| With the rapid development of the Internet of Things,all walks of life have an increasing demand for accurate location information in indoor scenarios.However,due to the complexity of the indoor environment,positioning with a single sensor can no longer meet the demand.Therefore,low-cost,high-precision indoor positioning technology has attracted more and more attention.In view of the above analysis,this paper deeply studies a variety of positioning methods based on Ultra Wide Band(UWB)and indoor positioning algorithms combined with Inertial Measurement Unit(IMU).The main research content and innovation points of the paper are summarized as follows:(1)Aiming at the problem that a single motion model in UWB indoor dynamic positioning cannot effectively achieve accurate tracking of targets,an interactive multiple model based Extended Kalman and Particle Filter(IMMEK-PF)indoor tracking algorithm is proposed.The algorithm first adopts two positioning methods based on arrival phase difference and two-way ranging.In order to compensate for the shortage of large errors caused by the arrival phase difference positioning method exceeding the effective measurement range,an adaptive weighting algorithm is designed for data fusion;and then In order to improve the environmental adaptability of the algorithm,the non-line-of-sight error in the measurement data is identified and processed to reduce its impact on the positioning accuracy;finally,combining extended Kalman filter and particle filter,an IMMEK-PF enhancement algorithm is proposed to improve the dynamic tracking performance on targets.Dynamic tracking performance on targets.Through the simulation experiments of two common motion modes of uniform motion and uniform circular motion in the room,the results show that the IMMEK-PF algorithm has the smallest tracking error due to the combination of two filters,and it is compatible with the IMM algorithm based on the extended Kalman filter and the particle-based algorithm.Compared with the IMM algorithm of the filter,the tracking accuracy is increased by 33.3% and 16.7% respectively.(2)Aiming at the problem that UWB positioning is susceptible to various noises and non-line-of-sight effects,resulting in increased positioning errors,an indoor positioning algorithm based on improved particle filter(IPF-UWB/IMU)was proposed,which combines UWB and IMU.The algorithm first filters the position data based on the twoway ranging positioning method,and performs time synchronization with the IMU data;then calculates the target moving speed through the UWB positioning results at adjacent moments,and compares it with the target moving speed calculated by the IMU,so as to realize Recognition of non-line-of-sight data;finally,the step of adaptively adjusting the number of particles is added to the standard particle filter to improve the calculation efficiency of the algorithm,and based on the improved particle filter,the UWB/IMU fusion data is optimally estimated to achieve accurate target position.The experimental results show that compared with the positioning scheme using only UWB sensors,the IPFUWB/IMU algorithm can effectively reduce the positioning error of UWB based on the prior information provided by the IMU,and has high reliability in non-line-of-sight environments;Compared with the combined algorithm of extended Kalman and unscented Kalman filter,its positioning accuracy is increased by 65.6% and 56.0% respectively;compared with the algorithm based on standard particle filter,its running time is shortened by 42.3%. |
PDF Full Text Request