Unconstrained Synthetic Aperture Sonar (U-SAS)

The project “Unconstrained Synthetic Aperture Sonar (U-SAS)” (in German: Nebenbedingungsfreies Synthetisches Apertur Sonar) is founded by the Deutsche Forschungsgemeinschaft (DFG).


Sonar is an essential sensor for underwater applications as it provides data under bad or even no visibility conditions and over longer distances. But its spatial resolution depends on a combination of transducers to (roughly) approximate a sampling beam via interference. A larger number of transducers placed on a larger area accordingly provide a higher resolution. But the number of transducers in a sonar sensor is limited by many factors like sensor size, power consumption and costs. A popular approach is hence the use of a synthetic aperture, i.e., the sonar with is positioned at different places to generate a virtual sensor with more virtual transducers.

The state of the art for this synthetic aperture sonar (SAS) is strongly coupled to constraints on the way it can be used. For example, the k poses often have to be equidistantly placed on a virtual line perpendicular to the sensor. This is motivated by the intention to ease the signal processing as well as by practical aspects: a vehicle, e.g., a surface vessel or an Autonomous Underwater Vehicle (AUV), with a sonar facing down to the sea-floor is only required to use its navigation sensors to travel with constant speed on a straight line. But it also significantly limits the scope of the vehicle’s mission.

The foundations for an unconstrained SAS are investigated in this project, i.e., a SAS that a) can be computed on arbitrary trajectories b) without the requirement of navigation sensor data (GPS, INS, etc.). The core idea is that a registration of the raw scans provides sufficiently precise location estimates to compute a higher-resolution spatial reconstruction of the scene. With respect to a) contributions to the formulation and solution to the unconstrained SAS problem are derived. These are also of interest to related areas, e.g., the use of radar on mobile systems like robots or automobiles, remote sensing with radar, or medical imaging. With respect to b) methods for robust 2.5D and 3D registration are investigated, especially spectral methods, which are very well suited for raw data that is strongly affected by noise. This also allows a synthetic scan formation that resides one level below state-of-the-art mapping with sonar and one level above the signal processing level of conventional SAS.


The following project-related publications have already appeared; several more are under review and in preparation – so, please occasionally re-visit this web-page for updates:

  • Heiko Bülow and Andreas Birk. Synthetic Aperture Sonar (SAS) without Navigation: Scan Registration as Basis for Near Field Synthetic Imaging in 2D. Sensors, 20(16), 4440, 2020, doi:10.3390/s20164440 [Open Access]
  • Andreas Birk. Seeing through the forest and the trees with drones: Signal-processing of thermal images that are autonomously collected by a drone detects people in densely occluded forests. Science Robotics, 6(55), 2021, https://robotics.sciencemag.org/content/6/55/eabj3947
  • Heiko Bülow and Andreas Birk. Registration of Magnetic Resonance Tomography (MRT) Data with a Low Frequency Adaption of Fourier-Mellin-SOFT (LF-FMS). Sensors, 21(8), 2581, 2021, doi:10.3390/s21082581 [Open Access]
  • Andreas Birk, Frederike Buda, Tim Hansen. Underwater Exploration with Sonar of the Flooded Basement of a WW-II Submarine Bunker in the Context of Digitization of Cultural Heritage. IEEE OCEANS, Limerick, 2023
  • Tim Hansen and Andreas Birk. Using Registration with Fourier-SOFT in 2D (FS2D) for Robust Scan Matching of Sonar Range Data. International Conference on Robotics and Automation (ICRA), London, UK, IEEE Press, 2023, doi: 10.1109/ICRA48891.2023.10160519
  • T. Hansen and A. Birk, Synthetic Scan Formation for Underwater Mapping with Low-Cost Mechanical Scanning Sonars (MSS), IEEE Access, 2023, doi:10.1109/ACCESS.2023.3312186 [Open Access]

Data & Code

Data and code from this project is openly released. We are among others currently moving our Git-repositories due to the name change of our institution – so, please occasionally re-visit this web-page for updates on this part, too.