A Hybrid Deep-learning/Fingerprinting for Indoor Positioning Based on IEEE P802.11az

Abstract

Many different technologies were proposed in the past few years for enhancing indoor positioning: WiFi, Radio Frequency Identification (RFID), Ultra Wide Band (UWB), and Bluetooth to mention some. This study followed the recent IEEE positioning standard (P802.11 az). The standard was developed to enhance indoor navigation by minimizing the consumption power with low hardware complexity. Therefore, this standard enables the usage of artificial intelligence algorithms with relatively high complexity. Also, the usage of this standard will enhance indoor localization and positioning for different commercial purposes. We proposed two methods: Time Of Arrival (TOA) and fingerprinting-deep learning, considering a simple Single Input-Single Input (SISO) system at five Gigahertz with the highest standard allowable bandwidth. The behavior of TOA had very low performance considering a realistic multi-path case. On the other hand, the deep learning algorithm achieved ultra-high indoor positioning resolution (around twelve centimeters). Although TOA is a technique that relies on a simple hardware algorithm relative to deep learning, this paper proved the failure of TOA in a simple indoor environment even using the latest IEEE positioning standard compared with the deep learning method.