. M. Hammersley, K. W. Morton. Poor man s Monte Carlo [J]. J. Royal StatisticalSociety,1954,16:23-28.
[52]J. E. Handschin, D. Q. Mayne. Monte Carlo techniques to estimate the conditionalexpectation in multi-state non-linear filtering [J]. Intern. Journal of Control,1969,9(5):547-559.
[53]H. Akashi, H. Kumamoto. Random sampling approach to state estimation inswitching environments [J]. Automatica,1977,13:429-434.
[54]B. P. Carlin, N. G. Polson and D. S. Stoffer. A Monte Carlo approach to non-normaland non-linear state space modeling [J]. J. Amer. Statistical Assoc.,1992,87:493-500.
[55]M. West. Modelling with mixtures [M]. Bayesian statistics4, Oxford UniversityPress,1992.
[56]P. Muller. Monte Carlo integration in general dynamic models [J]. Contemp. Math.,1991,115:145-163.
[57]N. J. Gordon, D. J. Salmond and A. F. M. Smith. Novel approach tononlinear/non-Gaussian Bayesian state estimation [J]. IEE Proc.-F,1993,140(2):107-113.
[58]A. H. Jaxwinski. Stochastic processes and filtering theory [M]. New York:Academic Press,1970.
[59]A. Doucet, J. de Freitas and N. J. Gordon, eds., Sequential Monte Carlo methods inpractice [M]. New York: Spring,2001.
[60]A. Doucet, S. Godsill and C. Andriea. On sequential Monte Carlo samplingmethods for Bayesian filtering [J]. Statistics and Computing,2000,10(3):197-208.
[61]G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear statespace models [J]. Journal of Computational and Graphical Statistics,1996,5(1):1-25.
[62]J. Carpenter, P. Clifford and P. Fearnhead. Improved particle filter for non-linearproblems [J]. IEE Proc.-F: Radar and Sonar Navigation,1999,146, pp.2-7.
[63]J. S. Liu and R. Chen. Sequential Monte Carlo methods for dynamic systems [J]. J.Amer. Statist. Assoc.,1994,89(425):278-288.
[64]R. van der Merwe, A. Doucet, N. de Freitas and E. Wan. The unscented particlefilter [R]. Tech. Rep. CUED/F-INFENG/TR380, Cambridge UniversityEngineering Department,2000.
[65]M. Pitt and N. Shephard. Filtering via simulation: Auxiliary particle filters [J].Journal of the American Statistical Association,1999,94(446):590-599.
[66]W. R. Gilks and C. Berzuini. Following a moving target–Monte Carlo inferencefor dynamic Baysian models [J]. Journal of the Royal Statistical Society, B,63, pp.127-146,2001.
[67]G. Storvik. Particle filters for state-space models with the presence of unknownstate parameters [J]. IEEE Trans. Signal Process.,2002,50(2):281-289.
[68]S. McGinnity and G. W. Irwin. Multiple model bootstrap filter for maneuveringtarget tracking [J]. IEEE Trans. Aerosp. Electron. Syst.,2000,36(3):1006-1012.
[69]P. M. Djuri, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo and J.Míguez. Particle filtering [J]. IEEE Signal Processing Magazine, Sept.2003,19-38.
[70]T. Ghirmai, M. F. Bugallo, J. Míguez and P. M. Djuri. A sequential Monte Carlomethod for adaptive blind timing estimation and data detection [J]. IEEE Trans.Signal Process.,2005,53(8):2855-2865.
[71]C. S. Maíz, E. M. Molanes-López, J. Míguez and P. M. Djuri. A particle filteringscheme for processing time series corrupted by outliers [J]. IEEE Trans. SignalProcess.,2012,60(9):4611-4627.
[72]S. Yang. Particle filtering based estimation of consistent motion and disparity withreduced search points [J]. IEEE Trans. Circuits and Systems for Video Technology,2012,22(1):91-104.
[73]J. MacCormick and A. Blake. A probabilistic exclusion principle for trackingmultiple objects [C]. Proc. Int. Conf. Computer Vision, pp.572-578,1999.
[74]P. D. Moral. Measure valued processes and interacting particle system: Applicationto nonlinear filtering problems [J]. Annals of Applied Probability,1998,8(2):438-495.
