Research On Communication Radiating Source Localization Technology

In recent years,with the development of 5G and artificial intelligence technology,passive localization technology has penetrated into various aspects of people’s lives,especially in application scenarios such as environmental monitoring,intelligent transportation,indoor positioning,and underwater rescue,where the technology plays an irreplaceable role in the localization of communication radiation sources.Communication radiation source localization technology not only meets people’s daily needs for positioning services but also plays a decisive role in electronic countermeasures and other military fields,with significant military significance.Therefore,this thesis focuses on the key and difficult problems of Time Difference of Arrival(TDOA)passive ranging and localization technology,based on the research results of predecessors in the field of passive localization,and mainly studies how to improve the localization accuracy of the positioning algorithm under the condition of large TDOA measurement errors and how to improve the performance of large-scale sensor network localization systems through autonomous selection of the best anchor node combination algorithm.The main research contents of this thesis are as follows:(1)Aiming at the low accuracy problem of traditional localization algorithms with large TDOA measurement errors,a TDOA iterative localization method based on improved Constrained Total Least Squares(ICTLS)is proposed.Firstly,the Constrained Total Least Squares(CTLS)solution with TDOA measurement errors is analyzed and deduced.Then,using the good global convergence performance demonstrated by the Alternating Direction Method of Multipliers(ADMM)in solving convex optimization problems,the traditional CTLS solution is improved by using the constraint relationship between the internal elements of the estimated vector in the traditional CTLS method as a new constraint,and modifying the objective optimization function to obtain the form of ICTLS solution.The ICTLS solution is rewritten into an augmented Lagrangian function form based on the principle of ADMM method to obtain an unconstrained localization optimization problem.Finally,the target position is estimated through ADMM iteration.The impact of the number of iterations,TDOA measurement errors,the number of anchor nodes,and the geometric distribution of anchor nodes on the performance of proposed ICTLS algorithm is analyzed and compared with the CTLS algorithm and the Two-step Weighted Least Squares(TSWLS)algorithm through simulation experiments,and the results verify that the proposed algorithm exhibits better ability to suppress TDOA measurement errors and has good localization performance under the condition of fewer anchor nodes and larger TDOA measurement errors.(2)Considering the requirements of large sensor networks in terms of power consumption and positioning accuracy,an anchor node selection algorithm based on the Improved Gray Wolf Optimization(IGWO)algorithm is proposed under TDOA measurement errors and anchor node position errors.It can not only reduce the overall energy consumption of the positioning system,prolong the service life of the anchor node,but also reduce the influence of double errors on the positioning accuracy.Firstly,the CTLS approximate closed-form solution under double error conditions is derived.Then,the localization estimation error covariance matrix of the closed-form solution is solved using perturbation method,and the mathematical model for anchor node selection problem was constructed using the minimized trace of the localization error covariance matrix as the objective optimization function.Then,the Grey Wolf Optimization(GWO)algorithm is improved from three aspects: nonlinear convergence factor,increased hunting and following ability of the head wolves,and adaptive weighted step strategy.And a solution for optimized anchor node selection problem based on the IGWO algorithm was designed.Finally,the global search and local refinement capabilities of IGWO algorithm are comprehensively analyzed from the aspects of the change of optimization time with the total number of anchor nodes,the change of fitness value with the number of iterations,and the change of algorithm performance with the anchor nodes position error and TDOA measurement error.And compared with the genetic algorithm(GA),particle swarm optimization(PSO)algorithm and GWO algorithm through simulation experiments,the anchor node selection algorithm based on IGWO algorithm was verified that can still maintain efficient optimization ability and good optimization accuracy under severe double error conditions,and provide a feasible solution for solving the anchor node selection problem under double error conditions.