Generative Signal Processing and Data Fusion for Sensor Networks

传感器网络的生成信号处理和数据融合

• 批准号: RGPIN-2020-04563
• 负责人: Leung, Henry
• 金额: $ 5.54万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752167
• 关键词: Generative; Signal; Processing; Data; Fusion

The recent trends in analytics and interconnectivity between internet of things (IoT) and sensors is transforming industries such as personalized healthcare, agriculture, manufacturing, energy, and surveillance to process data autonomously to gather insight and make informed decisions with minimal delay. Market studies suggest the number of connected sensors is expected to reach 20.8 billion by 2020, and $6 trillion investment is predicted for the sensing market over the next 5 years. Although there exists development in sensor technologies aimed at increasing resolution and efficiency, cost and physical barriers may limit the practical sensor deployment. Due to sensing limitations, phenomena frequently cannot be observed completely due to various factors including sensor failures, sampling or resolution, interference, occlusions from objects, or visibility issues from weather or night-time. These missing information in space, time, and sensor modalities are barriers to real-time continuous sensing. This motivates the need for developing signal processing tools for the sensing picture without being limited by physical sensing constraints. We propose a generative signal processing and data fusion framework to push the boundaries of sensing. Our goal is to develop a unified set of technologies for three fundamental aspects of generative sensing and processing. We propose theoretical approaches and models to find solutions to missing signals, objects, and tracks. We will investigate algorithms for augmenting detection, tracking, and recognition processing tasks with generative signal processing and combine these processes. Finally, we propose an artificial sensor concept and data fusion approaches that will bridge the gap between artificial and real physical sensing. The research results in a framework to generate, process data at desired times, spatial locations, and selected sensor modalities. This will be publicly beneficial in sensing, as personalization and approaches will be developed to acquire desired information in cases where it may be currently expensive or limited. We verify applications in remote sensing, urban and pipeline monitoring with academic and industrial collaborators to drive future commercial technologies, develop next generation sensing for phenomena which cannot be sensed by conventional approaches. Benefits to Canada include a general approach to make eventual trends in continuous sensing more cost-effective, derived algorithms for applications like pipeline, acoustic monitoring for public safety, maritime or arctic monitoring. The research will enhance robustness of decision making in scenarios of sensing failure, to complement current physical sensors and enable continuous sensing.

物联网（IoT）和传感器之间的分析和互连的最新趋势正在改变个性化医疗保健，农业，制造业，能源和监控等行业，以自主处理数据，以最小的延迟收集洞察力并做出明智的决策。市场研究表明，到2020年，连接传感器的数量预计将达到208亿，预计未来5年传感市场的投资将达到6万亿美元。虽然存在旨在提高分辨率和效率的传感器技术的发展，但成本和物理障碍可能限制实际的传感器部署。由于传感限制，由于各种因素，包括传感器故障、采样或分辨率、干扰、物体遮挡或天气或夜间的能见度问题，现象经常无法完全观察到。空间、时间和传感器模态中的这些缺失信息是实时连续感测的障碍。这激发了开发用于感测图像的信号处理工具而不受物理感测约束的需要。我们提出了一个生成信号处理和数据融合框架，以推动传感的边界。我们的目标是为生成传感和处理的三个基本方面开发一套统一的技术。我们提出了理论方法和模型，以找到解决丢失的信号，对象和跟踪。我们将研究增强检测，跟踪和识别处理任务的算法与生成信号处理和联合收割机这些过程。最后，我们提出了一个人工传感器的概念和数据融合的方法，将弥合人工和真实的物理传感之间的差距差距。研究结果在一个框架中生成，处理数据在所需的时间，空间位置和选定的传感器模态。这将在传感方面对公众有益，因为将开发个性化和方法，以在目前可能昂贵或有限的情况下获取所需信息。我们与学术界和工业界合作者一起验证遥感、城市和管道监测的应用，以推动未来的商业技术，开发下一代传感技术，用于传统方法无法感知的现象。对加拿大的好处包括一种通用方法，使连续传感的最终趋势更具成本效益，用于管道、公共安全声学监测、海洋或北极监测等应用的衍生算法。该研究将增强感知故障场景中决策的鲁棒性，以补充当前的物理传感器并实现连续感知。