millimeter-wave radar

This share presents raw data and Python source code for signal processing in overflow velocity measurement using millimeter-wave MIMO-FMCW radar on a fabricated real-scale pseudo embankment. The dataset and code offer insights into developing robust river embankments, crucial for mitigating failures during heavy rains in Japan. The methodology involves constructing a pseudo embankment recommended by the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) for technology validation.

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This dataset contains the extracted parameter data for the deep patellar tendon reflexes of four test subjects. Each subject was tapped with a reflex hammer with soft, medium, and hard taps three times. The dataset was collected by interpreting the spectrogram images from processed radar data and motion capture data.

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This synthetic dataset is generated using Matlab automotive driving toolbox to simulate a 77GHz FMCW millimeter-wave radar sensing in the road scenario. Especially for the Doppler ambiguity case, when the object vehicles move within or out of the unambiguous detecable velocity range. The dataset contains in total 20 recordings with the duration of 1 second each. Both time-division modulation (TDM) and binary phase modulation (BPM) data are provided. Each recording consists of complex ADC raw data and complex range-Doppler map, together with the ground-truth range and velocity.

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