Robust Wi-Fi location fingerprinting against device diversity based on spatial mean normalization

Abstract

Received signal strength (RSS) in Wi-Fi networks is commonly employed in indoor positioning systems; however, device diversity is a fundamental problem in such RSS-based systems. The variation in hardware is inevitable in the real world due to the tremendous growth in recent years of new Wi-Fi devices, such as iPhones, iPads, and Android devices, which is expected to continue. Different Wi-Fi devices performed differently in respect to the RSS values even at a fixed location, thus degrading localization performance significantly. This study proposes an enhanced approach, called spatial mean normalization (SMN), to design localization systems that are robust against heterogeneous devices. The main idea of SMN is to remove the spatial mean of RSS to compensate for the shift effect resulted from device diversity. The proposed algorithm was evaluated on an indoor Wi-Fi environment, where realistic RSS measurements were collected through heterogeneous laptops and smart phones. Experimental results demonstrate the effectiveness of SMN. Results show that SMN outperforms previous positioning features for heterogeneous devices.