  The uniform white noise assumption is one of the basic assumptions in most of
the existing directional-of-arrival (DOA) estimation methods. In many
applications, however, the non-uniform white noise model is more adequate. Then
the noise variances at different sensors have to be also estimated as nuisance
parameters while estimating DOAs. In this letter, different from the existing
iterative methods that address the problem of non-uniform noise, a
non-iterative two-phase subspace-based DOA estimation method is proposed. The
first phase of the method is based on estimating the noise subspace via
eigendecomposition (ED) of some properly designed matrix and it avoids
estimating the noise covariance matrix. In the second phase, the results
achieved in the first phase are used to estimate the noise covariance matrix,
followed by estimating the noise subspace via generalized ED. Since the
proposed method estimates DOAs in a non-iterative manner, it is computationally
more efficient and has no convergence issues as compared to the existing
methods. Simulation results demonstrate better performance of the proposed
method as compared to other existing state-of-the-art methods.
