MATLAB SIGNAL PROCESSING BLOCKSET 7 Guide de l'utilisateur Page 407

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Kalman Adaptive Filter
5-250
The variables are as follows.
The correlation matrices, Q
M
and Q
P
, are specified in the parameter dialog box
by scalar variance terms to be placed along the matrix diagonals, thus ensuring
that these matrices are symmetric. The filter algorithm based on this
constraint is also known as the random-walk Kalman filter.
The implementation of the algorithm in the block is optimized by exploiting the
symmetry of the input covariance matrix K(n). This decreases the total number
of computations by a factor of two.
The block icon has port labels corresponding to the inputs and outputs of the
Kalman algorithm. Note that inputs to the
In and Err ports must be
sample-based scalars. The signal at the
Out port is a scalar, while the signal at
the
Taps port is a sample-based vector.
Variable Description
n The current algorithm iteration
u(n) The buffered input samples at step n
K(n) The correlation matrix of the state estimation error
g(n) The vector of Kalman gains at step n
The vector of filter-tap estimates at step n
y(n) The filtered output at step n
e(n) The estimation error at step n
d(n) The desired response at step n
Q
M
The correlation matrix of the measurement noise
Q
P
The correlation matrix of the process noise
w
ˆ
n()
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