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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.