Research On Passive Localization Of Mobile Transmitters In Complex Electromagnetic Environment

In the context of high-tech and information technology,land,sea,and sky are linked into a multi-dimensional battlefield,which puts forward high requirements for the combat forces in the information battlefield to have good concealment and timely and accurate access to battlefield information.At the same time,the development of new technologies has brought safety hazards,and events such as unmanned aerial vehicle(UAV)"black flying" and signal interference are frequent.Interference source localization technology has become an important research topic.Passive positioning system plays an important role in modern information warfare and civil applications due to its advantages of high concealment,low cost,and high mobility.Considering the complex and volatile electromagnetic environment in the information battlefield,this thesis studies passive positioning technology for mobile signals,and innovates on multi-station time difference passive positioning systems.The proposed method in this thesis improves positioning accuracy while reducing computational complexity,which can better serve military electronic warfare and civilian communication systems.The main contributions of this thesis include:1.This thesis studies accuracy analysis and station optimization algorithms in positioning systems,models the station optimization problem in multi-station positioning systems,and designs a station optimization method based on differential evolution with the goal of minimizing the average error within the region.The method is divided into three modules:layout optimization scenario,optimization objectives and constraints,and differential evolution method.The results of simulation experiments indicate that the method designed in this thesis can effectively solve the optimization problem of station placement in multi station systems,and obtain the optimal station placement results that are in line with the research scenario.2.This thesis proposes a genetic Markov localization method based on compressed sensing,which is divided into three modules:multi station sampling module,reconstruction module,and localization module.In the multi station sampling module and reconstruction module,in order to reduce the computational complexity,the method uses compressed sensing technology to sample the signal and directly reconstruct the spectral function of the original signal from the low dimensional sampled signal.The simulation results show that compressed sensing technology can effectively reduce the running time of the method while ensuring the accuracy of the estimation results.Compared with other methods,the proposed method can achieve more ideal estimation results at low complexity.3.In view of the problem that most existing improved filtering methods are difficult to balance estimation performance and computational complexity,the method proposed in this thesis adopts genetic optimization ideas in the positioning module to optimize and correct inaccurate results in the preliminary results.Simulation experiments show that under the same conditions,the performance of the method proposed in the thesis is superior to other comparative methods,and the mean square error of the results is closer to the lower bound of the Cramer-Rao.High-accuracy positioning is achieved even under low SNR conditions,demonstrating the correctness of the method improvement idea.The above all demonstrate the high accuracy and low complexity of the improved method proposed in this thesis.