High-Resolution Augmented Multimodal Sensing of Distributed Radar Network

Advancement towards fully autonomous systems requires enhanced sensing and perception, particularly a 360° vision for safe manoeuvring. One approach to achieving this is through a distributed network of radar sensors, operating in homogeneous or heterogeneous configurations, strategically positioned to provide increased coverage and visibility in otherwise blind regions. Such a multi-perspective sensing network, complemented with multi-modal signal processing, can significantly improve the angular resolution of the radar, delivering high-fidelity scene imagery essential for region classification and path planning. This study presents a methodology for multi-modal and multi-perspective sensing using heterogeneous radar sensors, utilising Doppler beam sharpening (DBS) within Multiple-Input Multiple-Output (MIMO) radars to enhance resolution and coverage. Traditional frequency-modulated continuous wave (FMCW)-MIMO radars, currently the most widely used configuration, are prone to Doppler aliasing, limiting the Field of View (FoV) in DBS and MIMO-DBS processing. To address this limitation, the effective FoV in multi-perspective image is extended to that provided by the radar’s physical aperture. The proposed framework is validated using 77 GHz radar chipsets in both automotive and maritime conditions, with sensors mounted in front-looking, corner-looking, and side-looking orientations.