基于相似度的K阶临近定位算法

基于WIFI位置指纹的定位系统能实现较高精度的室内定位，其中基于接收信号强度指示（RSSI）的近邻选择算法在进行室内定位时容易引入奇异点，导致定位精度降低.针对该问题，本文提出了一种基于相似度的K阶临近定位算法（SKNN）.该算法借鉴二部分网络中求解节点相似性的思想，建立位置指纹与AP之间的二部分网络，并提出一个相似度参数，用该参数去修正K阶临近定位算法.实验结果表明，本文提出的SKNN算法可以有效的降低奇异点对定位结果的影响，提高定位精度，80%的定位误差均在2 m以内，且在大场景中效果明显.

Similarity-Based K-Nearest Neighborhood Location Algorithm

The positioning system based on WIFI location fingerprint can achieve high precision indoor location. The neighbor selection algorithm based on Received Signal Strength Indicator (RSSI) is easy to introduce singular points when locating indoors, which leads to the decrease of positioning accuracy. To solve this problem, this paper proposes a Similarity-based K-Nearest Neighborhood Location Algorithm (SKNN). Referring to the idea used to solve the problem of similarity of nodes in bipartite networks, this algorithm builds a bipartite network between the location fingerprint and the AP. It proposes a similarity parameter which can be used to modify the K-Nearest Neighborhood localization algorithm. The experimental results show that the SKNN algorithm proposed in this paper can effectively reduce the influence of singular points on the positioning results and improve the positioning accuracy, with 80% of the positioning errors within 2m, and the effect is obvious in the large scene.