关键词: 粒子滤波, 遗传算法, 自适应, 室内定位

Abstract:

Ultra wideband (UWB) technology, as a model of the new generation of indoor positioning technology, is often combined with inertial navigation systems (INS) to optimize the non line of sight error (NLOS) problem in positioning in practical applications. However, centralized information processing methods cannot effectively distinguish the sources of NLOS errors, resulting in redundant positioning anchor points, information waste, and increased costs. This paper proposes a UWB/INS indoor positioning method based on self resetting genetic particle filter (SGPF) to address the problem of non line of sight error identification and elimination in indoor positioning. The method uses the SGPF algorithm as a medium to trace the NLOS error in the measured values through INS system estimation, improving the tracking stability in NLOS environment. Firstly, by grouping physical anchor points and combining them with virtual anchor points to partition likelihood regions; Then, the NLOS error identification strategy is used to preliminarily estimate the high probability areas through the INS system and eliminate the NLOS anchor group and measurement values; Finally, considering whether to enable genetic resampling to optimize the diversity of the particle set and improve the robustness of the algorithm based on the effective number of particles to determine the state of the particle set. The SGPF algorithm combines the structures of standard particle filtering and genetic algorithm, which can effectively alleviate the problems of particle degradation and impoverishment, and achieve higher robustness with lower particle count and time consumption. Experiments have shown that in line of sight environments, the SGPF algorithm only requires 30% of the particle count of the PF algorithm to achieve the positioning effect of standard particle filtering, and the time consumption is much lower than that of traditional genetic particle filtering. In non line of sight environments, the average positioning error of SGPF algorithm is 0.0552m, which is 56.97% and 48.94% higher than traditional particle filtering and traditional genetic particle filtering, respectively.

Key words: particle filtering, genetic algorithm, adaptive, indoor positioning