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Localization of a wireless mobile device or a robot in indoor and GPS-deniedenvironments is a difficult problem, particularly in dynamic scenarios wheretraditional cameras and LIDAR-based alternative sensing and localizationmodalities may fail. We propose a method for estimating the location of amobile robot in relation to static wireless sensor nodes (WSN) deployed in theenvironment. The method employs a novel particle filter that updates itsweights using a Gauss probability over Direction of Arrival (DOA) estimate inconjunction with the mobile robot's mobility model. We evaluate and validatethe proposed method in terms of accuracy and computational efficiency throughextensive simulations and public real-world measurement datasets, comparingwith standard state-of-the-art localization approaches. The results showconsiderably high meter-level localization accuracy balanced by the highcomputational efficiency, enabling it to use online without a need for adedicated offline phase as in typical fingerprint-based localizationalgorithms.

Online Indoor Localization Using DOA of Wireless Signals