KalmanFormer: using transformer to model the Kalman Gain in Kalman Filters.

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Neurorobotics Pub Date : 2025-01-07 eCollection Date: 2024-01-01 DOI:10.3389/fnbot.2024.1460255
Siyuan Shen, Jichen Chen, Guanfeng Yu, Zhengjun Zhai, Pujie Han
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引用次数: 0

Abstract

Introduction: Tracking the hidden states of dynamic systems is a fundamental task in signal processing. Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and Gaussian systems, offering low computational complexity. However, real-world applications often involve non-linear dynamics, making it challenging for traditional Kalman Filters to achieve accurate state estimation. Additionally, the accurate modeling of system dynamics and noise in practical scenarios is often difficult. To address these limitations, we propose the KalmanFormer, a hybrid model-driven and data-driven state estimator. By leveraging data, the KalmanFormer promotes the performance of state estimation under non-linear conditions and partial information scenarios.

Methods: The proposed KalmanFormer integrates classical Kalman Filter with a Transformer framework. Specifically, it utilizes the Transformer to learn the Kalman Gain directly from data without requiring prior knowledge of noise parameters. The learned Kalman Gain is then incorporated into the standard Kalman Filter workflow, enabling the system to better handle non-linearities and model mismatches. The hybrid approach combines the strengths of data-driven learning and model-driven methodologies to achieve robust state estimation.

Results and discussion: To evaluate the effectiveness of KalmanFormer, we conducted numerical experiments in both synthetic and real-world dataset. The results demonstrate that KalmanFormer outperforms the classical Extended Kalman Filter (EKF) in the same settings. It achieves superior accuracy in tracking hidden states, demonstrating resilience to non-linearities and imprecise system models.

卡尔曼前:利用变压器对卡尔曼滤波器中的卡尔曼增益进行建模。
动态系统的隐藏状态跟踪是信号处理中的一项基本任务。递归卡尔曼滤波器(KF)被广泛认为是线性和高斯系统的有效解决方案,具有较低的计算复杂度。然而,现实世界的应用往往涉及非线性动力学,这使得传统的卡尔曼滤波器难以实现准确的状态估计。此外,在实际情况下,系统动力学和噪声的准确建模往往是困难的。为了解决这些限制,我们提出了KalmanFormer,一个混合模型驱动和数据驱动的状态估计器。通过利用数据,KalmanFormer提高了非线性条件和部分信息场景下状态估计的性能。方法:提出的KalmanFormer将经典卡尔曼滤波器与变压器框架相结合。具体来说,它利用变压器直接从数据中学习卡尔曼增益,而不需要事先知道噪声参数。然后将学习到的卡尔曼增益合并到标准卡尔曼滤波工作流程中,使系统能够更好地处理非线性和模型不匹配。混合方法结合了数据驱动学习和模型驱动方法的优势,以实现鲁棒状态估计。结果和讨论:为了评估KalmanFormer的有效性,我们在合成数据集和真实数据集上进行了数值实验。结果表明,在相同的条件下,卡尔曼前滤波器优于经典的扩展卡尔曼滤波器(EKF)。它在跟踪隐藏状态方面达到了卓越的精度,展示了对非线性和不精确系统模型的弹性。
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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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