Fingerprinting-Assisted UWB-based Localization Technique for Complex Indoor Environments

Abstract Among the numerous radio-based solutions for indoor localization, ultra-wideband (UWB) technology is of particular interest due to its signal characteristics. The wide bandwidth of the UWB signal provides a fine time resolution of the transmitted pulses that enables a centimetre-level ranging accuracy under line-of-sight (LOS) conditions even in multipath intensive indoor environments. Nevertheless, it is still a challenge to implement accurate UWB-based localization in complex multi-room indoor environments at low cost because of a large number of static UWB anchors that may need to be deployed in order to provide an adequate LOS coverage in every segment of the environment. Therefore, there is a strong interest in developing UWB-based localization techniques that will provide acceptable accuracy under partially LOS coverage conditions. In this paper, we present a novel hybrid method that combines two conventional localization techniques, trilateration and fingerprinting, to address the problem of cost-effective UWB-based localization in complex indoor environments. With the proposed method, the target location is determined by a trilateration algorithm, while a fingerprinting-based algorithm is used to provide additional distances for trilateration in cases when there is an insufficient number of available LOS measurements. The additional distances are generated by a non-parametric regression algorithm that relies on a fingerprint database to map all available online range measurements (LOS as well non-LOS) to distances between the target and the set of pre-defined reference points. To minimize human effort in fingerprint collection, the indoor environment is site-surveyed in a room-by-room fashion with auxiliary UWB anchors temporarily placed at up to three reference points in the surveyed room. The method is validated through an extensive indoor measurement campaign with commercially available UWB transceivers. The experimental results show that the proposed method achieves sub-decimetre level localization accuracy under typical real-world conditions.