Research On Indoor Positioning Method Based On Multi-sensor Information Fusion

With the development of 5G,people's demand for location services has gradually expanded,and GPS and Beidou satellite positioning are susceptible to non-line-of-sight and multipath effects,and cannot meet the positioning needs of indoor environments.Therefore,the hotspot of positioning research gradually gradually from outdoor environments Turn into the indoor environment.Since the introduction of infrared positioning technology in 1992,the application of positioning technology based on wireless sensor networks has gradually increased.Among them,indoor positioning technology based on WiFi has been widely sought-after because of its low cost and low power consumption;and the mobile phone has a built-in inertial sensor and WiFi module,Use inertial sensors to achieve autonomous,short-term positioning accuracy indoor positioning.This paper analyzes and compares the indoor positioning methods at home and abroad to determine the indoor positioning method that combines multi-sensor and WiFi technology,mainly uses the location fingerprint library positioning method to determine the initial point position,and then uses the pedestrian dead reckoning algorithm to achieve continuous positioning.The main research work and innovations are as follows:(1)Aiming at the problems caused by WiFi signal noise caused by multipath effects and personnel interference,analyze the WiFi signal strength(RSSI)transmission attenuation characteristics,propose a Gaussian filter optimization algorithm,and filter and de-noise the RSSI;at the same time,multi-directional multi-time RSSI measurement is used to obtain robust fingerprint information,Finally,the weighted K-nearest neighbor algorithm(WKNN)was used to obtain the initial point location information;the experimental results showed that the Gaussian filter optimization algorithm was used to process the WiFi signal strength in the experimental environment.The average positioning error is reduced to 0.975 m.(2)The pedestrian step estimation and step statistics methods in multi-sensor indoor positioning are improved.The step estimation method of nonlinear curve fitting is mainly proposed to model the step size in the asynchronous state.Experiments verify that the error of the step length estimation method in this paper can be within 1 m in a short distance;at the same time,a method based on threshold grading is proposed to form peaks and troughs,combined with the frequency of pedestrian steps to determine the statistics of pedestrian effective steps.The accuracy of statistical methods in a short distance can reach more than 96.7%.(3)In view of the multi-sensor indoor positioning,the acceleration signal is susceptible to pedestrian non-gait interference and environmental impact,causing data fluctuations;by analyzing the filtering algorithm and data transmission characteristics,an improved Kalman filtering algorithm is proposed to process acceleration data,which is obvious for single fluctuation The data and the overall data noise are filtered to obtain high-quality and robust acceleration data,and then the data is counted in steps,step length estimation and heading angle determination.Combined with the initial point position,the pedestrian dead reckoning algorithm is used to obtain continuous location point.The experimental results show that after the improved filtering algorithm proposed in this paper is processed,the probability of positioning error within 2 meters is increased to 84%,and the average positioning accuracy is increased to 1.4979 m.(4)According to the actual application requirements of the system,this paper designs and develops a set of indoor positioning system software,which mainly implements the filtering algorithm module,positioning algorithm module and location fingerprint library management module;determine Android Studio as the client development platform,complete WiFi signal collection Processing module,multi-sensor acquisition and processing module and user interface interaction module.The experimental results show that this system can achieve the desired effect.