Articles | Volume 14, issue 4
https://doi.org/10.5194/npg-14-395-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
https://doi.org/10.5194/npg-14-395-2007
© Author(s) 2007. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
16 Jul 2007
Merging particle filter for sequential data assimilation
S. Nakano
The Institute of Statistical Mathematics, Research Organization of Information and Systems, Japan
Japan Science and Technology Agency, Japan
G. Ueno
The Institute of Statistical Mathematics, Research Organization of Information and Systems, Japan
Japan Science and Technology Agency, Japan
T. Higuchi
The Institute of Statistical Mathematics, Research Organization of Information and Systems, Japan
Japan Science and Technology Agency, Japan
Viewed
Total article views: 3,386 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,964 | 1,233 | 189 | 3,386 | 239 | 280 |
- HTML: 1,964
- PDF: 1,233
- XML: 189
- Total: 3,386
- BibTeX: 239
- EndNote: 280
Cited
81 citations as recorded by crossref.
- A New Evolutionary Particle Filter for the Prevention of Sample Impoverishment . Seongkeun Park et al. https://doi.org/10.1109/TEVC.2008.2011729
- PREDICTED PERFORMANCE OF ESTIMATED SEEPAGE ANALYSIS MODEL BY DATA ASSIMILATION BASED ON MEASUREMENT DATA OF SOIL COLUMN TEST S. ITO et al. https://doi.org/10.2208/jscejge.76.4_350
- Absolute measurement of subcriticality in dollar units based on lag-1 autocovariance of zero-power reactor noise R. Hirota et al. https://doi.org/10.1080/00223131.2025.2530788
- Adaptive approximation of higher order posterior statistics W. Lee https://doi.org/10.1016/j.jcp.2013.11.015
- Bayesian updating of model parameters using adaptive Gaussian process regression and particle filter I. Yoshida et al. https://doi.org/10.1016/j.strusafe.2023.102328
- Parameter identification for Cam-clay model in partial loading model tests using the particle filter T. Shuku et al. https://doi.org/10.1016/j.sandf.2012.02.006
- A Localized Particle Filter for High-Dimensional Nonlinear Systems J. Poterjoy https://doi.org/10.1175/MWR-D-15-0163.1
- Estimation of electrohydrodynamic force and gas heating fields of plasma actuators using data assimilation Y. Kaneko et al. https://doi.org/10.1016/j.elstat.2026.104381
- PROPOSAL OF THE METHOD ESTIMATING SEEPAGE ANALYSIS MODEL INCLUDING A BOUNDARY CONDITION USING MERGING PARTICLE FILTER S. ITO et al. https://doi.org/10.2208/jscejge.76.1_52
- Two-Stage Filtering for Joint State-Parameter Estimation N. Santitissadeekorn & C. Jones https://doi.org/10.1175/MWR-D-14-00176.1
- ESTIMATION OF POSTERIOR PROBABILITY DENSITY DISTRIBUTIONS BY SOME FILTERING ALGORITHMS FOR OBSERVATION UPDATE T. SHUKU et al. https://doi.org/10.2208/jscejam.71.I_59
- Hybrid three-dimensional variation and particle filtering for nonlinear systems H. Leng & J. Song https://doi.org/10.1088/1674-1056/22/3/030505
- The statistical observation localized equivalent-weights particle filter in a simple nonlinear model Y. Zhao et al. https://doi.org/10.1007/s13131-021-1876-1
- ESTIMATION OF HYDRAULIC CONDUCTIVITY AND SWCC PARAMETERS FOR UNSATURATED SOIL BASED ON FIELD MINI DISK INFILTROMETER TESTS AND MERGING PARTICLE FILTER X. LI et al. https://doi.org/10.2208/journalofjscesp.25-15001
- Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography A. Schöniger et al. https://doi.org/10.1029/2011WR010462
- Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al. https://doi.org/10.1002/qj.3551
- Physics-informed data assimilation model for displacement prediction of hydrodynamic pressure-driven landslide Y. Liu et al. https://doi.org/10.1016/j.compgeo.2024.106085
- Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation M. Bocquet et al. https://doi.org/10.1175/2010MWR3164.1
- ESTIMATION OF BRIDGE DEFLECTION RESPONSE UNDER PASSING TRAIN LOADS BASED ON ACCELERATION K. MATSUOKA et al. https://doi.org/10.2208/jscejseee.69.527
- Estimating cellular parameters through optimization procedures: elementary principles and applications A. Kimura et al. https://doi.org/10.3389/fphys.2015.00060
- Estimating the parameters of neural mass models including time delay and nonlinearity using a particle filter: a preliminary study toward model‐based EEG analysis Y. Ogawa et al. https://doi.org/10.1002/tee.22481
