Self-Position Determination on V2I Communications Based on Alternating Least Square of Cross-Covariance Matrix

The Global Position System (GPS) is a reliable method for positioning in most scenarios, but it falls short in harsh environments like urban vehicular scenarios, where numerous trees or flyovers obstruct the signals. This presents an unprecedented challenge for autonomous vehicles or applications requiring high accuracy. Fortunately, vehicular ad-hoc networks (VANET) offer an effective solution, where vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are used to enhance location awareness. In V2I communications, the roadside units (RSU) transmit beacon packets, and the vehicle receives numerous packets from different RSUs to establish communication. To further improve localization accuracy, a cross-covariance matrices-alternating least square (CCM-ALS) algorithm is proposed. The algorithm relies on ALS of the CCM for obtaining the position of vehicles in V2I communications. The algorithm is highly precise compared to traditional angle of arrival (AOA) positioning and not inferior to direct position determination (DPD) approaches while being low in complexity, which is crucial for moving vehicles. The numerical results verify the superiority of the proposed method.