Parameter Estimation of 3D-GTD Model Based on a Multi-Neural Network

In this article, a novel parameter estimation method of three-dimensional geometrical theory of diffraction(3D-GTD) model based on a multi-neural network is proposed. By leveraging multi-view one-dimensional geometrical theory of diffraction(1D-GTD) parameters as input, the three-branch neural network simultaneously estimates 3D scattering center parameters—position, intensity, and type. Position reconstruction branch employs geometric encoding and gated fusion for joint feature extraction, combined with hierarchical attention and coordinate regression. Scattering intensity estimation branch achieves efficient feature aggregation via multi-level convolutional layers and parameter sharing mechanism, enabling high-precision intensity parameter estimation while ensuring model lightweight. Type identification branch establishes a mapping from numerical features to categorical indices, which converts the regression task into a classification task that conforms to discrete value constraints. It employs a sub-channel processing architecture and a shared multi-layer perceptron (MLP) to effectively aggregate multi-view predictive information, thereby achieving accurate identification of scattering center types. To address the challenge of difficult acquisition of training data, we construct a self-generated dataset with random characteristics, thereby enhancing the generalization performance of the network. Experimental results show the proposed method outperforms traditional approaches in estimation accuracy across different signal-to-noise ratio (SNR) levels and computational efficiency.