强非线性时间演化声速剖面的序贯反演
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摘要
受海面波浪起伏、降雨和内波等海洋动力学过程的影响,浅水声速剖面的时间演化具有高度非线性,针对该问题提出使用改进的粒子滤波方法进行声速剖面序贯反演.该方法通过建立声速剖面的经验正交模型(EOF)以及描述声速剖面时间演化特征的状态空间模型,将声速剖面反演问题建模为状态跟踪问题,利用不敏粒子滤波(UPF:Uncented Particle Filter)算法进行声速剖面序贯反演。仿真试验通过实测声速剖面数据和先验地声参数信息产生接收声场数据,再利用模拟声场数据估计声速剖面的时间变化.结果表明,相比于集合卡尔曼滤波(EnKF:Ensemble Kalman Filter),在计算效率等同的情形下,该方法可以在状态参数的时间跳变点保持良好的跟踪性能,一定程度上克服了现有反演算法在跳变点发散的问题,可以有效提高声速剖面反演精度,尤其在声速剖面时变性较强时具有显著优势.
Affected by ocean dynamic processes such as sea waves,rainfall and internal waves,the time evolution of sound speed profiles(SSPs)in shallow water is highly nonlinear.To solve the problem,an algorithm of the improved particle filter is implemented for the tracking of time-evolving SSPs.Based on the Empirical Orthogonal Functions(EOFs)and the state-space model which describe the evolution characteristics of SSPs,sequential inversion of SSPs are carried out through the acoustic pressure data received by the Vertical Line Array(VLA)using UPF.Time-evolving SSPs are estimated via the acoustic array data simulated by the measured SSPs and prior seabed acoustic properties.The algorithm was validated and result shows that under the comparable computational efficiency,the UPF-based method can overcome the divergences of Ensemble Kalman Filter(EnKF)algorithm and keeps up perfect tracking performance at the jump time.The estimated accuracy can be effectively increased,especially in the case of strongly time-evolving SSPs.
Affected by ocean dynamic processes such as sea waves,rainfall and internal waves,the time evolution of sound speed profiles(SSPs)in shallow water is highly nonlinear.To solve the problem,an algorithm of the improved particle filter is implemented for the tracking of time-evolving SSPs.Based on the Empirical Orthogonal Functions(EOFs)and the state-space model which describe the evolution characteristics of SSPs,sequential inversion of SSPs are carried out through the acoustic pressure data received by the Vertical Line Array(VLA)using UPF.Time-evolving SSPs are estimated via the acoustic array data simulated by the measured SSPs and prior seabed acoustic properties.The algorithm was validated and result shows that under the comparable computational efficiency,the UPF-based method can overcome the divergences of Ensemble Kalman Filter(EnKF)algorithm and keeps up perfect tracking performance at the jump time.The estimated accuracy can be effectively increased,especially in the case of strongly time-evolving SSPs.
引文
1 Munk W,Uunsch C.Ocean acoustic tomography:A scheme for large scale monitoring.Deep-Sea Res.,1979;26(2):123-161
2 Shang E C.Ocean acoustic tomography based on adiabaticmode theory.J.Acoust.Soc.Am.,1989; 85(4):1531—1537
3张之猛,刘伯胜.遗传模拟退火算法用于浅海声速反演的仿真研究.哈尔滨工程大学学报,2006; 27(4):505-513
4何利,李整林,彭朝晖等.南海北部海水声速剖面反演.中国科学:物理学力学天文学,2011; 41(1):49—57
5 Yardim C,Gerstoft P,Hodgkiss W S.An overview of sequential bayesian filtering in ocean acoustics.IEEE J.Oceanic Eng.,2011; 36(1):73—91
6李建龙,徐文,金丽玲,郭圣明.浅海环境下数据同化声层析方法研究.声学学报,2012; 37(1):10-17
7笪良龙,过武宏,赵建昕,范培勤.海洋-声学耦合模式捕捉水声环境不确定性.声学学报,2015; 40(3):477-486
8 Candy J V,Sullivan E J.Model-based environmental inversion:A shallow water ocean application.J.Acoust.Soc.Am.,1995; 98(3):1446-1454
9 Yardim C,Gerstoft P,Hodgkiss W S.Tracking of geoacoustic parameters using Kalman and particle filter.J.Acoust.Soc.Am.,2009; 125(2):746-760
10 Carriere O,Hermand J P,Le Gac J C et al.Full filed tomography and Kalman tracking of the range-dependent sound speed field in a coastal water environment.J.Mar.Syst.,2009; 78:S382-S392
11 Carriere O,Hermand J P,Candy J V.Inversion for timeevolving sound-speed field in a shallow ocean by ensemble Kalman filtering.IEEE J.Oceanic Eng.,2009; 34(4):586-602
12 Jin Liling,Li Jianlong,Xu Wen.Tracking of time-evolving sound speed profiles with an auto-regressive state-space model.Chinese Journal of Acoustics,2017; 36(43):302—312
13 Guo Xiaole,Yang Kunde,Ma Yuanliang.Trackingpositioning of sound speed profiles and moving acousticsource in shallow water.Chinese Journal of Acoustics,2017; 36(4):439-453
