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distributed recursive filtering for multi-rate uniform sampling systems with packet losses in sensor networks

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2023年第8期54卷 1729-1745页

作者： Shen, Yuqing Sun, Shuli Heilongjiang Univ Sch Elect Engn Harbin Peoples R China Heilongjiang Univ Sch Elect Engn Harbin 150080 Peoples R China

The distributed filtering problem is studied for a multi-rate uniform sampling linear discrete system over sensor networks, where packet losses may occur during data exchange between nodes. The multi-rate system is transformed into a single-rate system by using a virtual measurement method. At each sensor node, the estimates received from neighbour nodes are fused by matrix or scalar weighting according to their different estimation accuracy. A distributed recursive filter with the Kalman consensus filter structure is devised based on the fused estimate, where the filtering gain and consensus gain are solved by locally minimising an upper bound of the filtering error covariance. The obtained gains and the upper bound of the covariance depend on a group of free positive scalar parameters. The scalar parameters are optimised by minimising the upper bound of the covariance. It is proved that the upper bound of the covariance of the filter with optimal gains and parameters is smaller than the covariance of the local filter based on sensor measurements. It avoids calculating cross-covariance matrices between nodes and has small computational burden. The exponential boundedness in the mean square of the filtering error is analysed. A target tracking system verifies the effectiveness of the algorithm.

关键词： Sensor network distributed recursive filter multi-rate uniform sampling system packet loss exponential boundedness in mean square

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Design of distributed recursive filters based on data compression for sensor networks

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SIGNAL PROCESSING 2023年第1期207卷

作者： Shen, Yuqing Sun, Shuli Heilongjing Univ Sch Elect Engn Harbin 150080 Peoples R China

This paper is concerned with the distributed filtering problem based on data compression for linear dis-crete time-varying systems over sensor networks. Two kinds of sensors are considered: one kind only has a measuring capability and communicates measurements, while the other adds a calculating capability and communicates estimates. At each sensor node with calculating capability, a weighted measurement fusion algorithm is employed to compress the measurements of the sensor and its neighbor nodes with only measuring capability;and a weighted state fusion algorithm is employed to compress the estimates from neighbor nodes with calculating capability. Using compressed data, an optimal distributed recursive filter with a Kalman consensus filter form is devised in the linear unbiased minimum variance (LUMV) criterion. Cross-covariance matrices (CCMs) of the filtering errors between sensor nodes are derived. To avoid calculating CCMs, a suboptimal distributed recursive filter is presented, where a covariance inter-section fusion algorithm is employed to compress the estimates from neighbor nodes with calculating capability. The filtering and consensus gains are solved to minimize an upper bound of covariance ma-trix (CM) of the filtering error. It is proved that the filtering error is exponentially bounded in the mean square. Simulations illuminate the effectiveness of proposed algorithms.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Cross-covariance matrix Data compression distributed recursive filter Exponential boundedness in the mean square Sensor network

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distributed filter Based on SICI Data Compression over Sensor Networks 35

Distributed Filter Based on SICI Data Compression over Senso...

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35th Chinese Control and Decision Conference (CCDC)

作者： Shen, Yuqing Sun, Shuli Heilongjiang Univ Sch Elect Engn Harbin Peoples R China

ISBN: (纸本)9798350334722

The distributed filtering problem based on data compression is studied for linear discrete time-varying systems over sensor networks. At each sensor node, sequential inverse covariance intersection (SICI) fusion algorithm is used to compress the estimates received from neighbor nodes. A distributed recursive filter is presented based on compressed data. The filtering gain (FG) and consensus gain (CG) are solved by locally minimizing an upper bound of the filtering error covariance matrix (FECM). It avoids the calculation of cross-covariance matrices (CCMs) and reduces the computational burden. Simulation results verify the effectiveness of the algorithm.

关键词： sensor network distributed recursive filter SICI fusion data compression upper bound of FECM

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