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INS/GPS integration using neural networks for land vehicular navigation applications.

机译:使用神经网络的INS / GPS集成,用于陆地车辆导航应用。

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摘要

Most of the positioning technologies for modern land vehicular navigation systems have been available for 25 years. Virtually all of the systems augment two or more of these technologies. Typical candidates for an integrated navigation system are the Global Position System (GPS) and Inertial Navigation Systems (INS). The Kalman filter has been widely adopted as an optimal estimation tool for the INS/GPS integration, however, several limitations of such multi-sensor integration methodology have been reported, such as the impact of INS short term errors, model dependency, prior knowledge dependency, sensor dependency, and lineaization dependency.; To reduce the impact of short term INS sensor errors, the bandwidth of true motion dynamics were identified by spectrum analysis and the first generation denoising algorithm that used the Discrete Wavelet Transform (DWT) was applied to identify the limitations of the existing denoising algorithm. Consequently, this research proposed the cascade denoising algorithm to overcome the limitations of existing denoising algorithms. It was then evaluated using several INS/GPS integrated land vehicular systems and the results demonstrated superior performance to existing denoising algorithms in both the positioning and spectrum domains. In addition, the impact of proposed algorithms on different integrated systems was investigated extensively.; Furthermore, an alternative INS/GPS integration methodology, the conceptual intelligent navigator incorporating artificial intelligence techniques, was proposed to reduce the remaining limitations of traditional navigators that use the Kalman filter approach. The proposed conceptual intelligent navigator consisted of several different INS/GPS integration architectures that were developed using artificial neural networks to acquire the navigation knowledge. In addition, the ""brain"", a navigation information database, and a window based weight updating scheme were implemented to store and accumulate navigation knowledge. The conceptual intelligent navigator was evaluated using several INS/GPS integrated land vehicular systems and the results demonstrated superior performance to traditional navigator in the position domain. Finally, a low cost INS/GPS integrated system was considered to verify the advantages gained by incorporating the conceptual intelligent navigator as an alternative method toward developing next generation land vehicular navigation systems.
机译:现代陆地车辆导航系统的大多数定位技术已经使用了25年。实际上,所有系统都增强了这些技术中的两项或多项。集成导航系统的典型候选人是全球定位系统(GPS)和惯性导航系统(INS)。卡尔曼滤波器已被广泛用作INS / GPS集成的最佳估计工具,但是,已报告了这种多传感器集成方法的一些局限性,例如INS短期误差的影响,模型依赖性,先验知识依赖性,传感器相关性和线性化相关性。为了减少短期INS传感器误差的影响,通过频谱分析确定了真实运动动态的带宽,并应用了使用离散小波变换(DWT)的第一代降噪算法来确定现有降噪算法的局限性。因此,本研究提出了级联去噪算法,以克服现有去噪算法的局限性。然后使用几个INS / GPS集成陆地车辆系统对其进行了评估,结果在定位和频谱领域均表现出优于现有的去噪算法的性能。此外,广泛研究了所提出算法对不同集成系统的影响。此外,还提出了另一种INS / GPS集成方法,即结合了人工智能技术的概念性智能导航仪,以减少使用卡尔曼滤波方法的传统导航仪的剩余限制。拟议中的概念智能导航仪由几种不同的INS / GPS集成架构组成,这些架构是使用人工神经网络开发的,以获取导航知识。另外,实现了““大脑””,导航信息数据库和基于窗口的权重更新方案,以存储和积累导航知识。使用几种INS / GPS集成陆地车辆系统对概念智能导航仪进行了评估,结果证明了其在位置域上的性能优于传统导航仪。最后,考虑采用低成本的INS / GPS集成系统,以验证通过将概念性智能导航器作为开发下一代陆地车辆导航系统的替代方法而获得的优势。

著录项

  • 作者

    Chiang, Kai-Wei.;

  • 作者单位

    University of Calgary (Canada).;

  • 授予单位 University of Calgary (Canada).;
  • 学科 Geodesy.; Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 307 p.
  • 总页数 307
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 大地测量学;无线电电子学、电信技术;
  • 关键词

  • 入库时间 2022-08-17 11:41:23

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