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Published on: March 25, 2014
Energy-based jamming pattern open set recognition via spiking wavelet transformer
Xincheng Zhong1, Junbiao Cui2, Zhuangwei Ji1
1Department of Computer Science, Changzhi University, Changzhi, Shanxi, China.
This study introduces a new framework for jamming pattern recognition (JPR) to identify known and unknown jamming signals in communication systems. The novel approach enhances communication robustness by accurately classifying diverse jamming patterns.
Area of Science:
- Electrical Engineering
- Signal Processing
- Artificial Intelligence
Background:
- Jamming patterns (JPs) pose a significant threat to modern communication systems, disrupting operations in civil and industrial applications.
- Accurate identification of known JPs and detection of unknown JPs are crucial for maintaining reliable communication links.
Purpose of the Study:
- To address the challenge of jamming pattern open set recognition (JP-OSR).
- To propose a novel framework that effectively identifies known JPs and detects unknown JPs in communication signals.
Main Methods:
- Utilized a Spiking Wavelet Transformer (SWT) for high-resolution time-frequency feature extraction.
- Employed Hyperspherical Maximum Class Separation (MCS) to align features and enhance inter-class distinctions.
- Integrated an energy-based model with an adaptive threshold to handle feature variability across different Jamming-to-Signal Ratios (JSR) and JPs.
Main Results:
- The proposed framework demonstrated superior performance in jamming pattern open set recognition (JP-OSR).
- The SWT effectively captured intricate temporal and spectral characteristics of jamming signals.
- The adaptive thresholding mechanism enabled accurate recognition of both known and unknown jamming types under varying JSR conditions.
Conclusions:
- The novel framework integrating SWT, MCS, and adaptive thresholding is effective for JP-OSR.
- The method offers a robust solution for improving the resilience of communication systems against jamming.
- Simulation experiments validated the superiority of the proposed approach for jamming pattern recognition.
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