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Statistical Similarity Measure-Based Adaptive Outlier-Robust State Estimator With Applications | IEEE Journals & Magazine | IEEE Xplore

Statistical Similarity Measure-Based Adaptive Outlier-Robust State Estimator With Applications


Abstract:

This article presents an adaptive outlier-robust state estimator (AORSE) under the statistical similarity measures (SSMs) framework. Two SSMs are first proposed to evalua...Show More

Abstract:

This article presents an adaptive outlier-robust state estimator (AORSE) under the statistical similarity measures (SSMs) framework. Two SSMs are first proposed to evaluate the similarities between a pair of positive definite random matrices and between a pair of weighted random vectors, respectively. The AORSE is developed by maximizing a hybrid SSMs based cost function, wherein the posterior density function of the hidden state is assumed as a Gaussian distribution with the posterior covariance being approximately determined in a heuristic way. Simulation and experimental examples of moving-target tracking demonstrate the effectiveness of the proposed algorithm.
Published in: IEEE Transactions on Automatic Control ( Volume: 67, Issue: 8, August 2022)
Page(s): 4354 - 4361
Date of Publication: 23 May 2022

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