A Deep Learning-Based Target Radial Length Estimation Method through HRRP Sequence

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.

💡 Research Summary

The paper presents an end‑to‑end deep‑learning framework for estimating the radial length of a target from High‑Resolution Range Profile (HRRP) sequences. Traditional approaches treat HRRP as a one‑dimensional edge‑detection problem, relying on fixed threshold multiplication or FFT‑based super‑resolution techniques, which are highly sensitive to noise and require careful parameter tuning. To overcome these limitations, the authors first normalize each HRRP sequence to the