基于最优估计的传感器网络室内无线测距与定位问题研究
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摘要
无线传感器网络和物联网方兴未艾,UWB和ZigBee等适用于近距离、低功耗的无线通信技术成为重要的备选方案。室内环境下,常规的GPS定位技术失效,因此无线测距和定位是室内无线通信最基本的应用之一。室内无线通信时,存在多径干扰、非视距误差以及阴影衰落等影响因素,测距和定位面临许多新的需求和挑战。故基于无线传感器网络的室内无线测距和定位算法有待进一步研究和完善。
本文针对UWB和ZigBee等近距离无线通信技术,基于最优估计理论提出了几种到达时间(TOA)和接收信号强度(RSS)的测距与定位算法。主要工作和贡献包括以下几个方面：
1.针对能量检测模式下超宽带测距的精度低,算法复杂等问题,提出基于最优门限和次优门限的两种TOA估计算法。最优门限算法以接收机信号统计特性与UWB小尺度衰减特性关系式为基础推导出门限选择的闭合表达式,并在最小均方误差指标下求解TOA估计值；次优门限算法以最优门限分析为基础,在虚警概率约束下使用牛顿迭代给出求解门限的递推算法。
2.针对非视距条件下TOA测距精度低,正向偏差大的特点,提出一种基于偏置Kalman滤波和极大似然估计的标量加权信息融合算法,以消除UWB测距系统的非视距误差。该算法使用了TOA和RSS两种观测量来提高测距精度。首先,将IEEE802.15.4a给出的测距方法抽象为一个多传感器多尺度的采样过程。然后,分别在视距和非视距条件下使用信息融合算法估计测得的距离信息,并且重点考察了视距/非视距切换过程中误差消除算法的有效性。
3.针对路径损耗模型与实际信道衰减特性匹配性差,模型参数估计不准确等问题,提出一种先进行路径损耗最优模型筛选的RSS测距算法。首先分析一组路径损耗模型的统计特性,然后考虑RSS观测的非完全数据,提出基于数学期望最大化的参数估计算法,在准则函数的基础上筛选最优模型,进而进行RSS测距。
4.针对叶酸分布式检测对位置信息的要求及室内环境下RSS定位精度低的问题,提出基于Bayes估计和加权迭代的RSS定位算法。该算法首先使用极大似然估计在线估计路径损耗模型参数,然后利用Bayes准则建立关于位置信息的后验概率,最后在Bayes估计的位置信息的基础上使用加权迭代进行精确定位。该算法可与叶酸的分布检测有效结合。
总之,本文围绕无线传感器网络的室内测距与定位问题展开了研究,所得结果不仅具有重要的理论价值,而且具有广泛的实际应用价值。
With the rapid development of wireless sensor networks and the internet of things, the UWB and ZigBee technology for short range and low-power wireless communication have become an important alternative. For indoor environment, the conventional GPS location could not do work well, the indoor wireless ranging and positioning becomes one of the most fundamental applications of wireless sensor networks. Due to the multipath interference, non-Iine-of-sight error and shadow fading constraints of the indoor communication, the dynamic changes in the network topology and the nonlinear error of location algorithm, the approaches to indoor ranging and positioning in wireless sensor networks are required to solve the above problems.
Serval approaches to ranging and positioning with Time of Arrival (TOA) and Received Signal Strength (RSS) measurement in UWB and Zigbee systems are pro-posed based on the optimal estimation theory in this dissertation. The main works and contributions are summarized as follows:
1. In order to design precise and feasible ranging method with Impulse Radio Ultra Wide Band signal during energy detection, two new TOA estimation algo-rithms based on optimal and suboptimal thresholds are respectively proposed. For optimal method, with the relationship between energy's statistics in receiver and small-scale attenuation, a closed form of threshold is derived, and the TOA esti-mation is obtained under the minimum mean square error. For suboptimal method based on optimal threshold analysis, a recursive form of threshold selection using Newton iteration is developed with false alarm probability constraint.
2. A scalar weighting information fusion smoother with modified biased Kalman filter and maximum likelihood estimation is proposed to mitigate the ranging errors in UWB systems. The information fusion algorithm uses both the TOA and RSS measurement data to improve the ranging accuracy. At first, the ranging protocol of IEEE802.15.4a is considered as a multi-sensor system with multi-scale sampling. Then a scalar-based IF smoother is proposed to accurately estimate the range mea-surement in the line of sight (LOS) and non-line of sight (NLOS) condition of UWB sensor network. Investigation of the effectiveness of the IF in mitigating errors dur-ing the LOS/NLOS transitions is especially focused.
3. In order to reflect the actual channel attenuation, a model selection algorithm for path loss in wireless sensor networks is proposed for RSS ranging estimation. Firstly, the statistical properties of some path loss models are analyzed, and then ex-pectation maximization algorithm from incomplete data of received signal strength is proposed for parameter estimation, finally a set of weighted coefficients are given on the basis of criterion function, which could select an appropriate path loss model. Through experiment, the proposed model selection method could estimate parame-ters effectively, compared with other similar algorithms, this method could pick up a model fitting the experimental data better.
4. Since the location information is required to detect folate distributly and the positioning with RSS measurement in indoor environments faces many problem, a RSS-based positioning algorithm based on Bayes estimation and weighted iteration is proposed to improve the positioning accuracy. At first the maximum likelihood estimation is used for the estimation of path loss model parameters. And then Bayes criteria is utilized to establish the posterior probability of the location information. Finally, a weighted iteration algorithm based on the location information of Bayes estimation is proposed for precise positioning. This mathod is successfully applied to embedded folic acid detection systems.
