Researchers led by Balu P. Bhusari of the Ramrao Adik Institute of Technology, DY Patil Deemed to be University in Navi Mumbai, together with colleagues including Akshay A. Jadhav of SIES Graduate School of Technology, have developed a Dual-Path Attention-Augmented Residual Network, abbreviated DA-ResNet. The model is built on a ResNet-50 backbone, a widely used convolutional neural network architecture known for its ability to train very deep layers without suffering from vanishing gradients. What sets the new approach apart is not raw depth but a series of carefully engineered modules that address the specific physics of low Jamming-to-Noise Ratio, or JNR, conditions, where the distinguishing features of a jamming waveform are largely obscured by noise.

The problem the team set out to solve is fundamentally one of feature extraction. Radar jamming signals come in many varieties, including deceptive jamming that mimics real target returns, barrage jamming that blankets entire frequency bands, and compound jamming that combines multiple techniques. Each type leaves a characteristic fingerprint in the time-frequency domain, the two-dimensional representation of how a signal’s energy is distributed across frequency over time. When the jamming-to-noise ratio is high, these fingerprints are crisp and easy for machine learning models to read. As JNR drops toward and below zero decibels, however, the fingerprints fade into the noise floor, and models trained on cleaner data begin to fail dramatically.

To combat this, the researchers equipped their network with a Gabor-initialized convolutional front-end. Gabor filters are mathematical functions long prized in signal and image processing for their ability to capture oriented textures and periodic patterns, precisely the structures that jamming waveforms leave in spectrogram images. By initializing the first convolutional layer with Gabor filters rather than random weights, the network starts with an innate sensitivity to the directional ridges and oscillatory patterns that distinguish one jamming type from another, even when those patterns are faint. This biologically inspired initialization, which echoes the receptive fields found in the human visual cortex, gives the model a head start that random initialization cannot provide.

The second key ingredient is attention. The architecture incorporates Squeeze-and-Excitation, or SE, attention blocks, which perform channel-wise feature recalibration. In practical terms, the network learns to weigh the importance of each feature channel dynamically, amplifying the channels that carry discriminative information about the jamming type and suppressing those dominated by noise. This adaptive suppression is critical in low JNR regimes, where a substantial fraction of the raw input energy is useless or misleading. Rather than treating every feature equally, the model effectively learns which parts of the signal representation deserve scrutiny and which should be tuned out.

The third innovation is a hybrid dual-path pooling strategy at the end of the network. Most convolutional classifiers collapse their final feature maps using global average pooling, which summarizes the overall energy distribution of a signal but can wash out brief, sharp events. Global max pooling does the opposite, preserving the strongest transient peaks but ignoring the broader energy structure. The DA-ResNet combines both, running the features through parallel paths that capture global energy distribution and transient peak characteristics simultaneously. Because different jamming types differ in different ways, some through their overall spectral shape and others through sudden spikes or chirps, this dual-path design ensures that neither kind of evidence is lost before classification.

The team evaluated the model on two independent datasets containing time-frequency representations of radar jamming signals, with jamming-to-noise ratios spanning an unusually demanding range from minus 10 decibels to plus 30 decibels. The results were striking. DA-ResNet achieved an overall recognition accuracy of 97.07 percent, outperforming existing state-of-the-art methods across the tested conditions. Importantly, the model demonstrated strong cross-SNR generalization, meaning it retained high accuracy when tested on signal conditions different from those seen during training, a property that matters enormously in real deployments where the interference environment cannot be predicted in advance. The authors also note that the model achieves this performance with moderate computational overhead, an important consideration for radar systems where processing latency and hardware budgets are constrained.

Beyond raw accuracy, the researchers subjected their network to a battery of diagnostic analyses designed to confirm that it was learning meaningful, separable representations rather than exploiting artifacts. Confusion matrices showed clean separation among jamming classes with limited cross-confusion. Receiver operating characteristic curves quantified the trade-off between detection sensitivity and false alarms across classes. A t-SNE visualization, a technique that projects high-dimensional feature embeddings into two dimensions, revealed tightly clustered groups corresponding to individual jamming types, indicating that the learned features are genuinely discriminative. Perhaps most compelling for practitioners, Grad-CAM heatmaps highlighted the specific regions of the time-frequency images that the network attended to when making its decisions, offering a form of explainable artificial intelligence that builds trust in the model’s judgments and helps engineers verify that the network is focusing on physically meaningful signal structures.

The significance of this work extends beyond a single benchmark. Electronic warfare is escalating as a domain of geopolitical competition, and the ability of a radar to recognize the jamming it faces in real time determines which countermeasures it can deploy. A radar that can correctly identify a deceptive sweep jamming at minus 10 decibels JNR can switch to appropriate anti-jamming processing, such as sidelobe blanking, frequency agility, or adaptive beamforming, before the deception succeeds. Models like DA-ResNet, which remain robust deep into the noise floor, could therefore translate directly into survivability advantages for both military platforms and critical civilian infrastructure such as air traffic control and GNSS-dependent navigation, where jamming and interference are growing concerns.

The study also reflects broader trends in applied machine learning. Rather than inventing an entirely new network family, the authors combined proven components, residual learning, Gabor-based initialization, squeeze-and-excitation attention, and dual pooling, into an architecture tailored to the physics of the problem. This pattern of physics-informed deep learning, where domain knowledge shapes the inductive biases of the model, is proving especially valuable in signal processing fields where training data is limited and noise is adversarial. The researchers, who received no external funding for the work and report no competing interests, suggest that their framework’s balance of accuracy, robustness, and computational efficiency makes it a practical candidate for integration into next-generation electronic counter-countermeasure systems, where every decibel of recognition capability recovered from the noise could make the difference between a radar that sees through the storm and one that is blinded by it.

Subject of Research: Deep learning-based recognition of radar jamming signals under low jamming-to-noise ratio conditions

Article Title: Dual-Path Attention-Augmented ResNet for Robust Radar Jamming Recognition under Low JNR Conditions

Article References: Bhusari, B. P., Jadhav, A. A., Somani, S., More, S., & Patil, S. (2026). Dual-Path Attention-Augmented ResNet for Robust Radar Jamming Recognition under Low JNR Conditions. Mobile Networks and Applications. https://doi.org/10.1007/s11036-026-02550-4

Image Credits: AI Generated

DOI: 10.1007/s11036-026-02550-4

Keywords: radar jamming recognition, deep learning, ResNet-50, attention mechanism, Squeeze-and-Excitation, Gabor filters, time-frequency analysis, low JNR robustness, electronic counter-countermeasures, explainable AI, Grad-CAM, dual-path pooling