An Improved Filtering Algorithm and Its Application in Target Tracking
Aiming at the problem that the unscented Kalman filter algorithm may have a non positive covariance matrix in the process of filtering iteration,which leads to large tracking error and even easy divergence of filtering,the unscented Kalman filter algorithm is improved and designed.In the process of filtering iteration,QR decomposition and Cholesky decomposition are carried out on the state cova-riance matrix,and the filtering iteration is carried out through the decomposed covariance square root matrix,so as to ensure the positive definiteness of the state covariance matrix,and the weighted least square method is used for synchronous fusion of measurement data.In order to verify the effectiveness of the improved algorithm,the data fusion simulation test experiment of radar infrared joint tracking system is designed and compared with the traditional unscented Kalman filter algorithm.The simulation results show that the improved algorithm can effectively suppress the tracking error and improve the ro-bustness of the target tracking system,which can be applied to the design of multi-sensor track infor-mation fusion system of air defense weapon system.
data fusiontarget trackingtime registrationsquare-root unscented Kalman filtering
万方数据
维普
