Classification of human movements using Micro Doppler features in foliage environments.


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The focus of this work is measuring and classifying human motion in foliaged environments. A baseline study is performed using a vector network analyzer in open-space and foliaged environments for a variety of frequencies, activities, and formations. The results serve as a justification for a radar prototype’s parameters and a metric to gauge the prototype’s accuracy. The prototype is developed at 2.45 GHz to serve as a low cost and portable alternative to commercially available radars and to the vector network analyzer. Experimentation is repeated in open space and foliaged environments for a more thorough activity list and more participants. Data is post-processed to extract Doppler features and for classification. Results indicate that a low cost radar is capable of distinguishing human motion via human backscatter despite the attenuation, multipath, and blockage due to foliage.



Radar. Doppler. MicroDoppler. Human motion. Foliage. Signal processing. Machine learning.