A Cluster-Principal-Component-Analysis-Based Indoor Positioning Algorithm

Indoor location-based service is emerging as the crucial application of the Internet of Things, which promotes the advance of relevant technology in the indoor scenario. Several positioning algorithms are proposed for different indoor configurations in recent years. The fingerprint-based indoor positioning algorithm has drawn much attention because of the good positioning performance without additional hardware. However, the false fingerprint matching frequently incurs due to the complexity of the indoor positioning environment and affects the positioning accuracy. In this article, a principal component analysis (PCA)-assisted indoor positioning algorithm based on the adaptive hierarchical clustering algorithm (PAHC) is proposed, which can improve the positioning accuracy through aggregating the reference points (RPs) and conducting the cluster-based PCA (C-PCA) features extraction. More specifically, a clustering termination method is proposed to obtain reasonable RPs clusters adaptively according to the preset RPs. A two-stage fingerprint matching algorithm is proposed based on the C-PCA to further increase the difference between similar RPs and thus improving the positioning accuracy. To verify the proposed algorithm, an indoor wireless system is established in the practical indoor scenario. The experimental results indicate that the proposed PAHC algorithm can increase the positioning accuracy by 9.3% compared with the conventional fingerprint algorithm.

Automated summary

An indoor positioning algorithm based on principal component analysis (PCA) and adaptive hierarchical clustering has been proposed to improve positioning accuracy by aggregating reference points and conducting cluster-based PCA feature extraction. Experimental results show that this algorithm can increase positioning accuracy by 9.3% compared to traditional fingerprint algorithms.