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Scilab Help >> Signal Processing > Convolution - Correlation > hank

# hank

covariance to hankel matrix

### Syntax

hk =hank(m, n, cov)

### Arguments

- m
number of bloc-rows

- n
number of bloc-columns

- cov
sequence of covariances; it must be given as :[R0 R1 R2...Rk]

- hk
computed hankel matrix

### Description

This function builds the hankel matrix of size
`(m*d,n*d)`

from the covariance sequence of a vector
process. More precisely:

This function builds the hankel matrix of size `(m*d,n*d)`

from the covariance sequence of a vector process. More precisely:

### Examples

//Example of how to use the hank macro for //building a Hankel matrix from multidimensional //data (covariance or Markov parameters e.g.) // //This is used e.g. in the solution of normal equations //by classical identification methods (Instrumental Variables e.g.) // //1)let's generate the multidimensional data under the form : // C=[c_0 c_1 c_2 .... c_n] //where each bloc c_k is a d-dimensional matrix (e.g. the k-th correlation //of a d-dimensional stochastic process X(t) [c_k = E(X(t) X'(t+k)], ' //being the transposition in scilab) // //we take here d=2 and n=64 c = rand(2, 2 * 64) //generate the hankel matrix H (with 4 bloc-rows and 5 bloc-columns) //from the data in c H = hank(4, 5, c);

### See also

- toeplitz — Toeplitz matrix (chosen constant diagonal bands)

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