影响因子：6.8
发表刊物：IEEE Transactions on Vehicular Technology
关键字：GNSS, instantaneous velocity, time difference carrier phase, Doppler, kernel learning
摘要：GNSS has become a widely accessible technique for vehicle instantaneous velocimetry. GNSS time difference carrier phase (TDCP) velocimetry can provide high-accuracy displacement increments, through which the between-epoch average velocity can be derived. However, there are always so-called modelling errors in such velocity, i.e., the deviation between the average velocity and the instantaneous one. GNSS Doppler velocimetry offers exactly instantaneous velocity, but its measurement is much noisier. In this work, we propose to integrate TDCP with Doppler for estimating vehicle’s instantaneous velocity. The TDCP-derived displacements and the Doppler-derived instantaneous velocity are treated as two sets of measurements, whereas the vehicle’s kinematics is represented by kernel model. Rather than directly solving for vehicle’s velocity, we indirectly seek for the kernel weights to establish an analytical kernel model of vehicle’s motion state. Tikhonov regularization is introduced to deal with the ill-conditioned problem in kernel weights estimation, and it can significantly smooth/denoise the noisy Doppler measurements. The hyperparameters involved are optimized using generalized cross validation criterion. The constructed kernel model can provide vehicle’s velocity at any instants, not necessarily at the sampling epochs. The static and dynamic vehicle field experiments demonstrate that the proposed TDCP/Doppler integrated velocimetry can provide both high accuracy and efficiency
论文类型：期刊论文
学科门类：工学
一级学科：测绘科学与技术
文献类型：J
卷号：70
期号：5
页面范围：4190-4202
是否译文：否
发表时间：2021-05-01
收录刊物：SCI
发布期刊链接：https://doi.org/10.1109/TVT.2021.3076056