Fast Encoding of Synthetic Aperture Radar Raw Data using Compressed Sensing

Bhattacharya, Sujit; Blumensath, Thomas; Mulgrew, Bernard; Davies, Mike E.

doi:10.1109/ssp.2007.4301298

Cited by 106 publications

(54 citation statements)

References 5 publications

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“…In this paper we have used the Dantzig selector, solving the optimization problem using the primal-dual algorithm. In our implementation, this requires rewriting the (in general) complex matrices and vectors used above in (8) in terms of real entries. We denote these versions of the sensing matrix A, the scene vector x and the noise vector z by A * , x * and z * respectively.…”

Section: Computing the Reconstructionmentioning

confidence: 99%

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Wavelet-Based Compressive Sensing for Point Scatterers

Mishra¹,

Wilsenach²

2015

RADIOENGINEERING

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Abstract. Compressive Sensing (CS) allows for the sampling of signals at well below the Nyquist rate but does so, usually, at the cost of the suppression of lower amplitude signal components. Recent work suggests that important information essential for recognizing targets in the radar context is contained in the side-lobes as well, which are often suppressed by CS. In this paper we extend existing techniques and introduce new techniques both for improving the accuracy of CS reconstructions and for improving the separability of scenes reconstructed using CS. We investigate the Discrete Wavelet Transform (DWT), and show how the use of the DWT as a representation basis may improve the accuracy of reconstruction generally. Moreover, we introduce the concept of using multiple wavelet-based reconstructions of a scene, given only a single physical observation, to derive reconstructions that surpass even the best wavelet-based CS reconstructions. Lastly, we specifically consider the effect of the wavelet-based reconstruction on classification. This is done indirectly by comparing outputs of different algorithms using a variety of separability measures. We show that various wavelet-based CS reconstructions are substantially better than conventional CS approaches at inducing (or preserving) separability, and hence may be more useful in classification applications.

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Section: Computing the Reconstructionmentioning

confidence: 99%

“…The application of wavelet-based CS to Synthetic Aperture Radar (SAR) has been discussed in other papers (e.g [5][6][7][8]) and there has also been work done on the application of waveletbased CS to images (e.g. [9][10][11]).…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Based Compressive Sensing for Point Scatterers

Mishra¹,

Wilsenach²

2015

RADIOENGINEERING

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show abstract

“…Moreover, many reconstruction algorithms of compressive radar images have proposed. For example, Xie and co-workers proposed a smoothed L0 norm (SL0) algorithm to obtain fast radar imaging based on a compressive sensing [11]; Bhattacharya and co-workers used convex optimization through projection onto convex sets or greedy algorithms to decode compressive synthetic aperture radar (SAR) images [12,13]; and Yu and co-workers introduced a turbo-like iterative thresholding algorithm to recover SAR images [14]. The methods of compressive radar imaging based on these reconstruction algorithms can obtain high-resolution radar images with very small amounts of echo data, and improve the radar imaging speed remarkably, compared with conventional radar imaging methods.…”

Section: Introductionmentioning

confidence: 99%

2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging

Yang

Wang

et al. 2017

Remote Sensing

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Abstract:Compressive radar imaging has attracted considerable attention because it substantially reduces imaging time through directly compressive sampling. However, a problem that must be addressed for compressive radar imaging systems is the high computational complexity of reconstruction of sparse signals. In this paper, a novel algorithm, called two-dimensional (2D) normalized iterative hard thresholding (NIHT) or 2D-NIHT algorithm, is proposed to directly reconstruct radar images in the matrix domain. The reconstruction performance of 2D-NIHT algorithm was validated by an experiment on recovering a synthetic 2D sparse signal, and the superiority of the 2D-NIHT algorithm to the NIHT algorithm was demonstrated by a comprehensive comparison of its reconstruction performance. Moreover, to be used in compressive radar imaging systems, a 2D sampling model was also proposed to compress the range and azimuth data simultaneously. The practical application of the 2D-NIHT algorithm in radar systems was validated by recovering two radar scenes with noise at different signal-to-noise ratios, and the results showed that the 2D-NIHT algorithm could reconstruct radar scenes with a high probability of exact recovery in the matrix domain. In addition, the reconstruction performance of the 2D-NIHT algorithm was compared with four existing efficient reconstruction algorithms using the two radar scenes, and the results illustrated that, compared to the other algorithms, the 2D-NIHT algorithm could dramatically reduce the computational complexity in signal reconstruction and successfully reconstruct 2D sparse images with a high probability of exact recovery.

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“…More recently, there have been several applications of compressed sensing ideas to radar. [6][7][8][9] The authors in 6 accurately reconstruct a small number of targets on a time-frequency plane by transmitting a sufficiently incoherent pulse and employing the techniques of compressed sensing. The authors in 8 propose the use of chirp pulses and pseudo-random sequences for compressed sensing with imaging radars.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing of Monostatic and Multistatic SAR

Stojanovic,

Cetin,

Karl

2013

IEEE Geosci. Remote Sensing Lett.

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In this paper we study the impact of sparse aperture data collection of a SAR sensor on reconstruction quality of a scene of interest. Different mono and multi-static SAR measurement configurations produce different Fourier sampling patterns. These patterns reflect different spectral and spatial diversity trade-offs that must be made during task planning. Compressed sensing theory argues that the mutual coherence of the measurement probes is related to the reconstruction performance of sparse domains. With this motivation we compare the mutual coherence and corresponding reconstruction behavior of various mono-static and ultra-narrow band multi-static configurations, which trade-off frequency for geometric diversity. We investigate if such simple metrics are related to SAR reconstruction quality in an obvious way.

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Fast Encoding of Synthetic Aperture Radar Raw Data using Compressed Sensing

Cited by 106 publications

References 5 publications

Wavelet-Based Compressive Sensing for Point Scatterers

Wavelet-Based Compressive Sensing for Point Scatterers

2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging

Compressed Sensing of Monostatic and Multistatic SAR

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