Notes on Digital Signal Processing: Practical Recipes for Design, Analysis and Implementation

Note 73. Estimating Coefficients for Autoregressive Models: Burg Algorithm

The Burg algorithm [1, 2] is a technique for fitting an AR model to a signal that is represented by a sequence of N measured samples, x[0] through x[N –1]. What sets the Burg method apart from other techniques for estimating AR model parameters are the assumptions made about signal values x[n] for n > 0 and for n ≥ N. The autocorrelation method described in Note 70 assumes that unknown values of x[n] are zero. The covariance method described in Note 71 makes no assumptions about unknown values for x[n], but uses an optimization strategy that is structured to use only the N measured values. FFT-based methods assume the same periodic extension of values that is implicit ...