Back to feed
arXiv cs.LG
arXiv cs.LG
6/30/2026
Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter

Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter

Short summary

A new distributed filtering method (CA-NKCF) combines neural networks with Kalman-like updates to estimate system states without prior knowledge of noise statistics. Tested on linear dynamics, chaotic systems (Lorenz), and wireless tracking, it outperforms traditional Kalman filters, particle filters, and pure neural networks—even when motion models are misspecified. The approach enables decentralized inference across collaborating agents.

  • •Novel covariance-agnostic neural Kalman filter for distributed state estimation
  • •Outperforms traditional Kalman, particle filters, and pure neural networks
  • •Robust to model misspecification and varying noise levels

Generated with AI, which can make mistakes.

#research-breakthrough#ai-agents
Read full article at arXiv cs.LG

Is this a good recommendation for you?

Comments

Failed to load comments. Please try again.

Explore more

A Kalman Filter Is a Memory System
Dev.to
Feedback-Coupled Memory Systems in Continuous Time
arXiv CS.AI
STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting
arXiv cs.LG
QuantFlow: A Federated Mamba-Based Post-Transformer Foundation Model for Time-Series Forecasting
arXiv cs.LG