Improved adaptive Huber filter for relative navigation using global position system

Abstract

An improved adaptive Huber filter algorithm is presented to model error and measurement noise uncertainty. The adaptive algorithm for model error is obtained using an upper bound for the state prediction covariance matrix. The measurement noise is estimated at each filter step by minimizing a criterion function which was original from adaptive Huber filter. A recursive algorithm is provided for solving the criterion function. The proposed adaptive filter algorithm was successfully implemented in relative navigation using global position system for spacecraft formation flying in high earth orbits with real orbit perturbations and non-Gaussian random measurement error. Simulation results indicated that the proposed adaptive filter performed better in robustness and accuracy compared with previous adaptive algorithms.