用于室内惯性导航的记忆管理循环Kolmogorov-Arnold网络

IF 5.9 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Qiaolin Pu;Yunhai Li;Mu Zhou
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引用次数: 0

摘要

尽管惯性测量单元(imu)已成为室内定位的一种很有前途的解决方案,但由于其低成本,节能和基础设施无关的性质,测量误差的积累仍然是阻碍其广泛应用的关键挑战。近年来,随着深度学习的发展,数据驱动的方法已经被证明可以通过训练好的神经网络有效地解决这一问题。然而,目前主流的数据驱动模型主要采用简单的多层感知器(MLP)架构。当处理IMU数据时,这些架构在可扩展性和可解释性方面表现出固有的局限性,导致惯性导航应用中的参数利用率低下和定位精度欠佳。因此,本文提出了一种新的数据驱动模型KANet,它由两个关键部分组成:循环Kolmogorov-Arnold网络(RKANs)和长短期记忆(LSTM)启发的记忆管理单元。RKAN从根本上集成了Kolmogorov-Arnold网络(KANs)强大的函数逼近能力和递归神经网络(rnn)的顺序建模能力,而lstm启发的记忆机制增强了长序列的时间依赖性建模。通过利用具有有效记忆保留特征的非线性模式表示的可学习激活函数,该体系结构增强了模型处理复杂顺序IMU数据的能力。这种创新的设计克服了传统模型在处理复杂序列模式方面的局限性,在多步时间序列预测中显示出具有竞争力的准确性和提高的参数效率。实验结果表明,与传统的PDR模型和最先进的神经网络相比,该框架在不同数据集上表现出更高的参数效率,同时保持了较高的定位精度。此外,针对战术级IMU系统的全面基准测试表明,kanet辅助的消费级MEMS传感器可以实现定位精度,在某些方面,接近甚至匹配更高精度IMU系统的性能水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
KANet: Memory-Managed Recurrent Kolmogorov–Arnold Network for Indoor Inertial Navigation
Although inertial measurement units (IMUs) have emerged as a promising solution for indoor positioning, due to their low cost, energy efficiency, and infrastructure-independent nature, the accumulation of measurement errors remains a critical challenge that hinders their widespread application. In recent years, with the advancement of deep learning, data-driven methods have been proven to effectively solve this issue through trained neural networks. However, current mainstream data-driven models predominantly adopt simple multilayer perceptron (MLP) architectures. These architectures exhibit inherent limitations in scalability and interpretability when processing IMU data, resulting in inefficient parameter utilization and suboptimal positioning accuracy in inertial navigation applications. Hence, this article proposes a novel data-driven model named KANet, which consists of two key components: recurrent Kolmogorov–Arnold networks (RKANs) and long short-term memory (LSTM)-inspired memory management units. RKAN fundamentally integrates the powerful function approximation capability of Kolmogorov–Arnold networks (KANs) with the sequential modeling strength of recurrent neural networks (RNNs), while the LSTM-inspired memory mechanisms enhance temporal dependency modeling in long sequences. By leveraging learnable activation functions for nonlinear pattern representation with effective memory retention characteristics, this architecture enhances the model’s capacity to process complex sequential IMU data. This innovative design overcomes the limitations of traditional models in processing complex sequence patterns, demonstrating competitive accuracy and improved parameter efficiency in multistep time-series prediction. Experimental results demonstrate that the proposed framework achieves superior performance compared with traditional PDR models and state-of-the-art neural networks, exhibiting higher parameter efficiency across diverse datasets while maintaining high positioning accuracy. In addition, comprehensive benchmarking against tactical-grade IMU systems reveals that KANet-assisted consumer-grade MEMS sensors can achieve positioning accuracy that, in certain aspects, approaches or even matches the performance levels of higher precision IMU systems.
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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