Intelligent processing techniques are applied to a ballistic missile defense (BMD) application, focused on classifying the objects in a typical threat complex, using fused IR and ladar sensors. These techniques indicate the potential to improve designation robustness against "off-normal/unexpected" conditions, or when sensor data or classifier performance degrades. A Fused Sensor Discrimination (FuSeD) simulation testbed was assembled for designation experiments to evaluate test and simulation data, assess intelligent processor and classification algorithms, and evaluate sensor performance. Results were produced for a variety of neural net and other nonlinear classifiers, yielding high designation performance and low false alarm rates. Most classifiers yield a few percent in false alarm rate; rates are further improved when multiple techniques are applied vi a majority based fusion technique. Example signatures, features, classifier descriptions, intelligent controller design, and architecture are included. Work was performed for the Discriminating Interceptor Technology Program (DITP).
Intelligent fusion processing in BMD applications
1997
7 Seiten, 2 Quellen
Aufsatz (Konferenz)
Englisch
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