[75]K. Kanazawa, D. Koller and S. J. Russell. Stochastic simulation algorithms fordynamic probabilistic networks [C]. Proceedings of the Eleventh AnnualConference on Uncertainty inAI (UAI95),99.346-351,1995.
[76]A. Doucet. On sequential Monte Carlo methods for Bayesian filtering [R]. Dept.Eng., Univ. Cambridge, UK. Tech. Rep.,1998.
[77]N. Bergman. Recursive Bayesian estimation: Navigation and tracking applications
[D]. Ph. D. dissertation, Link ping Univ., Link ping, Sweden,1999.
[78]A. Doucet, N. Gordon and V. Krishnamurthy. Particle filters for state estimation ofjump Markov linear systems [J]. IEEE Trans. Signal Processing,2001,49:613-624.
[79]B. Rupley. Stochastic simulation [M]. New York: Wiley,1987.
[80]C. Musso, N. Oudjane, and F. LeGland. Improving regularized particle filters. InSequential Monte Carlo Methods in Practice (A. Doucet, N. De Freitas, and N. J.Gordon, eds.), New York: Springer,2001.
[81]J. Míguez, M. F. Bugallo, and P. M. Djuri. A new class of particle filters forrandom dynamic systems with unknown statistics [J]. EURASIP J. Appl. SignalProcess.,2004,2004(15):2278-2294.
[82]J. Míguez. Analysis of selection methods for cost-reference particle filtering withapplications to maneuvering target tracking and dynamic optimization [J]. Digit.Signal Process.,2007,17(4):787-807.
[83]J. Míguez. Analysis of parallelizable resampling algorithm for particle filtering [J].Signal Process.,2007,87(12):3155-3174.
[84]R. N. Bracewell. The Fourier transform and its application,2nded [M]. New York:McGraw-Hill,1978.
[85]J. C. Wood and D. T. Barry. Linear signal synthesis using the Radon-Wingertransform [J]. IEEE Trans. Signal Process.,1994,42(8):2105-2111.
[86]J. C. Wood and D. T. Barry. Radon transformation of time-frequency distributionsfor analysis of multicomponent signals [J]. IEEE Trans. Signal Process.,1994,42(11):3166-3177.
[87]M.-S. Wang, A. K. Chan, and C. K. Chui. Linear frequency-modulated signaldetection using Radon-ambiguity transform [J]. IEEE Trans. Signal Process.,1998,46(3):571-586.
[88]S. Mann and S. Haykin. The chirplet transform: Physical considerations [J]. IEEETrans. Signal Process.,1995,43(11):2745-2761.
[89]A. Bultan. A four-parameter atomic decomposition of chirplets [J]. IEEE Trans.Signal Process.,1999,47(3):731-745.
[90]G. Wang, X.-G. Xia, B. T. Root, V. C. Chen. Y. Zhang, and M. Amin. Maneuveringtarget detection in over-the-horizon radar using adaptive clutter rejection andadaptive chirplet transform [C]. IEE Proc. Radar Sonar Navig.,2003,150(4):292-298.
[91]G. Warren and R. Balasubramanian. Time-frequency detection of gravitationalwaves [J]. Phys. Rev. D.,1999,60(10):102001.
[92]E. Chassande-Mottin and A Pai. Best chirplet chain: Near-optimal detection ofgravita-tional wave chirps [J]. Phys. Rev. D.,2007,73(4):042003.
[93]E. J. Candes, P. R. Charlton, and H. Helgason. Detecting highly oscillatory signalsby chirplet path pursuit [J]. Applied and Computational Harmonic Analysis,2008,24(1):14-40.
[94]C. Steger. An unbiased detector of curvilinear structures [J]. IEEE Trans. PatternAnal. Machine Intell.,1998,20(2):113-125.
[95]P.-L. Shui, Z. Bao, and H.-T. Su. Nonparametric detection of FM signals usingtime-frequency ridge energy [J]. IEEE Trans. Signal Process.,2008,56(5):1749-1760.
[96]Y. Barniv. Dynamic programming solution for detecting dim moving targets [J].IEEE Trans. Aero. Electron. Syst.,1985,21:144-156.
