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Conventional direction of arrival (DOA) estimators are based on arrayprocessing using either time differences or beamforming. The proposed approachis based on the received power at each microphone, which enables simplehardware, low sampling frequency and small arrays. The problem is recast into alinear regression framework where the least squares method applies, and themain drawback is that different sound sources are not readily separable.  Our proposed approach is based on a training phase where the directionalsensitivity of each microphone element is estimated. This model is then used asa fingerprint of the observed power vector in a real-time estimator. Thelearned power vector is here modeled by a Fourier series expansion, whichenables Cram\'er-Rao lower bound computations. We demonstrate the performanceusing a circular array with eight microphones with promising results.

Using Received Power in Microphone Arrays to Estimate Direction of Arrival