As a promising research area in Internet of Things (IoT), Internet of Vehicles (IoV) has attracted much attention in wireless communication and network. In general, vehicle localization can be achieved by the Global Positioning Systems (GPS). However, in some special scenarios, such as cloud cover, tunnels or some places where the GPS signals are weak, GPS cannot perform well. The continuous and accurate localization services cannot be guaranteed. In order to improve the accuracy of vehicle localization, an assistant vehicle localization method based on Direction-of-Arrival (DOA) estimation is proposed in this paper. The assistant vehicle localization system is composed of three Base Stations (BS) equipped with a Multiple Input Multiple Output (MIMO) array. The locations of vehicles can be estimated if the positions of the three BSs and the DOAs of vehicles estimated by the BSs are known. However, the DOA estimated accuracy maybe degrade dramatically when the electromagnetic environment is complex. In the proposed method, a Sparse Bayesian Learning (SBL) based robust DOA estimation approach is first proposed to achieve the off-grid DOA estimation of the target vehicles under the condition of non-uniform noise, where the covariance matrix of non-uniform noise is estimated by a Least Squares (LS) procedure, and a grid refinement procedure implemented by finding the roots of a polynomial is performed to refine the grid points to reduce the off-grid error. Then, according to the DOA estimation results, the target vehicle is cross-located once by each two BSs in the localization system. Finally, robust localization can be realized based on the results of three-time cross-location. Plenty of simulation results demonstrate the effectiveness and superiority of the proposed method.

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In this paper, an algorithm of multiple signal classification (MUSIC) is proposed for two-dimensional (2-D) directionof-arrival (DOA) and polarization estimation of non-circular signal in three-dimensional (3-D) millimeter wave polarized largescale/massive multiple-input-multiple-output (MIMO) systems. The traditional MUSIC-based algorithms can estimate either the DOA and polarization for circular signal or the DOA for non-circular signal by using spectrum search. By contrast, in the proposed algorithm only the DOA estimation needs spectrum search, and the polarization estimation has a closedform expression. First, a novel dimension-reduced MUSIC (DR-MUSIC) is proposed for DOA and polarization estimation of circular signal with low computational complexity. Next, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signal with high estimation accuracy. Then based on the DOA estimation result using QNC-MUSIC, the polarization estimation of non-circular signal is acquired by using the closed-form expression of the polarization estimation in DR-MUSIC. In addition, the computational complexity analysis shows that compared with the conventional DOA and polarization estimation algorithms, our proposed QNC-MUSIC and DR-MUSIC have much lower computational complexity, especially when the source number is large. The stochastic Cramér-Rao Bound (CRB) for the estimation of the 2-D DOA and polarization parameters of the non-circular signals is derived as well. Finally, numerical examples are provided to demonstrate that the proposed algorithms can improve the parameter estimation performance when the large-scale/massive MIMO systems are employed.Index Terms-Direction-of-arrival (DOA) and polarization estimation, polarized large-scale/massive multiple-input-multipleoutput (LS-MIMO/massive MIMO), three-dimensional (3-D) millimeter wave communication, circular and non-circular signals, quaternion.

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