Learning algorithms for suppressing motion clutter in airborne array radar

A relatively new approach to maximizing the probability of target detection in airborne antenna radar is to implement a linear filter called an adaptive space-time processor (STP). The authors explore the applicability of artificial neural networks and learning algorithms for minimizing the effect of motion clutter on target detection. Artificial neural networks are adaptive, parallel, distributed processing systems capable of performing complex computations in real time. Learning algorithms are the mechanisms by which the long-term memory in artificial neural networks is updated, but not destroyed, to accomodate new or changing information. Because learning algorithms retain information over the lifetime of the system, but are also modifiable, they can minimizing the computational requirements faced by radar systems, yet still adapt to changing environmental conditions.