Micro-Doppler is a perturbation on an echo returned from a target which results from the movement of its component parts such as wheels on vehicles or swinging arms or legs on personnel. A great deal of information can potentially therefore be gained from analysing Micro-Doppler returns from a target which has been illuminated by radio frequency (eg radar) or acoustic wavelength radiation.This study aims to investigate the processing techniques which may be applied to acoustic micro-Doppler signature (uDS) data. Specifically, methods to extract, classify and track the uDS of individual targets from the background clutter and non-target backscatter signals will be developed.UCL has carried out extensive work in the area of uDS based target recognition using radar data in recent years. This has resulted in new algorithms and techniques which can be used in identifying and classifying targets. This work has particularly concentrated on identifying personnel and vehicle targets against the returns from the background environment. The work has been carried out in close collaboration with Thales Aerospace and has dealt with field data obtained by both Thales and UCL using personnel detecting radar. Much of this work could potentially be mapped on to the acoustic region and this proposal presents a study to examine how the knowledge gained using radar data can be used in the very different frequency ranges and propagation conditions that exist in the acoustic regime.An acoustic camera will be used to record audio and video data from a scene. Signal characterisation will then be performed using theoretical models and techniques developed using radar data in the previous work. Micro-Doppler classification techniques will be adapted to the acoustic regime, in addition to new methods, which may be suitable for the potentially longer acquisition times at acoustic frequencies. Tracking algorithms will then be applied to the target returns and methods to automate the entire detection and tracking process will be examined.The end result of the work should be a system that can detect, classify and track a range of targets based on their acoustic uDS returns in a range of different environments.

There are now large networks of CCTV cameras collecting colossal amounts of video data, of which many deploy not only fixed but also mobile cameras on wireless connections with an increasing number of the cameras being either PTZ controllable or embedded smart cameras. A multi-camera system has the potential for gaining better viewpoints resulting in both improved imaging quality and more relevant details being captured. However, more is not necessarily better. Such a system can also cause overflow of information and confusion if data content is not analysed in real-time to give the correct camera selection and capturing decision. Moreover, current PTZ cameras are mostly controlled manually by operators based on ad hoc criteria. There is an urgent need for the development of automated systems to monitor behaviours of people cooperatively across a distributed network of cameras and making on-the-fly decisions for more effective content selection in data capturing. Todate, there is no system capable of performing such tasks and fundamental problems need to be tackled. This project will develop novel techniques for video-based people tagging (consistent labelling) and behaviour monitoring across a distributed network of CCTV cameras for the enhancement of global situational awareness in a wide area. More specifically, we will focus on developing three critical underpinning capabilities:(a) To develop a model for robust detection and tagging of people over wide areas of different physical sites captured by a distributed network of cameras, e.g. monitoring the activities of a person travelling through a city/cities.(b) To develop a model for global situational awareness enhancement via correlating behaviours across a network of cameras located at different physical sites, and for real-time detection of abnormal behaviours in public space across camera views; The model must be able to cope with changes in visual context and on definitions of abnormality, e.g. what is abnormal needs be modelled by the time of the day, locations, and scene context.(c) To develop a model for automatic selection and controlling of Pan-Tilt-Zoom (PTZ)/embedded smart cameras (including wireless ones) in a surveillance network to 'zoom into' people based on behaviour analysis using a global situational awareness model therefore achieving active sampling of higher quality visual evidence on the fly in a global context, e.g. when a car enters a restricted zone which has also been spotted stopping unusually elsewhere, the optimally situated PTZ/embedded smart camera is to be activated to perform adaptive image content selection and capturing of higher resolution imagery of, e.g. the face of the driver.

Our proposal builds on the successful start made by Cambridge Centre for Analysis (CCA), a current EPSRC Centre for Doctoral Training. We propose to develop further our activity in two important and rapidly evolving areas of analysis, namely mathematics of information and statistics of complex systems. Beginning with Newton, for whom the development of calculus and the mathematical understanding of bodies in motion were closely intertwined, the mathematics used to describe real phenomena consistently involves notions of continuity, rate of change, average value, and basic challenges such as the relationship between discrete and continuum objects. This is the domain of analysis, encompassing modelling by partial differential equations and by random processes, and the mathematical theory which guides effective computation for such models. The centrality of mathematical analysis in the relationship between mathematics and its applications has been acknowledged by successive International Reviews of Mathematics, as has the need to increase the capacity of UK PhD training in analysis. Mathematical Analysis and its Applications is an EPSRC Priority Area. Beyond the established and important uses of analysis in modelling physical phenomena, digital technology has created new areas where mathematical analysis, in guiding the extraction of knowledge from massive discrete systems, plays an essential role. These include the fields of high-dimensional statistics and the mathematics of information, including compressed sensing. In each of these, one is looking for a reliable means to interpret massive high-dimensional data. Already several CCA students are working in these areas. Big Data is one of the Eight Great Technologies championed by the Minister for Universities and Science. Statistics and Data to Knowledge are EPSRC Priority Areas. We propose a first year training programme based on our current successful model, now expanded by two further core courses, one in Statistics of Complex Systems and one in Mathematics of Information. These new courses will be paired with postgraduate level courses from the existing Cambridge Masters' (MASt), which students can use to consolidate their understanding. The core courses themselves are based on supervised student team assignments leading to student presentations. The other main components of the first year are research mini-projects (often the route to a PhD project) and an industry workshop. Years two to four are devoted mainly to the PhD thesis. First year training establishes a collaborative ethos in the cohort and, by mixing students with different prior skills, encourages cross-fertilization of ideas across the different threads of analysis. This is sustained in later years through a programme of seminars, workshops and training in transferable skills. The students appreciate that their collective understanding of a given problem using different skills will often exceed each individual's understanding. This makes cohort-based training especially valuable in analysis. We already expose all our students to the role of mathematics and the opportunities for mathematicians in industry and society, and we encourage first-hand engagement with applications through mini-projects, industrial seminars and study weeks, and, for some, PhD projects with industrial partners. The development of core skills and eventually the ability to generate new ideas is the hardest and crucial part of training as a research mathematician. This is necessarily our overriding task, in which we seek synergy and inspiration from user engagement. In the new CDT, our network of industrial connections will be further enhanced, along with our collaborations with Cambridge engineering colleagues, and our links with the Smith Institute for Industrial Mathematics.

The aim of this proposal is to create a robustly-validated virtual prediction tool called a "digital twin". This is urgently needed to overcome limitations in current industrial practice that increasingly rely on large computer-based models to make critical design and operational decisions for systems such as wind farms, nuclear power stations and aircraft. The digital twin is much more than just a numerical model: It is a "virtualised" proxy version of the physical system built from a fusion of data with models of differing fidelity, using novel techniques in uncertainty analysis, model reduction, and experimental validation. In this project, we will deliver the transformative new science required to generate digital twin technology for key sectors of UK industry: specifically power generation, automotive and aerospace. The results from the project will empower industry with the ability to create digital twins as predictive tools for real-world problems that (i) radically improve design methodology leading to significant cost savings, and (ii) transform uncertainty management of key industrial assets, enabling a step change reduction in the associated operation and management costs. Ultimately, we envisage that the scientific advancements proposed here will revolutionise the engineering design-to-decommission cycle for a wide range of engineering applications of value to the UK.