Research On Indoor Positioning Algorithm Based On UWB

With the continuous development of information technology and the advancement of society,people's demand for spatial location information is increasing.From the early outdoor positioning navigation,outdoor route information query gradually changed to indoor positioning service.Outdoor,GPS positioning technology and base station positioning technology basically meet the needs of users for location services.However,people spend most of their time indoors,and a large number of positioning needs,such as individual users,service robots,and intelligent Internet of Things devices,occur indoors.Due to the occlusion of buildings and external walls in the room,GPS is not suitable for indoor positioning,so many indoor positioning technologies have been derived,such as ultra-wideband positioning technology,ultrasound,and WiFi.Because UWB has the advantages of low power consumption,strong penetrating power,good safety performance,high multipath resolution and high positioning accuracy,this paper deeply studies UWB-based positioning algorithm and ranging method,and combines with reverse neural network.The position coordinate results are optimized to improve the positioning accuracy of the target to be measured in a specific indoor environment.From the structural point of view,the research background and significance of this topic will be explained first.Secondly,the research status of UWB at home and abroad,the comparison between wireless positioning technologies,the research status of commonly used positioning algorithms,the value of UWB applications and the scenes are made.A brief introduction,the final outline of the basic definition and characteristics of UWB,as well as the UWB positioning principle,as well as the method of positioning system performance evaluation.In order to achieve indoor positioning of the target to be tested,there are two key steps.The first step is to select a suitable ranging model and measure the time,arrival time difference,and angle of arrival of the signal sent by the positioning tag to the base station.The second step is to select the appropriate positioning algorithm,and substitute the measured parameters into the selected positioning algorithm to calculate the position coordinates of the target to be tested.In the article,the Tang algorithm based on TDOA and the Chan algorithm will be deeply studied,and the simulation results will be obtained,and the results will be analyzed.Subsequently,in order to estimate the azimuth of the ultra-wideband pulse source,an algorithm based on the received signal angle method estimation algorithm is proposed.By using the equilateral triangle sparse antenna array with virtual array elements,the AA's estimation performance and system cost are optimized,which can effectively obtain accurate pulse source AOA estimation at low SNR.It is based on the Received Signal Time Difference Method that allows two array elements to be grouped and estimates several sets of AOA values.The inverse neural network algorithm is used to optimally group the array tuples to improve estimation accuracy.In this paper,Mathematical model is established for Fang algorithm,Chan algorithm and BP neural network based AOA algorithm estimation system,and its experimental environment is built.The proposed method is measured realistically,and the measurement results are simulated by MATLAB software.analysis.In the proposed BP neural network-based AOA algorithm system,the numerical simulation and system experiments show that the proposed method can achieve an AOA accuracy of 0.5-1 degrees in a typical indoor scene and within a radius of 2-5 meters.