International Journal of Science, Engineering and TechnologyAuthors: Mr. Akshai Vinu. K, Dr. F. Ramesh Dhanaseelan Professor, Dr. M. Jeya Sutha Associate Professor
Abstract: Distributed state estimation in nonlinear systems faces critical security and efficiency challenges, particularly under stealthy cyber-attacks and energy constraints. This project introduces a detection strategy for nonlinear consensus filters, allowing sensor nodes to verify local state estimates and error covariances to identify subtle intrusions. To enhance resource efficiency, an event-triggered distributed Cubature Kalman filtering (DKF) algorithm is proposed. Unlike traditional methods that require continuous data transmission, this approach activates updates only when necessary, significantly reducing communication overhead while maintaining estimation accuracy. Stability analysis confirms the reliability of the algorithm, ensuring robust performance even in adversarial conditions. Practical implementation in sensor networks demonstrates its effectiveness in mitigating stealthy attacks and optimizing energy consumption. By integrating advanced detection mechanisms with event-driven filtering, this work provides a secure, efficient, and resilient solution for nonlinear state estimation in distributed systems
