Results 21 to 30 of about 61,960 (301)
Multilevel Particle Filters [PDF]
SIAM Journal on Numerical Analysis, 2017 In this paper the filtering of partially observed diffusions, with discrete-time observations, is considered. It is assumed that only biased approximations of the diffusion can be obtained, for choice of an accuracy parameter indexed by $l$. A multilevel estimator is proposed, consisting of a telescopic sum of increment estimators associated to the ...
Ajay Jasra +3 more
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A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking
Remote Sensing, 2021 In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems.
Ning Zhou +3 more
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2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004 Using a Parzen density estimator, any distribution can be approximated arbitrarily close by a sum of kernels. In particle filtering, this fact is utilized to estimate a probability density function with Dirac delta kernels; when the distribution is discretized it becomes possible to solve an otherwise intractable integral.
Lehn-Schiøler, Tue +2 more
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Feedback Particle Filter [PDF]
IEEE Transactions on Automatic Control, 2013 A new formulation of the particle filter for nonlinear filtering is presented, based on concepts from optimal control, and from the mean-field game theory. The optimal control is chosen so that the posterior distribution of a particle matches as closely as possible the posterior distribution of the true state given the observations. This is achieved by
Tao Yang 0017 +2 more
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Particle Filtering With Invertible Particle Flow [PDF]
IEEE Transactions on Signal Processing, 2017 A key challenge when designing particle filters in high-dimensional state spaces is the construction of a proposal distribution that is close to the posterior distribution. Recent advances in particle flow filters provide a promising avenue to avoid weight degeneracy; particles drawn from the prior distribution are migrated in the state-space to the ...
Yunpeng Li 0001, Mark Coates
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A GENERIC PROBABILISTIC MODEL AND A HIERARCHICAL SOLUTION FOR SENSOR LOCALIZATION IN NOISY AND RESTRICTED CONDITIONS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 A generic probabilistic model, under fundamental Bayes’ rule and Markov assumption, is introduced to integrate the process of mobile platform localization with optical sensors.
S. Ji, S. Ji, X. Yuan
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SKELETONIZATION WITH PARTICLE FILTERS [PDF]
International Journal of Pattern Recognition and Artificial Intelligence, 2010 We present a novel method to obtain high quality skeletons of binary shapes. The obtained skeletons are connected and one pixel thick. They do not require any pruning or any other post-processing. The computation is composed of two major parts. First, a small set of salient contour points is computed.
Yuchun Tang +5 more
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Wrapped Particle Filtering for Angular Data
IEEE Access, 2022 Particle filtering is probably the most widely accepted methodology for general nonlinear filtering applications. The performance of a particle filter critically depends on the choice of proposal distribution.
Guddu Kumar +4 more
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Nudging the particle filter [PDF]
Statistics and Computing, 2019 AbstractWe investigate a new sampling scheme aimed at improving the performance of particle filters whenever (a) there is a significant mismatch between the assumed model dynamics and the actual system, or (b) the posterior probability tends to concentrate in relatively small regions of the state space.
Ömer Deniz Akyildiz, Joaquín Míguez
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A weighted likelihood criteria for learning importance densities in particle filtering
EURASIP Journal on Advances in Signal Processing, 2018 Selecting an optimal importance density and ensuring optimal particle weights are central challenges in particle-based filtering. In this paper, we provide a two-step procedure to learn importance densities for particle-based filtering.
Muhammad Javvad ur Rehman +2 more
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