Research On Filtering And Fusion Algorithms For Non-linear Target Tracking System

With the applications of non-linear filter expanding, the scopes of the target tracking systems have new features such as noise correlation, state mutation etc. The filters to deal with these specific filtering systems are not perfect. Therefore, it has important theoretic significance and application prospect.With the increasing complexity of the modern war, many multi-sensor systems toward application background appeared. In this case, the nonlinear model can describe the actual system more accurately. So, in this thesis, we proposed three kinds of multi-sensor fusion algorithms based on extended kalman filter (EIF) and compared the performance between the algorithms by simulation.This thesis carries out the following work:First, we propose a new UKF-STF-CN filter. Considering the correlation of process noise and measurement noise, we propose UKF-CN filter that can be used to deal with the systems with correlated noise. After that, in order to enhance the UKF-CN algorithm's performance to deal with the status mutation, we introduce the idea of STF to adjust the filter gain matrix online and propose the UKF-STF-CN. Simulation examples with bearings-only tracking are illustrated to verify the efficiency of the proposed algorithms.Second, we propose a new SCKF-STF-CN filter. This algorithm optimizes the sampling strategy of the UKF algorithm and enhances the estimated accuracy and the ability to cope with the system with high dimensional state. Similarly, several simulation examples with bearings-only tracking are illustrated to verify the efficiency of the proposed algorithms.Third, we propose three kinds of multi-sensor fusion algorithms based on extended kalman filter (EIF) and compare the performance between the algorithms by simulation.