超寬帶探地雷達多目標壓縮感知成像研究
發(fā)布時間：2018-10-31 17:19
【摘要】：壓縮感知成像要求信號在某個域上能滿足稀疏性要求,地下多目標在空域上降低了信號的稀疏性,導致成像出現(xiàn)散焦和虛像。擴大成像背景保證了稀疏性要求但又使得成像計算量上升,實時性不足。提出一種根據探地雷達回波特征預提取出潛在目標位置的壓縮感知成像方法。通過對數(shù)據進行去噪、滑動矩陣過濾來確定目標的水平位置,再對水平位置處的幾道A-Scan數(shù)據進行極值搜索,從而可以提取出成像區(qū)域目標位置信息,進而在建立成像冗余字典時只需考慮目標位置處的字典元素,無目標處字典元素直接剔除,減少字典建立所需的元素,降低了壓縮感知求解計算量。該方法由于只對潛在目標區(qū)域進行成像,因此在保證成像實時性的同時也保證了成像精度。實驗結果表明算法可行、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.
【作者單位】： 桂林電子科技大學信息與通信學院;西安電子科技大學電子工程學院;
【基金】：國家自然科學基金(61371186) 廣西自然科學基金(2013GXNSFFA019004) 廣西物聯(lián)網技術及產業(yè)化推進協(xié)同創(chuàng)新中心資助項目(WLW20060201)
【分類號】：TN957.52
本文編號：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.
【作者單位】： 桂林電子科技大學信息與通信學院;西安電子科技大學電子工程學院;
【基金】：國家自然科學基金(61371186) 廣西自然科學基金(2013GXNSFFA019004) 廣西物聯(lián)網技術及產業(yè)化推進協(xié)同創(chuàng)新中心資助項目(WLW20060201)
【分類號】：TN957.52
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