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Estimation fusion for distributed multi-sensor systems with uncertain cross-correlations

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  • Jianfang Tang
  • Jie Zhou
  • Yao Rong

Abstract

This paper addresses the estimation fusion problem in distributed multi-sensor systems with uncertain cross-covariance among local estimation errors. A robust linear estimation fusion method is proposed in the sense of minimising the worst mean square error of the fused estimator over the uncertain normalised cross-covariances (NCC). The weighted coefficient matrices of the fused estimator can be obtained by solving a semi-definite programming problem. This estimation fusion method is suitable for the situations with completely unknown NCC or partly known NCC. Two fusion estimators for the uncertain NCC with partly known prior information are presented. Some numerical simulations are provided to show the good performance of the proposed estimators.

Suggested Citation

  • Jianfang Tang & Jie Zhou & Yao Rong, 2019. "Estimation fusion for distributed multi-sensor systems with uncertain cross-correlations," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(7), pages 1378-1387, May.
    • Handle: RePEc:taf:tsysxx:v:50:y:2019:i:7:p:1378-1387
    DOI: 10.1080/00207721.2019.1615573
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