Exo-atmospheric Discrimination of Thrust Termination Debris and Missile Segments
نویسنده
چکیده
his article explores a time-delay neural network (TDNN) for exo-atmospheric discrimination of a missile reentry vehicle (RV) from other missile parts and thrust termination debris. The TDNN is an enhanced version of a back-propagation neural network that accounts for the features in the time domain by using the rate of change of the infrared signature over several seconds as a discriminant. We used simulated infrared signatures to train and test the TDNN on 90 randomly selected scenarios. Results showed that the TDNN could identify the RV in 97% of the cases, for a leakage rate of 3%; the false alarm rate (percentage of cases for which a non-RV was identified as an RV) was 5%. (
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