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Diffusion Distributed Quantized State Estimation With Variable Bandwidth.

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Bibliographic Details
Title: Diffusion Distributed Quantized State Estimation With Variable Bandwidth.
Authors: Liu, Jingzhi1 (AUTHOR) liujz1107@163.com, Chen, Feng1 (AUTHOR) fengchen.uestc@gmail.com, Feng, Minyu1 (AUTHOR) myfeng@swu.edu.cn, Wang, Shiyuan1 (AUTHOR) wsy@swu.edu.cn
Source: IEEE Transactions on Aerospace & Electronic Systems. Feb2022, Vol. 58 Issue 1, p406-419. 14p.
Subjects: Mean square algorithms, Bandwidths, Wireless communications
Abstract: In low-cost wireless sensor networks, the communication bandwidth between sensors may be variable due to power constraints on the sensors. Considering that quantization is an effective method to save communication bandwidth, a novel distributed adaptive quantization state estimation algorithm with variable bandwidth is proposed, where the quantization steps are time varying. Based on the minimum mean squared error criterion and diffusion strategy, the optimal local gain and neighborhood gain are designed to utilize quantized information for fusion estimation. These gains can be adjusted adaptively through local information fusion. Moreover, we also analyze the mean and mean-square performance of the proposed algorithm and find that the covariance is bounded under variable bandwidth. Finally, the effectiveness of the proposed algorithm is verified via numerical simulation. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:In low-cost wireless sensor networks, the communication bandwidth between sensors may be variable due to power constraints on the sensors. Considering that quantization is an effective method to save communication bandwidth, a novel distributed adaptive quantization state estimation algorithm with variable bandwidth is proposed, where the quantization steps are time varying. Based on the minimum mean squared error criterion and diffusion strategy, the optimal local gain and neighborhood gain are designed to utilize quantized information for fusion estimation. These gains can be adjusted adaptively through local information fusion. Moreover, we also analyze the mean and mean-square performance of the proposed algorithm and find that the covariance is bounded under variable bandwidth. Finally, the effectiveness of the proposed algorithm is verified via numerical simulation. [ABSTRACT FROM AUTHOR]
ISSN:00189251
DOI:10.1109/TAES.2021.3101568