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.

定位是指根据物体的各种形式的相对位置信息来估计物体的精确位置。源和传感器定位是在许多新兴应用中广泛有用的基本能力。例如，为打击生物恐怖主义而部署的传感器网络不仅必须检测潜在威胁的存在，而且必须查明威胁的来源。类似地，在普适计算中，定位错误的移动的用户允许计算机网络识别具有与用户匹配的能力的最合适的服务器。同样，在传感器网络中，各个传感器必须知道自己的位置，以便路由数据包，检测故障，并感测和记录事件。还有一个新兴的数十亿美元的无线本地化行业。这项研究将解决问题的关键，快速有效的本地化。调查人员将采取三管齐下的方法。首先，各种最佳估计将在各种实际的信号模型下进行研究。这些包括最大似然估计和最小方差估计。将确定理论性能限值。其次，研究人员将概括和分析各种算法，以低复杂度有效地获得这些估计。具体来说，研究人员将研究最佳定位，涉及最小化非凸成本函数，承认多个局部极小值。为了克服多个局部最小值的问题，研究人员将开发一个基于凸优化的松弛框架，以获得快速接近最优的解决方案。最后，无线传感器和锚点的正确放置影响定位的准确性和复杂性。因此，研究者将研究最佳锚放置，以帮助实现这两个属性。这些研究对于理解无线源定位的理论基础至关重要，并将从根本上影响其广泛的应用。