面向智能船舶的自校正加权融合估计算法
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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.
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.
引文
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[2]贺辞.CCS《智能船舶规范》六大功能模块要求[J].中国船检,2016(3):84-85.
[3]张崇猛,王戈,舒东亮,等.自适应滤波的舰船综合导航系统信息融合技术[J].火力与指挥控制,2012,37(7):39-42.
[4]牟立萍.舰船综合导航信息管理系统研究与设计[D].哈尔滨:哈尔滨工程大学图书馆,2011.
[5]YETENDJE A,DONA J A D,SERON M M.Multisensor fusion fault tolerant control[J].Automatica,2011,47(7):1461-1466.
[6]ZILIC Z.MSE minimization and fault-tolerant data fusion for multi-sensor systems[C]//Proc of the IEEE30th International Conference on Computer Design.Montreal:IEEE,2012:445-452.
[7]LI D,LIU J,LI O,et al.Fault tolerant navigation method for satellite based on information fusion and unscented Kalman filter[J].Journal of Systems Engineering and Electronics,2010,21(4):682-687.
[8]丁浩晗,冯辉,徐海祥,等.基于改进加权融合算法的动力定位数据融合[J].武汉理工大学学报:交通科学与工程版,2016,40(4):663-669.
[9]余伶俐.基于联邦滤波的多传感器主动容错估计方法[J].中国科技论文,2014(10):1124-1130.
[10]刘冠彤.传感器故障及其诊断技术[J].社会科学:文摘版,2016(6):180.
[11]胡绍林,傅娜,郭文明.动态测量数据的高保真容错Q-滤波算法[J].宇航学报,2016,37(1):112-117.
[12]徐海祥,冯辉.船舶动力定位系统原理[M].北京:国防工业出版社,2016.