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We have described how situational awareness can be integrated into an autonomous observing system by incorporating our theoretical and observational knowledge of the system. Metrics of what we do not know (state vector uncertainty) can be used to define what we need to measure and the required mode, time and location of the observations. The mathematical formalism of data assimilation provides us with the state vector uncertainty. Metrics of how important it is to know this information (information content) are used to assign a priority to each observation. A basic postulate of information theory is that information can be treated like a measurable physical quantity, such as density or mass. Consequently we can construct an optimization method for use in observing systems where there is an objective optimization for information content. This allows the best return of information for a given investment in measuring systems. Based on information content and level of uncertainty we can create a dynamic observation control system that adapts what measurements are made, where they are made, and when they are made, in an online fashion to maximize the information content, minimize the uncertainty in characterizing the system's state vector. The metrics are passed in real time to the Sensor Web observation scheduler to implement the observation plan for the next observing cycle.