Target tracking based on second-order converted measurement Kalman filter

To reduce the linearization errors of the Conventional Extended Kalman Filter (EKF) algorithm and the Converted Measurement Kalman Filter (CMKF) algorithm, the Second-order Converted Measurement Kalman Filter (SCMKF) algorithm is proposed in 3-dimensional space. The mean and the covariance of the converted measurements errors in Cartesian coordinates are inferred by the means of second-order Taylor series expansion. A more accurate and faster Kalman filter algorithm with debiased converted measurements is presented. Simulation results indicate that the SCMKF algorithm has higher tracking accuracy and faster convergence rate than the CMKF, the EKF, and the unscented Kalman filter, and the computation process of the SCMKF is more efficient than that of Debiased Converted Measurement Kalman Filter (DCMKF).