Model-Derived Multisensor Target Discrimination
Mark A. Vogel, Tony C. Chan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1989
Prediction of target signatures using complex computer models of targets and sensor characteristics permits the development of robust target discrimination algorithms in the absence of large data collections. This paper describes the methodology of employing model-derived data in multisensor target discrimination activities. The signature predictions not only provide reference data for algorithms but also aid in the design optimization of multisensor collection configurations. Due to their ability to process more information, multisensor target recognition algorithms are expected to outperform single sensor designs. Development of multisensor algorithms is hampered by extensive requirements for registered training and reference data. By relying heavily on predicted signatures for sensor parameter selection and algorithm development, the required quantity of sensed data collection is greatly reduced. The usefulness of the collected data is focused and enhanced by examination of modeling and validation requirements. Final expansion of algo-rithm designs from straightforward pattern recognition approaches to more highly-evolved model-based recognition concepts is more easily bridged when model-derived data plays a significant role at each developmental stage.