Sensor-Adaptive Signal Processing for Dynamic Decision Making

用于动态决策的传感器自适应信号处理

• 批准号: 251246-2013
• 负责人: Krishnamurthy, Vikram
• 金额: $ 5.46万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570955
• 关键词: Sensor; Adaptive; Signal; Processing; Dynamic

This project conducts fundamental research in adaptive decision making in signal processing systems. It involves the interplay of Bayesian signal processing, stochastic optimization, stochastic control and game theory. Such adaptive decision making problems have recently received significant attention in diverse areas including sensor networks, cognitive radars, biosensing, and automated decision systems. These problems transcend classical statistical signal processing (which deals with extracting signals from noisy measure- ments) to address the deeper issue of how sensors and signal processing algorithms can use feedback control to adapt to their environment thereby leading to a smart reconfigurable sensing systems. The research objectives of this project fall under the following three inter-related themes: (i) Sensor Resource Management: The objective is to devise adaptive resource allocation algorithms in sensor systems that are seeking to estimate the state of an underlying stochastic dynamical system. We are particularly interested in designing smart reconfigurable radars. (ii) Decentralized Decision Making: Motivated by energy, sensing and communication constraints, the aim is to devise autonomous strategies for individual sensors to activate themselves to detect events. Social learning and game theory will be used to synthesize and analyze decentralized decision making algorithms. (iii) Active Sensing using Molecular Biosensors: Our objective is to apply the fundamental theory, algorithms and analysis developed above to a remarkable biosensor that was built recently. The key unifying theme underpinning the above three objectives is the paradigm of sensor-adaptive signal processing: signal processing resources that adapt their behavior based on noisy measurements and previous decisions. Such adaptive decision making under uncertainty constitutes the fundamental theme of this project.

该项目进行信号处理系统中自适应决策的基础研究。它涉及贝叶斯信号处理，随机优化，随机控制和博弈论的相互作用。这种自适应决策问题最近受到了广泛的关注，包括传感器网络，认知雷达，生物传感和自动决策系统。这些问题超越了经典的统计信号处理（处理从噪声测量中提取信号），以解决传感器和信号处理算法如何使用反馈控制来适应其环境的更深层次的问题，从而导致智能可重构传感系统。 本项目的研究目标分为以下三个相互关联的主题： (i)传感器资源管理：我们的目标是设计自适应资源分配算法的传感器系统，正在寻求估计一个潜在的随机动态系统的状态。 我们对设计智能可重构雷达特别感兴趣。 (ii)分散决策：受能源、传感和通信限制的影响， 其目的是为各个传感器设计自主策略，以激活它们自己来检测事件。社会学习和博弈论将用于综合和分析分散决策算法。 (iii)使用分子生物传感器的主动传感：我们的目标是将上述开发的基本理论，算法和分析应用于最近建造的显着生物传感器。 支撑上述三个目标的关键统一主题是传感器自适应信号处理的范例：信号处理资源根据噪声测量和先前的决策调整其行为。这种不确定性下的适应性决策构成了本项目的基本主题。