A WiFi Indoor Location Tracking Algorithm Based on Improved Weighted K Nearest Neighbors and Kalman Filter

The weighted
$K$
-nearest neighbors (WKNN) algorithm is a widely adopted lightweight methodology for indoor WiFi positioning based on location fingerprinting. Nonetheless, it suffers from the disadvantage of a fixed
$K$
value and susceptibility to incorrect reference point matching. To address this issue, we present a novel algorithm in this paper, referred to as static continuous statistical characteristics-soft range limited-self-adaptive WKNN (SCSC-SRL-SAWKNN). Our algorithm not only takes into account location tracking in the motion state but also exploits the continuous statistical features of extended periods of inactivity to enhance localization. In the motion state, we initially employ the self-adaptive WKNN (SAWKNN) algorithm to determine the optimal
$K$
value, followed by the employment of the soft range limited KNN (SRL-KNN) algorithm to identify the correct reference point and ultimately estimate the position. When a prolonged stationary state is detected, we first utilize the moving window method to obtain a more stable position fingerprint, and then proceed with the positioning process in the same motion state. Ultimately, we use Kalman filter to generate the location trajectory. Our experimental findings demonstrate that the proposed SCSC-SRL-SAWKNN algorithm outperforms traditional WKNN, SAWKNN, and SRL-KNN techniques in terms of localization accuracy and location trajectory. Specifically, the localization accuracy of our algorithm is 56.7% and 36.6% higher than that of traditional WKNN in the static state and overall situation, respectively.