- Ensemble Riemannian data assimilation over the Wasserstein space S. Tamang et al. https://doi.org/10.5194/npg-28-295-2021
- Nonlinear data assimilation in geosciences: an extremely efficient particle filter P. van Leeuwen https://doi.org/10.1002/qj.699
- Identification of ship motion performance of coupled heave and pitch using experimental irregular time-series data T. Hanaki & M. Minoura https://doi.org/10.1007/s00773-022-00887-5
- State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems S. Vetra-Carvalho et al. https://doi.org/10.1080/16000870.2018.1445364
- On Two Localized Particle Filter Methods for Lorenz 1963 and 1996 Models N. Schenk et al. https://doi.org/10.3389/fams.2022.920186
- Bayesian Updating of Model Parameters by Iterative Particle Filter with Importance Sampling I. Yoshida & T. Shuku https://doi.org/10.1061/AJRUA6.0001047
- Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation J. Wang et al. https://doi.org/10.5194/nhess-19-1387-2019
- Dual-particle-filtering for Recursive Estimation of Agricultural-machinery Dynamics M. Cooper & T. Perez https://doi.org/10.1016/j.ifacol.2018.09.210
- A coherent structure approach for parameter estimation in Lagrangian Data Assimilation J. Maclean et al. https://doi.org/10.1016/j.physd.2017.08.007
- Ensemble data assimilation for earthquake sequences: probabilistic estimation and forecasting of fault stresses Y. van Dinther et al. https://doi.org/10.1093/gji/ggz063
- Numerical simulation of real-world flows T. Hayase https://doi.org/10.1088/0169-5983/47/5/051201
- Data assimilation using the particle filter for identifying the elasto‐plastic material properties of geomaterials A. Murakami et al. https://doi.org/10.1002/nag.2125
- Comparison and combination of EAKF and SIR-PF in the Bayesian filter framework Z. Shen et al. https://doi.org/10.1007/s13131-015-0757-x
- A new formulation of vector weights in localized particle filters Z. Shen et al. https://doi.org/10.1002/qj.3180
- Seepage analysis model based on field measurement data for estimation of posterior parameter distribution using merging particle filter S. Ito et al. https://doi.org/10.1016/j.sandf.2024.101442
- Efficient nonlinear data-assimilation in geophysical fluid dynamics P. van Leeuwen https://doi.org/10.1016/j.compfluid.2010.11.011
- Marginalized Particle Filtering Framework for Tuning of Ensemble Filters V. Šmídl & R. Hofman https://doi.org/10.1175/2011MWR3586.1
- Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. https://doi.org/10.5194/hess-25-3319-2021
- PROPOSAL FOR ESTIMATING UNSATURATED SOIL HYDRAULIC PARAMETERS BASED ON MEASUREMENT DATA FROM PORTABLE MINI DISK INFILTROMETER S. ITO & K. SAKO https://doi.org/10.2208/jscejj.23-00196
- Particle filter for model updating and reliability estimation of existing structures I. Yoshida & M. Akiyama https://doi.org/10.12989/sss.2013.11.1.103
- METHOD FOR ESTIMATING SEEPAGE ANALYSIS MODEL INCLUDING BOUNDARY CONDITION USING MERGING PARTICLE FILTER S. ITO et al. https://doi.org/10.2208/journalofjsce.10.1_368
- Data assimilation of RANS turbulence model parameters using pressure measurements on a 3D ship hull in towing-tank tests T. Hanaki et al. https://doi.org/10.1007/s00773-026-01126-x
- A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation S. Metref et al. https://doi.org/10.5194/npg-21-869-2014
- On Ensemble Kalman Filter, Particle Filter, and Gaussian Particle Filter M. Murata & K. Hiramatsu https://doi.org/10.5687/iscie.29.448
- Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters I. Hoteit et al. https://doi.org/10.1175/2011MWR3640.1
- Estimation of a Long-Term Variation of a Magnetic-Storm Index Using the Merging Particle Filter S. NAKANO & T. HIGUCHI https://doi.org/10.1587/transinf.E92.D.1382
- Efficient Kernel-Based Ensemble Gaussian Mixture Filtering B. Liu et al. https://doi.org/10.1175/MWR-D-14-00292.1
- Modelling changes in travel behaviour mechanisms through a high-order hidden Markov model Z. Zhu et al. https://doi.org/10.1080/23249935.2022.2130731
- Composing a surrogate observation operator for sequential data assimilation K. Akita et al. https://doi.org/10.14495/jsiaml.14.123
- Data assimilation to determine the electrohydrodynamic force of plasma actuator Y. Kaneko et al. https://doi.org/10.1016/j.sna.2024.115320
