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

In this work, a computationally efficient evolutionary algorithm is proposed for estimating the direction of arrival (DOA) of coherently distributed (CD) sources corrupted by additive impulsive noise. The typical method, such as distributed signal parameter estimation (DSPE) method, requires a 2-D spectral peak search and cannot allow for coherent signals. The proposed method in this article uses an infinite norm (IN) normalization combined with the maximum likelihood (ML) criteria. The resulting cost function is solved using the introduced multimodal quantum-inspired benchmarking algorithm (MQBA), which obviously reduces the computational complexity without grid errors. The Cramer-Rao bound (CRB) 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 with 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.

Automated summary

In this work, a computationally efficient evolutionary algorithm is proposed for estimating the DOA of CD sources corrupted by additive impulsive noise. The proposed method combines IN normalization with ML criteria and solves the resulting cost function using MQBA, reducing computational complexity. The CRB for the DOA estimation problem is also derived. Numerical simulations demonstrate that the proposed algorithm outperforms alternative approaches in terms of robustness and handling coherent signals.