An energy-efficient localization algorithm for mobile vehicles in vehicle to vehicle network

摘要

Traditional vehicle localization is based on global positioning system. However, the global positioning system is limited in shadowed environments such as underground carports, tunnels, and urban zones. This paper describes a vehicle positioning solution in an urban environment with the aid of vehicle to vehicle (V2V) networking, which utilizes the existing roadside sensors of the V2V network. The designed localization scheme is based on angle of arrival measurements. Compared with beamforming, minimum variance distortionless response, and subspace-based methods, an angle of arrival estimation method presented in this paper called sparsity angle sensing, based on a sparse representation of sensor measurements and compressive sensing theory, outperforms the other three methods in spatial resolution and robustness. Then the paper applies the novel angle of arrival estimation algorithm into an energy-efficient localization scheme design in a V2V network. Compared with sensor-based approaches, the proposed scheme relieves the requirements of sensor size and energy consumption, and reduces communication and computation overhead. Simulation results show the effectiveness of the proposed scheme in terms of reducing the positioning error.