Distributed Kalman filtering for sensor networks with random sensor activation, delays, and packet dropouts

ABSTRACT

This paper studies a distributed Kalman filtering problem for sensor networks, where sensor nodes may suffer from measuring the target state with a random activation nature and random delayed and lost state estimates of neighbour nodes due to unreliability of communication links. A distributed Kalman filter (DKF) is proposed, where predictor compensations for delayed and lost estimates of neighbour nodes and different consensus filter gains for state estimates of different neighbour nodes are used to improve estimation accuracy. Optimal filter gains with optimal parameters are designed to obtain a local minimum upper bound of filtering error covariance matrix, where optimal filter gains include an optimal Kalman filter gain for each sensor node and optimal multi-consensus filter gains for state estimates of its neighbour nodes. Our proposed DKF has a low computational cost because the calculation of cross-covariance matrices between estimates of sensor nodes is avoided. Besides, the boundedness of the proposed DKF is analysed. Finally, an example of a target tracking system in sensor networks demonstrates effectiveness of the proposed DKF.

KEYWORDS:

• Distributed Kalman filtering
• random sensor activation
• random delays and packet dropouts
• multi-consensus filter gains
• sensor network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by National Natural Science Foundation of China (No. 61573132), the Key Project of Natural Science Foundation of Heilongjiang Province, China (No. ZD2021F003).

Notes on contributors

Hao Jin

Hao Jin was born in Heilongjiang, China, in 1981. He received the BE degree and ME degree in the Department of Automation from Heilongjiang University, China, in 2004 and 2007, respectively. Currently, he is working for the PhD degree in Heilongjiang University. His main research interest is multi-sensor information fusion.

Shuli Sun

Shuli Sun was born in Heilongjiang, China, in 1971. He received the BS degree in applied mathematics from the Department of Mathematics, Heilongjiang University, Harbin, China, in 1996, the ME degree in control theory and control engineering from the Department of Automation, Heilongjiang University, Harbin, China, in 1999, and the PhD degree in aircraft design from the School of Astronautics, Harbin Institute of Technology, Harbin, China, in 2004. He was a Research Fellow with Nanyang Technological University, Singapore, from 2006 to 2007. Since 2006, he has been a Professor with the School of Electronic Engineering, Heilongjiang University. His research interests are in the areas of state estimation, signal processing, information fusion, and sensor network.