Algorithms for Single-Signal and Multisignal Minimum-Cross-Entropy Spectrum Analysis

摘要

Two algorithms are presented that implement multisignal minimum cross entropy spectral analysis (MCESA), a method for estimating the power spectrum of one or more independent signals when a prior estimate for each is available and new information is obtained in the form of values of the autocorrelation function of their sum. Single signal MCESA is included as a special case. One of the algorithms is slow, but general: the prior spectrum estimates and the resulting (posterior) spectrum estimates are represented by discrete frequency approximations with arbitrarily spaced frequencies, and the autocorrelation values may be given at arbitrarily spaced lags. The other algorithm is considerably faster and applies to an important special case: the prior and posterior spectrum estimates are of the all pole form that results from maximum entropy (or linear predictive) spectral analysis, and the autocorrelation values are given at equispaced lags beginning at zero.