Signal Processing 4 the Networked Battlespace

长期以来，传感器在我们所有军队的战斗意识中发挥了至关重要的作用，从雷达和声纳等先进成像技术到声学和电子监视。传感器是军方提供战术信息、协助识别和评估威胁的“耳目”。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高度复杂的传感系统，具有波形敏捷性和自适应波束模式，能够进行高分辨率成像，并能够检测和识别多个运动目标。今天，现代国防世界渴望建立一个相互连接的传感器网络，对感兴趣的场景进行持续和广泛的监视。这需要收集、传播和融合各种复杂程度和规模各异的传感器的数据--从卫星成像到移动电话。为了实现这种互连的传感，并避免数据过载的危险，有必要重新检查从传感器到最终决策的整个信号处理链。需要协调使用对计算要求更高的算法和潜在的大量数据增长与基本资源限制，无论是在计算和带宽方面，这都带来了新的数学和计算挑战。这导致近年来探索了一些新技术，如压缩传感、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，同时使其相关性最大化。虽然已经有许多有针对性的研究计划来探索这些新想法，如美国航空母舰的“集成传感和处理”计划及其“模拟信息”计划，但该领域总体上仍处于初级阶段。该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，从高效采样到分布式数据处理和融合，再到有效的实施。作为所有这些工作的基础，我们将研究在小型、轻量级和低功耗计算平台上实现复杂算法的重要问题。示范挑战将在整个项目中使用，涵盖所有主要的传感领域-雷达/无线电频率、声纳/声学和电光/红外-以展示我们开发的创新的性能。

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.