4.8 Article

Tailored Hidden Markov Model: A Tailored Hidden Markov Model Optimized for Cellular-Based Map Matching

期刊

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 69, 期 12, 页码 13818-13827

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2021.3135645

关键词

Hidden Markov model; map matching; cellular based positioning

资金

  1. National Natural Science Foundation of China [62072333, 62102263]
  2. Shenzhen Science and Technology Foundation [JCYJ20170816093943197]
  3. Shenzhen Science and Technology Program [JCYJ20210324094208024]

向作者/读者索取更多资源

This article discusses the advantages and disadvantages of GPS and cellular-based positioning. It highlights the challenges of cellular-based positioning and proposes a novel algorithm called THMM to improve its accuracy. The algorithm is optimized based on the characteristics of cellular-based data and the experimental results demonstrate its effectiveness.
Although the GPS-based positioning is ubiquitous for its high precision, the high power consumption brought by the high sampling frequency and the poor GPS signal penetration limits its availability in locating low-power mobile devices (especially mobile phones). As a promising complement, the cellular-based positioning has attracted great attention since it consumes much less power as well as its higher availability. However, the sparsity of cellular-based data (due to lower sampling rate) and large localization errors make the measurement accuracy becomes the main challenge of the cellular-based positioning. hidden Markov model can well solve the problem of positioning error of GPS data, but it is less accurate when applied to map matching of cellular-base data. Therefore, to improve accuracy, in this article, we propose a novel algorithm called the tailored hidden Markov model (THMM) that is optimized for the cellular-based data. Specifically, the geometric, the topological, and the probabilistic characteristics have been considered and fully exploited in the THMM design. Our proposed schemes are evaluated using real-world motor vehicle movement trajectories collected in Tianjin and the experimental results are encouraging compared with the state of the art algorithms.

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