面向多无人机协同观测的分布式无色信息滤波方法
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摘要
为解决复杂通信条件下的多无人机协同目标观测问题,提出了基于多智能体一致性理论的分布式状态估计方法.考虑到松散通信结构和通信条件的复杂性,设计双时窗递推迭代机制,即预测/更新时间窗和一致性融合时间窗,用来提高分布式系统的滤波精度.基于一致性融合算法,提出了分布式无色信息滤波方法.从理论上分析了一致性融合算法收敛性对估计精度的影响,揭示该方法的估计协方差劣于集中式方法的根本原因.蒙特卡洛仿真实验表明,该方法对复杂通信条件具有较强的鲁棒性,在平均估计误差、平均一致性误差及平均协方差矩阵迹等方面表现出色,能够满足复杂通信条件下多无人机系统对非线性目标模型状态实时估算的要求.
To solve the problem of cooperative target observation for a multiple UAV( unmanned aerial vehicle)system with a complex communication condition,we propose a distributed state estimation method based on multi-agent consensus theory. Considering the loose communication structure and the complexity of the communication condition,we design an iteration mechanism with double-time windows,including a local prediction / update window and a consensus fusion window,to improve the filter precision of the distributed system.Then,we propose a filter method for distributed unscented information based on the consensus algorithm. The influence of the convergence property of the consensus algorithm on the estimation precision is analyzed theoretically,revealing the fundamental reason why the covariance error of the proposed method is greater than that of the centralized method. The Monte Carlo simulation experiments indicate that the proposed method is robust to complex network constraints,and has outstanding performance on average estimation error,average consistency error and average trace of the covariance matrix. This method can meet the requirement for the real time estimation of non-linear targets for a multiple UAV system under a complex network.
To solve the problem of cooperative target observation for a multiple UAV( unmanned aerial vehicle)system with a complex communication condition,we propose a distributed state estimation method based on multi-agent consensus theory. Considering the loose communication structure and the complexity of the communication condition,we design an iteration mechanism with double-time windows,including a local prediction / update window and a consensus fusion window,to improve the filter precision of the distributed system.Then,we propose a filter method for distributed unscented information based on the consensus algorithm. The influence of the convergence property of the consensus algorithm on the estimation precision is analyzed theoretically,revealing the fundamental reason why the covariance error of the proposed method is greater than that of the centralized method. The Monte Carlo simulation experiments indicate that the proposed method is robust to complex network constraints,and has outstanding performance on average estimation error,average consistency error and average trace of the covariance matrix. This method can meet the requirement for the real time estimation of non-linear targets for a multiple UAV system under a complex network.
引文
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[12]Stankovic S S,Stankovic M,Stipanovic D M.Consensus based overlapping decentralized estimation with missing observations and communication faults[J].Automatica,2009,45(6):1397-1406.
[13]Wang L,Zhang Q J,Zhu H Y,et al.Adaptive consensus fusion estimation for MSN with communication delays and switching network topologies[C]//Proceedings of the 47th IEEE Conference on Decision and Control.Piscataway,NJ,USA:IEEE,2010:2087-2092.
[14]张庆杰,沈林成,朱华勇.多智能体系统实现鲁棒一致的时延相关稳定判据[J].控制与决策,2012,27(4):584-592.Zhang Q J,Shen L C,Zhu H Y.Delay-dependent stability criteria for robust consensus of multi-agent systems[J].Control and Decision,2012,27(4):584-592.
[15]Tian Y,Liu C.Robust consensus of multi-agent systems with diverse input delays and asymmetric interconnection perturbations[J].Automatica,2009,45(5):1347-1353.
[16]Lin P,Jia Y,Li L.Distributed robust H∞consensus control in directed networks of agents with time-delay[J].Systems&Control Letters,2008,57(8):643-653.
[17]Lin P,Jia Y.Robust H∞consensus analysis of a class of second-order multi-agent systems with uncertainty[J].IET Control Theory&Applications,2010,4(3):487-498.
[18]Hu J.On robust consensus of multi-agent systems with communication delays[J].Kybernetika,2009,45(5):768-784.
