极高频率
雷达跟踪器
跟踪(教育)
雷达成像
连续波雷达
遥感
雷达工程细节
火控雷达
计算机视觉
多普勒雷达
低截获概率雷达
作者
Khalid Z. Rajab,Bang Ye Wu,Peter Alizadeh,Akram Alomainy
摘要
The millimeterwave radar has made possible high resolution tracking, activity classification, and vital signs detection, at higher precisions than is possible with most other wireless approaches. However, detecting multiple moving targets is a challenge, as dynamic scene with a lot of motion leads to clutter and noise, which interfere with the responses of targets of interest. We present a digital beamforming approach using the MIMO radar, with a range resolution of 6.4 cm and a Doppler resolution of 0.18 m/s, which reduces interference between closely neighboring targets. Thus, measurements of individual target micro-Doppler signatures are possible, even in the presence of multiple other moving targets, and the signatures are, thereby, used to train a Deep Neural Network (DNN) for activity classification. The DNN has been applied to recognize six exercise-based classes, correctly predicting with over 95% classification accuracy for all classes, but that is extendable to fall detection and other activities.
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