In conclusion, this dissertation focuses on the indoor ranging and positioning in wireless sensor networks. The obtained results have not only important theoretic values, but also extensive practical values.
本文针对UWB和ZigBee等近距离无线通信技术,基于最优估计理论提出了几种到达时间(TOA)和接收信号强度(RSS)的测距与定位算法。主要工作和贡献包括以下几个方面：
1.针对能量检测模式下超宽带测距的精度低,算法复杂等问题,提出基于最优门限和次优门限的两种TOA估计算法。最优门限算法以接收机信号统计特性与UWB小尺度衰减特性关系式为基础推导出门限选择的闭合表达式,并在最小均方误差指标下求解TOA估计值；次优门限算法以最优门限分析为基础,在虚警概率约束下使用牛顿迭代给出求解门限的递推算法。
2.针对非视距条件下TOA测距精度低,正向偏差大的特点,提出一种基于偏置Kalman滤波和极大似然估计的标量加权信息融合算法,以消除UWB测距系统的非视距误差。该算法使用了TOA和RSS两种观测量来提高测距精度。首先,将IEEE802.15.4a给出的测距方法抽象为一个多传感器多尺度的采样过程。然后,分别在视距和非视距条件下使用信息融合算法估计测得的距离信息,并且重点考察了视距/非视距切换过程中误差消除算法的有效性。
3.针对路径损耗模型与实际信道衰减特性匹配性差,模型参数估计不准确等问题,提出一种先进行路径损耗最优模型筛选的RSS测距算法。首先分析一组路径损耗模型的统计特性,然后考虑RSS观测的非完全数据,提出基于数学期望最大化的参数估计算法,在准则函数的基础上筛选最优模型,进而进行RSS测距。
4.针对叶酸分布式检测对位置信息的要求及室内环境下RSS定位精度低的问题,提出基于Bayes估计和加权迭代的RSS定位算法。该算法首先使用极大似然估计在线估计路径损耗模型参数,然后利用Bayes准则建立关于位置信息的后验概率,最后在Bayes估计的位置信息的基础上使用加权迭代进行精确定位。该算法可与叶酸的分布检测有效结合。
总之,本文围绕无线传感器网络的室内测距与定位问题展开了研究,所得结果不仅具有重要的理论价值,而且具有广泛的实际应用价值。
With the rapid development of wireless sensor networks and the internet of things, the UWB and ZigBee technology for short range and low-power wireless communication have become an important alternative. For indoor environment, the conventional GPS location could not do work well, the indoor wireless ranging and positioning becomes one of the most fundamental applications of wireless sensor networks. Due to the multipath interference, non-Iine-of-sight error and shadow fading constraints of the indoor communication, the dynamic changes in the network topology and the nonlinear error of location algorithm, the approaches to indoor ranging and positioning in wireless sensor networks are required to solve the above problems.
Serval approaches to ranging and positioning with Time of Arrival (TOA) and Received Signal Strength (RSS) measurement in UWB and Zigbee systems are pro-posed based on the optimal estimation theory in this dissertation. The main works and contributions are summarized as follows:
1. In order to design precise and feasible ranging method with Impulse Radio Ultra Wide Band signal during energy detection, two new TOA estimation algo-rithms based on optimal and suboptimal thresholds are respectively proposed. For optimal method, with the relationship between energy's statistics in receiver and small-scale attenuation, a closed form of threshold is derived, and the TOA esti-mation is obtained under the minimum mean square error. For suboptimal method based on optimal threshold analysis, a recursive form of threshold selection using Newton iteration is developed with false alarm probability constraint.
2. A scalar weighting information fusion smoother with modified biased Kalman filter and maximum likelihood estimation is proposed to mitigate the ranging errors in UWB systems. The information fusion algorithm uses both the TOA and RSS measurement data to improve the ranging accuracy. At first, the ranging protocol of IEEE802.15.4a is considered as a multi-sensor system with multi-scale sampling. Then a scalar-based IF smoother is proposed to accurately estimate the range mea-surement in the line of sight (LOS) and non-line of sight (NLOS) condition of UWB sensor network. Investigation of the effectiveness of the IF in mitigating errors dur-ing the LOS/NLOS transitions is especially focused.
3. In order to reflect the actual channel attenuation, a model selection algorithm for path loss in wireless sensor networks is proposed for RSS ranging estimation. Firstly, the statistical properties of some path loss models are analyzed, and then ex-pectation maximization algorithm from incomplete data of received signal strength is proposed for parameter estimation, finally a set of weighted coefficients are given on the basis of criterion function, which could select an appropriate path loss model. Through experiment, the proposed model selection method could estimate parame-ters effectively, compared with other similar algorithms, this method could pick up a model fitting the experimental data better.
4. Since the location information is required to detect folate distributly and the positioning with RSS measurement in indoor environments faces many problem, a RSS-based positioning algorithm based on Bayes estimation and weighted iteration is proposed to improve the positioning accuracy. At first the maximum likelihood estimation is used for the estimation of path loss model parameters. And then Bayes criteria is utilized to establish the posterior probability of the location information. Finally, a weighted iteration algorithm based on the location information of Bayes estimation is proposed for precise positioning. This mathod is successfully applied to embedded folic acid detection systems.
In conclusion, this dissertation focuses on the indoor ranging and positioning in wireless sensor networks. The obtained results have not only important theoretic values, but also extensive practical values.
引文
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