GNSS technology is an effective all-weather, continuous and real-time method of detecting three-dimensional landslide displacement. However, current positioning methods such as the network real-time kinematic (RTK) technique based on virtual reference station (VRS) technology have some limitations, particularly a sensitivity to atmospheric modeling error. In this study, taking advantage of existing GNSS continuously operating reference stations (CORS), we propose a network RTK algorithm (virtual atmosphere constraint (VAC)) for high-precision landslide monitoring. Network modeling atmosphere delays were used as virtual observations for the terminal position solution, by which their accuracy information can be considered. Additionally, a noise reduction method that combines complementary ensemble empirical mode decomposition and median filtering was proposed to reduce GNSS signal residual errors in the original RTK coordinate time series. To validate the effectiveness of the algorithm, we processed one week of data from two monitoring receivers on the Heifangtai landslide and four surrounding CORS using the VAC and VRS methods. Results show that the original displacement time series processed with VRS was prone to jitter, whereas the VAC method exhibited better accuracy, stability, and ambiguity fixing rates. Horizontal and vertical RMS errors at a stable monitoring point were 1.7 cm and 3.9 cm for VRS-RTK and 0.9 cm and 2.3 cm for VAC-RTK; thus, VAC reduced the error by 40.1% and 20.5%, respectively. After noise reduction, the RMS values of the VAC method at the stable and the other collapsed monitoring point were both reduced to an mm-level accuracy.

GNSS技术是一种有效的全天候、连续、实时的三维滑坡位移检测方法。然而，目前基于虚拟参考站（VRS）技术的网络实时动态（RTK）技术等定位方法存在一定的局限性，尤其是对大气建模误差的敏感性。在这项研究中，利用现有的 GNSS 连续运行参考站 (CORS)，我们提出了一种用于高精度滑坡监测的网络 RTK 算法（虚拟大气约束 (VAC)）。网络建模大气延迟被用作终端位置解决方案的虚拟观测，通过它可以考虑它们的精度信息。此外，提出了一种结合互补系综经验模态分解和中值滤波的降噪方法来降低原始RTK坐标时间序列中的GNSS信号残差。为了验证算法的有效性，我们使用 VAC 和 VRS 方法处理了来自黑方台滑坡上的两个监测接收器和周围四个 CORS 的一周数据。结果表明，用VRS处理的原始位移时间序列容易出现抖动，而VAC方法表现出更好的精度、稳定性和模糊度固定率。VRS-RTK 稳定监测点的水平和垂直 RMS 误差分别为 1.7 cm 和 3.9 cm，VAC-RTK 分别为 0.9 cm 和 2.3 cm；因此，VAC 将错误分别减少了 40.1% 和 20.5%。降噪后，