带观测滞后和不确定噪声方差的多智能体传感网络鲁棒序贯协方差交叉融合Kalman滤波

齐文娟; 张鹏; 邓自立

doi:10.3724/SP.J.1004.2014.02632

带观测滞后和不确定噪声方差的多智能体传感网络鲁棒序贯协方差交叉融合Kalman滤波

doi: 10.3724/SP.J.1004.2014.02632

1.
黑龙江大学电子工程学院哈尔滨 150080;
2.
齐齐哈尔大学通信与电子工程学院齐齐哈尔 161000

基金项目:

Supported by National Natural Science Foundation of China (60874063) and Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province (YJSCX2012-263HLJ)

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出版历程
- 收稿日期: 2013-06-19
- 修回日期: 2013-10-29
- 刊出日期: 2014-11-20

Robust Sequential Covariance Intersection Fusion Kalman Filtering over Multi-agent Sensor Networks with Measurement Delays and Uncertain Noise Variances

1.
Department of Automation, Heilongjiang University, Harbin 150080, China

Funds:

Supported by National Natural Science Foundation of China (60874063) and Innovation and Scientific Research Foundation of Graduate Student of Heilongjiang Province (YJSCX2012-263HLJ)

摘要

摘要: 针对带观测滞后和不确定噪声方差的分簇多智能体传感网络系统,研究鲁棒序贯协方差交叉融合Kalman滤波器的设计问题.应用最邻近法则,传感网络被分成簇.应用极大极小鲁棒估计原理,基于带噪声方差最差保守上界的最差保守传感网络系统,提出了两级序贯协方差交叉(SCI)融合鲁棒稳态Kalman滤波器,可减小通信和计算负担并节省能量,且保证实际滤波误差方差有一个最小保守上界.一种Lyapunov方程方法被提出用于证明局部和融合滤波器的鲁棒性.提出了鲁棒精度的概念且证明了局部和融合鲁棒Kalman滤波器的鲁棒精度关系.证明全局SCI融合器的鲁棒精度高于每簇SCI融合器的精度且两者的鲁棒精度都高于每个局部鲁棒滤波器的精度.一个跟踪系统的仿真例子证明了鲁棒性和鲁棒精度关系.
- 多智能体传感网络 /
- 分簇网络 /
- 分布式融合 /
- 序贯协方差交叉融合 /
- 鲁棒Kalman滤波器 /
- 不确定噪声方差 /
- 观测滞后
Abstract: This paper deals with the problem of designing robust sequential covariance intersection (SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances. The sensor network is partitioned into clusters by the nearest neighbor rule. Using the minimax robust estimation principle, based on the worst-case conservative sensor network system with conservative upper bounds of noise variances, and applying the unbiased linear minimum variance (ULMV) optimal estimation rule, we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources, and guarantee that the actual filtering error variances have a less-conservative upper-bound. A Lyapunov equation method for robustness analysis is proposed, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter. A simulation example for a tracking system verifies the robustness and robust accuracy relations.
- Multi-agent sensor networks /
- clustering network /
- distributed fusion /
- sequential covariance intersection (SCI) fusion /
- robust Kalman filter /
- uncertain noise variances /
- measurement delay

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