Fundamental Bounds on Decentralized Adaptive Detection in Hidden Markov Models

隐马尔可夫模型中分散自适应检测的基本界限

• 批准号: 0830472
• 负责人: Yajun Mei
• 金额: $ 18.38万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830472&HistoricalAwards=false
• 关键词: Fundamental; Bounds; Decentralized; Adaptive; Detection

Abstract for ?TF08: Fundamental Bounds on Decentralized Adaptive Detection in Hidden Markov Models? (PI: Yajun Mei, NSF proposal #0830472)In modern information era, the advance of sensor, computing and communication technologies offers promising opportunities for the decision makers and organizations to make effective decisions quickly in many areas of real-world applications of sensor network systems. However, without timely updating or adaptation to reflect the changing environments, even the best decision-making methods are irrevocably vulnerable in applications such as threats detection in bioterrorism and hacking. The challenges become more difficult due to the complex spatio-temporal correlations among sensors and the constraints on communications, energy and computing. This research is concerned with the development of a general and systematic foundation and methodologies for decentralized adaptive detection when sensor observations are from hidden Markov models, with the focus on deriving the fundamental information limitation on the ability to reliably detect the changes. The investigator studies decentralized adaptive detection in hidden Markov models for two scenarios of sensor network systems. The first is the system where sensors do not have access to their past observations, in which the research topics include (i) optimal stationary quantizers; (ii) lower bounds on adaptive detection; (iii) robust detection via tandem quantizers; and (iv)adaptive detection with censored sensor observations. The second is the system where sensors have access their past observations, in which the research investigates: (i) universal information bounds; (ii) computing-friendly, effective schemes; (iii) schemes with controlled detection delay; and (iv) blockwise transmission.

抽象为？TF 08：隐马尔可夫模型中分散式自适应检测的基本界限？(PI：Yajun Mei，NSF proposal #0830472）在现代信息时代，传感器、计算和通信技术的进步为决策者和组织在传感器网络系统的许多实际应用领域提供了快速有效的决策机会。然而，如果没有及时更新或调整以反映不断变化的环境，即使是最好的决策方法在生物恐怖主义和黑客攻击中的威胁检测等应用中也是脆弱的。由于传感器之间复杂的时空相关性以及通信、能源和计算的限制，这些挑战变得更加困难。本研究关注的是一个通用的和系统的基础和方法的发展分散自适应检测时，传感器的观测是从隐马尔可夫模型，重点是推导出的基本信息的能力，可靠地检测到的变化的限制。研究了传感器网络系统中两种情况下隐马尔可夫模型的分散自适应检测。第一种是传感器无法访问其过去观测的系统，其中的研究主题包括：（i）最佳平稳量化器;（ii）自适应检测的下限;（iii）通过串联量化器的鲁棒检测;和（iv）具有删失传感器观测的自适应检测。第二种是传感器访问其过去的观测的系统，在该系统中，研究调查：（i）通用信息边界;（ii）计算友好，有效的计划;（iii）具有控制检测延迟的计划;和（iv）块传输。