Noise-blocking based receding horizon state estimation for constrained linear systems with multi-channels

This paper investigates the receding horizon state estimation for multi-channel constrained linear systems based on noise blocking. A fast receding horizon estimation algorithm is presented to improve the computation burden problem. In contrast to the standard receding horizon estimation, the move blocking structure is enforced into the estimated process noise sequence over the estimate window which can decreases the number of optimized variable and reduces the online computational burden when solving the receding horizon estimation problem. The stability of state estimation is proved by using the Lyapunov stability arguments. Finally, some comparisons to standard receding horizon estimation methods are adopted to illustrate the effectiveness of the proposed algorithm in terms of the computational burden.