传感技术学报2025，Vol.38Issue(10)：1775-1783,9.DOI:10.3969/j.issn.1004-1699.2025.10.007
改进自适应容积卡尔曼在温室数据鲁棒融合中的研究
Improved Adaptive Cubature Kalman for Robust Fusion of Greenhouse Data
摘要
Abstract
Targeting at the problems of low accuracy of greenhouse sensor acquisition and susceptibility to time-varying noise interfer-ence,a robust fusion algorithm for greenhouse data based on improved adaptive CKF is proposed.Firstly,the singular value decomposi-tion is used to replace the Cholesky decomposition in the standard CKF.Secondly,an adaptive factor is constructed to iteratively correct the covariance matrix.Thirdly,the volumetric transformation process is simplified by combining with the greenhouse observation model,which reduces the computational volume under the premise of guaranteeing the fusion performance.Finally,for the time-varying noise interference,the improved Sage-Husa algorithm and the sliding residual window factor are introduced to perform double adaptive adjust-ment for noise covariance array.The temperature is selected as the observation quantity for real measurement and simulation,and the re-al data collector is mainly composed of STM32F103C6T6 microprocessor and sensor modules.The traditional CKF,UKF and the pro-posed algorithm are fused for comparison experiments,and the results show that the proposed algorithm has a higher fusion accuracy and stronger fusion robustness,which has potential practical reference value.关键词
数据融合/自适应容积卡尔曼/奇异值分解/Sage-Husa算法/温室Key words
data fusion/adaptive cubature Kalman/singular value decomposition/Sage-Husa algorithm/greenhouse分类
信息技术与安全科学引用本文复制引用
沈家豪,李正权,邢松..改进自适应容积卡尔曼在温室数据鲁棒融合中的研究[J].传感技术学报,2025,38(10):1775-1783,9.基金项目
北京邮电大学网络与交换技术全国重点实验室开放课题资助项目(SKLNST-2023-1-13) （SKLNST-2023-1-13）