14 Vanleeuwen P J.An efficient variance-minimizing filter(and smoother)for large-scale problems.Mon.Wea.Rev.,2003; 131(9):2071-2084
15 T Lin,Michalopoulou Z H.Sound speed estimation and source localization with linearization and particle filtering.J.Acoust.Soc.Am.,2014; 135(5):1115—1126
16 Doucet A and Godsill S.On sequential Monte Carlo sampling methods for Bayesian filtering.Stat.Comput.,2000;10(3):197-208
17许枫,纪永强,郭占军等.基于混合粒子滤波的水下小目标跟踪.应用声学,2015; 34(4):297-302
18许彦伟,候朝焕,李军等.恒虚警率采样粒子滤波技术及其应用研究.应用声学,2013; 32(4):320-324
19 Haykin S.Kalman filtering and neural networks.USA:John Wiley&Sons,Inc,2001:123—132
20 YU Jiaxiang,XIAO Deyun,YANG Xiuting.Square root unscented particle filter with application to angle-only tracking.The Six World Congress on Intelligent Control and Automation,USA,2006:1548-1522
21 LeBlanc L R,Middleton F H.An underwater acoustic sound velocity data model.J.Acoust.Soc.Am., 1980;67(6):2055-2062
22 Ristic B,Arulampalam S,Gordon N.Beyond the Kalman filter:particle filters for tracking applications.Boston,London:Artech House Publishers,2004:35—44
23马树青.浅海孤立子内波对声传播的影响.哈尔滨工程大学,2011
24 Cheng Qi,Bondon P.A new unscented particle filter.IEEE International Conference on Acoustics Speech and Signal Processing,Las Vegas,2008:3417—3420
2 Shang E C.Ocean acoustic tomography based on adiabaticmode theory.J.Acoust.Soc.Am.,1989; 85(4):1531—1537
3张之猛,刘伯胜.遗传模拟退火算法用于浅海声速反演的仿真研究.哈尔滨工程大学学报,2006; 27(4):505-513
4何利,李整林,彭朝晖等.南海北部海水声速剖面反演.中国科学:物理学力学天文学,2011; 41(1):49—57
5 Yardim C,Gerstoft P,Hodgkiss W S.An overview of sequential bayesian filtering in ocean acoustics.IEEE J.Oceanic Eng.,2011; 36(1):73—91
6李建龙,徐文,金丽玲,郭圣明.浅海环境下数据同化声层析方法研究.声学学报,2012; 37(1):10-17
7笪良龙,过武宏,赵建昕,范培勤.海洋-声学耦合模式捕捉水声环境不确定性.声学学报,2015; 40(3):477-486
8 Candy J V,Sullivan E J.Model-based environmental inversion:A shallow water ocean application.J.Acoust.Soc.Am.,1995; 98(3):1446-1454
9 Yardim C,Gerstoft P,Hodgkiss W S.Tracking of geoacoustic parameters using Kalman and particle filter.J.Acoust.Soc.Am.,2009; 125(2):746-760
10 Carriere O,Hermand J P,Le Gac J C et al.Full filed tomography and Kalman tracking of the range-dependent sound speed field in a coastal water environment.J.Mar.Syst.,2009; 78:S382-S392
11 Carriere O,Hermand J P,Candy J V.Inversion for timeevolving sound-speed field in a shallow ocean by ensemble Kalman filtering.IEEE J.Oceanic Eng.,2009; 34(4):586-602
12 Jin Liling,Li Jianlong,Xu Wen.Tracking of time-evolving sound speed profiles with an auto-regressive state-space model.Chinese Journal of Acoustics,2017; 36(43):302—312
13 Guo Xiaole,Yang Kunde,Ma Yuanliang.Trackingpositioning of sound speed profiles and moving acousticsource in shallow water.Chinese Journal of Acoustics,2017; 36(4):439-453
14 Vanleeuwen P J.An efficient variance-minimizing filter(and smoother)for large-scale problems.Mon.Wea.Rev.,2003; 131(9):2071-2084
15 T Lin,Michalopoulou Z H.Sound speed estimation and source localization with linearization and particle filtering.J.Acoust.Soc.Am.,2014; 135(5):1115—1126
16 Doucet A and Godsill S.On sequential Monte Carlo sampling methods for Bayesian filtering.Stat.Comput.,2000;10(3):197-208
17许枫,纪永强,郭占军等.基于混合粒子滤波的水下小目标跟踪.应用声学,2015; 34(4):297-302
18许彦伟,候朝焕,李军等.恒虚警率采样粒子滤波技术及其应用研究.应用声学,2013; 32(4):320-324
19 Haykin S.Kalman filtering and neural networks.USA:John Wiley&Sons,Inc,2001:123—132
20 YU Jiaxiang,XIAO Deyun,YANG Xiuting.Square root unscented particle filter with application to angle-only tracking.The Six World Congress on Intelligent Control and Automation,USA,2006:1548-1522
21 LeBlanc L R,Middleton F H.An underwater acoustic sound velocity data model.J.Acoust.Soc.Am., 1980;67(6):2055-2062
22 Ristic B,Arulampalam S,Gordon N.Beyond the Kalman filter:particle filters for tracking applications.Boston,London:Artech House Publishers,2004:35—44
23马树青.浅海孤立子内波对声传播的影响.哈尔滨工程大学,2011
24 Cheng Qi,Bondon P.A new unscented particle filter.IEEE International Conference on Acoustics Speech and Signal Processing,Las Vegas,2008:3417—3420