A quasi-Gaussian Kalman filter

Suman Chakravorty, Mrinal Kumar, Puneet Singla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


In this paper, we present a Gaussian approximation to the nonlinear filtering problem, namely the quasi-Gaussian Kalman filter. Starting with the recursive Bayes filter, we invoke the Gaussian approximation to reduce the filtering problem into an optimal Kalman recursion. We use the moment evolution equations for stochastic dynamic equations to evaluate the prediction terms in the Kalman recursions. We propose two methods, one based on stochastic linearization and the other based on a direct evaluation of the innovations terms, to perform the measurement update in the Kalman recursion. We test our filter on a simple two dimensional example, where the nonlinearity of the system dynamics and the measurement equations can be varied, and compare its performance to that of an extended Kalman filter.

Original languageEnglish (US)
Title of host publicationProceedings of the 2006 American Control Conference
Number of pages6
StatePublished - Dec 1 2006
Event2006 American Control Conference - Minneapolis, MN, United States
Duration: Jun 14 2006Jun 16 2006


Other2006 American Control Conference
CountryUnited States
CityMinneapolis, MN


All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Chakravorty, S., Kumar, M., & Singla, P. (2006). A quasi-Gaussian Kalman filter. In Proceedings of the 2006 American Control Conference (Vol. 2006, pp. 970-975). [1655484]