A novel hierarchical classification for DoA estimation using coprime array with sensor location errors

Abstract

Deep learning-based methods have advantages for general Direction of Arrival (DoA) estimation in the array imperfections. However, existing methods using regression or classification are challenged to balance estimation performance and complexity. The good accuracy and adaptability of these methods is achieved by constructing complex deep models as well as large datasets. In this paper, we propose a novel hierarchical classification framework for multi-DoA estimation with coprime array, hoping to improve the accuracy and maintain the adaptation based on an appropriate complexity in the presence of array sensor location errors. Unlike existing data-driven methods, we use a hierarchical classifier that follows the idea of hierarchical modeling of mapping relationships. This makes it easier to learn the classification, thereby reducing the computational burden. The DoA estimation process is divided into multi-level according to the concept of general to specific direction. The complex classification task can then be divided into hierarchical subtasks. We construct a tree structure as priori to provide hierarchical relationships between labels. By learning the semantic relationships between label vectors, our proposed hierarchical model will provide a high-resolution spatial spectra. Our simulation results demonstrate the superiority of the proposed approach over the existing methods in accuracy, adaptation, and complexity.