Implicit sampling for particle filters.
We present a particle-based nonlinear filtering scheme, related to recent work on chainless Monte Carlo, designed to focus particle paths sharply so that fewer particles are required. The main features of the scheme are a representation of each new probability density function by means of a set of functions of Gaussian variables (a distinct function for each particle and step) and a resampling based on normalization factors and Jacobians. The construction is demonstrated on a standard, ill-conditioned test problem.
Chorin
Alexandre J
AJ
Department of Mathematics, University of California and Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. chorin@math.berkeley.edu
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Xuemin
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D019471:Elementary Particle Interactions; D008433:Mathematics; D008962:Models, Theoretical; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D011336:Probability; D035501:Uncertainty