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@article{189746,
        author = {Prashant Awasthi and Onkar Tiwari and Nirendra Tiwari},
        title = {AI-Based Satellite Swarm Surveillance for Autonomous Strategic Intelligence in National Defense},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {676-692},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189746},
        abstract = {In the face of rising asymmetric threats, space-based situational awareness is critical for national security. This paper proposes a fully autonomous, AI-enabled satellite swarm surveillance system capable of performing real-time strategic intelligence, threat detection, and anomaly analysis in Low Earth Orbit (LEO). Each satellite node in the swarm is equipped with onboard Convolutional Neural Networks (CNN) for image-based object recognition, Long Short-Term Memory (LSTM) models for spatio-temporal anomaly detection, and Reinforcement Learning (RL) agents for decentralized decision-making. The system enables inter-satellite coordination using a consensus-based mesh network with encrypted communication over Post-Quantum Cryptographic (PQC) protocols. We present a modular architecture supporting fault tolerance, autonomous task reallocation, and predictive path planning for persistent surveillance. Simulations conducted in MATLAB and STK validate our model's ability to maintain formation, process onboard inference, and respond to dynamic threats without ground intervention. The proposed system represents a significant step toward deploying sovereign, AI-governed orbital intelligence for defense.},
        keywords = {Satellite swarm, autonomous surveillance, defense AI, reinforcement learning, CNN, LSTM, post-quantum cryptography, inter-satellite communication, anomaly detection, mesh network, STK simulation, onboard inference, AI in space, military space systems},
        month = {January},
        }