Abstract
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -nearest neighbors (KNN) algorithms are widely used for indoor fingerprint positioning, but conventional KNN algorithms usually adopt received signal strength (RSS) similarity as a metric to select <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> reference points (RPs) for position determination, which may lead to inaccurate positioning results. This is because RSS similarity cannot well reflect position proximity due to the exponential relationship of RSS and propagation distance. To address these issues, this letter proposes a novel weighted adaptive KNN algorithm with historical information fusion for fingerprint positioning, which can choose a variable number of RPs according to both the improved RSS similarity and position proximity. Particularly, useful historical information extracted from a trajectory is used to improve further positioning accuracy. Finally, experiments are conducted on two different databases and the results validate performance of the proposed algorithm.
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