Airborne Tracking of Small Ground and Maritime Targets Under Realistic Conditions

现实条件下空中跟踪小型地面和海上目标

• 批准号: 535810-2018
• 负责人: Kirubarajan, Thia
• 金额: $ 26.23万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=720060
• 关键词: Airborne; Tracking; Small; Ground; Maritime

The monitoring of moving objects or persons on the ground using different types of sensors on one or more airborne platforms has many applications in safety & security, search & rescue, law enforcement, intelligent transportation, shipping and agriculture. The sensors on these platforms could be electro-optical/infrared (EO/IR), laser, radar or acoustic. The moving targets of interest could be vehicles, ships, boats, people or even submarines. The targets of interest could be evolving with different motion characteristics (e.g., constant velocity, maneuvering) under different environmental conditions (e.g., low light, rain, heavy traffic). The primary objectives in these airborne surveillance systems are to 1) detect different types of moving objects of interest, 2) track and classify the detected objects, and 3) analyze their behavior under different environmental conditions using the different sensors and limited computing resources onboard. The combination of different types of platforms, sensors, targets and environmental conditions, poses a challenging multiplatform-multisensor-multitarget tracking problem with many conflicting accuracy vs. speed vs. complexity requirements. The objective of this proposal is to address these conflicts in airborne surveillance systems to improve the state-of-the-art in airborne multiplatform-multisensor-multitarget tracking systems. Motivated by the extensive experience of the industry partner in airborne surveillance and their observations from fielded systems, we propose to develop accurate target tracking and fusion algorithms that factor in real-world challenges (e.g., small target sizes, environmental conditions, limited computational resources). While we aim to solve specific challenges in airborne surveillance systems, the results of this research can be applied to intelligent transportation, smart agriculture and law enforcement as well. In addition, the proposed research will train a number of highly qualified personnel in key areas of importance to Canada.

使用一个或多个空降平台上不同类型的传感器对地面上的移动物体或人员进行监控，在安全与保障，搜救，搜救，执法，智能运输，运输和农业方面有许多应用程序。这些平台上的传感器可以是电光/红外线（EO/ir），激光，雷达或声学。感兴趣的行动目标可能是车辆，船只，船只，人甚至潜艇。感兴趣的目标可以以不同的运动特性（例如，在不同的环境条件（例如，低光，雨水，交通繁忙）下的不同运动特征（例如，恒定速度，操纵）发展。 这些空中监视系统中的主要目标是1）检测感兴趣的移动对象，2）跟踪和对检测到的对象进行分类，3）使用不同的传感器和有限的计算资源在不同的环境条件下分析其行为。不同类型的平台，传感器，目标和环境条件的结合构成了一个具有挑战性的多平台 - 媒体传感器 - 穆尔多塔特跟踪问题，以及许多相互冲突的准确性与速度与复杂性要求。该提案的目的是解决机载监视系统中的这些冲突，以改善机载多平台 - 媒体传感器 - 穆尔多塔特跟踪系统的最新冲突。由行业合作伙伴在机载监视及其对现场系统的观察结果的广泛经验的推动，我们建议开发准确的目标跟踪和融合算法，从而影响实际挑战（例如，小目标，环境条件，环境条件，有限的计算资源）。尽管我们旨在解决空中监视系统中的具体挑战，但这项研究的结果也可以应用于智能运输，智能农业和执法部门。此外，拟议的研究将在对加拿大重要的关键领域中培训许多高素质的人员。