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

TL;DR

This paper presents a deep learning approach using GAF images and ResNet-101 for accurate target radial length estimation from HRRP sequences, outperforming traditional methods especially in noisy environments.

Contribution

The paper introduces a novel end-to-end deep learning method that transforms HRRP data into GAF images and fine-tunes a pretrained ResNet-101 for improved radial length estimation.

Findings

Superior noise resistance compared to traditional methods

Higher accuracy under low SNR conditions

Effective use of GAF images with deep CNNs

Abstract

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.