Scattering response modeling scheme based on combined neural network inspired by the equivalent scattering center

Abstract

A novel scheme is proposed in this paper to model the complex scattering pattern of radar target with a small training data set. By employing the ideal equivalent scattering center as transfer function, the frequency domain response can be represented by series of parameters so that the aspect and frequency domain dependency can be decoupled, and modeled, independently. In specific, neural network is employed to model the aspect dependency considering the complexity. To maintain the continuity of transformed parameters, a parameter extraction algorithm based on the Orthogonal Matching Pursuit is designed. With the same amount of training set, the proposed scheme exhibits a much better performance than the existing representative modeling techniques such as Geometrical Theory of Diffraction (GTD)-based model, the polynomial scattering center model and so on. At the same time, the training speed of the proposed model is also faster than those techniques.