Research on Unmanned Aircraft Radio Frequency Signal Recognition Algorithm Based on Wavelet Entropy Features

LIU Bing; SHI Mingxin; LIU Jiaqi

doi:10.11999/JEIT250051

Volume 47 Issue 8

Aug. 2025

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LIU Bing, SHI Mingxin, LIU Jiaqi. Research on Unmanned Aircraft Radio Frequency Signal Recognition Algorithm Based on Wavelet Entropy Features[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2736-2745. doi: 10.11999/JEIT250051

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LIU Bing, SHI Mingxin, LIU Jiaqi. Research on Unmanned Aircraft Radio Frequency Signal Recognition Algorithm Based on Wavelet Entropy Features[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2736-2745. doi: 10.11999/JEIT250051

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Research on Unmanned Aircraft Radio Frequency Signal Recognition Algorithm Based on Wavelet Entropy Features

doi: 10.11999/JEIT250051 cstr: 32379.14.JEIT250051

LIU Bing^{1, 2
,
,},
SHI Mingxin¹,
LIU Jiaqi¹

1.
School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
2.
Key Laboratory of Technology and Equipment of Tianjin Urban Air Transportation System, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Natural Science Foundation of China (6240011305), The Key Laboratory of Technology and Equipment of Tianjin Urban Air Transportation System Open Fund (TJKL-UAM-202403), Shijiazhuang Vertical Take-off and Landing Fixed-wing UAV Intelligence Research Key Laboratory Open Fund (KF2024-2), The Foundation of Yunnan Key Laboratory of Unmanned Autonomous Systems (202501YB02)

Received Date: 2025-01-22
Rev Recd Date: 2025-04-15

Available Online: 2025-04-29

Publish Date: 2025-08-27

Abstract

Abstract

Objective With the rapid development and broad application of Unmanned Aerial Vehicle (UAV) technology, its use in military reconnaissance, agricultural spraying, logistics, and film production presents growing challenges in signal classification and safety supervision. Accurate classification of UAV Radio Frequency (RF) signals in complex electromagnetic environments is critical for real-time flight monitoring, autonomous obstacle avoidance, and communication reliability in multi-agent coordination. However, conventional recognition methods exhibit limitations in both feature extraction and classification accuracy, particularly under interference or multipath propagation, which severely reduces recognition performance and constrains practical implementation. To address this limitation, this study proposes a recognition algorithm based on wavelet entropy features and an optimized Support Vector Machine (SVM). The method enhances classification accuracy and robustness by extracting wavelet entropy features from UAV RF signals and optimizing SVM parameters using the Great Cane Rat Optimization Algorithm (GCRA). The proposed approach offers a reliable strategy for UAV signal identification under complex electromagnetic conditions. The results contribute to UAV airspace regulation and unauthorized flight detection and establish a foundation for future applications, including autonomous navigation and intelligent route planning. This work holds both theoretical value and practical relevance for supporting the secure and standardized advancement of UAV systems. Methods This study adopts a systematic approach to achieve accurate classification and recognition of UAV RF signals, including four key stages: data acquisition, feature extraction, classifier design, and performance verification. First, the publicly available DroneRFa dataset is selected as the experimental dataset. It contains RF signals from 24 mainstream UAV models (e.g., DJI Phantom 3, DJI Inspire 2) across three ISM frequency bands—915 MHz, 2.4 GHz, and 5.8 GHz (Fig. 1). Data collection follows a “pick-store-pick-store” protocol to preserve signal integrity and ensure accurate classification. During preprocessing, 50,000 sampling points are extracted from each channel (RF0_I, RF0_Q, RF1_I, RF1_Q), balancing data continuity and feature representativeness under hardware read/write constraints. Signal magnitudes are normalized to eliminate amplitude-related bias. For feature extraction, a three-level wavelet transform using the Daubechies “db4” wavelet is applied to decompose the signal at multiple scales. A four-dimensional feature matrix is constructed by computing wavelet spectral entropy (Figs. 2 and 3), which captures both time-frequency characteristics and energy distribution. Feature differences among UAV models are confirmed by F-test analysis (Table 1), establishing a robust foundation for classification. In the classifier design stage, the GCRA is applied to optimize the penalty parameter C and Gaussian kernel parameter σ of the SVM. The classification error rate serves as the fitness function during optimization (Fig. 5). Inspired by the foraging behavior of cane rats, GCRA offers improved global search performance. Finally, algorithm performance is evaluated using 10-fold cross-validation and benchmarked against unoptimized SVM, PSO-SVM, GA-SVM, and GWO-SVM (Table 3), demonstrating the robustness and reliability of the proposed method. Results and Discussions This study yields several key findings. For wavelet entropy feature extraction, the F-test confirms that features from all four channels are statistically significant (p < 0.05), demonstrating their effectiveness in distinguishing among UAV models (Table 1). In classifier optimization, the GCRA exhibits strong parameter search capability, with fitness convergence achieved within 50 iterations at approximately 0.03 (Fig. 6). The optimized SVM classifier reaches an average recognition accuracy of 98.5%, representing a 6.8 percentage point improvement over the traditional SVM (Table 3). At the individual model level, the highest recognition accuracy is observed for DJI Inspire 2 (99.0%), with all other models exceeding 97% (Table 2). Confusion matrix analysis indicates that all misclassification rates are below 3% (Table2, Fig. 7). Notably, under identical experimental conditions, GCRA-SVM outperforms other optimization algorithms—achieving higher accuracy than PSO-SVM (94.7%) and GA-SVM (94.2%)—with lower variance (±0.00032), indicating greater stability (Table 3). These results validate the discriminative power of wavelet entropy features and highlight the enhanced performance and robustness of GCRA-based SVM optimization. Conclusions Through systematic theoretical analysis and experimental validation, this study reaches several key conclusions. The wavelet entropy-based feature extraction method effectively captures the time-frequency characteristics of UAV RF signals. By employing multi-scale decomposition and energy distribution analysis, it accurately identifies the unique signal features of various UAV models. Statistical tests confirm significant differences among the features of different UAV categories, providing a solid foundation for feature selection in UAV identification. The optimization of SVM parameters using the GCRA substantially enhances classification performance, achieving an average accuracy of 98.5% and a peak of 99% on the DroneRFa dataset, with excellent stability. This method addresses the technical challenge of UAV RF signal recognition in complex electromagnetic environments, with performance metrics fully meeting practical application needs. The findings offer a reliable technical solution for UAV flight supervision and lay the groundwork for advanced applications such as autonomous obstacle avoidance. Future research may focus on evaluating the method’s performance in high-noise environments and exploring fusion strategies with other models. Overall, this study provides significant contributions both in terms of theoretical innovation and engineering application.
- Wavelet entropy,
- Optimization neural network,
- Unmanned Aerial Vehicle Radio Frequency (UAV RF) signal,
- Recognition algorithm

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References(20)

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