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Category: Signal ProceSSingposter
Si01contact name
mark Barnell: [email protected]
Rick Zuber, Chris Capraro SRC, Inc., North Syracuse, NY [email protected], [email protected]
Mark Barnell, Michael Little Air Force Research Laboratory, Rome, NY
[email protected], [email protected]
Introduction The exploitation of data to provide situational awareness and timely actionable intelligence is of great importance to the modern warfighter. The challenge today is improved wide-area persistent surveillance. This is emphasized in the Air Force’s Technology Horizons 2010-2030: A vision for Air Force Science and Technology [1] under Service Core Function 8: Global Integrate Intelligence, Surveillance and Reconnaissance (ISR).
This work describes an adaptable, portable, distributed analytics framework to provide technology options for both legacy and future ISR platforms to facilitate persistent surveillance given various Size, Weight, and Power (SWaP) and time constraints. We demonstrate our framework by performing real-time Synthetic Aperture Radar (SAR) processing on a hybrid Central Processing Unit (CPU) - Graphics Processing Unit (GPU) system. Our framework is a novel because it was developed with scalability and portability in mind, such that the same software can run on a massively parallel supercomputer or a modest embedded system.
Objectives Fully adaptable radars that have increased degrees of spatial, frequency and waveform diversity have unique and demanding data throughput and processing requirements. As this diversity and functionality increases (multi-function/multi-mission) more complex processing will be required. Specifically, SAR generally requires the collection and movement of large quantities of data. The collected data can be exploited in many different ways and the algorithms are generally computationally demanding. Our near-term objective: Our long term objective:
Synthetic Aperture Radar (SAR) SAR full backprojection (BP) is a common algorithm used to form SAR imagery from stripmap or spotlight collections. The algorithm is flexible, in that it can be used with data that was collected with any geometry. While faster image formation algorithms exist, they require motion compensation to a line or point, which can introduce image artifacts, especially in images whose scene extent is large compared to the slant range. However, the image quality of full BP comes with significant computational burden. As a result, the implementation of real-time full BP for high resolution images with wide scene extents is a formidable challenge. The basic flow of full BP algorithm is illustrated in Figure 1.
Figure 1. Full backprojection SAR algorithm
For an image pixel at location locxy, the image value ixy is given as follows. where D represents the 2-dimensional matrix of range compressed data samples (fast time, slow time), Npulses represents the number of pulses integrated into the image, λ represents the imaging wavelength, locapc represents the per-pulse antenna phase center location, locxy represents pixel location, and rxy represents the per-pulse platform-to-pixel range.
Video SAR (also called dynamic imaging) can be generated at an almost arbitrary frame rate with little additional computational cost compared with full BP SAR. In a video SAR application, subaperture images can be formed and saved to memory at an interval defined by the desired frame rate. These subimages are also accumulated from frame to frame to form a composite.
Other SAR products can be generated from the SAR images including Coherent Change Detection (CCD) and Ground Moving Target Indicator (GMTI) detections.
Air WASP Framework We are developing a framework that will provide technology options through our framework that will allow a rapid response to quickly changing computing needs. In our approach, hardware components such as CPUs and GPUs may be connected via various I/O connections and software components will have multiple data input and output connections to each other. This will allow us to connect components together in a variety of configurations. The advantage to our component model is flexibility to re-organize the system in order to optimize processing based upon the raw data, the finished information products, and the system constraints (e.g. time, power, storage, etc.). However, the challenge is understanding how to properly distribute each algorithm so as to minimize data movement and maximum processing throughput.
Figure 2. Air WASP system framework The overall Air WASP framework is represented in Figure 2. The approach is to use a distributed memory architecture such as MPI to pass data and control processing on each compute node. Our design uses a shared-memory architecture such as OpenMP on the CPUs in each compute node to handle multithreading processing. Furthermore, our idea facilitates the creation of new software components, which are added to a library and are accessible to the system when needed. Figure 3 represents one way of distributing processing for a full backprojection algorithm.
Figure 3. An example full BP SAR processing topology
Conclusions and Future Work The Air WASP framework is designed to aid in persistent surveillance by leveraging enabling technologies in the areas of hardware, software and algorithms, which are more modular, capable and interoperable and integrating them. Instead of developing a system for a specific task, we will provide technology options through our framework that will allow a rapid response to quickly changing computing needs. We demonstrate this by implementing a modular, real-time SAR processing system that allows tradeoffs between various quality attributes, processing speeds, and available hardware resources. Furthermore, the system is portable and has been run on multiple hybrid CPU-GPU processing platforms.
References [1] Technology Horizons. (2012). Retrieved December 10, 2012, from The Official web site of the U.S. Air Force: http://www.af.mil/information/technologyhorizons.asp
Results Preliminary results were obtained using AFRL’s Condor cluster. X-Band Data from AFRL’s layered sensing collection in August 2008 was processed at high resolution. Video SAR results are shown in Figure 4.
Figure 4. Preliminary Results The results represent a trade-off between quality and speed. No upsampling or side-lobe reduction was performed and all calculations were performed in single precision floating-point arithmetic. A video SAR frame is shown in Figure 5.
Figure 5. Video SAR frame
Develop an adaptable, portable, distributed analytics framework to provide technology
options for both legacy and future ISR platforms to facilitate persistent surveillance
given various SWaP and time constraints.
Generate SAR complex images of a 10-20 km circular spot at sub-meter resolution and at a ½ frame per second with less than 30 seconds of
latency.
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DISTRIBUTION A. Approved for public release; distribution unlimited (88ABW-20130425)
