Aiming at the problem of large error and low timeliness occur while current indoor positioning

relying on Wi-Fi electromagnetic fingerprint library method to realize indoor personnel positioning is proposed. An improved density peak clustering（K-IDPC） algorithm fuses K-NN is proposed. The correlation coefficient and KNN idea are introduced to solve the problem that the ordinary density peak clustering（DPC） algorithm has unbalanced positioning data density and the distinction degree of clustering center is not high

which improves the robustness to the positioning environment. Combined with the data segmentation algorithm

the offline electromagnetic data is cut

so that the big data set is divided into several small data sets

which reduces the computational complexity. The experimental results show that the proposed indoor positioning method can effectively improve the indoor positioning effect compared with the traditional K-means

DBSCAN and DPC clustering algorithm.