Data assimilation for dispersion models

2006 9th International Conference on Information Fusion, FUSION

Volumn , Issue , 2006, Pages

  Reddy, K V Umamaheswara ^a   Yang, Cheng ^a   Singh, Tarunraj ^a   Scott, Peter D ^b  

^a the State University of New York   (United States)

^b UNIVERSITY AT BUFFALO   (United States)

Author keywords

Chem bio dispersion; Data assimilation; Ensemble Kalman filter; Ensemble square root filter; Particle filter

Indexed keywords

CONTROL THEORY; DATA FUSION; DIFFERENCE EQUATIONS; DIFFERENTIAL EQUATIONS; DIFFERENTIATION (CALCULUS); DISPERSION (WAVES); EQUATIONS OF STATE; ESTIMATION; FUSION REACTIONS; INFORMATION FUSION; KALMAN FILTERS; NUCLEAR PHYSICS; PARAMETER ESTIMATION; SENSOR DATA FUSION; SIGNAL FILTERING AND PREDICTION; STATE SPACE METHODS; WAVE FILTERS;

CHEM-BIO DISPERSION; COMPUTATIONALLY EXPENSIVE; DATA ASSIMILATION; DISPERSION MODELLING; ENSEMBLE KALMAN FILTER; ENSEMBLE SQUARE ROOT FILTER; HIGH DIMENSIONALITY; INTERNATIONAL CONFERENCES; KAIMAN FILTERS; KALMAN FILTER THEORY; LARGE-SCALE MODELLING; MODEL PARAMETERS; MONTE CARLO FILTERING; NON-GAUSSIAN; NON-LINEARITIES; ONE-DIMENSIONAL; PARTICLE FILTER; PARTICLE FILTERING; REAL-TIME ESTIMATION; SPHERICAL DIFFUSION MODEL; STATE-SPACE MODELING;

PARTIAL DIFFERENTIAL EQUATIONS;

EID: 50149102191     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2006.301615     Document Type: Conference Paper

References (12)

1. Andrew H. Jazwinski. Stochastic Processes and Filtering Theory, chapter 4-7. Academic Press, San Diego, GA, 1970.
2. Simon J. Julier, Jeffrey K. Uhlmann, and Hugh F. Durrant-Whyte. A new approach for filtering nonlinear systems. In Proceedings of the American Control Conference, pages 1628-1632, Seattle, WA, June 1995.
3. B. Ristic, S. Arulampalam, and N. Gordon. Beyond the Kalman Filter: Particle Filters for Tracking Applications, chapter 3. Artech House, Boston, MA, 2004.
4. F. Daum and J. Huang. Curse of dimensionality arid particle filters. In Proceedings of the IEEE Aerospace Conference, volume 4, pages 1979-1993, Big Sky, MT, March 2003.
5. John Lewis, S. Lakshmivarahan, and Sudarshan Dhall. Dynamic Data Assimilation: A Least Squares Approach. Cambridge University Press, Cambridge, UK, 2006.
6. Geir Evensen. The ensemble kalman filter: Theoritical formulation and practical implementation. Ocean Dynamics, 53:343-367, 2003.
7. Geir Evensen. Sampling strategies and square root analysis schemes for the enkf. Ocean Dynamics, 54:539-560, 2004.
8. M. K. Tippett, J. L. Anderson, C. H. Bishop, T. M. Hamill, and J. S. Whitaker. Ensemble square-root filters. Monthly Weather Review, 131:1485-1490, 2002.
9. Thomas M. Hamill, Jeffrey S. Whitaker, and Chris Snyder. Distance-dependent filtering of background error covariance estimates in an ensemble kalman filter. Monthly Weather Review, 129:2776-2790, 2001.
10. O. Kolditz. Computational Methods in Environmental Fluid Mechanics, chapter 6. Springer, Berlin, 2002.
11. Yaakov Bar-Shalom, X. Rong Li, and Thiagalingam Kirubarajan. Estimation with Applications to Tracking and Navigation. John Wiley & Sons, 2001.
12. A. Doucet, N. de Freitas, and N. Gordan, editors. Sequential Monte Carlo Methods in Practice, chapter 1. Springer, New York, NY, 2001.