Centimetre radar reflectance signal for material classification

Detecting and classifying materials is crucial for many real-world applications such as industrial robots, and precision agriculture. In recent years, radar sensor-based material classification become more and more popular. In this work, we employ a portable centimeter-wave radar sensor to collect the reflectance signal from nine different kinds of materials. Methods There are 956 observations in the dataset, categorized into 10 classes: 1) Air, 2) Beans, 3) Dirt, 4) Flour, 5) Lentils, 6) Oats, 7) Rice, 8) River Rocks, 9) Sand, and 10) Sugar. For one observation, it contains 8119 data points.