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Paper Title: Generalized Concentration-Based Performance Guarantees on Sensor Selection for State Estimation

Authors: Christopher I. Calle and Shaunak D. Bopardikar

In this paper, the authors develop novel matrix concentration inequalities and a convex optimization framework to formulate a priori performance guarantees on the sensor selection problem for the objective of performing state estimation via the discrete-time Kalman filter. By focusing on sampling with replacement policies to draw a sensor selection for state estimation, matrix concentration inequalities allow us to formulate a priori performance guarantees that do not depend on the submodularity property, a property that is typically necessary for providing combinatorial optimization problems with guarantees.

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Figure comparing the concentration-based upper bounds achievable by the baseline approach, the single-shot approach (SSA), and multi-shot approach (MSA).