Artificially inserted objects in synthetic aperture radar (SAR) images are an important component of modern electronic warfare, e.g. for the concealment or illusion of targets. Target simulation can be achieved through software and hardware techniques, by means of a radar target simulator (RTS). In this work, we designed an experiment involving an RTS on the ground and a synthetic aperture radar (SAR) mounted on an aircraft. The study aims to assess the performance of the RTS, analyse measured RTS signals in SAR imagery and its usefulness for future missions. The SAR sensor was the Fraunhofer’s (FHR) MIRANDA35 operating at Ka-band with signals of 600 MHz bandwidth. The RTS can simulate an adjustable number of targets, intensity, and slant range positions. During the experiments, the RTS was observed from different viewing angles, depending on the trajectory of the aircraft carrying MIRANDA35. The RTS signals were generated using the delay-based technique, so the target’s location in the focused SAR image varied depending on the time delay. Five corner reflectors were placed on the ground, enabling a comparison of the RTS signatures with those of the fixed reflectors. The theoretical position and backscatter of the simulated targets, based on the RTS configuration, were compared to those measured in the SAR images. The results showed that the measured and theoretical slant range values differed by about 2 meters.
FMCW airborne SAR can collect data from different viewing angles, and can offer rapidly informative imagery of strategic infrastructure, or disaster areas at local scales. As they transmit low power, they can be mounted on light platforms. In 2022 we conducted a measurement campaign with the FHR’s MIRANDA35 system. It allows 1) topographic mapping by means of a tomographic configuration, 2) along-track interferometry for air and ground moving target indication, and 3) polarimetry for target classification and recognition. We show the performance of the system, and diverse level-1 and level-2 products, such as multi-aspect digital elevation models.
Change detection for high resolution Synthetic Aperture Radar (SAR) imagery requires advanced denoising mechanisms to preserve details and minimize speckle. In this work, we propose a change detector based on a Morphological Component Analysis (MCA) of the scattering mechanisms provided with fully polarimetric data sets. With MCA, the power of each scattering mechanism is decomposed into diverse image features. By introducing a priori knowledge of the content of the scenes, and exploiting both the scattering mechanisms and their corresponding shapes, we can significantly improve performance, with fewer false alarms introduced by clutter, focusing errors, and inconsistent acquisition geometries.
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