2D-HASAP: Two-Dimensional Heading-Aided Single-Anchor Positioning via Hidden Markov Model Map-Matching

This paper proposes a two-dimensonal positioning method based on a hidden Markov model map-matching scheme. The states of the hidden Markov model are generated by dividing the area of interest into a grid. At each time instant, the method considers two types of measurements: the platform's heading and the two-dimensional distance between the platform and the single-anchor. A recursive Bayesian estimator exploits these measurements to estimate the platform's position. The platform's heading measurement is used to calculate the prior probability distribution. Following this, observation likelihood is computed by considering the two-dimensional distance measurement as the observation of the hidden Markov model. Finally, the most probable projection of these measurements on the states of the hidden Markov model is estimated as the platform's position. The proposed method can be efficiently used, especially in constrained indoor and outdoor environments. Moreover, the method provides a two-dimensional positioning solution with an increased robustness thanks to the bounded error on the distance measurements. Simulation studies are provided to demonstrate the effectiveness of the proposed method.

Keyword: hidden Markov model; map-matching; recursive Bayesian estimation; single-anchor positioning