FRAMEWORK FOR SCALABLE STATE ESTIMATION USING MULTI NETWORK OBSERVATIONS

摘要

A framework for state estimation using multi-network observation. Highly scalable qualitative probabilistic algorithms may be used to combine noisy, uncertain outputs having multi-modal event data from numerous networks into a relatively accurate and coherent estimate of the system state. Models of disparate networks may be pulled together to result in unified multi-modal event data. Information from multiple networks may be graphed and analyzed.