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We introduce a theoretical framework for the dynamic sensor coverage problem for a simple case with multiple discrete time linear dynamical systems located in different spacial locations. The objective is to keep an appreciable estimate of the states of the systems at all times by deploying a few mobile sensors. The sensors are assumed to have a limited range and they implement a Kalman filter to estimate the states of all the systems. The motion of the sensor is modeled as a discrete time discrete state Markov chain. Based on some recent results on the Kalman filtering problem with intermittent observations by Sinopoli et. al., we derive conditions under which a single sensor fails to solve the coverage problem. We also give conditions under which we can guarantee that a single sensor is enough to solve the dynamic coverage problem.