RF Dataset for Radar Target Classification

This is the dataset we collected for the article "Scalable Undersized Dataset RF Classification: Using Convolutional Multistage Training". 17 objects were collected in the laboratory and scanned using a 'cw radar' setup featuring 2x UWB antennas (1 transmit antenna, 1 receive antenna), inside anechoic chamber. There was no clutter added in the experiment.

Seventeen objects were collected from engineering and chemistry laboratories at UCLA for use as radar targets. The objects were selected to present a diversity of sizes, shapes, and material composition. All 17 targets were placed in the radar’s path and rotated through three different angles (0°, 45°, and 90°). Photos of all 17 rotated objects are shown in Figure 5 of the paper, whereas photos of the anechoic setup are shown in Figure S1 of the paper.

Twelve traces were recorded for each of the 17 objects and each of the three orientations per object. Additionally, 112 traces of the empty anechoic chamber were recorded under otherwise equal conditions. Each trace (S21 parameter) was recorded using a VNA as a string of complex numbers (real, imaginary) representing the complex-valued signal amplitude as a function of the sweep frequency.

From each complex-valued trace for each object (and orientation), the magnitude of the complex data was computed and stored in vectors of length 1,600, corresponding to linearly spaced frequency values in the range 675 MHz–8.5 GHz. For the empty anechoic chamber data, all 112 traces were averaged to provide a clean (low noise) trace. This low-noise trace was used to subtract the background signal for each object (and orientation) to produce traces whose features reflect only the characteristics of an object.