HRRPGraphNet: Make HRRPs to Be Graphs for Efficient Target Recognition

TL;DR

HRRPGraphNet transforms HRRP data into graph structures to improve target recognition accuracy and robustness, especially with limited training data, by leveraging graph-based feature extraction methods.

Contribution

The paper introduces HRRPGraphNet, a novel graph-based approach for HRRP recognition that captures internal relationships of range cells, enhancing performance over sequence-based models.

Findings

Achieved superior accuracy on electromagnetic simulation dataset.

Demonstrated robustness with limited training samples.

Outperformed traditional sequence-based methods.

Abstract

High Resolution Range Profiles (HRRP) have become a key area of focus in the domain of Radar Automatic Target Recognition (RATR). Despite the success of deep learning based HRRP recognition, these methods needs a large amount of training samples to generate good performance, which could be a severe challenge under non-cooperative circumstances. Currently, deep learning based models treat HRRP as sequences, which may lead to ignorance of the internal relationship of range cells. This letter introduces HRRPGraphNet, whose pivotal innovation is the transformation of HRRP data into a novel graph structure, utilizing a range cell amplitude(hyphen)based node vector and a range(hyphen)relative adjacency matrix. This graph(hyphen)based approach facilitates both local feature extraction via one(hyphen)dimensional convolution layers, global feature extraction through a graph convolution layer and a attention module. Experiments on the aircraft electromagnetic simulation dataset confirmed HRRPGraphNet superior accuracy and robustness, particularly in limited training sample environments, underscoring the potential of graph(hyphen)driven innovations in HRRP(hyphen)based RATR.