Probabilistic noise reduction
Probabilistic noise reduction
复制标题
概率降噪
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
Leonard A. Smith
中科院分区：
文献类型：
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作者：
J. Hansen;Leonard A. Smith
状态估计是产生准确预测的一个重要因素。为了产生对系统真实状态的“最佳”估计，在减少观测中固有的噪声上花费了很大的努力。但是有噪声的观测需要一种概率的，而不是确定性的方法来进行状态估计。状态的概率描述很少是高斯的，并且需要超出方差大小的信息；正确的分布受到系统吸引子的底层结构的限制。引入了有限时间稳定集和不稳定集的概念，并提出了基于数据同化的有限时间稳定集和不稳定集估计方法。四维变分同化善于在同化窗口开始时找到有效的有限时间稳定集，而集成卡尔曼滤波能够在观测可用的任何时间近似有限时间不稳定集。将这两种方案的结果结合起来，就产生了系统状态的概率估计，该估计优于单独的任何一种方案。
State estimation is an important factor in the production of accurate forecasts. Great effort isexpended in reducing the noise inherent in observations, to produce a “best” estimate of thetrue system state. But noisy observations necessitate a probabilistic, not a deterministic, approach to state estimation. A state’s probabilistic description is rarely Gaussian, and requiresinformation beyond variance magnitude; the correct distribution is restricted by the underlyingstructure of the system attractor. The concepts of finite-time stable and unstable sets are introducedand data assimilation-based methods for their estimation are developed. 4-dimensionalvariational assimilation proves adept at finding the finite-time stable set valid at the beginningof assimilation windows while the ensemble Kalman filter is capable of approximating the finitetimeunstable set at any time that an observation is available. Combining the results of the twoschemes produces a probabilistic estimate of the system state that is superior to either inisolation.