Multi-agent Systems increasingly rely on cooperative localisation to navigate GNSS-denied environments using only relative measurements. However, these purely relative networks inevitably accumulate global trajectory drift over time. This thesis investigates whether strategically landing a UAV to serve as a stationary anchor can mitigate this accumulated drift and improve the absolute localization performance of the overarching MAS.
Unlike traditional filtering methods that only estimate the current state, this work adopts Gaussian Belief Propagation to achieve scalable, fully distributed estimation, solving factor graphs locally to jointly optimize the trajectory history. Within this distributed architecture, low-cost IMU and range-bearing constraints were simulated to evaluate various landed anchor mathematical representations (Unary, Persistent Variable, and ZUPT) across continuous and multi-group flight topologies. The results demonstrate that in single-group, continuous flight scenarios, landing a drone fails to bound global drift because the dense network topology becomes highly overconfident and rigidly locks the anchor into a drifted state. Conversely, when applied to multi-group deployments, representing the landed anchor with a Zero-Velocity Update (ZUPT) model mathematically combats this overconfidence through artificial covariance inflation, allowing the anchor to absorb geometric corrections and successfully reset the drift of subsequent passing groups.
The findings imply that autonomous MAS should not simply deploy stationary anchors during continuous, dense flight without risking algorithmic divergence; instead, operators must explicitly structure missions into temporal, multi-wave batches to safely exploit this relative infrastructure. While the baseline GBP architecture was verified using real-world datasets, the conclusions regarding landed anchors rely on synthetic evaluations within a 2D simulation, meaning the proposed system must still be validated against the physical hardware and 3D flight complexities of real-world deployments.

This bachelor end project thesis is describing ways to use dry-electrode electroencephalography (EEG) measurements for authentication. There are five proposed methods based on the literature study, which in this report are called: ’frequency tagging’, ’pseudowords’, ’familiar music’, ’mental tasks’, and ’emotions’. First, all methods were tested, however, this proved too much work to perform each of them thoroughly and wellbounded, so two were selected for further investigation.

For the method involving frequency tagging, experiments were done and the obtained data was used to extract features. This analysis showed that tagging can be discovered in the EEG data, but re-induction of tagged words was hard to achieve, which would make it difficult to create an authentication system reliant on it. Analyzing results from experiments to test the pseudoword method was more promising. Some similarities between the features that were expected from papers and features from our results were found. These features were therefore used for classification, and resulted in small improvements in accuracy. This accuracy did however vary a lot between different data sets.