Auxiliary Vehicle Positioning Based on Robust DOA Estimation With Unknown Mutual Coupling

As an important branch of the Internet of Vehicles (IoV), vehicle positioning has drawn extensive attention. Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramér-Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. The proposed architecture can obtain robust self-localization with existing vehicular ad hoc networks, and it can collaborate with other positioning systems to provide a safe driving environment.