TY - JOUR
KW - Artificial Neural Networks
KW - Estimation
KW - Pareto Distributed Clutter
AU - Jesús Concepción Bacallao-Vidal
AU - José Raúl Machado-Fernández
AB - The 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.
IS - Regular Issue
M1 - 2
N2 - The 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.
PY - 2016
SP - 7
EP - 11
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks
UR - http://www.ijimai.org/journal/sites/default/files/files/2016/11/ijimai20164_2_1_pdf_46986.pdf
VL - 4
SN - 1989-1660
ER -