If you want to sign up for this seminar, you need to register with the following information in the registration form

• surname, first name,
• e-mail address,
• matriculation number,

Please note that the registration period starts 10.03.2025 at 10:00 h and ends 13.04.2025 at 23:59 h. All applications before and after this registration period will not be taken into account.

Registration will be possible within the before mentioned time by sending an e-mail with the desired seminar topic, name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it..

Only one student per topic is permitted (first come - first serve).

The registration is binding. A deregistration is only possible by sending an e-mail with your name and matriculation number to This email address is being protected from spambots. You need JavaScript enabled to view it. until Sunday, 13.04.2025 at 23:59 h. All later cancellations of registration will be considered as having failed the seminar.

Students write a scientific report on a topic closely related to the current research of the DSS group. Potential topics, therefore, deal with digital signal processing related to underwater applications.

Students will also present their findings in front of the other participants and the DSS group.

SONAR (sound navigation and ranging) systems are used to detect underwater targets. To achieve good resolution over long distances, multiple transmitters and receivers are used in arrays in multiple input multiple output (MIMO) systems. In addition to the design of the transmitted signals, an optimal arrangement of the array elements for the subsequent signal processing is also important. The aim of this seminar is therefore to find and summarize literature on the behaviour of different array geometries and to compare them with each other. The literature may also come from other fields (for example RADAR).

SONAR scans are used to visualize underwater environments, utilizing a Plan Position Indicator (PPI) plot to represent scan data. In these plots, accurately separating background environmental noise from correlated backscatter signal data using threshold values is crucial for reliable target detection. Various thresholding algorithms can be applied, including manual global and local thresholds, as well as automatically determined thresholds. For example, Otsu’s thresholding method analyzes the statistical properties of the data to determine an optimal threshold automatically. This seminar aims to provide an extensive review of thresholding techniques, evaluate their performance under varying noise conditions, and identify the most suitable methods for different SONAR applications.

Underwater transducers are used for transmitting and receiving acoustic signals and, therefore, an essential part of every underwater acoustic application, like SONAR (sound navigation and ranging) and underwater communication. Since, in reality, there are no perfect transducers, each one has different properties, which will significantly impact in multichannel signal processing the results. Since these properties only vary a little over time, a beforehand characterization is helpful to improve the performance of the overall system. Often measurement water tanks are used as a low-noise environment, which has the drawback of reflections due to relatively small dimensions. This seminar aims to find and summarize the literature about the characterization of underwater transducers, especially in measurement water tanks and the particular environment it creates.

SONAR systems often produce a basic map of the scanned environment called a Plan Position Indicator (PPI). It is up to the operator to analyse this and identify objects. Besides some conventional detection algorithms, it is also possible to use a specific type of neural network - for example a type of Convolutional Neural Network (CNN) - to take over the task of not only detecting objects using the PPI, but also to determine what kind of object is present using only the shape on the map. The aim of this seminar is to review and summarise the literature on neural network based object detection and classification for SONAR systems.

Distributed Acoustic Sensing (DAS) is a technology that transforms standard optical fibers into large-scale acoustic sensors by utilizing Rayleigh backscattering. By sending interrogating laser pulses and analyzing the backscattered signals, DAS can detect vibrations and acoustic events along the entire fiber length. To enhance signal quality and improve the signal-to-noise ratio (SNR), advanced signal processing techniques such as beamforming can be applied. Beamforming enables directional filtering by coherently combining signals from multiple virtual sensing points, thereby suppressing noise and enhancing desired signals. This integration improves the accuracy and robustness of DAS in applications such as seismic monitoring, infrastructure surveillance, and security systems, while leveraging existing fiber-optic networks for cost-effective, real-time sensing.

If the transmitter and receiver in a SONAR system are not positioned at the same location, the system is called a bistatic system or, in the case of additional transmitter-receiver combinations, a multistatic SONAR system. Due to the spatial separation, the direct sound from the transmitter to the receiver always arrives first. This means that targets that are in the direct path or too close to the direct path are masked by the direct blast. To prevent this, adaptive beamformer structures can be used to achieve the desired beamformer steering on the one hand and suppress the direct blast on the other. The aim of this seminar is therefore to create a comprehensive overview of suitable adaptive beamformers and to test their suitability for the application described here.

SONAR plays a crucial role in underwater target detection, localization, and tracking. One of the key challenges in SONAR-based detection is distinguishing moving targets from clutter and background noise. The Doppler shift, which arises due to the relative motion between the SONAR system and the target, can be used as a parameter for detecting and classifying moving objects. The aim of the seminar is to explore the principles of SONAR target detection with Doppler shift, highlighting its significance in determining target velocity, direction, and classification. Also, it will present popular algorithms and showcase examples of implemented systems along with their performance.