面向高效实时目标监测的阵列化传感器网络体系关键技术研究

项目名称： 面向高效实时目标监测的阵列化传感器网络体系关键技术研究

项目编号： No.61273079

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 王智

作者单位： 浙江大学

项目金额： 83万元

中文摘要： 面向高效实时的目标监测，本课题在阵列技术思想的启发下，以最新的压缩采样技术为支持，提出了一种新型的阵列化传感器网络体系架构，重点研究资源受限的实时保障和系统调度策略。实时保障方面，利用压缩采样技术和负载自适应的网络通信协议，构建网络化信息实时采集传输及处理一体化框架并分析相关性能；系统调度方面，设计面向不同应用需求的多工作模式，研究不同模式下的具有资源意识的节点调度策略，尤其针对资源严重受限情况下，提供性能折中的权衡策略。此外，针对高精度的目标监测需求，根据基于数据压缩的目标直接定位思想，研究适合新体系特点的目标跟踪框架及算法。在理论研究基础上，开发新体系下的原型系统，解决传感器节点高精度同步与自定位等支撑技术难题，并验证算法及模型的性能。此课题为大规模实时无线传感器网络突破实时性与资源限制提供了解决框架并进行实验验证，为其实用化提供了新途径。

中文关键词： 实时高效监测；实时信息传输；阵列化网络；压缩感知；无线传感器网络

英文摘要： An innovative array-forming sensor network is proposed to obtain efficient real-time target monitoring with Wireless Sensor Networks, which is inspired by array technologies and leverages modern compressive sampling theory. Technologies for real-time assurance and strategies for node scheduling are studied to facilitate the development of a distributed wireless sensor array network under resource limitations.On strategies for real-time assurance, compressive sampling and load-adaptive network protocols will be studied to build a real-time information collecting and processing framework. The performance for real-time will also be analyzed. In study of system scheduling, different modes will be designed for different requirements in order to achieve an optimum node scheduling strategies. Especially, a tradeoff strategy will be studied for occasions when resources are severely restricted. Considering the requirements for high-precision target monitoring, new target tracking framework and algorithms suitable for our system is studied using direct positioning methods, which is based on data compression. On the basis of theoretical researches, a prototype system for array-forming sensor network system will be developed to verify our models and algorithms, as well as solving the problems such as time synchronization, s

英文关键词： real-time target tracking；real-time information transmission；array-forming network；compressive sensing；wireless sensor networks