High range resolution radar target identification using multilayer feedforward neural network

In this paper range profiles offered by the high range resolution (HRR) radar are used as features and a multilayer feedforward neural network (MFNN) based classification scheme is presented for radar target identification (RTID). Typical experimental examples for identifying aircraft-type targets have shown that the range profiles can provide stable feature available for RTID, even in the situation where the change range of observing aspect angle is large. In addition, with a low ratio of learning patterns to test patterns, the trained network has good generalization capability.