Hybrid vehicle positioning and tracking using mobile phones

Tao, Shiquan; Manolopoulos, Vasileios; Rodriguez, Saul; Ismail, Mohammed; Rusu, Ana

doi:10.1109/itst.2011.6060074

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…However, the implementation of this method requires additional equipment and modification of the antenna, and the establishment of a location server and a DGPS base station. Therefore, Tao et al (9) proposed a hybrid vehicle tracking method based on Kalman filtering that combines coordinates collected by an A-GPS terminal with position estimates calculated from the observed TOA difference. Numerical simulation results show the effectiveness of this hybrid method in simulating vehicle scenarios.…”

mentioning

confidence: 99%

Research on Roadside Unit-Assisted Cooperative Positioning Method for a Connected Vehicle Environment

Liu

Yang

Chen

et al. 2021

Transportation Research Record

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With the rapid development of intelligent transportation systems and connected vehicle (CV) technology, vehicle-to-infrastructure communication technologies have provided new solutions to traditional traffic safety and efficiency issues. However, the current intelligent CVs often provide positioning services only through a single GPS. These modules’ positioning accuracy can be insufficient to support the safety and reliability of security applications. The question arises of how to enhance GPS positioning accuracy in a CV environment without adding additional equipment and using only the information that existing CV devices can access. This paper proposes a roadside unit (RSU)-assisted GPS-RSS (received signal strength) cooperative positioning method for a CV environment. The rough position information from GPS is combined with RSS ranging and dead reckoning to obtain preliminary position estimated coordinates of the CV. Bayesian filtering is performed to obtain a more accurate preliminary position estimate. The final position estimated coordinates, obtained after data fusion, are combined with the high-precision map data (MAP) sent by the RSU to match the lane where the vehicle is located. Simulation and field tests verify each other, and the results show that the lane positioning accuracy of GPS can be improved by 21% within the range from the RSU to the CV’s on-board unit.

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mentioning

confidence: 99%

Research on Roadside Unit-Assisted Cooperative Positioning Method for a Connected Vehicle Environment

Liu

Yang

Chen

et al. 2021

Transportation Research Record

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“…3, Kalman Filter of real and estimated velocities for each of the noisy To further compare estimation performance of U-TDOA, A-GPS and their fusion based on MF and SF algorithms, RMSE of 1000 different sample trajectories where each is filtered at 158 Kalman Filter steps is computed, used for performance metrics is defined as [23]:…”

Section: Simulation Resultsmentioning

confidence: 99%

Comparing Measurement and State Vector Data Fusion Algorithms for Mobile Phone Tracking Using A-GPS and U-TDOA Measurements

Habtie

Abraham

Midekso

2015

Lecture Notes in Computer Science

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Abstract. Multi-Sensor Data Fusion (MSDF) becomes one research area in different disciplines including science and engineering. To enhance reliability and accuracy of sensor measurements' multisensory data fusion techniques are applied. The aim of this paper is to evaluate estimation performance of measurement fusion and state vector fusion algorithms in tracking a moving mobile phone along all journey of a vehicle. These two algorithms based on Kalman Filter are implemented in the tracking system. Performance evaluation is computed using MAT-LAB and the analysis show position and velocity estimation accuracy of measurement fusion algorithm is better than state vector fusion algorithm.

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“…For example, tracking the trajectories of multiple sources can assist in effectively managing their resources in space [1]. Other applications include air traffic control [2], missile defence [3], asset tracking [4], mobile communications [5] and wireless sensor networks [6] [7].…”

Section: Introductionmentioning

confidence: 99%

Multisource Spatiotemporal Tracking Using Sparse Large Aperture Arrays

Kamil

Manikas

2017

IEEE Trans. Aerosp. Electron. Syst.

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Abstract-In this paper, a multi-source tracking technique is proposed using a sparse large aperture array of passive sensors of known geometry. Firstly a novel spherical-spatiotemporal-statespace model is introduced incorporating target ranges, directions and Doppler effects in conjunction with the array geometry. Subsequently, this array of sensors is integrated with an Extended Kalman Filter (EKF), defined as the Arrayed EKF, to track the trajectory of multiple mobile sources. In addition, a recursive lower bound on the performance of the proposed tracking method is obtained based on the Posterior Cramer-Rao Bound (Posterior CRB). Computer simulation studies show that the proposed approach can track the locations of sources, as these move in space, with a very high accuracy.

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Hybrid vehicle positioning and tracking using mobile phones

Cited by 8 publications

References 12 publications

Research on Roadside Unit-Assisted Cooperative Positioning Method for a Connected Vehicle Environment

Research on Roadside Unit-Assisted Cooperative Positioning Method for a Connected Vehicle Environment

Comparing Measurement and State Vector Data Fusion Algorithms for Mobile Phone Tracking Using A-GPS and U-TDOA Measurements

Multisource Spatiotemporal Tracking Using Sparse Large Aperture Arrays

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