复杂系统误差传递的主次作用分析与控制

系统观测目标误差渗透着系统误差和随机误差,系统误差和随机误差的分离与溯源理论和应用一直是误差分析的难点和热点。文中基于系统误差和随机误差的互相关系与传递特征,提出了以传递函数为基础的误差传递模型,并基于该模型,将复杂系统划分为若干个子系统,分析了各子系统在观测目标误差中的主次作用（ primary and secondary position a-nalysis,PSPA）。算例表明,该理论能够分析得出引起观测误差灵敏度较高的子系统,这对于误差溯源、分析和控制误差,提高观测目标的精度具有一定的指导意义。     

新误差分析方法对反舰导弹作战使用的影响

从随机误差和系统误差的定义出发,指出传统文献在计算捕捉概率时分析随机误差E和系统误差M时存在的问题,采用一种新的分析方法来研究E和M的变化对超视距反舰导弹的可用外部目标指示时间的影响。     

一种时间-传感器系统误差估计算法

由于时间的不一致和传感器系统误差直接影响目标探测信息的精度,假设卫星导航的精度足够高,不考虑导航误差的影响,在时间-传感器系统误差传递模型的基础上,利用对空中平台的实时观测和平台的导航信息,获取观测数据的新息相关函数序列,对未知的系统模型参数和噪声统计参数进行在线辨识,构建自适应滤波,实现对多平台时间-传感器系统误差的实时估计和配准。通过对比仿真实验表明,该算法收敛速度快,估计精度较高。     

面向船舶企业整体解决方案的数据建模内容与过程

本文以船舶企业为背景,从元信息共享的角度研究开展建立企业级全程数据模型和数据标准的活动,综述了国内外企业数据模型研究的现状,分析了数据建模是建立支持企业信息系统运行规则和事实的本体模型,提出利用分层结构的多维数据模型实施逐步建模,来建立描述企业管理活动,反映企业状态,为管理决策提供数据支持的企业集成数据模型。     

GPS在移动机器人导航定位系统中的应用

为了获得移动机器人定位数据的真实性，减少各种随机误差对定位精度的影响，通过运动载体当前统计模型，取位置作为观测量建立移动机器人动态定位模型，同时针对传统卡尔曼滤波的不足进行了分析，提出自适应卡尔曼滤波算法，该算法始终保持噪声模型接近于真实模型，从而较好地解决了GPS动态定位中状态噪声与观测噪声建模不准确和时变问题，在此基础上，通过模拟噪声和轨迹曲线，进行了仿真，结果显示效果显著。     

关于方位定位数值的解法

论述了两方位定位和系统误差影响下的三方位定位及随机误差影响下的三方位定位数值的解法问题。     

船位误差分析

分析了船位系统误差和随机误差的特性，给出了较大误差三角形的处理办法，从而增加了航海人员对估计船位的信赖程度。     

基于OLAP的船舶噪声数据模型

随着我国船舶噪声测试技术的发展,各单位积累了大量船舶噪声相关数据,这些数据隐含了丰富的信息和有用的知识是进行科学研究的宝贵资源,基于OLTP的船舶噪声数据库主要针对数据管理和简单查询开发的,所以其数据模型具有天生的缺陷,如果用于数据统计分析和大量复杂查询会显得效率低下、力不从心。本文主要针对基于事务的数据模型的局限性进行分析研究,并利用数据仓库技术构建一个高效的适合用于船舶噪声控制分析的数据模型。     

Riccati方程在电罗经直航向误差建模与预测中的应用

为减小舰船直航向运动情况下风、流、摇摆等对电罗经航向造成的随机误差影响,提高舰船直航向运动时电罗经指向精度,采用时间序列模型对其随机误差进行建模,利用随机误差自相关函数和偏相关函数特性确定模型阶数,获得了较为准确的误差模型.同时,基于建立的模型,采用Riccati递推方程与指数平滑式结合的方式对电罗经航向随机误差进行预测.结果表明,时间序列模型较为准确地描述了电罗经航向随机误差过程,预测精度较高,鲁棒性较强,为减小电罗经直航向误差提供一种有效的方法.     

管形数据转换及管件信息模型表达

针对常用三维CAD软件在处理和表达管形数据方面存在的不足,本文介绍了管形数据两种描述方法,并给出了管形设计数据至加工数据的转换模型。基于导管STL模型的几何信息自动提取算法能够实现常用CAD软件设计的导管模型信息的自动提取,从导管加工CAD/CAM集成的角度建立了基于特征的导管零件信息模型,该模型能够充分表达用户对导管的加工缺陷控制要求,为实现导管加工的CAD/CAM集成提供了统一的产品数据模型。     

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。     

微机械陀螺随机误差补偿研究

为了提高微机械陀螺使用精度,研究了随机漂移误差补偿方法。对静态陀螺输出数据进行预处理,提取出随机漂移数据,采用时序分析方法对其建立AR模型。基于模型设计Kalman滤波器,进行静态和动态仿真。结果表明：静态条件下滤波效果显著;动态条件下,滤波效果变差。针对此问题设计了标量因子时变的自适应Kalman滤波器,试验表明,此算法能够有效降低微机械陀螺的随机漂移。     

Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter

Ocean-biogeochemical models show typically significant errors in the representation of chlorophyll concentrations. The model state can be improved by the assimilation of satellite chlorophyll data with algorithms based on the Kalman filter. However, these algorithms do usually not account for the possibility that the model prediction contains systematic errors in the form of model bias. Accounting explicitly for model biases can improve the assimilation performance. To study the effect of bias estimation on the estimation of surface chlorophyll concentrations, chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are assimilated on a daily basis into the NASA Ocean Biogeochemical Model (NOBM). The assimilation is performed by the ensemble-based SEIK filter combined with an online bias correction scheme. The SEIK filter is simplified here by the use of a static error covariance matrix. The performance of the filter algorithm is assessed by comparison with independent in situ data over the 7-year period 1998–2004. The bias correction results in significant improvements of the surface chlorophyll concentrations compared to the assimilation without bias estimation. With bias estimation, the daily surface chlorophyll estimates from the assimilation show about 3.3% lower error than SeaWiFS data. In contrast, the error in the global surface chlorophyll estimate without bias estimation is 10.9% larger than the error of SeaWiFS data.     

