Intelligent sensor positioning and orientation through constructive neural network-embedded INS/GPS integration algorithms.
Intelligent sensor positioning and orientation through constructive neural network-embedded INS/GPS integration algorithms.
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DOI：
10.3390/s101009252
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Chang HW
中科院分区：
文献类型：
--
作者：
Chiang KW;Chang HW
在空间信息系统、三维城市模型等应用中，移动地图系统被广泛应用于获取空间信息。目前，移动测绘系统中最常用的定位定位技术包括以全球定位系统(GPS)为主的定位传感器和以惯性导航系统(INS)为主的定位传感器。在经典方法中，卡尔曼滤波(KF)方法的局限性和多传感器系统的整体价格限制了大多数陆基移动地图应用的普及。为了提高低成本的微电子机械系统(MEMS)INS/GPS组合系统的性能，已经提出了由多层前馈神经网络(MFNN)和KF/Smoolers组成的智能传感器定位和定向方案，但MFNN的自动化程度并不像最初预期的那么容易。因此，本研究不仅解决了以往研究中提出的用于INS/GPS组合系统的MFNN-KF/Smother算法中传统方法自动化程度低的问题，而且探索和分析了开发以更自动化的方式集成各种传感器的替代智能传感器定位和定向方案的思想。所提出的方案是使用最著名的构造性神经网络之一-级联相关神经网络(CCNNS)来实现的，以克服基于KF/Smother算法的传统技术以及以前开发的MFNN-Smoster方案的局限性。与MFNN相比，所应用的CCNN还具有更灵活的拓扑结构的优势。基于实验数据，本文给出的初步结果表明，与平滑算法以及MFNN平滑方案相比，所提出的方案是有效的。
Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks—the Cascade Correlation Neural Network (CCNNs)—to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN-smoother schemes.
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DOI：
10.1109/taes.2008.4560208
发表时间：
2008-04-01
影响因子：
4.4
作者：
Ciiiang, Kai-Wei;Noureldin, Aboelmagd;El-Sheimy, Naser
通讯作者：
El-Sheimy, Naser
影响因子：
5.4
作者：
Gustafsson, F;Gunnarsson, F;Nordlund, PJ
通讯作者：
Nordlund, PJ
影响因子：
2.4
作者：
Chiang, KW;Noureldin, A;El-Sheimy, N
通讯作者：
El-Sheimy, N
DOI：
10.3390/s90402586
发表时间：
2009
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Chiang KW;Chang HW;Li CY;Huang YW
通讯作者：
Huang YW
DOI：
10.3390/s8042886
发表时间：
2008-04-28
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Chang H;Xue L;Qin W;Yuan G;Yuan W
通讯作者：
Yuan W