[97]Y. Barniv and O. Kella. Dynamic programming solution for detecting dim movingtargets Part II: Analysis [J]. IEEE Trans. Aero. Electron. Syst.,1987,23:776-788.
[98]S. M. Tonissen and R. J. Evans. Target tracking using dynamic programming:Algorithm and performance [C]. Proceedings of the34th IEEE Conference onDecision and Control,3:2741-2746, New Orleans, LA,1995.
[99]J. D. R. Kramer and W. S. Reid. Track-before-detect processing for an airborne typeradar [C]. IEEE International Radar Conference1990, pp.422-427.
[100] A. Moqiseh, and M. M. Nayebi.3-D Hough transform for surveillance radartarget detection [C]. IEEE Radar Conference2008, pp.1-5, Rome,2008.
[1] R. E. Kalman. A new approach to linear filtering and prediction problems [J]. Trans.ASME, Journal of Basic Engineering,1960,82(D):35-45.
[2] B. Ristic, S. Arulampalam and N. Gordon. Beyond the Kalman filter: particle filtersfor tracking applications [M]. Boston·London: Artech House, Chapter1, pp.1-17,2004.
[3]赵树杰,赵建勋.信号检测与估计理论[M].北京：清华大学出版社，2005:380-449.
[4]陆光华，彭学愚，张林让，毛用才。随机信号处理[M]。西安：西安电子科技大学出版社，2002:48-58.
[5] M. West and J. Harrison. Bayesian forecasting and dynamic models [M]. New York:Springer-Verlag,1997.
[6] N. Bergman. Recursive Bayesian estimation [M]. Sweden: Linus&Linea AB,1999.
[7] N. J. Gordon, D. J. Salmond and A. F. M. Smith. Novel approach tononlinear/non-Gaussian Bayesian state estimation [J]. IEE proceedings-F,1993,140(2):107-113.
[8] B. Ristic, S. A. Arulampalam and N. Gordon. A tutorial on particle filters for onlinenonlinear/non-Gaussian Bayesian tracking [J]. IEEE Trans. Signal Process.,2002,50(2):174-188.
[9] A. Doucet. On sequential simulation-based methods for Bayesian filtering [R].Technical report CUED/F-INFENG/TR.310,1998.
[10]V. E. Benes. Exact finite-dimensional filters with certain diffusion nonlinear drift[J]. Stochastics,1981,5:65-92.
[11]F. E. Daum. Exact finite dimensional nonlinear filters [J]. IEEE Trans. AutomaticControl,1986,31(7):616-622.
[12]F. E. Daum. Beyond Kalman filters: practical design of nonlinear filters [J]. Proc.SPIE,1995,2561:252-262.
[13]B. D. O. Anderson and J. B. Moore. Optimal Filtering [M]. Englewood Cliffs, NJ:Prentice-Hall,1979.
[14]Y. Bar-Shalon and X. R. Li and T. Kirubarajan. Estimation with applications totracking and navigation [M]. New York: John Wiley&Sons,2001.
[15]S. Julier, J. Uhlmann and H. F. Durrant-White. A new method for nonlineartransformation of means and covariances in filters and estimators [J]. IEEE Trans.Automat Contr,2000,45(3):477-482.
[16]S. Julier and J. Uhlmann. Data fusion in nonlinear systems [M]. Handbook ofmultisensor data fusion (D. L. Hall and J. Linas, eds.), ch.13, Bova Raton, FL:CRPFC,2001.
[17]E. A. Wan and R. van der Merwe. The unscented Kalman filter for nonlinearestimation [C]. Proc. Symp. Adaptive System Signal Processing, Communication,Control, Lake Louise, AB, Canada,2000.
[18]J. M. Hammersley and K. W. Morton. Poor man s Monte Carlo [J]. Journal of theRoyal Statistical Society B,1954,16:23-38.
[19]J. E. Handschin and D. Q. Mayne. Monte Carlo techniques to estimate theconditional expectation in multi-stage non-linear filtering [J]. Intern. Journal ofControl,1969,9(5):547-559.
[20]H. Akashi and H. Kumamoto. Random sampling approach to state estimation inswitching environments. Automatica,1977,13:429-434.
[21]A. Smith. Sequential Monte Carlo Methods in practice [M]. New York:Spring-Verlag,2001.
