| 引用本文: | 李 海， 朱玥琪， 郭景瑞,罗 军， 张顺生. 基于KA⁃SRCN⁃pSTAP的低空风切变风速估计方法[J]. 雷达科学与技术, 2024, 22(3): 255-264.[点击复制] |
| LI Hai， ZHU Yueqi， GUO Jingrui,LUO Jun, ZHANG Shunsheng. Wind Speed Estimation of Low⁃Altitude Wind Shear Based on KA⁃SRCN⁃pSTAP[J]. Radar Science and Technology, 2024, 22(3): 255-264.[点击复制] |
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| 摘要: |
| 针对由于独立同分布（IID）样本严重不足，导致极化空时自适应（pSTAP）处理性能下降，进而导致低空风切变风速估计不准确的问题，本文提出了一种基于知识辅助的稀疏表示杂波零陷极化空时自适应（KA?SRCN?pSTAP）的低空风切变风速估计方法。该方法首先利用杂波脊的先验知识辅助构造极化空时稀疏字典，然后利用极化空时稀疏字典，通过SRCN算法挑选原子并对到杂波线性子空间补空间上的投影矩阵进行估计，从而得到pSTAP权矢量，最后构造pSTAP滤波器对地杂波进行抑制，准确估计低空风切变风速。该方法仅使用少量IID样本，将SRCN算法与极化?空时域相结合，完成对风切变风速的有效估计。仿真实验结果证明该方法可以有效实现少样本情况下的风速准确估计。 |
| 关键词: 机载双极化气象雷达 极化空时自适应处理 稀疏表示 地杂波抑制 风速估计 |
| DOI：DOI:10.3969/j.issn.1672-2337.2024.03.003 |
| 分类号:TN959.4 |
| 基金项目:国家重点研发计划项目（No.2021YFB1600600）；天津市自然基金重点项目（No.20JCZDJC00490）；中国民航大学蓝天教学名师培养经费 |
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| Wind Speed Estimation of Low⁃Altitude Wind Shear Based on KA⁃SRCN⁃pSTAP |
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LI Hai， ZHU Yueqi， GUO Jingrui
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1.Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China;2.Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
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| Abstract: |
| Due to the serious shortage of independent identically distributed （IID） samples, the performance of polarization space?time adaptive processing （pSTAP） is degraded, resulting in the inaccurate estimation of wind speed. To solve the problem, an approach to estimate low?altitude wind shear wind speed based on knowledge aided sparse representation based clutter null （KA?SRCN） ? pSTAP is proposed. This method first utilizes prior knowledge of clutter ridges to assist in constructing a polarized space?time sparse dictionary. Then, the polarized space?time sparse dictionary is used to select atoms through the SRCN algorithm and estimate the projection matrix on the linear subspace complement space of the clutter to obtain the pSTAP weighted vector. Finally, a pSTAP rejector is created to suppress the ground clutter and estimate the wind speed precisely. This method uses only a small number of IID samples and combines the SRCN algorithm with the polarization?space?time domain to achieve the effective estimation of wind speed. Simulation results show the validity of the devised approach. |
| Key words: airborne dual⁃polarization weather radar polarization space⁃time adaptive processing sparse representation ground clutter suppression wind speed estimation |
