Vehicle Positioning With Auxiliary Improved Differential-Evolution-Based 2-D DOA Estimation for Orthogonal Distributed Arrays

Direction-of-arrival (DOA) estimation using distributed arrays has emerged as a promising technique for autonomous vehicle (AV) positioning in Internet of Vehicles (IoV) systems. This article proposes a novel DOA estimation method based on orthogonal distributed arrays for accurate AV localization. Specifically, the covariance matrix between orthogonal arrays is exploited to construct two univariate polynomials for DOA estimation. As the polynomial degree depends on the array aperture and the discontinuous sensor layout, the resulting polynomials are often high-order and lacunary. To efficiently solve these polynomials, an improved differential-evolution (DE) algorithm is developed, featuring an adaptive mutation strategy to reduce computational cost and a counter-based mechanism to escape local optima. In addition, a covariance-based cost function is designed for 2-D angle pairing. The array aperture is further extended to enable simultaneous DOA estimation of multiple vehicles when the orthogonal arrays share a common sensor. Simulation results demonstrate that, compared with existing methods, the proposed approach achieves higher estimation accuracy and lower computational complexity, offering a promising solution for vehicle positioning in IoV environments.