High-Resolution Radar via Compressed Sensing

By Matthew A. Herman, Thomas Strohmer. IEEE Transactions on Signal Processing, 2008

1,091 citations recorded by the source corpus.

A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N times N grid. Assuming the number of targets K is small (i.e., K Lt N2), then we can transmit a sufficiently ldquoincoherentrdquo pulse and employ the techniques of compressed sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel-compressed sensing approach offers great potential for better resolution over classical radar.

Subfields: Aerospace Engineering, Biomedical Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/201dea99707c04e1b659932bb2d46c3e37a6435e

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Citations in the corpus

Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors (2008, background): “Other potential applications involving analog signals are: i) compressed sensing radar, where an IMV model can replace the discretization of the plane used in [16] and ii)”
Toeplitz Compressed Sensing Matrices With Applications to Sparse Channel Estimation (2010, background): “In particular, the works [34] and [35] considered the problem of recovering matrices that can be expressed as the superposition of a small number of component matrices from their action on a given probe signal—the so-called sparse matrix identification problem.”
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): “Certainly this interpretation includes the problem of identifying field elements from electromagnetic and acoustic images, multipath components in wireless communication, radiating sources in radar and sonar, and light sources in optical imaging and spec- trometry.”
General Deviants: An Analysis of Perturbations in Compressed Sensing (2009, background): “This can be realized in radar [7], remote sensing [8], telecommunications, source separation [5], [6], and countless other problems.”
Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference (2011, background): “Its potential applications include near-field source localization, radar imaging [42, 43] and channel estimation [44].”
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”
Multibeam for Joint Communication and Radar Sensing Using Steerable Analog Antenna Arrays (2018, background): “More recently, compressive sensing (CS) techniques have been widely applied in radar imaging [19], [20].”
SPARSE RECONSTRUCTION FOR SAR IMAGING BASED ON COMPRESSED SENSING (2010, positive): “In [17], high resolution radar was proposed by transmitting specially designed waveforms, where the theory of CS with random convolution was used by transmitting random noise-like signals.”
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].”
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].”
Sparsity and Compressed Sensing in Radar Imaging (2010, methodology): “For thespecialcaseofrange/Dopplerprocessingformonostatic radar with a single pulse, the Alltop cubic-chirp sequence has been employed[ 63 ] as adeterministic construction ofa low-coherence waveform; for prime length, the Alltop sequence nearly achieves the Welch bound on coherence, thusproducinganea”
Structured Compressed Sensing: From Theory to Applications (2011, methodology): “approach we present here to reduced-rate sampling is based on viewing analog signals in unions of subspaces, and is therefore fundamentally different than previous attempts to treat similar problems, [42,84,104,148,149]. The latter typically rely on discretization of the analog input, and are often ”
Ghost imaging lidar via sparsity constraints (2012, background): “Combined with sparsity constraints, super-resolution [12–14], compressive sensing [14–19], compressive radar [20] and other techniques (http://ecos.”
Compressive Sensing with structured random matrices (2012, background): “Although we will not cover specific applications in these notes, let us mention that compressive sensing may be applied in imaging [44, 109], A/D conversion [133], radar [69, 49] and wireless communication [126, 95], to name a few.”
Compressed Sensing (2012, methodology): “Other constructions are known for m×m2 matrices A generated from the Alltop sequence [36] or by using Grassmannian frames [56], in which cases the optimal lower bound is attained: μ(A) = 1 √ m .”