Genetic Algorithm for Path Loss Model Selection in Signal Strength-Based Indoor Localization

Abstract

Ranging methods using received signal strength (RSS) information are widely used for indoor localization because they can be easily implemented without conducting site surveys. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, we develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and non-line-of-sight (NLOS) states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, we formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, we apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92 m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09% over the benchmark scenario.