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Distributed Estimation with Novel Adaptive Data Selection Based on a Cross-Matching Mechanism

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Abstract

Distributed estimation using general data selection (DS) has always been applicable for reducing calculation loads in many fields. However, the traditional general DS (GDS) mode can deteriorate algorithm performance and usually neglects solving the problem of communication cost. These issues arise because distributed estimation is extremely susceptible to selecting the fused data and requires swapping all data. To solve these problems, a diffusion least-mean-square (DLMS) algorithm with an adaptive DS (ADS) is proposed to improve the GDS mode. The proposed algorithm can choose more reliable information in the data fusion process and diminish the communication cost (by using the saved intermediate data of previous iteration) and the calculation load. In addition, in GDS mode, the DS factor (DSF) selects data based on noise statistics (NS), resulting in some loss of selection ability. To further improve this situation, a novel cross-matching mechanism is proposed to improve the design of the DSF based on an intermediate estimation error. The mean stability and mean-square performance of the proposed DLMS algorithm with the ADS mode are analyzed theoretically, which can derive a convergence condition based on the step-size. Theoretical verification and target localization simulations are implemented to illustrate the effectiveness and robustness of the proposed ADS algorithm under satisfying the convergence condition as compared to other related DS algorithms.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 11972314, in part by the Innovation Capability Support Plan of Shaanxi Province under Grant D5140190076, and in part by the Emergency Management Technology Innovation Research Project of Shaanxi Province under Grant 2022HZ1390.

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Authors and Affiliations

  1. School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Fangyi Wan, Yi Hua, Bin Liao, Ting Ma & Xinlin Qing

  2. School of Aerospace Engineering, Xiamen University, Xiamen, 361000, People’s Republic of China

    Xinlin Qing

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  1. Fangyi Wan
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Contributions

FW contributed to the software, methodology, formal analysis, writing—original draft. YH was involved in the conceptualization, methodology, investigation, supervision, writing—original draft. BL contributed to the software, editing and validation. TM assisted in the visualization, software, formal analysis, and validation. XQ contributed to the validation, formal analysis, editing and software

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Correspondence to Yi Hua.

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Wan, F., Hua, Y., Liao, B. et al. Distributed Estimation with Novel Adaptive Data Selection Based on a Cross-Matching Mechanism. Circuits Syst Signal Process 42, 6324–6346 (2023). https://doi.org/10.1007/s00034-023-02410-6

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