Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years. It provides high-resolution, day-and-night and weather-independent images for a multitude of applications ranging from geoscience and climate change research, environmental and Earth system monitoring, 2-D and 3-D mapping, change detection, 4-D mapping (space and time), security-related applications up to planetary exploration. With the advances in radar technology and geo/bio-physical parameter inversion modeling in the 90s, using data from several airborne and spaceborne systems, a paradigm shift occurred from the development driven by the technology push to the user demand pull. Today, more than 15 spaceborne SAR systems are being operated for innumerous applications. This paper provides first a tutorial about the SAR principles and theory, followed by an overview of established techniques like polarimetry, interferometry and differential interferometry as well as of emerging techniques (e.g., polarimetric SAR interferometry, tomography and holographic tomography). Several application examples including the associated parameter inversion modeling are provided for each case. The paper also describes innovative technologies and concepts like digital beamforming, Multiple-Input Multiple-Output (MIMO) and bi- and multi-static configurations which are suitable means to fulfill the increasing user requirements. The paper concludes with a vision for SAR remote sensing.

/pdf/a-tutorial-on-synthetic-aperture-radar-1v8k9pqokm.pdf

A tutorial on synthetic aperture radar

Remote sensing with radar is typically an ill-posed linear inverse problem: a scene is to be inferred from limited measurements of scattered electric fields. Parsimonious models provide a compressed representation of the unknown scene and offer a means for regularizing the inversion task. The emerging field of compressed sensing combines nonlinear reconstruction algorithms and pseudorandom linear measurements to provide reconstruction guarantees for sparse solutions to linear inverse problems. This paper surveys the use of sparse reconstruction algorithms and randomized measurement strategies in radar processing. Although the two themes have a long history in radar literature, the accessible framework provided by compressed sensing illuminates the impact of joining these themes. Potential future directions are conjectured both for extension of theory motivated by practice and for modification of practice based on theoretical insights.

https://vpa.sabanciuniv.edu/vpadb/findfile_get_anonymous_paper.php?f=2011

Sparsity and Compressed Sensing in Radar Imaging

Spectral compressive sensing

This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint imaging and autofocusing from data with phase errors, 4) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and 5) recent work on compressed sensing (CS)-based analysis and design of SAR sensing missions.

/pdf/sparsity-driven-synthetic-aperture-radar-imaging-5a4zcvv5rb.pdf

Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

This paper is concerned about sparse, continuous frequency estimation in line spectral estimation, and focused on developing gridless sparse methods which overcome grid mismatches and correspond to limiting scenarios of existing grid-based approaches, e.g., $\ell_{1}$ optimization and SPICE, with an infinitely dense grid. We generalize AST (atomic-norm soft thresholding) to the case of nonconsecutively sampled data (incomplete data) inspired by recent atomic norm based techniques. We present a gridless version of SPICE (gridless SPICE, or GLS), which is applicable to both complete and incomplete data without the knowledge of noise level. We further prove the equivalence between GLS and atomic norm-based techniques under different assumptions of noise. Moreover, we extend GLS to a systematic framework consisting of model order selection and robust frequency estimation, and present feasible algorithms for AST and GLS. Numerical simulations are provided to validate our theoretical analysis and demonstrate performance of our methods compared to existing ones.

/pdf/on-gridless-sparse-methods-for-line-spectral-estimation-from-1ut4wh7kxx.pdf

On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data

Synthetic aperture radar (SAR) imaging is a valuable tool in a number of defense surveillance and monitoring applications. There is increasing interest in 3-D reconstruction of objects from radar measurements. Traditional 3-D SAR image formation requires data collection over a densely sampled azimuth-elevation sector. In practice, such a dense measurement set is difficult or impossible to obtain, and effective 3-D reconstructions using sparse measurements are sought. This paper presents wide-angle 3-D image reconstruction approaches for object reconstruction that exploit reconstruction sparsity in the signal domain to ameliorate the limitations of sparse measurements. Two methods are presented; first, we use lp penalized (for p ≤ 1) least squares inversion, and second, we utilize tomographic SAR processing to derive wide-angle 3-D reconstruction algorithms that are computationally attractive but apply to a specific class of sparse aperture samplings. All approaches rely on high-frequency radar backscatter phenomenology so that sparse signal representations align with physical radar scattering properties of the objects of interest. We present full 360° 3-D SAR visualizations of objects from air-to-ground X-band radar measurements using different flight paths to illustrate and compare the two approaches.

