Data Fusion of Infrared and Radar for Target Tracking

A target tracking method based on data fusion of infrared and radar is proposed to improve tracking precision. Unscented Kalman filter (UKF) is applied to process data on distributed fusion architectures. The method combines the advantages of UKF and track-to-track algorithms. The cross-covariances of the two sensors are used to estimate overall covariance and states. The overall estimation is obtained by the track-to-track fusion algorithm for the optimal combination of two correlated estimates. The proposed method is applied to simulating target tracking of infrared and radar. The simulation results show the proposed method has advantages in higher precision, and probability of detection is increased.