Deep distributed Kalman filter: an adaptive deep learning framework for distributed Kalman filtering

Kalman filtering is a widely used method for state estimation across various applications. Its distributed variant, the Distributed Kalman Filter (DKF), is crucial in decentralized systems, especially where sensor nodes have varying reliability. This paper introduces an Adaptive Deep Learning-based DKF that dynamically adjusts to changes in sensor reliability and network conditions. By integrating deep learning, the filter adapts in real-time, improving estimation accuracy in complex, heterogeneous environments. Simulations demonstrate the proposed approach’s enhanced performance over traditional DKF methods, making it a robust solution for decentralized applications in smart industries and IoT networks.