Moment-matching array processing technique for diffuse source estimation

Direction-of-Arrival (DOA) estimation is a fundamental problem in signal processing. Diffuse sources, whose power density cannot be represented by a single angular coordinate, are typically characterized based on prior assumptions, which associate the source angular density with a specific set of functions. However, these assumptions can lead to significant estimation biases when they are incorrect. This paper introduces the Moment-Matching Estimation Technique (MoMET), a low-complexity method for estimating the mean DOA, spread, and power of a diffuse source without requiring prior knowledge on the source distribution. The unknown source density is characterized by its mean DOA and its first central moments, which are estimated through covariance matching techniques. The MoMET parameterization is robust to incorrect model assumptions, only requires the optimization of the mean DOA, and applies to both Uniform Linear Arrays (ULAs) and non-ULAs as well as symmetric and asymmetric sources. The asymptotic bias and covariance of the new estimator are derived and its performance is demonstrated through simulations.