Particle-method-based formulation of risk-sensitive filter
A novel particle implementation of risk-sensitive filters (RSF) for nonlinear, non-Gaussian state-space models is presented. Though the formulation of RSFs and its properties like robustness in the presence of parametric uncertainties are known for sometime, closed-form expressions for such filters...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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2009
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| _version_ | 1826279202303770624 |
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| author | Sadhu, S Bhaumik, S Doucet, A Ghoshal, T |
| author_facet | Sadhu, S Bhaumik, S Doucet, A Ghoshal, T |
| author_sort | Sadhu, S |
| collection | OXFORD |
| description | A novel particle implementation of risk-sensitive filters (RSF) for nonlinear, non-Gaussian state-space models is presented. Though the formulation of RSFs and its properties like robustness in the presence of parametric uncertainties are known for sometime, closed-form expressions for such filters are available only for a very limited class of models including finite state-space Markov chains and linear Gaussian models. The proposed particle filter-based implementations are based on a probabilistic re-interpretation of the RSF recursions. Accuracy of these filtering algorithms can be enhanced by choosing adequate number of random sample points called particles. These algorithms significantly extend the range of practical applications of risk-sensitive techniques and may also be used to benchmark other approximate filters, whose generic limitations are discussed. Appropriate choice of proposal density is suggested. Simulation results demonstrate the performance of the proposed algorithms. © 2008 Elsevier B.V. All rights reserved. |
| first_indexed | 2024-03-06T23:55:14Z |
| format | Journal article |
| id | oxford-uuid:73fe6e0b-243b-4391-8b33-c6b827156b8d |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:55:14Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:73fe6e0b-243b-4391-8b33-c6b827156b8d2022-03-26T19:59:53ZParticle-method-based formulation of risk-sensitive filterJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:73fe6e0b-243b-4391-8b33-c6b827156b8dEnglishSymplectic Elements at Oxford2009Sadhu, SBhaumik, SDoucet, AGhoshal, TA novel particle implementation of risk-sensitive filters (RSF) for nonlinear, non-Gaussian state-space models is presented. Though the formulation of RSFs and its properties like robustness in the presence of parametric uncertainties are known for sometime, closed-form expressions for such filters are available only for a very limited class of models including finite state-space Markov chains and linear Gaussian models. The proposed particle filter-based implementations are based on a probabilistic re-interpretation of the RSF recursions. Accuracy of these filtering algorithms can be enhanced by choosing adequate number of random sample points called particles. These algorithms significantly extend the range of practical applications of risk-sensitive techniques and may also be used to benchmark other approximate filters, whose generic limitations are discussed. Appropriate choice of proposal density is suggested. Simulation results demonstrate the performance of the proposed algorithms. © 2008 Elsevier B.V. All rights reserved. |
| spellingShingle | Sadhu, S Bhaumik, S Doucet, A Ghoshal, T Particle-method-based formulation of risk-sensitive filter |
| title | Particle-method-based formulation of risk-sensitive filter |
| title_full | Particle-method-based formulation of risk-sensitive filter |
| title_fullStr | Particle-method-based formulation of risk-sensitive filter |
| title_full_unstemmed | Particle-method-based formulation of risk-sensitive filter |
| title_short | Particle-method-based formulation of risk-sensitive filter |
| title_sort | particle method based formulation of risk sensitive filter |
| work_keys_str_mv | AT sadhus particlemethodbasedformulationofrisksensitivefilter AT bhaumiks particlemethodbasedformulationofrisksensitivefilter AT douceta particlemethodbasedformulationofrisksensitivefilter AT ghoshalt particlemethodbasedformulationofrisksensitivefilter |