Detection of known targets in Weibull clutter based on Neural Networks. Robustness study against target parameters changes

The coherent detection of targets in presence of clutter and noise is considered in this study. Several clutter models are proposed in the literature, although the commonly used for sea and land clutter returns is the Weibull one. Our case of study involves that the target is known a priori, the clutter is Weibull-distributed and a white Gaussian noise is present. In this case, obtaining analytical expressions for the optimum detector is very difficult, so suboptimum solutions are taken as reference. One of this solutions is the target sequence known a priori (TSKAP) detector. This detector has several problems because it is designed for specific target and clutter parameters. So, in order to reduce these problems, a new solution is proposed, which is based in neural networks (NNs). The NNs selected are the MultiLayer Perceptrons (MLPs), which are able to learn from different environments. But, what does it happen if the radar (target or clutter) testing conditions are different of the design ones? In this case, a robustness study with respect to the target Doppler frequency is done for different radar conditions, which shows that the behavior of the proposed solution against this changes is better than the detector taken as reference, the TSKAP detector.