On the optimal and suboptimal nonlinear filtering problem for discrete time systems

Netto, Mendes; Gimeno, L.; Mendes, Mateus

doi:10.1109/cdc.1977.271644

Cited by 20 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Consider the application of SIS and SIR particle filtering to the following non‐Gaussian, nonlinear state‐space and observation model used as an illustrative example by several authors [ Andrade Netto et al , 1978; Gordon et al , 1993; Kitagawa , 1996; Doucet et al , 2000]:

where ω k and ν k are mutually independent Gaussian process and observation noises with σ w 2 = 10 and σ ν 2 = 1. The initial state is taken to be x 0 = 0.1.…”

Section: Particle Filteringmentioning

confidence: 99%

Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter

Moradkhani

Hsu

Gupta

et al. 2005

Water Resources Research

631

614

View full text Add to dashboard Cite

[1] Two elementary issues in contemporary Earth system science and engineering are (1) the specification of model parameter values which characterize a system and (2) the estimation of state variables which express the system dynamic. This paper explores a novel sequential hydrologic data assimilation approach for estimating model parameters and state variables using particle filters (PFs). PFs have their origin in Bayesian estimation. Methods for batch calibration, despite major recent advances, appear to lack the flexibility required to treat uncertainties in the current system as new information is received. Methods based on sequential Bayesian estimation seem better able to take advantage of the temporal organization and structure of information, so that better compliance of the model output with observations can be achieved. Such methods provide platforms for improved uncertainty assessment and estimation of hydrologic model components, by providing more complete and accurate representations of the forecast and analysis probability distributions. This paper introduces particle filtering as a sequential Bayesian filtering having features that represent the full probability distribution of predictive uncertainties. Particle filters have, so far, generally been used to recursively estimate the posterior distribution of the model state; this paper investigates their applicability to the approximation of the posterior distribution of parameters. The capability and usefulness of particle filters for adaptive inference of the joint posterior distribution of the parameters and state variables are illustrated via two case studies using a parsimonious conceptual hydrologic model. Hsu, H. Gupta, and S. Sorooshian (2005), Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter, Water Resour. Res., 41, W05012,

show abstract

where ω k and ν k are mutually independent Gaussian process and observation noises with σ w 2 = 10 and σ ν 2 = 1. The initial state is taken to be x 0 = 0.1.…”

Section: Particle Filteringmentioning

confidence: 99%

Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter

Moradkhani

Hsu

Gupta

et al. 2005

Water Resources Research

631

614

View full text Add to dashboard Cite

show abstract

“…Το θέμα της μη γραμμικής μοντελοποίησης έχει ελκύσει αξιοσημείωτη προσοχή και έχει μελετηθεί σε μεγάλο βαθμό από διαφορετικά σημεία περιλαμβάνοντας ένα μεγάλο αριθμό από αριθμητικούς προσεγγιστικά αλγόριθμους, για την εκτίμηση μη γραμμικών συστημάτων για προκαθορισμένα μοντέλα πληροφορίας (Lainiotis D.G. et al, 1998;Haykin S. & Li L., 1995;Andrade M. at al., 1978).…”

Section: ο αλγόριθμος αναγνώρισης μη γραμμικών συστημάτωνunclassified

Αλγόριθμοι Αναγνώρισης Δομής Συστημάτων Με Μεταβαλλόμενες Παραμέτρους

Ντεμίρης¹

View full text Add to dashboard Cite

Efficient inference for nonlinear state space models: An automatic sample size selection rule

Cheng

Chan

2019

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

On the optimal and suboptimal nonlinear filtering problem for discrete time systems

Cited by 20 publications

References 8 publications

Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter

Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter

Αλγόριθμοι Αναγνώρισης Δομής Συστημάτων Με Μεταβαλλόμενες Παραμέτρους

Efficient inference for nonlinear state space models: An automatic sample size selection rule

Contact Info

Product

Resources

About