基于压缩感知理论的稀疏孔径ISAR成像

在基于压缩感知理论的逆合成孔径雷达成像过程中,利用正交匹配追踪算法进行信号重构时存在重构精度较低、运算速度较慢的缺点,针对上述问题,提出了一种利用改进正交匹配追踪算法进行信号重构的稀疏孔径高分辨成像方法。首先,构造数据选择矩阵作为测量矩阵模拟回波缺失情况,然后利用稀疏基矩阵对回波信号进行稀疏表示,最后采取一种改进正交匹配追踪算法进行图像重构,相比于正交匹配追踪算法同时提高了运算速度和成像质量。通过仿真实验,在稀疏孔径数据随机缺失的情况下,改变数据缺失率,将该算法与距离-多普勒算法和正交匹配追踪算法的成像结果进行对比,验证了该算法的有效性和优越性。

ISAR Imaging by Exploiting Sparse Apertures Based on Compressive Sensing

In the process of inverse synthetic aperture radar ( ISAR) imaging based on compressive sensing, there is a drawback that the reconstruction precision is low and the operation speed is slow by using the orthogonal matching pursuit (OMP) algorithm for signal reconstruction. To solve the problem, a high resolution imaging method for sparse aperture of ISAR based on an improved orthogonal matching pursuit (IOMP) algorithm is proposed in this paper. First, a data selection matrix is built as the measurement matrix to simulate the sparse aperture. Second, the sparse basis matrix is used as the sparse representation of echoes. Finally, IOMP algorithm is adopted to reconstruct the image. Compared with OMP algorithm, the operation speed and imaging quality are improved simultaneously. Through the simulation experiment, compared with the results of Range-Doppler algorithm and orthogonal matching pursuit algorithm, the validity and superiority of the algorithm are verified by changing the data loss rate with the sparse aperture data randomly.