Sharaf Rashad, Noureldin Aboelmagd
IEEE Trans Neural Netw. 2007 Mar;18(2):589-94. doi: 10.1109/TNN.2006.890811.
Land vehicles rely mainly on global positioning system (GPS) to provide their position with consistent accuracy. However, GPS receivers may encounter frequent GPS outages within urban areas where satellite signals are blocked. In order to overcome this problem, GPS is usually combined with inertial sensors mounted inside the vehicle to obtain a reliable navigation solution, especially during GPS outages. This letter proposes a data fusion technique based on radial basis function neural network (RBFNN) that integrates GPS with inertial sensors in real time. A field test data was used to examine the performance of the proposed data fusion module and the results discuss the merits and the limitations of the proposed technique.
陆地车辆主要依靠全球定位系统（GPS）来提供始终如一的精确位置。然而，在城市地区，由于卫星信号被阻挡，GPS接收器可能会频繁遭遇GPS信号中断。为了克服这个问题，GPS通常会与安装在车内的惯性传感器相结合，以获得可靠的导航解决方案，尤其是在GPS信号中断期间。本文提出了一种基于径向基函数神经网络（RBFNN）的数据融合技术，该技术可实时将GPS与惯性传感器集成。使用现场测试数据来检验所提出的数据融合模块的性能，结果讨论了所提出技术的优点和局限性。
