Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy

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 distri...

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Main Authors: Xiaoyu Zhang, Yan Shen
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/23/6923
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author Xiaoyu Zhang
Yan Shen
author_facet Xiaoyu Zhang
Yan Shen
author_sort Xiaoyu Zhang
collection DOAJ
description 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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spelling doaj.art-916aad1b669d4e48bb6e763446c9da452023-11-20T23:24:17ZengMDPI AGSensors1424-82202020-12-012023692310.3390/s20236923Distributed Kalman Filtering Based on the Non-Repeated Diffusion StrategyXiaoyu Zhang0Yan Shen1College of Artificial Intelligence, Nankai University, Tianjin 300350, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaEstimation 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.https://www.mdpi.com/1424-8220/20/23/6923distributed Kalman filterdiffusion strategysensor networksdata fusion
spellingShingle Xiaoyu Zhang
Yan Shen
Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy
Sensors
distributed Kalman filter
diffusion strategy
sensor networks
data fusion
title Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy
title_full Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy
title_fullStr Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy
title_full_unstemmed Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy
title_short Distributed Kalman Filtering Based on the Non-Repeated Diffusion Strategy
title_sort distributed kalman filtering based on the non repeated diffusion strategy
topic distributed Kalman filter
diffusion strategy
sensor networks
data fusion
url https://www.mdpi.com/1424-8220/20/23/6923
work_keys_str_mv AT xiaoyuzhang distributedkalmanfilteringbasedonthenonrepeateddiffusionstrategy
AT yanshen distributedkalmanfilteringbasedonthenonrepeateddiffusionstrategy