Abstract
WiFi fingerprint positioning has been widely used in the indoor positioning field. The weighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditional WKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-based WKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy.
Highlights
In indoor environments, global navigation satellite systems (GNSS) can be affected by unfavorable factors, such as signal blocking and multipath propagation, which make it unable to achieve a satisfactory positioning performance [1]
To improve the positioning accuracy, we propose an improved WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED)
The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of reference points (RPs) and the proposed SWED-based weighed K-nearest neighbor (WKNN) (SWED-WKNN) algorithm can significantly improve the positioning accuracy
Summary
Global navigation satellite systems (GNSS) can be affected by unfavorable factors, such as signal blocking and multipath propagation, which make it unable to achieve a satisfactory positioning performance [1]. To reduce the computational complexity, the clustering-based fingerprinting methods are proposed; by the clustering, only the RPs of one cluster need be searched for each TP. Take the WKNN as an example: its working mechanism is to find K RPs which have the minimum signal distances from the online RSS, and weighted average the positions of the selected RPs according to their signal distances [14]. In [23], to deal with the noise in the calculation of signal distance, different weights are assigned to the RSS fingerprint according to their importance. To better demonstrate that our proposed method can achieve more appropriate fingerprint clustering and improve the positioning accuracy, our fingerprint collection work is still done manually. WiFi positioning method based on fingerprint clustering and signal weighted Euclidean distance (SWED).
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