Sensor Signal Processing

传感器信号处理

• 批准号: EP/J015180/1
• 负责人: Stephen McLaughlin
• 金额: $ 134.02万
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ015180%2F1
• 关键词: Sensor; Signal; Processing

The fundamental challenges for signal processing are: how best to sense; how to distribute the processing and communication of the data within the network to maximize performance and minimize cost; how to analyze it to extract the salient information. Signal processing is the glue which holds together much of modern technology. The algorithms underpinning mobile communications, medical imaging, image rendering for games and many other technologies were all developed within the global signal and image processing research community. Today the world is an environment of pervasive interconnected sensing with the associated requirement to extract useful information from the large volumes of data that arise. In applications of defence, homeland security and environmental monitoring there is a need to collect and combine data from a range of sensors of widely differing complexity (e.g. from satellite imaging to ground based motion detectors) to achieve persistent wide area monitoring of a scene of interest. This can assist in the assessment of threats, e.g. the planting of improvised explosive devices, the long-term ecological effects of deforestation, or the monitoring of time critical events such as devastation by fire or flood. On the roads the external monitoring of traffic flow by closed circuit television networks, junction-based pressures sensors and GPS create an opportunity when combined with on-vehicle sensors (e.g. lidar, radar and video) to provide driver assistance and ultimately automatic driving systems. This Platform proposal seeks funding for a foundation for our research team in addressing these challenges.

信号处理的基本挑战是：如何最好地感知;如何在网络内分配数据的处理和通信，以最大限度地提高性能并最小化成本;如何分析它以提取突出信息。信号处理是将许多现代技术结合在一起的粘合剂。支撑移动的通信、医学成像、游戏图像渲染和许多其他技术的算法都是在全球信号和图像处理研究界开发的。今天的世界是一个无处不在的互联传感环境，相关的要求是从大量的数据中提取有用的信息。在国防、国土安全和环境监测的应用中，需要收集和联合收割机来自复杂性大不相同的一系列传感器（例如，从卫星成像到基于地面的运动检测器）的数据，以实现对感兴趣场景的持久广域监测。这可以帮助评估各种威胁，例如简易爆炸装置的埋设、森林砍伐的长期生态影响，或监测时间紧迫的事件，如火灾或洪水造成的破坏。在道路上，通过闭路电视网络、基于路口的压力传感器和GPS对交通流量进行外部监控，与车载传感器（例如激光雷达、雷达和视频）相结合，为驾驶员提供辅助，最终实现自动驾驶系统。该平台提案旨在为我们的研究团队应对这些挑战提供资金。

项目成果

Spectral Unmixing of Multispectral Lidar Signals

• DOI: 10.1109/tsp.2015.2457401
• 发表时间: 2015-01
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Y. Altmann;A. Wallace;S. Mclaughlin

Efficient Range Estimation and Material Quantification from Multispectral Lidar Waveforms

根据多光谱激光雷达波形进行有效的范围估计和材料量化

• DOI: 10.1109/sspd.2016.7590596
• 发表时间: 2016
• 作者: Altmann Y

Lidar waveform based analysis of depth images constructed using sparse single-photon data

使用稀疏单光子数据构建的深度图像的基于激光雷达波形的分析

• DOI: 10.48550/arxiv.1507.02511
• 发表时间: 2015
• 作者: Altmann Y

Unsupervised nonlinear unmixing of hyperspectral images using Gaussian processes

• DOI: 10.1109/icassp.2012.6288115
• 发表时间: 2012-03
• 期刊: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Y. Altmann;N. Dobigeon;S. Mclaughlin;J. Tourneret

Target detection for depth imaging using sparse single-photon data

使用稀疏单光子数据进行深度成像的目标检测

• DOI: 10.1109/icassp.2016.7472279
• 发表时间: 2016
• 作者: Altmann Y