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Sensors 2018, 18(8), 2723; https://doi.org/10.3390/s18082723

Tracking Ground Targets with a Road Constraint Using a GMPHD Filter

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
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Received: 19 July 2018 / Revised: 14 August 2018 / Accepted: 15 August 2018 / Published: 18 August 2018
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Abstract

The Gaussian mixture probability hypothesis density (GMPHD) filter is applied to the problem of tracking ground moving targets in clutter due to its excellent multitarget tracking performance, such as avoiding measurement-to-track association, and its easy implementation. For the existing GMPHD-based ground target tracking algorithm (the GMPHD filter incorporating map information using a coordinate transforming method, CT-GMPHD), the predicted probability density of its target state is given in road coordinates, while its target state update needs to be performed in Cartesian ground coordinates. Although the algorithm can improve the filtering performance to a certain extent, the coordinate transformation process increases the complexity of the algorithm and reduces its computational efficiency. To address this issue, this paper proposes two non-coordinate transformation roadmap fusion algorithms: directional process noise fusion (DNP-GMPHD) and state constraint fusion (SC-GMPHD). The simulation results show that, compared with the existing algorithms, the two proposed roadmap fusion algorithms are more accurate and efficient for target estimation performance on straight and curved roads in a cluttered environment. The proposed methods are additionally applied using a cardinalized PHD (CPHD) filter and a labeled multi-Bernoulli (LMB) filter. It is found that the PHD filter performs less well than the CPHD and LMB filters, but that it is also computationally cheaper. View Full-Text
Keywords: Gaussian mixture probability hypothesis density filter; directional process noise; state constraint; ground moving target tracking; cardinalized PHD filter; labeled multi-Bernoulli filter Gaussian mixture probability hypothesis density filter; directional process noise; state constraint; ground moving target tracking; cardinalized PHD filter; labeled multi-Bernoulli filter
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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. (CC BY 4.0).
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Zheng, J.; Gao, M. Tracking Ground Targets with a Road Constraint Using a GMPHD Filter. Sensors 2018, 18, 2723.

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