Scilab Function
Last update : 12/01/2004

levin - Toeplitz system solver by Levinson algorithm (multidimensional)

### Calling Sequence

  `[la,sig]=levin(n,cov)`

### Parameters

- `n`: maximum order of the filter
- `cov`: matrix containing the

  (d x d matrices for a d-dimensional process). It must be given the following way :

- `la`: list-type variable, giving the successively calculated Levinson polynomials (degree 1 to n), with coefficients `Ak`
- `sig`: list-type variable, giving the successive mean-square errors.

### Description

function which solves recursively on n the following Toeplitz system (normal equations)

where {`Rk;k=1,nlag`} is the sequence of `nlag` empirical covariances

### Examples

    //We use the 'levin' macro for solving the normal equations
    //on two examples: a one-dimensional and a two-dimensional process.
    //We need the covariance sequence of the stochastic process.
    //This example may usefully be compared with the results from
    //the 'phc' macro (see the corresponding help and example in it)
    //
    //
    //1) A one-dimensional process
    //   -------------------------
    //
    //We generate the process defined by two sinusoids (1Hz and 2 Hz)
    //in additive Gaussian noise (this is the observed process);
    //the simulated process is sampled at 10 Hz (step 0.1 in t, underafter).
    //
    t1=0:.1:100;rand('normal');
    y1=sin(2*%pi*t1)+sin(2*%pi*2*t1);y1=y1+rand(y1);plot(t1,y1);
    //
    //covariance of y1
    //
    nlag=128;
    c1=corr(y1,nlag);
    c1=c1';//c1 needs to be given columnwise (see the section PARAMETERS of this help)
    //
    //compute the filter for a maximum order of n=10
    //la is a list-type variable each element of which
    //containing the filters of order ranging from 1 to n; (try varying n)
    //in the d-dimensional case this is a matrix polynomial (square, d X d)
    //sig gives, the same way, the mean-square error
    //
    n=15;
    [la1,sig1]=levin(n,c1);
    //
    //verify that the roots of 'la' contain the
    //frequency spectrum of the observed process y
    //(remember that y is sampled -in our example
    //at 10Hz (T=0.1s) so that we need to retrieve
    //the original frequencies (1Hz and 2 Hz) through
    //the log and correct scaling by the frequency sampling)
    //we verify this for each filter order
    //
    for i=1:n, s1=roots(la1(i));s1=log(s1)/2/%pi/.1;
    //
    //now we get the estimated poles (sorted, positive ones only !)
    //
    s1=sort(imag(s1));s1=s1(1:i/2);end;
    //
    //the last two frequencies are the ones really present in the observed
    //process ---> the others are "artifacts" coming from the used model size.
    //This is related to the rather difficult problem of order estimation.
    //
    //2) A 2-dimensional process
    //   -----------------------
    //(4 frequencies 1, 2, 3, and 4 Hz, sampled at 0.1 Hz :
    //   |y_1|        y_1=sin(2*Pi*t)+sin(2*Pi*2*t)+Gaussian noise
    // y=|   | with :
    //   |y_2|        y_2=sin(2*Pi*3*t)+sin(2*Pi*4*t)+Gaussian noise
    //
    //
    d=2;dt=0.1;
    nlag=64;
    t2=0:2*%pi*dt:100;
    y2=[sin(t2)+sin(2*t2)+rand(t2);sin(3*t2)+sin(4*t2)+rand(t2)];
    c2=[];
    for j=1:2, for k=1:2, c2=[c2;corr(y2(k,:),y2(j,:),nlag)];end;end;
    c2=matrix(c2,2,128);cov=[];
    for j=1:64,cov=[cov;c2(:,(j-1)*d+1:j*d)];end;//covar. columnwise
    c2=cov;
    //
    //in the multidimensional case, we have to compute the
    //roots of the determinant of the matrix polynomial
    //(easy in the 2-dimensional case but tricky if d>=3 !).
    //We just do that here for the maximum desired
    //filter order (n); mp is the matrix polynomial of degree n
    //
    [la2,sig2]=levin(n,c2);
    mp=la2(n);determinant=mp(1,1)*mp(2,2)-mp(1,2)*mp(2,1);
    s2=roots(determinant);s2=log(s2)/2/%pi/0.1;//same trick as above for 1D process
    s2=sort(imag(s2));s2=s2(1:d*n/2);//just the positive ones !
    //
    //There the order estimation problem is seen to be much more difficult !
    //many artifacts ! The 4 frequencies are in the estimated spectrum
    //but beneath many non relevant others.
    //

### See Also

`phc`,

### Author

G. Le Vey