CAREER: Distributed Space-Time Processing for Sensor Networks

职业：传感器网络的分布式时空处理

• 批准号: 0545571
• 负责人: Aleksandar Dogandzic
• 金额: $ 40万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-15 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0545571&HistoricalAwards=false
• 关键词: CAREER; Distributed; Space; Time; Processing

Large-scale sensor networks that can monitor an environment at close range with high spatial and temporal resolutions are expected to play an important role in various applications, e.g. assessing ``health'' of machines, aerospace vehicles, and civil-engineering structures; environmental, medical, food-safety, and habitat monitoring; energy management, inventory control, home and building automation, etc. Each node in the network will have limited sensing, signal processing, and communication capabilities, but by cooperating with each other they will accomplish tasks that are difficult to perform with conventional centralized sensing systems.This research focuses on novel solutions for prominent signal processing problems in sensor network design: efficiently extracting information through distributed (neighborhood-based) processing, mitigating practical difficulties such as node localization errors and spatially correlated measurements, and conserving energy through active node selection. Distributed Bayesian algorithms are being developed for estimating physical phenomena in the presence of node location uncertainties; ignoring these uncertainties may lead to poor estimation and detection performance. The investigators also study nonparametric distributed signal processing approaches under the practically important scenario where parametric models for the response function and noise distribution are unknown. Here, the goal is to provide reliable inference about the observed phenomenon that is comparable to that achieved using exact parametric models. Educational goals of the program include incorporating modern teaching techniques and statistical signal processing applications into the undergraduate engineering curriculum and integrating state-of-the-art signal processing into the graduate engineering curriculum at the Iowa State University. The research results and teaching tools developed in this project are made available to a broad scientific community through the Internet and publication in scientific journals.

大规模传感器网络可以近距离监测环境，具有高空间和时间分辨率，有望在各种应用中发挥重要作用，例如评估机器，航空航天飞行器和土木工程结构的“健康”；环境、医疗、食品安全和生境监测；能源管理、库存控制、家居及楼宇自动化等。网络中的每个节点将具有有限的传感、信号处理和通信能力，但通过相互合作，它们将完成传统集中式传感系统难以完成的任务。本研究主要针对传感器网络设计中突出的信号处理问题：通过分布式（基于邻域的）处理高效提取信息，减轻节点定位误差和空间相关测量等实际困难，以及通过主动节点选择节约能源。正在开发分布式贝叶斯算法，用于估计存在节点位置不确定性的物理现象；忽略这些不确定性可能导致较差的估计和检测性能。研究人员还研究了在响应函数和噪声分布的参数模型未知的实际重要场景下的非参数分布信号处理方法。在这里，目标是提供关于观察到的现象的可靠推断，这种推断与使用精确参数模型所获得的推断相当。该项目的教育目标包括将现代教学技术和统计信号处理应用纳入本科工程课程，并将最先进的信号处理融入爱荷华州立大学的研究生工程课程。本项目开发的研究成果和教学工具将通过互联网向广大科学界提供，并在科学期刊上发表。

项目成果

Sparse Signal Reconstruction via ECME Hard Thresholding

通过 ECME 硬阈值重建稀疏信号

• DOI: 10.1109/tsp.2012.2203818
• 发表时间: 2012
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Qiu, Kun;Dogandzic, Aleksandar
• 通讯作者: Dogandzic, Aleksandar

Bayesian Complex Amplitude Estimation and Adaptive Matched Filter Detection in Low-Rank Interference

低阶干扰中的贝叶斯复振幅估计和自适应匹配滤波器检测

• DOI: 10.1109/tsp.2006.887151
• 发表时间: 2007
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Dogandzic, Aleksandar;Zhang, Benhong
• 通讯作者: Zhang, Benhong

Sparse Signal Reconstruction from Quantized Noisy Measurements via GEM Hard Thresholding

• DOI: 10.1109/tsp.2012.2185231
• 发表时间: 2012-05
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Kun Qiu;Aleksandar Dogandzic

Distributed Estimation and Detection for Sensor Networks Using Hidden Markov Random Field Models

• DOI: 10.1109/tsp.2006.877659
• 发表时间: 2006-08
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Aleksandar Dogandzic;Benhong Zhang

Decentralized Random-Field Estimation for Sensor Networks Using Quantized Spatially Correlated Data and Fusion-Center Feedback

使用量化空间相关数据和融合中心反馈的传感器网络分散随机场估计

• DOI: 10.1109/tsp.2008.2005753
• 发表时间: 2008
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Dogandzic, A.