Stochastic sensor scheduling

统计信号处理处理从噪声测量中提取信号。在这个项目中，受能量、通信和计算限制的驱动，我们解决了如何动态调度和优化信号处理资源以从噪声测量中提取信号的更深层次的问题。这类传感器调度/管理问题涉及贝叶斯信号处理、随机优化、随机控制和博弈论中的思想的相互作用。我们的研究涉及四个相互关联的部分。在第一部分中，我们对被描述为约束马尔可夫决策过程、部分可观测马尔可夫决策过程和随机对策的传感器管理问题的结构性质进行了基础研究。在第二部分中，我们利用第一部分中的结构来推导用于传感器管理的复杂的基于模拟的优化算法。在第三部分中，我们利用第一部分和第二部分的基本结果来设计和分析监视和雷达资源管理中的网络化传感器的有效算法。在第四部分中，我们利用第二部分的基本结果设计了有效的多粒子布朗动力学和分子动力学模拟离子在生物膜离子通道中的渗透。支持我们研究的关键思想是动态系统的随机调度和对策的研究。这一统一的主题激励了我们的基本理论和应用。

Statistical signal processing deals with extracting signals from noisy measurements. In this project, motivated by energy, communication and computational constraints, we address the deeper issue of how to dynamically schedule and optimize signal processing resources to extract signals from noisy measurements. Such sensor scheduling/management problems involve the interplay of ideas in Bayesian signal processing, stochastic optimization, stochastic control and game theory. Our research involves four inter-related parts. In Part 1, we conduct fundamental research on the structural properties of sensor management problems formulated as constrained Markov decision processes, partially obeserved Markov decision processes and stochastic games. In Part 2, we exploit the structure in Part 1 to derive sophisticated simulation-based optimization algorithms for sensor management. In Part 3, we exploit the fundamental results in Part 1 and Part 2 to design and analyse efficient algorithms for networked sensors in surveillance and radar resource management. In Part 4, we use the fundamental results in Part 2 to devise efficient multiparticle Brownian dynamics and Molecular dynamics simulation of ion permeation in biological membrane ion channels. The key idea underpinning our research is the study of stochastic scheduling and games for dynamical systems. This unifying theme motivates our fundamental theory and applications.