An Enhanced Kalman Filter Algorithm for Multi-Sensor Data Fusion

This paper presents a novel enhanced Kalman filter algorithm specifically designed for multi-sensor data fusion applications. The key improvement lies in its sophisticated approach to handling inconsistent data from diverse sensors, enabling more accurate prediction outcomes during fusion processes. The algorithm incorporates adaptive covariance tuning mechanisms that dynamically adjust measurement noise matrices based on sensor reliability metrics. From an implementation perspective, it utilizes parallel processing architecture for real-time data integration, significantly improving computational efficiency while reducing memory requirements. We provide detailed explanations of the algorithm's mathematical foundation, including modified prediction and update equations that incorporate sensor confidence weights. The implementation leverages matrix decomposition techniques to optimize resource utilization, making it particularly advantageous for embedded systems with limited processing capabilities. Experimental results demonstrate superior performance in data fusion scenarios compared to conventional approaches. Furthermore, we explore potential applications in autonomous navigation, IoT sensor networks, and industrial monitoring systems, along with future research directions for adaptive threshold optimization and machine learning integration.