Our overall goal is to develop a method for incorporating knowledge of the environment, and in particular, environmental variability, into the signal processor. This talk begins with the assumption that all available information about the environment has been used to compute the probability density functions (pdfs) of relevant received signal parameters (e.g., amplitude or time spread). For the exponential class of pdfs that arises from applying the maximum entropy method to random signal parameters, the maximum likelihood detectors (MLD) can be implemented as an estimator‐correlator [Schwartz, ‘‘The estimator‐correlation for discrete‐time problems,’’ IEEE Trans. on Information Theory. 23, 93–100 (1977)]. The MLD detector correlates received data with the conditional mean estimate of the signal; hence we have named it the estimated ocean detector (EOD). Here, we present the general structure of the EOD and explore EOD performance for Gaussian signals and noise, which are members of the exponential class. ...