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Proceedings Paper

Multisensor feature fusion methods and results
Author(s): David Heagy; Roswell Barnes; Eric R. Bechhoefer
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Paper Abstract

Analysts responsible for supporting time dominated threat decisions are faced with a growing volume of sensor data. Most efforts to increase discrimination among targets using multiple types of sensors encounter the same problems: · Sensor data are received in large volumes. · Sensor data are highly variable. · Signature features are represented by many dimensions. · Feature values are inter-correlated, random, or not related to target differences. · Decision rules for classifying new target data are difficult to define. This paper describes a new methodology for solving several problems: selecting signature features, reducing variability, increasing discrimination accuracy, and developing decision rules for classifying new target signatures. The results from using a combination of exploratory and multi-variate statistical techniques show potential improvements over the traditional Dempster-Shafer approach. This project uses data from operational prototype sensors and vehicles of interest for threat analysis. Acoustic and seismic sensor data came from an unattended ground sensor and three military vehicles. Although the resulting algorithms are specific to the data set, the data screening and fusion methods tested in this project may be useful with other types of sensor and target data.

Paper Details

Date Published: 22 March 2001
PDF: 15 pages
Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); doi: 10.1117/12.421109
Show Author Affiliations
David Heagy, MITRE Corp. (United States)
Roswell Barnes, MITRE Corp. (United States)
Eric R. Bechhoefer, MITRE Corp. (United States)


Published in SPIE Proceedings Vol. 4385:
Sensor Fusion: Architectures, Algorithms, and Applications V
Belur V. Dasarathy, Editor(s)

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