I. Introduction
A historical perspective of spectral estimation described how the first spectral estimates in the early 19th Century used glass prisms to reveal spectral lines [1]. It would still last a long time before Wiener [2] and Khintchine [3] described the relation between the autocorrelation function and the spectrum of a stationary stochastic process. Wiener could use only a few lagged product autocovariances to estimate the spectrum. The rediscovery of the FFT algorithm was a major computational breakthrough [4]. This was foliowed by the maximum entropy spectral analysis of Burg [5] with autoregressive (AR) models. AR models still required order selection, for which Akaike developed the famous AIC criterion [6].