Research On Moving Multi-station Passive Location Algorithm Based On TDOA/FDOA

In the modern information warfare,with the rapid increase of military frequency equipment,the electromagnetic environment has become increasingly complex.Under such an environment and background,passive location technology has become an important research content in the field of electronic countermeasures.Compared with other location systems,the passive location system based on TDOA/FDOA information has been widely studied and applied by scholars at home and abroad because of its ability to simultaneously estimate the position and speed of the target and reduce the number of receiving stations.Therefore,this paper takes the passive TDOA/FDOA location system as the research object.The content studied in this article has the following aspects:Firstly,introduce the basic principles of location based on TDOA and FDOA information,namely TDOA location,FDOA location,and TDOA/FDOA joint location.The geometric accuracy factor formula of the TDOA/FDOA joint location model is derived,and the influence of TDOA measurement accuracy,FDOA measurement accuracy,receiver location uncertainties,station layout,target height,baseline length and other factors on location accuracy is analyzed.Secondly,several location algorithms based on TDOA and FDOA information are introduced: weighted least squares algorithm,Chan algorithm,and two-stage weighted least squares algorithm.In view of the long search time and poor location accuracy of existing search algorithms,this article proposes a hybrid location algorithm based on weighted least squares and particle swarm optimization algorithm is proposed.The algorithm first uses the weighted least squares method to obtain the initial estimate value of the target source position and velocity,and then uses the particle swarm algorithm to perform a global search near the initial estimated value to reduce the search range of the algorithm and shorten the search time.However,the particle swarm optimization algorithm is easy to converge prematurely,and it is easy to fall into a sub-optimal solution in the later stage of the algorithm.Aiming at this problem,this paper introduces the mechanism of natural selection.Simulation analysis show that the algorithm can improve the location accuracy of the algorithm.Finally,in order to solve the problem of the decrease in location accuracy of the traditional location algorithm when the position and velocity information of the receiving station have errors,a location method based on the sparrow search algorithm is proposed.This algorithm simulates the foraging and anti-predation behavior of the sparrow population and can intelligently respond the target space is searched,and the simulation results show that when the receiver location uncertainties is small,the root mean square error of the sparrow algorithm deviates from the Cramer-Rao Lower Bound,and the location performance is poor.In order to improve the location accuracy of the sparrow search algorithm in the case of low receiver location uncertainties,a sparrow search algorithm introduces Logistic chaotic mapping during initialization,which can make the distribution of sparrow individuals after initialization more uniform.To a certain extent,it can jump out of local extreme values and improve the search performance of the algorithm.