The Research Initiation Award project entitled - Spatial-temporal Information Fusion and Real-time Sensor Data Assimilation Using Sequential Monte Carlo Methods - has the overarching goal to develop tractable approaches for spatial-temporal information fusion and real time sensor data assimilation using state of the art probabilistic techniques based on sequential Monte Carlo (SMC) methods. New algorithms and methods will be developed to enhance the effectiveness and efficiency of information fusion and data assimilation for large-scale spatial temporal systems. A special focus is to effectively use real-time information and sensor data from geographically distributed data sources for more accurate state inference using SMC methods. Specific objectives of this project include: developing innovative algorithms to exploit the spatial-temporal state of a system for more effective sampling/resampling and convergence of SMC methods; developing advanced data integration methods to support assimilating real-time information and sensor data from distributed data sources; and developing new computing methods and infrastructure based on parallel environment to enhance performance of large-scale information fusion and data assimilation.
The project promises to have an impact on both theoretical and practical aspects of spatial-temporal information fusion and real time sensor data assimilation. The newly developed methods will increase the capability of seamlessly integrating real time data streams to support better analysis and prediction and real time decision making. The project involves undergraduate students by training them to conduct research in a stepwise manner, broadening their network, and preparing them for graduate studies or the workforce. The project will enhance the teaching of computer science classes on campus of Voorhees College by remotely accessing the cluster system at Georgia State University on which students can run parallel and distributed programs.
