RadarFuseNet: Complex-Valued Cross-Attention Fusion of Time-Frequency IQ Radar Features for Robust Classification

TL;DR

This paper introduces RadarFuseNet, a complex-valued cross-attention fusion network that enhances radar-based classification of materials and occluded objects by effectively combining IQ and FFT radar features, demonstrating high accuracy and generalization.

Contribution

It presents a novel bidirectional cross-attention fusion architecture using complex-valued CNNs for improved radar signal classification.

Findings

Material classification accuracy of 99.92% on known distances.

Occluded object classification accuracy of 94.20%.

Enhanced generalization to unseen measurement distances.

Abstract

Millimeter-wave (mmWave) radar has emerged as a compact and powerful sensing modality for advanced perception tasks that leverage machine learning. It is particularly effective in scenarios where vision-based sensors fail to capture reliable information, such as detecting occluded objects or distinguishing between different surface materials in indoor environments. Due to the nonlinear characteristics of mmWave radar signals, deep learning-based methods are well suited for extracting relevant information from in-phase and quadrature (IQ) data. However, the current state of the art in IQ signal-based occluded-object and material classification still offers substantial potential for further improvement. In this paper, we propose a bidirectional cross-attention fusion network that combines IQ signal and FFT-transformed radar features obtained by distinct complex-valued convolutional neural networks (CNNs). In our experiments, we achieve a material classification accuracy of 99.92% on samples collected at the same sensor distances used during training, and an accuracy of 65.56% on samples measured at previously unseen distances, demonstrating improved generalization across varying measurement conditions. Furthermore, our approach improves occluded object classification to 94.20%, outperforming all comparison and ablation models and underscoring the benefit of the proposed fusion strategy.