Cooperative Localisation: Distributed Optimisation with Hypothesis Testing

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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.