Research On Multi-sensor Target Tracking Technology In Complex Environment

In the modern electronic warfare environment,the number of targets is unknown and changeable,the density of clutter is high and the means of interference are endless.At the same time,the target tracking based on single sensor maintains less measurements and large error,which cannot meet the high-precision requirements of modern target tracking.Therefore,in order to obtain more comprehensive and accurate measurements and achieve more accurate and faster multi-target tracking,multi-sensor multi-target tracking technology has gradually become a research hotspot in the field of target tracking.In this thesis,the moving target tracking algorithm,multi-target data association algorithm and multi-sensor track fusion technology are studied for the multi-sensor target tracking technology in complex environment.By studying the principle of the interacting multiple model(IMM)algorithm,an improved IMM algorithm is proposed to solve the mismatch problem of its motion model.By focusing on the research of genetic algorithm-particle swarm optimization(GAPSO)algorithm,an improved GAPSO algorithm is proposed,and multi-target data association accuracy is improved.At the same time,multi-sensor track fusion strategy based on radar burst detection is studied.First,the principles of target motion models,Kalman filter algorithm and IMM algorithm are analyzed in this thesis,and their advantages and disadvantages are discussed.At the same time,the problem that the mismatch of motion models in IMM algorithm is studied,and an improved IMM algorithm is proposed.By supplementing the acceleration vector of the acceleration model with state vector,the problem of model mismatch is effectively improved.Then,the principles of classic data association algorithms such as joint probability data association(JPDA)are studied,and the characteristics of these algorithms are analyzed.When the JPDA algorithm deals with dense targets,there are problems that calculation amount increases rapidly and the association speed is slow.In order to solve the problem of slow association,the GAPSO algorithm is studied.This algorithm combines the emerging bionic intelligent optimization algorithm with data association,which realizes fast data association.On the basis of this,the GAPSO algorithm is improved,the dynamic adaptive crossover probability and mutation probability are introduced,and the elite retention strategy based on the coding factor is proposed.On the premise of ensuring fast data association,the accuracy of association is further improved.Finally,on the basis of studying the principle ofmulti-sensor track fusion structure and classical multi-sensor track fusion algorithm,the performance differences between single sensor tracking and multi-sensor tracking are compared and analyzed,and that multi-sensor track fusion has more advantages in actual combat is proved.At the same time,a multi-sensor track fusion strategy combining active sensor and passive sensor is proposed.The fusion strategy greatly shortens the boot time of the active sensor while ensuring the tracking accuracy,and improves the concealment of the combat system.