DOA Estimation of Special Non-uniform Linear Array Based on Quantum Honey Badger Search Algorithm

Aiming at the current lack of research related to the application of special non-uniform linear arrays in impulsive noise and multi-coherent signal environments, in this paper, a direction of arrival (DOA) estimation method with higher effectiveness and robustness has been proposed. The proposed matrix based on the sine transform exponential kernel low-order moment (SCELOM) can effectively suppress impulsive noise. The maximum likelihood algorithm (ML) is used to obtain good directional performance, and quantum optimization theory is applied to the honey badger bionics mechanism to construct the quantum honey badger algorithm (QHBA) to solve the problem of large computational complexity involved in the multidimensional nonlinear optimization problem of the maximum likelihood algorithm, which improves the search efficiency and estimation accuracy. Finally, the maximum likelihood equation with the SCELOM matrix named as QHBA-SCELOM-NLA-ML is designed. Monte Carlo simulation results show that the proposed algorithm has the advantages of fast convergence, high precision, strong anti-impact noise ability, scalable array aperture, good decoherence and wide applicability.