Coordinate ascent neural Kalman-MLE for state estimation
This addresses state estimation in dynamic systems, but appears incremental as it combines existing techniques like coordinate ascent and Kalman filters with neural networks.
The paper tackles the problem of learning dynamic and measurement models for state estimation by introducing a coordinate ascent algorithm that uses maximum likelihood estimation to train neural networks and noise covariances, then applies a non-linear Kalman filter for state estimation during testing.
This paper presents a coordinate ascent algorithm to learn dynamic and measurement models in dynamic state estimation using maximum likelihood estimation in a supervised manner. In particular, the dynamic and measurement models are assumed to be Gaussian and the algorithm learns the neural network parameters that model the dynamic and measurement functions, and also the noise covariance matrices. The trained dynamic and measurement models are then used with a non-linear Kalman filter algorithm to estimate the state during the testing phase.