Abstract
In this paper, we investigate the problem of time-varying sensor selection for linear time-varying (LTV) dynamical systems. We develop a framework to design an online sparse sensor schedule for a given large-scale LTV system with guaranteed performance bounds using randomized algorithms. In our online setting, the contribution of each sensor at each time is calculated on-the-fly, and we immediately decide to keep the corresponding sensor at each time in the sensor schedule or discard it without ever retracting these decisions. Furthermore, we provide new performance guarantees to approximate fully-sensed LTV systems up to a multiplicative approximation factor and an additive one by choosing on average a constant number of active sensors at each time.
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