Abstract
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.
Highlights
Aradar scans the surrounding area emitting electromagnetic waves that produce echoes after being reflected on nearby objects
In the specific case of coastal and ocean exploration, the reflective properties of the sea surface result in the generation of unwanted echoes that may reach high magnitudes. The elimination of these echoes, known as sea clutter, is one of the main problems faced by naval radars whose objective is to detect targets like ships or low altitude aircraft [3]
The Pareto distribution has been used in modeling the income of a population [32] and in several fields of engineering [27, 28, 33], including sonar [34] and radar [16, 35, 36] applications
Summary
Aradar scans the surrounding area emitting electromagnetic waves that produce echoes after being reflected on nearby objects. In the specific case of coastal and ocean exploration, the reflective properties of the sea surface result in the generation of unwanted echoes that may reach high magnitudes. The elimination of these echoes, known as sea clutter, is one of the main problems faced by naval radars whose objective is to detect targets like ships or low altitude aircraft [3]. The neural network, which was designed, achieves a precise and low computational cost estimation of the Pareto shape parameter in a wide range of possible values. In “Conclusions and Future Research” the contributions of the paper are summarized and recommendations are given for future research lines
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