Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

Xiaoyu Zhang; Yan Shen

doi:10.3390/s20236923

Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

Sensors (Basel). 2020 Dec 3;20(23):6923. doi: 10.3390/s20236923.

Authors

Xiaoyu Zhang¹, Yan Shen²

Affiliations

¹ College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
² College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.

Abstract

Estimation accuracy is the core performance index of sensor networks. In this study, a kind of distributed Kalman filter based on the non-repeated diffusion strategy is proposed in order to improve the estimation accuracy of sensor networks. The algorithm is applied to the state estimation of distributed sensor networks. In this sensor network, each node only exchanges information with adjacent nodes. Compared with existing diffusion-based distributed Kalman filters, the algorithm in this study improves the estimation accuracy of the networks. Meanwhile, a single-target tracking simulation is performed to analyze and verify the performance of the algorithm. Finally, by discussion, it is proved that the algorithm exhibits good all-round performance, not only regarding estimation accuracy.

Keywords: data fusion; diffusion strategy; distributed Kalman filter; sensor networks.