Related papers: Differentiable Adaptive Kalman Filtering via Optimal Transport

Differentiable Adaptive Kalman Filtering via Optimal Transport

URL: http://arxiv.org/abs/2508.07037v1
Date: Sat, 09 Aug 2025 16:36:33 GMT
Title: Differentiable Adaptive Kalman Filtering via Optimal Transport
Authors: Yangguang He, Wenhao Li, Minzhe Li, Juan Zhang, Xiangfeng Wang, Bo Jin,
Abstract summary: OTAKNet is the first online solution to noise-statistics drift within learning-based adaptive Kalman filtering.<n>We compare OTAKNet against classical model-based adaptive Kalman filtering and offline learning-based filtering.
Score: 23.40376181606577
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Learning-based filtering has demonstrated strong performance in non-linear dynamical systems, particularly when the statistics of noise are unknown. However, in real-world deployments, environmental factors, such as changing wind conditions or electromagnetic interference, can induce unobserved noise-statistics drift, leading to substantial degradation of learning-based methods. To address this challenge, we propose OTAKNet, the first online solution to noise-statistics drift within learning-based adaptive Kalman filtering. Unlike existing learning-based methods that perform offline fine-tuning using batch pointwise matching over entire trajectories, OTAKNet establishes a connection between the state estimate and the drift via one-step predictive measurement likelihood, and addresses it using optimal transport. This leverages OT's geometry - aware cost and stable gradients to enable fully online adaptation without ground truth labels or retraining. We compare OTAKNet against classical model-based adaptive Kalman filtering and offline learning-based filtering. The performance is demonstrated on both synthetic and real-world NCLT datasets, particularly under limited training data.

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