采用马氏决策过程和后验克拉美罗下界的多被动式移动传感器长期调度方法
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摘要
针对多被动式移动传感器协同工作时跟踪精度不稳定等问题,提出了一种基于多步预测的移动传感器长期调度方法。该方法结合部分可观马尔科夫决策过程(POMDP)构建多传感器调度模型,并基于后验克拉美罗下界(PCRLB)建立了传感器调度过程中的单步与长期代价函数;为有效减少计算复杂度,利用大量无迹采样粒子来近似估算长期代价值;通过将多约束非线性调度问题转化为决策树优化问题,可快速获取传感器长期调度方法,并给出了一种基于分支定界技术的改进决策树搜索算法。实验结果表明,所提方法能够实现移动式传感器的合理调度,在决策步长为2时,其目标跟踪精度相较于短期调度可平均提升6.08%;改进搜索算法的求解速度也更加迅速,能够有效满足在线调度的实时性要求。
A non-myopic scheduling method is proposed on the basis of multi-step prediction to solve the problem that tracking accuracies are not stable when multiple passive mobile sensors work together. A multi-sensor scheduling model is built based on the partially observable Markov decision process(POMDP), and a single-step cost function and a multi-step cost function in the scheduling process are given according to posterior Cramér-Rao lower bound(PCRLB). A large number of unscented sampling particles are used to approximate the multi-step prediction costs and to reduce the computation complexity. The sensor scheduling scheme is quickly obtained by transforming the multi-constraint nonlinear scheduling problem into a decision tree optimization problem, and solving the problem using an improved decision tree search algorithm based on the branch-and-bound technique. Simulation results and a comparison with the myopic scheduling method show that the proposed method can effectively make mobile sensors move reasonably, and the tracking accuracy improves by 6.08% on average when the decision step size is two. It is concluded that the improved search algorithm solves the problem faster and meets the real-time requirement of online scheduling.
A non-myopic scheduling method is proposed on the basis of multi-step prediction to solve the problem that tracking accuracies are not stable when multiple passive mobile sensors work together. A multi-sensor scheduling model is built based on the partially observable Markov decision process(POMDP), and a single-step cost function and a multi-step cost function in the scheduling process are given according to posterior Cramér-Rao lower bound(PCRLB). A large number of unscented sampling particles are used to approximate the multi-step prediction costs and to reduce the computation complexity. The sensor scheduling scheme is quickly obtained by transforming the multi-constraint nonlinear scheduling problem into a decision tree optimization problem, and solving the problem using an improved decision tree search algorithm based on the branch-and-bound technique. Simulation results and a comparison with the myopic scheduling method show that the proposed method can effectively make mobile sensors move reasonably, and the tracking accuracy improves by 6.08% on average when the decision step size is two. It is concluded that the improved search algorithm solves the problem faster and meets the real-time requirement of online scheduling.
引文
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[26] CADRE J E L,JAUFFRET C.Discrete-time observability and estimability analysis for bearings-only target motion analysis [J].IEEE Transactions on Aerospace & Electronic Systems,2018,33(1):178-201.
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[2] KALANDROS M.Covariance control for sensor management in cluttered tracking environments [J].Journal of Guidance Control & Dynamics,2015,27(27):493-496.
[3] ZHANG Duo,LIU Meiqin,ZHANG Senlin,et al.Mutual-information based weighted fusion for target tracking in underwater wireless sensor networks [J].Frontiers of Information Technology & Electronic Engineering,2018,19(4):544-556.
[4] HERNANDEZ M L,KIRUBARAJAN T,BARSHALOM Y.Multisensor resource deployment using posterior Cramer-Rao bounds [J].IEEE Transactions on Aerospace Electronic Systems,2004,40(2):399-416.
[5] 吴巍,柳毅,王国宏,等.辐射限制下有源无源协同跟踪技术 [J].信息与控制,2011,40(3):418-423.WU Wei,LIU Yi,WANG Guohong,et al.Active and passive synergy tracking technique with emission constraint [J].Information and Control,2011,40(3):418-423.
[6] WANG Y,HUSSEIN I.Risk-based sensor management for integrated detection and estimation [J].Journal of Guidance Control & Dynamics,2012,34(6):3633-3638.
[7] GOMESBORGES M E,MALTESE D,VANHEEGHE P,et al.A risk-based sensor management using random finite sets and POMDP [C]//Proceedings of the 20th International Conference on Information Fusion.Piscataway,NJ,USA:IEEE,2017:1588-1596.
[8] JOSHI S,BOYD S.Sensor selection via convex optimization [J].IEEE Transactions on Signal Processing,2009,57(2):451-462.
