Abstract
Atmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networks
Highlights
Prediction of radar coverage is a critical issue for military maritime and air surveillance
We propose a couple of novel hybrid models in which Genetic Algorithms (GA) and Artificial Neural Network (ANN) are designed to collaborate dynamically and have a potential of providing high performance
The success rate falls below 80% again for nine and higher values of ANN contribution, similar to Standard hybrid model (sHM) estimations
Summary
Prediction of radar coverage is a critical issue for military maritime and air surveillance. In our previous study [15], in order to estimate the M profile of the atmosphere from propagation factor curve, obtained from sea surface level radar clutter data, a cascade model of ANN and GA was presented where GA used the results obtained by the ANN only once. In the process of atmospheric refraction estimation, new estimates of the hybrid model are continuously added to the ANN dynamic training data set as new data. A more complex ANN structure has been designed to achieve faster predictive results with targeted accuracy Another main difference between the new study and the previous one comes from the increment of sample data representing the EM propagation factor curve. The proposed hybrid models with two different approaches (standard and adapted) are demonstrated, and all results are compared and discussed
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