Concept for Dense Network of Single-Chip Radar Sensors for Anonymous Human Detection and Tracking using Neuromorphic Computing

Combining Neuromorphic Computing (NC) and single-chip radar sensors could lead to innovative solutions in robotics, autonomous vehicles, and other sensor-based applications. NC architectures have the potential to enhance signal processing for radar data by incorporating their event-driven detection characteristics, enabling signal processing to be guided selectively, focusing on processing tasks only when events are detected. Consequently, neuromorphic algorithms can be efficiently trained for object detection and used to alleviate computational costs associated with the information-dense nature of radar data. Single-chip radar sensors can capture objects’ spatial and velocity information; neuromorphic algorithms can contribute by processing this information for accurate object recognition. We present a novel framework for orchestrating and calibrating a network of radar sensors, incorporating lightweight signal processing, NC, and event-based data monitoring.