A Bluetooth Indoor Positioning Method Based on Soft Range Limitation and Dynamic Threshold

ABSTRACT

This paper proposes a novel weighted ‐nearest neighbour algorithm for indoor positioning of continuously moving users based on soft range limitation and dynamic threshold. By introducing a position penalty function, reference points closer to the previous position are given higher weights resulting in a soft range limit. Meanwhile, a method for global fingerprint library simplification is proposed, and a dynamic threshold is introduced to avoid local extreme value phenomena. In addition, an adaptive parameter is adopted to maximize the positioning capability of the algorithm. Through numerical simulations and physical experiments, the results demonstrate that the proposed algorithm significantly outperforms existing five baseline positioning algorithms in terms of accuracy. Compared with the traditional ‐nearest neighbour algorithm, its performance in simulation and experimental tests is improved by and , respectively.