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Proceedings Paper

Force detection and identification system
Author(s): Michael A. Pagels; John F. Gilmore
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Paper Abstract

The Force Detection and Identification System (FDIS) provides an operationally capable near-real-time platform appropriate for the evaluation of state-of-the-art imagery exploitation components and architectural design principles on scalable platforms. FDIS's architectural features include: a highly modular component design allowing rapid component interchange, multiple intercomponent datapaths which support both fine- and course-grained parallelism, an infrastructure which supports heterogeneous computing on a range of high-performance computing platforms, and conceptual decoupling between image processing and non-image processing components while supporting multi-level evidence fusion. While none of these features are individually unique, when combined they represent a state-of-the-art imagery exploitation system. FDIS has demonstrated probability of detection and false alarm rates consistent with other SAR-based exploitation systems. FDIS, however, requires fewer computing resources, supports rapid insertion of new or changed components to support emerging technologies with an ease not encountered in legacy systems, and is smoothly scalable. In addition to exploiting these novel architectural features, FDIS includes a new multi-dimensional evidence fusion component; Force Estimation (FE). Previous exploitation systems have demonstrated the positive impact on probability of detection and false alarm rates obtained by clustering vehicle detections into groups. FE, however, as a fusion component extends evidence accrual beyond simple spatial characteristics. Based on a fast multipole algorithm, FE accrues probabilistic evidence on models of military unit compositions. Fused evidence includes vehicle classifications, cultural and terrain features, and electronic emission features. FE's algorithmic speed allows operation in near-real-time without requiring excessive computational resources. FE has demonstrated improved force detection results over a wide range of operational conditions.

Paper Details

Date Published: 16 August 2001
PDF: 6 pages
Proc. SPIE 4380, Signal Processing, Sensor Fusion, and Target Recognition X, (16 August 2001); doi: 10.1117/12.436970
Show Author Affiliations
Michael A. Pagels, Veridian Systems, Inc. (United States)
John F. Gilmore, Veridian Systems, Inc. (United States)


Published in SPIE Proceedings Vol. 4380:
Signal Processing, Sensor Fusion, and Target Recognition X
Ivan Kadar, Editor(s)

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