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

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 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 coexisted communication from roadside unit to vehicle. This information is used for codesigning a sparse MIMO array for enhanced automotive sensing and vehicle-to-roadside unit (V2R) communication. Note that both static roadside unit (RSU) and dynamic RSU are usually deployed in Internet of Vehicles, which can provide continuous transmission coverage and permanent connectivity. The bidirectional 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 codesign for both sensing and high quality V2R communications. Simulation results validate the enhanced automotive sensing performance assisted by the integrated bidirectional communications in the cycle of cognitive-driven optimization.