Research On Pedestrian Dead Reckoning Based On Indoor Multi-information Assistance

With the vigorous development of navigation and location service industry,indoor pedestrian navigation and positioning has received widespread attention.How to improve the accuracy and robustness of indoor pedestrian positioning is faced with many challenges.At present,the mainstream technical scheme is to use magnetometer,inertial sensor and WiFi sensor for positioning.There are some problems in traditional magnetic field fingerprint matching,inertial navigation positioning and multi-information fusion,such as fingerprint Fuzziness,cumulative error caused by direction drift,low accuracy of multisource fusion positioning and so on.To solve these problems,this paper studies the indoor multi-information assisted pedestrian dead reckoning technology.The purpose of the research is to provide a high-precision,high-reliability and low-cost indoor pedestrian positioning solution through the rational use of MEMS inertial sensor,magnetometer and WiFi sensor.The research content includes the following three parts:(1)Aiming at the problem of fingerprint fuzziness caused by indoor magnetic field fluctuation,based on inertial navigation,the magnetic field matching is modeled as a two-mode sequence matching model,and a three-dimensional dynamic time warping localization algorithm is proposed.The algorithm uses the inertial navigation attitude angle information to extract the vertical and horizontal components of the magnetic field fingerprint to form a two-dimensional magnetic field fingerprint,which expands the dimensional information of the magnetic field fingerprint and reduces the mismatching caused by the fuzziness of the magnetic field fingerprint.Finally,the positioning accuracy and robustness of the magnetic field/inertial navigation positioning algorithm are improved.The experimental results show that when using Nexus 5 to build fingerprint database,the average positioning errors of teaching building,study room and office building are 1.53 m,1.66 m and 3.42 m respectively.When using Redmi Note 7 to build fingerprint database,the average positioning errors of teaching building,study room and office building are 1.38 m,1.43 m and 2.8 m respectively.When using Samsung A5 to build fingerprint database,the average positioning error of Atlantis le Centre shopping center is 3.78 m.(2)Aiming at the problem of cumulative error caused by the direction drift of inertial navigation system,combined with building floor plan,an intelligent particle filter algorithm is proposed,which can effectively solve the problem of cumulative error in inertial navigation system.In this algorithm,the activity range of particles is constrained by the information of building map,and the firefly algorithm is used to make invalid particles migrate to effective particles,so as to eliminate invalid particles to participate in pedestrian state estimation and improve the effective particle diversity of intelligent particle filter algorithm.Finally,the indoor pedestrian positioning error is reduced.The experimental results show that the average positioning errors of teaching building,study room,office building and Atlantis le Centre shopping center are 1.64 m,1.06 m,1.28 m and 4.16 m respectively.(3)In order to solve the problems of system error of single sensor and low positioning accuracy of multi-source fusion algorithm,an indoor multi-source fusion positioning algorithm based on enhanced Kalman filter is proposed based on accelerometer,gyroscope,magnetometer and WiFi sensor.Firstly,the gross error elimination mechanism is used to remove the large position error in the magnetic field and WiFi fingerprint matching estimation,and then the weighted matrix and adaptive observation noise are used to improve the performance of the enhanced Kalman filter and reduce the position error of the multisource fusion algorithm.The experimental results show that when using Nexus 5 to build fingerprint database,the average positioning errors of teaching building,study room and office building are 0.99 m,1.02 m and 1.53 m respectively.When using Redmi Note 7 to build fingerprint database,the average positioning errors of teaching building,study room and office building are 1.16 m,1.39 m and 1.37 m respectively.When using Samsung A5 to build fingerprint database,the average positioning error of Atlantis le Centre shopping center is 2.5 m.Finally,the indoor multi-information-aided pedestrian dead reckoning studied in this paper is summarized,and the future work is prospected.