TIPS: Transformer Based Indoor Positioning System Using Both CSI and DoA of WiFi Signal

Abstract

In a channel state information (CSI) based indoor positioning system, the positioning performance becomes susceptible to multipath fading effects especially in non-line-of-sight environments. We propose a transformer-based indoor positioning system (TIPS) to address this challenge. The proposed TIPS utilizes a self-attention mechanism to process the continuous WiFi CSI observed from predetermined routes as fingerprints in a given indoor environment. Each route is then considered a sentence, whereas the position along the route is treated as a word in terms of natural language processing. Consequently, the problem of predicting the position with the fingerprints can then be considered the task of predicting the current word with previous words, which can be efficiently solved using the proposed TIPS. In order to fully exploit the relations among positions, we propose embedding the information of the direction of arrival (DoA) on top of the collected CSI as inputs to the TIPS. Thus, the transformer of the proposed TIPS can better capture the dependencies of the positions in the route and significantly boost positioning accuracy. To exhibit the superiority of the proposed TIPS in a radio frequency (RF) environment, we demonstrate a hardware implementation of an RF testbed consisting of an emulator of WiFi access point and user equipment. Through extensive computer simulations and experimental tests, it is demonstrated that the proposed TIPS can reduce the positioning error down to 20 cm, which is a significant improvement compared to the current state-of-the-art models.