Using Cognitive Digital Twin Framework for Autonomous Target Discrimination

Neoskye proposes an advanced Cognitive Digital Twin (CDT) framework designed for Autonomous Target Discrimination, integrating data from space-based infrared sensors and land based radars. This innovative system addresses the limitations of current missile defense technologies through sophisticated sensor data integration, real-time processing, and adaptive learning techniques. Our CDT framework utilizes Bayesian inference and Monte Carlo simulations to handle sensor resolution differences, coverage disparities, and viewing geometry limitations. This ensures robust data fusion and accurately predicts target behavior under various conditions, such as different weather scenarios and sensor performance variations. Higher-level contextual information is integrated using Long Short-Term Memory (LSTM) networks and domain-specific ontologies, providing a comprehensive understanding of threat evolution and the environment. We implement edge computing near the sensors, such as land based radars and space-based infrared systems, to optimize real-time data collection and minimize transmission for preliminary data processing. Adaptive data sampling adjusts the collection rate based on threat levels, ensuring efficient bandwidth and resource usage. Reinforcement learning algorithms like Q-learning and Deep Q-Networks (DQN) facilitate continuous model updates, enhancing the system's ability to identify high-priority targets even with incomplete data. Bayesian networks are employed for advanced anomaly detection, ensuring timely responses to potential threats. LSTM networks and Bayesian optimization enable real-time decision-making, reducing decision latency to under one second. Using tools like MATLAB and user feedback platforms, continuous feedback and performance evaluation mechanisms ensure iterative improvements and high reliability. Neoskye’s CDT framework offers a scalable, modular solution, providing significant advancements in missile defense capabilities and ensuring effective and precise target discrimination in complex and evolving environments. Approved for Public Release | 25-MDA-12052 (27 Mar 25)