Jamming Signal Classification in Radar Systems

Jamming signal classification lies at the heart of modern electronic warfare, enabling radar systems to distinguish between genuine echoes and deliberate interference. As adversaries deploy increasingly sophisticated deceptive and suppression jamming techniques—ranging from noise-like barrage jamming to coherent multi-pulse repeater jamming—accurate identification of the jamming type underpins adaptive counter-measures. Classical approaches rely on handcrafted signal features extracted in time, frequency or time-frequency domains, followed by statistical or machine-learning classifiers. In recent years, the advent of deep learning has shifted the focus towards automatic feature extraction using convolutional and recurrent neural networks, capable of handling high-dimensional representations such as spectrograms or raw IQ data. Key challenges include limited labelled data for rare jamming modes, rapid adaptation to novel emitter behaviours and real-time processing requirements on resource-constrained platforms. Progress in transfer learning, few-shot architectures and complex-valued networks promises enhanced generalisation across diverse scenarios. Ultimately, robust jamming classification not only supports agile waveform management and beamforming but also safeguards critical radar tasks including target detection, tracking and imaging.

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Jamming Signal Classification in Radar Systems publication trend

The graph below shows the total number of articles in jamming signal classification in radar systems across all publications each year (not limited to Nature Index journals).

Technical terms

Jamming: Deliberate transmission of signals to disrupt radar reception or interpretation.

Deception Jamming: Technique that creates false echoes or alters waveform parameters to mislead radar processing.

Suppression Jamming: Method that raises noise floor or sinks signal-to-noise ratio to mask genuine returns.

Convolutional Neural Network (CNN): Deep learning model using convolutional filters to extract hierarchical features from input data.

Siamese Network: Architecture that learns similarity metrics by comparing pairs of inputs through shared-weight subnetworks.

Short-Time Fourier Transform (STFT): Time-frequency analysis tool that computes local spectral content of non-stationary signals.

Complex-Valued Neural Network (CV-CNN): Neural model operating on complex input data to preserve phase information in signal processing.

References

1. Radar Jamming Recognition: Models, Methods, and Prospects. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024).
2. Convolutional Neural Network-Based Radar Jamming Signal Classification With Sufficient and Limited Samples. IEEE Access (2020).
3. Deep Fusion for Radar Jamming Signal Classification Based on CNN. IEEE Access (2020).
4. Fast Complex-Valued CNN for Radar Jamming Signal Recognition. Remote Sensing (2021).