{"ID":6621266,"CreatedAt":"2026-07-15T01:01:48.440468303Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.12135","arxiv_id":"2607.12135","title":"LQG solution for POMDP without estimating states: A minimum variance approach","abstract":"This paper investigates the control of discrete-time linear time-invariant (LTI) systems subject to incomplete and corrupted measurements. Specifically, we focus on designing a Linear Quadratic Gaussian (LQG) controller without relying on explicit state estimation. By leveraging minimum variance duality, our approach allows the current control input to be represented as a linear function of available measurements and previously applied inputs, successfully reducing the task to a tractable deterministic optimization problem. We provide theoretical justification for this framework and demonstrate its practical effectiveness through numerical experiments.","short_abstract":"This paper investigates the control of discrete-time linear time-invariant (LTI) systems subject to incomplete and corrupted measurements. Specifically, we focus on designing a Linear Quadratic Gaussian (LQG) controller without relying on explicit state estimation. By leveraging minimum variance duality, our approach a...","url_abs":"https://arxiv.org/abs/2607.12135","url_pdf":"https://arxiv.org/pdf/2607.12135v1","authors":"[\"Ranjan Sarkar\",\"Prabhat K. Mishra\"]","published":"2026-07-13T20:38:04Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false}