KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

• URL: http://arxiv.org/abs/2107.10043v1
• Date: Wed, 21 Jul 2021 12:26:46 GMT
• Title: KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
• Authors: Guy Revach, Nir Shlezinger, Xiaoyong Ni, Adria Lopez Escoriza, Ruud J. G. van Sloun, and Yonina C. Eldar
• Abstract summary: We present KalmanNet, a real-time state estimator that learns from data to carry out Kalman filtering under non-linear dynamics. We numerically demonstrate that KalmanNet overcomes nonlinearities and model mismatch, outperforming classic filtering methods.
• Score: 84.18625250574853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time state estimation of dynamical systems is a fundamental task in signal processing and control. For systems that are well-represented by a fully known linear Gaussian state space (SS) model, the celebrated Kalman filter (KF) is a low complexity optimal solution. However, both linearity of the underlying SS model and accurate knowledge of it are often not encountered in practice. Here, we present KalmanNet, a real-time state estimator that learns from data to carry out Kalman filtering under non-linear dynamics with partial information. By incorporating the structural SS model with a dedicated recurrent neural network module in the flow of the KF, we retain data efficiency and interpretability of the classic algorithm while implicitly learning complex dynamics from data. We numerically demonstrate that KalmanNet overcomes nonlinearities and model mismatch, outperforming classic filtering methods operating with both mismatched and accurate domain knowledge.

Related papers

• Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability [59.758009422067]
  We propose a standalone Kalman filter layer that performs closed-form Gaussian inference in linear state-space models. Similar to efficient linear recurrent layers, the Kalman filter layer processes sequential data using a parallel scan. Experiments show that Kalman filter layers excel in problems where uncertainty reasoning is key for decision-making, outperforming other stateful models.
  arXiv  Detail & Related papers  (2024-09-25T11:22:29Z)
• GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering [23.19392802641989]
  We study the tracking of graph signals using a hybrid model-based/data-driven approach. We develop the GSP-KalmanNet, which tracks the hidden graphical states from the graphical measurements. The proposed GSP-KalmanNet achieves enhanced accuracy and run time performance as well as improved robustness to model misspecifications.
  arXiv  Detail & Related papers  (2023-11-28T08:43:10Z)
• An Ensemble Score Filter for Tracking High-Dimensional Nonlinear Dynamical Systems [10.997994515823798]
  We propose an ensemble score filter (EnSF) for solving high-dimensional nonlinear filtering problems. Unlike existing diffusion models that train neural networks to approximate the score function, we develop a training-free score estimation. EnSF provides surprising performance, compared with the state-of-the-art Local Ensemble Transform Kalman Filter method.
  arXiv  Detail & Related papers  (2023-09-02T16:48:02Z)
• Kalman Filter for Online Classification of Non-Stationary Data [101.26838049872651]
  In Online Continual Learning (OCL) a learning system receives a stream of data and sequentially performs prediction and training steps. We introduce a probabilistic Bayesian online learning model by using a neural representation and a state space model over the linear predictor weights. In experiments in multi-class classification we demonstrate the predictive ability of the model and its flexibility to capture non-stationarity.
  arXiv  Detail & Related papers  (2023-06-14T11:41:42Z)
• Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM [101.32324781612172]
  Simultaneous localization and mapping (SLAM) is a method that constructs a map of an unknown environment and localizes the position of a moving agent on the map simultaneously. Extended Kalman filter (EKF) has been widely adopted as a low complexity solution for online SLAM. We present a robust EKF algorithm using the power of deep learning for online SLAM, referred to as Split-KalmanNet.
  arXiv  Detail & Related papers  (2022-10-18T07:10:38Z)
• Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering [0.20305676256390928]
  We show that the learning method outperforms gradient descent in a nonlinear system identification benchmark. We also explore the use of the algorithm in data-driven nonlinear model predictive control and its relation with disturbance models for offset-free tracking.
  arXiv  Detail & Related papers  (2021-11-04T07:49:15Z)
• Unsupervised Learned Kalman Filtering [84.18625250574853]
  unsupervised adaptation is achieved by exploiting the hybrid model-based/data-driven architecture of KalmanNet. We numerically demonstrate that when the noise statistics are unknown, unsupervised KalmanNet achieves a similar performance to KalmanNet with supervised learning.
  arXiv  Detail & Related papers  (2021-10-18T04:04:09Z)
• Neural Kalman Filtering [62.997667081978825]
  We show that a gradient-descent approximation to the Kalman filter requires only local computations with variance weighted prediction errors. We also show that it is possible under the same scheme to adaptively learn the dynamics model with a learning rule that corresponds directly to Hebbian plasticity.
  arXiv  Detail & Related papers  (2021-02-19T16:43:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.