A Data-Driven DOA Estimation-Based Target Localization for Internet of Unmanned System

With the rapid development of autonomous unmanned systems, the Internet of unmanned agents (IUAs) has emerged as a prominent research field. Direction of arrival (DOA) estimation enables intelligent base stations (IBSs) to detect the direction of unmanned device, facilitating essential functions such as target localization and tracking, and cooperative navigation, significantly enhancing the environmental perception capabilities and task execution efficiency of IUA systems. However, the traditional DOA estimation algorithms in practical complex electromagnetic mutual coupling environments are computationally intensive, incompatible with IUA’s high real-time requirements. To address these challenges, this article proposes a data-driven (DD) DOA estimation method for unmanned device localization in IUA. The IUA localization system comprises four IBS equipped with uniform linear arrays (ULAs). Device localization is achieved through DOA estimates from these four IBS. A deep learning (DL) is proposed to jointly address two key challenges: 1) IUA’s demand for real-time algorithm performance and 2) mutual coupling effects between IBS sensors. A novel DL architecture is designed to estimate off-grid angle parameters and mutual coupling coefficients. The framework incorporates two learnable modules, one focusing on mutual coupling coefficients and another aimed at precise DOA estimation and associated confidence levels. The target unmanned device position is estimated using the least squares method applied to the DOA measurements from all IBSs. The proposed DL-based algorithm surpasses existing methods while maintaining low computational complexity. Extensive simulations demonstrate the high performance and real-time capabilities of the DD-based solution.