# Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy

arXiv: https://arxiv.org/abs/1912.01790 · HTML: https://mlsanity.com/paper/1912.01790/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning](https://mlsanity.com/paper/2209.11820/) (2022)
- [Transferable and Adaptable Driving Behavior Prediction](https://mlsanity.com/paper/2202.05140/) (2022)
- [T4P: Test-Time Training of Trajectory Prediction via Masked Autoencoder and Actor-specific Token Memory](https://mlsanity.com/paper/2403.10052/) (2024)
- [Feedback Favors the Generalization of Neural ODEs](https://mlsanity.com/paper/2410.10253/) (2024)
- [Estimating Neural Network Robustness via Lipschitz Constant and Architecture Sensitivity](https://mlsanity.com/paper/2410.23382/) (2024)
- [1 Modular Parallel Manipulator for Long-Term Soft Robotic Data Collection](https://mlsanity.com/paper/2409.03614/) (2024)

## Abstract (arXiv)

High fidelity behavior prediction of intelligent agents is critical in many applications. However, the prediction model trained on the training set may not generalize to the testing set due to domain shift and time variance. The challenge motivates the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance. Inspired by Extended Kalman Filter (EKF), this paper introduces a series of online adaptation methods, which are applicable to neural network-based models. A base adaptation algorithm Modified EKF with forgetting factor (MEKFλ) is introduced first, followed by exponential moving average filtering techniques. Then this paper introduces a dynamic multi-epoch update strategy to effectively utilize samples received in real time. With all these extensions, we propose a robust online adaptation algorithm: MEKF with Exponential Moving Average and Dynamic Multi-Epoch strategy (MEKFEMA-DME). The proposed algorithm outperforms existing methods as demonstrated in experiments. The source code is open-sourced in the following link https://github.com/intelligent-control-lab/MEKFMAME.