Application Research and Improvement of Weighted Information Fusion Algorithm and Kalman Filtering Fusion Algorithm in Multi-sensor Data Fusion Technology

• Published in: Sensing and imaging Vol. 24; no. 1
• Main Author: Liu, Qiuxia
• Format: Journal Article
• Language: English
• Published: New York Springer US 16-11-2023
• Subjects: Electrical Engineering; Engineering; Imaging; Microwaves; Radiology; RF and Optical Engineering; Fusion algorithm; Simulations and analyses; Multi-sensor data fusion; Intelligent monitoring system; Data accuracy
• ISSN: 1557-2072; 1557-2072
• DOI: 10.1007/s11220-023-00448-z

In order to improve the accuracy and reliability of the collected data, the weighted information fusion algorithm and the Kalman filtering fusion algorithm in multi-sensor data fusion technology are researched and improved. Firstly, the definition of multi-sensor data fusion and the basic working principle of multi-sensor data fusion are expounded. Structural models of multi-sensor data fusion are introduced, the existing problems of multi-sensor data fusion are analyzed, the development trend of data fusion is pointed out. Secondly, the multi-sensor data fusion algorithms are analyzed, the weighted information fusion fusion algorithm and Kalman filtering algorithm in multi-sensor data fusion technology are focused on. Aiming at the deficiency of the weighted information fusion algorithm, an information fusion algorithm combining the jackknife method and the adaptive weighted method is proposed, and the basic steps of the improved fusion algorithm are given. The algorithm makes full use of the observed values and the estimated values of each historical moment, Quenouille estimation on the estimated values is performed by constructing pseudo-values. On the basis of the traditional Kalman filtering algorithm, an improved filtering algorithm is proposed, and a new state estimation equation is derived, which both treats the field value to prevent the filtering divergence, and introduces the observed value at the next moment to the state estimate at the current moment. Finally, improved fusion algorithms are applied and simulated in intelligent monitoring systems. Application and simulation results show that improved fusion algorithms are effective and superior, they have high reliability and anti-interference performances, the accuracy of the data is greatly increased, and they play a positive role in promoting the wide application of multi-sensor data fusion technology.