Wang Shouhua, Wang Shuaihu, Sun Xiyan
College of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China.
Key Laboratory of Cognitive Radio and Information Processing, School of Information and Communication, Guilin University of Electronic Technology, Ministry of Education, Guilin 541004, China.
Sensors (Basel). 2023 Oct 11;23(20):8396. doi: 10.3390/s23208396.
During short baseline measurements in the Real-Time Kinematic Global Navigation Satellite System (GNSS-RTK), multipath error has a significant impact on the quality of observed data. Aiming at the characteristics of multipath error in GNSS-RTK measurements, a novel method that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and adaptive wavelet packet threshold denoising (AWPTD) is proposed to reduce the effects of multipath error in GNSS-RTK measurements through modal function decomposition, effective coefficient sieving, and adaptive thresholding denoising. It first utilizes the ICEEMDAN algorithm to decompose the observed data into a series of intrinsic mode functions (IMFs). Then, a novel IMF selection method is designed based on information entropy to accurately locate the IMFs containing multipath error information. Finally, an optimized adaptive denoising method is applied to the selected IMFs to preserve the original signal characteristics to the maximum possible extent and improve the accuracy of the multipath error correction model. This study shows that the ICEEMDAN-AWPTD algorithm provides a multipath error correction model with higher accuracy compared to singular filtering algorithms based on the results of simulation data and GNSS-RTK data. After the multipath correction, the accuracy of the E, N, and U coordinates increased by 49.2%, 65.1%, and 56.6%, respectively.
在实时动态全球导航卫星系统（GNSS-RTK）的短基线测量中，多径误差对观测数据质量有显著影响。针对GNSS-RTK测量中多径误差的特点，提出了一种将改进的完备总体经验模态分解与自适应噪声（ICEEMDAN）和自适应小波包阈值去噪（AWPTD）相结合的新方法，通过模态函数分解、有效系数筛选和自适应阈值去噪来降低GNSS-RTK测量中多径误差的影响。该方法首先利用ICEEMDAN算法将观测数据分解为一系列固有模态函数（IMF）。然后，基于信息熵设计了一种新的IMF选择方法，以准确定位包含多径误差信息的IMF。最后，对所选的IMF应用优化的自适应去噪方法，以最大程度地保留原始信号特征，并提高多径误差校正模型的精度。本研究表明，与基于模拟数据和GNSS-RTK数据结果的奇异滤波算法相比，ICEEMDAN-AWPTD算法提供了精度更高的多径误差校正模型。经过多径校正后，E、N和U坐标的精度分别提高了49.2%、65.1%和56.6%。
