01445nas a2200205   4500000000100000000000100001008004100002260001200043653003100055653001500086653003100101100003800132700003500170245009100205856009800296300000900394490000600403520081600409022001401225       2016                            d  c12/201610aArtificial Neural Networks10aEstimation10aPareto Distributed Clutter1 aJesús Concepción Bacallao-Vidal1 aJosé Raúl Machado-Fernández00aImproved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/11/ijimai20164_2_1_pdf_46986.pdf  a7-110 v43 aThe main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE). The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications.  a1989-1660