基于块目标的频率步进连续波探地雷达压缩感知重建算法

压缩感知理论对于解决频率步进连续波探地雷达信号处理过程中存在的采样速率高、存储数据量大、信号处理时间长等问题具有重要意义. 针对雷达探测中块目标物体在探测区域不满足稀疏性的问题，提出一种适合块目标的压缩感知重构模型.利用某些稀疏正交基对块目标进行稀疏化处理使其满足稀疏性，将字典矩阵与稀疏矩阵结合形成适用于块目标物体的新观测矩阵，再通过压缩感知凸优化算法求解稀疏化系数，最后把该系数通过稀疏变换得到块目标的反射系数.通过实验仿真验证该方法的可行性，与未稀疏化处理的压缩感知重构模型相比具有更高的精度和分辨率.

Reconstruction Algorithm of Compressed Sensing for Stepped-Frequency Continuous Wave Ground Penetrating Radar Based on Block Objects

Compressed sensing (CS) is of great significance to solving such problems as high sampling rate， huge storage pressure and long processing time in the process of stepped-frequency continuous wave ground penetrating radar (SFCW-GPR). Aiming at the problem that block objects can’t meet the sparseness in the detecting area， and using the orthogonal basis for sparse processing of block objects to satisfy the sparsity condition， a new observation matrix that was suitable for block objects was formed by combining the dictionary matrix and sparse matrix. The sparse coefficients were solved by using the compressed sensing convex optimization algorithm. Finally， the reflection coefficients of block objects were obtained through sparse transformation of the sparse coefficients. The simulation results showed that the proposed method is feasible and has higher accuracy and resolution ratio compared with the compressed sensing reconstruction model without sparsity.