Estimation of Correlation Functions by Stochastic Approximation

Two techniques for estimating the correlation function of a stationary random process using the method of stochastic approximation are developed. Both provide updated estimates as successive T-second observations are processed. Both assume a functional form for the correlation function that depends on a number of parameters that are estimated. One technique is based on the mean-square error; the other maximizes a likelihood function. Examples are included.