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The problem of detecting the number of narrowband sources of energy and the direction of arrival (DOA) of each detected source using data received by an array of sensors is investigated. The well known difficulty of an unconstrained maximum likelihood (ML) approach for estimation of dimensionality parameters (e.g., model order) is addressed by using ML signal-to-noise ratio estimates of hypothesized sources as detection statistics rather than using the likelihood function (LF) with a penalty function. Performance comparisons are made to unstructured and structured techniques based on Akaike information theoretic criteria (AIC), minimum description length (MDL), and Bayesian predictive density (BPD) methods as well as minimum variance distortionless response (MVDR). The technique presented here offers better detection performance for multiple closely spaced uncorrelated signals, with the ability to trade off detection and false alarm performance.