An improved Burg-type recursive lattice method for autoregressive spectral analysis

A new, efficient recursive lattice method for autoregressive spectral analysis is presented. This method is based on an estimate of the covariance matrix, which is Toeplitz, while allowing an unbiased estimation of the frequencies of sinusoidal signals. The algorithm works recursively similarly to Burg´s (1975) algorithm for maximum entropy autoregressive spectral estimation. It is shown that for truncated sinusoids in additive white noise, this method is superior to the original Burg´s algorithm in resolution, positional bias (it is unbiased in the absence of noise), and spurious peaks in the spectrum, while having about the same arithmetic complexity. It also has better finite precision properties than the Levinson algorithm