Sparse Signal Methods for 3-D Radar Imaging

We study circular synthetic aperture radar (CSAR) systems collecting radar backscatter measurements over a
complete circular aperture of 360 degrees. This study is motivated by the GOTCHA CSAR data collection experiment
conducted by the Air Force Research Laboratory (AFRL). Circular SAR provides wide-angle information
about the anisotropic reflectivity of the scattering centers in the scene, and also provides three dimensional information
about the location of the scattering centers due to a non planar collection geometry. Three dimensional
imaging results with single pass circular SAR data reveals that the 3D resolution of the system is poor due to
the limited persistence of the reflectors in the scene. We present results on polarimetric processing of CSAR
data and illustrate reasoning of three dimensional shape from multi-view layover using prior information about
target scattering mechanisms. Next, we discuss processing of multipass (CSAR) data and present volumetric
imaging results with IFSAR and three dimensional backprojection techniques on the GOTCHA data set. We
observe that the volumetric imaging with GOTCHA data is degraded by aliasing and high sidelobes due to
nonlinear flightpaths and sparse and unequal sampling in elevation. We conclude with a model based technique
that resolves target features and enhances the volumetric imagery by extrapolating the phase history data using
the estimated model.

GOTCHA experience report: three-dimensional SAR imaging with complete circular apertures

We present a set of simulated X-band scattering data for civilian vehicles. For ten facet models of civilian vehicles, a high-frequency electromagnetic simulation produced fully polarized, far-field, monostatic scattering for 360 degrees azimuth and elevation angles from 30 to 60 degrees. The 369 GB of phase history data is stored in a MATLAB file format. This paper describes the CVDomes data set along with example imagery using 2D backprojection, single pass 3D, and multi-pass 3D.

/pdf/civilian-vehicle-radar-data-domes-3phykg3f37.pdf

Civilian vehicle radar data domes

We examine the relationship between sparse linear reconstruction and the classic problem of continuous parametric modeling. In sparse reconstruction, one wishes to recover a sparse amplitude vector from a measurement that is described as a linear combination of a small number of discrete additive components. Recent results in the compressive sensing literature have provided fast sparse reconstruction algorithms with guaranteed performance bounds for problems with certain structure. In this paper, we show an explicit connection between sparse reconstruction and parameter/order estimation and demonstrate how sparse reconstruction may be used to solve model order selection and parameter estimation problems. The structural assumption used in compressive sensing to guarantee reconstruction performance-the Restricted Isometry Property-is not satisfied in the general parameter estimation context. Nonetheless, we develop a method for selecting sparsity parameters such that sparse reconstruction mimics classic order selection criteria such as Akaike information criterion (AIC) and Bayesian information criterion (BIC). We compare the performance of the sparse reconstruction approach with traditional model order selection/parameter estimation techniques for a sinusoids-in-noise example. We find that the two methods have comparable performance in most cases, and that sparse linear modeling performs better than traditional model-based parameter/order estimation for closely spaced sinusoids with low signal-to-noise ratio.

On the Relation Between Sparse Reconstruction and Parameter Estimation With Model Order Selection

Typically in SAR imaging, there is insufficient data to form well-resolved three-dimensional (3D) images using
traditional Fourier image reconstruction; furthermore, scattering centers do not persist over wide-angles. In
this work, we examine 3D non-coherent wide-angle imaging on the GOTCHA Air Force Research Laboratory
(AFRL) data set; this data set consists of multipass complete circular aperture radar data from a scene at AFRL,
with each pass varying in elevation as a result of aircraft flight dynamics . We compare two algorithms capable
of forming well-resolved 3D images over this data set: regularized l p least-squares inversion, and non-uniform
multipass interferometric SAR (IFSAR).

Christian D. Austin

Papers

Sparse Signal Methods for 3-D Radar Imaging

GOTCHA experience report: three-dimensional SAR imaging with complete circular apertures

Civilian vehicle radar data domes

On the Relation Between Sparse Reconstruction and Parameter Estimation With Model Order Selection

Sparse multipass 3D SAR imaging: applications to the GOTCHA data set