DR-GAN：一種無(wú)監(jiān)督學(xué)習(xí)的探地雷達(dá)雜波抑制方法

雷文太; 毛凌青; 龐澤邦; 任強(qiáng); 王成浩; 隋浩; 辛常樂(lè)

doi:10.11999/JEIT221072

DR-GAN：一種無(wú)監(jiān)督學(xué)習(xí)的探地雷達(dá)雜波抑制方法

doi: 10.11999/JEIT221072

1.
中南大學(xué)計(jì)算機(jī)學(xué)院長(zhǎng)沙 410083
2.
中國(guó)電波傳播研究所青島 266107

基金項(xiàng)目: 中國(guó)電波傳播研究所穩(wěn)定支持科研經(jīng)費(fèi)(A131903W13)

詳細(xì)信息

作者簡(jiǎn)介:
雷文太：男，教授，研究方向?yàn)樘降乩走_(dá)信號(hào)處理技術(shù)

毛凌青：男，碩士生，研究方向?yàn)樘降乩走_(dá)信號(hào)處理技術(shù)

龐澤邦：男，博士生，研究方向?yàn)榛谏疃葘W(xué)習(xí)的探地雷達(dá)智能數(shù)據(jù)處理

任強(qiáng)：男，高級(jí)工程師，研究方向?yàn)樘降乩走_(dá)系統(tǒng)集成技術(shù)

王成浩：男，高級(jí)工程師，研究方向?yàn)樘降乩走_(dá)信息處理

隋浩：男，碩士生，研究方向?yàn)樘降乩走_(dá)信號(hào)處理技術(shù)

辛常樂(lè)：男，碩士生，研究方向?yàn)樘降乩走_(dá)信號(hào)處理技術(shù)

通訊作者:
龐澤邦　pangzebang@csu.edu.cn

中圖分類(lèi)號(hào): TN957.52
計(jì)量
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2022-08-15
- 修回日期: 2023-02-16
- 網(wǎng)絡(luò)出版日期: 2023-02-22
- 刊出日期: 2023-10-31

DR-GAN: An Unsupervised Learning Approach to Clutter Suppression for Ground Penetrating Radar

1.
School of Computer Science, Central South University, Changsha 410083, China
2.
China Research Institute of Radiowave Propagation, Qingdao 266107, China

Funds: The Stable-Support Scientific Project of China Research Institute of Radiowave Propagation (A131903W13)

摘要

摘要: 探地雷達(dá)(GPR)是一種基于電磁波的地下無(wú)損探測(cè)技術(shù)，廣泛應(yīng)用于市政工程、交通、軍事等領(lǐng)域。在數(shù)據(jù)采集過(guò)程中，由于發(fā)射天線(xiàn)和接收天線(xiàn)之間的耦合、起伏地面的散射以及地下隨機(jī)媒質(zhì)的復(fù)雜性等原因，采集得到的GPR B-scan回波中通常存在雜波，雜波嚴(yán)重影響了地下目標(biāo)的檢測(cè)和特征提取。該文提出一種用于GPR B-scan圖像雜波抑制的解糾纏表示生成對(duì)抗網(wǎng)絡(luò)(DR-GAN)，設(shè)計(jì)了目標(biāo)特征編碼器和雜波特征編碼器用來(lái)提取GPR B-scan圖像中的目標(biāo)特征和雜波特征，設(shè)計(jì)了雜波抑制生成器用來(lái)獲取雜波抑制后的GPR B-scan圖像。與現(xiàn)有的基于監(jiān)督學(xué)習(xí)的GPR雜波抑制方法相比，該方法在網(wǎng)絡(luò)訓(xùn)練時(shí)不需要成對(duì)的匹配數(shù)據(jù)，可以更好地應(yīng)用于實(shí)測(cè)GPR圖像的雜波抑制。在仿真和實(shí)測(cè)GPR數(shù)據(jù)上的實(shí)驗(yàn)結(jié)果表明，DR-GAN這一無(wú)監(jiān)督學(xué)習(xí)網(wǎng)絡(luò)具有更好的雜波抑制性能。對(duì)石英砂中埋設(shè)的鋼筋進(jìn)行數(shù)據(jù)采集，運(yùn)用DR-GAN對(duì)含雜波的實(shí)測(cè)數(shù)據(jù)進(jìn)行處理，處理結(jié)果的改善系數(shù)(IF)指標(biāo)較現(xiàn)有的魯棒非負(fù)矩陣分解(RNMF)方法提高了17.85 dB。
- 探地雷達(dá) /
- 雜波抑制 /
- 無(wú)監(jiān)督學(xué)習(xí) /
- 解糾纏表示 /
- 生成對(duì)抗網(wǎng)絡(luò)
Abstract: Ground Penetrating Radar (GPR) is an underground nondestructive detection technology based on electromagnetic wave, which is widely used in municipal engineering, transportation, military and other fields. In the process of data acquisition, due to the coupling between transmitting antenna and receiving antenna, scattering from undulating ground and the complexity of underground random media, there is usually clutter in the GPR B-scan, which affects seriously the detection and feature extraction of underground targets. A Disentanglement Representation Generative Adversarial network (DR-GAN) for clutter suppression in GPR B-scan images is proposed. A target feature encoder and a clutter feature encoder are designed to extract target features and clutter features in GPR B-scan images. A clutter suppression generator is designed to obtain the GPR B-scan image after clutter suppression. Compared with the existing GPR clutter suppression methods based on supervised learning, the proposed method does not need pairwise matching data during network training, and can be better applied to the clutter suppression of measured GPR images. Experimental results on simulated and measured GPR data show that DR-GAN is an unsupervised learning network with better clutter suppression performance. The data of reinforcement embedded in quartz sand are collected, and the measured data containing clutter are processed by DR-GAN. The Improvement Factor (IF) index of the processing results is 17.85 dB higher than that of the existing Robust Nonnegative Matrix Factorization (RNMF) method.
- Ground Penetrating Radar (GPR) /
- Clutter suppression /
- Unsupervised learning /
- Disentangled Representations (DR) /
- Generative Adversarial Networks (GAN)

