基于随机滤波的探地雷达成像方法

探地雷达是一种超宽带雷达系统，若按传统的奈奎斯特采样，雷达回波信号需要大量空间存储。压缩感知可以实现利用少量的测量值对稀疏信号进行重构，其中最为关键的是测量矩阵和重构算法的选择。本文将压缩感知应用于探地雷达成像，并利用随机滤波的思想选择测量矩阵，可以有效减少测量矩阵中非零值的个数。利用正交匹配追踪算法对信号进行重构，算法简单，降低了数据的存储量和运算复杂度，该算法同样可以对时间和空间上同时压缩的数据进行成像。最后，本文给出基于时间连续信号的GPR接收机一种CS实现方案。仿真结果表明，本文提出的成像方法可以以少量数据精确地对信号进行重构，并且运算量少。 

Ground Penetrating Radar Imaging via Random Filtering

Ground penetrating radar (GPR) is an ultra-wideband radar system, whose echo signal requires a lot of space for storage if using the conventional Nyquist sampling. However, the theory of compressive sensing (CS) enables the reconstruction of sparse signals from a small set of measurements, whereas the key point is the selection of the measurement matrix and the reconstruction algorithm. This paper presents an imaging algorithm for GPR based on CS. The measurement matrix is selected via random filters, which effectively reduces the number of non-zero elements in the measurement matrix. The simple Orthogonal Matching Pursuit (OMP) algorithm is adopted to reconstruct signal with less data storage and lower computational complexity. The proposed algorithm can also apply for the data compressed in both the temporal and spatial domain. Finally, this paper presents a CS scheme for GPR receiver based on time continuous signal. Some simulation experiments are taken on testing the proposed method on GPR imaging, and the results are provided to illustrate the performance of this method.