Indoor localization is very often a daunting task, especially over non-line-of-sight (NLOS) transmission links. Many solutions have been so far proposed in the literature. Most promising ones rely on the so-called fingerprinting scheme combined with machine learning and, hence, require an offline process referred to as learning phase that needs to be reconducted each time the user moves from one environment to another. An interesting solution alternatively exploits the estimates of both the directions of arrivals (DOAs) and the propagation time delays (TDs) for localization. Such technique, however, is sensitive to the online estimation of a moving user.
In this project, we aim to develop a new online localization solution for moving users in indoor scenarios.
In the first phase of this project, we will investigate the improvement of Monte Carlo based joint DOAs and TDs estimation technique using a population based optimization solutions.
In the second phase of this project, we will focus on assessing the performance of the localization technique in a realistic environment. The DOAs and delays will later be fed to a localization technique to provide position estimates.
In the third phase of this project, we will focus on the assessment of the new localization technique in realistic environments that involve use of multiple unmanned aerial vehicles (UAVs). In this case, we will conduct two types of experiments. The first one uses Wi-Fi cards (e.g., Intel NIC and Atheros) mounted on a scientific-type drone (i.e., DJI Matrix). The second will exploit nano-drones (i.e., crazyflie) with their own “Loco Positioning” system.
The candidate should have a strong background in:
- C/C++/Python/MATLAB programming languages;
- Linux operating system (e.g., Ubuntu distribution).
Basic knowledge of the IEEE 802.11xx PHY/MAC standards is recommended.