Analyzing Code Tracking Algorithms for Galileo Open Service Signal

The ever-increasing public interest on location and positioning services has originated a demand for higher performance Global Navigation Satellite Systems (GNSSs). Galileo Open Service (OS) signal, part of the European contribution to future GNSS, was designed to respond to the above demand. In all GNSSs, the estimation with high accuracy of the Line-Of-Sight (LOS) delay is a prerequisite. The Delay Lock Loops (DLLs) and their enhanced variants (i.e., feed-back code tracking loops) are the structures of choice for the commercial GNSS receivers, but their performance in severe multipath scenarios is still rather limited. In addition, the new satellite positioning system proposals specify the use of a new modulation, the Binary Offset Carrier (BOC) modulation, which triggers a new challenge in the code tracking stage. Therefore, in order to meet this emerging challenge and to improve the accuracy of the delay estimation in severe multipath scenarios, this thesis analyzes feed-back as well as feed-forward code tracking algorithms and proposes a novel algorithm, namely Peak Tracking (PT), which is a combination of both feed-back and feed-forward structures and utilizes the advantages inherent in these structures. In this thesis, the code tracking algorithms are studied and analyzed for Sine BOC (SinBOC) modulated Galileo OS signal for various multipath profiles in Rayleigh fading channel model. The performance of the analyzed algorithms are measured in terms of various well-known criteria such as Root-Mean-Square-Error (RMSE), Mean-Time-to-Lose Lock (MTLL), delay error variance and Multipath Error Envelopes (MEEs). The simulation results show that the proposed PT algorithm outperforms all other analyzed algorithms in various multipath profiles in good Carrier-to-Noise-Ratios (CNRs). The simulation results are compared with the theoretical Cramer-Rao Bound (CRB) and the comparison shows that the delay error variance for PT algorithm approaches the theoretical limit with the increase in CNR. Therefore, the proposed algorithm can be considered as an excellent candidate for implementation in future Galileo receivers, especially when tracking accuracy is a concern. /Kir1

An Enhanced FGI-GSRx Software-Defined Receiver for the Execution of Long Datasets

The Global Navigation Satellite System (GNSS) software-defined receivers offer greater flexibility, cost-effectiveness, customization, and integration capabilities compared to traditional hardware-based receivers, making them essential for a wide range of applications. The continuous evolution of GNSS research and the availability of new features require these software-defined receivers to upgrade continuously to facilitate the latest requirements. The Finnish Geospatial Research Institute (FGI) has been supporting the GNSS research community with its open-source implementations, such as a MATLAB-based GNSS software-defined receiver `FGI-GSRx’ and a Python-based implementation `FGI-OSNMA’ for utilizing Galileo’s Open Service Navigation Message Authentication (OSNMA). In this context, longer datasets are crucial for GNSS software-defined receivers to support adaptation, optimization, and facilitate testing to investigate and develop future-proof receiver capabilities. In this paper, we present an updated version of FGI-GSRx, namely, FGI-GSRx-v2.0.0, which is also available as an open-source resource for the research community. FGI-GSRx-v2.0.0 offers improved performance as compared to its previous version, especially for the execution of long datasets. This is carried out by optimizing the receiver’s functionality and offering a newly added parallel processing feature to ensure faster capabilities to process the raw GNSS data. This paper also presents an analysis of some key design aspects of previous and current versions of FGI-GSRx for a better insight into the receiver’s functionalities. The results show that FGI-GSRx-v2.0.0 offers about a 40% run time execution improvement over FGI-GSRx-v1.0.0 in the case of the sequential processing mode and about a 59% improvement in the case of the parallel processing mode, with 17 GNSS satellites from GPS and Galileo. In addition, an attempt is made to execute v2.0.0 with MATLAB’s own parallel computing toolbox. A detailed performance comparison reveals an improvement of about 43% in execution time over the v2.0.0 parallel processing mode for the same GNSS scenario.Peer reviewe

Analyzing Code Tracking Algorithms for Galileo Open Service Signal

The ever-increasing public interest on location and positioning services has originated a demand for higher performance Global Navigation Satellite Systems (GNSSs). Galileo Open Service (OS) signal, part of the European contribution to future GNSS, was designed to respond to the above demand. In all GNSSs, the estimation with high accuracy of the Line-Of-Sight (LOS) delay is a prerequisite. The Delay Lock Loops (DLLs) and their enhanced variants (i.e., feed-back code tracking loops) are the structures of choice for the commercial GNSS receivers, but their performance in severe multipath scenarios is still rather limited. In addition, the new satellite positioning system proposals specify the use of a new modulation, the Binary Offset Carrier (BOC) modulation, which triggers a new challenge in the code tracking stage. Therefore, in order to meet this emerging challenge and to improve the accuracy of the delay estimation in severe multipath scenarios, this thesis analyzes feed-back as well as feed-forward code tracking algorithms and proposes a novel algorithm, namely Peak Tracking (PT), which is a combination of both feed-back and feed-forward structures and utilizes the advantages inherent in these structures. In this thesis, the code tracking algorithms are studied and analyzed for Sine BOC (SinBOC) modulated Galileo OS signal for various multipath profiles in Rayleigh fading channel model. The performance of the analyzed algorithms are measured in terms of various well-known criteria such as Root-Mean-Square-Error (RMSE), Mean-Time-to-Lose Lock (MTLL), delay error variance and Multipath Error Envelopes (MEEs). The simulation results show that the proposed PT algorithm outperforms all other analyzed algorithms in various multipath profiles in good Carrier-to-Noise-Ratios (CNRs). The simulation results are compared with the theoretical Cramer-Rao Bound (CRB) and the comparison shows that the delay error variance for PT algorithm approaches the theoretical limit with the increase in CNR. Therefore, the proposed algorithm can be considered as an excellent candidate for implementation in future Galileo receivers, especially when tracking accuracy is a concern. /Kir1

Aroma based localization in GNSS-denied environments

This paper studies infrastructure less localization solutions using aroma fingerprints. These fingerprints are collected under varying conditions from different indoor locations using Ion Mobility Spectrometry based Electronic Noses. A supervised machine learning algorithm for data processing location estimation is proposed. The non-parametric system is trained with data from all locations, and its performance evaluated using data from the same locations collected under different environmental conditions. Five different classifiers are studied and tested for location estimation. The Stochastic Gradient Descent classifier achieved the highest accuracy, with the 푘NN with Euclidian distance also performing reliably under different conditions