Neural net algorithm for target ID trained on simulated data

Christopher L. Howell; Kimberly Manser; Jeffrey Olson

doi:10.1117/12.2305660

26 April 2018 Neural net algorithm for target ID trained on simulated data

Christopher L. Howell, Kimberly Manser, Jeffrey Olson

Proceedings Volume 10625, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIX; 106250Q (2018) https://doi.org/10.1117/12.2305660
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Simulation-based training for target acquisition algorithms is an important goal for reducing the cost and risk associated with live data collections. To this end, the US Army Night Vision and Electronic Sensors Directorate (NVESD) has developed high-fidelity virtual scenes of terrains and targets using the DIRSIG in pursuit of a virtual DRI (detect, recognize, identify) capability. In this study, the NVESD has developed a neural network (NN) algorithm that can be trained on simulated data to classify targets of interest when presented with real data. This paper discusses the classification performance of a NN algorithm and the potential impact training with simulated data has on algorithm performance.

Citation Download Citation

Christopher L. Howell, Kimberly Manser, and Jeffrey Olson "Neural net algorithm for target ID trained on simulated data", Proc. SPIE 10625, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIX, 106250Q (26 April 2018); https://doi.org/10.1117/12.2305660

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