A HMM map-matching approach enhancing indoor positioning performances of an inertial measurement system

Lamy-Perbal, Sylvie; Guénard, Nicolas; Boukallel, Mehdi; Landragin-Frassati, Anne

doi:10.1109/ipin.2015.7346774

Cited by 19 publications

(6 citation statements)

References 12 publications

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“…When they use points with low sampling rate as their data, the matching error is over 50% [8,9]. Therefore, in view of the track point with a low sampling point, the paper will put forward an improved algorithm of map-matching.…”

Section: Application Conditions Of the Algorithmmentioning

confidence: 99%

A novel algorithm of low sampling rate GPS trajectories on map-matching

Liu

2017

J Wireless Com Network

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Map-matching is the process of matching the GPS locus to the road network on the digital map. However, due to the most existing map-matching algorithms that are based on high sampling rate, when the sampling interval is increased, the correct rate of the algorithm will be greatly reduced. Based on this, this paper proposed a new algorithm of map-matching for low sampling rate GPS trajectories. The algorithm gave full consideration to the road network of the geometric structure and topological structure and the mutual influence between adjacent points (time, speed information) by calculating the probability of each trajectory point of candidate points to determine matching results. At the end of this paper, we use the data of Beijing UCAR Inc.'s car in a case study. This case demonstrates: For low sampling rate matching track points in the complex road, the algorithm has a good uptime, and an exact match was found.

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Section: Application Conditions Of the Algorithmmentioning

confidence: 99%

A novel algorithm of low sampling rate GPS trajectories on map-matching

Liu

2017

J Wireless Com Network

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“…However, these methods have high infrastructure costs and require a large workforce to build a complete indoor positioning navigation system. Another way to improve the navigation accuracy is using maps that need no additional infrastructure or equipment [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

Zhang¹,

Zhang²,

Guo³

et al. 2022

Journal of Compurters

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<p>With the rapid development of smart city, the indoor positioning services became more and more important. During the existing solutions, inertial measurement unit (IMU) with pedestrian dead reckoning (PDR) was a promising scheme since they did not require external equipment in the environment. However, the orientation drift of low-cost IMU limited their application in practical. To address this problem, a zero-velocity update (ZUPT) framework included Kalman filter and particle filter is designed based on the foot-based low-cost IMU and digital floor plan to provide the service of personal navigation. In the designed Smoothing for ZUPT-aided INSs framework, the Kalman filter is used to estimate the position and attitude by zero velocity correction technique. Then, the particle filter is used to improve the localization and heading accuracy by map matching. The position estimation presented in this study achieves an average position error of 1.16 m. The experimental results show that the designed framework can solve the personal navigation problem in the case of building plan information assistance and help improve the accuracy and reliability of continuous position determination of personal navigation systems effectively. </p> <p> </p>

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“…BLE can successfully improve the accuracy of INS and be adopted to determine the initial position of the users [17]. A map-matching method can be used to correct the DR system to realize positioning continuously [18]. The accuracy of subsistent radio maps can be improved based on both crowd-sourced WiFi fingerprints that are post-processed offline and on the logged pedestrian trajectory [19].…”

Section: Introductionmentioning

confidence: 99%

WiFi/PDR integrated navigation with robustly constrained Kalman filter

Long

Qin

et al. 2020

Meas. Sci. Technol.

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Nowadays, global navigation satellite systems (GNSSs) are widely used in location-based services (LBSs) as they can provide high-accuracy position services continuously. However, their performance deteriorates in indoor scenarios, in which GNSS signal reception is limited or completely impossible. In this paper, an enhanced constrained Kalman filter is presented to enhance the indoor positioning performance of a LBS and for use in a WiFi/pedestrian dead reckoning (PDR) integrated navigation algorithm. A robust scheme for computing the gross error in constrained conditions is suggested to make the performance of the constrained condition model in the WiFi/PDR integrated system more robust. The results of simulation analysis indicate that the robustly constrained Kalman filter can reliably determine gross errors in constrained conditions. An indoor field experiment was conducted to test the performance of the proposed filter algorithm, and the results show that the improved filter can eliminate the effect of gross error from constrained conditions in the WiFi/PDR system.

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A HMM map-matching approach enhancing indoor positioning performances of an inertial measurement system

Cited by 19 publications

References 12 publications

A novel algorithm of low sampling rate GPS trajectories on map-matching

A novel algorithm of low sampling rate GPS trajectories on map-matching

Pedestrian Inertial Navigation with Building Floor Plans for Indoor Environments via Particle Filter

WiFi/PDR integrated navigation with robustly constrained Kalman filter

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