The optimal two-stage Kalman estimator (OTSKE) is analyzed (CS Hsieh and FC Chen, see
ibid., vol. 44. p. 194-9, 1999). Comparison with two estimators shows that the optimal two …

An approach to the control of divergence in Kalman filter algorithms<xref ref-type='fn' rid='FN0001'>†&l
Page 1 INT. J. CONTROL, l('73, VOL. 17, NO.4, 741-746 An approach to the control of …

This article presents a Kalman-type recursive estimator for discrete-time systems with a
measurement noise modeled by a Gaussian-uniform mixture. The objective is to deal with …

The ensemble Kalman filter is a Monte Carlo method for state estimation of nonlinear
models, developed as an alternative or improvement of the extended Kalman filter. In this …

The optimal filtering equations, as derived by Kalman [1],[2], require the specification of a
number of models for a given application. This paper concerns itself with the effect of errors …

The problem of estimating the state of a linear dynamic system driven by additive Gaussian
noise with unknown time varying statistics is considered. Estimates of the state of the system …

Some extensions to the results of Hsieh's and Ignagni's work for the two-stage Kalman filter
are given, in which the bias vector is expressed by a first-order auto-regressive model. Two …

Recently, the adaptive two‐stage Kalman filter, which can track unknown random bias, was
proposed. This filter can be used for systems with unknown random bias on the assumption …

In recent years, minimum-variance recursive estimators, such as the Kalman filter, have
been used successfully in many practical applications. However, a common problem, known …

It is well known that the optimality of the Kalman filter relies on the Gaussian distribution of
process and observation model errors, which in many situations is well justified–. However …