A Kalman filter for integrating dead reckoning, map matching and GPS positioning

Krakiwsky, Edward J.; Harris, C.B.; Wong, R.C.K.

doi:10.1109/plans.1988.195464

Cited by 90 publications

(44 citation statements)

References 4 publications

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“…Map-matching based on probabilistic strategy, including HMM-based map-matching for GPS positioning [23][24][25][26], and multiple hypothesis [27][28][29][30] focuses on the perspective of the total situation for all position data and all candidate road links [7,15,16,[31][32][33][34], instead of calculation between individual positions and nearby candidate road links. These algorithms typically take three steps.…”

Section: Brief Review Of Map-matching Algorithmsmentioning

confidence: 99%

Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning

Luo

Chen²,

Xv³

2017

IJGI

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Numerous 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.

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Section: Brief Review Of Map-matching Algorithmsmentioning

confidence: 99%

Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning

Luo

Chen²,

Xv³

2017

IJGI

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“…These models encode complete distributions over random variables using graph-based representations, and some special cases such as the state-space Kalman Filter have long found vast applications in global positioning systems (e.g. Krakiwsky et al (1988); Gustafsson et al (2002);Fung & Grimble (1983); Sasiadek et al (2000)). Additionally, Hidden Markov Models (HMMs), Maximum-Entropy Markov Models (MEMMs) and Conditional Random Fields (CRFs) have also been studied for diverse supervised labelling problems with sequential data (Dietterich (2002) (2012)).…”

Section: Related Workmentioning

confidence: 99%

Unsupervised labelling of sequential data for location identification in indoor environments

Perez

Pinchin

Brown

et al. 2016

Expert Systems with Applications

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In this paper we present indoor positioning within unknown environments as an unsupervised labelling task on sequential data. We explore a probabilistic framework relying on wireless network radio signals and contextual information, which is increasingly available in large environments. Thus, we form an informative spatial classifier without resorting to a pre-determined map, and show the potential of the approach using both simulated and real data sets.Results demonstrate the ability of the procedure to segregate structures of radio signal observations and form clustered regions in association to areas of interest to the user; thus, we show it is possible to differentiate location between closely spaced zones of variable size and shape.

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“…These techniques are able to detect offroad situations. In the framework of Bayesian filtering, Kalman approaches [3], [15], [26] and particle filter [10] techniques have been developed. The matching relies in this case on a map pose-tracking paradigm in which the map data is treated as an observation.…”

Section: Architecture Of the Road-matching Strategymentioning

confidence: 99%

Road Selection Using Multicriteria Fusion for the Road-Matching Problem

Najjar

Bonnifait

2007

IEEE Trans. Intell. Transport. Syst.

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This paper presents a road selection strategy for novel road-matching methods designed to support real-time navigational features within Advanced Driving-Assistance Systems. Selecting the most likely segment(s) is a crucial issue for the road-matching problem. The selection strategy merges several criteria using Belief Theory. Particular attention is given to the development of Belief functions from measurements and estimations of relative distances, headings and velocities. Experimental results, using data from ABS sensors, differential GPS receiver and accurate digital roadmap, illustrate the performances of this approach, especially in ambiguous situations.

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A Kalman filter for integrating dead reckoning, map matching and GPS positioning

Cited by 90 publications

References 4 publications

Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning

Enhanced Map-Matching Algorithm with a Hidden Markov Model for Mobile Phone Positioning

Unsupervised labelling of sequential data for location identification in indoor environments

Road Selection Using Multicriteria Fusion for the Road-Matching Problem

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