无线传感器网络中功率受限的分布式矢量估计

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. The Bayesian CRB will be derived and linear estimators will be developed. The fusion center (FC) uses the transmitted discrete messages 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. (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.

分布式参数估计作为WSNs中的一个挑战性研究内容，引起了学者的广泛兴趣。本课题拟针对未知的矢量参数估计系统从三方面展开研究：（1）贝叶斯克拉美罗界(Bayesian Cramer-Rao bound ,CRB)和线性估计器的确定。我们将从理论上推导出待估计矢量的Bayesian CRB，从而确定系统的线性估计器。我们考虑基于融合中心的分布式估计系统，融合中心利用接收到的数据进行估计融合。本课题重点考虑系统观察模型、信道衰落和噪声、调制方式等对矢量估计系统的影响，并研究传输功率、速率和估计准确性之间的折中问题。（2）分布式矢量估计中的信道估计问题。本课题重点考虑信道估计误差对分布式信号处理的影响。我们也会研究信道估计误差和信道估计功率对矢量估计准确性的影响。（3）基于簇的分层网络结构中的分布式矢量估计。我们重点考虑簇头之间的功率分配对估计准确性的影响。

无线传感器网络在环境监控、医疗、军事等领域有着广泛而重要的应用前景。分布式参数估计作为WSNs中的一个挑战性研究内容引起了学者的广泛兴趣。本课题针对无线传感器节点功率受限的缺点对未知的矢量参数估计系统功率优化问题展开研究，这里我们假定该系统基于常见的线性观察模型和数字化传输策略。.我们主要针对三方面的问题展开研究：（1）信道估计及估计误差分析。提出了迭代对消的信道估计策略。该算法对基于相关原理的信道估计算法进行改进，利用迭代的思路消除其引入的估计误差，逐步逼近信道的精确估计值。（2）研究信道特性及对应的信道估计误差对分布式信号处理的影响。建立存在信道估计误差下的矢量估计器和最优化的功率分配策略，得到了导频信息和数据传输之间功率比的闭式解。（3）将信道估计误差引入软检测器设计，并数值分析了其导频信息和数据传输之间功率比的最优值。（4）将瞬态无线信息与功率传输（SWIPT）技术引入两跳中继系统，建立可持续供电的中继传输方案，得到了导频功率、数据功率和能量功率之间比值的闭式解。.本课题的研究成果可以大大提高系统对信道估计误差的鲁棒性和降低系统功率，从而延长无线传感器网络寿命，特别是将SWIPT技术引入到后续研究中，为可持续供电的绿色无线传感器网络的构建奠定了重要的研究基础。