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Location awareness creates a new paradigm for distributing scalable multimedia data over wireless networks, enabling a variety of contextaware applications that require precise location information of network nodes. An emerging concept for robust and accurate network localization is to exploit cooperation and heterogeneous design for harnessing multimodal fusion of sensing measurements to extract location information. This article gives a brief introduction to visionand radio-based positioning technologies, and then presents illustrative machine-learning methodology to successfully integrate vision information and radio time-of-arrival measurements for cooperative localization of ultra-wideband visual radios in harsh indoor environments.
Nguyen et al. (Fri,) studied this question.