<p>Characterization of polarimetric backscattering phenomenon is investigated through the ground-based synthetic aperture radar (GB-SAR) data that are collected from a test site consisting of manmade targets distributed over a vegetated terrain at L-band frequencies. The characterization of either synthetic or natural targets has been made by applying two main polarization interpretation schemes, namely amplitude-based interpretation and target-based interpretation in extracting the features of those targets. Polarimetric backscattering signatures of objects are analyzed by assessing the Pauli, total power, entropy/mean-alpha (<italic>H</italic> / α¯ ) images of the terrain for the goal of identification and classification of scattering mechanisms. After applying the classification methodology presented, obtained polarimetric images have demonstrated that target features can be effectively discriminated from each other providing a successful characterization of natural and manmade objects based on GB-SAR measurements. Specifically, <italic>H</italic> / α¯ classification results are shown to be well capable of clearly identifying the distinct scattering mechanisms of the terrain. Full-polarimetric measurements of this particular scene confirmed the ability to retrieve the physical target features to a certain extent using the high-resolution GB-SAR imagery and the relevant polarimetric analyses.</p>
We present a radar sensor that was designed to detect and image moving objects/targets on the other side of a wall. The radar sensor was composed of a linear array of Vivaldi antenna elements, an radio frequency (RF) switch, a microcontroller unit, and an RF transceiver. For the linear array, a total of eight antenna elements were used as sensors in synthetic aperture radar (SAR) configuration in the cross-range axis to improve the resolution in this dimension. Design steps of Vivaldi antenna elements and the entire linear array were presented. After the design, the prototyping procedure and the details of the radar sensor were given. Through-the-wall radar (TWR) imaging experiments were performed for stationary and moving targets using the assembled sensor. The resultant TWR images after these experiments were presented. During the image formation, a back-projection type image focusing algorithm was implemented and applied to increase the signal-to-noise ratio of the raw images. The constructed radar images demonstrated that our radar sensor could successfully detect and image both stationary and moving targets on the other side of the wall.