Research On Indoor Positioning Algorithm Based On Ultra-wideband

With the development of technology,people are increasingly demanding the accuracy of real-time positioning systems.In a general outdoor environment,the Global Positioning System(GPS)is already able to provide accurate location information.However,in the indoor environment,GPS signals will be extremely weak,and it is easy to be blocked and interfered by various obstacles,resulting in a sharp decline in positioning accuracy,which can no longer meet people's positioning needs.Therefore,it is a serious challenge for people to build an indoor positioning system that can effectively adapt to different indoor environments and maintain relatively high accuracy.The emergence of Ultra-wideband(UWB)technology provides a solution to this challenge.UWB signals have many advantages such as high transmission rate,low power consumption and strong anti-interference ability,which is very significant for the research of indoor high-precision positioning system.At present,the most influential factor on indoor positioning accuracy is the propagation of signals in a non-line-of-sight(NLOS)environment.In order to effectively suppress the influence of non-line-of-sight error on the positioning accuracy,this paper focuses on the research of NLOS error suppression algorithm based on UWB.Firstly,several representative NLOS error suppression algorithms are introduced.These algorithms can be divided into two categories:One way is directly suppressing the error of NLOS and another way is reconstructing measurements by identifying NLOS base stations before positioning.Based on these two points,this paper individually proposes an algorithm and analyzes their performances by simulation.Then it points out the shortcomings of existing positioning algorithms.This paper propose an improved algorithm which revises TDOA measurements by BP neural network and complete the positioning by the least square method based on QR decomposition,namely BP-QRLS algorithm.Finally,through the simulation and analysis of this algorithm,we can prove that this algorithm can effectively improve the positioning accuracy.The research content of this paper can be summarized as follows:(1)Explain the background and research significance of this project,summarize the development history and transmission characteristics of UWB technology,and investigate the research status of UWB technology at home and abroad,and clarify the research objectives of this paper.(2)Analyze the transmission pulse and modulation method of UWB system,and then study eight indoor channel models under IEEE802.15.4a standard,and simulate the channel impulse response.(3)By comprehensively comparing and analyzing the advantages of common UWB wireless positioning technology,TDOA is selected as the positioning scheme of UWB system in this paper.(4)In order to suppress the influence of NLOS error on positioning accuracy,the least square method based on QR decomposition(QRLS)and the NLOS error identification method based on TDOA residual were proposed,and the two algorithms were simulated and analyzed.(5)Analyzing the formula derivation and principle of traditional positioning algorithm,including Fang algorithm,Taylor algorithm,Chan algorithm and so on.An improved QRLS algorithm which modifies TDOA measurements by BP neural network is proposed,namely BP-QRLS algorithm.(6)The BP-QRLS algorithm is compared with the traditional positioning algorithm through MATLAB simulation,and evaluate the accuracy of the positioning algorithm from the perspective of system measurement error and the number of base stations.The simulation results show that the BP-QRLS algorithm does not need to rely on the prior information of the NLOS base station,and the positioning accuracy is much better than other traditional positioning algorithms,which can achieve the requirements of indoor high-precision positioning system.