  This paper introduces an innovative deep learning-based method for end-to-end
target radial length estimation from HRRP (High Resolution Range Profile)
sequences. Firstly, the HRRP sequences are normalized and transformed into GAF
(Gram Angular Field) images to effectively capture and utilize the temporal
information. Subsequently, these GAF images serve as the input for a pretrained
ResNet-101 model, which is then fine-tuned for target radial length estimation.
The simulation results show that compared to traditional threshold method and
simple networks e.g. one-dimensional CNN (Convolutional Neural Network), the
proposed method demonstrates superior noise resistance and higher accuracy
under low SNR (Signal-to-Noise Ratio) conditions.