直接优化解析姿态确定方法

基于惯性系的姿态确定方法是近阶段关于动基座姿态确定问题的热点研究方向,分析了惯性系姿态确定方法的误差源产生机理,发现传统的惯性系首先解析出起始姿态阵,继而通过惯性器件量测输出,更新至当前姿态完成姿态确定,这一更新过程不仅加大了计算量,同时会重新引入器件误差和系统误差。因此,针对这一问题提出了一种基于惯性系的瞬时姿态阵解析姿态确定方法。该方法直接解析出当前时刻的姿态阵,避免姿态更新过程中引入误差,充分发挥了优化解析法的优势,对准精度得到大大提高,并利用仿真和实测数据验证了算法的可行性和有效性。     

导航装备动态性能测试系统天线安装误差补偿方法研究

针对目前船舶计程仪及陀螺导航设备误差标校方法手段落后、过程周期长等问题,采用MS860作为测定误差的基准平台搭建导航装备动态性能测试系统,选用Nport5450串口服务器接收处理多路串口信号进行数据通信,结合轴角信号转换装置进行模拟、数字信号转换,利用信息处理机终端处理与显示导航参数信息。另外,分析了GPS天线安装偏差导致船体纵横摇、GPS天线高程差、方位偏差、GPS测量基线长和基座高程差等因素对GPS航向产生的影响,推导了GPS动态航向测量模型的误差补偿方程。此系统对降低导航设备误差标校工作成本,提高导航装备的保障性具有十分重要的意义。     

舰艇声呐测向精度试验方法

声呐系统测向精度指标是舰艇声呐系统的一个十分重要的战技指标.声呐系统作为舰艇作战系统的一个重要组成部分,它所提供测量数据精度的大小,直接关系到指控系统对鱼雷射击诸元的解算精度,从而影响鱼雷的射击效果.从理论上明确测向精度指标的描述问题,然后通过对声呐测向系统误差的校准,测向精度海上试验的航路约定的分析,研究并提出一种适应现代声呐高测向精度指标要求的测向精度真值解算方法,并对算法的误差进行详细分析.     

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.     

GPS车载导航系统的地图匹配算法研究

分析了影响车载导航系统定位精度的各种GPS数据误差,提出将改进的地图匹配投影算法和基于路网拓扑关系的匹配算法进行整合,以此来实时提高车载导航系统的定位准确度,同时给出了误差模型和算法描述以及算法的实现方法。最后通过仿真实验来验证算法的准确性和可靠性,并在此基础上提出了几点改进策略。     

Wave data assimilation with the Kalman filter

A new data assimilation method for ocean waves is presented, based on an efficient low-rank approximation to the Kalman filter. Both the extended Kalman filter and a truncated second-order filter are implemented. In order to explicitly estimate past wind corrections based on current wave measurements, the filter is extended to a fixed-lag Kalman smoother for the wind fields. The filter is tested in a number of synthetic experiments with simple geometries. Propagation experiments with errors in the boundary condition showed that the KF was able to accurately propagate forecast errors, resulting in spatially varying error correlations, which would be impossible to model with time-independent assimilation methods like OI. An explicit comparison with an OI assimilation scheme showed that the KF also is superior in estimating the sea state at some distance from the observations. In experiments with errors in the driving wind, the modeled error estimates were also in agreement with the actual forecast errors. The bias in the state estimate, which is introduced through the nonlinear dependence of the waves on the driving wind field, was largely removed by the second-order filter, even without actually assimilating data. Assimilation of wave observations resulted in an improved wave analysis and in correction of past wind fields. The accuracy of this wind correction depends strongly on the actual place and time of wave generation, which is correctly modeled by the error estimate supplied by the Kalman filter. In summary, the KF approach is shown to be a reliable assimilation scheme in these simple experiments, and has the advantage over other assimilation methods that it supplies explicit dynamical error estimates.     

总体最小二乘的改进与信道估计应用

总体最小二乘法同时考虑了数据矩阵和观察值的误差,在参数估计中得到广泛应用。然而总体最小二乘法没有针对具体问题利用误差的先验信息。总体最小二乘的改进算法—结构总体最小二乘法设定了误差矩阵的结构,通过迭代运算估计参数及误差。基于叠加训练序列的时不变信道估计中,信息序列均值构成的误差矩阵为Toeplitz矩阵。结构总体最小范数法作为一种结构总体最小二乘法,可以设定误差矩阵具有Toeplitz结构,有效提高叠加训练信道估计性能。论文对比了最小二乘,数据最小二乘,总体最小二乘和结构总体最小范数在叠加训练序列信道估计中的应用,仿真结果表明,基于结构总体最小范数的估计算法的归一化信道均方误差最小。