Abstract
The Schur recursion is equivalent to the Levinson-Wiggins-Robinson algorithm and computes the reflection matrices (or Schur parameters) associated with the autocorrelation function of a stationary process. It produces a sequence that converges towards the causal minimum phase spectral factor of the corresponding matricial power spectrum density. In this paper, we intend to extend some of its properties already known in the case of scalar signals to the case of vectorial signals: firstly, it is shown that a minimum phase estimation of the spectral factor is computed at each step of the recursion, this result is based on the canonical form of the Schur recursion; secondly, it is shown that all the data computed during the recursion are bounded which may be useful for a fixed point implementation, and a method for performing this fixed point implementation is proposed.
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