船用陀螺漂移误差模型辨识及及其应用

在对船用陀螺漂移数据建立时间序列模型的基础上，采用卡尔曼滤波算法对捷联陀螺漂移数据进行了处理，以提高陀螺静态漂移误差系数的估计精度，并把得到的陀螺漂移误差模型实时补偿到捷联系统中，得到了满意的效果。     

基于双轴速率转台的IMU440惯性测量单元快速标定方法与实验

在捷联惯导系统中,惯性器件的确定性误差是系统误差的主要原因之一。为了提高惯导系统的输出精度,必须对这一误差加以补偿。以美国Crossbow公司开发的IMU440惯性测量单元为对象进行了快速标定实验,建立了陀螺仪和加速度计的误差模型方程,提出了用于辨识陀螺仪和加速度计误差模型参数的速率和位置标定法,根据两种标定方法得到了IMU440惯性测量单元的误差模型,最后对误差模型进行了校验。实验结果表明,误差补偿后的惯性器件输出值可以很好地接近理想输出值,大大降低了捷联惯导系统的输出误差。     

温度变化对船用动力调谐陀螺性能影响的研究

本文结合某船用动力调谐陀螺，研究了温度变化对转子体质心位移，力矩器性能，调谐状态，信号器性能及风阻力矩的影响，建立了温度误差模型。实测证明该模型具有实用参考价值。     

动力调谐陀螺仪角位移测试中的数据处理技术

坦克炮控系统的性能测试关键为角位移的测试,因此,研制了基于动力调谐陀螺仪的角位移测试系统。介绍了系统组成及工作原理,并着重分析了角位移测量原理和误差,以及在线实时数据处理技术。实验结果表明：基于动力调谐陀螺仪并采用改进零点渐变算法的角位移测试系统零点漂移小,测角精度可达0．48％,完全能满足炮控系统性能测试的需求。     

全天候天文字位系统研究

对用陀螺仪代替天体来实现全天候天文定位系统问题进行了研究。结果表明该系统可以在阴雨天、水下等任何空间实现实时、连续、高精度定位。同时，通过对该系统与惯性导航系统的比较发现，该系统基本不存在时间积累误差。     

微型线振动陀螺仪中机械静电力耦合变形研究

针对此现象建立微型线振动陀螺仪结构不对称造成的静电力耦合误差模型以及梳齿变形模型,使用Matlab软件进行仿真分析,同时根据有限元分析的方法,使用ANSYS软件进行分析,得到由于结构不对称造成的电场分布图、静电力分布图和变形图,这些分析研究对提高微型线振动陀螺仪的精度起着非常重要的作用。     

捷联式陀螺仪闭环控制电路的设计

为了提高捷联式陀螺组件动、静态性能,必须对陀螺仪数学模型和校正网络进行优化.通过对陀螺仪模型的系统分析和仿真,对陀螺仪控制电路中的校正网络进行改进设计,确定电路的具体参数.测试结果表明,回路的噪声得到有效抑制,固有频率明显提高,动态与静态性能均达到技术指标要求.     

基于有限元仿真的船用陀螺仪高加速应力筛选试验设计

在船载导航系统中,陀螺仪的作用很大,尤其是在强磁场干扰时,陀螺仪性能的好坏直接决定了船舶航行的安全性,因此在设计船舶导航系统时,需要对陀螺仪的可靠性进行试验。本文通过采用有限元模型,对陀螺仪的力学作用方式和工作原理进行研究,通过对组成系统进行抽象化的仿真,得到在一些情况下的陀螺仪作用力的时域和频域响应特性曲线。通过对加速应力的模拟和筛选,得到最佳的陀螺仪作用力模型,从而为其性能的提升提供理论指导。     

