Driven by the underlying need for an as yet undeveloped framework for fusing heterogeneous data and information at different semantic levels coming from both sensory and human sources, we present some results of the research conducted within the NATO Research Task Group IST-106/RTG-051 on “Information Filtering and Multi Source Information Fusion.” As part of this ongoing effort, we discuss here a first outcome of our investigation on multi-level fusion. It deals with removing the first hurdle between data/information sources and processes being at different levels: representation. Our contention here is that a common representation and description framework is the premise for enabling processing overarching different semantic levels. To this end, we discuss here the use of the Battle Management Language (BML) as a way (“lingua franca”) to encode sensor- and text-based data and a priori and contextual knowledge, both as hard and soft data. We here expand on our previous works [1, 2] further detailing and exemplifying the use of BML and clarifying aspects related to the use of contextual information and the exploitation of uncertain soft input along with sensor readings
Wildlife Hazard Management is nowadays a very serious problem, mostly at airports and wind farms. If ignored, it may lead to repercussions in human safety, ecology, and economics. One of the approaches that is widely implemented in small and medium-size airports, as well as on wind turbines is based on a stereo-vision. However, to provide long-term observations allowing the determination of the hot spots of birds’ activity and forecast future events, a robust tracking algorithm is required. The aim of this paper is to review tracking algorithms widely used in Radar Science and assess the possibilities of application of these algorithms for the purpose of tracking birds with a stereo-vision system. We performed a survey-of-related works and simulations determined five state-of-the art algorithms: Kalman Filter, Nearest-Neighbour, Joint-Probabilistic Data Association, and Interacting Multiple Model with the potential for implementation in a stereo-vision system. These algorithms have been implemented and simulated in the proposed case study
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.