Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning
AbstractNumerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data. View Full-Text
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Luo, A.; Chen, S.; Xv, B. Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning. ISPRS Int. J. Geo-Inf. 2017, 6, 327.
Luo A, Chen S, Xv B. Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning. ISPRS International Journal of Geo-Information. 2017; 6(11):327.Chicago/Turabian Style
Luo, An; Chen, Shenghua; Xv, Bin. 2017. "Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning." ISPRS Int. J. Geo-Inf. 6, no. 11: 327.
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