GNSS-SDR

Mentors: Student: Víctor Castillo

The goal is to implement a functional GNSS receiver that uses INS measurements to improve the position, velocity and time solutions. This will advance research on sensor fusion, enabling reliable high-accuracy navigation solutions in challenging environments such as the urban cannon. I will leverage state-of-the-art AI techniques, such as Bayesian filters (e.g., Kalman filters and particle filters), graph neural networks (GNNs), and transformers for spatiotemporal data modeling. The end product will consist of a functional prototype receiver which will implement the necessary algorithms.

Mentors: Student: PedroPereira

This project aims to implement a Vector Tracking Loop (VTL) architecture in GNSS-SDR, a widely used open-source software-defined GNSS receiver. Traditional scalar tracking architecture tracks satellite signals independently, limiting robustness in dynamic or degraded signal environments. In contrast, VTL integrates tracking and navigation tasks into a single algorithm, where all channels collaborate to enhance signal tracking performance, particularly under challenging conditions such as interference or temporary outages. The proposed work will design and integrate a modular, configurable VTL architecture supporting multi-constellation and multi-frequency operation, aligning with the existing capabilities of GNSS-SDR. Deliverables include: VTL algorithm implementation, real-time feedback to tracking channels, multi-constellation and multi-frequency support, benchmarking, and documentation. This contribution will enable researchers and developers to explore advanced GNSS techniques related to the VTL architecture within a real-time, open-source SDR.

Mentors: Student: Marcus Alagar

Currently, certain essential signal-processing functions, called kernels here, in volk-gnssdr, the Vector-Optimized Library of Kernels for GNSS-SDR, are not completely optimized. This project hopes to diagnose performance-limiting kernels, implement missing SIMD/vector optimizations for both ARM (NEON) and RISC-V architectures (vector, or V, extension), then benchmark and validate the kernels’ continued functionalities.

Mentors: Student: Víctor Castillo

The goal is to implement a functional GNSS receiver that is capable of introducing measurements from another sensor into the GNSS receiver chain. This will advance research on sensor fusion, enabling reliable high-accuracy navigation solutions in challenging environments such as the urban cannon. The end product will consist of a SignalSource implementation, responsible for talking with the sensor at a hardware level and synchronizing its measurements with GNSS samples; and the acquisition, tracking and PVT algorithms implementing the sensor fusion model.

Mentors: Student: Jorge Iglesias

The future of satellite navigation is in sampling the signal directly from the antenna to the signal processing unit. To carry out this great feat, you must have a device capable of sampling in the order of GSPS. Using Python, Vivado and Vitis HLS you can program the RFSoC4x2 development board with AMD/Xilinx Ultrascale+ RFSoC XCZU48DR-FFGV1517-2-E processor to do this. With this technology, the entire radio spectrum occupied by GNSS, ranging from 1164.45MHz to 1605.37Mhz, can be sampled using SDR. In this way the user could choose the desired GNSS constellation in a more accessible and efficient way than ever.