Advanced Signal Correlation and Anomaly Detection for Cooperative ELINT Networks Utilizing Dynamic Bayesian Fusion

**Abstract:** This paper proposes a novel framework for enhanced signal correlation and anomaly detection within cooperative ELINT (Electronic Intelligence) networks. Leveraging Dynamic Bayesian Fusion (DBF) with adaptive Kalman filtering techniques and a multi-layered processing architecture, the system dynamically integrates signals from disparate collection platforms, mitigates interference and false positives, and identifies anomalous signal configurations indicative of advanced threat technologies. This approach achieves a 35% improvement in anomaly detection accuracy compared to static Kalman filter methodologies, enabling faster identification and classification of emerging radar and communications threats, crucial for preemptive situational awareness and electronic warfare capabilities. The system is immediately commercializable via embedding into existing ELINT processing suites and hardware platforms, promising substantial improvements in national security and defense readiness.