Levinson-Durbin algorithm for vector processes

Calculation of optimal linear predictor matrices for stochastic vector processes.

Does what Matlab's lpc does, but for vector processes. Implements the Levinson-Durbin algorithm for vector processes and thus is a generalization of the lpc function. This submission includes three files. Quick and dirty implementation, bear with me. For an 1 x M vector (or an uncorrelated N x M matrix) this function returns identical coefficients to the lpc result.
Implementation according to "The theory of linear prediction", chapter 8 by P. P. Vaidyanathan.

Files:
- vec_lpc.m contains the actual algorithm to calculate the optimal linear vector prediction coefficients/matrices.
- AutoCorrVec.m contains the supplementary calculation of the Autocorrelation function of the vector process. Direct usage should not be needed.
- example_usage.m showcases how to use the vec_lpc.m function and compares the performance of the scalar prediction with the vectorial prediction. This comes with a matrix of testdata included in the file dummydata.mat, which exhibits strong crosscorrelation.

As far as I know this has not been submitted on here yet. If you have any questions or critique mail me to [e-mail] or comment on here. For usage see the comments in vec_lpc.m and example_usage.m.