Two-Dimensional Central DOA Estimation for Incoherently Distributed Sources in Unknown Nonuniform Noise

Most existing direction-of-arrival (DOA) estimation methods are presented for a point source model, which could suffer from substantial performance degradation in multipath transmission scenarios. Although there have been some reports on DOA estimation in the case of multipath transmission or a distributed source model, they all rely on the prior knowledge of the number of sources under the assumption of uniform noise, which may not be available or satisfied in practice. This paper proposes two efficient two-dimensional (2-D) DOA estimation methods for incoherently distributed (ID) sources in unknown nonuniform noise, building on the generalized array manifold (GAM) of an L-shaped array and array covariance and cross covariance vectors. In particular, the first method employs a conjugate symmetry operation to enlarge the array aperture and two 1-D subspace spectral searches to estimate 2-D central DOAs independently. The second method constructs a sparse total least squares (STLS) problem to handle the impact of model bias and finite number of samples, and applies the alternating descent algorithm to achieve an improved 2-D central DOA estimation without knowing the number of sources. Finally, a simple and effective parameter pairing scheme is designed to avoid the ambiguity problem. Simulation results are provided to validate the effectiveness and superiority of the proposed methods.