Cognitive Inference and Signal Processing in Adversarial Uncertain Environments

为了收集、处理数据并使其可供广泛的潜在应用程序随时访问，各种网络技术正在高度分布式的体系结构中涌现，以适应数量和类型不断增加的不同类型的传感器。提出的研究计划(RP)的目标是发明和分析处理数据的认知分布式算法。传统方法在实践中往往失败，因为它们没有考虑到不确定和敌对的环境(通信、认知被动雷达、压缩传感和稀疏信号处理、房颤标测、机器学习、高光谱成像和递归贝叶斯估计)。这项研究的成果是以新方法的形式加强对环境的感知和监测，例如使用多种类型和数量的分布式传感器，如雷达、照相机、电极，对源和目标进行定位和检测。 这个RP将以先进和低成本的认知信号处理算法的形式产生解决方案，并发明新的方法，使关键系统能够在U&AE中学习和认知操作。这些解决方案将在能源、通信、汽车、雷达、安全、生物医学、金融和大数据等各个行业得到应用。这些部门的进展将对加拿大的经济和就业产生重大影响，因为相关部门每年产生大量收入。加拿大的工业也将从这一RP的结果中受益，因为认知范式促进了更有效的资源使用，它们在提高效率方面的成功将使它们更具竞争力。此外，6名博士、4名硕士和5名本科生将接受尖端技术培训，以帮助满足该领域对专业知识日益增长的需求。 最近多个行业在智能、机器学习和信号处理领域创造了大量就业机会，证明了这项研究的及时性。经过培训以填补这些必要角色的HQP对于支持在加拿大企业的有利可图投资至关重要。

To collect data, process and make it available and readily accessible for a wide range of potential applications, various cyber technologies are emerging in a highly distributed architecture for increasing numbers and types of heterogeneous sensors. The goal of the proposed research program (RP) is to invent and analyze cognitive distributed algorithms for processing data. Traditional methods often fail in practice because they are not designed to take into account uncertain and adversarial environments (Umunication, cognitive passive radars, compressed sensing and sparse signal processing, atrial fibrillation mapping, machine learning, hyper-spectral imaging and recursive Bayesian estimation. The outcomes of this research are in the form of novel methods for enhanced sensing and monitoring of the environment such as localization and detection of sources and objects using multiple types and number of distributed sensors such as radars, cameras, electrodes. This RP will yield solutions in the form of advanced and low-cost cognitive signal processing algorithms and invent new methods to enable critical systems to learn and cognitively operate in U&AEs. These solutions will have applications in various industries such as: energy, communication, automotive, radar, security, biomedical, financial and big data. Advances in these sectors will have a significant impact upon the Canadian economy and employment, as relevant sectors generate significant annual revenue. Canada's industries will also stand to benefit from the outcomes of this RP because the cognitive paradigm promotes more efficient resource use, and their success in increasing efficiency will make them more competitive. In addition, six PhD, four MSc and five undergraduate students will be trained in leading-edge technology to help meet the growing demand for expertise in this area. The timeliness of this research is evidenced by recent massive job creation by multiple industries in the areas of intelligence, machine learning, and signal processing. HQP trained to fill these necessary roles are critical to supporting profitable investment in Canadian business.