Network Element Layout Optimization Methods For Indoor Location Accuracy Robustness

With the rapid development of economy society and communication technology,more than 70% of people's activities are currently concentrated on indoor places.Actually,the indoor location services have been in great demand nowadays.This paper will cover the main topics in indoor location,including the accuracy robustness and location stability of indoor location systems.Though Wireless Fidelity(Wi-Fi),Bluetooth,Radio Frequency Identification(RFID),and Ultra Wideband(UWB)are widely used in indoor wireless location technology,the cost of deploying these technologies in urban scale is still a problem.In the coming 5G era,the communication integration based on operators will provide feasible solutions for indoor localization.However,due to the non-sight characteristics of indoor building,the robustness of localization accuracy is still very poor.In addition,the regional location vulnerability and drastic changes in accuracy problems come into existence because of unreasonable deployment of network elements.Therefore,this paper researches on the methods of indoor location accuracy robustness.The main work of the paper is as follows:Firstly,to study the aspect on the static target localization of the non-sight characteristics and noise measurement,this paper brings up a three-dimensional TDOA/AOA hybrid localization model for error suppression.And its locating process takes into account the effect on localization of noise measurement.Furthermore,an AOA noise equation is derived,which can be utilized to measure the noise caused in the process,thus reducing the localization error.Combined with the actual localization situation,the influence of noise on localization is considered.In order to reduce the localization error,the AOA noise equation is derived.Then the paper presents a real-time localization method,which is based on PDR technology for moving targets.Also,the method combines PDR technology with network element localization data by using the extended Kalman filtering method.The method locates according to moving state to ensure localization stability and accuracy robustness of moving targets.Secondly,the paper provides a network element layout fusion optimization method for indoor location accuracy robustness in terms of the problems of signal coverage vulnerability and low localization accuracy.The optimization model considers two factors: signal coverage and regional average localization accuracy.Compared with the adaptive algorithm,the traditional genetic algorithm is prone to "premature" phenomenon and the quality of the solution is disappointing in the process of network element optimization.Therefore,the paper aims to improve the genetic algorithm and simulated annealing.Then the improved algorithm is fused to develop the convergence speed of the algorithm and the quality of the network element layout and then the signal coverage and overall positioning stability of the localization area.Finally,the testing experiment shows the validity and feasibility of the proposed method.The experiment relies on the positioning simulation system and the network element deployment planning platform.It is given that the proposed hybrid positioning algorithm has better positioning performance and anti-noise ability than the Chan and Taylor positioning algorithms.Moreover,the fusion optimization method proposed in this paper turns out to have been improved.Besides,the experiments show that the method leads to a higher indoor localization signal coverage percentage and a lower average localization error in order to speed up the convergence of the algorithm.And the results suggest that the real-time positioning method of the PDR has stabler and more accurate localization performance for the moving target.