A Data-Driven Deep Neural Network for Modeling of Ionospheric Clutter in HFSWR

Abstract

In the practical application of HFSWR, ionospheric clutter has become the principal obstacle to target detection. Since ionospheric clutter has nonlinear and time-varying characteristics, the current suppression methods still require improvement. In this paper, we proposed a method merging the deep neural networks and linear Koopman operator from the dynamic perspective, combining the powerful learning capacity of neural networks and the interpretability of Koopman theory, and applied linear Koopman mode to represent nonlinear ionospheric clutter in the state space based on time-delayed coordinates to achieve the finite-dimensional linear approximate reconstruction of ionospheric clutter. The validation results of the measured data reveal that the method has satisfactory modelling accuracy, which proves the effectiveness of the approach and lays the foundations for the subsequent breakthrough in new methods for ionospheric clutter suppression.