Diffusion Logarithm-Correntropy Algorithm for Parameter Estimation in Non-Stationary Environments over Sensor Networks.

Limei Hu, Feng Chen, Shukai Duan, Lidan Wang

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Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Limei HuCollege of Electronic and Information Engineering, School of Mathematics and Statistics, Southwest University, Chongqing 400715, China. hlm0903@email.swu.edu.cn.

Feng ChenCollege of Electronic and Information Engineering, School of Mathematics and Statistics, Southwest University, Chongqing 400715, China. fengchenmit@swu.edu.cn.

Shukai DuanKey Laboratory of Nonlinear Circuits and Intelligent Information Processing, and College of Electronic and Information Engineering, Southwest University, and Chongqing Collaborative Innovation Center for Brain Science, Chongqing 400715, China. duansk@swu.edu.cn.

Lidan WangKey Laboratory of Nonlinear Circuits and Intelligent Information Processing, and College of Electronic and Information Engineering, Southwest University, and Chongqing Collaborative Innovation Center for Brain Science, Chongqing 400715, China. ldwang@swu.edu.cn.

Funding

Chongqing Research Program of Basic Research and Frontier Technology No. cstc2017jcyjAX0265the Fundamental Research Funds for the Central Universities Nos. XDJK2017B053, XDJK2017D176, XDJK2017D180, XDJK2017D181

6 · The paper itself

Abstract

This paper considers the parameter estimation problem under non-stationary environments in sensor networks. The unknown parameter vector is considered to be a time-varying sequence. To further promote estimation performance, this paper suggests a novel diffusion logarithm-correntropy algorithm for each node in the network. Such an algorithm can adopt both the logarithm operation and correntropy criterion to the estimation error. Moreover, if the error gets larger due to the non-stationary environments, the algorithm can respond immediately by taking relatively steeper steps. Thus, the proposed algorithm achieves smaller error in time. The tracking performance of the proposed logarithm-correntropy algorithm is analyzed. Finally, experiments verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been proposed for parameter estimation.

Indexed as

diffusion logarithm-correntropy algorithmnon-stationaryparameter estimationsensor networkstracking performance

Identifiers

PMID30309002

DOI10.3390/s18103381

PMCPMC6209990

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