Site-Specific RSS Signature Modeling for WiFi Localization

A number of techniques for indoor and outdoor WiFi localization using received signal strength (RSS) signatures have been published. Little work has been performed to characterize the RSS signatures used by these WiFi localization techniques or to assess the accuracy of current channel models to represent the signatures. Without accurate characterization and models of the signatures, a large amount of empirical data is needed to evaluate the performance of the WiFi localization techniques. The goal of this paper is to characterize the RSS signatures and present a novel wall breakpoint model for use in WiFi localization simulations to eliminate the need for large empirical databases. In this paper, we present our empirical database of RSS signatures measured in the campus of the Worcester Polytechnic Institute, characterize the RSS signatures used in WiFi localization, evaluate the performance of the current WiFi channel model, and propose a novel wall breakpoint model, which exploits site-specific information to provide a tighter fit to the empirical RSS signatures. From the RSS signature characterization, it was observed that the behavior of the RSS signatures is dependant on the location of the Access Point (AP) with a building due to shadow fading. Our proposed model improves upon the simpler WiFi channel model by adding a dynamic site-specific wall breakpoint to account for the location of the AP within a building and allowing for proper simulation of the outdoor path-loss environment.