The ability to discriminate a reentry vehicle (RV) from booster parts and other debris is critical to theater ballistic missile defense (TBMD). As it travels along its trajectory, a threat missile separates into a reentry vehicle (RV) and clutter. The latter consists of several tanks, separation debris and fragments of hot fuel. Interception of the RV requires discrimination of the RV from the clutter. The required discrimination must be performed no later than 30 seconds before intercept. A time-delay neural network (TDNN) is proposed for discrimination of the RV from other missile parts debris. The rate of change of the IR signature over several seconds is used as a discriminant. The performances of two different approaches are compared: 1) A TDNN that employs backpropagation weight updates is used to calculate activation levels for output nodes. The RV is selected via winner-take-all. A TDNN that updates weights using a cross-entropy error function with a softmax activation function is used to estimate assignment probabilities. The RV is subsequently selected via a probabilistic assignment algorithm that imposes the constraint that there can only be a single RV. We found that the TDNN employing backpropagated softmax learning performed better than the TDNN employing backpropagated least mean square learning.
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