Airborne Application of Information Fusion Algorithms to Classification

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Authors
  1. Valin, P.
  2. Bossé, É.
  3. Jouan, A.
Corporate Authors
Defence R&D Canada - Valcartier, Valcartier QUE (CAN)
Abstract
The objective of the report is to survey the reasoning frameworks common in the artificial intelligence field for identity information fusion, and to select those that are appropriate to deal with dissimilar data coming from sensors involved in airborne data/information fusion. The Image Support Module (ISM) for the existing Forward-Looking Infra Red (FLIR) will make use of many of these reasoning frameworks in parallel, and actually fuse the results coming from these complementary classifiers. The ISM for the upcoming Spotlight Synthetic Aperture Radar (SSAR) will incorporate some of these reasoning methods in a hierarchical manner to provide multiple inputs to the Multi-Sensor Data Fusion (MSDF) module. The data used are a combination of simulated and real imagery for the SSAR and unclassified airborne data for the FLIR, obtained from the Naval Air Warfare Center at China Lake (USA) through the University of California at Irvine.

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Report Number
DRDC-VALCARTIER-TR-2004-282 — Technical Report
Date of publication
01 May 2006
Number of Pages
82
DSTKIM No
CA027339
CANDIS No
525415
Format(s):
CD ROM

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