A Lightweight Dual-Stream Convolutional Network Feature Fusion Method for UAV RF Recognition

DONG Pengyu; XIANG Xin; LV Siting; LIANG Yuan; WANG Rui; MAO Hu

doi:10.11999/JEIT260464

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DONG Pengyu, XIANG Xin, LV Siting, LIANG Yuan, WANG Rui, MAO Hu. A Lightweight Dual-Stream Convolutional Network Feature Fusion Method for UAV RF Recognition[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260464

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DONG Pengyu, XIANG Xin, LV Siting, LIANG Yuan, WANG Rui, MAO Hu. A Lightweight Dual-Stream Convolutional Network Feature Fusion Method for UAV RF Recognition[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260464

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A Lightweight Dual-Stream Convolutional Network Feature Fusion Method for UAV RF Recognition

doi: 10.11999/JEIT260464 cstr: 32379.14.JEIT260464

Air Force Engineering University, Xi’an, 710038, China

Received Date: 2026-04-30
Accepted Date: 2026-08-10
Rev Recd Date: 2026-07-31

Available Online: 2026-08-20

Abstract

Abstract

Objective With the rapid proliferation of Unmanned Aerial Vehicles (UAVs) and the escalating demand for airspace security, radio frequency (RF) fingerprint recognition has emerged as a pivotal technology for identifying non-cooperative UAVs. However, existing methods grapple with significant challenges, including poor robustness in low signal-to-noise ratio (SNR) environments and prohibitive computational complexity, which severely hinder their deployment on resource-constrained tactical edge devices. To address these critical limitations, this paper proposes a novel lightweight dual-stream convolutional network tailored for UAV RF recognition. This network is designed to extract static spectral texture features and dynamic temporal gradient features in parallel, complemented by a meticulously crafted lightweight feature fusion strategy. Methods The proposed network architecture is ingeniously designed to process RF signals. The input signal undergoes a Short-Time Fourier Transform (STFT) to generate a two-dimensional spectrogram, which serves as the primary input. The network is bifurcated into two parallel streams: a static stream and a dynamic stream. The static stream is engineered to capture the inherent static spectral patterns and energy distributions within the STFT spectrogram. It comprises a series of stacked convolutional blocks, each integrating convolutional layers, batch normalization, and ReLU activation functions, followed by max-pooling layers to progressively downsample the feature maps and increase the channel depth. Conversely, the dynamic stream is dedicated to enhancing feature discriminability, particularly in low-SNR scenarios. It begins by computing the temporal gradient of the input spectrogram, effectively suppressing static background noise and accentuating dynamic signal variations. This gradient map is then processed by a symmetric set of convolutional blocks, mirroring the structure of the static stream. To maintain model efficiency, an element-wise addition fusion strategy is employed to integrate the features from both streams, ensuring a balance between feature complementarity and computational overhead. The fused features are subsequently fed into a classification head, consisting of an adaptive average pooling layer, dropout layers for regularization, and fully connected layers to produce the final classification output. Extensive experiments are conducted on the publicly available DroneRF dataset, encompassing ablation studies to dissect the contribution of each component, comparative analyses of various fusion strategies, and rigorous evaluations of the model’s lightweight characteristics. Results and Discussions The experimental results unequivocally demonstrate the efficacy of the proposed method. The dual-stream network achieves a remarkable 95.65% accuracy on the test set, representing a substantial 5 percentage point improvement over the best-performing single-stream network. A critical analysis reveals that the temporal gradient operation contributes significantly to this enhancement by improving the average SNR by 1 dB, thereby bolstering feature discriminability in challenging low-SNR environments. Furthermore, the model’s lightweight design is a standout feature, with a mere 0.58 million parameters, making it eminently suitable for deployment on tactical edge devices. Ablation studies and feature visualization analyses provide compelling evidence for the complementary nature of static and dynamic features. The static stream adeptly captures broad spectral contours, while the dynamic stream focuses on fine-grained temporal variations. The element-wise addition fusion strategy proves superior, outperforming other approaches like feature concatenation and attention-based fusion in terms of both performance and computational efficiency, thereby validating the rationale behind the lightweight design. Conclusions This paper presents a comprehensive solution to the challenges of UAV RF recognition in complex environments by proposing a lightweight dual-stream convolutional network. The method effectively enhances recognition accuracy and robustness through the synergistic combination of dual-stream feature extraction and the SNR-enhancing properties of temporal gradient features, all while maintaining a lightweight architecture suitable for edge deployment. The proposed approach offers a significant advancement in the field, providing a robust and efficient solution for UAV identification. Future research endeavors will focus on further enhancing the model’s adaptability to complex electromagnetic environments, incorporating the effects of sensor noise, and extending the framework to multi-UAV cooperative scenarios.
- UAV RF Recognition,
- Lightweight Dual-Stream Convolutional Network,
- Feature Fusion,
- Temporal Gradient,
- Short-Time Fourier Transform (STFT)

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References(25)

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