A sequential processing method for converted measurement Kalman filters based on orthogonal transform

Abstract

To effectively reduce computational requirements of converted measurement Kalman filters (CMKF), a sequential processing method was presented in this paper. Firstly, the spherical measurement of the target position was converted into Cartesian coordinate domain, resulting in converted measurement error and the corresponding statistics (mean and covariance). Secondly, a sequential processing method which sequentially treated scalar components of the converted measurement vector, was derived based on orthogonal transform. Finally, the proposed sequential processing method was utilized to implement the CMKF algorithm for a target tracking scenario. The simulation results show that the proposed method can effectively reduce the computational requirements of CMKF algorithm while achieving desired tracking performance. © 2012 Springer Science+Business Media B.V.