On the design of a stable adaptive filter for state estimation in high dimensional systems

Hoang, Hong Son; Baraille, Rémy; Talagrand, O.

doi:10.1016/s0005-1098(00)00175-8

Cited by 21 publications

(53 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As proved in [7], the real dominant ScVs (DScVs) play the same role in ensuring filter stability as the dominant EVs or SVs. The Schur approach enjoys all advantages of the singular vector approach and in addition, it does not require the adjoint code.…”

Section: Order Reductionmentioning

confidence: 99%

“…In [7,8] the choice of r and parameterization of the gain e P K are studied from the point of view of filter stability. It is well known whatever is a filter, the question of ensuring its stability is of the first importance: instability causes the error growth and it can completely destroy the filter.…”

Section: Order Reductionmentioning

confidence: 99%

“…In [7]   e M  should be SPD. For adaptation purpose, the following stabilizing gain structure is of more interest due to linear dependence of the gain on the tuning parameters…”

Section: Gain Parametrizationmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Filter for High Dimensional Inverse Engineering Problems: From Theory to Practical Implementation

Hoang¹,

Barailles²

2013

ENG

View full text Add to dashboard Cite

The inverse engineering problems approach is a discipline that is growing very rapidly. The inverse problems we consider here concern the way to determine the state and/or parameters of the physical system of interest using observed measurements. In this context the filtering algorithms constitute a key tool to offer improvements of our knowledge on the system state, its forecast… which are essential, in particular, for oceanographic and meteorologic operational systems. The objective of this paper is to give an overview on how one can design a simple, no time-consuming Reduced-Order Adaptive Filter (ROAF) to solve the inverse engineering problems with high forecasting performance in very high dimensional environment.

show abstract

Section: Order Reductionmentioning

confidence: 99%

Section: Order Reductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Filter for High Dimensional Inverse Engineering Problems: From Theory to Practical Implementation

Hoang¹,

Barailles²

2013

ENG

View full text Add to dashboard Cite

show abstract

“…[12], the different classes of parameterized filters are found which belong to the class of stable reduced-order filters (ROF) [10,13].…”

Section: Adaptive Filtering (Af)mentioning

confidence: 99%

“…The choice of a reduced space is of primary importance since it depends on the main characteristics of the filter known as stability. As proved in [12], under detectability condition, stability of the filter is ensured by forming the columns of P r, k from unstable and stable eigenvectors (or singular vectors, Schur vectors) of the fundamental matrix Φ k , and one can choose…”

Section: Adaptive Filtering (Af)mentioning

confidence: 99%

A Comparison Study on Performance of an Adaptive Filter with Other Estimation Methods for State Estimation in High-Dimensional System

Hoang¹,

Baraille²

2017

Advances in Statistical Methodologies and Their Application to Real Problems

View full text Add to dashboard Cite

In this chapter, performance comparison between the adaptive filter (AF) and other estimation methods, especially with the variational method (VM), is given in the context of data assimilation problem in dynamical systems with (very) high dimension. The emphasis is put on the importance of innovation approach which is a basis for construction of the AF as well as the choice of a set of tuning parameters in the filter gain. It will be shown that the innovation representation for the initial dynamical system plays essential role in providing stability of the assimilation algorithms for stable and unstable system dynamics. Numerical experiments will be given to illustrate the performance of the AF.

show abstract

A Posteriori Validation of Assimilation Algorithms

Talagrand

2003

Data Assimilation for the Earth System

View full text Add to dashboard Cite

On the design of a stable adaptive filter for state estimation in high dimensional systems

Cited by 21 publications

References 30 publications

Adaptive Filter for High Dimensional Inverse Engineering Problems: From Theory to Practical Implementation

Adaptive Filter for High Dimensional Inverse Engineering Problems: From Theory to Practical Implementation

A Comparison Study on Performance of an Adaptive Filter with Other Estimation Methods for State Estimation in High-Dimensional System

A Posteriori Validation of Assimilation Algorithms

Contact Info

Product

Resources

About