Improved Kalman Filtering and Adaptive Weighted Fusion Algorithms for Enhanced Multi-Sensor Data Fusion in Precision Measurement

Abstract

Multi-sensor data fusion plays a crucial role in achieving accurate and reliable measurements in precision measurement systems. This study focuses on the application of multi-source data fusion technology based on an improved Kalman filtering algorithm in precision measurement. The fundamental principles and structural models of multi-sensor data fusion are analyzed, highlighting the importance of effective fusion algorithms. Improvements are proposed for the weighted information fusion algorithm and the Kalman filtering fusion algorithm to enhance their performance in handling uncertainties and inconsistencies in sensor data. The improved weighted information fusion algorithm combines the Jackknife method with an adaptive weighting approach, while the improved Kalman filtering fusion algorithm incorporates a weight factor, a state transition matrix, a measurement transition matrix, and a process noise distribution matrix. The effectiveness of the improved algorithms is validated through simulations and practical applications, demonstrating significant improvements in estimation accuracy, precision, and robustness compared to traditional methods. The study also discusses the challenges and opportunities for further research in multi-sensor data fusion, including scalability, computational efficiency, and the integration of advanced techniques such as machine learning and deep learning. The findings contribute to the advancement of multi-sensor data fusion techniques and their applications in precision measurement, providing insights for future research and development.