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We consider the problem of steering, via output feedback, the statedistribution of a discrete-time, linear stochastic system from an initialGaussian distribution to a terminal Gaussian distribution with prescribed meanand maximum covariance, subject to probabilistic path constraints on the state.The filtered state is obtained via a Kalman filter, and the problem isformulated as a deterministic convex program in terms of the distribution ofthe filtered state. We observe that, in the presence of constraints on thestate covariance, and in contrast to classical Linear Quadratic Gaussian (LQG)control, the optimal feedback control depends on both the process noise and theobservation model. The effectiveness of the proposed approach is verified usinga numerical example.

Chance Constrained Covariance Control for Linear Stochastic Systems With Output Feedback