- A Hybrid Ensemble Kalman Filter to Mitigate Non-Gaussianity in Nonlinear Data Assimilation T. TSUYUKI https://doi.org/10.2151/jmsj.2024-027
- Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter H. Cheng et al. https://doi.org/10.1007/s10035-017-0781-y
- Comparison of sequential data assimilation methods for the Kuramoto–Sivashinsky equation M. Jardak et al. https://doi.org/10.1002/fld.2020
- Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model A. Subramanian et al. https://doi.org/10.1175/JAS-D-11-0332.1
- Computational aspects of sequential Monte Carlo filter and smoother G. Kitagawa https://doi.org/10.1007/s10463-014-0446-0
- Particle Filtering in Geophysical Systems P. van Leeuwen https://doi.org/10.1175/2009MWR2835.1
- A sequential Bayesian approach for the estimation of the age–depth relationship of the Dome Fuji ice core S. Nakano et al. https://doi.org/10.5194/npg-23-31-2016
- Exploring Practical Estimates of the Ensemble Size Necessary for Particle Filters L. Slivinski & C. Snyder https://doi.org/10.1175/MWR-D-14-00303.1
- An Extension of the Ensemble Kalman Filter for Estimating the Observation Error Covariance Matrix Based on the Variational Bayes’s Method A. Nakabayashi & G. Ueno https://doi.org/10.1175/MWR-D-16-0139.1
- Non-storm irregular variation of the <I>D</I><sub>st</sub> index S. Nakano & T. Higuchi https://doi.org/10.5194/angeo-30-153-2012
- Obstacles to High-Dimensional Particle Filtering C. Snyder et al. https://doi.org/10.1175/2008MWR2529.1
- International journal of computational fluid dynamics real-time prediction of unsteady flow based on POD reduced-order model and particle filter R. Kikuchi et al. https://doi.org/10.1080/10618562.2016.1198782
- An iterative Bayesian filtering framework for fast and automated calibration of DEM models H. Cheng et al. https://doi.org/10.1016/j.cma.2019.01.027
- PROPOSAL OF A TRAFFIC STATE ESTIMATION METHOD FOR ACCIDENTS BY FUSING SENSING DATA AND TRAFFIC FLOW MODEL Y. KAWASAKI et al. https://doi.org/10.2208/jscejj.23-20011
- A local particle filter for high-dimensional geophysical systems S. Penny & T. Miyoshi https://doi.org/10.5194/npg-23-391-2016
- From the Ensemble Kalman Filter to the Particle Filter: a comparative study in rainfall-runoff models* D. Plaza et al. https://doi.org/10.3182/20110828-6-IT-1002.02326
- Traffic state estimation on a two-dimensional network by a state-space model Y. Kawasaki et al. https://doi.org/10.1016/j.trc.2019.03.016
- Application of Kalman Filter to Large-scale Geospatial Data: Modeling Population Dynamics H. Akatsuka & M. Terada https://doi.org/10.1145/3563692
- Improved particle filter in data assimilation . Leng Hong-Ze et al. https://doi.org/10.7498/aps.61.070501
- Key Points on The Reliability Engineering Y. NOMURA et al. https://doi.org/10.2472/jsms.65.403
- Entropy Based Student’s t-Process Dynamical Model A. Nono et al. https://doi.org/10.3390/e23050560
- Landslide displacement prediction based on time series analysis and data assimilation with hydrological factors J. Wang et al. https://doi.org/10.1007/s12517-020-05452-1
- Data assimilation: Particle filter and artificial neural networks H. Furtado et al. https://doi.org/10.1088/1742-6596/135/1/012073
- Estimation of Intracellular Calcium Ion Concentration by Nonlinear State Space Modeling and Expectation-Maximization Algorithm for Parameter Estimation T. Tsunoda et al. https://doi.org/10.1143/JPSJ.79.124801
- Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter C. Montzka et al. https://doi.org/10.1016/j.jhydrol.2011.01.020
- A Moment Matching Ensemble Filter for Nonlinear Non-Gaussian Data Assimilation J. Lei & P. Bickel https://doi.org/10.1175/2011MWR3553.1
- A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation J. Maclean & E. Spiller https://doi.org/10.3934/fods.2021019
- Neural networks for emulation variational method for data assimilation in nonlinear dynamics H. Furtado et al. https://doi.org/10.1088/1742-6596/285/1/012036
- A modified ensemble Kalman particle filter for non‐Gaussian systems with nonlinear measurement functions Z. Shen & Y. Tang https://doi.org/10.1002/2014MS000373
- CONSTRUCTION OF ESTIMATION METHOD OF TRAFFIC FLOW RATE AT TRAFFIC ACCIDENT BY STATE SPACE MODEL Y. KAWASAKI et al. https://doi.org/10.2208/jscejipm.76.5_I_1297
Latest update: 26 Sep 2026
