Real-time locating and tracking Technology plays a significant role in location-based IoT applications. With the extensive installation of WiFi access points, the WiFi based indoor positioning approach has become one of the most widely used location technologies. However, due to the limitations of wireless signals, the classic WiFi-based method has become labor-intensive. Recently, the WiFi-based twoway ranging approach was introduced into the 802.11-REVmc2 protocol, which is built on a new packet type known as fine timing measurement (FTM) frame. In this work, we introduce the round-trip time measurement with clock skew and analyze the distribution of the round trip time (RTT) ranging error. A calibration method is presented to eliminate the RTT range offset at the transmitter end. We also develop an integrated ranging algorithm based on the WiFi round trip time range and received signal strength to enhance the scalability and robustness of the positioning system. The experimental results demonstrate that the proposed fusion method achieves remarkable improvement in scalability and precision in both static and dynamic tests, including outdoor and indoor environments. Compared with the classic fingerprinting approach, the performance of the system is remarkably improved, and achieves an average positioning accuracy of 1.435 m with an update rate of every 0.19 s. INDEX TERMSIndoor localization, smartphone, WiFi fine time measurement (FTMs), round trip time (RTT), received signal strength (RSS), Kalman filter.

Sensors-based and radio frequency (RF)-based indoor localization technology is one of the keys in location-based services. The IEEE 802.11-2016 introduced the Wi-Fi fine timing measurement (FTM) protocol, which provides a new approach for Wi-Fi-based indoor localization. However, Wi-Fi signals are susceptible to complex indoor environments. To improve the positioning accuracy and stability, an enhanced particle filter (PF) with two different state update strategies, a new criterion for divergence monitoring and rapid re-initialization is proposed to integrate the advantages of pedestrian dead reckoning (PDR) and Wi-Fi FTM. In addition, an adaptive tilt compensation is proposed to improve real-time heading estimation of conventional PDR, and the Wi-Fi FTM outliers are detected by displacement estimation of the PDR. The experimental results show that the proposed PF has better localization performance than the single source positioning methods in a typical indoor scenario. The accuracy of final localization is within 1 m in 86.7% of the dynamic cases and the average calculation time is less than 0.5 s when the number of particles is 2000. INDEX TERMSWi-Fi FTM, pedestrian dead reckoning, particle filter, multi-sensors, indoor localization.

More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.