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.

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Footmounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

Indoor positioning services have become necessary in many situations. Radio frequency (RF) signals are suitable for being used for positioning because of their ubiquity and imperceptibility. This paper utilizes the information from the baseband of a Bluetooth low energy (BLE) transceiver for angle estimation and signal strength for distance estimation. The scheme constitutes a single-anchor based solution to calculate the position of a client. It significantly reduces the cost of installation by avoiding traditional methods like multilateration or triangulation that require three or more anchors, even in a small space. To improve the performance, we design a fusion algorithm based on a Kalman filter to integrate measurements of the anchor station and simplified pedestrian dead reckoning (PDR) results from the client. Experiments show that the proposed solution estimates positions in high precision without initial user location or heading information. The mean error of the implementation is less than 1 m and can be improved to less than 0.5 m with a precise ranging measurement. INDEX TERMS BLE, data fusion, indoor positioning, Karman filter, pedestrian dead reckoning (PDR), single-anchor.

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Abstract. In recent years, audio positioning technology has been more favoured because it is not only highly accurate but also universally applicable to the mass users. In line with this trend, a chip dedicated to indoor positioning, the Kepler A100, has been released lately, which ranges by estimating the arrival time of the audio chirp. As with radio ranging techniques, sound-based ranging methods is subject to significant observation errors due to Non-Line-Of-Sight (NLOS) or multi-path effects, while Pedestrian Dead Reckoning (PDR) based on inertial data have the disadvantage of error accumulation. Both make localization challenging. In this paper, the ranging performance of the Kepler A100 chip is evaluated and experiments are conducted in two typical indoor scenarios to verify the stability and sub-metre accuracy of the ranging results. Meanwhile, this paper implements an indoor positioning system combining audio chirp/Micro-Electro-Mechanical Systems (MEMS)/floor map based on particle filtering algorithm. A new mapping constraint method is applied in the system, which can effectively constrain the particles in a reasonable space. Experiments are conducted in two complex office environments and the results show that the error of the proposed algorithm is about 0.65m in 95% of cases, a reduction of more than 40% compared to only-PDR or only-audio. The effectiveness and stability of the method is demonstrated.