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

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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

While many synthetic aperture radar (SAR) image formation techniques exist, two of the most intuitive methods
for implementation by SAR novices are the matched filter and backprojection algorithms. The matched filter and
(non-optimized) backprojection algorithms are undeniably computationally complex. However, the backprojection
algorithm may be successfully employed for many SAR research endeavors not involving considerably large
data sets and not requiring time-critical image formation. Execution of both image reconstruction algorithms
in MATLAB is explicitly addressed. In particular, a manipulation of the backprojection imaging equations is
supplied to show how common MATLAB functions, ifft and interp1, may be used for straight-forward SAR
image formation. In addition, limits for scene size and pixel spacing are derived to aid in the selection of an
appropriate imaging grid to avoid aliasing. Example SAR images generated though use of the backprojection
algorithm are provided given four publicly available SAR datasets. Finally, MATLAB code for SAR image
reconstruction using the matched filter and backprojection algorithms is provided.

SAR image formation toolbox for MATLAB

Renewable energy based Distributed Generation (DG) has been the solution to researchers to combat the problem of increasing load. In DG based microgrids, the loads and generators are in the close vicinity to aid continuous power supply. However, the power electronic interfacing towards DG systems gives rise to some of the serious power quality problems, such as, the reactive power compensation and the generation of harmonics that pollutes the power distribution system. Reactive power compensation is becoming a challenging task to sustain an acceptable degree of power quality in microgrids due to tightly coupled generation and distribution. Therefore, current research is to cope up with the expanding microgrid system and mitigation of these concerned issues. Recent trends are geared towards the realization of multitasking devices to tackle several power quality problems simultaneously. Hence, the objective of this paper is to present an overview of a microgrid and its modeling utilizing the actual environmental data. Subsequently, the challenges and power quality issues faced in the microgrid are observed and succeeded by a review of compensation methods against these concerns using various control techniques, algorithms, and devices.

A review of reactive power compensation techniques in microgrids

In this letter, we present an autofocus routine for backprojection imagery from spotlight-mode synthetic aperture radar data The approach is based on maximizing image sharpness and supports the flexible collection and imaging geometries of BP, including wide-angle apertures and the ability to image directly onto a digital elevation map While image-quality-based autofocus approaches can be computationally intensive, in the backprojection setting, we demonstrate a natural geometric interpretation that allows for optimal single-pulse phase corrections to be derived in closed form as the solution of a quartic polynomial The approach is applicable to focusing standard backprojection imagery, as well as providing incremental focusing in sequential imaging applications based on autoregressive backprojection An example demonstrates the efficacy of the approach applied to real data for a wide-aperture backprojection image

An Autofocus Method for Backprojection Imagery in Synthetic Aperture Radar

This paper describes a challenge problem whose scope is the 2D/3D imaging of stationary targets from a volumetric data
set of X-band Synthetic Aperture Radar (SAR) data collected in an urban environment. The data for this problem was
collected at a scene consisting of numerous civilian vehicles and calibration targets. The radar operated in circular SAR
mode and completed 8 circular flight paths around the scene with varying altitudes. Data consists of phase history data,
auxiliary data, processing algorithms, processed images, as well as ground truth data. Interest is focused on mitigating
the large side lobes in the point spread function. Due to the sparse nature of the elevation aperture, traditional imaging
techniques introduce excessive artifacts in the processed images. Further interests include the formation of highresolution
3D SAR images with single pass data and feature extraction for 3D SAR automatic target recognition
applications. The purpose of releasing the Gotcha Volumetric SAR Data Set is to provide the community with X-band
SAR data that supports the development of new algorithms for high-resolution 2D/3D imaging.

A challenge problem for 2D/3D imaging of targets from a volumetric data set in an urban environment

This document describes a challenge problem whose scope is the detection, geolocation, tracking
and ID of moving vehicles from a set of X-band SAR data collected in an urban environment. The
purpose of releasing this Gotcha GMTI Data Set is to provide the community with X-band SAR data
that supports the development of new algorithms for SAR-based GMTI. To focus research onto
specific areas of interest to AFRL, a number of challenge problems are defined.
The data set provided is phase history from an AFRL airborne X-band SAR sensor. Some key
features of this data set are two-pass, three phase center, one-foot range resolution, and one
polarization (HH). In the scene observed, multiple vehicles are driving on roads near buildings.
Ground truth is provided for one of the vehicles.

A challenge problem for SAR-based GMTI in urban environments

Synthetic aperture radar (SAR) is a form of remote sensing where coherent radar echoes transmitted from a moving platform are processed to form an image of a scene, usually on the ground. There are several algorithms that have been developed with varying levels of complexity and accuracy. In applications with large scene size requirements, the choice of image formation algorithm is important. Exact imaging algorithms like the back-projection algorithm (BPA) can form large images without errors, but they are computationally expensive. Another well-known algorithm is the polar format algorithm (PFA), which is significantly faster than BPA, but it uses approximations that cause image errors in large scenes. In this paper, we evaluate the scene size limitations of the PFA in terms of image defocus. This is caused by residual quadratic phase errors that arise due to approximations in the algorithm. We derive this residual quadratic phase error using a Taylor series expansion in the slow time dimension. Then, we derive simplified expressions for image defocus for two flight paths: circular and linear. We also use the Taylor series expansion to derive accurate corrections for image distortion caused by PFA. These distortion corrections are used in conjunction with the residual quadratic phase errors to derive accurate scene size limitations that are notably different from the circular regions of focus determined in earlier works.

Scene size limits for polar format algorithm

This document describes a challenge problem whose scope is two-fold. The first aspect is to develop SAR CCD algorithms that are applicable for X-band SAR imagery collected in an urban environment. The second aspect relates to effective data compression of these complex SAR images, where quality SAR CCD is the metric of performance. A set of X-band SAR imagery is being provided to support this development. To focus research onto specific areas of interest to AFRL, a number of challenge problems are defined. The data provided is complex SAR imagery from an AFRL airborne X-band SAR sensor. Some key features of this data set are: 10 repeat passes, single phase center, and single polarization (HH). In the scene observed, there are multiple buildings, vehicles, and trees. Note that the imagery has been coherently aligned to a single reference.

https://www.sdms.afrl.af.mil/content/challenge_areas/documents/A_challenge_problem_for_SAR_change_detection_and_data.pdf

LeRoy A. Gorham

Papers

SAR image formation toolbox for MATLAB

A challenge problem for 2D/3D imaging of targets from a volumetric data set in an urban environment

A challenge problem for SAR-based GMTI in urban environments

Scene size limits for polar format algorithm

A Challenge Problem for SAR Change Detection and Data Compression.