DANSE : Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Bayesian Setup

• We propose DANSE - a data-driven non-linear state estimation method. DANSE provides a closed-form posterior of the state of a model-free process, given linear measurements of the state in a Bayesian setup, like the celebrated Kalman filter (KF). Non-linear dynamics of the state are captured by data-driven recurrent neural networks (RNNs). The training of DANSE combines maximum-likelihood and gradient-descent in an unsupervised framework, i.e. only measurement data and no process data are required. Using simulated linear and non-linear process models, we demonstrate that DANSE - without knowledge of the process model - provides competitive performance against model-based approaches such as KF, unscented KF (UKF), extended KF (EKF), and a hybrid approach such as KalmanNet.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Keyword

deep learning

neural networks

recurrent neural networks

state estimation

Bayesian networks

Deep neural networks

Gradient methods

Maximum likelihood estimation

Bayesian

Closed form

Data driven

Linear state estimation

Model free

Neural-networks

Nonlinear state

Process-models

State estimation methods

Publication and Content Type

ref (subject category)

kon (subject category)