Compressive Radar Imaging

By Richard Baraniuk, Philippe Steeghs. International Radar Conference, 2007

1,001 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Biomedical Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/ae68bd958143856009f199d12aad71bac0d8ea4a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Biomedical Engineering

In Computational Mechanics

Citations in the corpus

Array imaging using intensity-only measurements (2010, methodology): “This idea is called basis pursuit (BP) or compressed sensing (CS), under different conditions on Af̂ (ω), and has been used in some formulations of array imaging [3, 15, 24, 25].”
Compressive Sensing by Random Convolution (2009, methodology): “A preliminary investigation of using ideas from compressive sensing in radar imaging can be found in [ 5 ].”
Single-pixel imaging 12 years on: a review. (2020, background): “Examples include x-ray imaging [10–12], terahertz imaging [13–15], compressive radar [16], a VIS-NIR telescope [17] and fluorescence microscopy [18].”
Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices (2013, background): “Efficient recovery of sparse signals and low-rank matrices has been a very active area of recent research in applied mathematics, statistics, and machine learning, with many important applications, ranging from signal processing [28, 16] to medical imaging [22] to radar systems [3, 21].”
The Split Bregman Method for L1-Regularized Problems (2009, background): “This formulation has been shown to be useful in many areas, including medical imaging [16, 17], radar [1], and other signal processing applications.”
Compressed Synthetic Aperture Radar (2010, background): “The two-dimensional image formed is interpreted in the dimensions of range and cross-range or azimuth.”
Compressive sensing: From theory to applications, a survey (2013, methodology): “CS theory contributes to RADAR system design by eliminating the need of pulse compression matched ﬁlter at receiver and reducing the analog to digital conversion bandwidth from Nyquist rate to information rate, simplifying hardware design [71].”
MIMO Radar Using Compressive Sampling (2009, background): “In [17], in the context of radar imaging, compressive sampling was shown to have the potential to reduce the typically required”
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals (2009, background): “Additional potential applications include geophysical imaging, where two- and three-dimensional seismic data can be modeled as piecewise smooth (hence, sparse in a local Fourier representation) [7], as well as radar and sonar imaging [8], [9].”
Sensitivity to Basis Mismatch in Compressed Sensing (2010, background): “Several articles (see, e.g., [31]–[37]) consider the use of compressed sensing theory for discrete radar/sonar imaging, and sensor array processing, when the targets are taken to be on a regular grid in delay, Doppler, and wavenumber, and study this theory as a new high resolution imaging principle.”
Compressed sensing based compression of SAR raw data (2009, methodology): “Compressed sensing has also been proposed in [9][10][11] with encouraging simulation results.”
On compressive sensing applied to radar (2010, background): “The paper [8], which is one of the first publications on CS-radar, investigates an A/D”
Sparsity and Compressed Sensing in Radar Imaging (2010, background): “However, only recently have low-coherence properties been mated with ‘1-regularized or greedy reconstruction algorithms for sparse imaging; examples include random binary waveforms for recovery of onedimensional range profiles [ 60 ], random phase waveforms for multistatic images [16], frequency-hop”
Spectral compressive sensing (2013, methodology): “Immediate applications of this formulation include sparsity-based localization [20, 21, 22, 23, 70], radar imaging [24, 25, 26], and sparse timefrequency representations [12].”
Compressed Sensing (2014, neutral): “Estas abarcan diversas áreas como: imagenoloǵıa medica [49, 45], conversión analógico/digital [68], detección mediante RADAR [4, 42], corrección de errores [18], cámaras digitales [34, 21, 51], Sismoloǵıa [41], Radioastronomı́a [71], comunicación inalámbrica [63, 3, 65] y redes de sensores inalámbri”
A Novel Strategy for Radar Imaging Based on Compressive Sensing (2008, neutral): “In [25], the authors propose a new “compressive radar receiver,” based on the notion of CS.”
SPARSE RECONSTRUCTION FOR SAR IMAGING BASED ON COMPRESSED SENSING (2010, positive): “A compressive radar imaging scheme based on CS was proposed in [16], the system can eliminate the need for the matched ﬁlter in the radar receiver and reduce the required receiver analog-to-digital conversion bandwidth.”
High-Resolution Radar via Compressed Sensing (2008, background): “As such, we will only be concerned with the range and velocity of the targets.”