2020
DOI: 10.1109/tvt.2020.3004832
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Carrier-Phase-Based Multi-Vehicle Cooperative Positioning Using V2V Sensors

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Cited by 27 publications
(17 citation statements)
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“…Table 3 compare more detailed performance for all control groups. 2 -FDE excludes the faulty measurements during the navigation, thus has better performance than the case without FDE in most cases. And the proposed GKLD-FDE has the best performance, for RMSE, GKLD-FDE is 1.63 m at the last epoch, better than that of 2 -FDE (1.68 m) and the case without FDE (1.75 m).…”
Section: Resultsmentioning
confidence: 99%
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“…Table 3 compare more detailed performance for all control groups. 2 -FDE excludes the faulty measurements during the navigation, thus has better performance than the case without FDE in most cases. And the proposed GKLD-FDE has the best performance, for RMSE, GKLD-FDE is 1.63 m at the last epoch, better than that of 2 -FDE (1.68 m) and the case without FDE (1.75 m).…”
Section: Resultsmentioning
confidence: 99%
“…To guarantee the safety and efficiency of systems, many applications, such as intelligent transportation systems and location-based services, 1 are very dependent on reliable and accurate relative positioning solutions. 2,3 The state-of-the-art relative navigation is based on the carrier-phase differences of global navigation satellite system (GNSS) observations between nodes (e.g. carrier-phase differential global positioning system (CP-DGPS)) to provide decimeter or centimeter level relative navigation accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…On average, each ICV has 4.19 ITS-station measurements can be used in ideal EGKF. Because measurements and ICVs are not matched in EG algorithm, there are some measurements are wasted within the time steps [1,17]. And there are some measurements not belonging to ICVs are used incorrectly within time steps [96, 115], [147, 164] and [197,214].…”
Section: Highway Scenariomentioning
confidence: 99%
“…First, the host intelligent connected vehicle (ICV) named ego receives the GNSS position of neighbour ICVs through basic safety message [10] or cooperative awareness message [11]. Then, the ego uses V2V signal [12][13][14][15][16] or combined with special equipment [17] to estimate the relative distance and/or relative angle between ego and neighbour ICVs. Finally, data fusion is implemented through Bayesian filter, such as Kalman filter or extended Kalman filter to obtain a higher-precision positioning for ego [18,19], where the relative distance is used as measurements.…”
Section: Introductionmentioning
confidence: 99%
“…Early cooperative positioning technology mainly utilized ranging and direction finding to obtain the positions of nodes. The first generation of cooperative positioning technology mainly carried out the centralized processing of data to achieve navigation and positioning [11][12][13][14][15]. By measuring the distance between the central node and the surrounding nodes, combined with the node's own positioning information, the positioning information of all nodes could be obtained.…”
Section: Introductionmentioning
confidence: 99%