Moment conditions for convergence of particle filters with unbounded importance weights
@article{Mbalawata2016MomentCF, title={Moment conditions for convergence of particle filters with unbounded importance weights}, author={Isambi S. Mbalawata and Simo S{\"a}rkk{\"a}}, journal={Signal Process.}, year={2016}, volume={118}, pages={133-138} }
10 Citations
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- 2016
The convergence of the algorithm with the number of particles tending to infinity is proved by requiring a moment condition and a step-wise initial condition boundedness for the stochastic exponential process giving the likelihood ratio of the SDEs.
Particles resampling scheme using regularized optimal transport for sequential Monte Carlo filters
- EngineeringInternational Journal of Adaptive Control and Signal Processing
- 2018
A resampling method is presented for improving the performance of particle filters by using adaptive numbers of the resampling particles. The proposed method replaces the resampling step of a…
Particle filtering with applications in networked systems: a survey
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This survey paper first provides an overview of the particle filtering methods as well as networked systems, and then investigates the recent progress in the design of particle filter for networked system.
Data Fusion With Inverse Covariance Intersection for Prior Covariance Estimation of the Particle Flow Filter
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- 2020
The prior covariance estimation method based on inverse covariance intersection (ICI) is proposed to apply the particle flow filter. The proposed method has better estimate performance and guarantees…
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- 2019
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
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- 2017
This review examines the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty.
References
SHOWING 1-10 OF 34 REFERENCES
Weight moment conditions for L4 convergence of particle filters for unbounded test functions
- Mathematics2014 22nd European Signal Processing Conference (EUSIPCO)
- 2014
Novel moment conditions for importance weights of sequential Monte Carlo based particle filters are derived, which ensure the L4 convergence of particle filter approximations of unbounded test functions.
Stability properties of some particle filters
- Mathematics
- 2013
Under multiplicative drift and other regularity conditions, it is established that the asymptotic variance associated with a particle filter approximation of the prediction filter is bounded…
A Basic Convergence Result for Particle Filtering
- Computer ScienceIEEE Transactions on Signal Processing
- 2008
The basic nonlinear filtering problem for dynamical systems is considered, and a general framework, including many of the particle filter algorithms as special cases, is given.
A survey of convergence results on particle filtering methods for practitioners
- Computer ScienceIEEE Trans. Signal Process.
- 2002
The aim of this paper is to present a survey of convergence results on particle filtering methods to make them accessible to practitioners.
Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
- Mathematics
- 2004
The term sequential Monte Carlo methods or, equivalently, particle filters, refers to a general class of iterative algorithms that performs Monte Carlo approximations of a given sequence of…
Optimality of the auxiliary particle filter
- Mathematics
- 2009
In this article we study asymptotic properties of weighted samples produced by the auxiliary particle filter (APF) proposed by Pitt and Shephard [17]. Besides establishing a central limit theorem…
On the L4 convergence of particle filters with general importance distributions
- Engineering2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2014
The result essentially shows that with general importance distributions the particle filter converges provided that the importance weights are bounded and by numerical simulations this condition is often also a practical requirement for a good performance of a particle filter.
A General Convergence Result for Particle Filtering
- MathematicsIEEE Transactions on Signal Processing
- 2011
The present contribution is that it is proved that the particle filter converge for unbounded functions in the sense of Lp-convergence, for an arbitrary p ≥ 2.
Convergence of Sequential Monte Carlo Methods
- Computer Science, Mathematics
- 2007
This paper presents a general sequential Monte Carlo (SMC) method which includes most of the important features present in current SMC methods and generalizes and encompasses many recent algorithms.