A new weight updating method for INS/GPS integration architectures based on neural networks
A new weight updating method for INS/GPS integration architectures based on neural networks
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DOI：
10.1088/0957-0233/15/10/015
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发表时间：
2004-10-01
影响因子：
2.4
通讯作者：
El-Sheimy, N
中科院分区：
文献类型：
--
作者：
Chiang, KW;Noureldin, A;El-Sheimy, N
惯性导航系统（INS）和全球定位系统（GPS）技术已经广泛地用于许多定位和导航应用中。每种制度都有其独特的特点和局限性。因此，这两个系统的合并提供了一些优点，并克服了每个系统的不足之处。INS/GPS组合通常使用卡尔曼滤波器来实现。然而，卡尔曼滤波器仅在某些预定义的动态模型下才能充分执行，并且存在与可观测性和对噪声影响的免疫力相关的几个问题。最近提出了一种基于人工神经网络的INS/GPS组合方法，用于融合INS测量值和差分全球定位系统（DGPS）测量值。虽然能够提供高性能的INS/DGPS集成与GPS中断期间的位置分量的准确预测，更新ANN权重的传统方法限制了实时能力。针对传统权值更新方法的局限性，提出了一种新的权值更新准则，并利用位置更新和位置速度更新两种不同的结构进行权值更新。
Inertial navigation system (INS) and global position system (GPS) technologies have been widely utilized in many positioning and navigation applications. Each system has its own unique characteristics and limitations. Therefore, the integration of the two systems offers a number of advantages and overcomes each system's inadequacies. INS/GPS integration is usually implemented using Kalman filters. However, Kalman filters perform adequately only under certain predefined dynamic models and suffer from several problems related to observability and immunity to noise effects. An INS/GPS integration method based on artificial neural networks (ANNs) to fuse INS measurements and differential global positioning system (DGPS) measurements has been recently suggested. Although able to provide high performance INS/DGPS integration with accurate prediction of position components during GPS outages, the conventional methods of updating the ANN weights limit the real-time capabilities. This paper offers a new weight updating criterion to improve the limitation of traditional weight updating methods with the utilization of two different architectures; the position update architecture and position and velocity update architecture.