Volume 11 Issue 5
Oct.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(5): 765-777
GAO Yongchan, PAN Liyan, LI Yachao, et al. Multi-rank range-spread target detection method for space/time symmetric array radar under non-Gaussian clutter background[J]. Journal of Radars, 2022, 11(5): 765–777. doi: 10.12000/JR22013
Citation: GAO Yongchan, PAN Liyan, LI Yachao, et al. Multi-rank range-spread target detection method for space/time symmetric array radar under non-Gaussian clutter background[J]. Journal of Radars, 2022, 11(5): 765–777. doi: 10.12000/JR22013
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Multi-rank Range-spread Target Detection Method for Space/Time Symmetric Array Radar under Non-Gaussian Clutter Background

DOI: 10.12000/JR22013

  • School of Electronic Engineering, Xidian University, Xi’an 710071, China

Funds:  The National Natural Science Foundation of China (61701370, 61871307, 61971432), China Postdoctoral Science Foundation under Grant (2019M653561, 2020T130493)
More Information
  • Corresponding author: GAO Yongchan, ycgao@xidian.edu.cn; PAN Liyan, lypan@stu.xidian.edu.cn
  • Received Date: 2022-01-13
  • Accepted Date: 2022-03-11
  • Rev Recd Date: 2022-03-10
    • Available Online: 2022-03-21
  • Publish Date: 2022-04-14
    Abstract
  • This study proposes a multi-rank range-spread target detection method for multi-channel array radar under a non-Gaussian clutter background. The method aims to detect the target from real clutter using the multi-channel array radar. First, a multi-rank range-spread target model was formulated using a subspace matrix with a rank greater than one and the coordinate vectors of corresponding range bins. Then, by exploiting the persymmetric structure information of the clutter covariance matrix under the detection scenario, wherein the radar receiver units were central symmetric in space or time, a small sample estimation strategy for the parameters to be solved through the unitary transformation was constructed. Further, a non-Gaussian clutter background multi-rank range-spread target detection method was designed based on the generalized likelihood ratio, Rao, and Wald tests. Finally, a theoretical derivation proved that the proposed detection method has the constant false alarm rate property. The experimental results based on both the simulated and measured data showed that the proposed detection method can ensure the constant false alarm rate property of the clutter covariance matrix. Additionally, compared with the existing detection methods, the proposed detection method improves the target detection performance under small sample support. Besides, the proposed detection method effectively improves the robustness of target detection under the condition of steering vector mismatch.

     

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