A Novel Fingerprint Positioning Method Applying Vision-Based Definition for WIFI-Based Localization

Wireless fidelity (WIFI) can transmit data efficiently, but it has the advantages of low computing cost and simple deployment. However, the WIFI signal is susceptible to interference from human movement, multipath propagation, and indoor temperature changes, resulting in problems such as low positioning accuracy and poor stability. To improve positioning accuracy and stability, we propose a novel fingerprint positioning technology applying vision-guided definition for WIFI-based localization, defined as KV. There are two stages in localization: WIFI-based coarse localization and fusion localization. In the WIFI-based coarse positioning stage, we propose a two-metric adaptive
${k}$
-nearest neighbor (KNN) localization method to improve the accuracy and robustness of WIFI-based localization. Unlike the traditional KNN method, a difference-based search approach is introduced to obtain the
${K}$
value intelligently and reasonably instead of manual determination. In the fusion localization stage, we employ an effective multiangle unsupervised fusion positioning method applying the fusion of WIFI and vision to enhance positioning accuracy and positioning stability further. Experimental results show that our proposed fusion localization method achieves 1.24 m in localization accuracy and the positioning error is 60% within 0.8 m. The performance of the proposed method surmounts other state-of-the-art methods, which proves its effectiveness. This method has important practical significance in multisource indoor positioning technology.