Results 11 to 20 of about 1,703,706 (235)
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. Moreover, there are different derivations for discrete and continuous time dynamic models. In this paper we offer a new simple derivation
Torben Knudsen, John Leth
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Particle filters for partially-observed diffusions. [PDF]
, 2008 In this paper we introduce novel particle filters for a class of partially-observed continuous-time dynamic models where the signal is given by a multivariate diffusion process.
Fearnhead, Paul +2 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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Multiple object tracking using particle filters [PDF]
, 2006 The particle filtering technique with multiple cues such as colour, texture and edges as observation features is a powerful technique for tracking deformable objects in image sequences with complex backgrounds.
Canagarajah, N. +3 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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A convolution particle filtering approach for tracking elliptical extended objects [PDF]
, 2013 This paper proposes a convolution particle filtering approach for extended object tracking. Convolution particle filters (CPFs) are likelihood free filters. They are based on convolution kernel probability density representation.
Gning, Amadou +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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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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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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Localisation of mobile nodes in wireless networks with correlated in time measurement noise. [PDF]
, 2011 Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in ...
Mihaylova, Lyudmila +3 more
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