Volume 8 Issue 5
Oct.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(5): 598-605
FANG Linlin, ZHOU Chao, WANG Rui, et al. RCS feature-aided insect target tracking algorithm[J]. Journal of Radars, 2019, 8(5): 598–605. doi: 10.12000/JR19067
Citation: FANG Linlin, ZHOU Chao, WANG Rui, et al. RCS feature-aided insect target tracking algorithm[J]. Journal of Radars, 2019, 8(5): 598–605. doi: 10.12000/JR19067
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RCS Feature-aided Insect Target Tracking Algorithm

doi: 10.12000/JR19067
  • 1.

    Radar Research Lab, School of Information and Electronics, Institute of Technology, Beijing 100081, China

  • 2.

    Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute ofTechnology), Ministry of Education, Beijing 100081, China

Funds:  The National Natural Science Foundation of China (31727901)
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  • Corresponding author: ZHOU Chao, 7520180050@bit.edu.cn
  • Received Date: 2019-07-12
  • Rev Recd Date: 2019-09-23
    • Available Online: 2019-10-11
  • Publish Date: 2019-10-01
    Abstract
  • Pest migration has the characteristics of large scale and strong suddenness, which will lead to the outbreaks of pests and diseases, the decline of grain yield, and considerable economic losses. Entomological radar is an effective means of monitoring migratory pests. However, the Radar Cross Section (RCS) of an insect target is small, whereas the echo power is weak. High detection probability will result in a high false alarm probability. In the data association step of target tracking, the association error occurs due to the influence of false measurement. By utilizing the amplitude difference between the target and noise, the amplitude information-assisted tracking algorithm can effectively improve the recognition degree toward the target and noise and improve the tracking performance. However, the RCS fluctuation model of the target is needed as prior information to calculate the amplitude likelihood ratio. Therefore, in this paper, the insect RCS fluctuating characteristics are analyzed based on Ku-band entomological radar experiment data. The results show that gamma distribution can fit well the RCS probability distribution of the insect target. On this basis, we derive the amplitude likelihood ratio of the gamma fluctuation target in Gaussian white-noise background. By analyzing the simulation results and performance under different signal-to-noise ratios, measurement noises, and fluctuation model parameters, compared with probabilistic data association filter, the RCS feature-aided tracking algorithm can effectively improve the insect target tracking accuracy.

     

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