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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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