Point Source Target Passive Tracking Algorithm Research

As an important branch of target tracking techniques, passive target tracking plays important roles in improving the viable ability and battle effectiveness of weapon systems in modern electronic warfares. This thesis focuses on the bearing-only target positioning and bearing-only target tracking problems .The main works are as follows:The problem of bearing-only target positioning is first outlined. The dynamic model in the spherical coordinate is presented for bearing-only target positioning. However, such model has unknown covariance of process noise, which is related to the range between the target and the passive sensor. Here two parameter identification methods are proposed: 1) Based on the principle of the orthogonality among filter residuals, a parameter identification method is proposed, which results in Parameter-Identification Based Filter (PIBF). 2) Based on multiple model idea, the parameter is quantified in its value range and then adaptively identified via model switches. In addition, the decoupling two-channel filter model is also presented, which is shown much low computation burden and satisfactory accuracy in computer simulations.The nonlinear problem is inevitable in bearing-only target tracking problem. Here the Monte Carlo based nonlinear filters are compared with the classical Extended Kalman Filter (EKF) in this field: the Unscented Kalman Filter (UKF), the Particle Filter (PF) and the Unscented Particle Filter (UPF). Furthermore, a multi-sensor passive tracking algorithm, through combining the UKF and Probability Data Association (PDA), is provided for bearing-only target tracking in dense clutters.