| Autor: |
Daniel A. Cooper, Richard Wronski, James B. Costales, Nathan D’Olympia, Krzysztof E. Kamieniecki, Robert J. Ledoux, Michael Gallagher, David Hempstead, Camille Monnier, Lauren Janney, Rustam Niyazov, Stephen E. Korbly |
| Rok vydání: |
2015 |
| Předmět: |
|
| Zdroj: |
2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). |
| DOI: |
10.1109/nssmic.2015.7581757 |
| Popis: |
A robust network of distributed sensors has been proposed in response to the Radiation Awareness and Interdiction Network (RAIN) Broad Agency Announcement (BAA) issued by DHS/DNDO in March of 2014. The testbed system is designed to detect, track, and identify potential threatening radiation sources in moving vehicles without interrupting the flow of traffic in typical highway scenarios. The algorithmic basis for the system depends on a number of data fusion methodologies to optimally combine and exploit multi-sensor, multi-modal data. Specifically, data-level fusion of radiation measurements is being used to enhance detection and identification of radiation sources, while extracted feature-level data from auxiliary video sensors is used both to improve computational speed and accuracy as well as provide operationally relevant source attribution information. An overview of the current development work will be provided in two parts: 1) A theoretical description of the data fusion algorithms and their expected utility will be provided; and 2) Performance results from both simulated and real measurements will be used to demonstrate the efficacy of a system using the proposed data fusion algorithms. The expected value of the testbed system using the advanced algorithms will also be discussed. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
