Atmospheric Refractivity Estimation from Radar Sea Clutter Using Novel Hybrid Model of Genetic Algorithm and Artificial Neural Networks

This paper is focused on solving the inversion problem of refractivity from clutter (RFC) technique. A novel hybrid model is developed that can estimate the atmospheric refractivity (M profile) with a high accuracy, for surface based duct case, which is most effective non¬standard propagation condition on radar observation. The model uses propagation factor curve in horizontal axis, whose characteristics is determined by M profile for esti¬mation. The model is based on artificial neural network, which includes a dynamic training data approach, and a problem adapted genetic algorithm. Dynamic training data set application is a nonstandard approach in neural network applications, in which every obtained result are dynamically added to data set during the estimation pro¬cess, for a better estimation. Firstly, neural network and genetic algorithm have been adapted to the characteristics of inversion problem separately. Then, the mentioned two methods have been harmonized and run together. Ulti-mately, the final algorithm has evolved into a complex adapted hybrid model, which is easily applicable to clutter data obtained by any real radar from the real environment. The results show that the proposed model presents consid¬erably effective solution to refractivity estimation problem.