Results 281 to 290 of about 223,064 (315)
Some of the next articles are maybe not open access.
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
Particle filtering is a sequential signal processing methodology that uses discrete random measures composed of particles and weights to approximate probability distributions of interest. The quality of approximation depends on many factors including the number of particles used for filtering and the way new particles are generated by the filter.
Petar M. Djuric +2 more
openaire +1 more source
Particle filtering is a sequential signal processing methodology that uses discrete random measures composed of particles and weights to approximate probability distributions of interest. The quality of approximation depends on many factors including the number of particles used for filtering and the way new particles are generated by the filter.
Petar M. Djuric +2 more
openaire +1 more source
Monte Carlo filter particle filter
2015 European Control Conference (ECC), 2015We propose a new realization method of the sequential importance sampling (SIS) algorithm to derive a new particle filter. The new filter constructs the importance distribution by the Monte Carlo filter (MCF) using sub-particles, therefore, its non-Gaussianity nature can be adequately considered while the other type of particle filter such as unscented
Masaya Murata +2 more
openaire +1 more source
Particle flow auxiliary particle filter
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015Particle flow filters have been recently developed as an alternative approach for nonlinear filtering. The particles approximating the prior are migrated using differential equations to be distributed according to the posterior. Computationally tractable exact solutions only exist for linear Gaussian models.
Yunpeng Li 0001 +2 more
openaire +1 more source
Particle Filters for Magnetoencephalography
Archives of Computational Methods in Engineering, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +3 more sources
2010
Particle filtering is an effective sequential Monte Carlo approach to solve the recursive Bayesian filtering problem in non-linear and non-Gaussian systems. The algorithm is based on importance sampling. However, in the literature, the proper choice of the proposal distribution for importance sampling remains a tough task and has not been resolved yet.
Xiaoqin Zhang 0002 +2 more
openaire +1 more source
Particle filtering is an effective sequential Monte Carlo approach to solve the recursive Bayesian filtering problem in non-linear and non-Gaussian systems. The algorithm is based on importance sampling. However, in the literature, the proper choice of the proposal distribution for importance sampling remains a tough task and has not been resolved yet.
Xiaoqin Zhang 0002 +2 more
openaire +1 more source
Proceedings of the IEEE, 2004
Particle filters estimate the state of dynamic systems from sensor information. In many real-time applications of particle filters, however, sensor information arrives at a significantly higher rate than the update rate of the filter. The prevalent approach to dealing with such situations is to update the particle filter as often as possible and to ...
Cody C. T. Kwok +2 more
openaire +2 more sources
Particle filters estimate the state of dynamic systems from sensor information. In many real-time applications of particle filters, however, sensor information arrives at a significantly higher rate than the update rate of the filter. The prevalent approach to dealing with such situations is to update the particle filter as often as possible and to ...
Cody C. T. Kwok +2 more
openaire +2 more sources
IEEE Signal Processing Magazine, 2007
This article provides an overview of nonlinear statistical signal processing based on the Bayesian paradigm. The next-generation processors are well founded on MC simulation-based sampling techniques. The development of the sequential Bayesian processor is reviewed using the state-space models.
openaire +1 more source
This article provides an overview of nonlinear statistical signal processing based on the Bayesian paradigm. The next-generation processors are well founded on MC simulation-based sampling techniques. The development of the sequential Bayesian processor is reviewed using the state-space models.
openaire +1 more source
Particle Filter-Weight Estimation and Dual Particle Filter
2009 International Workshop on Intelligent Systems and Applications, 2009When the clean state is not available, a dual estimation approach is required. A dual algorithm, dual particle filter, for nonlinear state and parameters estimation is presented. Dual filter is combined with particle filter for nonlinear situation. Two separate particle filters run con-currently: one for signal estimation which is called particle state
Pengpai Fan +3 more
openaire +1 more source
2008
The Kalman and particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the particle filter does so by a sequential Monte Carlo method.
openaire +1 more source
The Kalman and particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the particle filter does so by a sequential Monte Carlo method.
openaire +1 more source
Consistency checks for particle filters
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006An "inconsistent" particle filter produces--in a statistical sense--larger estimation errors than predicted by the model on which the filter is based. Two test variables are introduced that allow the detection of inconsistent behavior. The statistical properties of the variables are analyzed.
openaire +2 more sources