Volume 12 Issue 6
Dec.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(6): 1125-1137
CHEN Hongmeng, YU Jizhou, ZHANG Wenjie, et al. Probability model-driven airborne Bayesian forward-looking super-resolution imaging for multitarget scenario[J]. Journal of Radars, 2023, 12(6): 1125–1137. doi: 10.12000/JR23080
Citation: CHEN Hongmeng, YU Jizhou, ZHANG Wenjie, et al. Probability model-driven airborne Bayesian forward-looking super-resolution imaging for multitarget scenario[J]. Journal of Radars, 2023, 12(6): 1125–1137. doi: 10.12000/JR23080
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Probability Model-driven Airborne Bayesian Forward-looking Super-resolution Imaging for Multitarget Scenario

DOI: 10.12000/JR23080
  • 1.

    Beijing Institute of Radio Measurement, Beijing 100854, China

  • 2.

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

Funds:  The National Natural Science Foundation of China (62101396, 62171337)
More Information
  • Corresponding author: YU Jizhou, 2917161774@qq.com; LU Yaobing, luyaobing65@163.com
  • Received Date: 2023-05-09
  • Rev Recd Date: 2023-08-15
    • Available Online: 2023-08-15
  • Publish Date: 2023-09-06
    Abstract
  • Forward-looking imaging is crucial in many civil and military fields, such as precision guidance, autonomous landing, and autonomous driving. The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history. The deconvolution method can be used to improve the quality of forward-looking imaging; however, it will not work well for complex imaging scenes. To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations, an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution. First, the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene. Then, the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model. Finally, the super-resolution imaging problem was solved using the Bayesian framework. Because the sparsity characterization parameters are embedded in the entire process of imaging, the forward-looking imaging parameters will be updated during each iteration. The effectiveness of the proposed algorithm was verified using simulation and real data.

     

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