Speaker
Description
Wireless positioning systems are essential enablers for modern applications such as smart city services, indoor navigation, autonomous systems, and industrial automation. However, accurate location estimation remains a complex challenge, particularly in environments affected by non-line-of-sight (NLOS) conditions and multipath effects, or where infrastructure is limited. This talk presents a journey through innovative wireless positioning solutions—from multi-base station geometric models to hybrid multipath-aware approaches using a single base station. The first part of the presentation highlights the development of a geometry-based positioning framework using the Multiple Linear Lines of Position (MLOP) technique in WiMAX MIMO systems. This method significantly improves accuracy by integrating spatial diversity and is further enhanced with Virtual Base Stations (VirBS) to reduce GDOP in NLOS scenarios. The second part introduces a hybrid method using Time of Arrival (TOA) and Direction of Arrival (DOA) information from a single MIMO base station. The use of beamforming, angle estimation, and simulation of virtual sources enables a practical, low-complexity solution. The final segment highlight a cutting-edge Single Base Station (SMBS) approach that leverages multipath reflections and MIMO diversity to estimate positions with minimal infrastructure. The talk concludes with future directions including AI-enhanced models, cooperative positioning, and integration with emerging wireless technologies like 5G and UWB. These innovations aim to make high-precision, scalable wireless positioning systems a reality across diverse environments.
