End-to-end Machine Learning (ML) detector with satellite radar architecture
Computer vision transitioned from traditional image processing to Machine Learning (ML) based solutions. Zendar believes the emerging radar architecture with satellite front-ends and central processing provides an opportunity for bringing machine learning to Radar signal processing.Traditional radar processing pipeline uses a variant of threshold detector to extract point cloud from the radar data cube. Majority of threshold detectors have a limited field of view, which reduces them to be a local peak detector. By using spatial and temporal information combined with a multi-scale field of view, ML detector can achieve higher true positive rate with significantly lower false positive rate. It also enables the ML detector to be able to detect and remove ghost targets which is not possible with current threshold detectors. End-to-end training of such ML based approach utilizes semantic and discriminative features encoded in the satellite radar architecture data.Zendar will present its advancements in the past three years of research to bring machine learning to Automotive radar.
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Reza Mostajabi Ph.D.
Head of Machine Learning