共查询到18条相似文献，搜索用时 140 毫秒
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在隧道、城市峡谷、多径效应明显等恶劣环境中,GNSS定位结果易受异常值影响。GNSS异常值的存在使得观测噪声服从Gauss分布的假设不再成立,从而将严重影响GNSS/INS组合系统中Kalman滤波的性能。本文将抗差Kalman滤波用于GNSS/INS组合导航系统中异常观测的探测与抑制。根据设定的显著性水平对Kalman滤波的新息向量进行c2检验,原假设被拒绝时认为异常观测存在;然后引入尺度化因子对新息向量的协方差矩阵进行膨胀,以抑制异常观测对滤波结果的影响,并通过解析方法求解该因子。数值仿真的结果验证了算法的有效性。 相似文献
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最小二乘配置法由于其能融合不同种类重力观测数据进行局部重力场逼近的特性而受到广泛关注,但最小二乘配置结果的不稳定严重影响该方法的推广应用。 基于对重力观测量协方差矩阵的谱分解,分析出该协方差矩阵存在病态性,协方差矩阵的求逆过程是信号放大的非平稳过程,微小的观测误差会被协方差矩阵的小奇异值放大,从而导致配置结果的不稳定且精度偏低。 引入 Tikhonov 正则化算法,通过 L 曲线法选择正则化参数,利用正则化参数修正重力观测量协方差矩阵的小奇异值,能抑制其对观测误差的放大影响。 通过以 EGM2008 重力场模型分别计算的山区、丘陵和海域重力异常作为基础数据确定相应区域大地水准面的实验,验证了本文算法的有效性。 相似文献
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基于罗经/DVL/水声定位系统的水下组合导航方法研究 总被引：1，自引：0，他引：1
针对基于罗经/DVL航位推算系统位置误差积累的问题,研究基于罗经/DVL/水声定位系统的水下运载体组合导航定位方法,设计了组合导航方案,建立了基于航位推算系统的误差方程,在此基础上引入水声定位系统的位置量测信息,设计了卡尔曼滤波器,实现了罗经/DVL/水声定位系统的信息融合。最后对研究的方法进行了计算机仿真和半物理仿真实验。实验结果表明,该方法抑制了航位推算系统位置误差发散,且减小了水声定位数据波动幅度,实现了水下运载体的高精度导航定位。 相似文献
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提出 1种基于奇异值分解 (SVD)的多通道带乘性噪声系统的最优滤波方法。该方法基于多通道带乘性噪声系统的最优滤波理论[1] ,利用奇异值分解作为工具 ,将原算法中的协方差矩阵P进行奇异值分解 ,可以在一定程度上避免在递推过程中 ,由于计算误差和舍入误差的积累而引起的协方差矩阵P失去对称性 ,因而导致算法失效的问题。在保证算法在线性最小方差意义下为最优的同时 ,具有很好的数值稳定性和鲁棒性。仿真中对改进后算法和原算法估计效果做了对比 ,仿真结果证明了本文方法的有效性。 相似文献
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为了提升磁梯度数据相关成像法对不同深度的多磁性目标的成像效果,提出了一种自适应滤波方法。首先将待成像空间划分为三维规则网格,通过解析法计算位于不同网格的磁性体在观测平面的理论磁梯度,然后依据各磁性体的磁梯度数据频谱特征来决定观测数据的滤波阈值,最后将滤波后的观测数据用于相关成像。仿真模型实验表明：对于位置较浅磁性体,根据成像结果估计的磁性体中心位置与实际基本相符, 滤波处理最多减小了约 76%的误差,对于平均 1.7 m 深的多个磁性体,估计的中心位置最大误差 0.3 m,相比于无滤波处理的结果误差减小了 46%左右,信噪比不足时,对近似球体的模型仍然有较好的定位效果。说明自适应滤波在一定程度上能改善三维相关成像的分辨率,提升水下磁性目标定位的准确度。 相似文献
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Yosuke Fujii 《Journal of Oceanography》2005,61(1):167-181
I present the derivation of the Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar), which is developed for adopting a non-diagonal background error covariance matrix in nonlinear variational analyses (i.e., analyses employing a non-quadratic cost function). POpULar is based on the idea of a linear preconditioned conjugate gradient method widely adopted in ocean data assimilation systems. POpULar uses the background error covariance matrix as a preconditioner without any decomposition of the matrix. This preconditioning accelerates the convergence. Moreover, the inverse of the matrix is not required. POpULar therefore allows us easily to handle the correlations among deviations of control variables (i.e., the variables which will be analyzed) from their background in nonlinear problems. In order to demonstrate the usefulness of POpULar, we illustrate two effects which are often neglected in studies of ocean data assimilation before. One is the effect of correlations among the deviations of control variables in an adjoint analysis. The other is the nonlinear effect of sea surface dynamic height calculation required when sea surface height observation is employed in a three-dimensional ocean analysis. As the results, these effects are not so small to neglect. 相似文献
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MICHAEL K. TIPPETT STEPHEN E. COHN RICARDO TODLING DAN MARCHESIN 《地球，A辑:动力气象学与海洋学》2000,52(5):533-553
