Work by Rahul Moghe, Renato Zanetti, & Maruthi R. Akella, AIAA JGCD 2019

Discussion by Ben Gravell and Venkatraman Renganathan

Keywords: Adaptive, Kalman filter, linear system, learning, optimal, estimation

Summary

Unlike the classic Kalman filter requires knowledge of the measurement and process noise covariance matrices, the adaptive Kalman filter proposed by the authors does not. It is shown that under mild assumptions, the online data-driven estimates of the noise covariances asymptotically converge to the true values, rendering the proposed adaptive Kalman filter asymptotically optimal.

Read the paper on AIAA here.

Play with our demo code on Binder, view the Jupyter Notebook, or clone the GitHub repo.

Presentation + Code

We discuss and walk through the paper in a presentation and Python code.