Abstract
An algorithm similar to the Burg algorithm is given for autoregressive estimation in one dimension. Coefficients of the filter used to decorrelate the signal are found recursively from lower-order filters. The coefficients needed to get the larger-sized filters can be found at each stage of the recursion from the correlations of lower-order filter outputs. Thus, the resulting estimate does not assume knowledge of ensemble averages. It offers a computational saving of 25% over the commonly used method, in which an estimate of the correlation matrix is first computed and then inverted.

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