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.

使用来自异构和地理分布传感器的数据进行传感或推理具有许多民用和国防应用。例如，在面向防御的监视系统中，使用来自雷达、声纳、光电或红外摄像机等传感器的噪声数据来跟踪多个不明目标，以识别其位置和路线并对其类型进行分类。在民用城市监控或智慧城市系统中，使用多个摄像头来监控交通并确保某个区域内人员的安全。传感器技术的进步带来了我们周围无处不在的价格实惠的高质量传感器（例如摄像机、声学设备、短程雷达）。此外，与以前通常由对监视感兴趣的人部署传感器不同，现在也可以获得来自临时机会传感器的数据。诚然，计算技术也随着传感器技术的进步而进步。然而，为了实现实时传感能力，需要开发高效的算法来处理来自多个传感器的大量数据（例如高帧率的4K视频数据、误报率极高的声纳数据）具有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征和传感器配置的算法。我们建议开发多级（例如，在传感器、平台、系统和系统级）自适应算法，以处理来自安装在平台上的一组随时间变化的传感器的数据，这些传感器可能会随着时间的推移而演变，目的是准确地跟踪、分类和态势感知。除了培训高素质人才和推进最先进的传感技术之外， 拟议的工作将产生重大的经济和社会影响。该项目中开发的算法经过一些修改后可以应用于国防和民用传感系统。