Nonlinear filtering algorithm for improving opto-electric target tracking

In order to reduce the linear errors and improve the long-range target tracking accuracy, the Second-order debiased Converted Measurement Kalman Filter (SCMKF) algorithm is presented based on conventional CMKF, which is obtained by taking the second-order terms of a Taylor series expansion for the converted measurement functions to approximate the Cartesian coordinate errors. The mean and covariance of Cartesian measurement errors have been derived and the debiasing compensation is applied to SCMKF algorithm, which is helpful to improve long-range tracking accuracy. Simulation results show that the tracking accuracy of SCMKF is much higher than those of EKF and conventional CMKF, and the SCMKF provides faster convergence rate than the EKF.