Signal Processing Solutions for the Networked Battlespace

现代战场的性质正在发生戏剧性的变化。电子通信允许平台之间史无前例的数据和信息交换。电子技术的进步使低成本联网无人值守传感器成为可能。因此，对从各种联网通信和武器平台获得的大量多传感器数据进行智能和可靠的处理，对于保持军事优势和减轻在无政府和扩大的作战区(战场空间)内构成多种威胁的智能对手至关重要。因此，我们组成了一个由来自拉夫堡、萨里、斯特拉斯克莱德和加的夫大学的学术专家组成的独特联盟，以及六个工业项目合作伙伴QinetiQ、Selex-Galileo、Thales、Texas Instruments、PrismTech和Steepest Ascent，以开发变革性的新信号处理解决方案，以造福于DSTL、国防部和整个英国。为实现这一目标，我们提出了一个由以下五个相互关联的工作包组成的五年综合工作方案：(1)联网战场空间的高维度自动统计异常检测和分类，其中我们的目标不仅是检测异常，而且确定其性质和在高维度复杂网络环境中获得的细微差别。将使用数据质量和模糊性度量来确保常态模型不会被不可靠和模糊的数据破坏；(2)处理不确定性并纳入领域知识，其中我们的目标是利用网络化战场空间的世界模型来提高性能和信心，并将不确定性降低到前所未有的水平。这类信息的例子是关于地形和场地布局、平台之间的几何关系和作战条件(如天气)的数字地图；(3)信号分离和宽带分布式波束形成，其中我们的目标是设计用于欠确定和卷积信源分离的低复杂度稳健算法，以及宽带分布式波束形成，通过低阶和稀疏表示促进其及其快速实现；(4)多输入多输出(MIMO)和分布式传感，其中我们打算为在杂乱的网络化战场空间中运行的分布式MIMO雷达系统创建新的范例；(5)低复杂度的算法和高效的实现，其中我们的目标是与德州仪器、PrismTech和Steepest Ascent一起制定和实现一系列复杂信号处理算法在网络环境下的新实现策略。这些相互关联的工作包经过非常仔细的设计，以配合DSTL和EPSRC确定的研究主题和挑战，我们有明确的战略来获得数据集、进行评估和沟通发现。我们设计了一个结构严谨的财团管理团队，包括一个由具有工作计划范围专业知识的知名外部独立专家组成的总括指导小组。财团的运作将由财团董事和财团管理团队负责。我们联合体管理的一个关键组成部分是鼓励研究人员和受雇的学生定期借调到联合体内其他合作者的实验室，以受益于合作伙伴大学和行业的互补知识和技能；获得特权数据集和/或设备；或共享资源，并在应对特定的DSTL挑战时提供临界质量。管理结构和协调措施旨在使联合体有能力承担牵头联合体的角色，如果需要，与DSTL和EPSRC合作，建立信号和数据处理方面的实践社区，并确保英国在该领域拥有世界领先的能力。

The nature of the modern battlefield is changing dramatically. Electronic communication is allowing unprecedented interchange of data and information between platforms. Advances in electronics are allowing the possibility of low cost networked unattended sensors. Intelligent and robust processing of the very large amount of multi-sensor data acquired from various networked communications and weapons platforms is, therefore, crucial to retain military advantage and mitigate smart adversaries who present multiple threats within an anarchic and extended operating area (battlespace). Hence we have composed a unique consortium of academic experts from Loughborough, Surrey, Strathclyde and Cardiff universities together with six industrial project partners QinetiQ, Selex-Galileo, Thales, Texas Instruments, PrismTech & Steepest Ascent, to develop transformational new signal processing solutions to the benefit of Dstl, the MoD, and the UK in general. To achieve this goal we are proposing a five-year integrated programme of work composed of the following five interlinked work packages: (1) Automated statistical anomaly detection and classification in high dimensions for the networked battlespace, in which we aim not only to detect anomaly, but also to identify its nature and nuance, when acquired in a high dimensional complex network environment. Data quality and ambiguity measures will be used to ensure the models of normality are not corrupted by unreliable and ambiguous data; (2) Handling uncertainty and incorporating domain knowledge, within which we aim to exploit the world model of the networked battlespace to improve performance and confidence, and to reduce uncertainty to an unprecedented level. Examples for such information are digital maps about terrain and layout of the field, geometric relations between platforms and operational conditions such as weather; (3) Signal separation and broadband distributed beamforming, in which we target at designing low-complexity robust algorithms for underdetermined and convolutive source separation, and broadband distributed beamforming, facilitated by low-rank and sparse representations, and their fast implementations; (4) Multi-input and multi-output (MIMO) and distributed sensing, within which we intend to create novel paradigms for distributed MIMO radar systems operating in the cluttered networked battlespace; and (5) Low complexity algorithms and efficient implementation, in which with Texas Instruments, PrismTech & Steepest Ascent we aim to formulate and realize novel implementation strategies for a range of complex signal processing algorithms in a networked environment. These interlinked workpackages have been very carefully designed to marry up with the research themes and challenges identified by Dstl & the EPSRC and we have clear strategies for attaining datasets, performing evaluation, and communicating findings.We have designed a carefully structured consortium management team including an overarching steering group with renowned external independent experts with expertise covering the scope of the work programme. The operation of the consortium will be the responsibility of the Consortium Director and the Consortium Management Team. A key component of our consortium management is to encourage research staff and students employed to be periodically seconded to the labs of other collaborators within the consortium to benefit from complementary knowledge and skills at partner universities and industry; gain access to privileged datasets and/or equipment; or share resources & provide critical mass when addressing a particular Dstl challenge.The management structure and coordination measures have been designed for the consortium to have the capacity to assume the role of lead consortium, if required, working with Dstl & EPSRC to establish a community of practice in signal and data processing, and to ensure the UK has world leading capability in the area.