منابع مشابه
Parallel resampling in the particle filter
Modern parallel computing devices such as the graphics processing unit (GPU) have gained significant traction in scientific computing, and are particularly well-suited to dataparallel algorithms such as the particle filter. Of the components of the particle filter, the resampling step is the most difficult to implement well on such devices, as it often requires a collective operation, such as a...
متن کاملEnsemble Particle Filter with Posterior Gaussian Resampling
An ensemble particle filter(EnPF) was recently developed as a fully nonlinear filter of Bayesian conditional probability estimation, along with the well known ensemble Kalman filter(EnKF). A Gaussian resampling method is proposed to generate the posterior analysis ensemble in an effective and efficient way. The Lorenz model is used to test the proposed method. With the posterior Gaussian resamp...
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Optical sensors based vehicle tracking can be widely implemented in traffic surveillance and flow control. The vast development of video surveillance infrastructure in recent years has drawn the current research focus towards vehicle tracking using high-end and low cost optical sensors. However, tracking vehicles via such sensors could be challenging due to the high probability of changing vehi...
متن کاملSaturated Particle Filter: Almost sure convergence and improved resampling
Nonlinear stochastic dynamical systems are widely used to model physical processes. In many practical applications, the state variables are defined on a compact set of the state space, i.e., they are bounded or saturated. To estimate the states of systems with saturated variables, the Saturated Particle Filter (SPF) has recently been developed. This filter exploits the structure of the saturate...
متن کاملResampling the ensemble Kalman filter
Ensemble Kalman filters (EnKF) based on a small ensemble tend to provide collapse of the ensemble over time. It is shown that this collapse is caused by positive coupling of the ensemble members due to use of one common estimate of the Kalman gain for the update of all ensemble members at each time step. This coupling can be avoided by resampling the Kalman gain from its sampling distribution i...
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ژورنال
عنوان ژورنال: Journal of Computational and Graphical Statistics
سال: 2016
ISSN: 1061-8600,1537-2715
DOI: 10.1080/10618600.2015.1062015