Covariance Matching based robust Adaptive Cubature Kalman Filter

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 adjust 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.

A new state estimation approach-Adaptive Fading Cubature Kalman filter

“…Simulation study of a bench-marking target tracking example [23] is presented in this section for comparing the performance assessment of the proposed algorithm with the existed algorithms; the CKF, ACKF, AFCKF-P adaption approaches. The nonlinear system and measurement models for target tracking example can be expressed as follows [6], [22]. The state vector, x k = [x 1,k x 2,k x 3,k x 4,k ] ⊤ including the vehicle position and velocity in x and y-plane.…”

“…The innovation and residual sequence are represented already in equation ( 9) and (10). By defining predicted and estimated state errors [22] are…”