双平衡环挠性陀螺接头疲劳寿命的可靠性预计

本文以一种双平衡环挠性陀螺接头为结构模型,运用可靠性方法对挠性接头的疲劳寿命进行了分析,提出了一种预计挠性接头对于无限寿命的可靠度的方法。     

陀螺随机漂移引起的惯导系统误差特性分析

为研究陀螺随机漂移作用下的惯导系统误差发散规律,当陀螺随机漂移为白噪声时,基于静基座下的惯导系统误差方程,得到了惯导系统误差的计算方法。基于白噪声的统计特性,理论推导了惯导误差方差的解析解,并分析了各因素对惯导系统误差的影响。基于理论推导公式,对白噪声作用下的惯导系统误差做了仿真计算。结果表明:在陀螺随机漂移作用下,惯导系统误差与航行纬度有关。方差中包含舒拉、傅科、地球三种周期振荡和非周期项。其中的非周期项与陀螺漂移率的方差成线性关系,同时是时间的斜坡函数;经度误差的方差中,三种周期性振荡受非周期项的调制作用,振荡幅值随时间线性增大。     

Errors in dynamical fields inferred from oceanographic cruise data: Part I. The impact of observation errors and the sampling distribution

Diagnostic studies of ocean dynamics based on the analysis of oceanographic cruise data are usually quite sensitive to observation errors, to the station distribution and to the synopticity of the sampling. Here we present an error analysis of the first two sources. The third one is evaluated in Part II of this work (J. Mar. Sys. (2005), this issue). For observed variables and those linearly related to them, we use the Optimal Statistical Interpolation (OI) formulation. For variables which are not linearly related to observed variables (e.g., the vertical velocity), we carry out numerical experiments in a consistent way with OI statistics. Best results are obtained when some kind of scale selection or spatial filtering is applied in order to suppress small scales that cannot be properly resolved by the station distribution.The formulation is first applied to a high resolution (SeaSoar) sampling aimed to the recovery of mesoscale features in a region of large spatial variability (noise-to-signal fraction of the order of 0.002). Fractional errors (rms error divided by the standard deviation of the field) are estimated in about 2% for dynamic height and between 4% and 20% for geostrophic vorticity and vertical velocity. For observed variables, observation errors and sampling limitations are shown to contribute in similar amounts to total errors. For derived variables, sampling errors are by far the dominant contribution. For less dense samplings (e.g., equally spaced CTD stations), fractional errors are about 6% for dynamic height and between 15% and 30% for geostrophic vorticity and vertical velocity. For this sampling strategy, errors of all variables are mostly associated with sampling limitations.     

人为失误与海上事故发生的机理分析

海上事故的发生与航海人员在完成航行任务和处理海上风险过程中的能力有非常密切的关系。保障此过程中的行为能有效地完成成为关键，而航海人员完成任务的能力又直接受到人为失误的影响，也就是说，人为失误的发生直接降低了人的处理能力，使航行任务归于失败。现从行为学的观点对人为失误的发生进行了初步的分析，寻求减少人为失误发生的途径。并从统计学的观点解释了由人为失误引起的海上事故发生的原因。     

机动对SINS对准中惯性仪表误差估计的影响

为了尽可能估计出捷联惯导系统中惯性仪表的误差,建立捷联惯导系统误差方程和量测方程,运用传递对准技术,构建了速度匹配方式下的Kalman滤波器模型,研究了线加速和拐弯机动下对惯性仪表误差估计的影响,并对计算机仿真结果进行比较分析,仿真结果表明:线加速情况下可以提高陀螺漂移误差的估计精度,拐弯情况下可以提高加速度计偏置误差的估计精度。     

The Economics of Using Ocean Observing Systems to Improve Beach Closure Policy

Beach closure policies in the United States suffers from two shortcomings. Type I errors, in which clean beaches are closed, results when managers resort to extensive beach closures because they are unsure of the spatial extent of water contamination. Type II errors, in which contaminated beaches remain open, occur because the time from sampling to public notification can be between two and nine days. Coastal Ocean Observing Systems (COOS) could reduce the impact of both Type I and II errors. The COOS could reduce the spatial extent of beach closures by better predicting the fate of contaminants in coastal waters. An improved COOS also could reduce the time from sampling to public notification of contamination events. I estimate the lost recreational value associated with Type I errors (unnecessary closures) and the public health costs associated with Type II errors (unnecessary exposure to waterborne illnesses) for beaches in Southern California.     

