Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy
1
College of Artificial Intelligence, Nankai University, Tianjin 300350, China
2
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(23), 6923; https://doi.org/10.3390/s20236923
Received: 6 November 2020
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Revised: 25 November 2020
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Accepted: 30 November 2020
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Published: 3 December 2020
(This article belongs to the Section Sensor Networks)
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.
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MDPI and ACS Style
Zhang, X.; Shen, Y. Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy. Sensors 2020, 20, 6923. https://doi.org/10.3390/s20236923
AMA Style
Zhang X, Shen Y. Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy. Sensors. 2020; 20(23):6923. https://doi.org/10.3390/s20236923
Chicago/Turabian StyleZhang, Xiaoyu, and Yan Shen. 2020. "Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy" Sensors 20, no. 23: 6923. https://doi.org/10.3390/s20236923
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