Model-Derived Multisensor Target Discrimination

Mark A. Vogel; Tony C. Chan

doi:10.1117/12.948936

5 January 1989 Model-Derived Multisensor Target Discrimination

Mark A. Vogel, Tony C. Chan

Proceedings Volume 1003, Sensor Fusion: Spatial Reasoning and Scene Interpretation; (1989) https://doi.org/10.1117/12.948936
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States

Abstract

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.

Citation Download Citation

Mark A. Vogel and Tony C. Chan "Model-Derived Multisensor Target Discrimination", Proc. SPIE 1003, Sensor Fusion: Spatial Reasoning and Scene Interpretation, (5 January 1989); https://doi.org/10.1117/12.948936

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