Radio-visual signal fusion for localization in cellular networks

Location based services in wireless networks is a quite demanding application especially in urban areas. Cellular network provides measurements regarding the signal attenuations from serving and neighbouring base stations for managing radio resources. Localization based on this inconsistent received signal strength is a challenging problem. This paper describes a novel bimodal localization idea for mobile users in cellular networks. A series of vision-based algorithms are applied to extract user position from monocular vision and then augment it with extracted location in cellular network. A probabilistic framework based on particle filters developed to fuse the bimodal data as well as localize the mobile user from inconsistent measurements. An adaptive particle weighting scheme based on the modal confidence coefficient is also developed. This approach can be easily implemented to utilize available online visual databases to increase accuracy of conventional localization methods for wireless networks even in indoor environments that other navigation signals are not available.