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Suggested Citation:"VESTA/PARD TECHNOLOGY." National Research Council. 2004. Summary of the Sensing and Positioning Technology Workshop of the Committee on Nanotechnology for the Intelligence Community: Interim Report. Washington, DC: The National Academies Press. doi: 10.17226/11032.
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Page 19

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RADIO/RADAR/OPTICAL TAGS 19 TOPIC 5: RADIO/RADAR/OPTICAL TAGS Four papers were presented on these topics, by Mark Shellans, Pathfinder Technology, Inc.; Bill Hurley, Inkode; Dennis Prather, University of Delaware; and Stephen Griggs, DARPA. VESTA/PARD TECHNOLOGY Mark Shellans discussed two related technologies for the acquisition or analysis of data on subjects at considerable distances: vibro-electronic signature target analysis (VESTA) and passive acoustic reflection devices (PARDs). These technologies could be used to identify vehicles on the battlefield as well as communicate covertly with forces on the ground. The VESTA technology is based on the fact that physical motions of target objects will cause slight modulations of the reflected signals—for example, the sound vibrations of a moving vehicle modulate the return signal when the vehicle is illuminated by radar. Based on the modulated return, empirical, non-pattern-matching algorithms can be used to identify the class of vehicle and even to recognize a particular vehicle's unique vibrational pattern, provided the algorithm was previously “trained” on that vehicle's signal. Shellans's approach is based on the principle that the number of features that describe the physical state of a system can be represented by a multidimensional polynomial, and any arbitrary multidimensional polynomial can be matched by expansion of a McLauren series. A typical recognition problem might involve analyzing a polynomial surface in 12 dimensions; the sheer number of possible patterns in a library of templates for pattern matching would make a pattern-matching approach unmanageable. Instead, one must limit the dynamic range of the variables or cluster around variable values for targets one expects to see. Shellan's algorithm uses a “Twenty- Questions” approach to narrow down the phase space. The VESTA approach enables fine-grain discrimination of signals requiring orders of magnitude less storage and processing than would otherwise be needed to analyze the signal, and Shellans believes this property will make the technology more useful for nanoscale devices with limited processing capabilities. Shellans has used radar analysis techniques and recorded the acoustic characteristics of five different cars from a distance to train the system using a single 2- to 3-second scan of a Doppler radar. Later, the cars were driven past the detector many times in random order and could be identified with 100 percent accuracy. A similar technique was also used successfully with multiband, polarimetric synthetic aperture radar images of forested terrain to locate objects—for example, downed aircraft, tanks, or even cars—under the foliage. The technique can be applied to other kinds of sensor data, including biometric sensors. Shellans also described a passive acoustic reflection device that superimposes information onto reflected radar signals. A radar receiver would be able to extract the superimposed information using the same analytical methods used to identify unique vehicle sounds. Because the PARD is not a transmitter,

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The second activity performed by the NRC for the Intelligence Technology Innovation Center was a workshop to explore how nanotechnology might enable advances in sensing and locating technology. Participants at this workshop focused on tagging, sensing, and tracking applications of interest to the intelligence community. This report presents a summary of that workshop. In includes an overview of security technologies, and discussions of systems, natural chemical/biological tags, passive chemical/biological tags, and radio/radar/optical tags.

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