Covariance Matching based robust Adaptive Cubature Kalman Filter

TL;DR

This paper introduces a covariance matching-based adaptive robust cubature Kalman filter that adaptively estimates measurement noise covariance, improving robustness and performance in state estimation tasks.

Contribution

It proposes a novel covariance matching approach for adaptive robust CKF, enhancing noise covariance estimation and filter robustness.

Findings

Improved estimation accuracy demonstrated in simulations

Enhanced adaptive capability over conventional ACKF

Effective noise covariance adaptation in dynamic scenarios

Abstract

This letter explores covariance matching-based adaptive robust cubature Kalman filter (CMRACKF). In this method, the innovation sequence is used to determine the covariance matrix of measurement noise that can overcome the limitation of conventional CKF. In the proposed algorithm, weights are adaptively adjusted and used for updating the measurement noise covariance matrices online. It can also enhance the adaptive capability of the ACKF. The simulation results are illustrated to evaluate the performance of the proposed algorithm.