A Robust Direction of Arrival Estimation Method for Coherently Distributed Sources in an Impulsive Noise Environment

Abstract

In this work, a computationally efficient evolutionary algorithm is proposed for estimating the direction of arrival (DOA) of coherently distributed sources corrupted by additive impulsive noise. The typical method, such as distributed signal parameter estimation method, requires a two-dimensional spectral peak search, and cannot allow for coherent signals. The proposed method in this paper employs an infinite norm normalization combined with the maximum likelihood criteria. The resulting cost function is solved using the introduced multimodal quantum-inspired benchmarking algorithm, which obviously reduces the computational complexity without grid errors. The Cramér-Rao bound for the considered DOA estimation problem is also derived. Finally, numerical simulations are conducted to show that our algorithm can perform superior robustness and allow for coherent signals without any additional process compared to alternative approaches for the considered scenarios. In addition, the proposed scheme can be popularized to address other DOA estimation problems and promote the development of DOA estimation.