Abstract
The received signal strength (RSS) fingerprint-based indoor localization has been considered as a promising solution, due to its relatively high localization accuracy and its ease of use in widespread Wireless Local Area Network (WLAN) infrastructure. A major bottleneck is that the offline fingerprint calibration is time consuming and labor intensive. In this study, inspired by our analysis that multi-density is inherent to the RSS distribution, we present a new radio map construction scheme, called Adaptive Density Graph-based Manifold Alignment (ADG-MA), which can reduce the number of Reference Points (RPs) in offline phase. In particular, it utilizes the density features to capture the exact neighborhood relations of RSS. Furthermore, the approach labels the RSS from user traces to construct the radio map. The extensive experiments demonstrate that the proposed method can construct an accurate radio map at a low deployment cost, as well as achieve a high localization accuracy.
Highlights
With the unceasing development of wireless communication technology, computer technology and the demand of Location-based Services (LBSs), the indoor wireless localization techniques are of growing interest and becoming increasingly prevalent [1]
The localization technology based on Wireless Local Area Network (WLAN) mainly includes localization based on Time of Arrival (TOA) [4], Time Difference of Arrival (TDOA) [5], Angle of Arrival (AOA) [6] and Received Signal Strength (RSS) [7]
· In this paper, inspired by our analysis that multi-density is inherent to the RSS distribution, we present a new radio map construction scheme, called Adaptive Density Graph-based Manifold Alignment (ADG-MA), which can reduce the number of Reference Points (RPs) in offline phase
Summary
With the unceasing development of wireless communication technology, computer technology and the demand of Location-based Services (LBSs), the indoor wireless localization techniques are of growing interest and becoming increasingly prevalent [1]. Compared with other localization algorithms, WLAN fingerprint-based localization scheme is widely used. The WLAN fingerprint-based localization approach mainly involves two phases, namely offline phase and online phase [8]. Due to the complexity of wireless Wi-Fi signal propagation and the effect of multi-path fading and shadow effect, the RSS signal of the same AP at a certain RP usually has complex time-changing characteristics, so it needs to collect data repeatedly at the RP to improve the accuracy of the radio map. The newlycollected RSS signals are matched against the signals in the radio map by matching algorithms (e.g. KNN [9]) to calculate the location of the user
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