Volume 44 Issue 4
Apr.  2018
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CHEN Weishi, YAN Jun, LI Jinget al. Joint optimization of detection and tracking with Rao-Blackwellized Monte Carlo data association[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 700-708. doi: 10.13700/j.bh.1001-5965.2017.0228(in Chinese)
Citation: CHEN Weishi, YAN Jun, LI Jinget al. Joint optimization of detection and tracking with Rao-Blackwellized Monte Carlo data association[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 700-708. doi: 10.13700/j.bh.1001-5965.2017.0228(in Chinese)

Joint optimization of detection and tracking with Rao-Blackwellized Monte Carlo data association

doi: 10.13700/j.bh.1001-5965.2017.0228
Funds:

Joint Research Foundation of National Natural Science Foundation of China (NSFC) and Civil Aviation Administration of China (CAAC) U1633122

National Key R&D Program 2016YFC0800406

More Information
  • Corresponding author: CHEN Weishi, E-mail: chenwsh@mail.castc.org.cn
  • Received Date: 13 Apr 2017
  • Accepted Date: 21 Jul 2017
  • Publish Date: 20 Apr 2018
  • A joint optimization algorithm was proposed for radar target detection and tracking with Rao-Blackwellized Monte Carlo data association. Rao-Blackwellization made the separation of single target tracking and data association, where the data association was solved by the sequential Monte Carlo method (particle filtering), leading to the multiple target tracking in the environment of clutter and false alarm measurements. Meanwhile, the size of the wave gate depended on the distribution range of particles. Under the consideration of the particle weights, the detection threshold was modified with the relative position of the detection units to all the particles, improving the detection rate. Finally, combined with the algorithm for clutter suppression with spatial features achieved in the previous research, the proposed algorithm was applied to the simulated data as well as the ground-truth data collected by the S-band incoherent and coherent radars. It is demonstrated that the proposed algorithm can realize the detection and tracking of small targets with relatively small number of particles.

     

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