Research On Target Tracking Algorithm Based On Single Direction Finding Station

As the development of modern science technology and industry are growing,there are various kinds of targets can be tracked.The mobility of the target is growing,so people propose higher demand for the accuracy of tracking.How to make use of the observation measurement of the target,by using target tracking filter algorithm,to realize the consistent tracking,is the key point of target tracking.This paper takes the stationary single direction locating statistic as the observation measurement,the moving radiation source as the tracking target,models the common motion states of target radiation sources.Focusing on how to improve the tracking and positioning accuracy of the moving radiation source,did the in-depth study of the target tracking algorithm.The work and research results completed in this paper are summarized as follows:1.The traditional linear Kalman filter algorithm is studied.The coordinate transformation Kalman filter algorithm is derived by combining the observation characteristics.The commonly used nonlinear Kalman filter algorithm,namely extended Kalman filter algorithm and unscented Kalman filter algorithm,is studied,and the simplified unscented Kalman filter algorithm is proposed.Through simulation,the performance of conventional filtering algorithm and several tracking filtering algorithms based on Kalman filtering are analyzed and compared.It is found that the unscented Kalman filtering algorithm has better tracking filtering performance under the background of this paper.2.The method of adaptive processing of system model noise and observation noise in the tracking filtering process is studied.Combined with the best-performance optimal Kalman filter algorithm,the adaptive unscented Kalman filter algorithm is proposed.Realtime judgment and correction of the possible filter divergence are made and improved adaptive unscented Kalman filter algorithm is proposed.Through simulation,the feasibility of the proposed algorithm to prevent filter divergence is verified,also we verified that the adaptive noise processing process can improve the efficiency of the tracking accuracy of the target.3.The common motion states of the target radiation source are studied.The near uniform motion model,the near uniform acceleration motion model and the near uniform motion model are established in the two-dimensional plane rectangular coordinate system and the three-dimensional rectangular coordinate system.Considering the target maneuvering motion,the interactive multi-model algorithm is studied,and the interactive multi-model algorithm is combined with the unscented Kalman filter algorithm to propose the unscented Kalman filter algorithm of the interactive multi-model.To improve the tracking accuracy.An adaptive noise estimator is introduced,and an adaptive unscented Kalman filter algorithm for preventing multi-models of divergence is proposed.The superiority of the performance of the algorithm is verified by simulation and field experiments.