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ZHOU Haowen and WANG Qi. Extended object tracking based on variational marginalized particle filter for automotive radar[J]. Journal of Radars, in press. doi: 10.12000/JR25005
Citation: ZHOU Haowen and WANG Qi. Extended object tracking based on variational marginalized particle filter for automotive radar[J]. Journal of Radars, in press. doi: 10.12000/JR25005
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Extended Object Tracking Based on Variational Marginalized Particle Filter for Automotive Radar

DOI: 10.12000/JR25005 CSTR: 32380.14.JR25005
  • 1.

    Nanjing ChuHang Technology Co., Ltd., Nanjing 211800, China

  • 2.

    Hella Nanjing Technology Center, Nanjing 211106, China

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  • Corresponding author: ZHOU Haowen, zhouhaowen@nuaa.edu.cn
  • Received Date: 2025-01-03
  • Rev Recd Date: 2025-05-01
    • Available Online: 2025-05-17
    Abstract
  • The measurement from automotive millimeter-wave radar consists of position coordinates in the polar coordinate system and doppler velocity, which has a complex, nonlinear relationship with the extended object state modeled in the Cartesian coordinate system. To address this nonlinear state estimation problem, a Variational Marginalized Particle Filter-based Extended Object Tracking (VMPF-EOT) algorithm is proposed. First, the object’s two-dimensional planar contour is modeled as an ellipse with an explicitly defined orientation angle. A parameterized inverse gamma distribution is constructed as the conjugate prior distribution for the contour size. Second, the measurement source position is introduced as an auxiliary variable to establish a measurement model for extended objects detected by automotive millimeter-wave radar. To enhance the contour estimation performance for maneuvering objects, the joint distribution of the extended object state is marginalized with respect to the contour orientation angle. The posterior distribution of the contour orientation angle is estimated independently using a particle filter. The approximate analytical solution for the posterior distributions of the remaining state variables—including the target’s center motion state and contour size—is derived using the variational Bayesian inference. The simulation results demonstrate that the proposed algorithm achieves higher state estimation accuracy than existing algorithms. In tracking maneuvering targets, the proposed algorithm offers a more significant advantage in terms of estimating the contour orientation angle and contour size.

     

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