Constructive neural-networks-based MEMS/GPS integration scheme
Constructive neural-networks-based MEMS/GPS integration scheme
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DOI：
10.1109/taes.2008.4560208
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发表时间：
2008-04-01
影响因子：
4.4
通讯作者：
El-Sheimy, Naser
中科院分区：
文献类型：
--
作者：
Ciiiang, Kai-Wei;Noureldin, Aboelmagd;El-Sheimy, Naser
本文利用的想法，开发一种替代的数据融合方案，集成了低成本的微机电系统（MEMS）的惯性测量单元（伊穆斯）和全球定位系统（GPS）的接收器的输出。所提出的方案是使用一个建设性的神经网络（级联相关网络（CCN）），以克服传统的技术，主要是基于卡尔曼滤波器（KF）的局限性。在这项研究中应用的CNN具有灵活的拓扑结构的优势，如果与最近使用的多层前馈神经网络（MFNN）的惯性导航系统（INS）/GPS集成。本文中提出的初步结果表明，建议CCN的有效性，基于MFNN和卡尔曼滤波技术的INS/GPS集成。
This article exploits the idea of developing an alternative data fusion scheme that integrates the outputs of low-cost micro-electro-mechanical systems (MEMS) inertial measurements units (IMUs) and receivers of the Global Positioning System (GPS). The proposed scheme is implemented using a constructive neural network (cascade-correlation network (CCNs)) to overcome the limitations of conventional techniques that are predominantly based on the Kalman filter (KF). The CNN applied in this research has the advantage of having a flexible topology if compared with the recently utilized multi-layer feed-forward neural networks (MFNNs) for inertial navigation system (INS)/GPS integration. The preliminary results presented in this article illustrate the effectiveness of proposed CCNs over both MFNN-based and Kalman filtering techniques for INS/GPS integration.