[19]Lee D J.Nonlinear estimation and multiple sensor fusion using unscented information filtering[J].IEEE Signal Processing Letters,2008,15:861-864.
[2]Su H,Wang X,Lin Z.Flocking of multi-agents with a virtual leader[J].IEEE Transactions on Automatic Control,2009,54(2):293-307.
[3]Lin J,Morse A S,Anderson B D O.The multi-agent rendezvous problem,Part 1:The synchronous case[J].SIAM Journal on Control and Optimization,2007,46(6):2096-2119.
[4]Bauso D,Giarre L,Pesenti R.Distributed consensus protocols for coordinating buyers[C]//Proceedings of the 42nd IEEE Conference on Decision and Control.Piscataway,NJ,USA:IEEE,2003:588-592.
[5]Jadbabaie A,Motee N,Barahona M.On the stability of the Kuramoto model of coupled nonlinear oscillators[C]//Proceedings of the 2004 American Control Conference.Piscataway,NJ,USA:IEEE,2004:4296-4301.
[6]Saber R O.Distributed Kalman filter with embedded consensus filters[C]//Proceedings of the 44th IEEE Conference on Decision and Control&European Control Conference.Piscataway,NJ,USA:IEEE,2005:8179-8184.
[7]Spanos D P,Olfati S R.Approximate distributed Kalman filtering in sensor networks with quantifiable performanc[C]//Proceedings of the 4th International Symposium on Information Processing in Sensor Networks.Piscataway,NJ,USA:IEEE,2005:133-139.
[8]Saber R O,Shamma J S.Consensus filters for sensor networks and distributed sensor fusion[C]//Proceedings of the 44th IEEE Conference on Decision and Control&European Control Conference.Piscataway,NJ,USA:IEEE,2005:6698-6703.
[9]Casbeer D W,Beard R.Distributed information filtering using consensus filters[C]//Proceedings of the 2009 American Control Conference.Piscataway,NJ,USA:IEEE,2009:1882-1887.
[10]Yang P,Freeman R A,Lynch K M.Distributed cooperative active sensing using consensus filters[C]//Proceedings of the IEEE International Conference on Robotics and Automation.Piscataway,NJ,USA:IEEE,2007:405-410.
[11]石晓航,梁青阳,张庆杰,等.DC-IMM估计方法在多UAV协同目标跟踪中的应用[J].中南大学学报,2013,44(7):52-57.Shi X H,Liang Q Y,Zhang Q J,et al.The multi-UAV target tracking using DC-IMM estimate method[J].Journal of Southeast University,2013,44(7):52-57.
[12]Stankovic S S,Stankovic M,Stipanovic D M.Consensus based overlapping decentralized estimation with missing observations and communication faults[J].Automatica,2009,45(6):1397-1406.
[13]Wang L,Zhang Q J,Zhu H Y,et al.Adaptive consensus fusion estimation for MSN with communication delays and switching network topologies[C]//Proceedings of the 47th IEEE Conference on Decision and Control.Piscataway,NJ,USA:IEEE,2010:2087-2092.
[14]张庆杰,沈林成,朱华勇.多智能体系统实现鲁棒一致的时延相关稳定判据[J].控制与决策,2012,27(4):584-592.Zhang Q J,Shen L C,Zhu H Y.Delay-dependent stability criteria for robust consensus of multi-agent systems[J].Control and Decision,2012,27(4):584-592.
[15]Tian Y,Liu C.Robust consensus of multi-agent systems with diverse input delays and asymmetric interconnection perturbations[J].Automatica,2009,45(5):1347-1353.
[16]Lin P,Jia Y,Li L.Distributed robust H∞consensus control in directed networks of agents with time-delay[J].Systems&Control Letters,2008,57(8):643-653.
[17]Lin P,Jia Y.Robust H∞consensus analysis of a class of second-order multi-agent systems with uncertainty[J].IET Control Theory&Applications,2010,4(3):487-498.
[18]Hu J.On robust consensus of multi-agent systems with communication delays[J].Kybernetika,2009,45(5):768-784.
[19]Lee D J.Nonlinear estimation and multiple sensor fusion using unscented information filtering[J].IEEE Signal Processing Letters,2008,15:861-864.
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