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.

中文翻译：

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通过 Tikhonov 正则化核学习结合 GNSS 多普勒和载波相位测量的车辆瞬时速度重建

GNSS 已成为一种广泛使用的车辆瞬时测速技术。GNSS 时差载波相位 (TDCP) 测速可以提供高精度位移增量，通过它可以推导出历元间平均速度。然而，这种速度总是存在所谓的建模误差，即平均速度与瞬时速度之间的偏差。GNSS 多普勒测速仪提供准确的瞬时速度，但其测量噪声要大得多。在这项工作中，我们建议将 TDCP 与多普勒相结合来估计车辆的瞬时速度。TDCP 导出的位移和多普勒导出的瞬时速度被视为两组测量值，而车辆的运动学由内核模型表示。而不是直接求解车辆的速度，我们间接寻找核权重来建立车辆运动状态的解析核模型。引入 Tikhonov 正则化来处理核权重估计中的病态问题，它可以显着平滑/去噪嘈杂的多普勒测量。所涉及的超参数使用广义交叉验证标准进行优化。构建的内核模型可以提供任何时刻的车辆速度，不一定是在采样时期。静态和动态车辆现场实验表明，所提出的 TDCP/多普勒综合测速仪可以提供高精度和高效率。引入 Tikhonov 正则化来处理核权重估计中的病态问题，它可以显着平滑/去噪嘈杂的多普勒测量。所涉及的超参数使用广义交叉验证标准进行优化。构建的内核模型可以提供任何时刻的车辆速度，不一定是在采样时期。静态和动态车辆现场实验表明，所提出的 TDCP/多普勒综合测速仪可以提供高精度和高效率。引入 Tikhonov 正则化来处理核权重估计中的病态问题，它可以显着平滑/去噪嘈杂的多普勒测量。所涉及的超参数使用广义交叉验证标准进行优化。构建的内核模型可以提供任何时刻的车辆速度，不一定是在采样时期。静态和动态车辆现场实验表明，所提出的 TDCP/多普勒集成测速仪可以提供高精度和效率。