[9] MARTINS F V,CARRANO E G,WANNER E F,et al.On a vector space representation in genetic algorithms for sensor scheduling in wireless sensor networks [J].Evolutionary Computation,2014,22(3):361-403.
[10] ZHANG Zhenkai,TIAN Yubo.A novel resource scheduling method of netted radars based on Markov decision process during target tracking in clutter [J].EURASIP Journal on Advances in Signal Processing,2016,2016(1):1-9.
[11] ZAPPONE A,BUZZI S,JORSWIECK E.Energy-efficient power control and receiver design in relay-assisted DS/CDMA wireless networks via game theory [J].IEEE Communications Letters,2011,15(7):701-703.
[12] CHAVALI P,NEHORAI A.Managing multi-modal sensor networks using price theory [J].IEEE Transactions on Signal Processing,2012,60(9):4874-4887.
[13] 庞策,黄树彩,刘锦昌,等.多传感器交叉提示技术在传感器联盟中的应用 [J].西安交通大学学报,2017,51(7):148-155.PANG Ce,HUANG Shucai,LIU Jinchang,et al.Application of multi-sensor cross cueing technology in sensor alliance [J].Journal of Xi’an Jiaotong University,2017,51(7):148-155.
[14] HOANG H G.Control of a mobile sensor for multi-target tracking using multi-target/object multi-Bernoulli filter [C]//Proceedings of the 2012 International Conference on Control,Automation and Information Sciences.Piscataway,NJ,USA:IEEE,2012:7-12.
[15] RISTIC B,BANGU V.Sensor control for multi-object state-space estimation using random finite sets [J].Automatica,2010,46(11):1812-1818.
[16] WANG Xiaoying,HOSEINNEZHAD R,GOSTAR A K,et al.Multi-sensor control for multi-object Bayes filters [J].Signal Processing,2018,142:260-270.
[17] 陈辉,韩崇昭.机动多目标跟踪中的传感器控制策略的研究 [J].自动化学报,2016,42(4):512-523.CHEN Hui,HAN Chongzhao.Sensor control strategy for maneuvering multi-target tracking [J].Acta Automatica Sinica,2016,42(4):512-523.
[18] XU Enyang,DING Zhi,DASGUPTA S.Target tracking and mobile sensor navigation in wireless sensor networks [J].IEEE Transactions on Mobile Computing,2014,3(8):177-186.
[19] ZHOU Ke,ROUMELIOTIS S.Optimal motion strategies for range-only constrained multisensor target tracking [J].IEEE Transactions on Robotics,2008,24(5):1168-1185.
[20] 娄柯,崔宝同,李纹.基于蜂拥控制的移动传感器网络目标跟踪算法 [J].控制与决策,2013(11):1637-1642.LOU Ke,GUI Baotong,LI Wen.Target tracking algorithm of mobile sensor networks based on flocking control [J].Control and Decision,2013(11):1637-1642.
[21] FODERARO G,ZHU Pingping,WEI Hongchuan,et al.Distributed optimal control of sensor networks for dynamic target tracking [J].IEEE Transactions on Control of Network Systems,2018,5(1):142-153.
[22] ARASARATNAM I,HAYKIN S.Cubature Kalman filters [J].IEEE Transactions on Automatic Control,2009,54(6):1254-1269.
[23] MUNIR A,ATHERTON D P.Adaptive interacting multiple model algorithm for tracking a manoeuvring target [J].IEE Proceedings of the Radar,Sonar and Navigation,1995,142(1):11-17.
[24] KESHAVARZ-M A,KHALOOZADEH H.Interacting multiple model and sensor selection algorithms for maneuvering target tracking in wireless sensor networks with multiplicative noise [J].International Journal of Systems Science,2017,48(5):899-908.
[25] HU Xiaoqing,MING Bao,ZHANG Xiaoping,et al.Quantized Kalman filter tracking in directional sensor networks [J].IEEE Transactions on Mobile Computing,2018,17(4):871-883.
[26] CADRE J E L,JAUFFRET C.Discrete-time observability and estimability analysis for bearings-only target motion analysis [J].IEEE Transactions on Aerospace & Electronic Systems,2018,33(1):178-201.
[27] LIU Zhigang,WANG Jinkuan,XUE Yanbo.PCRLB-based sensor selection for maneuvering target tracking in range-based sensor networks [J].Future Generation Computer Systems,2013,29(7):1751-1757.
[28] HUBE R,MARCO F.Optimal pruning for multi-step sensor scheduling [J].IEEE Transactions on Automatic Control,2012,57(5):1338-1343.