Estimating 1D Dynamical Plasma Parameters Using Data-Driven Techniques

One of the key challenges of time-dependent (dynamic) plasma phenomena is the measurement of plasma properties due to the multiscale nature of the plasma flows. An extended Kalman filter (EKF), a state estimation technique that uses both a physics-based model and measurement data, was successfully coupled with a zero-dimensional (0D) plasma model to study the plasma dynamics in Hall effect thrusters (HETs) and pulsed inductively coupled plasma [1], [2]. In this work, we present a state estimation approach for 1D plasma phenomena to estimate the spatiotemporal evolution of plasma properties using a Kalman Filter. The Kalman filter is compared with stochastic differential equations and applied to discharge oscillations in HETs. The results show that the technique provides an effective and efficient estimation method that can be extended to a variety of plasma dynamics applications. This approach has the potential for increased understanding and control in plasma dynamics, leading to improved design and performance of plasma-based systems.