Nonlinear filtering algorithms for GPS using pseudorange and Doppler shift measurements
Nonlinear filtering algorithms for GPS using pseudorange and Doppler shift measurements
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DOI：
10.1109/itsc.2002.1041342
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发表时间：
2002-09
期刊：
影响因子：
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通讯作者：
X. Mao;M. Wada;H. Hashimoto
中科院分区：
文献类型：
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作者：
X. Mao;M. Wada;H. Hashimoto
本文介绍了我们在将现代非线性滤波技术应用于基于 GPS 的位置估计的研究中获得的结果。描述了使用 GPS 原始数据、伪距和多普勒频移测量的基于独立 GPS 的位置估计问题。然后开发了用于非线性滤波的位置和速度估计的新模型。该模型是非线性的并且具有可变的测量数量以应对任意数量的卫星。研究该模型并将其应用于两个不同的非线性滤波器。第一个是目前最常用的非线性滤波器：扩展卡尔曼滤波器。还提出使用无味过滤器作为构成基于 GPS 的系统和模型参数学习的替代过滤器。然后给出了第一个实验结果，包括使用不同滤波器的滤波模式获得的估计结果的比较。还讨论了未来的研究方向。
This paper presents the results obtained in our research about application of modern nonlinear filtering techniques to GPS based position estimation. The stand-alone GPS based position estimation problem using GPS raw data, pseudo-range and Doppler shifts measurements are described. A new model for position and velocity estimation are then developed for nonlinear filtering. The model is nonlinear and has variable measurement number for coping with an arbitrary number of satellites. The model is investigated applying it to two different nonlinear filters. The first one is the presently most used nonlinear filter: the extended Kalman filter. The use of unscented filter as an alternative filter for composing GPS based system and model parameter learning is also proposed. The first experimental results that comprise the comparison of estimation results obtained with the filtering modal using different filters are then presented. Future research directions are also discussed.