Signal Procssing in the Information Age

持久的实时、多传感器、多模式监视能力将是国防部未来行动环境的核心；这种技术也将是现代社会的核心技术。除了传统的基于物理的传感器，如雷达、声纳和光电传感器，来自电话、分析师报告、社交媒体的“人类传感器”将提供新的有价值的信号和信息，这些信号和信息可能会提高态势感知、信息优势和自主性。将这些广泛的数据转换和处理成满足这些要求的可操作信息对现有的传感器信号处理技术提出了许多新的挑战。在未来大规模部署多模式、多源传感器将分布在各种环境中的情况下，需要新的信号处理技术。因此，在处理大容量、高速、非传统来源和高度不确定的数据时，考虑多源数据的可扩展性、适应性和资源管理等基本问题是及时的。UDRC第三阶段项目，信息时代的信号处理是一项雄心勃勃的倡议，汇集了来自5个领先的信号处理、数据科学和机器学习中心的国际领先专家和10个行业合作伙伴。由爱丁堡大学数字通信研究所牵头，与爱丁堡信息学学院、赫里奥特-瓦特大学、斯特拉斯克莱德大学和贝尔法斯特女王大学合作。这个多学科联盟汇集了这些研究中心在传感、处理和机器学习方面的独特专业知识。该联盟通过UDRC第一阶段和第二阶段、国防部国防企业中心和美国海军研究办公室参与了国防信号处理研究。该团队在技术转让方面拥有丰富的经验，包括：跟踪和监视(DSTL)、高级雷达处理(莱昂纳多，SEA)、宽带波束形成(泰利斯)、汽车激光雷达和雷达系统(意法半导体、捷豹路虎)以及深度学习人脸识别安全(AnyVision)。该项目将研究基本的数学信号和数据处理技术，这些技术将为未来运营环境所需的未来技术奠定基础。我们将开发支持推理算法，以提供可操作的信息，这些信息在计算上高效、可伸缩和多维，并纳入非传统和不同种类的信息源。我们将研究包括物理传感器和人工传感器的动态传感器网络的多目标资源管理。我们还将使用强大的机器学习技术，包括深度学习，以实现对与运营安全相关的新任务、异常情况、威胁和机会的更快、更可靠的学习。

Persistent real-time, multi-sensor, multi-modal surveillance capabilities will be at the core of the future operating environment for the Ministry of Defence; such techniques will also be a core technology in modern society. In addition to traditional physics-based sensors, such as radar, sonar, and electro-optic, 'human sensors', e.g. from phones, analyst reports, social media, will provide new valuable signals and information that could advance situational awareness, information superiority, and autonomy. Transforming and processing this broad range of data into actionable information that meets these requirements presents many new challenges to existing sensor signal processing techniques.In a future where a large-scale deployment of multi-modal, multi-source sensors will be distributed across a range of environments, new signal processing techniques are required. It is therefore timely to consider the fundamental questions of scalability, adaptability, and resource management of multi-source data, when dealing with data that is high-volume, high-velocity, from non-traditional sources, and with high uncertainty.The UDRC Phase 3 project, Signal Processing in an Information Age is an ambitious initiative that brings together internationally leading experts from 5 leading centres for signal processing, data science and machine learning with 10 industry partners. Led by the Institute of Digital Communications at the University of Edinburgh, in collaboration with the School of Informatics at Edinburgh, Heriot-Watt University, University of Strathclyde and Queen's University Belfast. This multi-disciplinary consortium brings together unique expertise in sensing, processing and machine learning from across these research centres. The consortium has been involved in defence signal processing research through the UDRC phases 1 & 2, the MOD's Centre for Defence Enterprise, and the US Office of Naval Research. The team have significant experience in technology transfer, including: tracking and surveillance (Dstl), advanced radar processing (Leonardo, SEA); broadband beamforming (Thales); automotive Lidar and radar systems (ST Microelectronics, Jaguar Land Rover), and deep learning face recognition for security (AnyVision).This project will investigate fundamental mathematical signal and data processing techniques that will underpin future technologies required in the future operating environment. We will develop the underpinning inference algorithms to provide actionable information, that are computationally efficient, scalable, and multi-dimensional, and incorporate non-conventional and heterogeneous information sources. We will investigate multi-objective resource management of dynamic sensor networks that include both physical and human sensors. We will also use powerful machine learning techniques, including deep learning, to enable faster and robust learning of new tasks, anomalies, threats, and opportunities, relevant to operational security.