基于实时3D 60 ghz雷达系统的ai驱动事件识别

A. Tzadok, A. Valdes-Garcia, P. Pepeljugoski, J. Plouchart, M. Yeck, Huijuan Liu
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引用次数: 6

摘要

提出了一种垂直集成的天线-人工智能系统。以前为Gb/s NLOS通信开发的60 ghz 16元相控阵发射器和接收器模块用于实现3D雷达系统,该系统以高帧率从场景中提取体积信息。该系统采用1 ghz带宽的FMCW信号,每秒可以处理1250个雷达读数。雷达电子器件和相控阵模块控制之间的有效定时控制方案能够从单独的波束方向获得每个雷达读数。该系统每秒可以扫描一帧5×5方向50次。所有雷达系统组件,包括信号产生和ADC都组装在一个便携式机箱中。系统中还包括一个摄像头,可以同时捕获雷达和视频流。开发了一种深度神经网络,用于从三维雷达信息流中提取时间和体积特征,并实现对快速演变事件的自动识别。作为一个应用示例,DNN被训练来执行自动手势识别。整个雷达系统和相关的深度神经网络对涉及两只手的9种不同手势的识别准确率达到93%。
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AI-driven Event Recognition with a Real-Time 3D 60-GHz Radar System
A vertically integrated antennas-to-AI system is presented. 60-GHz 16-element phased array transmitter and receiver modules, previously developed for Gb/s NLOS communications, are used to implement a 3D radar system that extracts volumetric information from a scene at a high frame rate. The system employs an FMCW signal with 1-GHz bandwidth and can process 1250 radar readouts per second. An efficient timing control scheme between the radar electronics and the phased array module control enables obtaining each of the radar readouts from a separate beam direction. The system can scan a frame of 5×5 directions 50 times per second. All the radar system components including signal generation and ADC are assembled in a single portable chassis. A camera is also included in the system to enable the simultaneous capture of radar and video streams. A DNN was developed to extract temporal and volumetric features from the 3D radar information stream and enable the automatic recognition of fast evolving events. As an application example, the DNN was trained to perform automatic hand gesture recognition. The overall radar system and the associated DNN achieved a recognition accuracy of 93% on a set of 9 different gestures involving two hands.
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