Research On High Precision Positioning Technology Based On Mobile Communication Signal In Complex Post Disaster Environment

With the widespread application of mobile communication technology,mobile terminals are increasingly closely connected with populations' living styles.It also provides a good application basis for emergency rescue that entails mobile terminals to search and rescue trapped people.In recent years,the wireless location parameter estimation technology based on communication signal has gradually developed to the direction of multi-dimension,amalgamation and high-efficiency production.Meanwhile,it has significant theoretical value and application prospect to develop research on high-precision positioning technology based on communication signals in intricate post-disaster environments with higher location accuracy and better performance.To improve the positioning accuracy and time efficiency,this dissertation considers various aspects comprehensively,such as the communication conditions,resources of signal frequency band is limited,low parameter estimation precision and high extent of computational complexity in post-disaster environments,to further explore the channel resource allocation optimization algorithm,first of diameter and high precision of multipath signal recognition algorithm,time delay and nonline-of-sight environments angle joint estimation method.And to construct a higher precision positioning platform based on mobile communication signals in complex post-disaster environments.The primary works cover the following factors:1.Facing the situation of channel estimated difficulty that caused by random deformation of buildings and low-level electromagnetic signal transmission in post-disaster environments,this dissertation proposes a channel fading model based on wireless communication signal measurement.First,to modeling the large-scale fading pattern and the small scale fading multi-path channel impulse response pattern.Then,to establish LTE signal spectrum multipath channel impulse response model on the strength of the real buildings damaged environment.And to propose a tufted point channel model with an improved ESPRIT algorithm based on post-disaster environment.Simulation and experiment shows that the proposed algorithm improves the delay estimation by 16.07%,the amplitude estimation by 27.08%,and the frequency estimation by 14.04%.2.To respond to the situations of less available communication base stations and more user equipments after the disaster,the uplink channel will be blocked.Therefore,a new dynamic channel resource allocation model is proposed.Firstly,a resource allocation algorithm based on threedimensional spiral approximation is proposed,which can realize global optimization.At the same time,it can realize rational channel resource allocation and orderly access of uplink signals.And the prediction model of system throughput and the number of user terminals is constructed.Simulation and experiment shows that the proposed algorithm improves the QoS by 21.92%and the system throughput by 27.27%.The prediction accuracy can reach 98%.3.In view of the serious multipath interference of signal propagation after disaster so that cannot accurately identify the first path.Therefore,this dissertation proposes a method based on MEDLL-SRA.Firstly,the frequency domain of multiple parameters of each subcarrier is modeled,and then the amplitude,phase and arrival time deviation of multipath signals are stripped.After that,the rough searches are completed and the parameter estimated values are obtained.Furthermore,the search parameters of the next level are dynamically adjusted,and the refined parameter estimation is completed step by step through loop iteration.The parameter estimation of all detected multiple components is generated.Finally,the exact arrival time of the first path is obtained.Simulation and experiment shows that this algorithm improves the convergence speed by 53%and the first-path resolution by 34.6%.4.In view of the situations that the signals received from the base station are blocked by irregular buildings and only provied the limited information so that the positioning accuracy is bad.This dissertation proposes a TDOA/AOA fusion localization algorithm based on near-end strategy optimization.Firstly,as an agent,near-end strategy optimization can ensure that hierarchical reinforcement learning generates stable empirical data during training.Then,A reward function based on non-lineof-sight error compensation is designed to make the model take into account both sub-targets and high-reward targets in the process of iteration,so as to achieve non-line-of-sight error correction for the original measured values.At last,the accuracy of localization algorithm is improved by adaptive weight allocation.Simulation and experiment shows that the positioning accuracy of this algorithm is improved by 54.45%.