MOC: Wi-Fi FTM With Motion Observation Chain for Pervasive Indoor Positioning

The IEEE 802.11-2016 standard enables devices to gather precise ranging information through the time-of-flight evaluation, facilitating the development of accurate indoor location-based services. Researchers have indicated that the protocol's most effective performance is in scenarios with direct line-of-sight, despite providing meter-level ranging accuracy. In real indoor environments, the accuracy diminishes considerably due to random errors caused by interference such as multipath effects and non-line-of-sight signal propagation. Therefore, it is essential to accurately evaluate the reliability of each ranging measurement and effectively leverage neighboring high-quality observations to improve positioning accuracy. This study presents a novel optimization algorithm that relies on the motion observation series by incorporating adjacent ranging observations and a priori motion knowledge into a factor graph model, resulting in a unified optimization objective. Consequently, our system can dynamically estimate the confidence of fine time measurements ranging measurements. It optimizes the position estimation of the current user by maximizing the probability of not only the current ranging measurements but also the adjacent historical measurements and a priori motion. Additionally, to enable real-time positioning, a fast-solving procedure employing an adaptive gradient is proposed, capable of providing evaluations in under 10ms. The system has been tested in real indoor environments, showing improved performance compared to existing methods. It achieves meter-level real-time positioning accuracy at 1 sigma without requiring a specific device pose, additional sensor, or expensive site survey. This makes our proposal highly applicable for wide adoption and readiness for the market.