Enhanced Automotive Sensing Assisted by Joint Communication and Cognitive Sparse MIMO Radar

Abstract

Automotive radar is mandated to provide high accuracy direction of arrival (DOA) estimation for safe driving, while remaining a low cost device for feasible mass production. Sparse multiple input multiple output (MIMO) arrays emerge as a primary candidate to meet these requirements. As DOA estimation accuracy is a main indicator of tracking performance, Cramer Rao Bound (CRB) is chosen as the goodness measurement for sparse MIMO array optimization, but its application requires prior information of the road environment. We propose a cognitive sparse MIMO array automotive radar, which “perceives” the road environment via automotive sensing supplemented by co-existed communication from roadside unit to vehicle (R2V). This information is used for co-designing a sparse MIMO array for enhanced automotive sensing and vehicle to roadside unit (V2R) communication. Note that both static RSU and dynamic RSU are usually deployed in Internet of Vehicles (IoV), which can provide continuous transmission coverage and permanent connectivity. The bi-directional communications are integrated into the automotive radar. This is achieved by joint transmit waveform design with spectral nulls for communications and with shared sparse MIMO array co-design for both sensing and high quality V2R communications. Simulation results validate the enhanced automotive sensing performance assisted by the integrated bi-directional communications in the cycle of cognitive-driven optimization.