超宽带探地雷达多目标压缩感知成像研究
发布时间：2018-10-31 17:19
【摘要】：压缩感知成像要求信号在某个域上能满足稀疏性要求,地下多目标在空域上降低了信号的稀疏性,导致成像出现散焦和虚像。扩大成像背景保证了稀疏性要求但又使得成像计算量上升,实时性不足。提出一种根据探地雷达回波特征预提取出潜在目标位置的压缩感知成像方法。通过对数据进行去噪、滑动矩阵过滤来确定目标的水平位置,再对水平位置处的几道A-Scan数据进行极值搜索,从而可以提取出成像区域目标位置信息,进而在建立成像冗余字典时只需考虑目标位置处的字典元素,无目标处字典元素直接剔除,减少字典建立所需的元素,降低了压缩感知求解计算量。该方法由于只对潜在目标区域进行成像,因此在保证成像实时性的同时也保证了成像精度。实验结果表明算法可行、有效。
[Abstract]:Compression sensing imaging requires that the signal can satisfy the sparsity requirement in a certain domain, and underground multi-targets reduce the sparsity of the signal in the airspace, resulting in defocusing and virtual image of the imaging. The expansion of imaging background ensures sparsity, but it increases the amount of imaging computation, and the real-time performance is insufficient. A compression sensing imaging method based on GPR echo features to extract potential target location is proposed. By de-noising and sliding matrix filtering, the horizontal position of the target is determined, and several A-Scan data at the horizontal position are searched for extremum value, which can extract the position information of the target in the imaging region. Furthermore, only the dictionary elements at the target location should be considered, and the non-target dictionary elements should be eliminated directly in order to reduce the elements needed for the establishment of the dictionary and reduce the computational complexity of the compression perception solution. The method only imaged the potential target area, so it can guarantee the real-time imaging and the imaging accuracy. Experimental results show that the algorithm is feasible and effective.
【作者单位】： 桂林电子科技大学信息与通信学院;西安电子科技大学电子工程学院;
【基金】：国家自然科学基金(61371186) 广西自然科学基金(2013GXNSFFA019004) 广西物联网技术及产业化推进协同创新中心资助项目(WLW20060201)
【分类号】：TN957.52
本文编号：2303012
[Abstract]:Compression sensing imaging requires that the signal can satisfy the sparsity requirement in a certain domain, and underground multi-targets reduce the sparsity of the signal in the airspace, resulting in defocusing and virtual image of the imaging. The expansion of imaging background ensures sparsity, but it increases the amount of imaging computation, and the real-time performance is insufficient. A compression sensing imaging method based on GPR echo features to extract potential target location is proposed. By de-noising and sliding matrix filtering, the horizontal position of the target is determined, and several A-Scan data at the horizontal position are searched for extremum value, which can extract the position information of the target in the imaging region. Furthermore, only the dictionary elements at the target location should be considered, and the non-target dictionary elements should be eliminated directly in order to reduce the elements needed for the establishment of the dictionary and reduce the computational complexity of the compression perception solution. The method only imaged the potential target area, so it can guarantee the real-time imaging and the imaging accuracy. Experimental results show that the algorithm is feasible and effective.
【作者单位】： 桂林电子科技大学信息与通信学院;西安电子科技大学电子工程学院;
【基金】：国家自然科学基金(61371186) 广西自然科学基金(2013GXNSFFA019004) 广西物联网技术及产业化推进协同创新中心资助项目(WLW20060201)
【分类号】：TN957.52
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