In this article we will delve into the architecture, features, and training process of the Spot Jamming DetectionNet developed for the FreeScopes environment. This specialized model is designed to detect spot jamming in radar systems and accurately localize targets, even under challenging conditions of intentional interference.
In previous articles we introduced a Radar Classification model and its deployment.
The Importance of Spot Jamming Detection in ATC
In Air Traffic Control (ATC), reliable detection and localization of targets—such as aircraft—is crucial for ensuring safety and operational efficiency. Jamming, a common electronic warfare technique, disrupts radar systems with noise or deceptive signals, complicating target identification. By deploying the Spot Jamming DetectionNet, ATC systems gain a robust tool to mitigate these effects and maintain accurate target tracking in real-time scenarios.
Model Workflow and Preprocessing
The Spot Jamming DetectionNet begins its workflow with the LoadInputData block, which loads recorded radar data and corresponding position labels. These inputs form the foundation of the training dataset, enabling the model to learn how to distinguish between genuine targets and jamming signals.
The data is then processed in the PreProcessInputData block, where several critical steps are performed:
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Normalization: The IQ (in-phase and quadrature) data from the radar simulator is normalized to reduce variability and ensure consistency.
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Feature Extraction Using the Welch Method: This involves calculating advanced features from the IQ data to distinguish jamming signals from genuine targets:
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Power Spectral Density (Pxx): Reveals power distribution across frequencies, helping identify anomalies.
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Centroid: Indicates the dominant frequency where genuine signals are likely present.
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Bandwidth: Differentiates the frequency range characteristics of real targets versus jamming signals.
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Entropy: Quantifies signal randomness, highlighting jamming noise.
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Time-Frequency Spectrogram (Sxx): Provides insights into signal evolution over time, useful for detecting jamming patterns.
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Mean and Variance of IQ Data: Helps assess baseline signal strength and fluctuations caused by jamming.
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Kurtosis and Skewness of IQ Data: Highlights unique features of targets versus noise.
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These features empower the model to differentiate between legitimate target signals and the effects of spot jamming. Additionally, Gaussian labeling is applied to position labels, smoothing the position information for improved localization accuracy under interference conditions.
Model Architecture
The Spot Jamming DetectionNet follows a carefully structured architecture to perform its dual tasks:
Feature Extraction
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Conv1D Block: Extracts spatial patterns with a kernel size of 20x1x16, 16 biases, and a ReLU activation function. This block identifies features indicative of genuine targets versus jamming signals.
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Flatten Block: Reshapes features into a 1D vector for processing by dense layers.
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Dense Block: Learns high-level representations with 256 neurons, 256 biases, and a ReLU activation function.
Dual Task Branches
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Localization Branch:
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PositionInput Block: Features 64 units with a ReLU activation function.
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PositionOutput Block: Contains 400 softmax-activated units representing positions with a resolution of 0.05 meters over a 20-meter range. This branch outputs a probability distribution, accurately localizing the target even under jamming conditions.
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Detection Branch:
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DetectionInput Block: Features 256 units with a ReLU activation function.
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DetectionOutput Block: A single sigmoid-activated unit performs binary classification to determine whether a target is present (Class 1) or absent (Class 0).
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Training and Performance Evaluation
The training process, conducted over 10 epochs, highlights the model’s ability to learn and adapt:
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Loss Function: Decreases steadily, indicating successful minimization of errors.
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Accuracy: Improves consistently, reflecting the model’s growing competence in detecting targets and mitigating jamming effects.
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Confusion Matrix: Provides a clear evaluation of the model’s effectiveness in differentiating genuine targets from jamming signals, demonstrating readiness for real-world deployment.
Practical Implications
This Spot Jamming DetectionNet is a significant advancement for ATC and other radar systems, ensuring robust detection and localization of targets even in the presence of jamming signals. Its architecture and training process exemplify the application of AI in solving real-world challenges, reinforcing operational safety and efficiency.
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AI Addressing Complex Challenges
The Spot Jamming DetectionNet exemplifies the power of AI in addressing complex challenges in radar systems. By combining innovative preprocessing techniques, structured architecture, and dual-task capabilities, this model delivers reliable performance in detecting and localizing targets under jamming conditions. Thank you for exploring this article, and we look forward to sharing more insights in the next part of this series.
Continue with Part 5
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Stay Tuned
Stay connected with our ongoing publications on Artificial Intelligence as we continue to explore its impact on Air Traffic Control. Our aim is to contribute to the evolving conversation around AI’s role in ATC, particularly the shifting responsibilities and qualifications of ATSEP. This will help ensure readiness for the challenges ahead in this important field.
