Volume 6 Issue 3
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Article Navigation >  Journal of Radars  > 2017  >  6(3): 252-258
Wu Sunyong, Xue Qiutiao, Zhu Shengqi, Yan Qingzhu, Sun Xiyan. Track-Before-Detect Algorithm for Weak Extended Target Based on Particle Filter under Clutter Environment[J]. Journal of Radars, 2017, 6(3): 252-258. doi: 10.12000/JR16128
Citation: Wu Sunyong, Xue Qiutiao, Zhu Shengqi, Yan Qingzhu, Sun Xiyan. Track-Before-Detect Algorithm for Weak Extended Target Based on Particle Filter under Clutter Environment[J]. Journal of Radars, 2017, 6(3): 252-258. doi: 10.12000/JR16128
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Track-Before-Detect Algorithm for Weak Extended Target Based on Particle Filter under Clutter Environment

DOI: 10.12000/JR16128
  • ①.

    School of Computational Science and Mathematics, Guilin University of Electronic Technology, Guilin 541004, China

  • ②.

    Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin 541004, China

  • ③.

    National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds:  The National Natural Science Foundation of China (61261033, 61561016, 61362005), The Natural Science Foundation of Guangxi (2014GXNSFAA118352, 2014GXNSFBA118280, 2016GXNSFAA380073), Guangxi Key Laboratory of Precision Navigation Technology and Application (DH201502), Guangxi Key Laboratory of Cryptography and Information Security (GCIS201611)
  • Received Date: 2016-11-18
  • Rev Recd Date: 2017-01-26
    • Available Online: 2017-04-14
  • Publish Date: 2017-06-28
    Abstract
  • The Track-Before-Detect (TBD) algorithm based on the particle filter is proposed for weak extended target detection and tracking in low signal to clutter noise radio. The rod-shaped object is analyzed by dividing the cell on range and azimuth under the Weibull clutter. On the basis of a point target, the likelihood function and particle weights can be obtained by the target spread function. In the TBD algorithm, the binary target variable and the target shape parameters is added to the state vector and the scattering points in the sample collection is given based on the particle filter, which can detect and estimate the target state and the shape parameters under the clutter environment. Simulation results show that the stability of the algorithm is very good.

     

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