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 resolveMsignals inK-dimensional space, whereMmay be unknown. The resulting procedure first generates aK-dimensional cross-ambiguity function and then passes this function through aK-dimensional linear filter. The procedure effectively reduces the problem from its original form of optimally searching for a maximum in the(M \times K)-dimensional space to searching forMmaxima in theK-dimensional parameter space. The method is obviously sub-optimal; its advantage lies in the relatively simple form of the detection scheme.