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.

使用一个或多个机载平台上的不同类型的传感器监测地面上的移动物体或人员在安全和安保、搜索和救援、执法、智能交通、航运和农业中具有许多应用。这些平台上的传感器可以是光电/红外（EO/IR）、激光、雷达或声学传感器。感兴趣的移动目标可以是车辆、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，恒定速度，机动）在不同的环境条件下（例如，低光、下雨、交通繁忙）。 这些机载监视系统的主要目标是1）检测不同类型的感兴趣的移动对象，2）跟踪和分类检测到的对象，以及3）使用不同的传感器和有限的机载计算资源分析它们在不同环境条件下的行为。不同类型的平台，传感器，目标和环境条件的组合，提出了一个具有挑战性的多平台，多传感器，多目标跟踪问题与许多冲突的精度与速度与复杂性的要求。本建议的目的是解决这些冲突，在机载监视系统，以提高国家的最先进的机载多平台，多传感器，多目标跟踪系统。受行业合作伙伴在机载监视方面的丰富经验以及他们对现场系统的观察的启发，我们建议开发精确的目标跟踪和融合算法，这些算法考虑到现实世界的挑战（例如，小的目标尺寸、环境条件、有限的计算资源）。虽然我们的目标是解决机载监控系统的具体挑战，但这项研究的结果也可以应用于智能交通、智能农业和执法。此外，拟议的研究将在对加拿大重要的关键领域培训一些高素质的人员。