Research On Filter Tracking Technology In Multiple Passive Detection And Positioning Modes

In response to the increasingly complex passive detection and positioning requirements,how to use different observations in different passive positioning modes to estimate the position and status information of the target radiation source,and realize real-time positioning and tracking is becoming more and more important.This paper takes this as the research goal to carry out corresponding theoretical and simulation research.The work done in this article is summarized as follows:1.In the time difference positioning,firstly,in order to solve the problem of missing time difference data in practical applications,a variety of data filling methods are studied,and comparative simulation analysis is carried out.The simulation results show that the data error filled by the least squares curve recursive fitting method is small.On the basis of obtaining complete time difference observation data,the Least Square(LS)algorithm,Recursive Least Square(RLS)tracking filter algorithm,and standard Kalman Filtering(KF)are studied respectively.The tracking performance of the tracking algorithm,the conclusion is drawn that the tracking performance of the RLS algorithm and the KF algorithm are better.Finally,aiming at the problem of time difference positioning and tracking of mobile radiation source targets,combined with Interacting Multiple Model(IMM),the RLS-IMM algorithm and the KF-IMM algorithm are studied,and their tracking performance is simulated and compared.The simulation results show that IMM The algorithm can effectively improve the positioning accuracy.2.In the direction finding intersection positioning,the two-station direction finding intersection positioning and the three-station direction finding intersection positioning are used as the application backgrounds.The RLS algorithm,the Unscented Kalman Filtering(UKF)algorithm,and simplified Tracking filtering performance of Simplified Unscented Kalman Filtering(SUKF)algorithm.The simulation results show that the UKF algorithm has better filtering tracking performance within the acceptable range of calculation.Based on the UKF-IMM algorithm,and further research on the use of Adaptive Unscented Kalman Filtering(AUKF)algorithm to deal with the target radiator tracking problem under the timevarying conditions of observation noise,simulation results show that this method can be effective Improve positioning accuracy.3.In the time difference-direction combined positioning,the performance of the two processing methods of direct filtering tracking positioning and filtering smoothing after positioning are compared through simulation experiments.Then,using the UKF algorithm combined with the least squares data filling method and the IMM algorithm to process the actual observation data,a better positioning and tracking accuracy is obtained.