Papers by Elisa Shahbazian
The NATO Security Through Science Program and the Defence Investment Division requested and spons... more The NATO Security Through Science Program and the Defence Investment Division requested and sponsored the organization of a NATO Advanced Research Workshop (ARW) on the topic of Data Fusion Technologies for Harbour Protection, which was held June 27-July 1, 2005 in Tallinn, Estonia. The goal of the workshop was to help knowledge exchange between the technology experts and the security
Geoscience and Remote Sensing IEEE International Symposium, 1998
This paper describes an on-going effort to build an Adaptable Data Fusion Testbed (ADFT) based on... more This paper describes an on-going effort to build an Adaptable Data Fusion Testbed (ADFT) based on a Knowledge-Based System (KBS) BlackBoard (BB) architecture to perform data fusion of imaging and nonimaging sensors present on-board the CP-140 Canadian maritime patrol aircraft. The algorithms incorporate state-of-the-art tracking in clutter and evidential reasoning for target identification. The end result offers the user a
Sensor Fusion: Architectures, Algorithms, and Applications, 1997
This paper describes a phased incremental integration approach for application of image analysis ... more This paper describes a phased incremental integration approach for application of image analysis and data fusion technologies to provide automated intelligent target tracking and identification for Airborne Surveillance on board an Aurora Maritime Patrol Aircraft. The sensor suite of the Aurora consists of a radar, an Identification Friend or Foe (IFF) system, an Electronic Support Measures (ESM) system, a Spotlight Synthetic Aperture Radar (SSAR), a Forward Looking Infra-Red (FLIR) sensor and a Link-11 tactical datalink system. Lockheed Martin Canada (LMCan) is developing a testbed, which will be used to analyze and evaluate approaches for combining the data provided by the existing sensors, which were initially not designed to feed a fusion system. Three concurrent research proof-of-concept activities provide techniques, algorithms and methodology into three sequential phases of integration of this testbed. These activities are: (a) analysis of the fusion architecture (track/contact/hybrid) most appropriate for the type of data available, (b) extraction and fusion of simple features from the imaging data into the fusion system performing automatic target identification, and (c) development of a unique software architecture which will permit integration and independent evolution, enhancement and optimization of various decision aid capabilities, such as Multi-Sensor Data Fusion (MSDF), Situation and Threat Assessment (STA) and Resource Management (RM).
This paper describes a phased incremental integration approach for application of image analysis ... more This paper describes a phased incremental integration approach for application of image analysis and data fusion technologies to provide automated intelligent target tracking and identification for airborne surveillance on board an Aurora Maritime Patrol Aircraft. The sensor suite of the Aurora consists of a radar, an identification friend or foe (IFF) system, an electronic support measures (ESM) system, a spotlight
Information Fusion coordinates large-volume data processing machines to address user needs. Users... more Information Fusion coordinates large-volume data processing machines to address user needs. Users expect a situational picture to extend their ability of sensing events, movements, and activities. Typically, data is collected and processed for object location (e.g. target identification) and movement (e.g. tracking); however, high-level reasoning or situational understanding depends on the spatial, cultural, and political effects. In this paper, we explore opportunities where information fusion can aid in the selection and processing of the data for enhanced tacit knowledge understanding by (1) display fusion for data presentation (e.g. cultural segmentation), (2) interactive fusion to allow the user to inject a priori knowledge (e..g. cultural values), and (3) associated metrics of predictive capabilities (e.g. cultural networks). In a simple scenario for target identification with deception, cultural information impacts on situational understanding is demonstrated using the Technology-Emotion-Culture-Knowledge (TECK) attributes of the Observe-Orient-Decide-Act (OODA) model.
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Papers by Elisa Shahbazian