Abstract
In this paper, we propose an AHP‐WKNN method for indoor localization which combines the Analytic Hierarchy Process (AHP) technique and the Weighted K‐nearest Neighbor (WKNN) algorithm. AHP serves to assign weights when WKNN is employed to select fingerprints for indoor positioning. The AHP technique can reasonably enlarge the influence that the received signal strength (RSS) gap between reference points has on the weights, achieving better performance in positioning. This paper also modifies the adaptive Kalman filter (AKF) noise reduction method by correcting the output based on the error between the RSS measurement and the expected output. The modified AKF can track the changes of RSS more effectively and achieve better performance of noise reduction. The simulation result shows that the proposed AHP‐WKNN method and the modified AKF can improve positioning accuracy effectively.
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