LQG solution for POMDP without estimating states: A minimum variance approach

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.