On the general Kalman filter for discrete time stochastic fractional systems

Mechatronics ◽  
2013 ◽  
Vol 23 (7) ◽  
pp. 764-771 ◽  
Author(s):  
Hoda Sadeghian ◽  
Hassan Salarieh ◽  
Aria Alasty ◽  
Ali Meghdari
Keyword(s):  
Kalman Filter ◽  
Discrete Time ◽  
Fractional Systems

Estimation of Steady-State Optimal Filter Gain From Nonoptimal Kalman Filter Residuals

1994 ◽  
Vol 116 (3) ◽  
pp. 550-553 ◽  
Author(s):  
Chung-Wen Chen ◽  
Jen-Kuang Huang
Keyword(s):  
Kalman Filter ◽  
Steady State ◽  
Finite Number ◽  
Discrete Time ◽  
Stochastic System ◽  
Moving Average ◽  
Ar Model ◽  
Numerical Example ◽  
Measurement Noises ◽  
Time Invariant

This paper proposes a new algorithm to estimate the optimal steady-state Kalman filter gain of a linear, discrete-time, time-invariant stochastic system from nonoptimal Kalman filter residuals. The system matrices are known, but the covariances of the white process and measurement noises are unknown. The algorithm first derives a moving average (MA) model which relates the optimal and nonoptimal residuals. The MA model is then approximated by inverting a long autoregressive (AR) model. From the MA parameters the Kalman filter gain is calculated. The estimated gain in general is suboptimal due to the approximations involved in the method and a finite number of data. However, the numerical example shows that the estimated gain could be near optimal.

scholarly journals Ellipsoidal and Gaussian Kalman Filter Model for Discrete-Time Nonlinear Systems

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1168 ◽  
Author(s):  
Ligang Sun ◽  
Hamza Alkhatib ◽  
Boris Kargoll ◽  
Vladik Kreinovich ◽  
Ingo Neumann
Keyword(s):  
Kalman Filter ◽  
Nonlinear Systems ◽  
State Estimation ◽  
Discrete Time ◽  
Probability Distributions ◽  
Nonlinear Problems ◽  
The State ◽  
Filter Model ◽  
Highly Nonlinear ◽  
Correction Step

In this paper, we propose a new technique—called Ellipsoidal and Gaussian Kalman filter—for state estimation of discrete-time nonlinear systems in situations when for some parts of uncertainty, we know the probability distributions, while for other parts of uncertainty, we only know the bounds (but we do not know the corresponding probabilities). Similarly to the usual Kalman filter, our algorithm is iterative: on each iteration, we first predict the state at the next moment of time, and then we use measurement results to correct the corresponding estimates. On each correction step, we solve a convex optimization problem to find the optimal estimate for the system’s state (and the optimal ellipsoid for describing the systems’s uncertainty). Testing our algorithm on several highly nonlinear problems has shown that the new algorithm performs the extended Kalman filter technique better—the state estimation technique usually applied to such nonlinear problems.

Discrete‐time invariant extended Kalman filter on matrix Lie groups

2020 ◽  
Vol 30 (12) ◽  
pp. 4449-4462
Author(s):  
Karmvir Singh Phogat ◽  
Dong Eui Chang
Keyword(s):  
Kalman Filter ◽  
Lie Groups ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Time Invariant

Discrete-time distributed Kalman filter design for formations of autonomous vehicles

2018 ◽  
Vol 75 ◽  
pp. 55-68 ◽  
Author(s):  
Daniel Viegas ◽  
Pedro Batista ◽  
Paulo Oliveira ◽  
Carlos Silvestre
Keyword(s):  
Kalman Filter ◽  
Discrete Time ◽  
Autonomous Vehicles ◽  
Filter Design
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