[22]P. M. Djuri, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo and J.Míguez. Particle filtering [J]. IEEE Signal Processing Magazine,2003,20(5):19-38.
[23]T. Ghirmai, M. F. Bugallo, J. Míguez and P. M. Djuri. A sequential Monte Carlomethod for adaptive blind timing estimation and data detection [J]. IEEE Trans.Signal Process.,2005,53(8):2855-2865.
[24]G. S. Maíz, E. M. Monlanes-López, J. Míguez and P. M. Djuri. A particle filteringscheme for processing time series corrupted by outliers [J]. IEEE Trans. SignalProcess.,2012,60(9):4611-4627.
[25]S. Yang. Particle filtering based estimation of consistent motion and disparity withreduced search points [J]. IEEE Trans. Circuits and Syst. Video Tech.,2011,22(1):91-104.
[26]P. M. Djuri, M. Khan and D. E. Johnston. Particle filtering of stochastic volatilitymodeled with leverage [J]. IEEE Journal of selected topics in signal processing,2012,6(4):327-336.
[27]周文瑜，焦培南等.超视距雷达技术[M].北京：电子工业出版社,2008.
[28]J. M. headrick and M. I. Skolnik. Over-the-horizon radar in the HF band [J].Proceedings of the IEEE,1974,62(6):665-673.
[29]J. Míguez, M. F. Bugallo and P. M. Djuri. A new class of particle filters forrandom dynamic systems with unknown statistics [J]. EURASIP J. Appl. SignalProcess.,2004,2004(15):2278-2294.
[30]J. Míguez. Analysis of selection methods for cost-reference particle filtering withapplications to maneuvering target tracking and dynamic optimization [J]. DigitalSignal Process.,2007,17(4):787-807.
[31]J. Míguez. Analysis of parallelizable resampling algorithm for particle filtering,Signal Process.,2007,87(12):3155-3174.
[32]Y. C. Ho and R. C. K. Lee. A Bayesian approach to problems in stochasticestimation and control [J]. IEEE Trans. Automat. Contr.,1964, AC-9:333–339.
[33]S. Julier. A skewed approach to filtering [J]. Proc. SPIE,1998,3373:271–282.
[34]E. A. Wan and R. Van der Merwe. The unscented Kalman filter for nonlinearestimation [C]. Proc. Symp. Adaptive Syst. Signal Process., Commun. Contr., LakeLouise, AB, Canada,2000.
[35]E. A. Wan and R. van der Merwe.“The unscented Kalman filter,” in KalmanFiltering and Neural Netwarks (S. Haykin). New York: John Wiley&Sons,2001.
[36]F. Martinerie and P. Forster. Data association and tracking using hidden Markovmodels and dynamic programming [C].1992IEEE Internaltion Conference onAcostics, Speech, and Signal Processig (ICASSP-92), San Francisco, CA,1992.
[37]L. R. Rabiner. A tutorial on hidden Markov models and selected applications inspeech recognition [J]. Proc. IEEE,1989,77(2):257–285.
[38]L. R. Rabiner and B. H. Juang. An introduction to hidden Markov models [J]. IEEEAcoust., Speech, Signal Processing Mag.,1986,3(1):4–16.
[39]R. L. Streit and R. F. Barrett. Frequency line tracking using hidden Markov models[J]. IEEE Trans. Acoust., Speech, Signal Process.,1990,38(4):586–598.
[40]A. Kong, J. S. Liu and W. H. Wong. Sequential imputations and Bayesian missingdata problem [J]. Journal of the American Statistical Association,1994,89:278-288.
[41]A. Doucet, S. Godsill and C. Andrieu. On sequential Monte Carlo samplingmethods for Bayesian filtering [J]. Statistics and Computing,2000,10:197-208.
[42]A. Doucet, N. Gordon and V. Krishnamurthy. Particle filters for state estimation ofjump Markov linear systems. IEEE Trans. Signal Process.,2001,49(3):613-624.
[43]卢锦,苏洪涛,水鹏朗.代价参考粒子滤波实现天波雷达目标状态估计[J].西安电子科技大学学报,2013,40(5):20-25+59.
[44]H. W. Sorenson. On the development of practical nonlinear filters [J]. InformationSciences,1974,7:253-270.
