Research On Motion Modeling And Tracking Of Maneuvering Target

In today’s era,information and technology are developing rapidly,and aerospace technology has also developed greatly with the efforts of various countries.Various types of missiles and aircraft with high-speed and high-mobility movement characteristics are emerging one after another.With the greatly enhanced ability of various types of aircraft evasion maneuver and penetration attack,the technical requirements for the positioning and tracking of these targets are getting higher and higher.In recent years,the United States and its allies have increasingly engaged in arms exercises in the Asia-Pacific region,posing a great threat to China’s national security.Therefore,studying motion modeling and accurate stability tracking of maneuvering targets is essential for deploying military strategies and safeguarding national security.Based on the principle of maneuvering target tracking,this paper studies the motion modeling and tracking system of maneuvering target.The main work includes the following aspects:(1)This paper introduces the development of maneuvering target tracking algorithms and models.Then,introduces the traditional typical motion model and filtering algorithm of maneuvering targets,and their tracking performance is compared through simulation experiments.Then analyze their respective application ranges based on the experimental results.(2)On the basis of the theoretical research of Singer model and collaborative turning model,I propose a new maneuvering target tracking simulation model for verification of the performance of the tracking model and algorithm,and verify the adaptability and complexity of the model through simulation.(3)The correction factor is constructed by filtering new information to adaptively amplifies the sampling range of the UKF algorithm.The improved UKF algorithm is used to track turning maneuvering targets,so that the filter can be adaptively adjusted under the condition that the prior information is inaccurate,which effectively improves the tracking performance of the algorithm.(4)Improve the multi-model algorithm by using the idea of sequential filtering.During the filtering process,two different sets of measurement noise matrices are set by adjusting different detection accuracy information,and two sets of filtering outputs are obtained by sequential filtering.Use the information in the filtered output to filter out mismatched models and reassign model probabilities to the remaining models.The statistical distance is constructed as the tracking accuracy judgment standard,and a set of filter outputs with small statistical distance is selected to update the target status.