Abstract
The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.
Highlights
Positioning technology is one of the key technologies of location service, the Internet of Things, and artificial intelligence, and the indoor positioning system has attracted much attention
This paper proposed an improved weighted K-nearest neighbor algorithm
The proposed algorithm adopted both spatial distance and physical distance to select reference points (RPs) for position determination; algorithm adopted both spatial distance and physical distance to select RPs for position a weighted algorithm based on the fusion of weights of these two distances was used for determination; a weighted algorithm based on the fusion of weights of these two distances was position calculation
Summary
Positioning technology is one of the key technologies of location service, the Internet of Things, and artificial intelligence, and the indoor positioning system has attracted much attention. The global navigation satellite system (GNSS) [1] can meet most of the needs of positioning in the outdoor environment, but it does not work in indoor environments because there is no GNSS signal in the interior of buildings To overcome this shortcoming, some indoor positioning technologies have been proposed. Visual positioning [5] is a popular positioning technology; it locates a camera by estimating its posture or matching a captured photo with stored images associated with known locations. It requires an accurate image database, which is challenging and impractical
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