Crammer-Rao Lower Bound of Vehicular Position & Orientation using a Lens-based MIMO in the Cooperative Vehicle-to-Vehicle (V2V) Systems

Abstract

We derive theoretical limits of estimation on the position and orientation of vehicles equipped with lens based arrays in cooperative Vehicle to Vehicle (V2V) scenario. Then, we analyze and compare its performance with conventional linear array. We determine a received signal of a lens-embedded antenna array and V2V geometric model, with which we next derive the Cramér-Rao lower bound (CRLB) on position and orientation estimation. We verify that a lens-based multiple-input multiple-output (MIMO) outperforms a conventional uniform linear array (ULA) in a certain constraint of a lens’s structure.