Aircraft trajectory filtering method based on Gaussian‐sum and maximum correntropy square‐root cubature Kalman filter

Abstract

Aiming at meetiing the need to filtering flight trajectory data for aircraft testing, a novel adaptive cubature Kalman filter (CKF) is proposed based on the maximum correntropy and Gaussian-sum in this paper. Firstly, based on the traditional CKF algorithm, we introduced a Gaussian-sum method to approximate non-Gaussian noise to get more accurate filtering results in view of the problem of reduced filtering accuracy caused by the inherent non-Gaussian nature of the noise and the system non-linearity. Secondly, the maximum correntropy criterion is introduced to solve further the problem of improving the filtering accuracy of the system in the case of non-linearity. Simulation results and actual data verification showed that the Square-root cubature Kalman filter algorithm based on the maximum correntropy and Gaussian-sum has higher accuracy than traditional filtering algorithms, which verified the algorithm's effectiveness in the application.