This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.

'The Micro-Doppler Effect in Radar' by V.C. Chen

The aim of this paper is to design a novel parallel computing solution for the processing chain implementing the Small BAseline Subset (SBAS) Differential SAR Interferometry (DInSAR) technique. The proposed parallel solution (P-SBAS) is based on a dual-level parallelization approach and encompasses combined parallelization strategies, which are fully discussed in this paper. Moreover, the main methodological aspects of the proposed approach and their implications are also addressed. Finally, an experimental analysis, aimed at quantitatively evaluating the computational efficiency of the implemented parallel prototype, with respect to appropriate metrics, has been carried out on real data; this analysis confirms the effectiveness of the proposed parallel computing solution. In the current scenario, characterized by huge SAR archives relevant to the present and future SAR missions, the P-SBAS processing chain can play a key role to effectively exploit these big data volumes for the comprehension of the surface deformation dynamics of large areas of Earth.

SBAS-DInSAR Parallel Processing for Deformation Time-Series Computation

This paper presents the first fully polarimetric high-resolution circular synthetic aperture radar (CSAR) images at L-band (1.3 GHz). The circular data were acquired in 2008 by the Experimental SAR (E-SAR) airborne system of the German Aerospace Center (DLR) over the airport of Kaufbeuren, Germany. The obtained images resulting from the coherent integration of the whole circular flight are investigated and discussed in terms of two of the main CSAR properties, namely, the theoretical subwavelength resolution in the horizontal plane (x, y) and the 3-D imaging capabilities. The 3-D imaging capabilities are of special interest due to the penetration of L-band in vegetated areas. These results were compared with images processed by the incoherent addition of the full synthetic aperture. The coherent approach showed a better performance since scatterers are focused at their maximum resolution. Due to the nonlinearity of the tracks and the high-computational burden, an efficient fast factorized back-projection (FFBP) has been developed. Unlike frequencydomain processors, it accommodates azimuthal variances and topography changes. Limits and considerations of the proposed algorithm are described and discussed. To further accelerate this process, the FFBP was also implemented in a graphics processing unit (GPU). Processing performance has been assessed with the direct BP (DBP) as a reference, obtaining speedup factors up to 1800. Residual motion errors have been estimated with a new frequency-based autofocus approach for CSAR configurations based on low signal-to-clutter ratio (SCR) isotropic scatterers. High-resolution images of man-made and distributed scatterers have been analyzed and compared with a stripmap SAR, both concerning anisotropic and isotropic-like scatterers. Results include a single-channel tomogram of a Luneburg lens and a fully polarimetric tomogram of a tree.

Fully Polarimetric High-Resolution 3-D Imaging With Circular SAR at L-Band

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

The publicly-available Moving and Stationary Target Acquisition and Recognition (MSTAR) synthetic aperture radar (SAR) dataset has been an valuable tool in the development of SAR automatic target recognition (ATR) algorithms over the past two decades, leading to the achievement of excellent target classification results. However, because of the large number of possible sensor parameters, target configurations and environmental conditions, the SAR operating condition (OC) space is vast. This leads to the impossible task of collecting sufficient measured data to cover the entire OC space. Thus, synthetic data must be generated to augment measured datasets. The study of synthetic data fidelity with respect to classification tasks is a non-trivial task. To that end, we introduce the Synthetic and Measured Paired and Labeled Experiment (SAMPLE) dataset, which consists of SAR imagery from the MSTAR dataset and well-matched synthetic data. By matching target configurations and sensor parameters among the measured and synthetic data, the SAMPLE dataset is ideal for investigating the differences between measured and synthetic SAR imagery. In addition to the dataset, we propose four experimental designs challenging researchers to investigate the best ways to classify targets in measured SAR imagery given synthetic SAR training imagery.

A SAR dataset for ATR development: the Synthetic and Measured Paired Labeled Experiment (SAMPLE)

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

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

A Challenge Problem for SAR Change Detection and Data Compression.

Radar resolution in three dimensions is considered for circular synthetic apertures at a constant elevation angle.
A closed-form expression is derived for the far-field 3-D point spread function for a circular aperture of 360 degrees
azimuth and is used to revisit the traditional measures of resolution along the x, y and z spatial axes. However,
the limited angular persistence of reflectors encountered in practice renders the traditional measures inadequate
for circular synthetic aperture radar imaging. Two alternative measures for 3-D resolution are presented: a
nonparametric measure based on level sets of a reflector's signature and a statistical measure using the Cramer-
Rao lower bound on location estimation error. Both proposed measures provide a quantitative evaluation of
3-D resolution as a function of scattering persistence and radar system parameters. The analysis shows that
3-D localization of a reflector requires a combination of large radar cross section and large angular persistence.
In addition, multiple elevations or a priori target scattering models, if available, may be used to significantly
enhance 3-D resolution.

Three-dimensional resolution for circular synthetic aperture radar

This paper presents a design for the parallel processing of synthetic aperture radar data using one or more Field Programmable Gate Arrays (FPGAs). Our design supports real-time computation of a two-dimensional image from a matrix of echo pulses and their corresponding response values. Components of this design include: (a) central processing pipeline to perform back projection calculations, (b) pre-fetch cache to minimize external memory access latency, (c) memory bridge that serves as the primary on-chip storage for pulse data, and (d) a pixel queue to direct image data in and out of the pipeline. Design parameters may be adjusted to achieve optimum performance, and multiple instances of this design may be replicated on-chip to achieve prespecified performance objectives. We provide a complexity analysis as a function of the input and output parameters. Simulation results based on an implementation of this design show that our design achieves 160 GFLOPs per instance on a simulated Altera Stratix III EP3SL150 FPGA, and scales well for output image size ranging from 500 × 500 pixels to 5,000 × 5,000 pixels.

/pdf/parallel-processing-techniques-for-the-processing-of-4kdf3b5fk5.pdf

Parallel processing techniques for the processing of synthetic aperture radar data on FPGAs

Convolutional neural networks (CNNs) are state-of-the-art techniques for image classification; however, CNNs require an extensive amount of training data to achieve high accuracy. This demand presents a challenge because the existing amount of measured synthetic aperture radar (SAR) data is typically limited to just a few examples and does not account for articulations, clutter, and other target or scene variability. Therefore, this research aimed to assess the feasibility of combining synthetic and measured SAR images to produce a classification network that is robust to operating conditions not present in measured data and that may adapt to new targets without necessarily training on measured SAR images. A network adapted from the CIFAR-10 LeNet architecture in MATLAB Convolutional Neural Network (MatConvNet) was first trained on a database of multiple synthetic Moving and Stationary Target Acquisition and Recognition (MSTAR) targets. After the network classified with almost perfect accuracy, the synthetic data was replaced with corresponding measured data. Only the first layer of filters was permitted to change in order to create a translation layer between synthetic and measured data. The low error rate of this experiment demonstrates that diverse clutter and target types not represented in measured training data may be introduced in synthetic training data and later recognized in measured test data.

Linda J. Moore

Papers

SAR image formation toolbox for MATLAB

A Challenge Problem for SAR Change Detection and Data Compression.

Three-dimensional resolution for circular synthetic aperture radar

Parallel processing techniques for the processing of synthetic aperture radar data on FPGAs

Blending synthetic and measured data using transfer learning for synthetic aperture radar (SAR) target classification