Abstract
A density-based spatial clustering of applications with noise (DBSCAN) and three distances (TD) integrated Wi-Fi positioning algorithm was proposed, aiming to enhance the positioning accuracy and stability of fingerprinting by the dynamic selection of signal-domain distance to obtain reliable nearest reference points (RPs). Two stages were included in this algorithm. One was the offline stage, where the offline fingerprint database was constructed and the other was the online positioning stage. Three distances (Euclidean distance, Manhattan distance, and cosine distance), DBSCAN, and high-resolution distance selection principle were combined to obtain more reliable nearest RPs and optimal signal-domain distance in the online stage. Fused distance, the fusion of position-domain and signal-domain distances, was applied for DBSCAN to generate the clustering results, considering both the spatial structure and signal strength of RPs. Based on the principle that the higher resolution the distance, the more clusters will be obtained, the high-resolution distance was used to compute positioning results. The weighted K-nearest neighbor (WKNN) considering signal-domain distance selection was used to estimate positions. Two scenarios were selected as test areas; a complex-layout room (Scenario A) for post-graduates and a typical large indoor environment (Scenario B) covering 3200 m2. In both Scenarios A and B, compared with support vector machine (SVM), Gaussian process regression (GPR) and rank algorithms, the improvement rates of positioning accuracy and stability of the proposed algorithm were up to 60.44 and 60.93%, respectively. Experimental results show that the proposed algorithm has a better positioning performance in complex and large indoor environments.
Highlights
Introduction iationsThe global navigation satellite system (GNSS) is very hard to realize the high-precision indoor positioning because GNSS signals arrived at rooms are weak, or there are no GNSS signals [1]
Based high-resolution distance selection principle, the optimal signal-domain distances could be on the high-resolution distance selection principle, the optimal signal-domain distances determined by the maximum number of clusters
ED3Nor + MD3Nor + CD3Nor where three distances (TD) denotes the sum of three normalized signal-domain distances, and Euclidean distance (ED), Manhattan distance (MD), and cosine distance (CD) represent the signal-domain distances based on ED, MD, and CD, respectively, and EDiNor,EDiNor, and EDiNor represent the normalized values of ED, MD, CD between the online fingerprint and the ith offline fingerprint
Summary
There are offline and online stages in the Wi-Fi fingerprint positioning method. The aim of the offline stage is mainly to construct the fingerprint database. Li et al [55] showed that the MD-based signaldomain distance behaved better than the ED-based signal-domain distance in terms of positioning accuracy when they were utilized to estimate location. It showed that the nearest RPs found with different signal-domain distances may be different. It is useful for fingerprinting to select an optimal signal-domain distance to estimate theoffice location. There were some an studies about reducing thedistance influence unreliable nearest RPs for fingerprinting to select optimal signal-domain to of estimate the location.
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