Vehicle positioning has played an important role in intelligent transportation systems. Previous research has had difficulties in increasing the sensor aperture and reducing the computational complexity of the vehicle positioning algorithm. This paper proposes a new sensor model to extend the sensor aperture, which is similar to the nested sensor model combined with fourth-order cumulants. The proposed algorithm estimates the number of vehicles, which is much higher than the actual number of sensors. An ideal characteristic equation-based method is used to avoid the use of eigenvalue decomposition and spectrum peak search, thereby greatly reducing the computation complexity. In addition, the weighted coefficient matrix is introduced for optimization. Theoretical analysis and simulation results show that the proposed algorithm has lower computational complexity, avoids 2-D parameter matching, and has a high utilization of arrays while still ensuring accurate parameter estimation.

Array optimization has recently received significant attention owing to its several advantages, such as larger array aperture and greater degrees of freedom (DOFs). However, current works focus on far-field sources, while array optimization for near-field sources has not been adequately investigated. Therefore, this work develops a new symmetry sparse array model for near-field sources based on the improved maximum inter-element spacing constraint (IMISC). The proposed symmetry IMISC (S-IMISC) array model has all the advantages of traditional sparse array models. Compared with traditional sparse array models, the S-IMISC array model affords more uniform DOFs and is less affected by mutual coupling. Additionally, in order to improve the real-time performance of near-field sources localization, the characteristic equation-based method (CEM) is used to obtain the azimuth information of near-field sources which can avoid eigenvalue decomposition (EVD), and a spectrum peak search and compression scheme is used to obtain the distance information by searching the partial area instead of the whole Fresnel area, thereby significantly reducing computation complexity. Extensive simulations verify the advantages of the proposed algorithm and the S-IMISC array model.