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

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.

Automated summary

This article presents an adaptive outlier-robust state estimator (AORSE) under the statistical similarity measures (SSMs) framework. The AORSE is developed by maximizing a hybrid SSMs based cost function, which improves the accuracy of the algorithm. Simulation and experimental examples show the effectiveness of the proposed algorithm.