Composite Weighted Average Consensus Filtering

Hao Chen; Jianan Wang; Zhang Jun; Chunyan Wang; Jiayuan Shan

doi:10.1109/GNCC42960.2018.9018865

Composite Weighted Average Consensus Filtering

Hao Chen
, Jianan Wang
, Zhang Jun
, Chunyan Wang
, Jiayuan Shan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, a composite weighted average consensus filtering algorithm (CWACF) is proposed by applying multiple heterogeneous nonlinear filters. In light of the sensors' different sensing accuracy and computational capability, extended Kalman filter (EKF) and sparse-grid quadrature filter (SGQF) are compositely adopted on different sensors as local filters. Then, estimation from neighbours are fused based on the weighted average consensus framework to attain better estimation performance. Moreover, it has been proved that the estimation error is exponentially bounded in mean square. The performance of the proposed algorithm and distributed extended Kalman filtering (DEKF) are compared by a target localization case through a sensor network.

Original language	English
Title of host publication	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538611715
DOIs	https://doi.org/10.1109/GNCC42960.2018.9018865
Publication status	Published - Aug 2018
Externally published	Yes
Event	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 - Xiamen, China Duration: 10 Aug 2018 → 12 Aug 2018

Publication series

Name	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

Conference

Conference	2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Country/Territory	China
City	Xiamen
Period	10/08/18 → 12/08/18

Access to Document

10.1109/GNCC42960.2018.9018865

Cite this

Chen, H., Wang, J., Jun, Z., Wang, C., & Shan, J. (2018). Composite Weighted Average Consensus Filtering. In 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 Article 9018865 (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GNCC42960.2018.9018865

@inproceedings{766a8f29651a4bff89f81453982faff6,

title = "Composite Weighted Average Consensus Filtering",

abstract = "In this paper, a composite weighted average consensus filtering algorithm (CWACF) is proposed by applying multiple heterogeneous nonlinear filters. In light of the sensors' different sensing accuracy and computational capability, extended Kalman filter (EKF) and sparse-grid quadrature filter (SGQF) are compositely adopted on different sensors as local filters. Then, estimation from neighbours are fused based on the weighted average consensus framework to attain better estimation performance. Moreover, it has been proved that the estimation error is exponentially bounded in mean square. The performance of the proposed algorithm and distributed extended Kalman filtering (DEKF) are compared by a target localization case through a sensor network.",

author = "Hao Chen and Jianan Wang and Zhang Jun and Chunyan Wang and Jiayuan Shan",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 ; Conference date: 10-08-2018 Through 12-08-2018",

year = "2018",

month = aug,

doi = "10.1109/GNCC42960.2018.9018865",

language = "English",

series = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",

address = "United States",

}

Chen, H, Wang, J, Jun, Z, Wang, C & Shan, J 2018, Composite Weighted Average Consensus Filtering. in 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018., 9018865, 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018, Xiamen, China, 10/08/18. https://doi.org/10.1109/GNCC42960.2018.9018865

Composite Weighted Average Consensus Filtering. / Chen, Hao; Wang, Jianan; Jun, Zhang et al.
2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 9018865 (2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Composite Weighted Average Consensus Filtering

AU - Chen, Hao

AU - Wang, Jianan

AU - Jun, Zhang

AU - Wang, Chunyan

AU - Shan, Jiayuan

PY - 2018/8

Y1 - 2018/8

N2 - In this paper, a composite weighted average consensus filtering algorithm (CWACF) is proposed by applying multiple heterogeneous nonlinear filters. In light of the sensors' different sensing accuracy and computational capability, extended Kalman filter (EKF) and sparse-grid quadrature filter (SGQF) are compositely adopted on different sensors as local filters. Then, estimation from neighbours are fused based on the weighted average consensus framework to attain better estimation performance. Moreover, it has been proved that the estimation error is exponentially bounded in mean square. The performance of the proposed algorithm and distributed extended Kalman filtering (DEKF) are compared by a target localization case through a sensor network.

AB - In this paper, a composite weighted average consensus filtering algorithm (CWACF) is proposed by applying multiple heterogeneous nonlinear filters. In light of the sensors' different sensing accuracy and computational capability, extended Kalman filter (EKF) and sparse-grid quadrature filter (SGQF) are compositely adopted on different sensors as local filters. Then, estimation from neighbours are fused based on the weighted average consensus framework to attain better estimation performance. Moreover, it has been proved that the estimation error is exponentially bounded in mean square. The performance of the proposed algorithm and distributed extended Kalman filtering (DEKF) are compared by a target localization case through a sensor network.

UR - https://www.scopus.com/pages/publications/85082451789

U2 - 10.1109/GNCC42960.2018.9018865

DO - 10.1109/GNCC42960.2018.9018865

M3 - Conference contribution

AN - SCOPUS:85082451789

T3 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

BT - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018

Y2 - 10 August 2018 through 12 August 2018

ER -

Composite Weighted Average Consensus Filtering

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