根据节点到观察节点的UWB通道差异的成对节点定位
Pairwise Node Localization From Differences in Their UWB Channels to Observer Nodes
摘要：
我们认为在多径传播环境中的一对无线节点之间的定位和距离估计的问题，但不是通常的方式处理它们之间的信道测量。我们提出了一种新的范例，比较两个节点的超宽带（UWB）信道的其他节点，称为观察员。主要思想是信道脉冲响应（CIR）之间的相异性随着$d$而增加，并且允许估计$\hat{d}$。我们的方法依赖于提取共同的多径组件（MPC）的CIR。这在室内或城市场景中是现实的，并且如果$d$比观察者距离小得多。我们提出的距离估计，利用MPC延迟差异中包含的丰富的位置信息。同样，我们提出了估计的相对位置向量处理MPC延迟和MPC方向。我们这样做的各种重要的情况下：有和没有时间同步，延迟测量误差，和知识的MPC之间的CIR关联。该估计器具有很大的技术优势：它们不需要视线条件，观察者的位置知识，或环境知识。我们研究的估计精度与随机抽样的基础上的数值评估，此外，在室内环境中的测量的基础上进行实验评估。该建议显示了在理论和实践中具有很大准确性的潜力。我们描述了如何将新的测量范式纳入合作定位框架的时空跟踪。这可以在动态多路径设置中实现可负担得起的无线网络定位。
英文摘要：
We consider the problem of localization and distance estimation between a pair of wireless nodes in a multipath propagation environment, but not the usual way of processing a channel measurement between them. We propose a novel paradigm which compares the two nodes' ultra-wideband (UWB) channels to other nodes, called observers. The main idea is that the dissimilarity between the channel impulse responses (CIRs) increases with $d$ and allows for an estimate $\hat{d}$. Our approach relies on extracting common multipath components (MPCs) from the CIRs. This is realistic in indoor or urban scenarios and if $d$ is considerably smaller than the observer distances. We present distance estimators which utilize the rich location information contained in MPC delay differences. Likewise, we present estimators for the relative position vector which process both MPC delays and MPC directions. We do so for various important cases: with and without time synchronization, delay measurement errors, and knowledge of the MPC association between the CIRs. The estimators exhibit great technological advantages: they do not require line-of-sight conditions, observer location knowledge, or environment knowledge. We study the estimation accuracy with a numerical evaluation based on random sampling and, additionally, with an experimental evaluation based on measurements in an indoor environment. The proposal shows the potential for great accuracy in theory and practice. We describe how the paradigm could incorporate novel measurements into cooperative localization frameworks for spatio-temporal tracking. This could enable affordable wireless network localization in dynamic multipath settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
