A randomly perturbed iterative proper orthogonal decomposition (RI-POD) technique for filtering distributed parameter systems

In this paper, we consider the filtering of distributed parameter systems (DPS), i.e., systems governed by partial differential equations (PDE). We adopt a reduced order model (ROM) based strategy to solve the problem. We propose a randomly perturbed iterative version of the snapshot proper orthogonal decomposition (POD) technique, termed RI-POD, to construct ROMs for DPS that is capable of capturing their global behaviour. Further, the technique is entirely data based, and is applicable to forced as well as unforced systems. We apply the ROM generated using the RI-POD technique to construct reduced order Kalman filters to solve the DPS filtering problem. The methodology is tested on the 1-dimensional heat equation.