Autonomous learning approach for automatic target recognition processor

Tien-Hsin Chao; Thomas Lu

doi:10.1117/12.886145

26 April 2011 Autonomous learning approach for automatic target recognition processor

Tien-Hsin Chao, Thomas Lu

Proceedings Volume 8055, Optical Pattern Recognition XXII; 805502 (2011) https://doi.org/10.1117/12.886145
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

JPL is developing a comprehensive Automatic Target Recognition (ATR) system that consists of an innovative anomaly detection preprocessing module and an automatic training target recognition module. The anomaly detection module is trained with an imaging data feature retrieved from an imaging sensor suite that represents the states of the normalcy model. The normalcy model is then trained from a self-organizing learning system over a period of time and fed into the anomaly detection module for scene anomaly monitoring and detection. The "abnormal" event detection will be sent to a human operator for further investigation responses. The target recognition will be continuously updated with the "normal' input sensor data. The combination of the anomaly detection preprocessing module to the re-trainable target recognition processor will result in a dynamic ATR system that is capable of automatic detection of anomaly event and provide an early warning to a human operator for in-time warning and response.

Citation Download Citation

Tien-Hsin Chao and Thomas Lu "Autonomous learning approach for automatic target recognition processor", Proc. SPIE 8055, Optical Pattern Recognition XXII, 805502 (26 April 2011); https://doi.org/10.1117/12.886145

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