Abstract
AbstractRecently, the fingerprint-based wireless local area network (WLAN) positioning has gained significant interest. A probability distribution-aided indoor positioning algorithm based on the affinity propagation clustering is proposed. Different from the conventional fingerprint-based WLAN positioning algorithms, the paper first utilizes the affinity propagation clustering to minimize the searching space of reference points (RPs). Then, we introduce the probability distribution-aided positioning algorithm to obtain the target's refined position. Furthermore, because the affinity clustering can effectively lead to a reduction of the computational cost for the RP searching which is involved in the probability distribution-aided positioning algorithm, the proposed algorithm can lower the difficulty and minimize the power consumption when estimating the user's position. Experimental results conducted in the real environments show that our proposed algorithm will significantly improve the performance of the probability distribution-aided positioning algorithm in both the positioning accuracy and real-time ability.
Highlights
In recent decade, the indoor wireless local area network (WLAN) positioning technology has caught significant attention by a variety of universities and research institutes [1-3]
By using received signal strength (RSS) readings collected from nine public access points (APs) (Cisco WRT54G), we will compare the performance of our proposed algorithm with other three typical positioning algorithm; (1) k-nearest neighbor (kNN) positioning algorithm with K-means clustering (K-means + Knn); (2) probability distribution-aided positioning algorithm with K-means clustering (K-means + Probability Distribution); and (3) kNN positioning algorithm with affinity propagation clustering (Affinity Propagation + Knn)
5.1 Clustering results In the experiments, we only focus on the situations that the damping factor is in the range of [0.5, 0.9] because only the damping factor falling into this range can guarantee that the affinity clustering results converge when the clustering process ended
Summary
The indoor wireless local area network (WLAN) positioning technology has caught significant attention by a variety of universities and research institutes [1-3]. The basic idea of probabilistic approach is to pre-store the RSS distribution with respect to each hearable AP into a radio map and use it to conduct the position estimation. This algorithm contains two phases: (1) in the off-line phase, we construct the radio map and conduct the affinity propagation clustering; and (2) in the on-line phase, the cluster matching-based coarse and probability distributionaided fine positioning will be performed, respectively.
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