User`s guide

Kalman Adaptive Filter

5-250

The variables are as follows.

The correlation matrices, Q

and Q

, 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

The correlation matrix of the measurement noise

The correlation matrix of the process noise

n()