Medium-high correlated Weibull-distributed clutter reduction by neural networks in coherent radar systems

This paper presents a clutter reduction system when medium-high correlated Weibull-distributed clutter governs the environment of a coherent radar system. This proposal is based on the capabilities of learning of some artificial intelligence techniques, such as the neural networks. This capability of learning of the neural networks is used to learn some statistical characteristics of the radar environment. The results obtained with this proposal show how the desired signals (targets) are emphasized with respect to the environmental interference (clutter), which is reduced. Moreover, several advantages are found if it is compared with other classical approaches to reduce the clutter level, such as Target Sequence Known A Priori techniques.