524 research outputs found
Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters
This paper investigates an approximation scheme of the optimal nonlinear
Bayesian filter based on the Gaussian mixture representation of the state
probability distribution function. The resulting filter is similar to the
particle filter, but is different from it in that, the standard weight-type
correction in the particle filter is complemented by the Kalman-type correction
with the associated covariance matrices in the Gaussian mixture. We show that
this filter is an algorithm in between the Kalman filter and the particle
filter, and therefore is referred to as the particle Kalman filter (PKF). In
the PKF, the solution of a nonlinear filtering problem is expressed as the
weighted average of an "ensemble of Kalman filters" operating in parallel.
Running an ensemble of Kalman filters is, however, computationally prohibitive
for realistic atmospheric and oceanic data assimilation problems. For this
reason, we consider the construction of the PKF through an "ensemble" of
ensemble Kalman filters (EnKFs) instead, and call the implementation the
particle EnKF (PEnKF). We show that different types of the EnKFs can be
considered as special cases of the PEnKF. Similar to the situation in the
particle filter, we also introduce a re-sampling step to the PEnKF in order to
reduce the risk of weights collapse and improve the performance of the filter.
Numerical experiments with the strongly nonlinear Lorenz-96 model are presented
and discussed.Comment: Accepted manuscript, to appear in Monthly Weather Revie
К проблеме методологического статуса персонализированной медицины как практикоориентарованной учебной дисциплины
МЕДИЦИНСКИЕ УЧЕБНЫЕ ЗАВЕДЕНИЯОБРАЗОВАНИЕ МЕДИЦИНСКОЕСТУДЕНТЫУЧЕБНЫЕ ДИСЦИПЛИНЫПЕРСОНАЛИЗИРОВАННАЯ МЕДИЦИНАПРАКТИКО-ОРИЕНТИРОВАННАЯ ОБРАЗОВАТЕЛЬНАЯ СРЕДАПРАКТИКО-ОРИЕНТИРОВАННОЕ ОБУЧЕНИЕМЕТОДОЛОГИЧЕСКИЕ ПОДХОД
Multichannel Photon Counting Lidar Measurements Using USB-based Digital Storage Oscilloscope
We present a simple method of making multichannel photon counting measurements of weak lidar signal from large ranges, using commonly available USB-based digital storage oscilloscopes. The single photon pulses from compact photomultiplier tubes are amplified and stretched so that the pulses are large and broad enough to be sampled efficiently by the USB oscilloscopes. A software interface written in Labview is then used to count the number of photon pulses in each of the prescribed time bins to form the histogram of LIDAR signal. This method presents a flexible alternative to the modular multichannel scalers and facilitate the development of sensitive lidar systems
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