Optimal Deployment Method Of Short Baseline Direction Finding Based On Time Difference Of Arrival

The multi-station passive time-difference positioning system has always been the research focus of many scholars at home and abroad,and has broad application prospects in aerospace integrated networks,wireless sensor networks and so on.When the baseline between the receiver stations is too short,the entire receiver station network forms a point-to-point geometry with the distant radiation source,which leads to the use of the time difference of arrival(TDOA)between the receiver station system and the target radiation source.During direction finding or positioning,there is a large error in the connection between the radiation source and the receiving station.In this case,part of the radial positioning accuracy can be sacrificed to ensure that each receiving station can improve the direction finding accuracy of passive TDOA for the radiation source.Therefore,many scholars have carried out research on the DF information measurement method and DF algorithm based on DF information measurement in the short-baseline passive time difference DF problem,but these studies seldom consider the optimal deployment of DF nodes.In order to improve the short-baseline passive time difference direction finding accuracy,it is urgent to carry out theoretical research on the optimal deployment of direction finding nodes.This thesis mainly studies the optimal deployment method of short-baseline passive TDOA direction finding nodes,constructs the basic optimization framework for optimal deployment of direction-finding nodes in the passive TDOA direction-finding scenario of radiation sources,and constructs a basic optimization framework for the optimal deployment of direction-finding nodes in the regional passive TDOA direction-finding scenario.The basic optimization framework for optimal deployment of direction finding nodes is proposed based on particle swarm optimization as the optimal deployment algorithm for direction finding nodes,and the algorithm proposed in this paper is simulated by MAILAB software to verify its performance.The contents studied in this paper are as follows:The optimal deployment method for passive TDOA direction finding of radiation sources is studied.Aiming at the passive TDOA direction finding scenario of radiation source,a mathematical model of the optimal station placement problem of passive TDOA direction finding of radiation source is constructed.And the particle swarm algorithm in the swarm intelligence algorithm is selected to solve the optimization problem,so as to obtain the optimal deployment configuration of the direction finding node.The simulation results show that when the direction-finding nodes selected by the passive TDOA directionfinding optimal station layout algorithm based on the particle swarm algorithm proposed in this paper participate in direction finding,compared with the traditional rectangular station layout,arc station layout,and Cartesian station layout The station layout method obtained from the same optimization problem with the trace of the mean square error(MSE)of the short-baseline passive TDOA positioning system in the coordinate system significantly reduces the direction finding error for single and multiple radiation sources.At the same time,it is demonstrated that when other influence conditions remain unchanged,the more the number of DF nodes,the better the DF performance.The optimal deployment method for regional passive TDOA direction finding is studied.The regional passive TDOA direction finding scene is designed,and the mathematical model of the regional passive TDOA direction finding optimization problem is constructed.And the solution process for this optimization problem is proposed,so as to obtain the node optimization strategy for regional passive TDOA direction finding.The simulation results show that when the passive time difference direction finding node is deployed according to the regional passive TDOA direction finding optimization algorithm proposed in this paper,compared with the traditional rectangular layout,arc layout,and Cartesian coordinate system.The average value of the mean square error(MSE)trace of the multisampling point short-baseline passive TDOA positioning system is the same optimization problem as the objective function.Direction finding error.