An Introduction to Twisted Particle Filters and Parameter Estimation in Non-Linear State-Space Models
| Autor: | Robert Piche, Juha Ala-Luhtala, Nick Whiteley, Kari Heine |
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| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Computer science Monte Carlo method Markov process 02 engineering and technology sequential Monte Carlo Statistics - Computation 01 natural sciences 010104 statistics & probability symbols.namesake Particle filter Resampling 0202 electrical engineering electronic engineering information engineering State space particle MCMC 0101 mathematics Electrical and Electronic Engineering Computation (stat.CO) Simulation Markov chain Estimation theory Autocorrelation 020206 networking & telecommunications Markov chain Monte Carlo Marginal likelihood Nonlinear system Gaussian noise Gaussian state-space model Signal Processing symbols Multinomial distribution parameter estimation Algorithm |
| Zdroj: | Ala-Luhtala, J, Whiteley, N, Heine, K & Piche, R 2016, ' An Introduction to Twisted Particle Filters and Parameter Estimation in Non-linear State-space Models ', IEEE Transactions on Signal Processing, vol. 64, no. 18, pp. 4875-4890 . https://doi.org/10.1109/TSP.2016.2563387 |
| ISSN: | 1941-0476 1053-587X |
| DOI: | 10.1109/tsp.2016.2563387 |
| Popis: | Twisted particle filters are a class of sequential Monte Carlo methods recently introduced by Whiteley and Lee to improve the efficiency of marginal likelihood estimation in state-space models. The purpose of this article is to extend the twisted particle filtering methodology, establish accessible theoretical results which convey its rationale, and provide a demonstration of its practical performance within particle Markov chain Monte Carlo for estimating static model parameters. We derive twisted particle filters that incorporate systematic or multinomial resampling and information from historical particle states, and a transparent proof which identifies the optimal algorithm for marginal likelihood estimation. We demonstrate how to approximate the optimal algorithm for nonlinear state-space models with Gaussian noise and we apply such approximations to two examples: a range and bearing tracking problem and an indoor positioning problem with Bluetooth signal strength measurements. We demonstrate improvements over standard algorithms in terms of variance of marginal likelihood estimates and Markov chain autocorrelation for given CPU time, and improved tracking performance using estimated parameters. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible |
| Databáze: | OpenAIRE |
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