Review and Simulation-Based Analysis of Artificial Intelligence for Detection and Response to Air Threats
DOI:
https://doi.org/10.1590/jatm.v18.1457Keywords:
Artificial intelligence, Machine learning, Air defense, Target recognition, Decision support systems, Computerized simulationAbstract
The study aimed to analyze how artificial intelligence (AI) systems could be used to make air defense for critical infrastructure more effective. An analytical review method was used to systematically analyze how AI is used in air defense systems based on open sources from the United States, Israel, Ukraine, Germany, and South Korea, with an emphasis on the automation of processes for detecting, classifying, and prioritizing air threats for the period from 2020 to 2024. The study established that convolutional neural networks provide 92.8% accuracy in air target recognition, with an F1 score of 0.91. Random forest and eXtreme Gradient Boosting models achieved classification accuracies of 90.4% and 93.8%, respectively, at the final threat classification stage. The use of clustering algorithms (k-means, density based spatial clustering of applications with noise, DBSCAN) reduced the average time to determine coordinates to 1.4 seconds and the full response cycle to 2.1 seconds. The integration of AI into the centralized control system increased the probability of successful interception of air threats by 12-15%. The study provides analytical and simulation-based support for the potential use of AI systems to improve the effectiveness of air defense for critical infrastructure and identifies promising directions for the development of adaptive and automated defense technologies.
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