• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors
【2h】

A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors

机译：基于数字滤波器和重构观测矢量的粗对准方法

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors’ outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber’s M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS) is under entirely self-contained conditions.
机译：本文提出了一种基于视在重力运动的粗对准方法。由于复杂情况的干扰，由视在重力计算出的真实观测向量受到污染。详细分析了干扰源，然后设计了一种低通数字滤波器，以消除测量观测向量的高频噪声。为了从惯性传感器的输出中提取有效的观测矢量，设计了一种参数识别和矢量重建方法，其中采用了自适应卡尔曼滤波器来估计未知参数。此外，还开发了一种基于Huber的M估计理论的鲁棒滤波器，用于解决由于车辆机动而导致的测量观测向量的异常值。设计了一个包含模拟测试和物理测试的综合实验，以验证该方法的性能。结果表明，该方法在摇摆模式下与流行的视在速度方法等效，但优于该方法。当捷联惯性导航系统（SINS）处于完全独立的条件下时，当前方法处于运动模式。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号