Md Arifur Rahman

Publication

Enhancing Ballistic Missile Defence: Comparing AI-Integrated Systems with Traditional Approaches

The Ballistic Missile Defence (BMD) systems play a significant role in national security, and are designed to detect, track and intercept incoming missiles. Beginning in the 1950s, they have undergone rapid evolution in consonance with the advancement in technology. Artificial Intelligence (AI), in particular, has proven to be a transformative force in bolstering the capabilities of BMD systems. Against this backdrop, this article analyses the efficiency and effectiveness of AI-integrated missile defence systems compared to traditional non-AI systems under different operational conditions and scenarios. Leveraging machine learning algorithms, neural networks and real-time data processing, AI increases detection accuracy, reduces false positives and improves interception success rates. The article relies on quantitative analysis based on t-tests, statistical performance analysis and simulations under diverse conditions. The findings indicate that AI-integrated systems significantly outperform traditional systems in detection latency, false positive rates and interception success. Furthermore, the article analyses the potentially vulnerable sites, challenges and ethical considerations related to AI integration in missile defence, stressing the need for human oversight in the decision-making process. This research underscores the strategic advantages and limitations of AI-enhancing defence capabilities against advanced missile threats.