Implementation of Kalman Filtering with Spiking Neural Networks

Alejandro Juárez-Lora, Luis M. García-Sebastián, Victor H. Ponce-Ponce, Elsa Rubio-Espino, Herón Molina-Lozano, Humberto Sossa

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

A Kalman filter can be used to fill space–state reconstruction dynamics based on knowledge of a system and partial measurements. However, its performance relies on accurate modeling of the system dynamics and a proper characterization of the uncertainties, which can be hard to obtain in real-life scenarios. In this work, we explore how the values of a Kalman gain matrix can be estimated by using spiking neural networks through a combination of biologically plausible neuron models with spike-time-dependent plasticity learning algorithms. The performance of proposed neural architecture is verified with simulations of some representative nonlinear systems, which show promising results. This approach traces a path for its implementation in neuromorphic analog hardware that can learn and reconstruct partial and changing dynamics of a system without the massive power consumption that is typically needed in a Von Neumann-based computer architecture.

Original languageEnglish
Article number8845
JournalSensors
Volume22
Issue number22
DOIs
StatePublished - Nov 2022

Keywords

  • Kalman filter
  • artificial intelligence
  • dynamics
  • robotics
  • spiking neural networks

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