基于卡尔曼滤波的INS／USBL水下导航系统模型研究

简述惯性导航技术(INS)和超短基线定位技术(USBL),并分析了各自的优缺点.为了满足深海作业对导航定位高精度、高数据更新率的技术需求,提出基于卡尔曼滤波技术(Kalman Filter)实现INS和USBL的融合集成,研究了系统模型,进行了水下定位试验,给出了误差分析结果,证明了模型的可靠性.     

基于序列非线性滤波SAR影像水体自动提取

由于合成孔径雷达（SAR）图像中固有斑点噪声的强烈影响,某些对于光学图像有很好效果的目标提取区域分割方法,但是对于SAR图像来说,效果不很好,针对水体目标的亮度及形状分布特征,不作阈值分割处理,而采用序列非线性滤波处理方法,可以快速有效地实现SAR影像水体目标的自动提取识别。     

分数阶微积分在医学图像处理中的应用

简要介绍了分数阶微积分和医学图像的概念,然后从分数阶微分基本定义出发,得到了可以作用于二维医学图像的分数阶微分掩模,掩模可以根据对图像的需求进行增强,最后通过实验证明,这个方法可以有效完成对医学图像的处理,并且弥补了传统方法不能连续改变处理效果的缺点,是一种简单可行并且效果较好的图像增强方法。     

一种新ⅡR分数阶微分滤波器

从频域角度出发,分析了几种基于典型微分算子的ⅡR分数阶微分滤波器的数字实现,提出一种频率响应更接近理想微分的新算子,在此基础上运用连续分数扩充方法实现了分数阶微分滤波器的设计,并详细推导基于新算子的ⅡR分数阶微分滤波器的数字实现。实验证明,基于新算子的ⅡR分数阶微分滤波器频率响应整体上优于Tustin和Simpson等其他ⅡR分数阶滤波器。     

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基于趋势面的多波束测深数据滤波方法

简要的介绍了基于统计特性进行多波束测深数据滤波的方法,并对这些滤波方法的特点及实际应用效果进行了对比分析,在此基础上,提出了趋势面滤波法,并对其进行了实践验证,取得了理想的效果。     

单体喷水推进船舶动力定位控制方法研究

面向一型喷水推进器为动力定位执行机构的单体喷水推进船舶,开展了船舶仿真模型建立、推力分配及进一步动力定位相关控制算法设计与仿真模拟。针对传统的定步长Kalman滤波提出了改进方案,同时考虑到单体喷水推进船舶的特殊性,对推力分配算法进行了优化,并基于荷载前馈的PD控制算法开展了不同工况下的仿真试验。试验结果表明,所提出的喷水推进推力分配算法能够有效控制一型单体喷水推进船舶实现定点、定艏向、移位、转艏等动力定位典型功能,并具备一定抗外部干扰能力。     

Variations of the suspended matter and the particulate organic carbon in the entry of the Gironde Estuary,France

The effects of using different types of glass fiber filters (GF/F, GF/C) and of sample treatments were evaluated. Studies on the variation of suspended matter (SM) and particulate organic carbon (POC) showed that: 1) the transversal and day to night variations are important and must be taken into account in order to get a correct river flux; 2) no regular seasonal variations of SM and POC were observed, as they are controlled essentially by the climatological, hydrologic, physiochemical, biological, and geological conditions of the drainage area. Contribution No 2246 from the Institute of Oceanology, Chinese Academy of Sciences.     

湛江气温的多时间尺度特征及其变化趋势预测

利用MHAT小波函数分析了湛江市近50年来年平均气温的多时间尺度变化特征，并采用Kalman滤波模型对年平均温度的变化趋势进行了预测研究。结果表明，在半个多世纪的气候变化过程中包含了16a、4a和准2a的周期振荡，20世纪80年代中期以来湛江气温持续升高，且未来几年仍处于偏高阶段。卡尔曼滤波模型对湛江年平均气温的预测中，独立样本预测的平均绝对误差（MAE）等于0．30℃，相对误差为1．24％，对短期气候预测工作具有较高的参考价值。     

Data assimilation in groundwater modelling: ensemble Kalman filter versus ensemble smoothers

Groundwater modelling calls for an effective and robust data integrating method to fill the gap between the model and observation data. The ensemble Kalman filter (EnKF), a real‐time data assimilation method, has been increasingly applied in multiple disciplines such as petroleum engineering and hydrogeology. In this approach, a groundwater model is updated sequentially with measured data such as hydraulic head and concentration. As an alternative to the EnKF, the ensemble smoother (ES) has been proposed for updating groundwater models using all the data together, with much less computational cost. To further improve the performance of the ES, an iterative ES has been proposed for continuously updating the model by assimilating measurements together. In this work, we compare the performance of the EnKF, the ES, and the iterative ES using a synthetic example in groundwater modelling. Hydraulic head data modelled on the basis of the reference conductivity field are used to inversely estimate conductivities at unsampled locations. Results are evaluated in terms of the characterization of conductivity and groundwater flow predictions. It is concluded that (a) the iterative ES works better than the standard ES because of its continuous updating and (b) the iterative ES could achieve results comparable with those of the EnKF, with less computational cost. These findings show that the iterative ES should be paid much more attention for data assimilation in groundwater modelling.