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.
Multi-target tracking and activity classification with millimeter-wave radar
Note: This paper is part of the APL Special Collection on Advances in 5G Physics, Materials, and Devices.
Khalid Z. Rajab, Bang Wu, Peter Alizadeh, Akram Alomainy; Multi-target tracking and activity classification with millimeter-wave radar. Appl. Phys. Lett. 19 July 2021; 119 (3): 034101. https://doi.org/10.1063/5.0055641
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