Application of An Adaptive Cubature Kalman Filter in Target Tracking Model of Infrared Semi-strapdown Seeker

Abstract

According to the characteristics of the cubature Kalaman filter, the precision of filter can be improved by considering the statistical characters of the noise, thus an adaptive CKF is applied to solve the estimation problem existed in the semi-strapdown seeker system. By calculating the mean value in each iteration, the algorithm can estimate and correct the statistical characters of the noise on-line by using Sage-Husa maximum a posterior (MAP) estimator in the filtering process therefore, and then can effectively improve the estimation accuracy and stability of the CKF. When the normal and adaptive CKF methods are applied in the target tracking model of infrared semi-strapdown seeker, the simulation results show that the adaptive CKF algorithm is more feasible and effective, and it has better performance than the CKF both in stability and precision, thus demonstrating that the ACKF could obviously improve the filtering effect of normal CKF algorithm.