Abstract
Fingerprint-based positioning in a wireless local area network (WLAN) environment has received much attention recently. One key issue for the positioning method is the radio map construction, which generally requires significant effort to collect enough measurements of received signal strength (RSS). Based on the observation that RSSs have high spatial correlation, we propose an efficient radio map construction method based on low-rank approximation. Different from the conventional interpolation methods, the proposed method represents the distribution of RSSs as a low-rank matrix and constructs the dense radio map from relative sparse measurements by a revised low-rank matrix completion method. To evaluate the proposed method, both simulation tests and field experiments have been conducted. The experimental results indicate that the proposed method can reduce the RSS measurements evidently. Moreover, using the constructed radio maps for positioning, the positioning accuracy is also improved.
Highlights
With the rapidly increasing location-based services (LBS), such as positioning, tracking, navigation, and location-based security, the positioning issue has been extensively studied
To evaluate the proposed method, we implement the fingerprint radio map construction experiment both on simulated and real data, and the constructed results are compared with inverse distance weighting (IDW), radial basis function (RBF), and the basic lowrank (BLR) method
The results indicate that the smoothing low-rank (SLR) methods yield the best performance, especially at low sampling rate
Summary
With the rapidly increasing location-based services (LBS), such as positioning, tracking, navigation, and location-based security, the positioning issue has been extensively studied. The fingerprint-based positioning method is implemented in two phases: the off-line training phase and the online positioning phase. In the off-line training phase, at the selected reference points, the RSSs from different access points (APs) are measured by a mobile device. The RSS measurements and the correspondent locations are generally formulated as the radio map, which infers the relation between the RSS distribution and the spatial locations. In the online positioning phase, an observed RSS measurement is matched to the radio map, and the location can be estimated by many proposed methods, such as k-nearest neighbor algorithm (KNN) [1], kernel-based algorithm [3], or Bayesian estimation method [4]
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