Abstract
Indoor positioning and navigation is an essential field of location-based service. Non-line of sight (NLOS) error restricts the accuracy of indoor positioning. Many researchers have studied the localization problem in indoor NLOS environments, but there is still a problem that NLOS error cannot be mitigated in unknown areas. To solve the above problems, this paper proposes a method of constructing virtual base stations in unknown areas (UA-VBS), and presents the corresponding positioning algorithm to calculate the location of the user equipment. Firstly, the base stations are selected and the initial positioning is carried out. Then, multiple virtual base stations are constructed according to the user equipment positions in the first three steps. The LOS base stations and virtual base stations participate in the TDOA calculation together, and calculate the base stations’ combination with the minimum residual and the corresponding positioning result. Finally, the pedestrian dead reckoning fusion weight is updated by the residual value, and the accurate positioning result in NLOS environment is obtained. Simulation and experimental results show that the proposed algorithm has high positioning accuracy and stability in NLOS environment.
Highlights
In recent years, with the rapid development of mobile communication technology, location-based service (LBS) is playing an increasingly important role in people’s daily life
The Pedestrian Dead Reckoning (PDR) fusion weight is updated by the residual value, and the accurate positioning result in LOS/Non-line of sight (NLOS) environment is obtained
The combination with the minimum residual is selected as the positioning result, and the PDR fusion weight is updated by the residual value
Summary
With the rapid development of mobile communication technology, location-based service (LBS) is playing an increasingly important role in people’s daily life. All of these methods can improve the positioning accuracy in mixed LOS/NLOS environments. L. Yan [21] et al proposed a three-step positioning method to identify and mitigate the effects of NLOS by Bayesian Sequence detection and modified Kalman filter pre-process the signal and perform position estimation by residual weighting algorithm, which is an effective method to improve the positioning accuracy in NLOS environment. In our previous study [26], we proposed a GDOP-assisted base station selection method based on channel conditions and GDOP value variation, which can effectively improve the positioning accuracy in a mixed NLOS/LOS environment. We will describe the UA-VBS method in detail
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