A K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold

Abstract

Abstract There are two problems with traditional indoor fingerprint location methods. First, irrelevant fingerprints in a fingerprint database interfere with the matching phase, which leads to poor positioning accuracy and stability of positioning results, and second, there is a large amount of computational overhead in the matching phase. Therefore, this paper proposes a K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold. In this method, a circular domain is formed in a coarse positioning process to narrow the positioning range. It solves the problem of the interference of irrelevant fingerprints. At the same time, a fault-tolerant mechanism is introduced to adjust the circular domain dynamically to ensure that the coarse positioning circular domain contains high similarity reference points and improve the fault tolerance of the coarse positioning. This method consists of offline and online phases. In the offline phase, the values of the received signal strength from Bluetooth low energy are preprocessed using a Gaussian filter to construct a fingerprint database. In the online phase, irrelevant fingerprints are filtered out by using the coarse positioning method. The filtered fingerprints are then matched with a testing point by the K-nearest neighbor algorithm, and the weighted centroids of the nearest reference points are solved. Finally, the coordinate of the testing point is obtained. The experimental results show that this method can effectively improve indoor positioning accuracy when compared with the traditional K-nearest neighbor. The average positioning error of the proposed method is 0.844 m.