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We consider the optimal design of a sensing system in which a sensor can choose how and when to communicate to an estimator. The optimal choice of transmission and estimation policies is made difficult by the fact that the sensor and the estimator may use their entire history of observations. Traditionally, Markov decision theory is used to analyze such multi-stage decision problems. But, Markov decision theory assumes a single decision maker-an assumption that is not satisfied in an active sensing system that has two decision makers with different information. In this paper, we use the approach of Nayyar et al (2011) to investigate the system as a dynamic team. Using a series of structural results, we show that the optimal policy is easy to implement. We also obtain a dynamic programming decomposition to find optimal sensing and estimation policies.