Kalman Filter for Dynamic Estimation of Stochastic Radio Source Power and DoA

Interferometric measurements corresponds to sample covariance matrices of signals received by several sensors. In scenarios like dynamic radio astronomy imaging, the properties of these signals can vary over time, presenting a challenging case for study. In this context, this work tackles the problem of estimating stochastic radio source power from sample covariance measurements. A novel approach is developed, introducing a nonstandard Kalman filter tailored for Gaussian noise and signals, expanding the Kalman filter's applicability range to situations where the underlying measurement model is unknown. The effectiveness of this approach for source power and direction of arrival estimation are illustrated through simulations using synthetic data.