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基于改进U-Net++和近红外光谱技术的羊毛含量快速定性分析方法研究
- Release time:2021-05-01
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Impact Factor:
6.8Journal:
IEEE Transactions on Vehicular TechnologyKey Words:
GNSS, instantaneous velocity, time difference carrier phase, Doppler, kernel learningAbstract:
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 efficiencyIndexed by:
Journal paperDiscipline:
EngineeringFirst-Level Discipline:
Surveying and MappingDocument Type:
JVolume:
70Issue:
5Page Number:
4190-4202Translation or Not:
noDate of Publication:
2021-05-01Included Journals:
SCILinks to published journals:
https://doi.org/10.1109/TVT.2021.3076056
- Pre One:Nijia Qian, Guobin Chang*, Jingxiang Gao. (2020). Smoothing for continuous dynamical state space models with sampled system coefficients based on sparse kernel learning
- Next One:Guobin Chang*, Nijia Qian, Chao Chen， Jingxiang Gao. (2020). Precise instantaneous velocimetry and accelerometry with a stand-alone GNSS receiver based on sparse kernel learning