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面向智能船舶的自校正加权融合估计算法 被引量:2

Self-tuning weighted fusion estimation method for intelligent ship
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摘要 针对智能船舶多传感器系统因未知海洋环境干扰和设备间干扰等因素,导致一个或数个传感器产生间歇性随机故障,进而导致融合估计结果出现偏差甚至失真的问题,设计了一种自适应加权融合估值算法,并引入了衰减记忆因子降低旧测量数据对融合估计的影响权重.为增强融合估值器对于量测故障信号的容错性,添加了故障检测与校正模块对量测信号进行检测与校正.为了验证算法的容错性能和融合估计的精度,对带有间歇性随机故障的三传感器系统进行了仿真实验,并与改进前的自适应加权融合结果进行了对比.结果表明:对于带间歇性随机故障的多传感器系统而言,设计的自校正加权融合估计算法不仅具有鲁棒性,而且具有较高的融合精度. In order to solve the problem that the estimation result of fusion was error or even distorted due to the random intermittent failure of one or several sensors,which resulted from unknown marine environment interference and equipment interference,an improved weighted fusion estimation method was designed,and the fading memory factor was introduced to decrease the effect weight of the old measurement data.And fault detection and correction(FDC) module was added to detect and correct the measurement signals so as to enhance the fault tolerance of the fusion estimator for measuring fault signals.In order to verify the effectiveness of the algorithm and the accuracy of the estimation,three sensor systems with intermittent faults were simulated and compared with the results of fusion estimation method before improvement.It is proved that the proposed fusion estimation method is not only robust to multi-sensor systems with intermittent faults,but also has high fusion accuracy.
作者 徐海祥 周志杰 韩鑫 李文娟 XU Haixiang;ZHOU Zhijie;HAN Xin;LI Wenjuan(Key Laboratory of High Performance Ship Technology,Ministry of Education,Wuhan University of Technology,Wuhan 430063,China;School of Transportation,Wuhan University of Technology,Wuhan 430063,China;Marine Equipment Technology Institute,Jiangsu University of Science and Technology,Zhenjiang 212003,Jiangsu China)
出处 《华中科技大学学报(自然科学版)》 EI CAS CSCD 北大核心 2019年第3期25-30,共6页 Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金 国家自然科学基金资助项目(51879210) 高性能船舶技术教育部重点实验室开放基金资助项目(2016gxnc01)
关键词 智能船舶 容错融合 融合估计 自适应加权 衰减记忆因子 intelligent ship fault tolerant fusion fusion estimation self-tuning weighted fading memory factor
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