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Article summary:

1. WiFi indoor positioning technology is widely studied due to the extensive deployment of wireless networks in indoor environments and the fact that most mobile devices are equipped with built-in WiFi modules.

2. The weighted K-nearest neighbors (WKNN) algorithm is a prevalent method for indoor WiFi positioning based on location fingerprinting, but it suffers from challenges such as a fixed K value and susceptibility to incorrect reference point matching.

3. The proposed static continuous statistical characteristics-soft range limited-self-adaptive WKNN (SCSC-SRL-SAWKNN) algorithm takes into account location tracking in both motion and stationary states, employs SAWKNN and SRL-KNN algorithms to achieve the best positioning accuracy, and uses Kalman filter to generate the location trajectory. It outperforms traditional WKNN, SAWKNN, and SRL-KNN techniques in terms of localization accuracy and location trajectory.

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