John Nehrbass

Civilian vehicle radar data domes

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.

Octave and Python: High-Level Scripting Languages Productivity and Performance Evaluation

Octave and Python are open source alternatives to MATLAB, which is widely used by the High Performance Computing Modernization Program (HPCMP) community. These languages are two well known examples of high-level scripting languages that promise to increase productivity without compromising performance on HPC systems. In this paper, we report our work and experience with these two non-traditional programming languages at the HPCMP Centers. We used a representative sample of SIP codes for the study, with special emphasis given to the understanding of issues such as portability, degree of complexity, productivity and suitability of Octave and Python to address signal/image processing (SIP) problems on the HPCMP HPC platforms. We implemented a relatively simple two-dimensional (2D) FFT and a more complex image enhancement algorithm in Octave and Python and benchmarked these SIP codes on several HPCMP platforms, paying special attention to usability, productivity and performance aspects. Moreover, we performed a thorough benchmark containing important low level SIP core functions and algorithms and compared the outcome with the corresponding results for MATLAB. We found that the capabilities of these languages are comparable to MATLAB and they are powerful enough to efficiently implement complex SIP algorithms. Productivity and performance results for each language vary depending on the specific task and the availability of high level functions in each system to address such tasks. Therefore, the choice of the best language to use in a particular instance will strongly depend upon the specifics of the SIP application that needs to be addressed. We concluded that Octave and Python look like promising tools that may provide an alternative to MATLAB without compromising performance and productivity. Their syntax and functionality are similar enough to MATLAB to present a very shallow learning curve for experienced MATLAB users

A computational science IDE for HPC systems: design and applications

Software engineering studies have shown that programmer productivity is improved through the use of computational science integrated development environments (or CSIDE, pronounced “sea side”) such as MATLAB. Scientists often desire to use high-performance computing (HPC) systems to run their existing CSIDE scripts with large data sets. ParaM is a CSIDE distribution that provides parallel execution of MATLAB scripts on HPC systems at large shared computer centers. ParaM runs on a range of processor architectures (e.g., x86, x64, Itanium, PowerPC) and its MPI binding, known as bcMPI, supports a number of interconnect architectures (e.g., Myrinet and InfiniBand). On a cluster at Ohio Supercomputer Center, bcMPI with blocking communication has achieved 60% of the bandwidth of an equivalent C/MPI benchmark. In this paper, we describe goals and status for the ParaM project and the development of applications in signal and image processing that use ParaM.

Wide-area wide-angle SAR focusing

This study started with a data set that leveraged the latest autofocusing methods to obtain the cleanest radar data set appropriate for generating large SAR imagery over a 5-km spot. The authors intended to spotlight individual smaller nonmoving targets within the larger area; however, the images appeared blurred and varied greatly when generated by different passes of the circular SAR radar system. This study concentrated on using widely dispersed QTs combined with an algorithm to correct for both range and phase errors to improve imaging. The wide-angle QT imaging and vehicle identification experiments showed a significant improvement over all orbits and provided higher quality imagery to more robustly perform image registration. Focusing showed significant improvement in visualizations quad-trihedrals and a vehicle.

Discrimination of civilian vehicles using wide-angle SAR

At high frequencies, synthetic aperture radar (SAR) imagery can be represented as a set of points corresponding to scattering centers. Using a collection of sequential azimuths with a fixed aperture we build a cube of points for each of seven civilian vehicles in the Gotcha public release data set (GPRD). We present a baseline study of the ability to discriminate between the vehicles using strictly 2D geometric information of the scattering centers. The comparison algorithm is independent of pose and translation using a novel application of the partial Hausdorff distance (PHD) minimized through a particle swarm optimization. Using the PHD has the added benefit of reducing the effects of occlusions and clutter in comparing vehicles from pass to pass. We provide confusion matrices for a variety of operating parameters including azimuth extent, various amplitude cutoffs, and various parameters within PHD. Finally, we discuss extension of the approach to near-field imaging and to additional point attributes, such as 3D location and polarimetric response.

