Probabilistic Snapshot GNSS

Lead Research Organisation: University of Oxford
Department Name: Autonom Intelligent Machines & Syst CDT


Brief description of the context of the research including potential impact: Conventional global nav-igation satellite system (GNSS) receivers operate in multiple consecutive steps to estimate their position. Instead, direct position estimation (DPE) is based on a probabilistic model of the received GNSS signal and performs position estimation in one step using maximum-likelihood estimation (MLE). This approach has the potential to be robust in scenarios where conventional GNSS fails, such as low-quality signals recorded with an energy-saving low-cost device, weak signals, e.g., in a multi-path environment, or signals as short as one millisecond. Furthermore, Bayesian DPE allows to directly integrate prior knowledge into the probabilistic model. Advancements of DPE such that it can be employed in practice would allow to build GNSS receivers with significantly lower costs and lower energy consumption. Such devices would, e.g., enable conservationists to perform more affordable wildlife tracking on a broader scale.
Aims and objectives: One of the main goals is to develop open-source hardware and software for low-cost low-energy wildlife tracking. This requires improving DPE such that it can work with real data, especially, short signals (snapshots) with low amplitude and frequency resolution. A first im-portant step is to revisit the underlying probabilistic model to make it more robust and allow faster likelihood optimisation. Second, the development of new tailored optimisation methods for MLE in DPE is required to speed up the processing of large amounts of signal captures. Other questions are how to efficiently use multiple GNSS in DPE and how to address uncertain time and frequency measurements resulting from an imprecise receiver clock. Finally, domain specific prior knowledge shall be integrated to improve positioning accuracy and robustness.
Novelty of the research methodology: While DPE has already been proposed in theory, it has not been transferred to real-world applications yet. This is due to different hurdles, some of which are described above, some of which might yet have to be discovered. For this reason, this project aims at developing hardware and software for a snapshot GNSS receiver, which can be employed by end-users for a real-world application. In this way, the project will identify challenges that prevent snapshot GNSS with DPE from being applied in practice and will develop probabilistic approaches to address them.
Alignment to EPSRC's strategies and research areas: Digital signal processing, sensors and instru-mentation, artificial intelligence technologies, (robotics)


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024050/1 01/10/2019 31/03/2028
2242651 Studentship EP/S024050/1 01/10/2019 30/09/2023 Jonas Beuchert