Signal Processing 4 the Networked Battlespace

长期以来，传感器在我们所有武装部队的战斗意识中发挥了至关重要的作用，从先进的成像技术，如雷达和声纳，到声学和电子监视。传感器是军队的“眼睛和耳朵”，提供战术信息，协助识别和评估威胁。实现这些目标的关键是信号处理。事实上，通过现代信号处理，我们已经看到基本雷达转变为具有波形敏捷性和自适应波束模式的高度复杂的传感系统，能够进行高分辨率成像，并检测和识别多个移动目标。今天，现代国防世界渴望建立一个相互连接的传感器网络，为感兴趣的场景提供持久和广泛的监视。这需要收集、传播和融合来自一系列复杂程度和规模差别很大的传感器的数据——从卫星成像到移动电话。为了实现这种互联传感，并避免数据过载的危险，有必要重新审视从传感器到最终决策的整个信号处理链。需要协调更多计算要求较高的算法的使用和潜在的大量数据增长以及基本资源限制（在计算和带宽方面），这提供了新的数学和计算挑战。这导致近年来探索了许多新技术，如压缩感知、自适应传感器管理和分布式处理技术，以最大限度地减少通过传感器网络获取或传输的数据量，同时最大限度地提高其相关性。虽然已经有一些有针对性的研究项目来探索这些新想法，比如美国的“集成传感和处理”项目和他们的“模拟到信息”项目，但这一领域总体上仍处于起步阶段。这个项目将在一个连贯的工作方案中研究多传感器系统的处理，从有效的抽样，通过分布式数据处理和融合，到有效的实施。在所有这些工作的基础上，我们将研究在小、轻、低功耗的计算平台上实现复杂算法的重要问题。范例挑战将在整个项目中使用，涵盖所有主要的传感领域——雷达/射频、声纳/声学和光电/红外——以展示我们开发的创新性能。

Sensors have for a long time played a vital role in battle awareness for all our armed forces, ranging from advanced imaging technologies, such as radar and sonar to acoustic and the electronic surveillance. Sensors are the "eyes and ears" of the military providing tactical information and assisting in the identification and assessment of threats. Integral in achieving these goals is signal processing. Indeed, through modern signal processing we have seen the basic radar transformed into a highly sophisticated sensing system with waveform agility and adaptive beam patterns, capable of high resolution imaging, and the detection and discrimination of multiple moving targets.Today, the modern defence world aspires to a network of interconnected sensors providing persistent and wide area surveillance of scenes of interest. This requires the collection, dissemination and fusion of data from a range of sensors of widely varying complexity and scale - from satellite imaging to mobile phones. In order to achieve such interconnected sensing, and to avoid the dangers of data overload, it is necessary to re-examine the full signal processing chain from sensor to final decision. The need to reconcile the use of more computationally demanding algorithms and the potential massive increase in data with fundamental resource limitations, both in terms of computation and bandwidth, provides new mathematical and computational challenges. This has led in recent years to the exploration of a number of new techniques, such as, compressed sensing, adaptive sensor management and distributed processing techniques to minimize the amount of data that is acquired or transmitted through the sensor network while maximizing its relevance. While there have been a number of targeted research programs to explore these new ideas, such as the USs "Integrated Sensing and Processing" program and their "Analog to Information" program, this field is still generally in its infancy. This project will study the processing of multi-sensor systems in a coherent programme of work, from efficient sampling, through distributed data processing and fusion, to efficient implementations. Underpinning all this work, we will investigate the significant issues with implementing complex algorithms on small, lighter and lower power computing platforms. Exemplar challenges will be used throughout the project covering all major sensing domains - Radar/radio frequency, Sonar/acoustics, and electro-optics/infrared - to demonstrate the performance of the innovations we develop.