Web Interface for Querying/Searching RDF Database

The resource description framework (RDF) is a language for representing information about resources on the web. However, RDF can also be used to describe other data and relationships between objects in the data. Many applications in the signal/image processing (SIP) community (such as radar imaging, electromagnetics, etc.) generate large amounts of data. Researchers would like to have online access to this data as well as the ability to easily explore and mine the data. Our applications RDF metadata representation is similar to that of a conventional database, and users can use forms to search the database, or use the standard RDF query language SPARQL, to create queries. In most cases, all the data as well as the RDF description of the data resides on secure Department of Defense (DoD) major shared resource center (MSRC) resources. In order to provide a web interface for exploring this data, we need a secure way to access the user data. Towards this goal, we use the user interface toolkit (UIT) to provide a web application that allows users to browse and search the RDF metadata of large SIP databases securely and conveniently on their desktop. The UIT uses the same Kerberos technology and Secure ID cards that are used to access all MSRC machines and provides an application programming interface (API) for building clients to access computing resources in the DoD high performance computing and modernization program (HPCMP).

High-performance computing for automatic target recognition in synthetic aperture radar imagery

Many research efforts have been devoted to applying machine learning (ML) algorithms to the task of Automatic Target Recognition (ATR). In the 90’s, ML techniques such as Neural Networks were less popular due to various technological barriers and applications. Computational resources were scarce and expensive. Today, computational resources are not as expensive as in the past; however, an abundance of sensors and business data need to be analyzed in real-time. High performance computing (HPC) enables ML-based decision making in real-time or near real-time. This research explores the application of deep learning algorithms, specifically convolutional neural networks, to the task of ATR in synthetic aperture radar (SAR) imagery. We developed a Convolution Neural Networks (CNN) architecture for achieving ATR in SAR imagery and found that classification accuracy levels of 99% can be achieved through the application of neural networks. We used graphics processing units (GPU) to accomplish the computational tasks.

The SIP High Productivity Toolset for Parameter Sweeps and Monte Carlo Runs

Many high performance computing tasks take the form of parameter sweeps. The same task is run many times with different sets of input parameters. For example, a researcher may run thousands of radar signature jobs over various azimuth and elevation angles. Keeping track of the status of the overall situation can be daunting and tedious, leading the user to manually track the state of all the jobs. Time that could otherwise be spent on analysis is spent managing the parameter sweep itself. The signal and image processing high productivity toolset (SIPHPT) automates much of the parameter sweep management activity. With the user supplying a single configuration (setup) file, the SIPHPT provides utilities to submit jobs, verify proper job setup, and monitor overall status. The SIPHPT creates underlying master scripts to manage the jobs. In the event jobs do not complete successfully, the exact same commands (and setup file) can be used to submit and monitor the jobs again. The master script will only run jobs that have not yet been successful. The SIPHPT thus provides utilities to reduce manual job management activity associated with large parameter sweep jobs, availing users more time to concentrate on analysis. We describe the SIPHPT and provide information on where to find it on Department of Defense (DoD) high performance computing (HPC) Aeronautical Systems Center (ASC) and Army Research Laboratory (ARL) Major Shred Resource Center (MSRC) systems.

SAR data exploitation: computational technology enabling SAR ATR algorithm development

A fundamental issue with synthetic aperture radar (SAR) application development is data processing and exploitation in real-time or near real-time. The power of high performance computing (HPC) clusters, FPGA, and the IBM Cell processor presents new algorithm development possibilities that have not been fully leveraged. In this paper, we will illustrate the capability of SAR data exploitation which was impractical over the last decade due to computing limitations. We can envision that SAR imagery encompassing city size coverage at extremely high levels of fidelity could be processed at near-real time using the above technologies to empower the warfighter with access to critical information for the war on terror, homeland defense, as well as urban warfare.