CIF: Small: Power-constrained distributed vector estimation in wireless sensor networks

CIF：小：无线传感器网络中的功率受限分布式矢量估计

• 批准号: 1319770
• 负责人: Azadeh Vosoughi
• 金额: $ 37.33万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319770&HistoricalAwards=false
• 关键词: CIF; Small; Power; constrained; distributed

Wireless sensor networks (WSNs) have the potential to significantly impact many aspects of our everyday life and are anticipated to be critically important for environmental monitoring, health monitoring, surveillance, military, and homeland security applications. Distributed parameter estimation serves as one of the main challenges in WSNs, where spatially distributed battery-powered sensors are deployed over a sensing field to monitor physical or environmental conditions. This research will investigate distributed estimation of an unknown vector with a general observation model, when the transmission strategy is digital. The following three problems will tackled: (1) Bayesian Cramer-Rao bound (CRB) and linear estimators for hard-decoding and soft-decoding link layer designs. The Bayesian CRB will be derived and linear estimators will be developed. In hard-decoding design, the fusion center (FC) uses the recovered transmitted discrete messages for estimation, whereas in soft-decoding design, the FC uses directly the continuous-valued channel outputs for estimation. The impacts of the observation model, fading and communication channel noises and choice of modulation on the optimization solutions will be studied, and the existing tradeoffs between transmit power, rate, and estimation accuracy will be explored. Contrasting the power-rate-distortion regions will enable the investigator to quantify the performance improvement provided by soft-decoding (compared with hard-decoding) link layer design; (2) channel estimation problems in distributed vector estimation. The influence of wireless channel estimation errors on the distributed signal processing designs will be studied. Also, the investigator will study how the combined effects of channel estimation errors and energy cost of transmitting training symbols further limit the estimation accuracy; and, (3) distributed vector estimation in a cluster-based hierarchical network architecture with digital transmission. The existing tradeoffs between transmit power allocation among the clusters and the estimation accuracy will be explored.

无线传感器网络（WSNs）有可能显着影响我们日常生活的许多方面，并预计将是至关重要的环境监测，健康监测，监视，军事和国土安全应用。分布式参数估计是无线传感器网络中的主要挑战之一，其中空间分布的电池供电的传感器部署在感测场上以监测物理或环境条件。本研究将探讨在一般观测模式下，当传输策略为数位时，未知向量的分散式估计。本文将解决以下三个问题：（1）硬解码和软解码链路层设计的贝叶斯克拉美-罗界（CRB）和线性估计器。将推导贝叶斯CRB，并开发线性估计量。在硬解码设计中，融合中心（FC）使用恢复的传输离散消息进行估计，而在软解码设计中，FC直接使用连续值的信道输出进行估计。将研究观测模型、衰落和通信信道噪声以及调制选择对优化解的影响，并探索发射功率、速率和估计精度之间现有的权衡。对比功率率失真区域将使研究者能够量化软解码（与硬解码相比）链路层设计提供的性能改善;（2）分布式矢量估计中的信道估计问题。无线信道估计误差对分布式信号处理设计的影响将被研究。此外，调查员将研究如何信道估计误差和传输训练符号的能量成本的综合影响，进一步限制估计精度;和，（3）分布式矢量估计在一个基于集群的分层网络架构与数字传输。现有的集群之间的发射功率分配和估计精度之间的权衡将被探讨。