A New Indoor Localization Method Based on Inversion Propagation Model

Abstract

The popularity of WLAN and mobile devices makes the development and widely use of location-aware systems and services possible. Since the Received Signal Strength Indication (RSSI) can be obtained by most mobile devices, the fingerprinting method based on RSSI becomes a simple and efficient method of Indoor Location (IL). This method needs to use the empirical measurements, propagation modeling or their combination to build the fingerprinting map in the offline phase, and the map plays an important role in the location process. The inversion propagation model can predict the propagation loss between any two points along the virtual propagation path from the basestation to the mobile-station, which precision has been presented in [1], [2]. In this paper, the algorithm of Inversion Propagation Model is used to build the fingerprinting map in order to find a new method that can keep a high degree of accuracy as well as simplify the process and improve the efficiency of map building.

A Review on Indoor Localization Techniques using Received Signal Strength

“…RF-fingerprinting involves of two stages; the offline stage and the online stage. In the offline stage (Training phase) the area of interest is divided into grids, in each grid, many RSS are scanned from nearby APs and averaged to remove the fast fading effect, averaged RSS with corresponding location (also called reference points RP) are stored in a database known as Radio map [23].…”

“…One popular approach is to find the smallest Euclidean distance between the test point measurements and the radio map database [24]. The RP whose Euclidean distance with TP is the smallest represents the nearest location to TP [23].…”

The implementation of indoor localization based on an experimental study of RSSI using a wireless sensor network

“…Then, several other studies have been done to improve this method. In [19], they got a localization accuracy of 1.5 m by applying a Bayesian method, and based on the inverse propagation model, a new algorithm was proposed in [20]. An enhancement has been proposed in [21] for the target tracking applications by reducing the amount of data used.…”