Signal Procssing in the Information Age

信息时代的信号处理

• 批准号: EP/S000631/1
• 负责人: Mike Davies
• 金额: $ 521.43万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FS000631%2F1
• 关键词: Signal; Procssing; Information; Age

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.

持续的实时、多传感器、多模式监视能力将是国防部未来业务环境的核心;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，声纳和电光，“人类传感器”，例如来自电话，分析报告，社交媒体，将提供新的有价值的信号和信息，可以提高态势感知，信息优势和自主性。将这些广泛的数据转换和处理为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，多模态，多源传感器的大规模部署将分布在一系列环境中，需要新的信号处理技术。因此，在处理大量、高速、非传统来源和高度不确定性的数据时，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”是一项雄心勃勃的计划，汇集了来自5个领先信号处理中心的国际领先专家，数据科学和机器学习与10个行业合作伙伴。由爱丁堡大学数字通信研究所牵头，与爱丁堡信息学院、赫瑞瓦特大学、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来自这些研究中心的传感，处理和机器学习方面的独特专业知识。该联合体通过UDRC第1和第2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），先进的雷达处理（列奥纳多，SEA）;宽带波束形成（Thales）;汽车激光雷达和雷达系统（意法半导体、捷豹路虎）、和深度学习人脸识别技术（AnyVision）本项目将研究基础数学信号和数据处理技术，这些技术将成为未来所需技术的基础。操作环境。我们将开发基础推理算法，以提供可操作的信息，这些信息是计算效率高，可扩展和多维的，并结合非传统和异构的信息源。我们将研究多目标的动态传感器网络，包括物理和人类传感器的资源管理。我们还将使用强大的机器学习技术，包括深度学习，以更快、更强大地学习与运营安全相关的新任务、异常、威胁和机会。

项目成果

Expectation-propagation Algorithms for Linear Regression with Poisson Noise: Application to Photon-limited Spectral Unmixing

具有泊松噪声的线性回归的期望传播算法：在光子限制光谱解混中的应用

• DOI: 10.1109/icassp.2019.8682479
• 发表时间: 2019
• 作者: Altmann Y

Graph Filter Design for Distributed Network Processing: A Comparison between Adaptive Algorithms

• DOI: 10.1109/sspd51364.2021.9541468
• 发表时间: 2021-09
• 期刊: 2021 Sensor Signal Processing for Defence Conference (SSPD)
• 作者: Atiyeh Alinaghi;Stephan Weiss;V. Stanković;I. Proudler

Expectation-propagation for weak radionuclide identification at radiation portal monitors.

辐射入口监测器弱放射性核素识别的期望传播。

• DOI: 10.1038/s41598-020-62947-3
• 发表时间: 2020
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Altmann Y

Compact Order Polynomial Singular Value Decomposition of a Matrix of Analytic Functions

解析函数矩阵的紧阶多项式奇异值分解

• DOI: 10.1109/camsap58249.2023.10403445
• 发表时间: 2023
• 作者: Bakhit M

Fast Task-Based Adaptive Sampling for 3D Single-Photon Multispectral Lidar Data

• DOI: 10.1109/tci.2022.3150974
• 发表时间: 2021-09
• 期刊: IEEE Transactions on Computational Imaging
• 影响因子: 5.4
• 作者: Mohamed Amir Alaa Belmekki;Rachael Tobin;G. Buller;S. Mclaughlin;Abderrahim Halimi