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.
This paper is a follow-up of a paperl given in the 1980 SPIE
Conference, "Modern Utilization of Infrared Technology VI".
A brief review is given of the earlier paper describing the
test target, "BTP#4". This target is used in estimating the
effects of various image chain processes, particularly
displays on intage quality for image exploitation. The paper
then presents an abstracted version of a study2, "Effects of
CRT Display Variable and Image Analysis" conducted by D.
Heagy and R. Holmes in 1982. This study supports the
hypothesis that the BTP#4 target scores can be used to
evaluate the quality of a monitor for presenting images for
exploitation. A correlation of .74 was found between average
BTP#4 scores and image interpretability quality ratings on a
monitor calibrated and set to 12 operating characteristics.
The paper concludes with some examples of BTP#4 applications
and coimuents on its advantages and disadvantages.
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