In recent years, the Wi-Fi-based indoor positioning technology has become a research hotspot. This technology mainly locates the indoor Wi-Fi based on the received signal strength indicator (RSSI) signals. The most popular Wi-Fi positioning algorithm is the k-nearest neighbors (KNN) algorithm. Due to the excessive amount of RSSI data, clustering algorithms are generally adopted to classify the data before KNN positioning. However, the traditional clustering algorithms cannot maintain data integrity after the classification. To solve the problem, this paper puts forward an improved public c-means (IPC) clustering algorithm with high accuracy in indoor environment, and uses the algorithm to optimize the fingerprint database. After being trained in the database, all fingerprint points were divided into several classes. Then, the range of each class was determined by comparing the cluster centers. To optimize the clustering effect, the points in the border area between two classes were allocated to these classes simultaneously, pushing up the positioning accuracy in this area. The experimental results show that the IPC clustering algorithm achieved better accuracy with lighter computing load than FCM clustering and k-means clustering, and could be coupled with KNN or FS-KNN to achieve good positioning effect. INDEX TERMS Wi-Fi, indoor positioning, improved public c-means (IPC) clustering algorithm, the k-nearest neighbors (KNN) algorithm.

Considering the high accuracy needed for indoor positioning, this paper develops a novel indoor positioning algorithm for the wireless sensor network (WSN) in the following steps. First, the RSSIs of the network nodes were sampled and analyzed, and the excess errors were filtered to enhance positioning accuracy. Next, the initial position was iteratively obtained by the weighted centroid algorithm, and a correction matrix was developed to improve the Taylor series expansion (TSE), and the final position was determined through improved TSE iteration. The proposed positioning method was verified through simulation.

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