Unscented Kalman Filter/Smoother for a CBRN puff-based dispersion model

Author

Terejanu, Gabriel ; Singh, Tarunraj ; Scott, Peter D.

Author_Institution

Univ. at Buffalo, Buffalo

fYear

2007

fDate

9-12 July 2007

Firstpage

1

Lastpage

8

Abstract

Fixed interval smoothing for systems with nonlinear process and measurement models is studied and applied to the assimilation of sensor data in a Chemical, Biological, Radiological or Nuclear (CBRN) incident scenario. A two-filter smoother that uses a Backward Sigma-Point Information Filter, and also a forward-backward Rauch-Tung-Striebel (RTS) smoothing form are re-derived using the weighted statistical linearization concept. Both methods are derived in the context of the Unscented Kalman Filter. The square root version of the resulting RTS Unscented Kalman Filter / Smoother is applied to a CBRN dispersion puff-based model with variable state dimension, and the data assimilation performance of the method is compared with a Particle Filter implementation.

Keywords

Kalman filters; data assimilation; smoothing methods; statistical analysis; CBRN Puff-based dispersion model; Rauch-Tung-Striebel smoothing; backward sigma-point information filter; chemical biological radiological nuclear; data assimilation performance; nonlinear process; statistical linearization concept; unscented Kalman filter-smoother; variable state dimension; Biological system modeling; Biosensors; Chemical and biological sensors; Chemical processes; Data assimilation; Information filters; Nuclear measurements; Particle filters; Sensor systems; Smoothing methods; chemical dispersion; data assimilation; puff-based model; sigma-point filtering; unscented kalman smoother; variable state dimension;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2007 10th International Conference on

Conference_Location

Quebec, Que.

Print_ISBN

978-0-662-45804-3

Electronic_ISBN

978-0-662-45804-3

Type

conf

DOI

10.1109/ICIF.2007.4408076

Filename

4408076