Ensemble and reduced‐rank approaches to prediction and assimilation rely on low‐dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time‐independent systems are used to identify factors that cause the steady‐state analysis error covariance to admit a low‐dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix , a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady‐state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time‐dependent systems. If much of the steady‐state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady‐state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting non‐modal transient growth. Failure to observe growing modes leads to increased steady‐state analysis error variances. Leading eigenvectors of the steady‐state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest‐order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady‐state analysis error covariance matrix. 相似文献
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现代海洋/大气资料同化方法的统一性及其应用进展 总被引：9，自引：3，他引：9
海洋／大气资料同化的理论基础是用数值模式作为动力学强迫对观测信息进行提炼，或者说，从包含观测误差(噪声)的空间分布不均匀的实测资料中依据动力系统自身的演化规律(动力学方程或模式)来确定海洋／大气系统状态的最优估计。本文对主要的现代海洋／大气资料同化方法，包括最优插值(()ptimal Interpolation，简称()Ⅰ)、变分方法(3—Dimensional Variational和4—Dimensional Variational，分别简称3DVAR和4DVAR)和滤波方法(Filtering)的原理、算法设计和实际应用进行系统地回顾，并对这些资料同化方法的优缺点进行分析和讨论。在滤波框架下，所有的现代资料同化方法都被统一了：()Ⅰ和3DVAR是不随时间变化的滤波器，4DVAR和卡曼滤波是线性滤波器，即非线性滤波的退化情形；而集合滤波能构建非线性的滤波器，因为集合在某种程度上体现了系统的非高斯信息。一个非线性滤波器的主要优点是能计算和应用随时间变化的各阶误差统计距，如误差协方差矩阵。将非线性滤波器计算的随时间变化的误差协方差矩阵引入到（）Ⅰ或4DVAR中，也许能实质性地改进这些传统方法。在实际应用中，方法的优劣可能取决于所选用的数值模式和可获得的计算资源，因此需针对不同的问题选取不同的资料同化方法。由于各种资料同化方法具有统一性，因此可建立测试系统来评价这些方法，从而对各种方法获得更深入的理解，改进现有的资料同化技术，并提高人们对海洋／大气环境的预测能力。 相似文献
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Pan-Tai Liu Fu Li Heng Xiao 《Oceanic Engineering, IEEE Journal of》1996,21(3):256-259
If a system is unobservable, the error covariance associated with a Kalman filter will be nearly singular. As a consequence, an optimum estimation in the sense of minimum error covariance does not exist. In this paper, we show that this (unobservable) system can be transformed into a nonlinear system with a linear measurement equation. In addition to other useful features, this transformation also serves to decouple the state in such a way that an observable part can be extracted and estimated while no information can be gained and processed for the unobservable part 相似文献
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This paper presents an integrated navigational algorithm for unmanned underwater vehicles (UUV) using two acoustic range transducers and strap-down inertial measurement unit (SD-IMU). A range measurement model is derived for a UUV having one acoustic transducer and cruising around two reference transponders at sea floor or surface. The proposed algorithm, called pseudo long base line (PLBL), estimates the position of the vehicle integrating the SD-IMU signals corrected with the two range measurements. Extended Kalman filter was applied to propagate error covariance, to update measurement errors and to correct state equation whenever the external measurements are available. Simulations were conducted to illustrate the effectiveness of the PLBL using the 6-d.o.f. nonlinear numerical model of a UUV at current flow, excluding bottom-fixed DVL. This paper also shows the error convergence of the vehicle's initial position by the additional range measurements without velocity information. 相似文献
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针对传统模拟积分法的图斑椭球面积误差模型构造复杂及绝对精度无法自动匹配问题,以梯形图幅面积理论计算公式为关联纽带,采用纬线等分割补的手段构建了梯形图块面积的通用计算公式。在分析梯形图块面积替代误差数值分布规律的基础上,通过纬度积分步长变量的引入建立了严密的面积替代误差数列模型;阐明了纬度积分步长变量调控下面积替代误差数列的极限收敛特性,设计了面积替代误差的超限判定准则;构建了面积误差限定条件下纬度积分步长的自动调控模型,提出了一种绝对精度自动匹配的图斑椭球面积解算方法。实验结果表明:该算法可实现任意图斑椭球面积的精确量算,且绝对精度控制在给定误差阈值之内。 相似文献