Abstract: Aiming at the defects of indoor ultra-wide band (UWB) positioning technology, such as poor positioning effect and inaccurate positioning in complex occlusion environment, this paper proposes a hybrid positioning method based on Chan algorithm and particle swarm optimization algorithm. First, the Chan algorithm is used to obtain the initial estimated position coordinates of the positioning tag, and in a non-line-of-sight (NLOS) environment, a threshold θ is set to filter the position coordinates calculated by the Chan algorithm. The distance difference received by the known base station is summed with the distance difference between different base stations obtained by the tag position information calculated by Chan algorithm. If the sum of the differences is less than the threshold, the position coordinates are directly output. Otherwise, the position coordinates are used as the initial value of the particle swarm algorithm for iteration. Optimization keeps track of individual extreme values and local extreme values, updates individual positions and speeds, and finds the global optimal solution before outputting. The simulation results and the actual field experiment results show that compared with a single algorithm, the hybrid positioning algorithm proposed in this paper improves the positioning accuracy of 27%~31% in the non-line-of-sight environment. The convergence speed is fast, the algorithm complexity is low, and it meets the requirements of indoor positioning.

Key words: indoor positioning, non-line-of-sight, TDOA, Chan algorithm, threshold filtering, particle swarm algorithm, co-location algorithm