A Compressive Sensing Data Acquisition and Imaging Method for Stepped Frequency GPRs

Gürbüz, Ali Cafer; McClellan, J.H.; Scott, Waymond R.

doi:10.1109/tsp.2009.2016270

Cited by 281 publications

(211 citation statements)

References 43 publications

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“…One is (Carrier-Free Pulse CFP-GPR), which images the subsurface by transmitting short electromagnetic (EM) pulses and processing the reflections due to permittivity discontinuities in the ground [5]. The other is the stepped-frequency continuous-wave GPR (SFCW-GPR) [6][7][8][9]. When it works, a stepped frequency signal probes the environment with a discrete set of frequencies.…”

Section: Introductionmentioning

confidence: 99%

“…When it works, a stepped frequency signal probes the environment with a discrete set of frequencies. Compared with CFP-GPR, SFCW-GPR has several advantages, such as greater measurement accuracy, greater dynamic range, lower noise, easier frequency band and frequency steps selecting [9]. Therefore, it is becoming increasingly popular.…”

Section: Introductionmentioning

confidence: 99%

“…One important reason for this is long data acquisition time [9]. There are tens of scan positions and hundreds of measurements at each scan position, which means large quantities of data and long measurement time and thus make it too slow for some applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

3d Imaging Method for Stepped Frequency Ground Penetrating Radar Based on Compressive Sensing

Cai¹,

Tong²,

Wang³

et al. 2012

PIER M

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Abstract-Long data collecting time is one of the bottlenecks of the stepped-frequency continuous-wave ground penetrating radar (SFCW-GPR). We discuss the applicability of the Compressive Sensing (CS) method to three dimensional buried point-like targets imaging for SFCW-GPR. It is shown that the image of the sparse targets can be reconstructed by solving a constrained convex optimization problem based on l 1 -norm minimization with only a small number of data from randomly selected frequencies and antenna scan positions, which will reduce the data collecting time. Target localization ability, performance in noise, the effect of frequency bandwidth, and the effect of the wave travel velocity in the soil are demonstrated by simulated data. Numerical results show that the presented CS method can reconstruct the point-like targets in the right position even with 10% additive Gaussian white noise and some wave travel velocity estimation error.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3d Imaging Method for Stepped Frequency Ground Penetrating Radar Based on Compressive Sensing

Cai¹,

Tong²,

Wang³

et al. 2012

PIER M

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“…Once CS emerges, it attracts extensive attentions in signal processing, radar, and many other communities. Up to now, lots of works on CS theory and its applications in radar have been conducted [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], and some of them are briefly summarized as follows. Baraniuk and Steeghs applied CS to SAR imaging with pseudo-noise sequence as transmitted signal [4].…”

Section: Introductionmentioning

confidence: 99%

“…Ender addressed the generic system architectures and implementation considerations for applying CS, CS based ISAR imaging of satellite was processed using TIRA real data [9]. Gurbuz et al [10], Yoon and Amin [11,12] and Huang et al [13] applied CS to ground penetrating radar (GPR) and through-wall-radar-imaging (TWRI). Subotic et al applied CS to distributed radar waveforms design, showed their impact and constraints in the distributed radar [14].…”

Section: Introductionmentioning

confidence: 99%

Fpga Based Realization of Aic for Applying Cs to Radar

Chen¹,

Zhang²,

Zhang³

2011

PIER C

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Abstract-Research on digital modeling and realization of noncorrelation measurement frame for compressive sensing (CS) is conducted aiming at applying CS to imaging radar. FPGA based Analogue-to-Information Converter (AIC) is proposed and implemented. Real measurement data from AIC hardware platform and simulation data from AIC software platform are compressed to get range profiles, and the results agree well with what expected. The results show that the noise and synchronization error in real system deteriorate the performance of AIC thus CS remarkably.

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Compressed Sensing‐Based Imaging of Millimeter‐Wave ISAR Data

Demirci

Özdemir

2013

Micro & Optical Tech Letters

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High‐resolution radar imaging requires wide bandwidths and highly dense samplings of both time/frequency and spatial domains. However, the rapidly developing field of compressive sensing (CS) breaks this rule and paves a way for simultaneously achieving high‐resolution imaging with a significantly reduced number of samples. Although CS is seemed to be an intuitively attractive approach, the ongoing studies have shown the application of CS to synthetic aperture radar (SAR) data to be a challenging task in practice. In this work, as a contribution to these efforts, we address the potential use of CS in millimeter‐wave inverse SAR (ISAR) imaging. Various simulation and experimental imaging results are presented to demonstrate the validity and the robustness of the proposed CS‐based reconstruction from very limited samples. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:2967–2972, 2013

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A Compressive Sensing Data Acquisition and Imaging Method for Stepped Frequency GPRs

Cited by 281 publications

References 43 publications

3d Imaging Method for Stepped Frequency Ground Penetrating Radar Based on Compressive Sensing

3d Imaging Method for Stepped Frequency Ground Penetrating Radar Based on Compressive Sensing

Fpga Based Realization of Aic for Applying Cs to Radar

Compressed Sensing‐Based Imaging of Millimeter‐Wave ISAR Data

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