  Localization of a wireless mobile device or a robot in indoor and GPS-denied
environments is a difficult problem, particularly in dynamic scenarios where
traditional cameras and LIDAR-based alternative sensing and localization
modalities may fail. We propose a method for estimating the location of a
mobile robot in relation to static wireless sensor nodes (WSN) deployed in the
environment. The method employs a novel particle filter that updates its
weights using a Gauss probability over Direction of Arrival (DOA) estimate in
conjunction with the mobile robot's mobility model. We evaluate and validate
the proposed method in terms of accuracy and computational efficiency through
extensive simulations and public real-world measurement datasets, comparing
with standard state-of-the-art localization approaches. The results show
considerably high meter-level localization accuracy balanced by the high
computational efficiency, enabling it to use online without a need for a
dedicated offline phase as in typical fingerprint-based localization
algorithms.
