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针对部分可观多传感器多目标协同跟踪问题,提出了一种基于潜博弈的分布式优化算法。以传感器为博弈方,选择广义Fisher信息矩阵(Generalized Fisher Information Matrix, GFIM)为跟踪收益函数,将探测和通信约束下的多传感器多目标分配问题描述为一个局部信息博弈模型,证明了该模型是一个潜博弈模型,至少存在一个可行的纯策略纳什均衡(Nash Equilibrium, NE)。为了提高计算效率,设计了一种改进并行最佳响应动态(Modified Parallel Best Response Dynamic, MPBRD)的分布式决策算法,分析了算法的复杂度。仿真结果显示,在小规模场景下,基于潜博弈的分布式优化算法能够达到集中式全枚举优化算法的跟踪性能,计算时间大大缩短。在大规模场景下,基于潜博弈的分布式优化算法具有较好的收敛性,满足大规模传感器决策的实时性需求。
Abstract：A distributed optimal assignment algorithm based on potential game theory is proposed to solve the partially observable multi-sensor collaborative tracking problem. The sensor is selected as the game player and the multi-sensor multi-target assignment problem under detection and communication constrains is constructed as a game model based on local information with the target tracking reward function of Generalized Fisher Information Matrix( GFIM) metric. The model is proved to be a potential game model with at least one feasible pure strategy Nash Equilibrium( NE) point. A Modified Parallel Best Response Dynamic( MPBRD) algorithm is developed to solve the game problem efficiently. Then its computation complexity is analyzed. Simulation results show that in smallscale scenario, the proposed algorithm can achieve the tracking performance of the full enumeration algorithm in greatly reduced time,and in large-scale scenario, it has a convergence good enough to meet the real-time decision requirements of large-scale sensor network.
[1]何友,关欣,王国宏.多传感器信息融合研究进展与展望[J].宇航学报,2005(4):524-530.
[2] HERO A O, COCHRAN D. Sensor Management:Past,Present,and Future[J]. IEEE Sensors Journal,2011,11(12):3064-3075.
[3]闫涛,韩崇昭,张光华.空中目标传感器管理方法综述[J].航空学报,2018,39(10):26-36.
[4] SHEN X J,VARSHNEY P K. Sensor Selection Based on Generalized Information Gain for Target Tracking in Large Sensor Networks[J]. IEEE Transactions on Signal Processing,2014,62(2):363-375.
[5]张华睿,杨宏文,胡卫东,等.基于Fisher信息距离的传感器管理方法[J].系统工程与电子技术,2012,34(8):1587-1591.
[6]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.
[7] KALANDROS M. Covariance Control for Multisensor Systems[J]. IEEE Transactions on Aerospace and Electronic Systems,2002,38(4):1138-1157.
[8] GOSTAR A K,HOSEINNEZHAD R,BAB-HADIASHAR A. Multi-bernoulli Sensor Control via Minimization of Expected Estimation Errors[J]. IEEE Transactions on Aerospace and Electronic Systems,2015,51(3):1762-1773.
[9]徐公国,单甘霖,段修生,等.基于马尔科夫决策过程的多传感器协同检测与跟踪调度方法[J].电子与信息学报,2019,41(9):2201-2208.
[10] JOSHI S,BOYD S. Sensor Selection via Convex Optimization[J]. IEEE Transactions on Signal Processing,2009,57(2):451-462.
[11] FUDENBERG D, TIROLE J. Game Theory[M]. Cambridge:MIT Press,1991.
[12]郝晓辰,姚宁,汝小月,等.基于生命期模型的无线传感器网络信道分配博弈算法[J].物理学报,2015,64(14):5-15.
[13]邵鸿翔,孙有铭,冀保峰,等.多无人机辅助NOMA网络的联合无线资源分配算法[J].无线电工程,2024,54(3):565-572.
[14]贾杰,张桂园,陈剑,等.无线传感器网络中基于潜在博弈的分布式节点定位[J].电子学报,2014,42(9):1724-1730.
[15]冉晓旻,方德亮.基于势博弈的分布式目标跟踪传感器分配算法[J].电子与信息学报,2017,39(11):2748-2754.
[16]韩鹏,丁桂强,刘国彬.基于博弈论的多雷达多目标分配方法研究[J].现代雷达,2020,42(2):16-22.
[17]靳标,邝晓飞,彭宇,等.基于合作博弈的组网雷达分布式功率分配方法[J].航空学报,2022,43(1):427-437.
[18] MOSTAANI A,SABAHI M F,ASADI N. Obtaining a Potential Game Through Pricing to Achieve Optimal Resource Management in Non-cooperative Cognitive Radio Networks[J]. Wireless Personal Communications:An International Journal,2016,87(1):45-62.
[19] VAN TREES H L. Detection,Estimation,Modulation Theory[M]. Hoboken:Wiley,2001.
[20]郭军军,韩崇昭.基于贝叶斯理论框架的传感器选择算法[J].自动化学报,2018,44(8):1425-1435.
[21]马玲,左燕,彭冬亮,等.基于POMDP的多机无源传感器协同任务规划[J].无线电工程,2022,52(7):1260-1265.
[22]杨标,朱圣棋,余昆,等.贪婪的量测划分机制下的多传感器多机动目标跟踪算法[J].电子与信息学报,2021,43(7):1962-1969.
基本信息:
中图分类号:E91;TP212;TP18
引用信息:
[1]张淯铧,林庆宝,左燕,等.基于潜博弈的多传感器协同跟踪分布式优化算法[J].无线电工程,2025,55(03):672-678.
基金信息:
国家自然科学基金(61673146); 浙江省自然科学基金重点项目(LZ23F030002)~~
2024-10-22
2024-10-22
2024-10-22