A new state estimation approach-Adaptive Fading Cubature Kalman filter

Abstract

This paper presents a novel adaptive fading cubature Kalman filter (AFCKF) based on double transitive factors. The developed adaptive algorithm is explained in two stages; stage (i) a single transitive factor is used to update the predicted state error covariance, P_k^- based on innovation or residual vector, whereas, in stage (ii), the measurement noise covariance matrix, R_k^* is scaled by another transitive factor. Furthermore, showing the proof concept for estimation of the process noise, Q_k^* and measurement noise covariance matrices by combining the innovation and residual vector in the AFCKF algorithm. It can provide reliable state estimation in the presence of unknown noise statistics. Bench-marking target tracking example is considered to show the performance improvement of the developed algorithms. As compared with existing adaptive approaches, the proposed fading algorithm can provide better estimation results.

Tasks

Reproductions