HTML全文

圖 1 DR-GAN的網(wǎng)絡(luò)框架

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圖 2 GPR圖像的雜波抑制流程

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圖 3 DR-GAN的編碼器與生成器的網(wǎng)絡(luò)結(jié)構(gòu)

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圖 4 DR-GAN的判別器網(wǎng)絡(luò)結(jié)構(gòu)

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圖 5 仿真場(chǎng)景模型

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圖 6 DR-GAN在仿真數(shù)據(jù)上的雜波抑制效果

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圖 7 不同方法對(duì)仿真數(shù)據(jù)的雜波抑制效果

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圖 8 實(shí)測(cè)場(chǎng)景

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圖 9 實(shí)測(cè)雜波數(shù)據(jù)的制作

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圖 10 DR-GAN在實(shí)測(cè)數(shù)據(jù)上的雜波抑制效果

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圖 11 不同方法對(duì)實(shí)測(cè)數(shù)據(jù)的雜波抑制效果

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表 1 仿真場(chǎng)景參數(shù)

模型參數(shù)		掃描場(chǎng)景參數(shù)
模型尺寸	1.8 m×0.002 m×0.45 m	發(fā)射源波形	瑞克子波
單元格大小	0.002 m×0.002 m×0.002 m	發(fā)射中心頻率	2 GHz
土壤沙子重量百分比	50%	仿真時(shí)窗	10 ns
土壤粘土重量百分比	50%	發(fā)射天線(xiàn)起點(diǎn)	(0.1 m,0.002 m,0.4 m)
土壤的容重	2.0 g/cm³	接收天線(xiàn)起點(diǎn)	(0.2 m,0.002 m,0.4 m)
土壤的沙粒密度	2.66 g/cm³	每次掃描天線(xiàn)移動(dòng)距離	0.01 m
土壤體積含水率范圍	0.001～0.150	目標(biāo)形狀	圓柱、方柱
土壤材料種類(lèi)	50種	圓柱半徑	0.03～0.05 m
土壤厚度	0.4 m	方柱邊長(zhǎng)	0.04～0.06 m
土壤表面高度起伏范圍	0.38～0.41 m	目標(biāo)高度	0.2～0.3 m
		目標(biāo)水平位置	0.35～1.45 m

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表 2 各種雜波抑制算法的平均PSNR(dB)/平均SSIM

目標(biāo)類(lèi)型	MS	SVD	NMF	RPCA	RNMF	RAE	本文DR-GAN
PVC圓柱體	0.85/0.014	22.38/0.365	22.23/0.305	23.54/0.259	22.72/0.205	22.56/0.154	36.66/0.965
空洞圓柱體	6.62/0.060	22.96/0.420	22.96/0.398	25.94/0.470	24.35/0.338	22.99/0.363	42.73/0.989
金屬圓柱體	12.58/0.126	25.26/0.588	25.18/0.562	27.59/0.612	25.86/0.471	25.92/0.432	44.70/0.992
PVC方柱體	6.41/0.021	23.09/0.357	24.06/0.365	27.11/0.437	25.84/0.351	25.22/0.287	43.65/0.980
空洞方柱體	12.45/0.072	25.79/0.535	25.38/0.494	28.78/0.597	26.43/0.444	25.80/0.369	47.92/0.993
金屬方柱體	16.97/0.181	25.97/0.538	25.39/0.504	29.15/0.652	27.45/0.538	26.72/0.458	48.00/0.993

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表 3 各種雜波抑制方法的平均用時(shí)(s)

	MS	SVD	NMF	RPCA	RNMF	RAE	DR-GAN(GPU)
時(shí)間	0.002 9	0.010 2	0.013 6	2.083 9	6.063 6	3.872 6	0.103 0

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表 4 實(shí)測(cè)數(shù)據(jù)雜波抑制的平均IF(dB)

目標(biāo)類(lèi)型	MS	SVD	NMF	RPCA	RNMF	RAE	本文DR-GAN
空心PVC管	10.49	24.19	24.59	20.68	24.74	12.01	41.34
空心塑料瓶	13.26	24.50	23.82	23.18	24.67	18.40	45.79
鋼筋	13.19	26.07	25.96	26.28	28.16	26.65	46.01

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