Journal Article

A Method for Few-Shot Radar Target Recognition Based on Multimodal Feature Fusion

Zhou, Yongjing, Zhu, Weigang, Li, Yonggang

2025

Overview

Enhancing generalization capabilities and robustness in scenarios with limited sample sizes, while simultaneously decreasing reliance on extensive and high-quality datasets, represents a significant area of inquiry within the domain of radar target recognition. This study introduces a few-shot learning framework that leverages multimodal feature fusion. We develop a cross-modal representation optimization mechanism tailored for the target recognition task by incorporating natural resonance frequency features that elucidate the target’s scattering characteristics. Furthermore, we establish a multimodal fusion classification network that integrates bi-directional long short-term memory and residual neural network architectures, facilitating deep bimodal fusion through an encoding-decoding framework augmented by an energy embedding strategy. To optimize the model, we propose a cross-modal equilibrium loss function that amalgamates similarity metrics from diverse features with cross-entropy loss, thereby guiding the optimization process towards enhancing metric spatial discrimination and balancing classification performance. Empirical results derived from simulated datasets indicate that the proposed methodology achieves a recognition accuracy of 95.36% in the 5-way 1-shot task, surpassing traditional unimodal image and concatenation fusion feature approaches by 2.26% and 8.73%, respectively. Additionally, the inter-class feature separation is improved by 18.37%, thereby substantiating the efficacy of the proposed method.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Artificial intelligence / Big Data / Classification / Comparative analysis / Data fusion / Deep learning / Design / Energy / few-shot learning / Methods / multimodal fusion / natural resonant frequency / Neural networks / Object recognition (Computers) / Pattern recognition / Physical properties / Radar systems / Signal processing / Target recognition / Technology application