Sensors 2014, 14(1), 995-1009; doi:10.3390/s140100995
Article

Multiple Vehicle Cooperative Localization with Spatial Registration Based on a Probability Hypothesis Density Filter

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Received: 13 November 2013; in revised form: 22 December 2013 / Accepted: 26 December 2013 / Published: 8 January 2014
(This article belongs to the Section Physical Sensors)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: This paper studies the problem of multiple vehicle cooperative localization with spatial registration in the formulation of the probability hypothesis density (PHD) filter. Assuming vehicles are equipped with proprioceptive and exteroceptive sensors (with biases) to cooperatively localize positions, a simultaneous solution for joint spatial registration and state estimation is proposed. For this, we rely on the sequential Monte Carlo implementation of the PHD filtering. Compared to other methods, the concept of multiple vehicle cooperative localization with spatial registration is first proposed under Random Finite Set Theory. In addition, the proposed solution also addresses the challenges for multiple vehicle cooperative localization, e.g., the communication bandwidth issue and data association uncertainty. The simulation result demonstrates its reliability and feasibility in large-scale environments.
Keywords: random finite set; PHD filter; spatial registration
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MDPI and ACS Style

Zhang, F.; Buckl, C.; Knoll, A. Multiple Vehicle Cooperative Localization with Spatial Registration Based on a Probability Hypothesis Density Filter. Sensors 2014, 14, 995-1009.

AMA Style

Zhang F, Buckl C, Knoll A. Multiple Vehicle Cooperative Localization with Spatial Registration Based on a Probability Hypothesis Density Filter. Sensors. 2014; 14(1):995-1009.

Chicago/Turabian Style

Zhang, Feihu; Buckl, Christian; Knoll, Alois. 2014. "Multiple Vehicle Cooperative Localization with Spatial Registration Based on a Probability Hypothesis Density Filter." Sensors 14, no. 1: 995-1009.

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