Probabilistic Snapshot GNSS

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)

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.