[45]S. C. Kramer and H. W. Sorenson. Recursive Bayesian estimation using piece-wiseconstant approximations [J]. Automatica,1988,24(6):789-801.
[46]K. Kastella. Finite difference methods for nonlinear filtering and automatic targetrecognition, in Multigarget-multisensory tracking (Y. Bar-Shalom and W. D. Blair,eds.), vol. III, ch.5, Norwood, MA: Artech House,2000.
[47]L. D. Stone. A Bayesian approach to multiple-target tracking, in Handbook ofmultisensory data fusion (D. L. Hall and J. Linas, eds.), ch.10, Boca Raton, FL:CRPFC Press,2001.
[48]L. R. Rabiner. A tutorial on hidden Markov models and selected applications inspeech recognition [C]. Proc. Of the IEEE,1989,77(2):257-285.
[1]吴顺君，梅晓春.雷达信号处理和数据处理技术[M].北京：电子工业出版社，2008.
[2]赵树杰,赵建勋.信号检测与估计理论[M].北京：清华大学出版社，2005.
[3] S. M. Kay著,罗鹏菲等译.统计信号处理基础——估计与检测理论[M].北京：电子工业出版社，2011.
[4] M. I. Skolnik著，左群声等译.雷达系统导论（第三版）[M].北京：电子工业出版社，2010.
[5] H. T. Su, T. P. Wu, H. W. Liu and Z. Bao. Rao-Blackwellised particle filter basedtrack-before-detect algorithm [J]. IET signal processing,2008,2(2):169-176.
[6] Y. Barniv. Dynamic programming solution for detecting dim moving targets [J].IEEE Trans. Aeros. Electron. Syst.,1985,21(1):144-156.
[7] S. M. Tonissen, R. J. Evans. Performance of dynamic programming techniques fortrack-before-detect [J]. IEEE Trans. Aerosp. Electron. Syst.,1996,32(4):1440-1451.
[8] L. Johnston, V. Krishnamurthy. Performance analysis of a dynamic programmingtrack before detect algorithm [J]. IEEE Trans. Aerosp. Electron. Syst.,2002,38(1):228-242.
[9] J. Arnold, S. Shaw and H. Pasternack. Efficient target tracking using dynamicprogramming [J]. IEEE Trans. Aerosp. Electron. Syst.,1993,29(1):44-56.
[10]S. M. Tonissen, Y. Bar-Shalom. Maximum likelihood track-before-detect withfluctuating target amplitude [J]. IEEE Trans. Aerosp. Electron. Syst.,1998,43(3):796-809.
[11]P.-L. Shui, Z. Bao and H.-T. Su. Nonparametric detection of FM signals usingtime-frequency ridge energy [J]. IEEE Trans. Signal Process.,2008,56(5):1749-1760.
[12]G. Warren and R. Balasubramanian. Time-frequency detection of gravitationalwaves [J]. Phys. Rev. D.,1999,60(10):102001.
[13] J. C. Wood and D. T. Barry. Linear signal syntheses using the Radon-Wignertransform [J]. IEEE Trans. Signal Process.,1994,42(8):2105–2111.
[14]M.-S. Wang, A. K. Chan, and C. K. Chui. Linear frequency-modulated signaldetection using Radon-ambiguity transform [J]. IEEE Trans. Signal Process.,1998,46(3):571–586.
[15]S. Mann and S. Haykin. The chirplet transform: Physical considerations [J]. IEEETrans. Signal Process.,1995,43(11):2745–2761.
[16]B. D. Carlson. E. D. Evans and S. L. Wilson. Search radar detection and track withthe Hough transform, part I: System concept [J]. IEEE Trans. Aerosp. Electron.Syst.,1994,30:102-108.
[17]B. D. Carlson, E. D. Evans, S. L. Wilson. Search Radar Detection and Track withthe Hough Transform. Part II: Detection Statistics [J]. IEEE Trans Aerosp.Electron.Syst.,1994,30(1):109-115.
[18]B. D. Carlson, E. D. Evans, S. L. Wilson. Search Radar Detection and Track withthe Hough Transform. Part III: Detection Performance with Binary Integration [J].IEEE Trans Aerosp. Electron. Syst.,1994,30(1): l16-125.