Errors in dynamical fields inferred from oceanographic cruise data: Part II. The impact of the lack of synopticity

Diagnostic studies of ocean dynamics based on the analysis of oceanographic cruise data are usually quite sensitive to observation errors, to the station distribution and to the synopticity of the sampling. The first two sources have been evaluated in the Part I of this work. Here we evaluate synopticity errors for different sampling strategies applied to simulated unstable baroclinic waves. As suggested in previous studies, downstream and upstream cross-front samplings produce larger errors than along-front samplings. In our particular case study, the along-front sampling results in fractional errors (rms error divided by the standard deviation of the field) of about 15% for dynamic height and more than 50% for relative vorticity and vertical velocity. These values are significantly higher than those obtained in Part I for typical observation errors and sampling limitations (about 6% for dynamic height and between 15 and 30% for geostrophic vorticity and vertical velocity).We also propose and test two methods aimed at reducing the impact of the lack of synopticity. The first one corrects the observations using the quasi-geostrophic tendency equation. The second method combines the relocation of stations (based on a system velocity) and the correction of observations (through the estimation of a growth rate). For the fields simulated in this work, the second method gives better results than the first, being able to eliminate practically all synopticity errors in the case of the along-front sampling. In practice, the error reduction is likely to be less effective, since actual fields cannot be expected to have a system velocity as homogeneous as for the single-mode waves simulated in this work.     

基于误差分析的DCT域图像隐藏算法

为有效减少在AWGN信道下取整误差对秘密信息提取造成的影响,通过分析取整误差、DCT系数误差和秘密信息提取误差三者的关系,得出DCT系数误差是服从N（0,0.0833）正态分布,进而推导出在DCT域扩频算法和量化索引调制（QIM）盲提取算法中秘密信息提取误差的概率密度函数。基于误差分析分别提出采取容错和纠错编码方式的两种改进算法,提高图像隐藏的隐蔽性和秘密信息提取的准确率。仿真结果验证了这两种算法的可行性和有效性。     

Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea

Sea surface temperature fields of the North Sea and Baltic Sea have been constructed for the year 2001 using a multiplatform Optimal Interpolation scheme. The analyzed fields are constructed every 12 h on a 10 km spatial grid. The product is based upon observations from the three NOAA satellites 12, 14 and 16 together with a large amount of in situ observations. Space dependent covariance functions are estimated from the satellite observations and account for spatial and temporal lags. Several independent methods have been used to assess the error on the sea surface temperature product. Compared against independent in situ observations, the mean RMS difference for the year 2001 is 0.78 °C. The spatial distribution of the errors reveals that the Baltic Sea in general show higher errors than the North Sea. The error statistics throughout the year show a temporal variation of the errors with maximum during summer and winter. Tests with a varying number of satellite observations show that the accuracy of the satellite observations is the most important parameter in terms of reducing the errors on the interpolated sea surface temperature product.     

Impact of forcing errors in the CAMCAT oil spill forecasting system. A sensitivity study

The CAMCAT oil spill forecasting system is presented in this paper, and an evaluation of the impact of errors in the forcing fields over its forecasts is carried out. The system is formed by several independent modules which provide forecasts of winds, currents and waves to an oil spill module which predicts the evolution of the spill.The typical twin-experiments experience is used paying special attention to a realistic characterization of the errors when perturbing the forcing fields. The results suggest that errors in the wind and current fields are the main limiting factor for the quality of the oil spill forecasts. The pollutant identification is also crucial to determine the final vertical position and characteristics of the product.     

Comparative analysis between operational weather prediction models and QuikSCAT wind data near the Galician coast

Wind measurements from SeaWinds scatterometer on the NASA QuikSCAT satellite and wind forecasts from two different operational numerical models provided by MeteoGalicia were compared for a 4-year period (2002–2005) in Galician coast environment. Available wind data buoy measurements were also used to complement the analysis. A statistical analysis based on mean errors, root mean square errors and complex correlation was performed from spatial, temporal and directional points of view.In the spatial comparison no significant differences between models and satellite were observed and the error magnitudes of the models are compatible with typical QuikSCAT errors. The suitability of satellite wind estimations for data assimilation in these models must be further investigated. Negative bias of models with respect to the satellite was also confirmed with buoy data, in such a way that models overestimation is smaller than the satellite one. Big errors in wind direction appear in southeasterly and southwesterly winds for both satellite and models, contributing to high RMSE values when compared to buoy data. These errors were mainly attributed to the effect of insufficient spatial resolution near shore.