基于同伦稀疏STAP的低空风切变风速估计

doi:10.12305/j.issn.1001-506X.2022.04.13

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (4): 1174-1181.doi: 10.12305/j.issn.1001-506X.2022.04.13

基于同伦稀疏STAP的低空风切变风速估计

李海*, 程伟杰, 谢瑞杰

中国民航大学天津市智能信号与图像处理重点实验室, 天津 300300

收稿日期:2020-10-13 出版日期:2022-04-01 发布日期:2022-04-01
通讯作者: 李海
作者简介:李海(1976—), 男, 教授, 硕士研究生导师, 博士, 主要研究方向为机载气象雷达信号处理、分布式目标检测与参数估计、自适应信号处理、阵列信号处理、动目标检测与参数估计|程伟杰(1996—), 男, 硕士研究生, 主要研究方向为机载气象雷达信号处理|谢瑞杰(1996—), 男, 硕士研究生, 主要研究方向为机载气象雷达信号处理
基金资助:
民机项目(MJ-2018-S-28);天津市自然基金重点项目(20JCZDJC00490);航空基金项目(20182067008);中央高校基本科研业务费中国民航大学专项(3122019041);中国民航大学蓝天教学名师培养经费资助课题

Wind speed estimation of low-altitude wind-shear based on homotopy sparse STAP

Hai LI*, Weijie CHENG, Ruijie XIE

Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

Received:2020-10-13 Online:2022-04-01 Published:2022-04-01
Contact: Hai LI

摘要/Abstract

摘要：

在机载气象雷达前视阵下, 由于杂波的距离依赖性导致独立同分布(independent identically distributed, IID)训练样本严重不足, 传统的空时自适应处理(space-time adaptive processing, STAP)算法性能下降, 风场速度估计不准。针对此问题, 提出一种基于同伦稀疏STAP的低空风切变风速估计方法, 该方法仅需少量IID训练样本就能够达到较好的风场速度估计效果。首先，利用同伦稀疏恢复算法估计杂波协方差矩阵, 从而重构出高分辨率的杂波空时二维谱。然后，通过求解STAP处理器的最优权矢量自适应滤除杂波。最后，实现对风场风速的准确估计。仿真结果证明所提方法的有效性。

关键词: 机载气象雷达, 同伦稀疏空时自适应处理, 低空风切变, 风速估计

Abstract:

In airborne weather radar front view array, due to the distance dependence of clutter, the training samples of independent identically distributed (IID) are seriously insufficient. The performance of traditional space-time adaptive processing (STAP) algorithm is reduced, and wind field velocity estimation is inaccurate.To solve this problem, this paper proposes a low-altitude wind-shear wind speed estimation method based on homotopy sparse STAP. This method only needs a small number of IID training samples to achieve a better wind field velocity estimation effect.Firstly, the homotopy sparse recovery algorithm is used to estimate the clutter covariance matrix, and then the high resolution space-time spectrum of clutter is reconstructed. Then, the optimal weight vector of STAP processor is solved to filter the clutter adaptively. Finally, the wind speed is estimated accurately.Simulation results show the effectiveness of the proposed method.

Key words: airborne weather radar, homotopy sparse space-time adaptive processing (STAP), low-altitude wind-shear, wind speed estimation

中图分类号:

TN959.4

李海, 程伟杰, 谢瑞杰. 基于同伦稀疏STAP的低空风切变风速估计[J]. 系统工程与电子技术, 2022, 44(4): 1174-1181.

Hai LI, Weijie CHENG, Ruijie XIE. Wind speed estimation of low-altitude wind-shear based on homotopy sparse STAP[J]. Systems Engineering and Electronics, 2022, 44(4): 1174-1181.

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参数	参数值	参数	参数值
载机高度/m	600	阵元数	8
载机速度/(m/s)	75	相干脉冲数	32
波长/m	0.032	主瓣角度/(°)	(60, 0)
脉冲重复频率/Hz	7 000	距离分辨率/m	150
杂噪比/dB	40	信噪比/dB	5
风场中心距离/km	12.5	风场中心方向/(°)	(60, 0)
风场水平方向范围/km	8.5~16.5	风场垂直方向范围/km	0~0.6
风场水平风速范围/(m/s)	-40~40	风场垂直风速范围/(m/s)	-20~20

IID样本数	风速估计方法	均方根误差/(m/s)
100	LSMI	2.039 4
	M-CAP	1.659 1
	OMP	1.573 4
	同伦稀疏STAP	1.553 5
50	LSMI	22.532 5
	M-CAP	1.787 6
	OMP	1.602 3
	同伦稀疏STAP	1.584 7
10	LSMI	31.838 1
	M-CAP	24.799 4
	OMP	7.903 9
	同伦稀疏STAP	1.642 8

杂噪比/dB	风速估计方法	均方根误差/(m/s)
40	LSMI	2.039 4
	M-CAP	1.659 1
	OMP	1.573 4
	同伦稀疏STAP	1.553 5
50	LSMI	14.478 0
	M-CAP	7.670 2
	OMP	1.825 5
	同伦稀疏STAP	1.762 6
60	LSMI	21.302 6
	M-CAP	11.962 3
	OMP	2.568 2
	同伦稀疏STAP	2.453 3

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基于同伦稀疏STAP的低空风切变风速估计

Wind speed estimation of low-altitude wind-shear based on homotopy sparse STAP

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