基于压缩感知的稀疏采样数据大转角ISAR 成像研究

针对方位向稀疏采样条件下,大带宽大转角逆合成孔径雷达(ISAR)高分辨成像时,一维距离像中目标散射点的距离徙动问题,提出了基于贝叶斯压缩感知的稀疏ISAR 成像方法。对于方位向稀疏采样数据,该方法在包络对齐和相位补偿后,通过傅里叶变换将数据变换到距离频率域,对每一距离单元数据,根据方位向稀疏采样的位置构造相应的Keystone基矩阵,利用贝叶斯压缩感知算法重建目标在各距离频域单元的多普勒域系数,最后,通过距离向逆傅里叶变换和方位向自聚焦完成ISAR 成像。计算机仿真验证了该方法的有效性。

A Study on Wide Angle ISAR Imaging of Sparse Sampling Data Based on Compressive Sensing

For the problem of range migration in inverse synthetic aperture radar ( ISAR) imaging under azimuth sparse sampling with wide angle and wide bandwidth, an algorithm is proposed to eliminate range migration based on Bayesian compressive sensing. The envelope alignment and phase compensation is applied to the sparse sampling data, and then the fast Fourier transformation (FFT) is used in range direction to transform the data into rang frequency domain. For the sparse sampling data of each range cell, the corresponding Keystone base matrix is constructed to recovery the Doppler coefficient in azimuth direction, and ISAR image can be obtained through inverse FFT in range direction and autofocus. The simulation denotes the validity of the method proposed in the paper.