AI-Aided Kalman Filters

• URL: http://arxiv.org/abs/2410.12289v1
• Date: Wed, 16 Oct 2024 06:47:53 GMT
• Title: AI-Aided Kalman Filters
• Authors: Nir Shlezinger, Guy Revach, Anubhab Ghosh, Saikat Chatterjee, Shuo Tang, Tales Imbiriba, Jindrich Dunik, Ondrej Straka, Pau Closas, Yonina C. Eldar,
• Abstract summary: The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
• Score: 65.35350122917914
• License:
• Abstract: The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on mathematical representations in the form of simple state-space (SS) models, which may be crude and inaccurate descriptions of the underlying dynamics. Emerging data-centric artificial intelligence (AI) techniques tackle these tasks using deep neural networks (DNNs), which are model-agnostic. Recent developments illustrate the possibility of fusing DNNs with classic Kalman-type filtering, obtaining systems that learn to track in partially known dynamics. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms. We review both generic and dedicated DNN architectures suitable for state estimation, and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented. The usefulness of each approach in preserving the individual strengths of model-based KFs and data-driven DNNs is investigated in a qualitative and quantitative study, whose code is publicly available, illustrating the gains of hybrid model-based/data-driven designs. We also discuss existing challenges and future research directions that arise from fusing AI and Kalman-type algorithms.

Related papers

• Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services [55.0337199834612]
  Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics.
  arXiv  Detail & Related papers  (2024-11-03T07:01:13Z)
• Reinforcing POD-based model reduction techniques in reaction-diffusion complex networks using stochastic filtering and pattern recognition [0.09324035015689712]
  Complex networks are used to model many real-world systems. Dimensionality reduction techniques like POD can be used in such cases. We propose an algorithmic framework that combines techniques from pattern recognition and filtering theory.
  arXiv  Detail & Related papers  (2023-07-19T05:45:05Z)
• Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles [0.8258451067861933]
  We present an automated approach to deep neural network (DNN) discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. We demonstrate the feasibility of this framework for two tasks - forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature.
  arXiv  Detail & Related papers  (2023-02-20T03:57:06Z)
• Deep Variational Models for Collaborative Filtering-based Recommender Systems [63.995130144110156]
  Deep learning provides accurate collaborative filtering models to improve recommender system results. Our proposed models apply the variational concept to injectity in the latent space of the deep architecture. Results show the superiority of the proposed approach in scenarios where the variational enrichment exceeds the injected noise effect.
  arXiv  Detail & Related papers  (2021-07-27T08:59:39Z)
• KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics [84.18625250574853]
  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.
  arXiv  Detail & Related papers  (2021-07-21T12:26:46Z)
• Latent Space Data Assimilation by using Deep Learning [0.0]
  Performing Data Assimilation (DA) at a low cost is of prime concern in Earth system modeling. We incorporate Deep Learning (DL) methods into a DA framework. We exploit the latent structure provided by autoencoders (AEs) to design an Ensemble Transform Kalman Filter with model error (ETKF-Q) in the latent space.
  arXiv  Detail & Related papers  (2021-04-01T12:25:55Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control [0.0]
  We present a novel approach that captures the underlying dynamics of a system by incorporating control in a neural ordinary differential equation framework. Results indicate that a simple DyNODE architecture when combined with an actor-critic reinforcement learning algorithm outperforms canonical neural networks.
  arXiv  Detail & Related papers  (2020-09-09T12:56:58Z)
• Online learning of both state and dynamics using ensemble Kalman filters [0.0]
  This paper investigates the possibility to learn both the dynamics and the state online, i.e. to update their estimates at any time. We consider the implication of learning dynamics online through (i) a global EnKF, (i) a local EnKF and (iii) an iterative EnKF. We then demonstrate numerically the efficiency and assess the accuracy of these methods using one-dimensional, one-scale and two-scale chaotic Lorenz models.
  arXiv  Detail & Related papers  (2020-06-06T13:19:26Z)

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.