强杂波下基于压缩感知的低空风切变速度解模糊

doi:10.12305/j.issn.1001-506X.2022.10.05

Abstract

Abstract:

When millimeter wave linear frequency modulated continuous wave (LFMCW) radar detects low-altitude wind-shear, it is severely interfered by ground clutter signals, and the wind velocity estimation is ambiguous. In response to this problem, this paper proposes a low-altitude wind-shear velocity ambiguity resolution method based on compressed sensing in the background of strong clutter. According to the non-uniform undersampling characteristics of radar echo in multi frequency and pulse group stagger mode, the unambiguous redundant dictionary (sensing matrix) is constructed in the angle Doppler domain. Then, the main components of clutter spectrum are extracted by compressed sensing technology, the clutter energy support area is estimated, and the clutter suppression matrix is established. Then the clutter suppression based on the weighted minimum l₁ norm optimization model is implemented, the unambiguous sparse vector of the low-altitude wind-shear is restored in the angle Doppler domain and the unambiguous Doppler information of the wind field is obtained. The accurate velocity is finally obtained.

Key words: millimeter wave linear frequency modulated continuous wave (LFMCW) radar, compressed sensing, strong clutter, low-altitude wind-shear, velocity ambiguity resolution

CLC Number:

TN959.4

Hai LI, Ze HUYAN, Zhijie MAO. Low-altitude wind-shear velocity ambiguity resolution based on compressed sensing under strong clutter[J]. Systems Engineering and Electronics, 2022, 44(10): 3029-3036.

Figures/Tables 13

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References 17

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参数	取值
平台高度/m	600
飞行速度/(m·s^-1)	75
载频/GHz	30
重频1/Hz	7 000
重频2/Hz	8 000
调频带宽/MHz	1
一个CPI内脉冲数	64
一个子CPI脉冲数	32
发射天线阵元数	1
接收天线阵元数	16
主瓣方向/(°)	(90, 0)
信噪比/dB	5
杂噪比/dB	40

风速解模糊方法	均方根误差/(m·s^-1)
剩余定理法	2.689 7
一维集算法	2.211 4
本文所提算法	2.218 9

方法	运行环境	计算机CPU	运行时间/s
CMCAP-剩余定理法			463.201 3
CMCAP-一维集算法	Matlab R2014a	Intel Xeon E5-1650 v3 @ 3.50 GHz	471.523 7
本文所提方法			197.452 3

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Low-altitude wind-shear velocity ambiguity resolution based on compressed sensing under strong clutter

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