Cooperative Localisation: Distributed Optimisation with Hypothesis Testing

关键位置信息长期以来一直在各种军事环境中使用。在许多这些设置中，如果可以传达关于特定实体和/或事件的空间位置的信息将是有利的。本地化要求也变得更加普遍，并在地面部队中发现了许多战略应用。例如，部队的安全可以直接与部队及其敌人在战场上的位置意识程度有关。对于非军事应用，跟踪移动用户的第一需要似乎是1996年美国联邦通信委员会(FCC)发布的命令中的一项基本公共安全功能，该命令要求所有无线服务提供商向公共安全应答点提供紧急911(E-911)呼叫者的准确位置。从那时起，市场上出现了各种基于位置的服务(LBS)，从识别朋友或员工的下落到个性化的LBS，比如发现最近的提款机。据估计，LBSS每年将在全球范围内产生约100亿美元的收入。在某些情况下，提供有用的本地化将需要通过空中达到米级的完美分辨率。然而，基本的物理挑战，如信道衰落、低信噪比、多用户干扰和多径条件，阻碍了目标的实现。我们对下一代(XG)本地化系统的愿景将是提供动态、分布式、健壮和高分辨率的LBS。这些xG LBS的实现将需要在节点处进行先进的信号处理和智能，以解决干扰问题，并检测直接视线(LOS)的存在，以实现可靠的测距和定位。移动终端将采用先进的多天线技术，如多输入多输出(MIMO)，以提供增强的位置信息，并减轻多径和多用户干扰。为了实现所需的LBSS，本项目建议研究使用移动用户协作进行定位。用户或节点协作的新颖性在于，节点可以通过适当的中继来协作，以减轻多径干扰，从而有助于在存在延迟路径的情况下识别测距损失。更重要的是，协作可以将位置信息从一个节点交换到另一个节点，从而可以消除由于缺少LOS信号路径而引起的位置模糊，并且还可以实现更高的分辨率。该方案的另一个创新之处是使用基于假设检验的机器学习来检测视距，这将与用于无线定位的协作信号处理相结合。这一极具挑战性的目标也有可能重新定义无线网络的体系结构，提供一种新的系统解决方案，用于在这种协作和自我调节的体系结构中组织用户对系统资源的访问，并彻底改变21世纪ICT的关键领域。与DSTL的利益特别相关，该项目涵盖的领域包括：(1)宽带信号分离-LOS和非LOS信号的分类；(2)检测-使用协作信号处理在多用户和多径干扰信道中增强接收；(3)高分辨率定位-确定移动用户在无线环境中的位置；(4)多径抑制--利用协作信号处理抑制多径干扰。该项目的最终结果不仅将阐明使用移动节点协作的无线定位问题的好处，还将提供实现高分辨率定位的分布式算法以及解决通信系统中的一些关键问题。

Critical locational information has long been used in a variety of military settings. In many of these settings, it would be advantageous if information concerning the spatial locations of particular entities and/or events could be conveyed. Localisation requirement has also become more pervasive and has found many strategic applications to the ground forces. For instance, the troops' safety can be directly linked to the degree of location-awareness of the troops and their enemies in a battlefield. For non-military applications, the first need to track a mobile user appeared as an essential public safety feature from the order issued by the Federal Communications Commission (FCC) in the US in 1996, which mandated all wireless service providers to deliver accurate location of an emergency 911 (E-911) caller to public safety answering points. Since then, various location-based services (LBSs) have emerged in the market, from identifying the whereabouts of a friend or employee to personalised LBS such as discovering the nearest cash machine. It is estimated that LBSs will generate annual revenues of the order of 10 billions worldwide.Providing a useful localisation will require, in some cases, metre-perfect resolution to be achieved over air. Yet, the fundamental physical challenges such as channel fading, low signal-to-noise ratios (SNRs), multiuser interference, and multipath conditions have put obstacles on meeting the objective. Our vision of next-generation (xG) localisation systems will be the provision of dynamic, distributed, robust and high-resolution LBSs. The realisation of these xG LBSs will require advanced signal processing and intelligence at the nodes to resolve the problem of interference and to detect the presence of a direct line-of-sight (LoS) for reliable ranging and localisation. Advanced multi-antenna technologies such as multiple-input multiple-output (MIMO) are expected to be adopted at mobile terminals for providing enhanced locational information as well as mitigating the multipath and multiuser interference.To achieve the needed LBSs, this project proposes to investigate the use of mobile user cooperation for localisation. The novelty of user or node cooperation lies in that nodes can work collaboratively by proper relaying to mitigate the multipath interference that can help identify the LoS for ranging in the presence of delay paths. The cooperation can, more importantly, exchange locational information from one node to another so that location ambiguity due to the lack of LoS signal paths could be removed and higher resolution can also be achieved. Another novelty of this proposal is the use of hypothesis testing based machine learning for the detection of LoS, which will be integrated with the cooperative signal processing for wireless localisation. This exceptionally challenging objective also has the potential to redefine the architecture of wireless networks, provide a novel system solution for organising the access of users to the system resources in this cooperative and self-regulating architecture, and revolutionise key areas of the 21st century ICT.Of particular relevance to the interests of DSTL, the areas that this project covers include:(1) Broadband signal separation - classification of LoS and non-LoS signals;(2) Detection - enhanced reception in multiuser and multipath interference channels using cooperative signal processing;(3) High-resolution localisation - determining the locations of mobile users in wireless environments;(4) Multipath mitigation - suppressing multipath interference using cooperative signal processing.The final outcomes of this project will not only elucidate the benefits of a wireless positioning problem using mobile node cooperation but also offer distributed algorithms for realising high-resolution localisation as well as address some key problems in communications systems.