Research And Application Of Airborne Surveillance Radar Target Tracking Algorithm

In modern warfare characterized by electronic and information,airborne surveillance radar have unique advantages in acquiring enemy information as compared to traditional ground-based radar.Therefore,they play an indispensable role in modern warfare.Uninterrupted tracking of the target of interest is one of the main functions of airborne surveillance radar.Therefore,the problem of airborne surveillance radar tracking has received considerable attention and has been further studied in related fields.The problems of airborne surveillance radar target tracking mainly includes two difficulties.First,target's maneuvering brings great difficulties to accurately estimate target's state.Second,in the multi-target tracking,the dense echo makes target Data associations between measurements and targets face great difficulties.The thesis mainly studies maneuvering target tracking algorithm and multi-target tracking algorithm in clutter environment.Firstly,the principle of traditional maneuvering target tracking algorithm is analyzed,the factors affecting tracking accuracy of algorithm are studied.A "current" statistical model is proposed which adaptively adjusts the maneuvering frequency of algorithm by target innovation change;And then several classical data association algorithm are studied.Then discusses the application environment and performance of various association algorithms,and proposes an improved data association algorithm based on chaotic neural network for high-complexity defects of JPDA algorithm.The work done and obtained research results by the thesis are summarized as follows:1.From the principle of Bayesian filtering,several Bayesian filtering algorithms commonly used in target tracking algorithms are studied,such as Kalman filtering,extended Kalman filtering,Unscented Kalman filtering and particle filter.The performance of various filtering algorithms was compared and analyzed through simulated experiments.It was found that when the degree of nonlinearity of the system equation is high,the particle filter has better performance than other filter algorithms.2.From the principle of maneuvering target tracking,several traditional maneuvering target tracking algorithms are studied.The applicable scenarios and the advantages and disadvantages of each tracking method are analyzed and discussed.The effect cause by fixed maneuvering frequency parameters in the CS model are emphatically analyzed.And a method of adaptively adjusting the maneuvering frequency by changing of target innovation is proposed.This method can effectively improve the real-time performance and tracking accuracy of the CS model.Finally,through simulation experiments,the superiority of this method is verified.3.Starting from data association algorithm in multi-target tracking problem,the thesis deeply analyzes the tracking performance of three traditional data association methods in different environments,and concludes that the JPDA method has better tracking performance.The algorithm for the JPDA method neural network implementation due to the slow convergence,a JPDA algorithm based on an improved transient chaotic neural network is proposed.Theoretical analysis and simulation results verify that the algorithm can improve the network convergence speed by changing the setting of network parameters in different search phases.And effectively reduce the running time of the algorithm.