Conditional bias-penalized Kalman filter for improved estimation and prediction of extremes

2017 ◽  
Vol 32 (1) ◽  
pp. 183-201 ◽  
Author(s):  
Dong-Jun Seo ◽  
Miah Mohammad Saifuddin ◽  
Haksu Lee
Keyword(s):  
Kalman Filter ◽  
Estimation And Prediction

scholarly journals Estimation and Prediction of the Vehicle’s Motion Based on Visual Odometry and Kalman Filter

2012 ◽  
pp. 491-502 ◽  
Author(s):  
Basam Musleh ◽  
David Martin ◽  
Arturo de la Escalera ◽  
Domingo Miguel Guinea ◽  
Maria Carmen Garcia-Alegre
Keyword(s):  
Kalman Filter ◽  
Visual Odometry ◽  
Estimation And Prediction

A Kalman filter method for estimation and prediction of space–time data with an autoregressive structure

2019 ◽  
Vol 203 ◽  
pp. 117-130 ◽  
Author(s):  
Bernardo Lagos-Álvarez ◽  
Leonardo Padilla ◽  
Jorge Mateu ◽  
Guillermo Ferreira
Keyword(s):  
Kalman Filter ◽  
Space Time ◽  
Filter Method ◽  
Time Data ◽  
Estimation And Prediction ◽  
Kalman Filter Method

scholarly journals Iterative and Algebraic Algorithms for the Computation of the Steady State Kalman Filter Gain

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Nicholas Assimakis ◽  
Maria Adam
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Prediction Error ◽  
Covariance Matrices ◽  
Asymptotically Stable ◽  
System Parameters ◽  
Estimation And Prediction ◽  
Error Covariance ◽  
Algebraic Algorithms ◽  
Time Invariant

The Kalman filter gain arises in linear estimation and is associated with linear systems. The gain is a matrix through which the estimation and the prediction of the state as well as the corresponding estimation and prediction error covariance matrices are computed. For time invariant and asymptotically stable systems, there exists a steady state value of the Kalman filter gain. The steady state Kalman filter gain is usually derived via the steady state prediction error covariance by first solving the corresponding Riccati equation. In this paper, we present iterative per-step and doubling algorithms as well as an algebraic algorithm for the steady state Kalman filter gain computation. These algorithms hold under conditions concerning the system parameters. The advantage of these algorithms is the autonomous computation of the steady state Kalman filter gain.

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