# Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation

arXiv: https://arxiv.org/abs/2309.07016 · HTML: https://mlsanity.com/paper/2309.07016/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2023.

## Built on by (most cited first, 9 of 9)

- [AI-Aided Kalman Filters](https://mlsanity.com/paper/2410.12289/) (2024)
- [Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements](https://mlsanity.com/paper/2407.07368/) (2024)
- [Sun sensor calibration algorithms: A systematic mapping and survey](https://mlsanity.com/paper/2507.21541/) (2025)
- [Differentiable Adaptive Kalman Filtering via Optimal Transport](https://mlsanity.com/paper/2508.07037/) (2025)
- [One Filters All: A Generalist Filter for State Estimation](https://mlsanity.com/paper/2509.20051/) (2025)
- [pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements](https://mlsanity.com/paper/2510.27503/) (2025)
- [Hierarchical Variational Kalman Filtering](https://mlsanity.com/paper/2607.00877/) (2026)
- [Edge-Aware Thermal Infrared UAV Swarm Tracking](https://mlsanity.com/paper/2607.12544/) (2026)
- [Unscented KalmanNet: Structure-Preserving Deep Learning with Calibrated Posterior Uncertainty under Incomplete Physics and Unknown Noise](https://mlsanity.com/paper/2608.04201/) (2026)

## Abstract (arXiv)

Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models. A major limitation of current DNN-aided designs stems from the need to train them to filter data originating from a specific distribution and underlying SS model. Consequently, changes in the model parameters may require lengthy retraining. While the KF adapts through parameter tuning, the black-box nature of DNNs makes identifying tunable components difficult. Hence, we propose Adaptive KalmanNet (AKNet), a DNN-aided KF that can adapt to changes in the SS model without retraining. Inspired by recent advances in large language model fine-tuning paradigms, AKNet uses a compact hypernetwork to generate context-dependent modulation weights. Numerical evaluation shows that AKNet provides consistent state estimation performance across a continuous range of noise distributions, even when trained using data from limited noise settings.