Correas Serrano, AitorAitorCorreas SerranoGonzalez Huici, Maria AntoniaMaria AntoniaGonzalez HuiciSimoni, RenatoRenatoSimoniBredderman, TobiasTobiasBreddermanWarsitz, ErnstErnstWarsitzMüller, ThomasThomasMüllerKirsch, OliverOliverKirsch2023-07-122023-07-122022https://publica.fraunhofer.de/handle/publica/44552510.23919/IRS54158.2022.99049872-s2.0-85140442655This 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.enarray designAutomotive radarCompressed SensingDoA estimationgroup sparsityMIMO radarOrthogonal Matching Pursuitradar networkssidelobe cancellationconference paper