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