Multisensor feature fusion methods and results

David Heagy; Roswell Barnes; Eric R. Bechhoefer

doi:10.1117/12.421109

22 March 2001 Multisensor feature fusion methods and results

David Heagy, Roswell Barnes, Eric R. Bechhoefer

Proceedings Volume 4385, Sensor Fusion: Architectures, Algorithms, and Applications V; (2001) https://doi.org/10.1117/12.421109
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

David Heagy, Roswell Barnes, and Eric R. Bechhoefer "Multisensor feature fusion methods and results", Proc. SPIE 4385, Sensor Fusion: Architectures, Algorithms, and Applications V, (22 March 2001); https://doi.org/10.1117/12.421109

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