Robust particle filter formulations with application to terrain-aided navigation: ROBUST PARTICLE FILTERS FOR TERRAIN-AIDED NAVIGATION

Teixeira, Francisco Curado; Quintas, João; Maurya, Pramod; Pascoal, António

doi:10.1002/acs.2692

Title: Robust particle filter formulations with application to terrain-aided navigation: ROBUST PARTICLE FILTERS FOR TERRAIN-AIDED NAVIGATION

Journal Article · Thu Jun 09 00:00:00 EDT 2016 · International Journal of Adaptive Control and Signal Processing

DOI:https://doi.org/10.1002/acs.2692· OSTI ID:1400764

^[1]; Quintas, João ^[1]; Maurya, Pramod ^[2]; Pascoal, António ^[3]

Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST), University of Lisbon, Portugal
National Institute of Oceanography, Marine Instrumentation Division, Dona Paula Goa India
Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST), University of Lisbon, Portugal, National Institute of Oceanography, Marine Instrumentation Division, Dona Paula Goa India

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Sponsoring Organization:: USDOE Office of Nuclear Energy (NE), Fuel Cycle Technologies (NE-5)

Grant/Contract Number:: PEst-OE/EEI/LA0009/2011

OSTI ID:: 1400764

Journal Information:: International Journal of Adaptive Control and Signal Processing, Journal Name: International Journal of Adaptive Control and Signal Processing Vol. 31 Journal Issue: 4; ISSN 0890-6327

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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Cited by: 43 works

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References (13)

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Particle filter theory and practice with positioning applications Gustafsson, Fredrik IEEE Aerospace and Electronic Systems Magazine, Vol. 25, Issue 7 https://doi.org/10.1109/MAES.2010.5546308	journal	July 2010
Filtering via Simulation: Auxiliary Particle Filters Pitt, Michael K.; Shephard, Neil Journal of the American Statistical Association, Vol. 94, Issue 446 https://doi.org/10.1080/01621459.1999.10474153	journal	June 1999
Harold Jeffreys’s Theory of Probability Revisited Robert, Christian P.; Chopin, Nicolas; Rousseau, Judith Statistical Science, Vol. 24, Issue 2 https://doi.org/10.1214/09-STS284	journal	May 2009
Sequential Imputations and Bayesian Missing Data Problems Kong, Augustine; Liu, Jun S.; Wong, Wing Hung Journal of the American Statistical Association, Vol. 89, Issue 425 https://doi.org/10.1080/01621459.1994.10476469	journal	March 1994
Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference Chopin, Nicolas The Annals of Statistics, Vol. 32, Issue 6 https://doi.org/10.1214/009053604000000698	journal	December 2004
A Bayesian approach to problems in stochastic estimation and control Ho, Y.; Lee, R. IEEE Transactions on Automatic Control, Vol. 9, Issue 4 https://doi.org/10.1109/TAC.1964.1105763	journal	October 1964
Novel approach to nonlinear/non-Gaussian Bayesian state estimation Gordon, N. J.; Salmond, D. J.; Smith, A. F. M. IEE Proceedings F Radar and Signal Processing, Vol. 140, Issue 2 https://doi.org/10.1049/ip-f-2.1993.0015	journal	January 1993
Terrain Navigation for Underwater Vehicles Using the Correlator Method Nygren, I.; Jansson, M. IEEE Journal of Oceanic Engineering, Vol. 29, Issue 3 https://doi.org/10.1109/JOE.2004.833222	journal	July 2004
Following a moving target-Monte Carlo inference for dynamic Bayesian models Gilks, Walter R.; Berzuini, Carlo Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 1 https://doi.org/10.1111/1467-9868.00280	journal	February 2001
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo Cappe, Olivier; Godsill, Simon J.; Moulines, Eric Proceedings of the IEEE, Vol. 95, Issue 5 https://doi.org/10.1109/JPROC.2007.893250	journal	May 2007

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