Multi-sensor datasets of ultrasonic and mmWave reflected signals for material classification

This article details a publicly accessible dataset of reflected signals from ultrasonic and millimetre-wave (mmWave) sensors used for material classification. The dataset was gathered using two sensing methods: the URM09 ultrasonic sensor operating at 40 kHz and the DFRobot mmWave C4001 radar sensor operating at 24 GHz. Six common materials were chosen for data collection: wood, plastic, metal, glass, cardboard, and asbestos. Each material was tested at three thicknesses and one size (25 cm × 25 cm). All measurements were taken from a fixed distance of 55 cm in a controlled indoor setting with foam-lined enclosures to reduce external noise. The raw analogue signals were processed with Savitzky-Golay filtering and sliding window techniques. Time-domain features (mean, standard deviation, energy, RMS, kurtosis, skewness) and frequency-domain features (spectral centroid, spectral bandwidth, dominant frequency) were extracted from each sample. For URM09 the dataset includes 1789 refined data points across all six material-thickness combinations and 565 records for six material-only classification. On the other hand, For C4001 the dataset includes 1504 refined data points across all five material-thickness combinations and 470 records for five material-only classification. This dataset supports research in robotics, industrial inspection, non-destructive testing, and sensor fusion applications where contactless material identification is essential.