Recent Articles and Multimedia
Paper Title: State and Sparse Input Estimation in Linear Dynamical Systems using Low-Dimensional Measurements
Authors: Rupam Kalyan Chakraborty, Geethu Joseph, and Chandra R. Murthy
In this work, we tackle the joint estimation of system states and sparse inputs in a linear dynamical system, a problem that arises in networked control and cyber-physical systems. By integrating sparse recovery techniques into Kalman smoothing, we introduce new algorithms to reconstruct the states and inputs from low-dimensional (compressive) measurements, which are insufficient for conventional smoothing methods to succeed.
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Schematic illustration of sparse inputs applied to a linear system that generates compressive measurements. The proposed observer processes a block of measurements and reconstructs the states and sparse inputs by iteratively alternating between Kalman smoothing and sparse prior hyperparameter tuning.