Title: An ensemble Kalman filtering algorithm for state estimation of jump Markov systems

Authors: S. Vasuhi; V. Vaidehi

Addresses: M.I.T., Anna University, Chennai, India ' M.I.T., Anna University, Chennai, India

Abstract: This paper presents a target model whose dynamics is modelled as a jump Markov process. The problem of state estimation of jump Markov system using ensemble Kalman filter (EnKF) is dealt here. The EnKF is designed for higher order nonlinear state estimation. In target tracking using jump process, accuracy of the state estimate is defined as a function of ensemble size. The jump Markov system minimises state error at the state estimation and assumes that all probability distributions involved are to be Gaussian. This paper proposes state estimation of jump Markov systems using EnKF, at each step the ensemble of state estimates are calculated. From the result, calculate the error statistics from ensemble forecasts which are dynamically propagated through nonlinear system.

Keywords: ensemble Kalman filter; EnKF; nonlinear state estimation; jump Markov systems; dymanic modelling; target tracking.

DOI: 10.1504/IJESMS.2016.073301

International Journal of Engineering Systems Modelling and Simulation, 2016 Vol.8 No.1, pp.1 - 7

Accepted: 24 Jul 2014
Published online: 30 Nov 2015 *

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