A 5G indoor positioning method based on Chan-White Shark optimization algorithm

Aiming at the problems of poor robustness and low accuracy of 5G positioning caused by non-line-of-sight errors and environmental occlusion in indoor environments, this paper proposes a 5G indoor positioning method based on Chan-White Shark optimization algorithm. The method uses Chan algorithm and White Shark optimization algorithm for co-localization, adopts Chan algorithm as the first level of co-localization algorithm, and initially solves the rough position of the moving target, which is used as the basis for the initial population generation of the White Shark algorithm, and then searches for the optimal solution within the moving target's movement area using White Shark optimization algorithm to optimize the Chan algorithm solution results. The experimental results show that the White Shark algorithm is better than the particle swarm optimization algorithm and the sparrow search algorithm in the cooperative positioning algorithm, and the 5G positioning method based on the Chan-White Shark optimization algorithm has higher positioning accuracy and robustness than the 5G positioning method based on the single Chan algorithm or the White Shark optimization algorithm.