Multi-Static Synthetic Aperture Radar for Earth Monitoring: Challenges, Innovative Solutions, and Demonstrations Using Swarms of Drones - Villano, Michelangelo; Peixoto, Maxwell Nogueira; Kim, Sumin; Mustieles Perez, Victor; Ustalli, Nertjana; Scala, Francesca; Börner, Thomas; Mittermayer, Josef; Krieger, Gerhard; Moreira, Alberto (APSAR 2023 - 2023 8th Asia-Pacific Conference on Synthetic Aperture Radar, 2023)
Spaceborne synthetic aperture radar (SAR) is an essential tool for Earth observation applications. By combining multiple SAR images taken from different viewing angles, precise digital elevation models and high-resolution tomograms can be created, revealing the three-dimensional structure of vegetation, ice, and dry soil. Currently, such images are typically acquired sequentially, impacting product quality and limiting our ability to monitor rapid changes effectively. Multi-static SAR systems bring new possibilities by enabling the simultaneous collection of all necessary data, promising more effective and robust planetary monitoring. However, a significant challenge lies in developing multi-static concepts capable of generating high-quality digital elevation models and tomograms from sets of noisy and undersampled radar data acquired by clusters of smallsats with compact antenna apertures. To address this, swarms of drones equipped with radars and localization systems offer an affordable option for testing multi-static concepts and acquiring data with sub-hour temporal resolution.
CRIS-Team Scopus Importer:2024-12-16
Keywords: digital elevation models (DEMs); drones; interferometry; multi-static concepts; satellite swarms; synthetic aperture radar (SAR); tomography

New Insights into Wideband Synthetic Aperture Radar Interferometry - Mustieles Perez, Victor; Kim, Sumin; Krieger, Gerhard; Villano, Michelangelo (IEEE Geoscience and Remote Sensing Letters, 2024)
Synthetic aperture radar interferometry (InSAR) is a technique that exploits the phase difference between two synthetic aperture radar (SAR) images to generate digital elevation models (DEMs) of the imaged terrain. InSAR has a well-established theoretical background, but approximations for narrowband signals are often made. To unwrap the interferometric phase, dual-baseline techniques are also commonly employed. Modern InSAR systems will use wider bandwidths to improve the accuracy and resolution of the DEMs. This letter generalizes the expressions of the baseline decorrelation and the critical baseline for InSAR systems with wide bandwidths and large baselines, where the spectral shrinkage is not negligible. It is also shown that radargrammetry offers high potential to unwrap the interferometric phase already on a pixel-by-pixel basis. The proposed approach can also exploit acquisitions with multiple baselines or frequency bands. The results show that a single wideband interferometric acquisition may suffice to perform phase unwrapping for high interferometric coherences. The concepts discussed in this paper will help to design and enhance the performance of future wideband and multi-band InSAR missions to deliver a new generation of DEMs with unprecedented resolution and accuracy.
CRIS-Team Scopus Importer:2024-09-20
Keywords: drones; interferometric coherence; multi-band; radargrammetry; SAR interferometry; spectral shift; Synthetic aperture radar (SAR); unmanned aerial vehicle (UAV); wide bandwidth

Towards Detecting Climate Change Effects With UAV-Borne Imaging Radars - Ullmann, Ingrid; Bonfert, Christina; Grathwohl, Alexander; Lahmeri, Mohamed Amine; Mustieles Perez, Victor; Kanz, Julian; Sterk, Elena; Bormuth, Frederik; Ghasemi, Roghayeh; Fenske, Patrick; Villano, Michelangelo; Schober, Robert; et al. (IEEE Journal of Microwaves, 2024)
Climate change is associated with a variety of environmental phenomena, such as the melting of glaciers, the drying out of crops and soils, and an increase in the risk of avalanches. To detect and monitor these changes, satellite-based radar imaging is widely used, as it enables the large-scale mapping of the Earth's surface independent of weather and sunlight. An emerging field of research is to use radar systems mounted on unmanned aerial vehicles (UAVs) for this purpose. UAV-borne radar systems image smaller areas, but can capture them in greater detail, more flexibly in terms of flight trajectory, and at shorter repetition intervals. This article presents the capabilities of UAV-based radar systems and the associated challenges in terms of system design and imaging techniques for detecting and monitoring the effects of climate change. Current research results are shown. In addition, we give an outlook on using a swarm of cooperative UAVs, each carrying a radar. UAV swarms will enable a higher imaging quality with respect to resolution and detectability, but at the same time come with a number of additional challenges, which will be discussed in the paper as well.
CRIS-Team Scopus Importer:2024-09-27
Keywords: Climate change; microwaves in climate change; radar; radar imaging; remote sensing; UAV

UAV Formation Optimization for Communication-Assisted InSAR Sensing - Lahmeri, Mohamed Amine; Mustieles Perez, Victor; Vossiek, Martin; Krieger, Gerhard; Schober, Robert (IEEE International Conference on Communications, 2024)
Interferometric synthetic aperture radar (InSAR) is an increasingly important remote sensing technique that enables three-dimensional (3D) sensing applications such as the generation of accurate digital elevation models (DEMs). In this paper, we investigate the joint formation and communication resource allocation optimization for a system comprising two unmanned aerial vehicles (UAVs) to perform InSAR sensing and to transfer the acquired data to the ground. To this end, we adopt as sensing performance metrics the interferometric coherence, i.e., the local correlation between the two co-registered UAV radar images, and the height of ambiguity (HoA), which together are a measure for the accuracy with which the InSAR system can estimate the height of ground objects. In addition, an analytical expression for the coverage of the considered InSAR sensing system is derived. Our objective is to maximize the InSAR coverage while satisfying all relevant InSAR-specific sensing and communication performance metrics. To tackle the non-convexity of the formu-lated optimization problem, we employ alternating optimization (AO) techniques combined with successive convex approximation (SCA). Our simulation results reveal that the resulting resource allocation algorithm outperforms two benchmark schemes in terms of InSAR coverage, while satisfying all sensing and real-time communication requirements. Furthermore, we highlight the importance of efficient communication resource allocation in facilitating real-time sensing and unveil the trade-off between InSAR height estimation accuracy and coverage.
CRIS-Team Scopus Importer:2024-09-13