Results 11 to 20 of about 11,537 (265)
Proceedings of the IEEE, 2004 ctkwok,fox£ Particle filters estimate the state of dynamical 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.
Marina Meilă, Cody Kwok, Dieter Fox
exaly +4 more sources
2020 Australian and New Zealand Control Conference (ANZCC), 2020 Particle filters are often explained by either heuristics arguments or complex mathematics. Present day particle filters rely on various methods such as importance sampling, resampling method and resampling strategy.
Torben Knudsen +3 more
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Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
SignalsThe goal of this study is to explore the effectiveness of applying multi-objective particle swarm optimization (MOPSO) algorithms in the design of infinite impulse response (IIR) filters.
Te-Jen Su +5 more
doaj +3 more sources
Applied Sciences, 2021 A laboratory study was conducted to evaluate 11 vehicular cabin filters (including electrostatic filters) in removing fine particles. Two filters with charcoal were also evaluated to understand their usefulness in removing five common volatile organic ...
Tak W. Chan +3 more
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Particle Filters: A Hands-On Tutorial
Sensors, 2021 The particle filter was popularized in the early 1990s and has been used for solving estimation problems ever since. The standard algorithm can be understood and implemented with limited effort due to the widespread availability of tutorial material and ...
Jos Elfring +2 more
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Performance Analysis of Resampling Algorithms of Parallel/Distributed Particle Filters
IEEE Access, 2021 Particle filters have been widely used in various fields due to their advantages in dealing with non-linear and/or non-Gaussian systems. A large number of particles are needed to guarantee the convergence of particle filters for the state estimation ...
Xudong Zhang +3 more
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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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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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