Proceedings Article | 7 May 2007
KEYWORDS: Sensors, Data modeling, Computing systems, RGB color model, Target detection, Physics, Infrared sensors, Target recognition, Surveillance, Infrared radiation
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.
