Collaborative Research: Robust Low Complexity Approaches to Source Localization and Sensor Placement in Wireless Networks

协作研究：无线网络中源定位和传感器放置的鲁棒低复杂性方法

• 批准号: 0830747
• 负责人: Soura Dasgupta
• 金额: $ 22.03万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830747&HistoricalAwards=false
• 关键词: Collaborative; Research; Robust; Low; Complexity

AbstractLocalization involves the estimation of the precise location of an object based on various forms of relative position information available of the object. Source and sensor localization is a fundamental capability broadly useful in a number of emerging applications. For example a network of sensors deployed to combat bioterrorism, must not only detect the presence of a potential threat, but also must pinpoint the source of the threat. Similarly, in pervasive computing, locating an errant mobile user permits the computer network to identify the most appropriate server with matching capabilities for the user. Likewise, in sensor networks individual sensors must know their own positions, so as to route packets, detect faults, and sense and record events. There is also an emerging multibillion dollar wireless localization industry. This research will address issues that hold the key to fast efficient localization.The investigators will adopt a three pronged approach. First, various optimum estimates will be investigated under a variety of practical signal models. These include maximum likelihood and minimum variance estimates. Theoretical performance limits will be determined. Secondly, the investigators will generalize and analyze various algorithms that obtain these estimates efficiently with low complexity. Specifically, the investigators will study optimal localization involving minimization of non-convex cost functions that admit multiple local minima. To overcome the problem of multiple local minima, the investigators will develop a relaxation framework based on convex optimization to obtain fast near optimal solutions. Finally, the proper placement of wireless sensors and anchors impacts both the accuracy and the complexity with which localization is performed. Thus, the investigators will study optimum anchor placement to aid both these attributes. These investigations are critical to the understanding of the theoretical foundation of wireless source localization, and will fundamentally impact its broad applications.

AbstractLocalization涉及基于对象可用的各种相对位置信息的对象的精确位置的估计。来源和传感器定位是一种基本能力，在许多新兴应用程序中广泛有用。例如，部署用于打击生物恐怖主义的传感器网络，不仅必须检测到潜在威胁的存在，而且还必须确定威胁的来源。同样，在Pervasive计算中，找到错误的移动用户允许计算机网络识别具有用户匹配功能的最合适的服务器。同样，在传感器网络中，单个传感器必须知道自己的位置，以路由数据包，检测故障以及感官和记录事件。还有一个新兴的无线本地化行业。这项研究将解决持有快速效率本地化关键的问题。研究人员将采用三种规律的方法。首先，将在各种实用信号模型下研究各种最佳估计。这些包括最大似然和最小方差估计。将确定理论性能限制。其次，研究人员将概括和分析各种算法，以低复杂性有效地获得这些估计值。具体而言，研究人员将研究最佳定位，涉及最小化的非凸成本函数，这些函数吸收了多个局部最小值。为了克服多个局部最小值的问题，研究人员将基于凸优化的放松框架开发一个放松框架，以便快速获得接近最佳的解决方案。最后，适当的无线传感器和锚定位会影响执行定位的准确性和复杂性。因此，研究人员将研究最佳锚定位置以帮助这两个属性。这些调查对于理解无线源本地化的理论基础至关重要，并从根本上影响其广泛的应用。