Maximum likelihood approach to joint array detection/estimation

The problem of detecting the number of (possibly correlated) narrowband sources of energy and estimating the direction of arrival (DOA) of each detected source using data received by an array of sensors is investigated. A combined detection and estimation approach based on the likelihood function (LF) is used. The approach is motivated by detection theoretic considerations instead of information theoretic criteria and uses maximum likelihood (ML) signal-to-noise ratio (SNR) estimates of hypothesized sources as detection statistics rather than maximizing the 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) approaches as well as the Minimum Variance Distortionless Response (MVDR) approach. An important feature that distinguishes this approach is the ability to trade off detection and false alarm performance, which is not possible with the other LF-based approaches, while achieving performance levels comparable to or exceeding the LF-based and MVDR approaches.