Shi, Y. (Yifang)

Filtering

20181

Settings

Author search.filters.isAuthorOfPublication.9ae4faf4-b354-4af5-8fe2-f910bc3a6c0f

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Improved multitarget tracking in clutter using bearings-only measurements
    (MDPI AG, 2018) Peng, D. (Dongliang); Shi, Y. (Yifang); Ding, Y. (Yuemin); Xue, M. (Mengfan)
    Multitarget tracking in clutter using bearings-only measurements is a challenging problem. In this paper, a performance improved nonlinear filter is proposed on the basis of the Random Finite Set (RFS) theory and is named as Gaussian mixture measurements-based cardinality probability hypothesis density (GMMbCPHD) filter. The GMMbCPHD filter enables to address two main issues: measurement-origin-uncertainty and measurement nonlinearity, which constitutes the key problems in bearings-only multitarget tracking in clutter. For the measurement-origin-uncertainty issue, the proposed filter estimates the intensity of RFS of multiple targets as well as propagates the posterior cardinality distribution. For the measurement-origin-nonlinearity issue, the GMMbCPHD approximates the measurement likelihood function using a Gaussian mixture rather than a single Gaussian distribution as used in extended Kalman filter (EKF). The superiority of the proposed GMMbCPHD are validated by comparing with several state-of-the-art algorithms via intensive simulation studies.