  The maximum likelihood (ML) and maximum a posteriori (MAP) estimation
techniques are widely used to address the direction-of-arrival (DOA) estimation
problems, an important topic in sensor array processing. Conventionally the ML
estimators in the DOA estimation context assume the sensor noise to follow a
Gaussian distribution. In real-life application, however, this assumption is
sometimes not valid, and it is often more accurate to model the noise as a
non-Gaussian process. In this paper we derive an iterative ML as well as an
iterative MAP estimation algorithm for the DOA estimation problem under the
spherically invariant random process noise assumption, one of the most popular
non-Gaussian models, especially in the radar context. Numerical simulation
results are provided to assess our proposed algorithms and to show their
advantage in terms of performance over the conventional ML algorithm.
