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Received:April 17, 2015 Revised:June 08, 2015
Received:April 17, 2015 Revised:June 08, 2015
中文摘要: 分析了QAR数据中影响飞机性能衰减的主要因素,采用自适应加权数据融合算法和扩展卡尔曼滤波算法对相关性能参数进行了状态参量的估计,并验证了自适应加权融合算法在外界环境影响较小时的便捷性和外界环境影响过大时的局限性.引入扩展卡尔曼滤波算法,加入高斯噪声的计算,提高了状态估计值的精确度,为航空公司改善飞机运行提供了参考.
Abstract:This paper analyses the main factors affecting aircraft performance degradation in QAR data, uses the optimal weighted data fusion algorithm and the extended Calman filter algorithm for the estimationof state parameter which are related to aircraft performance parameters. When there is less external environment effect, the convenience of the optimal weighted fusion algorithm is verified, and when the effect is overlarge, the precision of this algorithm is limited. Thus we use the extended Calman filter algorithm, take Gauss noise into account, and improve the accuracy of state estimate for the airline to improve the operation of the aircraft provides a reference.
keywords: QAR data fusion algorithm adaptive weighted fusion extended calman filter aircraft performance
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基金项目:国家科技支撑计划(2011BAH24B10);中国民航大学科研基金(08CAUC-E08);中国民航大学科研启动基金(08QD16X);中央高校基本科研业务费(3122014D042)
引用文本：
谷润平,黄磊,赵向领.QAR数据的数据融合算法.计算机系统应用,2016,25(1):136-140
GU Run-Ping,HUANG Lei,ZHAO Xiang-Ling.Data Fusion Algorithm Based on QAR Data.COMPUTER SYSTEMS APPLICATIONS,2016,25(1):136-140
谷润平,黄磊,赵向领.QAR数据的数据融合算法.计算机系统应用,2016,25(1):136-140
GU Run-Ping,HUANG Lei,ZHAO Xiang-Ling.Data Fusion Algorithm Based on QAR Data.COMPUTER SYSTEMS APPLICATIONS,2016,25(1):136-140
