RCS Prediction for Flexible Targets with Uncertain Shape Based on CNN-LSTM

Traditional radar cross-section (RCS) prediction methods struggle with dynamically uncertain shapes of flexible targets, because they cannot disentangle intrinsic geometry from transient deformation, leading to degraded accuracy and prohibitive computational cost. To bridge this gap, we propose a dual-branch deep learning architecture that explicitly separates static geometric features from dynamic deformation characteristics, suppressing deformation noise in target identity representation. Training data are generated by coupling non-uniform rational B-spline (NURBS) parametric modeling with computational electromagnetics. The dynamic branch employs a one-dimensional convolutional neural network-long short-term memory-Transformer (1D-CNN-LSTM-Transformer) to extract temporal deformation features, while the static branch encodes baseline geometry via fully connected layers; their fused outputs deliver high-fidelity RCS predictions. Trained and tested on 1000 deformed metasurface samples, the proposed method achieves mean squared error (MSE) = 0.0541, root mean squared error (RMSE) = 0.2326 and coefficient of determination (R2) = 0.9997. The results demonstrate end-to-end accurate prediction under shape uncertainty, extending RCS modeling for flexible targets beyond recent studies that focus on static scenarios, and offering a reliable tool for flexible stealth design and high-resolution radar target recognition.