基于自复位遗传粒子滤波的UWB/INS室内定位方法OA

As a paradigm of the new-generation indoor positioning technology,ultra-wideband(UWB)technology is often combined with the inertial navigation system(INS)in practical applications to solve the non-line-of-sight(NLOS)error issue in positioning.However,the centralized information processing method fails to effectively distin-guish the sources of NLOS errors.To ensure positioning accuracy,additional anchor nodes need to be deployed,which leads to redundancy of positioning anchor nodes,and further results in information waste and increased costs.Aiming at the problems of NLOS error identification and elimination in indoor positioning,this paper pro-posed a UWB/INS indoor positioning method based on self-reset genetic particle filtering(SGPF).With the SGPF algorithm as its core,this method traces the source of NLOS errors in measured values using the estimated values of the INS system,so as to improve the tracking stability under NLOS environments.The method first groups physical anchor nodes and divides likelihood regions in combination with virtual anchor nodes.Then,based on the prelimi-nary estimation of the INS,it identifies high-probability regions through an NLOS error identification strategy,while eliminating NLOS anchor node groups and their corresponding measured values.Finally,it judges the state of the particle set by combining the number of effective particles,determines whether to enable genetic resampling to opti-mize particle diversity,and ultimately improves the robustness of the algorithm.The SGPF algorithm integrates the structural advantages of the standard particle filter(PF)and genetic algorithms,and can effectively alleviate the problems of particle degradation and impoverishment and achieve higher robustness with a smaller number of par-ticles and lower time consumption.Experimental results show that:under line-of-sight environments,the SGPF algorithm requires only 30%of the number of particles used in the PF algorithm to achieve the same positioning effect,and its calculation time is much lower than that of the traditional genetic particle filter algorithm;under NLOS environments,the SGPF algorithm has an average positioning error of 0.055 2 m.Compared to traditional particle filter and traditional genetic particle filter algorithms,the localization error is reduced by 56.98%and 48.94%respectively.