This paper presents a method for resolving signals closely spaced in parameter space in the sense that the parameters of the signals being measured (i.e., time of arrival, frequency, etc.) are close together. A maximum-likelihood method is used to resolve

signals in

-dimensional space, where

may be unknown. The resulting procedure first generates a

-dimensional cross-ambiguity function and then passes this function through a

-dimensional linear filter. The procedure effectively reduces the problem from its original form of optimally searching for a maximum in the

-dimensional space to searching for

maxima in the

-dimensional parameter space. The method is obviously sub-optimal; its advantage lies in the relatively simple form of the detection scheme.