Multi-level adaptive systems and algorithms for agile and opportunistic sensing

用于敏捷和机会感知的多级自适应系统和算法

• 批准号: 501206-2016
• 负责人: Kirubarajan, Thia
• 金额: $ 13.11万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Department of National Defence / NSERC Research Partnership
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=655679
• 关键词: Multi; level; adaptive; systems; algorithms

Sensing or inference using data from heterogeneous and geographically-distributed sensors has many civilian as well as defense applications. For example, in defense-oriented surveillance systems, multiple unidentified targets are tracked using noisy data from sensors such as radar, sonar, electro-optical or infrared cameras to identify their locations and courses and classify their types. In civilian urban-monitoring or smart-city systems, multiple cameras are used to monitor traffic and to ensure the safety and security of people in an area. Advances in sensor technologies have resulted in affordable high-quality sensors (e.g., video cameras, acoustic devices, short-range radars) that are ubiquitous around us. Also, unlike before when sensors were usually deployed by those interested in surveillance, now data from ad-hoc sensors-of-opportunity are also available. Admittedly, computing technology has also improved along with advances in sensor technology. However, in order to achieve real-time sensing capability, it is necessary to develop efficient algorithms to process the vast amounts of data from a multitude of sensors (e.g., 4K video data at high framerates, sonar data with extremely high false alarm rates) with a time-varying sensor architecture or configuration. That is, we need algorithms that can adapt to ever-changing target characteristics and sensor configurations at design-time as well as at run-time. We propose to develop multi-level (e.g., at sensor, platform, system and system-of-systems levels) adaptation algorithms to process data from a time-varying set of sensors mounted on platforms that may evolve over time with the objective of accurate tracking, classification and situational awareness. In addition to training highly qualified personnel and advancing the state-of-the-art in sensing technology, the**proposed work will have significant economic and societal impacts. The algorithms to be developed in this project can be applied, with some modifications, to defense as well as civilian sensing systems.

使用来自异构和地理分布的传感器的数据进行感测或推断具有许多民用和国防应用。例如，在面向防御的监视系统中，使用来自诸如雷达、声纳、光电或红外摄像机的传感器的噪声数据来跟踪多个未识别的目标，以识别它们的位置和路线并对其类型进行分类。在民用城市监控或智能城市系统中，多个摄像头用于监控交通并确保区域内人员的安全。传感器技术的进步已经产生了负担得起的高质量传感器（例如，摄像机、声学设备、短程雷达），这些设备在我们周围无处不在。此外，与以前传感器通常由那些对监视感兴趣的人部署不同，现在也可以使用来自特设机会传感器的数据。诚然，计算技术也随着传感器技术的进步而沿着改进。然而，为了实现实时感测能力，有必要开发有效的算法来处理来自多个传感器的大量数据（例如，高帧率下的4K视频数据、具有极高误报率的声纳数据）与时变传感器架构或配置。也就是说，我们需要能够在设计时和运行时适应不断变化的目标特性和传感器配置的算法。我们建议开发多层次（例如，在传感器、平台、系统和系统的系统级别）的自适应算法，以处理来自安装在平台上的时变传感器组的数据，这些传感器组可以随着时间的推移而发展，目标是精确跟踪、分类和态势感知。除了培训高素质的人员和推进传感技术的最新发展外，** 拟议的工作将产生重大的经济和社会影响。在这个项目中开发的算法可以应用，与一些修改，国防以及民用传感系统。