Neural Network For Exo-Atmospheric Target Discrimination

In response to a threat missile, an interceptor missile with a kinetic warhead (KW) is launched with the intention of intercepting and killing the lethal reentry vehicle (RV) in the exo-atmosphere before it reaches its target. Data from an IR sensor on-board the KW is to be used to discriminate the RV from the other pieces in the field of view. A time-delay neural network (TDNN) is proposed for discrimination. A TDNN was trained using simulated data, and tested using simulated and flight data. The flight data includes IR signatures for RVs, boosters, and thrust termination debris. The TDNN is able to distinguish RVs from other missile parts and debris. This paper describes the performance of a TDNN for discrimination in ballistic missile defense when tested using flight data.