基于量测补偿的多传感器分布式滚动时域估计
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摘要
针对网络中单个传感器无法对目标状态完全观测的情况,基于量测补偿策略与滚动时域估计方法,提出了一种分布式估计算法。针对网络中单个传感器量测对状态不完全可观测的情况,令各传感器利用其对完全量测的预测来补偿实际量测;进一步针对传感器网络中所有传感器估计的一致性问题,令各传感器利用本身的量测和邻居传感器的传输信息对状态进行滚动时域估计,并在此基础上构造分布式的滚动时域估计优化问题和设计实施算法。仿真结果表明,在整个传感器网络全局可观测的前提下,该算法可以使得传感器网络中的所有传感器实现对目标状态的完整、准确估计。
Considering the incomplete observability of each sensor's measurement in the network,and a distributed state estimation algorithm is proposed based on the measurement compensation strategy moving horizon estimation(MHE).To achieve the completeness of state estimation,each sensor constructs the measurements prediction and sends it to its neighbor sensors for measurement compensation.For the consensus of the distributed estimation,each sensor adopts the MHE approach by using its local and its neighbor sensors' measurements.Then,the distributed MHE(DMHE)optimization problem and algorithm are presented for each sensor.Simulation results show that by implementing the presented DMHE algorithm,all the incompletely measured sensors can give completed and precise object state estimation in the observable whole sensor network.
Considering the incomplete observability of each sensor's measurement in the network,and a distributed state estimation algorithm is proposed based on the measurement compensation strategy moving horizon estimation(MHE).To achieve the completeness of state estimation,each sensor constructs the measurements prediction and sends it to its neighbor sensors for measurement compensation.For the consensus of the distributed estimation,each sensor adopts the MHE approach by using its local and its neighbor sensors' measurements.Then,the distributed MHE(DMHE)optimization problem and algorithm are presented for each sensor.Simulation results show that by implementing the presented DMHE algorithm,all the incompletely measured sensors can give completed and precise object state estimation in the observable whole sensor network.
引文
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[4]ZHAI Y,YEARY M B,HAVLICEK J P,et al.A new centralized sensor fusion-tracking methodology based on particle filtering for power-aware systems[J].IEEE Trans.on Instrumentation and Measurement,2008,57(10):2377-2387.
[5]DAVEY S J,GORDON N J,SABORDO M.Multi-sensor trackbefore-detect for complementary sensors[J].Digital Signal Processing,2011,21(5):600-607.
[6]WANG Y F,NGUYEN B M,FUJIMOTO H,et al.Multirate estimation and control of body slip angle for electric vehicles based on onboard vision system[J].IEEE Trans.on Industrial Electronics,2014,61(2):1133-1143.
[7]ZHANG J,LIU J F.Two triggered information transmission algorithms for distributed moving horizon state estimation[J].Systems&Control Letters,2014,65:1-12.
[8]JIN X B,BAO J,ZHANG J L.Centralized fusion estimation for uncertain multi-sensor system based on LMI method[C]∥Proc.of the IEEE International Conference on Mechatronics and Automation,2009:2383-2387.
[9]SHIMOKAWA T,FUJIWARA A.Centralized algorithms for the connected target coveragein wireless sensor networks[C]∥Proc.of the 3rd International Conference on Networking and Computing,2012:307-310.
[10]OLFATI-SABER R,SHAMMA J S.Consensus filters for sensor networks and distributed sensor fusion[C]∥Proc.of the44th IEEE Conference on Decision and Control,and the European Control Conference,2005:6698-6703.
[11]OLFATI-SABER R.Distributed Kalman filter with embedded consensus filters[C]∥Proc.of the 44th IEEE Conference on Decision and Control,and the European Control,2005:8179-8184.
[12]ALRIKSSON P,RANTZER A.Distributed Kalman filtering using weighted averaging[C]∥Proc.of the International Symposium on Mathematical Theory of Networks&System,2006:1-6.
[13]CHEN B,ZHANG W A,YU L.Distributed finite-horizon fusion Kalman filtering for bandwidth and energy constrained wireless sensor networks[J].IEEE Trans.on Signal Processing,2014,62(4):797-812.
[14]CARLI R,CHIUSO A,SCHENATO L,et al.Distributed Kalman filtering based on consensus strategies[J].IEEE Journal on Selected Areas in Communications,2008,26(4):622-633.
[15]RAO C V,RAWLINGS J B,LEE J H.Constrained linear state estimation-a moving horizon approach[J].Automatica,2001,37(10):1619-1628.
[16]RAO C V,RAWLINGS J B,MAYNE D Q.Constrained state estimation for nonlinear discrete-time systems:stability and moving horizon approximations[J].IEEE Trans.on Automatic Control,2003,48(2):246-258.
[17]PHILIPP P,SCHMID-ZUREK T.Distributed moving horizon estimation via dual decomposition[C]∥Proc.of the 51st IEEE Conference on Decision and Control,2012:4792-4798.
[18]ZHANG J,LIU J F.Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties[J].Journal of Process Control,2013,23(9):1281-1295.
[19]FARINA M,FERRARI-TRECATE G,SCATTOLINI R.Distributed moving horizon estimation for linear constrained systems[J].IEEE Trans.on Automatic Control,2010,55(11):2462-2475.
[20]ZENG J,LIU J F.Distributed moving horizon state estimation:simultaneously handling communication delays and data losses[J].Systems&Control Letters,2015,75:56-68.