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: $ 7.29万
• 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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=696637
• 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.

使用来自异质和地理分布的传感器的数据进行传感或推理具有许多平民和国防应用。例如，在面向防御的监视系统中，使用来自雷达，声纳，电子光学或红外摄像机等传感器的嘈杂数据来跟踪多个身份不明的目标，以识别其位置和课程并对其类型进行分类。在平民城市监测或智能城市系统中，使用多个摄像机来监视流量并确保该地区人们的安全和保障。传感器技术的进步导致我们周围无处不在的高质量传感器（例如，摄像机，声学设备，短距离雷达）。同样，与通常受到监视感兴趣的人部署传感器不同，现在也可以使用来自临时传感器的数据。诚然，随着传感器技术的进步，计算技术也有所提高。但是，为了获得实时传感能力，有必要开发有效的算法来处理来自众多传感器的大量数据（例如，高帧速率的4K视频数据，具有极高虚假警报速率的声纳数据，具有极高的错误警报率），并具有时间变化的传感器架构或配置。也就是说，我们需要算法，这些算法可以适应设计时间和运行时的不断变化的目标特性和传感器配置。我们建议开发多层次（例如，在传感器，平台，系统和系统级别）适应算法，以从安装在平台上的时变传感器集来处理数据，这些传感器可能会随着时间的推移而发展，以实现准确的跟踪，分类，分类和情境差异的目标。除了培训高素质的人员并促进传感技术的最先进之外， 拟议的工作将产生重大的经济和社会影响。可以在该项目中开发的算法，并进行一些修改，以防御和平民传感系统。