App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Efficient unmanned aerial vehicle signal classification method based on Mamba state space model

Wang Baiyanga
Fang Minga
Wang Xujiana
Wang Hongjuna
Wang Xindib
a. School of Information Science and Engineering, b. School of Artificial Intelligence, Shandong University, Jinan 250100, China
DOI: 10.19734/j.issn.1001-3695.2026.07.0169

Abstract

To meet the requirements of unmanned aerial vehicle (UAV) radio-frequency (RF) signal monitoring for classification accuracy, computational efficiency, and deployment adaptability, this study proposes an efficient state space model named Drone-Mamba. Drone-Mamba constructs a hybrid basic block that integrates convolutional operations, residual connections, multilayer perceptrons, and state space modeling to extract local time-frequency features, enhance long-range dependency modeling, and reduce computational overhead by exploiting the linear-complexity advantage of state space models. Experiments on a UAV RF signal classification dataset show that Drone-Mamba achieves accuracies of 100%, 99.00%, and 97.96% on binary, four-class, and ten-class tasks, respectively. On the ten-class task, it achieves a precision of 98.20%, a recall of 97.78%, and an F1-score of 97.75%. These results demonstrate that Drone-Mamba balances classification performance and computational efficiency and provides an efficient and feasible solution for UAV RF monitoring and intelligent identification in resource-constrained scenarios.

Foundation Support

信号快速检测与智能识别算法开发国家重点研发项目(2023YFF0717402)
山东省自然科学基金资助项目(ZR2025QC636)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.07.0169
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-22] Accepted Paper

Cite This Article

王百洋, 房明, 王绪建, 等. 基于Mamba状态空间模型的高效无人机信号分类方法 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.07.0169. (Wang Baiyang, Fang Ming, Wang Xujian, et al. Efficient unmanned aerial vehicle signal classification method based on Mamba state space model [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.07.0169. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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