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Kalman Filtering Toolbox Examples
and Applications
To
go to one of the below example/application click the link
The M-KFToolbox manual
includes numerous illustrative examples that can be used to solve typical
problems encountered in discrete Kalman filtering applications. The following 13
examples are presented in detail (with input/output data):
1.
Generation
of a random walk process, see XRWALK
2.
Generation
of a first order Gauss-Markov
process, see XGMP1
3.
Generation
of a second order Gauss-Markov process, see XGMP2
4.
Generation
of observed data (measurements) for a linear time-invariant model, see XGOBSD
5.
Covariance analysis by
using conventional or alternate conventional discrete Kalman filter formulation,
see XKFCOV
6.
Decorrelation of the
measurement noise by using U-D decomposition, see XMNDEC
7.
Steady state solution of
a discrete Riccati equation, see XMDRIC
8.
Suboptimal
(constant gain) discrete Kalman filter design, see XSDKF
9.
Smoothing
process by using the Rauch-Tung-Striebel algorithm, see
XSMCOVPS
10.
Decomposition and
reconstruction of covariance matrix into or from its U-D factors, see XMUDDU
11.
Time propagation of U-D factors, see XTPUD
12.
Measurement incorporation by using U-D factors, see XMUDM
13.
U-D
implementation of the discrete Kalman filter, see XKFUD
In
addition the following 4 applications are included:
Application 1:
Ship navigation fixes
Application
2: 5-state
GPS receiver model covariance analysis
Application 3:
8-state GPS receiver model covariance analysis
Application
4: Simplified
Schuler loop model
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