On the Choice of Access Point Selection Criterion and Other Position Estimation Characteristics for WLAN-Based Indoor Positioning

Abstract

The positioning based on Wireless Local Area Networks (WLAN) is one of the most promising technologies for indoor location-based services, generally using the information carried by Received Signal Strengths (RSS). One challenge, however, is the huge amount of data in the radiomap database due to the enormous number of hearable Access Points (AP) that could make the positioning system very complex. This paper concentrates on WLAN-based indoor location by comparing fingerprinting, path loss and weighted centroid based positioning approaches in terms of complexity and performance and studying the effects of grid size and AP reduction with several choices for appropriate selection criterion. All results are based on real field measurements in three multi-floor buildings. We validate our earlier findings concerning several different AP selection criteria and conclude that the best results are obtained with a maximum RSS-based criterion, which also proved to be the most consistent among the different investigated approaches. We show that the weighted centroid based low-complexity method is very sensitive to AP reduction, while the path loss-based method is also very robust to high percentage removals. Indeed, for fingerprinting, 50% of the APs can be removed safely with a properly chosen removal criterion without increasing the positioning error much.

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

“…P i,ap represents the signal strength measured at the i-thRP sent from the ap-th AP, where P Tap represents the signal propagation strength of the ap-th AP, and η i,ap represents a noise factor as normal distribution. Unlike Nurminen [47], Laitinen [48] obtained only a few pieces of fingerprint data at several key RPs and then obtained the entire radio map through the model. Laitinen [48] optimized the fingerprint database in this way, which improved positioning efficiency.…”

“…n represents the path fading index and W-N(0, σ 2 ) represents a random variable as normal distribution, which indicates the slow fading (shadow fading) of indoor signal propagation. Nurminen [47] utilized this model based on a large number of fingerprint data measured to optimize his research results, which had some effects.…”

“…Laitinen et al [48] improved the model proposed by Nurminen [47] and built the single slope model, shown in Equation (8).…”

“…Nurminen et al [47] used the classical logarithmic model (power-law model) as the indoor channel propagation model. The classical logarithmic mode combines LOS (Line of Sight) and NLOS (Not Line of Sight) propagation by using numbers of correction factors to offset the uncertainty caused by these two propagations.…”

“…The classical logarithmic mode combines LOS (Line of Sight) and NLOS (Not Line of Sight) propagation by using numbers of correction factors to offset the uncertainty caused by these two propagations. Equation (7) shows the logarithmic model Nurminen [47] used.…”