基于零空间l1范数最小化的ISAR成像方法

在目标场景散射率分布满足稀疏性假设下,压缩感知(compressive sensing, CS)成像与传统距离-多普勒成像方法相比,可以使用很少的数据获得良好的图像,图像对比度高,没有旁瓣干扰。本文提出了一种基于零空间l₁范数最小化的逆合成孔径雷达(inverse synthetic aperture radar, ISAR) CS成像方法。从解欠定方程组的角度,将待重建目标图像分解为初猜值与残余值两部分。首先使用加权最小二乘(weighted lease square, WLS)法估计初猜值,作为目标初像；然后将待重建目标场景散射率的l₁范数作为额外的一个非线性测量值引入到图像重建中,在卡尔曼滤波框架下,利用非线性“伪测量”值,最小化待重建目标场景的l₁范数来估计零空间中残余值的解。实测ISAR数据处理验证了所提算法的有效性。与正交匹配追踪算法(matching pursuit algorithm, OMP)和primal-dual l₁范数最小化方法相比,所提方法获得的成像效果更好,成像时间比primal-dual l₁范数最小化方法更短。

ISAR imaging using null space l1 norm minimization

Under the assumption that the target scene is sparse, the compressive sensing (CS) imaging can use very few data to obtain good images with high contrast and no side-lobe interference as compared with the conventional range-Doppler imaging methods. In this paper, an inverse synthetic aperture radar (ISAR) CS imaging method based on the minimization of the null-space l₁ norm is proposed. The solution of the underdetermined linear imaging system is decomposed into two parts: the preliminary value and the residual value. First, the weighted lease square method is used to estimate the preliminary value, which is used as the target initial image. Then, the l₁ norm of the target scene reflectivity is introduced as an additional non-linear measurement and used in the image reconstruction. Within the Kalman filter framework, the residual value in the null space is estimated by minimizing the l₁ norm of the target scene using the nonlinear pseudo-measurement. The ISAR real data processing verifies the effectiveness of the proposed method. The image quality obtained by the proposed method is better than that of the orthogonal matching pursuit algorithm (OMP) and the primal-dual l₁ norm minimization method. The imaging time is much less than the primal-dual l₁ norm minimization method and comparable to OMP.