稀疏带状测量矩阵在压缩感知ISAR成像中的应用

Application in compressed sensing ISAR imaging based on sparse banded measurement matrices

将压缩感知(CS)理论用于逆合成孔径雷达(ISAR)成像,可以有效利用缺损的雷达回波数据,解决了因数据缺损造成成像质量下降的问题。目前压缩感知中常用的高斯或伯努利等随机测量矩阵独立随机元数目过多,存储空间过大,从而导致硬件实现成本过高。所构造的稀疏带状测量矩阵,通过将测量矩阵进行带状循环移位置零稀疏化,可大幅减少测量矩阵中非零元素数目,降低系统采样要求,节约硬件实现成本,使得压缩感知ISAR成像工程化更容易实现。最后通过仿真和微波暗室实验数据验证了点目标模型下稀疏带状测量矩阵进行ISAR成像的可行性和有效性。

The application of compressed sensing （CS） theory to inversed synthetic aperture radar （ISAR） can make effective use of the data of defective radar echo, and solve the lower quality of imaging, which is caused by the defective data. By analyzing the present situation, it was found that the number of independent random elements in the commonly used Gauss and Bernoulli random measurement matrices was too huge, and the storage space was too large, which led to the high hardware implementation cost. Sparse banded measurement matrices were constructed in this paper, which significantly reduced the number of nonzero elements in measurement matrix and the requirement of system sampling, and saved the hardware implementation cost, by sparsifying measurement matrices banded cyclic shift zero. Finally, the data of simulation and anechoic chamber experiment verify the feasibility and effectiveness of the application of sparse banded measurement matrices into ISAR imaging through the point target model.