In this article, we will explore the deployment of our trained TargetClassificationNet into the FreeScopes Platform and demonstrate its effectiveness in real-time target classification.
Real-Time Integration and Visualization
As established in the second article of this series, the model has been deployed and seamlessly integrated into the FreeScopes environment. This integration enables live visualization of classification results, offering an intuitive and dynamic representation of radar data.
Example 1: Classification of a Low-RCS Target
To begin, we introduce a static target with a low radar cross-section (RCS) of less than 1 m² into the SkySim simulator. The results are displayed on the PPI Scope 1, where an orange dot appears, indicating the presence of the detected target.
This orange dot signifies that the model has accurately classified the target as a low-RCS object. Complementary signal responses are also visible in the AScope 1 and PPI Scope 2, further validating the detection of this small target within the radar dataset.
Example 2: Classification of a High-RCS Target
In the second example, a target with a high RCS exceeding 10 m² is injected into the simulator. Upon introducing this high-RCS target, the orange dot in PPI Scope 1 disappears, and a green dot appears in its place at the corresponding location. This transition from orange to green demonstrates that the model has correctly classified the target as a high-RCS object.
These real-time visualizations highlight the precision and reliability of the TargetClassificationNet. The model’s ability to differentiate between low-RCS and high-RCS targets, represented dynamically through distinct color-coded markers, underscores its effectiveness in practical applications.
Practical Implications and Reliability
The successful deployment of the TargetClassificationNet demonstrates its readiness for real-world scenarios. By accurately classifying targets based on their radar cross-sections, the model proves itself as a valuable asset in radar systems where precise identification and classification are critical. This capability is particularly relevant for air traffic control (ATC) and military radar operations, where decision-making depends on accurate and timely information.
Watch the Video
Please note that from time to time we paused the simulation to give us more time to comment. This explains why from time to time the amplitude in the A-Scope freezes.
Advanced Applied Training on AI
In summary, the deployment of the TargetClassificationNet in the FreeScopes environment showcases the power and precision of AI-driven radar systems in dynamic environments. From the accurate classification of low-RCS to high-RCS targets, the platform’s real-time capabilities provide a foundation for advanced training and operational excellence. Thank you for exploring this demonstration, and we look forward to sharing more insights in our next publication.
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.
