Coordinate ascent neural Kalman-MLE for state estimation
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Abstract (arXiv)
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.
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References · 5
- 2021KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamicscited by 128
- 2020Deep State Space Models for Nonlinear System Identificationcited by 30
- 2013Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMCcited by 24
- 2019DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Predictioncited by 12
- 2015Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systemscited by 6
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