[19]Y. Boers and H. Driessen. A particle-filter-based detection scheme [J]. IEEE SignalProcess. letter,2003,10(10):300-302.
[20]胡洪涛，胡敬忠，胡士强.基于辅助粒子滤波的红外小目标检测前跟踪算法[J].控制与决策,2005,20(11):1208-1211.
[21]卢锦，苏洪涛，水鹏朗.辅助粒子滤波实现天波超视距雷达弱目标似然比检测[J].西安交通大学学报,2013,47(8):90-95.
[22]丁露飞,耿富录.雷达原理[M].西安：西安电子科技大学，2006.
[23]T. Thayaparan, S. Kennedy. Detection of maneuvering air target in sea-clutter usingjoint time–frequency analysis techniques [J]. IEE Proc. Radar Sonar Navig.,2004,151(1):19–30.
[24]J. Míguez, M. F. Bugallo and P. M. Djuri. A new class of particle filters forrandom dynamic systems with unknown statistics [J]. EURASIP J. Appl. SignalProcess.,2004,2004(15):2278-2294.
[25]J. Míguez. Analysis of selection methods for cost-reference particle filtering withapplications to maneuvering target tracking and dynamic optimization [J]. DigitalSignal Process.,2007,17(4):787-807.
[26]J. Míguez. Analysis of parallelizable resampling algorithm for particle filtering,Signal Process.,2007,87(12):3155-3174.
[27]J. Lu, P.-L. Shui and H.-T. Su. Track-before-detect method based on cost-referenceparticle filter in non-linear dynamic systems with unknown statistics [J]. IET SignalProcess.,2014,8(1):85-94.
[28]M. Novey, T. Adali and A. Roy. A complex generalized Gaussiandistribution-characterization, generation, and estimation [J]. IEEE Trans. SignalProcess.,2010,58(3):1427-1433.
[29]J. M. Headrick, J. F. Thomason. Application of high-frequency radar [J]. Radio Sci.,1998,33(4):1045-1054.
[30]周文瑜,焦培南.超视距雷达技术[M].北京:电子工业出版社,2008.
[31]L. R. Wyatt. Progress in the interpretation of HF sea echo: HF radar as a remotesensing tool [J]. IEE Proc., Radar Sonar Navig.,1990,137(2):139-147.
[32]S. J. Anderson. Adaptive remote sensing with HF skywave radar [J]. IEE Proc. F,Radar Signal Process.,1992,139(2):182-192.
[33]M. Six, J. Parent, A. Bourdillon and J. Delloue. A new multi-beam receivingequipment for the valensole skywave HF radar: description and applications [J].IEEE Trans. Geosci. Remote Sens.,1996,34(3):708-719.
[34]G. A. Fabrizio, A. B. Gershmanand M. D. Turley. Robust adaptive beamformingfor HF surface wave over-the-horizon radar [J]. IEEE Trans. Aerosp. Electron. Syst.,2004,40(2):510-525.
[35]G. Wang, X.-G. Xia, B. T. Root, etc. Maneuvering target detection inover-the-horizon radar by using adaptive chirplet transform with subspace clutterrejection [J]. IEE Proc., Radar Sonar Navig.,2003,150(4):292-198.
[36]Y. Zhang, M. G. Amin and G. J. Frazer. High-resolution time-frequencydistribution for maneuvering target detection in over-the-horizon radars [J]. IEEProc., Radar Sonar Navig.,2003,150(4):299-304.
[37]张卓奎，陈慧婵.随机过程[M].西安:西安电子科技大学出版社，2003.
[38]B. Ristic, S. Arulampalam and N. Gordon. Beyond the Kalman filter: particle filtersfor tracking applications [M]. Boston·London: Artech House, chpter11, pp.239,2004.
[1] D. Musicki, R. Evans, and S. Stankovic. Integrated probabilistic data association [J].IEEE Trans. Automatic Control,1994,39(6):1237-1241.
[2] S. B. Colegrove, A. W. Davis, and J. K. Ayliffe. Track initiation and nearestneighbors incorporated into probabilistic data association [J]. Journal of electricaland electronics engineers,1986,6:191-198.
[3] Y. Bar-Shalom, K. C. Chang and H. A. P. Blom. Automatic track formation inclutter with a recursive algorithm. Multi-target-Multi-sensor tracking [M].Norwood, MA: Artech House,1992.
