| Citation: | YANG Y J，GAN Y，LI C H，et al. Amended SRCKF algorithm based on minimum variance of innovation[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（1）：138-144 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0202
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The model errors in the target tracking process will lead to the degraded performance and decreased filtering accuracy of the square-root cubature Kalman filter (SRCKF). Amended Kalman filter (AKF) can solve this problem effectively, but it is difficult to be applied to nonlinear filtering. To overcome the negative impact of the model errors and to further improve the application scope of the amendment thought, the vector form of the amendment coefficient is derived by minimizing innovation covariance on the basis of the SRCKF. Then, the amended SRCKF (ASRCKF) algorithm is proposed. By using posterior measurements, the ASRCKF algorithm can increase confidence level to measurement, so that the target model error can be compensated. The simulation results indicate the ASRCKF can suppress the model errors effectively with better filtering performance, compared with SRCKF and STF-SRCKF algorithms.
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