Error Factor Analysis of DNN-based Fingerprinting Localization through Virtual Space

Localization using low-power wireless devices is one of the promising methods even in outdoor environments because the Global Navigation Satellite System (GNSS) devices generally consume much power to result in frequent battery replacements. Received Signal Strength Indicator (RSSI) is often utilized in such methods due to availability in most wireless devices. Even in outdoor open space, however, various factors such as path-loss characteristics and antenna directivity possibly cause difficulty in RSSI-based localization. This paper investigates the error factors of Deep Neural Network (DNN)-based fingerprinting localization in terms of antenna directivity. Most existing studies have analyzed the error factor in localization algorithms or methods to improve accuracy. On the other hand, the error factors on the environments had not been paid attention yet at all. In this paper, we investigate distance errors of the DNN-based fingerprinting method in terms of antenna directivity as one of the environmental factors using virtual space reproduced in a computer. Through experiments where an actual Bluetooth Low Energy (BLE) tag system was emulated, we found that the tag antenna directivity could be the factor to worsen the localization accuracy while receiver antenna directivity to better.