随机丢包的无迹卡尔曼滤波估计以及性能分析

doi:10.16180/j.cnki.issn1007-7820.2024.02.004

Abstract

Abstract:

In engineering applications, wireless network control systems are mostly nonlinear systems. Due to long-distance transmission and unreliable communication networks, the measured values of system sensors may be lost in the transmission process, which influences accuracy estimation and system performance. In this study, the problem of unscented Kalman filtering for a class of nonlinear discrete stochastic systems affected by correlated noise and sensor measurement loss is studied. By introducing a random variable that obeys Bernoulli distribution and has known conditional probability to describe the random sensor measurement loss, an algorithm is proposed to compensate the data. The results are verified by standard numerical software. The results show that the filter compensated by the algorithm can estimate the system well, greatly reduce the impact of sensor measurement loss on the filter performance, and increase the accuracy of estimation.

Key words: wireless network control system, nonlinear discrete stochastic systems, state estimation, unscented Kalman filter, packet losses, system covariance, estimation error, system noise

CLC Number:

TP393

BAI Rui,REN Zhu. Estimation and Performance Analysis of Unscented Kalman Filter with Randomly Missing Measurements[J].Electronic Science and Technology, 2024, 37(2): 23-29.

Figures/Tables 10

Table 1.

Data transmission diagram"

k	λ_k	z_k	y_k
1	1	z₁	z₁
2	0	丢包	$z 2 \| 1$
3	1	z₃	z₃
4	0	丢包	$z 4 \| 3$
5	0	丢包	$z 5 \| 3$
6	1	z₆	z₆
7	1	z₇	z₇

Table 1.

Figure 1.

Figusre 2.

Figure 3.

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Figure 5.

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Figure 8.

Figure 9.

References 21

[1]	李建宁. 无线网络控制系统的建模与控制[D]. 杭州: 浙江大学, 2013:31-50.
	Li Jianning. Modeling and control of wireless network control system[D]. Hangzhou: Zhejiang University, 2013:31-50.
[2]	郭小华. 基于无线传感器网络的无线网络控制系统研究[D]. 杭州: 浙江大学, 2008:12-42.
	Guo Xiaohua. Research on wireless network control system built on wireless sensor networks[D]. Hangzhou: Zhejiang University, 2008:12-42.
[3]	Hassan M, Salut G, Singh M, et al. A decentralized computational algorithm for the global Kalman filter[J]. IEEE Transactions on Automatic Control, 1978, 23(2):262-268. doi: 10.1109/TAC.1978.1101713
[4]	Alriksson P, Rantzer A. Experimental evaluation of a distributed Kalman filter algorithm[C]. New Orleans: The Forty-sixth IEEE Conference on Decision and Control, 2007:562-570.
[5]	Shi L. Resource optimization for networked estimator with guaranteed estimation quality[D]. Pasadena: California Institute of Technology, 2009:34-40.
[6]	Shi L, Johansson K H, Murray R M. Estimation over wireless sensor networks:Tradeoff between communication,computation and estimation qualities[J]. IFAC Proceedings Volumes, 2008, 41(2):605-611. doi: 10.3182/20080706-5-KR-1001.00102
[7]	孙杨燕, 周秀莹, 任祝. 针对偏差攻击的安全状态估计与检测[J]. 电子科技, 2021, 34(12):75-80.
	Sun Yangyan, Zhou Xiuying, Ren Zhu. Security state estimation and detection for biasing sttack[J]. Electronic Science and Technology, 2021, 34(12):75-80.
[8]	Sinopoli B, Schenato L, Franceschetti M, et al. Kalman filtering with intermittent observations[J]. IEEE Transactions on Automatic Control, 2004, 49(9):1453-1464. doi: 10.1109/TAC.2004.834121
[9]	Huang M, Dey S. Kalman filtering with markovian packet losses and stability criteria[C]. San Diego: Proceedings of the Forty-fifth IEEE Conference on Decision and Control, 2006:604-610.
[10]	Shi L, Epstein M, Murray R M. Kalman filtering over a packet-dropping network:A probabilistic perspective[J]. IEEE Transactions on Automatic Control, 2010, 55(3):594-604. doi: 10.1109/TAC.2009.2039236
[11]	Mohammadzadeh A, Tavassoli B, Moaveni B. Simultaneous estimation of state and packet-loss occurrences in networked control systems[J]. ISA Transactions, 2020, 107(1):307-315. doi: 10.1016/j.isatra.2020.08.006
[12]	郭戈, 王宝凤. 多丢包不确定离散系统的鲁棒Kalman滤波[J]. 自动化学报, 2010, 36(5):767-772. doi: 10.3724/SP.J.1004.2010.00767
	Guo Ge, Wang Baofeng. Robust Kalman filteringfor uncertain discrete-time systems with multiple packet dropouts[J]. Acta Automatica Sinica, 2010, 36(5):767-772. doi: 10.3724/SP.J.1004.2010.00767
[13]	Liu Y G, Xu B G, Shi B H. Kalman filtering for stochastic systems with consecutive packet losses and measurement time delays[J]. Control Theory & Applications, 2013, 30(7):898-908.
[14]	王帅, 杨文, 侍洪波. 带丢包一致性滤波算法研究[J]. 自动化学报, 2010, 36(12):1689-1696.
	Wang Shuai, Yang Wen, Shi Hongbo. Consensus-based f-iltering algorithm with packet-dropping[J]. Acta Auto-matica Sinica, 2010, 36(12):1689-1696.
[15]	谢可心, 杨春曦, 刘华, 等. 卡尔曼一致性滤波器的丢包性能分析及能量优化[J]. 控制理论与应用, 2018, 35(8):1177-1185.
	Xie Kexin, Yang Chunxi, Liu Hua, et al. Packet-dropout performance and energy optimization of the distributed Kalman consensus filter[J]. Control Theory & Applications, 2018, 35(8):1177-1185.
[16]	Wang J B, Luo X L. Research on airborne passive location based on extend Kalman filter with control inputs[C]. Beijing: The Third International Conference on Information Science and Control Engineering, 2016:702-708.
[17]	Han L, Cai W T, Pu X J, et al. Method on fetal electr ocardiogram extraction utilizing light GBM combinedwith EKF[C]. Chongqing: The Thirteenth International Conference on Communication Software and Networks, 2021:142-149.
[18]	Julier S J, Uhlmann J K. A new method for nonlinear transformation of means and covariances in filters and estimators[J]. IEEE Transactions on Automatic Control, 2000, 45(3):477-482. doi: 10.1109/9.847726
[19]	谯小康, 屈小媚. 基于车辆与车辆的车联网分布式协同感知定位[J]. 控制理论与应用, 2021, 38(7):988-996.
	Qiao Xiaokang, Qu Xiaomei. Vehicle to vehicle-based distributed cooperative sensing positioning for internet of vehicles[J]. Control Theory & Applications, 2021, 38(7):988-996.
[20]	Li L, Yu D D, Xia Y Q, et al. Remote nonlinear state estimation with stochastic event-triggered sensor schedule[J]. IEEE Transactions on Cybernetics, 2019, 49(1):734-745. doi: 10.1109/TCYB.6221036
[21]	Xu L, Ma K, Fan H X. Unscented Kalman filtering for nonlinear state estimation with correlated noises and missing measurements[J]. International Journal of Control,Automation and Systems, 2018, 16(3):1011-1020. doi: 10.1007/s12555-017-0495-2

Estimation and Performance Analysis of Unscented Kalman Filter with Randomly Missing Measurements

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