Second order state and covariance estimation for nonlinear stochastic systems

Olusegun M. Otunuga, Gangaram S. Ladde

Research output: Contribution to journalArticlepeer-review


The Kalman Filter is one of the most commonly used methods in state and parameter estimation problems in linear and nonlinear stochastic systems analysis. This paper deals with further extensions of the existing Extended Kalman Filter (EKF) approach to study state and parameter estimation problems of nonlinear system of stochastic differential equations. The main focus in this paper is to reduce the magnitude of errors in the existing Kalman Filter schemes. This is achieved by approximating the state conditional mean and covariances using a second degree polynomials. In addition, the drift, diffusion and the observations are approximated using a multidimensional extension of Sterling's interpolation formula.

Original languageEnglish (US)
Pages (from-to)89-126
Number of pages38
JournalNeural, Parallel and Scientific Computations
Issue number1-2
StatePublished - 2014
Externally publishedYes


  • Algorithm
  • Kalman filter
  • Nonlinear
  • Stirling interpolation
  • Taylor's series

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Networks and Communications
  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'Second order state and covariance estimation for nonlinear stochastic systems'. Together they form a unique fingerprint.

Cite this