A note on Kalman Filter

A practical note on evaluating Kalman filter performance optimality and degradation

This paper presents useful remarks to the readers on the Kalman filter (KF) performance optimality, degradation, and some innovation related parameters. Guidelines for efficient approach for evaluation of the KF performance optimality and sensitivity analysis are presented. Performance degradation due to uncertainty in process and measurement noise statistics is discussed. Consistency check bet...

A Pipelined Kalman Filter Architecture

Presented in this paper is a hardware-efficient pipelined Kalman filter architecture for recursive leastsquares (RLS) identification. The proposed architecture as developed via the relaxed look-ahead technique. This technique results in an extremely low hardware overhead at the expense of a slightly degraded performance. The hardware overhead due to pipelining consists of only the pipelining la...

A New Adaptive Extended Kalman Filter for a Class of Nonlinear Systems

This paper proposes a new adaptive extended Kalman filter (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed filter is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the n...

Tutorial on the Ensemble Kalman Filter ∗

The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component o...

On improving sensitivity of the Kalman filter