This thesis presents research advances on cooperative localization and communication. These two macro trends are investigated in the general context of mobile multi-agent networks for situational awareness applications, where time-varying agents of unknown absolute location are asked to fulfill positioning and information sharing tasks. The research on localization aims to develop an integrated solution where…
Accurate and robust positioning remains a fundamental challenge in Global Navigation Satellite System (GNSS) navigation, especially in weak-signal, high-dynamics, interference-prone, and model-mismatched environments. Conventional two-step positioning approaches and Kalman Filter (KF)-based methods rely heavily on accurate observation models and state-space dynamics, which often degrade under practical imperfections such as multipath, interference, and structural modeling errors….
A new robust time scale algorithm, Autonomous Time scale using the Student’s T-distribution (ATST), has been proposed and validated using simulated clock data. Designed for use in a nanosatellite swarm, ATST addresses phase jumps, frequency jumps, anomalous measurement noise, and missing data by making a weighted average of the residuals contained in the Basic Time…
This dissertation advances generative speech enhancement by investigating both unsupervised and supervised machine learning approaches, with a focus on integrating visual information to improve robustness. The work is organized into three main contributions: The first contribution focuses on unsupervised generative speech enhancement. We explore a Bayesian framework combining variational autoencoders (VAEs) trained on clean speech…
Quality-of-experience (QoE) of interactive applications transmitted over TCP/IP networks has recently gained considerable attention, and is mainly influenced by transmission delays due to TCP/IP?s retransmission characteristic. This thesis shows that interactive Internet applications share the commonality of a recurring request- response cycle that is highly vulnerable to such transmission delays. In the context of two…
This work provides a precise performance analysis of precoding in the asymptotic regime of the multi-user massive multiple-input multiple-output (MIMO) setting. Considering practical challenges, such precoders are typically formulated as non-linear, non-smooth optimization problems without closed-form solutions, often followed by non-linear post-processing operations. These aspects make it difficult to analyze the system behavior in multiple…
The Large Hadron Collider (LHC) is the world?s highest energy particle collider, which has already delivered data for numerous physical discoveries. To continue this quest for discovering new physics, the Compact Linear Collider (CLIC) and the Future Circular Collider (FCC) aim to push the boundaries of fundamental physics at high collision energies. However, as their…
A major goal in distant-speech recognition is to transform speech signals of a target speaker into symbols in order to trigger a dialog manager. Spatio-temporal filters, so called beamformers, usually enhance the target speaker’s speech signals in a noisy and reverberant environment. However, a beamformer requires information on the target speaker’s position. A source localizer…
Over the last decade, deep learning methods have gained increasing traction in indus trial applications, ranging from image-based automated quality control, over signal enhancement to condition monitoring tasks. While deep learning has immensely in creased the performance and capabilities of machine learning models, it also increased the vulnerability of those models. Moreover, these models require…
We consider Bayesian networks (BNs) with discriminatively optimized parameters and structures, i.e. BNs that are optimized to maximize a kind of probabilistic margin. These maximum margin Bayesian networks (MM BNs) are inspired by support vector machines (SVMs) that aim to separate samples from different classes by a large margin in some feature space. MM BNs…
