The Kalman Evolve: Closing the Gap in Kalman Filtering via Interpretable Algorithm Discovery

ArXi:2605.26830v1 Announce Type: cross State estimation is a fundamental problem in control and signal processing, for which the Kalman Filter provides an optimal solution under linear dynamics, Gaussian noise, and known noise covariances. However, these assumptions often fail in realistic sensing settings such as Doppler radar and LiDAR. In these cases, the optimal estimator is inherently nonlinear, which leads to systematic performance degradation.