[4] C. Musso, N. Oudjane, and F. LeGland. Improving regularized particle filters [M].Sequential Monte Carlo Methods in Practice (A. Doucet, N. de Freitas, and N. J.Gordon, eds.), New York: Spring,2001.
[5] S. McGinnity and G. E. Irwin. Multiple model bootstrap filter for maneuveringtarget tracking [J]. IEEE Trans. Aerosp. Electron. Syst.,2000,36(3):1006-1012
[6] D. S. Angelova, T. A. Semerdjiev, V. P. Jilkov, and E. A. Semerdjiev. Application ofMonte Carlo method for tracking maneuvering target in clutter [J]. Mathematicsand Computers in Simulation,2001,55:15-23.
[7] Y. Boers and H. Driessen. Hybrid state estimation: a target tracking application [J].Automatica,2002,38:2153-2158.
[8] C. Hue, J. L. Cadre, and P. Pérez. Sequential Monte Carlo methods for multipletarget tracking and data fusion [J]. IEEE Trans. Signal Process.,2002,50:309-325.
[9] B. Ristic, S. A.rulampalam, N. Gordon. Beyond the Kalman filter: particle filtersfor tracking application [M]. Boston London: Artech House,2004.
[10]D. J. Salmond and H. Birch. A particle filter for track-before-detect[C]. Proceedingsof American Control Conference, Arlington, VA,2001.
[11]H.-T. Su, T.-P. Wu, H.-W. Liu and Z. Bao. Rao-Blackwellised particle filter basedtrack-before-detect algorithm [J]. IET Signal Process.,2008,2(2):169-176,2008.
[12]张卓奎，陈慧婵.随机过程[M].西安:西安电子科技大学,2003.
[13]施仁杰.马尔科夫链基础及其应用[M].西安：西安电子科技大学,1992.
[14]王梓坤.生灭过程和马尔科夫链[M].北京:科学出版社,1980.
[15]S. M. Tonissen, Y. Bar-Shalom. Maximum likelihood track-before-detect withfluctuating target amplitude [J]. IEEE Trans. Aeros. Electron. Syst.,1998,34(3):796-809.
[16]吴兆平.雷达信号长时间处理算法研究[D].西安：西安电子科技大学，2012.
[17]M. Rollason and D. Salmond. A particle filter for track-before-detect of a targetwith unknown amplitude [C]. IEE Int Seminar Target Tracking: Algorithms andApplications, Enscheda, Netherlands,2001
[18]D. J. allantyne, H, Y. Chan and M. A. Kouritzin. Novel branching particle methodfor tracking [C]. Proc. SPIE, Signal and Data Processing of Small targets,vol.4048,2000.
[19]J. Liang, X.-Y. Peng, and Y.-T. Ma. Particle estimation algorithm using correlationof observation for nonlinear system state [J]. Electronics Letters,2008,44(8):553-554.
[20]J. Liang, and X.-Y. Peng. Improved particle filter for nonlinear system state [J].Electronics Letters,2008,44(21):1275-1277.
[21]M. Novey, T. Adali, and A. Roy. A complex generalized Gaussiandistribution-characterization, generation, and estimation [J]. IEEE Trans. SignalProcess.,2010,58(3):1427-1433.
[22]P.-L. Shui, Z. Bao, and H.-T. Su. Nonparametric detection of FM signals using
time-frequency ridge energy [J]. IEEE Trans. Signal Process.,2008,56(5):
1479-1460.
[1] P.-L. Shui, Z. Bao, and H.-T. Su. Nonparametric detection of FM signals usingtime-frequency ridge energy [J]. IEEE Trans. Signal Process.,2008,56(5):1749-1760.
[2] S. M. Kay. Fundamentals of statistical signal processing, Volume II: detectiontheory [M]. Englewood Cliffs, NJ: Prentice-Hall,1998.
[3] M-S Wang A. K. Chan, and C. K. Chui. Linear frequency-modulated signaldetection using Radon-ambiguity transform [J]. IEEE Trans. Signal Process.,1998,46(3):571-586.
