Performance Analysis and Design of a Distributed Radar Network for Automotive Application

This work deals with the problem of joint direction-of-arrival (DoA) estimation in a network of forward-facing automotive radars with partially overlapping fields of view (FOVs). Assuming monostatic operation, we show performance improvements achieved by using block-sparse reconstruction and array optimization compared to individual estimation with ad-hoc array designs. For a preexisting network consisting of two symmetric corner-mounted radars, we investigate the benefits of adding a third central sparse array optimized for joint operation with the corner-mounted sensors. Simulations show that adding a very-sparse central sensor explicitly designed to achieve sidelobe-cancellation with the supporting corner-mounted sensors significantly improves angular resolution without increasing the number of false alarms in the network.