Simultaneous estimation of parameters and the state of an optical parametric oscillator system

TL;DR

This paper develops algorithms for simultaneously estimating the state and pump power of an optical parametric oscillator, addressing environmental disturbances that cause uncertainties in the system model.

Contribution

It introduces dual and joint extended Kalman filter methods for concurrent state and parameter estimation in an OPO system, a novel approach for this application.

Findings

Algorithms effectively estimate system state and pump power.

Numerical examples validate the proposed methods.

Enhanced robustness to environmental disturbances.

Abstract

In this paper, we consider the filtering problem of an optical parametric oscillator (OPO). The OPO pump power may fluctuate due to environmental disturbances, resulting in uncertainty in the system modeling. Thus, both the state and the unknown parameter may need to be estimated simultaneously. We formulate this problem using a state-space representation of the OPO dynamics. Under the assumption of Gaussianity and proper constraints, the dual Kalman filter method and the joint extended Kalman filter method are employed to simultaneously estimate the system state and the pump power. Numerical examples demonstrate the effectiveness of the proposed algorithms.