影响因子：5.6
发表刊物：Measurement
关键字：Global navigation satellite system; Time difference carrier phase; Instantaneous velocity and acceleration; Sparse kernel learning; Fast iterative shrinkage thresholding algorithm
摘要：A new method is proposed to determine instantaneous velocities and accelerations with a stand-alone GNSS receiver. It is based on sparse kernel learning theory. Kernel trick is employed to represent the kinematics, and L1 norm regularization is used to get sparse solution. Analytical models rather than data series are provided, with which velocity and acceleration at any instant could be calculated. Between-epoch displacements, provided by GNSS time difference carrier phase technique, are used as training data. Efficient numerical algorithms, such as the tri-diagonal matrix algorithm and the fast iterative shrinkage thresholding algorithm, are employed to deal with the between-epoch correlations in the data and the L1 norm regularization, respectively. The hyperparameters are optimized using general cross validation or Akaike information criterion, both tailored for the L1 norm regularization problem. Both simulation and real-data results show the superior performance of the proposed method compared to the conventional finite difference method. Especially for dynamic cases, the proposed method can better represent the real dynamics and provide high-accuracy velocimetry and accelerometry results.
论文类型：期刊论文
论文编号：107803
学科门类：工学
一级学科：测绘科学与技术
文献类型：J
卷号：159
是否译文：否
发表时间：2020-04-05
收录刊物：SCI
发布期刊链接：https://doi.org/10.1016 /j.measurement.2020.107803