探地雷達信號預(yù)處理及成像技術(shù)
發(fā)布時間：2018-10-16 11:13
【摘要】：探地雷達(GPR)利用了電磁波在不同電磁特性物體分界面上的折射和反射原理,實現(xiàn)對地下目標場景的反演。由于探地雷達的探測環(huán)境較為復(fù)雜,回波信號中不僅存在著有效信號分量,還有收發(fā)天線之間的直耦波分量、經(jīng)地表直接反射波分量、噪聲分量以及射頻干擾分量等,如果不進行GPR信號預(yù)處理技術(shù),有效回波信號容易被淹沒在能量較強的直耦波和地表直接反射波當中,導(dǎo)致無法有效探測目標。本文針對GPR回波信號模型,研究了針對直達波去除和噪聲抑制的信號預(yù)處理技術(shù),結(jié)合實測數(shù)據(jù)分析了信號預(yù)處理技術(shù)的有效性。GPR成像是將GPR探測的地下目標回波信息轉(zhuǎn)化為圖像信息以更加直觀的方式顯示地下場景的信號處理技術(shù)。由于分層介質(zhì)場景、雙站工作模式的復(fù)雜性,本文從時域后向投影(BP)成像、頻域距離偏移(RM)成像以及壓縮感知(CS)成像三類成像算法入手,研究適合分層介質(zhì)場景以及雙站模式的GPR成像算法。其中,針對BP算法在GPR場景中計算復(fù)雜度較高的問題,研究了計算效率改進的BP算法,同時針對旁瓣和干擾能量較高的問題,本文提出了基于互相關(guān)信息的BP改進算法,并將此方法與計算效率改進的查找表BP算法結(jié)合起來,在提高計算效率的同時又保證了旁瓣和干擾的抑制能力,理論分析并實測數(shù)據(jù)以及仿真數(shù)據(jù)驗證了算法的有效性。然后,針對傳統(tǒng)RM算法在分層介質(zhì)以及雙站模式中應(yīng)用的局限性,研究了基于分層介質(zhì)的RM(LRM)算法,并將其推廣到雙站模式的LRM(BLRM)算法,實測數(shù)據(jù)以及仿真數(shù)據(jù)驗證了算法在分層介質(zhì)以及雙站模式中應(yīng)用的有效性。最后,研究了基于壓縮感知(CS)理論的GPR成像算法,結(jié)合GPR成像模型以及地下目標的稀疏環(huán)境,利用三種稀疏重構(gòu)算法實現(xiàn)了壓縮感知理論下的成像結(jié)果,從聚焦度角度獲得了比傳統(tǒng)算法更好的成像效果,并從成像位置成功概率角度研究三種壓縮感知算法隨信噪比(SNR)、壓縮數(shù)據(jù)比以及目標之間間隔的變化情況。
[Abstract]:The ground penetrating radar (GPR) (GPR) uses the principle of refraction and reflection of electromagnetic waves on the interface of objects with different electromagnetic characteristics to realize the inversion of the underground target scene. Because the detection environment of GPR is more complex, there are not only effective signal components in echo signal, but also direct-coupled wave components between transceiver antennas, direct reflection wave components, noise components and radio frequency interference components through the surface, etc. If the GPR signal preprocessing technique is not carried out, the effective echo signal is easily submerged in the direct coupling wave and the surface direct reflection wave, which makes it impossible to detect the target effectively. In this paper, for the GPR echo signal model, the signal pre-processing technology for direct wave removal and noise suppression is studied. The effectiveness of the signal preprocessing technique is analyzed by combining the measured data. GPR imaging is a signal processing technique that converts the echo information of the underground target detected by GPR into the image information to display the underground scene in a more intuitive way. Due to the complexity of layered media scene and bistatic operation mode, this paper starts with three imaging algorithms: time-domain backward projection (BP) imaging, frequency-domain range offset (RM) imaging and compression sensing (CS) imaging. The GPR imaging algorithm suitable for layered media scene and bistatic mode is studied. In order to solve the problem of high computational complexity of BP algorithm in GPR scene, the improved BP algorithm is studied. At the same time, aiming at the problem of high sidelobe and interference energy, an improved BP algorithm based on cross-correlation information is proposed in this paper. This method is combined with the improved lookup table (BP) algorithm, which not only improves the computational efficiency, but also ensures the ability of sidelobe and interference suppression. The effectiveness of the algorithm is verified by theoretical analysis, measured data and simulation data. Then, in view of the limitation of traditional RM algorithm in layered medium and bistatic mode, the RM (LRM) algorithm based on layered medium is studied and extended to the LRM (BLRM) algorithm of bistatic mode. The effectiveness of the algorithm in layered media and bistatic mode is verified by the measured data and simulation data. Finally, the GPR imaging algorithm based on compression sensing (CS) theory is studied. Combined with GPR imaging model and sparse environment of underground target, three sparse reconstruction algorithms are used to realize the imaging results of compression sensing theory. From the angle of focusing degree, the imaging effect is better than the traditional algorithm, and the variation of three compression sensing algorithms with SNR (SNR), compression data ratio and the interval between targets is studied from the point of view of image location success probability.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN957.51
本文編號：2274180
[Abstract]:The ground penetrating radar (GPR) (GPR) uses the principle of refraction and reflection of electromagnetic waves on the interface of objects with different electromagnetic characteristics to realize the inversion of the underground target scene. Because the detection environment of GPR is more complex, there are not only effective signal components in echo signal, but also direct-coupled wave components between transceiver antennas, direct reflection wave components, noise components and radio frequency interference components through the surface, etc. If the GPR signal preprocessing technique is not carried out, the effective echo signal is easily submerged in the direct coupling wave and the surface direct reflection wave, which makes it impossible to detect the target effectively. In this paper, for the GPR echo signal model, the signal pre-processing technology for direct wave removal and noise suppression is studied. The effectiveness of the signal preprocessing technique is analyzed by combining the measured data. GPR imaging is a signal processing technique that converts the echo information of the underground target detected by GPR into the image information to display the underground scene in a more intuitive way. Due to the complexity of layered media scene and bistatic operation mode, this paper starts with three imaging algorithms: time-domain backward projection (BP) imaging, frequency-domain range offset (RM) imaging and compression sensing (CS) imaging. The GPR imaging algorithm suitable for layered media scene and bistatic mode is studied. In order to solve the problem of high computational complexity of BP algorithm in GPR scene, the improved BP algorithm is studied. At the same time, aiming at the problem of high sidelobe and interference energy, an improved BP algorithm based on cross-correlation information is proposed in this paper. This method is combined with the improved lookup table (BP) algorithm, which not only improves the computational efficiency, but also ensures the ability of sidelobe and interference suppression. The effectiveness of the algorithm is verified by theoretical analysis, measured data and simulation data. Then, in view of the limitation of traditional RM algorithm in layered medium and bistatic mode, the RM (LRM) algorithm based on layered medium is studied and extended to the LRM (BLRM) algorithm of bistatic mode. The effectiveness of the algorithm in layered media and bistatic mode is verified by the measured data and simulation data. Finally, the GPR imaging algorithm based on compression sensing (CS) theory is studied. Combined with GPR imaging model and sparse environment of underground target, three sparse reconstruction algorithms are used to realize the imaging results of compression sensing theory. From the angle of focusing degree, the imaging effect is better than the traditional algorithm, and the variation of three compression sensing algorithms with SNR (SNR), compression data ratio and the interval between targets is studied from the point of view of image location success probability.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN957.51
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