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Positioning accuracy is a critical requirement for vehicle-to-everything(V2X) use cases. Therefore, this paper derives the theoretical limits ofestimation for the position and orientation of vehicles in a cooperativevehicle-to-vehicle (V2V) scenario, using a lens-based multiple-inputmultiple-output (lens-MIMO) system. Following this, we analyze theCram$\acute{\text{e}}$r-Rao lower bounds (CRLBs) of the position andorientation estimation and explore a received signal model of a lens-MIMO forthe particular angle of arrival (AoA) estimation with a V2V geometric model.Further, we propose a lower complexity AoA estimation technique exploiting theunique characteristics of the lens-MIMO for a single target vehicle; as aresult, its estimation scheme is effectively extended by the successiveinterference cancellation (SIC) method for multiple target vehicles. Giventhese AoAs, we investigate the lens-MIMO estimation capability for thepositions and orientations of vehicles. Subsequently, we prove that thelens-MIMO outperforms a conventional uniform linear array (ULA) in a certainconfiguration of a lens's structure. Finally, we confirm that the proposedlocalization algorithm is superior to ULA's CRLB as the resolution of the lensincreases in spite of the lower complexity.

AoA-based Position and Orientation Estimation Using Lens MIMO in Cooperative Vehicle-to-Vehicle Systems