Research On Indoor Positioning Method Based On Multi-Source Information Fusion Through Weighted Adaptive Particle Filtering

With the development of Internet of Things technology and mobile device,location-based services have gradually been known and applied.At present,outdoor positioning technology has developed relatively maturely,but there is still no good solution for indoor scene positioning.The reason for this is: the satellite positioning system,which is relatively mature in outdoor positioning scenarois,cannot be used in indoors.In addition,the indoor environment is relatively complex,signal multipath propagation,non-line of sight,etc.They are all the reasons why indoor positioning cannot be popularized.Therefore,the positioning scheme that only uses single source cannot meet the needs of indoor positioning.This makes the positioning scheme integrating multi-source information a research hotspot.Compared with other typical positioning methods,UWB(Ultra Wide Band)communication technology has great advantages in the field of indoor positioning because of its high temporal and spatial resolution.And IMU(Inertial Measurement Units)technology collects data related to the motion process for positioning,which does not require signal exchange and avoids noise problems during signal propagation.Therefore,taking UWB and IMU as the core positioning technologies,this paper studies and implements a multi-source information fusion indoor positioning method based on weighted adaptive particle filtering based on the pedestrian positioning object of handheld mobile intelligent terminal,and builds an actual positioning system for verification.The main work of the thesis is as follows:(1)The principle of pedestrian dead reckoning algorithm using IMU and non-line-of-sight identification using UWB channel impulse response information is studied.In addition,by studying the construction process of UWB positioning model and the characteristics of classical positioning algorithms,a secondary verification algorithm using UWB positioning technology is designed for single-point localization under signal line-of-sight propagation conditions.Based on the ranging results of UWB,the algorithm constructs a suitable geometric positioning model to achieve high-precision positioning of mobile terminals.The simulation results show that the algorithm is still very close to the lower bound of Cramero under the condition of large noise.(2)A multi-source information fusion positioning algorithm based on weighted adaptive particle filter algorithm is proposed.The algorithm takes the unified particle filter fusion algorithm as the basic framework,improves the time update step and measurement update step of the algorithm,integrates the UWB ranging results and the displacement vector information obtained by IMU measurement,and finally obtains more accurate and stable indoor positioning results.The structure of the fusion algorithm is more flexible and scalable.In addition,in order to avoid the influence of non-line-of-sight propagation,the algorithm uses the channel impulse response of UWB signal to identify non-line-of-sight propagation,the algorithm is very concise,will not increase excessive computational burden,but can obtain more accurate identification results.The simulation results show that the positioning accuracy of the proposed algorithm is higher than that of the existing algorithms,and the real-time performance meets the requirements of real-time positioning.(3)A multi-source fusion indoor positioning system applied to mobile intelligent terminals is designed and realized.Among them: the Raspberry Pi + STM32 mode is adopted on the side of the base station,which is used to send positioning signals and measure the distance between the base station and the mobile intelligent terminal;Develop mobile apps located on mobile smart terminals for data collection and real-time display of user location;The server adopts a distributed processing architecture,which distributes the functions of controlling data collection and the functions used for data processing to two sub-servers,reducing the amount of computing in the server-side system.Finally,in the two scenarios of office and classroom,the second verification algorithm and multi-source information fusion localization algorithm proposed in this paper are experimentally verified.The results show that the indoor positioning method proposed in this paper outperforms the existing methods.