Signal Processing for Localization and Environment Mapping
In the upcoming years we face the reality of everyday objects being connected to a large network, the ‘Internet of Things’ (IoT). A key aspect of the IoT is dependable communication and localization, where the participants act as ‘Smart Things’, communicating with each other and being aware of their environment.
In this regard, our research group has already developed rich models of wireless communication channels in harsh indoor environments with dense multipath propagation. UWB-based localization and tracking methods that achieve centimeter-level accuracies were created and first steps to realize the concept of ‘Cognitive Radio Networks’ were taken to approach a state were network participants learn and adapt to the environment to reach location awareness.
The purpose of this thesis is to build on top of the existing models and methods to create (radio) environment maps and perform channel predictions. The model parameters will be used to develop performance metrics that assess the robustness of wireless links and thus the dependability for wireless communications.
The image below shows one measurement scenario for the corridor of our institute. Using our channel model, the contributions of individual multipath components to the channel capacity are evaluated, indicating the robustness of wireless links other than the line of sight.