[4] G. Wang, X.-G. Xia, B. T. Root, V. C. Chen, Y. Zhang and M. Amin. Manoeuvringtarget detection in over-the-horizon radar by using adaptive chirplet transformwith subspace clutter rejection [J]. IEE Proc., Radar Sonar Navig.,2004,40(2):510-525.
[5] Y. Zhang, M. Amin and G. Frazer. High-resolution time-frequency distribution formanoeuvring target detection in over-the-horizon radars [J]. IEE Proc., RadarSonar Navig.,2003,150(4):299-304.
[6] A. Yasotharan and T. Thayaparan. Time-frequency method for detecting anaccelerating target in sea clutter [J]. IEEE Trans. Aerosp. Electron. Syst.,2006,42(4):1289-1310.
[7] R. A. Carmaoa, W. L. Huang, and B. Torresani. Multiridge detection andtime-frequency reconstruction [J]. IEEE Trans. Signal Process.,1999,47(2):480-492.
[8] M. Morvidone, B. Torresani. Time scale approach for chirp detection [C]. Int. J.Wavelets Multireslut. Inf. Process.,2003,1(1):19-49,2003.
[9] P-L Shui, H-Y Shang, and Y-B Zhao. Instantaneous frequency estimation based ondirectionally smoothed pseudo-Wigner-Ville distribution bank [J]. IET Radar,Sonar and Navigation,2007,1(4):317-325.
[10]E. Chassande-Mottin and A. Pai.Best chirplet chain: Near-optimal detection ofgravita-tional wave chirps [J]. Phys. Rev. D.,2007,73(4):042003.
[11]E. J. Candes, P. R. Charlton, and H. Helgason. Detecting highly oscillatory signalsby chirplet path pursuit [C]. Applied and Computational Harmonic Analysis,2008,24(1):14-40.
[12]M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particlefilters for nonlinear/non-Gaussian Bayesian tracking [J]. IEEE Trans. SignalProcess.,2002,50(2):174-188.
[13]B. Ristic, M. Arulampalam and N. Gordon. Beyond the Kalman filter: particlefilters for tracking application. Boston·London: Artech House,2004.
[14]M. Pitt and N. Shephard. Filtering via simulation: auxiliary particle filters [J]. J.Amer. Statist. Assoc.,1999,94(446):590-599.
[15]G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear statespace models [J]. J. Comput. Graph. Statist.,1996,5(1):1-25.
[16]S. M. Tonissen and Y. Bar-Shalom. Maximum likelihood track-before-detect withfluctuating target amplitude [J]. IEEE Trans. Aerosp. Electron. Syst.,1998,34(3):796-809.
[17]Y. Boers, H.Driessen. A particle-filter-based detection scheme [J]. IEEE SignalProcess. Lett.,2003,10(10):300-302.
[18]H. T. Su, P. L. Shui, H. W. Liu, and Z. Bao. Rao-Blackwellised particle filterbased track-before-detect algorithm [J]. IET Signal Process.,2008,2(2):169-176.
[19]J. Míguez, M.Bugallo, and P.Djuri. A new class of particle filters for randomdynamic systems with unknown statistics [J]. EURASIP J. Appl. Signal Process.,2004,15(2004):2278-2294.
[20]J.Míguez. Analysis of selection methods for cost-reference particle filtering withapplications to manoeuvring target tracking and dynamic optimization [J]. DigitalSignal Process.,2007,17(4):787-807.
[21]J.Míguez. Analysis of parallelizable resampling algorithm for particle filtering [J].Signal Process.,2007,87(12):3155-3174.
[22]M. Boli, P. M. Djuri, and S. Hong. Resampling algorithms and architectures fordistributed particle filters [J]. IEEE Trans. Signal Process.,2005,53(7):2442-2450.
[23]Yang S. Particle filtering based estimation of consistent motion and disparity withreduced search points [J]. IEEE Trans. Circ. Syst. Video Tech.,2012,22(1):91-104.
[24]W. Rudin. Real and complex analysis [M]. McGraw-Hill series in HigherMathematics, New York: McGraw-Hill,1966.
[25]R. L. Burden and J. D. Faires. Numerical analysis [M]. Thomson Learning, Inc.,(Seventh edition),2001.
[26]J. P. Marques de Sa. Pattern recognition [M]. Springer-Verlag, Berlin Heidelberg,2001.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |