Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/630828
Title: Weighted Average Consensus-Based Unscented Kalman Filtering
Authors: Wangyan Li;Guoliang Wei;Fei Han;Yurong Liu
subject: distributed unscented Kalman filters (DUKFs)|distributed state estimation (DSE)|sensor networks|Consensus filtering|weighted average consensus
Year: 2016
Publisher: IEEE
Abstract: In this paper, we are devoted to investigate the consensus-based distributed state estimation problems for a class of sensor networks within the unscented Kalman filter (UKF) framework. The communication status among sensors is represented by a connected undirected graph. Moreover, a weighted average consensus-based UKF algorithm is developed for the purpose of estimating the true state of interest, and its estimation error is bounded in mean square which has been proven in the following section. Finally, the effectiveness of the proposed consensus-based UKF algorithm is validated through a simulation example.
URI: http://localhost/handle/Hannan/182553
http://localhost/handle/Hannan/630828
ISSN: 2168-2267
2168-2275
volume: 46
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7152915.pdf569.76 kBAdobe PDFThumbnail
Preview File
Title: Weighted Average Consensus-Based Unscented Kalman Filtering
Authors: Wangyan Li;Guoliang Wei;Fei Han;Yurong Liu
subject: distributed unscented Kalman filters (DUKFs)|distributed state estimation (DSE)|sensor networks|Consensus filtering|weighted average consensus
Year: 2016
Publisher: IEEE
Abstract: In this paper, we are devoted to investigate the consensus-based distributed state estimation problems for a class of sensor networks within the unscented Kalman filter (UKF) framework. The communication status among sensors is represented by a connected undirected graph. Moreover, a weighted average consensus-based UKF algorithm is developed for the purpose of estimating the true state of interest, and its estimation error is bounded in mean square which has been proven in the following section. Finally, the effectiveness of the proposed consensus-based UKF algorithm is validated through a simulation example.
URI: http://localhost/handle/Hannan/182553
http://localhost/handle/Hannan/630828
ISSN: 2168-2267
2168-2275
volume: 46
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7152915.pdf569.76 kBAdobe PDFThumbnail
Preview File
Title: Weighted Average Consensus-Based Unscented Kalman Filtering
Authors: Wangyan Li;Guoliang Wei;Fei Han;Yurong Liu
subject: distributed unscented Kalman filters (DUKFs)|distributed state estimation (DSE)|sensor networks|Consensus filtering|weighted average consensus
Year: 2016
Publisher: IEEE
Abstract: In this paper, we are devoted to investigate the consensus-based distributed state estimation problems for a class of sensor networks within the unscented Kalman filter (UKF) framework. The communication status among sensors is represented by a connected undirected graph. Moreover, a weighted average consensus-based UKF algorithm is developed for the purpose of estimating the true state of interest, and its estimation error is bounded in mean square which has been proven in the following section. Finally, the effectiveness of the proposed consensus-based UKF algorithm is validated through a simulation example.
URI: http://localhost/handle/Hannan/182553
http://localhost/handle/Hannan/630828
ISSN: 2168-2267
2168-2275
volume: 46
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7152915.pdf569.76 kBAdobe PDFThumbnail
Preview File