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Proceedings Paper

Improving throughput for temporal target nomination using existing infrastructure
Author(s): Peter G. Raeth
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Paper Abstract

Earlier, we reported on predictive anomaly detection (PAD) for nominating targets within data streams generated by persistent sensing and surveillance. This technique is purely temporal and does not directly depend on the physics attendant on the sensed environment. Since PAD adapts to evolving data streams, there are no determinacy assumptions. We showed PAD to be general across sensor types, demonstrating it using synthetic chaotic data and in audio, visual, and infrared applications. Defense-oriented demonstrations included explosions, muzzle flashes, and missile and aircraft detection. Experiments were ground-based and air-to-air. As new sensors come on line, PAD offers immediate data filtering and target nomination. Its results can be taken individually, pixel by pixel, for spectral analysis and material detection/identification. They can also be grouped for shape analysis, target identification, and track development. PAD analyses reduce data volume by around 95%, depending on target number and size, while still retaining all target indicators. While PAD's code is simple when compared to physics codes, PAD tends to build a huge model. A PAD model for 512 x 640 frames may contain 19,660,800 Gaussian basis functions. (PAD models grow linearly with the number of pixels and the frequency content, in the FFT sense, of the sensed scenario's background data). PAD's complexity in terms of computational and data intensity is an example of what one sees in new algorithms now in the R&D pipeline, especially as DoD seeks capability that runs fully automatic, with little to no human interaction. Work is needed to improve algorithms' throughput while employing existing infrastructure, yet allowing for growth in the types of hardware employed. In this present paper, we discuss a generic cluster interface for legacy codes that can be partitioned at the data level. The discussion's foundation is the growth of PAD models to accommodate a particular scenario and the need to reduce false alarms while preserving all targets. The discussion closes with a view of future software and hardware opportunities.

Paper Details

Date Published: 7 May 2007
PDF: 12 pages
Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65670J (7 May 2007); doi: 10.1117/12.719439
Show Author Affiliations
Peter G. Raeth, Ball Aerospace & Technologies Corp. (United States)


Published in SPIE Proceedings Vol. 6567:
Signal Processing, Sensor Fusion, and Target Recognition XVI
Ivan Kadar, Editor(s)

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