一种稀疏孔径逆合成孔径雷达成像算法

doi:10.19665/j.issn1001-2400.2019.03.019

摘要/Abstract

摘要：

针对逆合成孔径雷达成像在稀疏孔径条件下存在方位向分辨率低、易受噪声干扰等问题,利用目标二维分布的稀疏性将成像问题转换为多测量向量模型下稀疏信号的重构问题,采用零范数最小均方法并行处理以提高运行效率,使用最优步长公式代替固定步长以避免步长设置不当对收敛速度和性能的影响,并利用平滑零范数法逼近零范数以提高重构精度和抗噪性能。相较于已有算法,新算法能够获得更高质量的目标图像,对噪声鲁棒性强,并且计算量更低。仿真和实测数据的处理结果,验证了该算法的有效性。

关键词: 逆合成孔径雷达, 压缩感知, 稀疏孔径, 自适应滤波, 平滑零范数, 多测量向量

Abstract:

Under sparse aperture conditions, some problems arise with inverse synthetic aperture radar imaging such as low azimuth resolution and susceptibility to noise. To solve them, the two-dimensional sparseness of a target is used to transform the imaging problem into the sparse signal reconstruction problem under the multiple measurement vectors model. The zero norm-least mean square algorithm is processed in parallel to improve the efficiency. The optimal step-size formula is used instead of the fixed step-size to avoid the influence of the improper step-size on the convergence speed and accuracy. And the smoothed zero norm is introduced to approximate the zero norm to improve the reconstruction accuracy and noise immunity ability. In comparison with existing methods, the proposed algorithm can obtain a clearer target image, which is robust to noise and requires less computation. The effectiveness of the proposed method is verified by simulation and the real data processing result.

Key words: inverse synthetic aperture radar, compressed sensing, sparse aperture, adaptive filtering, smoothed zero norm, multiple measurement vectors

中图分类号:

TN95

曾创展,朱卫纲,贾鑫. 一种稀疏孔径逆合成孔径雷达成像算法[J]. 西安电子科技大学学报, 2019, 46(3): 123-129.

ZENG Chuangzhan,ZHU Weigang,JIA Xin. Sparse-aperture ISAR imaging algorithm[J]. Journal of Xidian University, 2019, 46(3): 123-129.

图/表 8

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参考文献 12

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CS Problem	Adaptive Filter
θ_j,j∈(1,2,…,M)	x(n)
σ	h(n)
s_j=θ_jσ+v_j	d(n)=x^T(n)h+e(n)

图像	熵
图2 (a)	10.950	1
图2 (b)	8.908	8
图2 (c)	10.608	2
图2 (d)	10.617	5
图2 (e)	10.940	6
图2 (f)	7.334	5

图像	熵
图5 (a)	9.811	1
图5 (b)	6.983	2
图5 (c)	7.965	3
图5 (d)	8.847	4
图5 (d)	8.847	4
图5 (f)	6.110	4

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