@article {15912,
	title = {Compressed Synthetic Aperture Radar},
	journal = {IEEE Journal of Selected Topics in Signal Processing},
	volume = {4},
	year = {2010},
	month = {2010/04//},
	pages = {244 - 254},
	abstract = {In this paper, we introduce a new synthetic aperture radar (SAR) imaging modality which can provide a high-resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. This new imaging scheme, requires no new hardware components and allows the aperture to be compressed. It also presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements.},
	keywords = {Compressed sensing, compressed sensing (CS), compressive sensing, electromagnetic waveforms, image resolution, radar imaging, SAR imaging modality, synthetic aperture radar, Synthetic aperture radar (SAR), targets spatial distribution},
	isbn = {1932-4553},
	doi = {10.1109/JSTSP.2009.2039181},
	author = {Patel, Vishal M. and Easley,G. R and Healy,D. M and Chellapa, Rama}
}
@article {12463,
	title = {Compressive Video Acquisition, Fusion and Processing},
	year = {2010},
	month = {2010/12/14/},
	abstract = {Modern developments in sensor technology, signal processing, and wireless communications have enabled the conception and deployment of large-scale networked sensing systems spanning numerous collection platforms and varied modalities. These systems have the potential to make intelligent decisions by integrating information from massive amounts of sensor data. Before such benefits can be achieved, significant advances must be made in methods for communicating, fusing, and processing this evergrowing volume of diverse data. In this one-year research project, we aimed to expose the fundamental issues and pave the way for further careful study of compressive approaches to video acquisition, fusion, and processing. In doing so, we developed a theoretical definition of video temporal bandwidth and applied the theory to compressive sampling and reconstruction. We created a new framework for compressive video sensing based on linear dynamical systems, lowering the compressive measurement rate. Finally, we applied our own joint manifold model to a variety of relevant image processing problems, demonstrating the model{\textquoteright}s effectiveness and ability to overcome noise and occlusion obstacles. We also showed how joint manifold models can discover an object{\textquoteright}s trajectory, an important step towards video fusion.},
	keywords = {*DATA FUSION, *DETECTORS, *SIGNAL PROCESSING, *VIDEO SIGNALS, ACQUISITION, COLLECTION, COMMUNICATION AND RADIO SYSTEMS, COMPRESSIVE PROPERTIES, COMPRESSIVE SAMPLING, compressive sensing, COMPRESSIVE VIDEO, decision making, DEPLOYMENT, DETECTION, DYNAMICS, IMAGE PROCESSING, Linear systems, Linearity, MANIFOLDS(ENGINES), measurement, MISCELLANEOUS DETECTION AND DETECTORS, MODELS, sampling, THEORY, TRAJECTORIES},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA533703},
	author = {Baraniuk,Richard G. and Chellapa, Rama and Wakin,Michael}
}
