Priori information aided compressive sensing for time domain synchronous OFDM

2012 ◽  
Vol 48 (13) ◽  
pp. 800 ◽  
Author(s):  
Zhaocheng Wang ◽  
Linglong Dai ◽  
Jun Wang
Keyword(s):  
Compressive Sensing ◽  
Time Domain ◽  
Priori Information

Time Domain Impulsive Noise Mitigation in UWA Systems using Compressive Sensing Approach

2021 ◽  
Author(s):  
Zee Ang Sim ◽  
Esther Xin Fui Wong ◽  
Filbert H. Juwono ◽  
Lenin Gopal ◽  
Catur Apriono
Keyword(s):  
Compressive Sensing ◽  
Time Domain ◽  
Impulsive Noise ◽  
Noise Mitigation

scholarly journals Direct Under-Sampling Compressive Sensing Method for Underwater Echo Signals and Physical Implementation

2019 ◽  
Vol 9 (21) ◽  
pp. 4596 ◽  
Author(s):  
Tongjing Sun ◽  
Ji Li ◽  
Philippe Blondel
Keyword(s):  
Compressive Sensing ◽  
A Priori ◽  
Original Data ◽  
Measurement Matrix ◽  
Critical Elements ◽  
Under Sampling ◽  
Information Sampling ◽  
Priori Information ◽  
Practical Field ◽  
Dedicated Hardware

Compressive sensing can guarantee the recovery accuracy of suitably constrained signals by using sampling rates much lower than the Nyquist limit. This is a leap from signal sampling to information sampling. The measurement matrix is key to implementation but limited in the acquisition systems. This article presents the critical elements of the direct under-sampling—compressive sensing (DUS–CS) method, constructing the under-sampling measurement matrix, combined with a priori information sparse representation and reconstruction, and we show how it can be physically implemented using dedicated hardware. To go beyond the Nyquist constraints, we show how to design and adjust the sampling time of the A/D circuit and how to achieve low-speed random non-uniform direct under-sampling. We applied our method to data measured with different compression ratios (volume ratios of collected data to original data). It is shown that DUS-CS works well when the SNR is 3 dB, 0 dB, −3 dB, and −5 dB and the compression ratio is 50%, 20%, and 10%, and this is validated with both simulation and actual measurements. The method we propose provides an effective way for compressed sensing theory to move toward practical field applications that use underwater echo signals.

scholarly journals Ultrawideband Impulse Radar Through-the-Wall Imaging with Compressive Sensing

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Wenji Zhang ◽  
Moeness G. Amin ◽  
Fauzia Ahmad ◽  
Ahmad Hoorfar ◽  
Graeme E. Smith
Keyword(s):  
Compressive Sensing ◽  
Time Domain ◽  
Measurement Data ◽  
Acquisition Time ◽  
Time Domain Signal ◽  
Spatially Extended ◽  
Data Volume ◽  
Order Of Magnitude ◽  
New Perspective ◽  
The Time Domain

Compressive Sensing (CS) provides a new perspective for addressing radar applications requiring large amount of measurements and long data acquisition time; both issues are inherent in through-the-wall radar imaging (TWRI). Most CS techniques applied to TWRI consider stepped-frequency radar platforms. In this paper, the impulse radar two-dimensional (2D) TWRI problem is cast within the framework of CS and solved by the sparse constraint optimization performed on time-domain samples. Instead of the direct sampling of the time domain signal at the Nyquist rate, the Random Modulation Preintegration architecture is employed for the CS projection measurement, which significantly reduces the amount of measurement data for TWRI. Numerical results for point-like and spatially extended targets show that high-quality reliable TWRI based on the CS imaging approach can be achieved with a number of data points with an order of magnitude less than that required by conventional beamforming using the entire data volume.

scholarly journals Multi-Frequency GPR Microwave Imaging of Sparse Targets through a Multi-Task Bayesian Compressive Sensing Approach

2021 ◽  
Vol 7 (11) ◽  
pp. 247
Author(s):  
Marco Salucci ◽  
Nicola Anselmi
Keyword(s):  
Compressive Sensing ◽  
Quantitative Imaging ◽  
A Priori ◽  
Wide Band ◽  
Frequency Diversity ◽  
A Priori Information ◽  
Spectral Components ◽  
Priori Information ◽  
The One ◽  
Ground Penetrating

An innovative inverse scattering (IS) method is proposed for the quantitative imaging of pixel-sparse scatterers buried within a lossy half-space. On the one hand, such an approach leverages on the wide-band nature of ground penetrating radar (GPR) data by jointly processing the multi-frequency (MF) spectral components of the collected radargrams. On the other hand, it enforces sparsity priors on the problem unknowns to yield regularized solutions of the fully non-linear scattering equations. Towards this end, a multi-task Bayesian compressive sensing (MT-BCS) methodology is adopted and suitably customized to take full advantage of the available frequency diversity and of the a-priori information on the class of imaged targets. Representative results are reported to assess the proposed MF-MT-BCS strategy also in comparison with competitive state-of-the-art alternatives.

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