Indoor Wireless Location And Tracking Scheme Based On Multi-source Data Fusion

In recent years,with the rapid development of the fifth-generation mobile communication and Internet of Things(IOT)technology,indoor wireless positioning technology has gradually become a research hotspot of people and the results are outstanding.Among them,indoor positioning based on RSSI and Pedestrian Dead Reckoning(PDR)based on inertial sensors are the two most widely used technologies in indoor positioning.The RSSI-based positioning technology can obtain the absolute position information of the target,but it requires a sufficient number of anchor nodes to be pre-deployed in the indoor environment,and the RSSI value is vulnerable to environmental factors,resulting in greater volatility of positioning results.The PDR technology can obtain the relative position information of the target and does not need to deploy additional nodes in advance.The positioning accuracy mainly depends on the accuracy of pedestrian gait,heading,and step size estimation.However,with the increase of positioning time,this technique is prone to generate cumulative error.For the above problems,it's very difficult to improve the positioning performance only using positioning technology.Based on this,this article mainly studies indoor wireless positioning and tracking scheme based on RSSI and PDR fusion.This paper first analyzes the key technologies involved in RSSI positioning from three aspects: data acquisition,distance conversion,and positioning algorithm.Aiming at the problem of low control rate of distance conversion errors in RSSI positioning,a dynamic estimation method for path loss coefficients is proposed.This method makes full use of the anchor information in the positioning system and improves the anti-jamming performance of the system.At the same time,it focuses on the analysis of pedestrian gait detection,pedestrian step size estimation,and pedestrian heading detection in PDR positioning.This paper proposes a solution to the problem of low accuracy of step-size estimation model in PDR positioning and insensitivity of gait detection algorithm to changes in walking status.The PDR step size of the joint RSSI information assists the calculation strategy.The method can dynamically adjust the step size for pedestrians' different walking states.The traditional peak detection algorithm is improved by introducing dynamic thresholds and time thresholds,which greatly improves the step accuracy.Experiments show that the improvement of the two positioning technologies has improved the positioningperformance.Further,in the tracking of moving targets,aiming at the fluctuation of RSSI positioning results and the cumulative error of PDR positioning,this paper proposes an RSSI/PDR fusion tracking algorithm based on Extended Kalman Filter.By adding the pedestrian heading angle as a state vector,the algorithm makes the system's state equation and observation equation closer to the actual system state.Through practical tests,the fusion algorithm effectively suppresses the fluctuation of the RSSI positioning results and the cumulative error of PDR positioning,making the positioning tracking trajectory closer to the pedestrian's true trajectory.Finally,for the proposed RSSI/PDR fusion location tracking scheme,this paper builds a centralized location tracking system based on Zig Bee and JY901 modules.The software design of the host computer based on Visual Studio 2013 and the MFC framework was completed at PC.At the same time,the development of the underlying software of each node was completed based on the Z-Stack protocol stack.In the system debugging stage,the positioning and tracking experiment was performed to verify the performance of the location tracking system.