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一种基于联邦卡尔曼滤波（FKF）的多全球导航卫星系统（GNSS）时间传递方法。

A Multi-Global Navigation Satellite System (GNSS) Time Transfer Method with Federated Kalman Filter (FKF).

机构信息

School of Electronics and Information Engineering, Beijing Jiaotong University (BJTU), Beijing 100044, China.

National Institute of Metrology (NIM), Beijing 100029, China.

出版信息

Sensors (Basel). 2023 Jun 4;23(11):5328. doi: 10.3390/s23115328.

DOI:10.3390/s23115328
PMID:37300055
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10256004/
Abstract

Relative to single Global Navigation Satellite System (GNSS) measurements, i.e., the measurements from a single GNSS system, a single GNSS code, and a single GNSS receiver, multi-GNSS measurements for time transfer could improve reliability and provide better short-term stability. Previous studies applied equal weighting to different GNSS systems or different GNSS time transfer receivers, which, to some extent, revealed the improvement in the additional short-term stability from the combination of two or more kinds of GNSS measurements. In this study, the effects of the different weight allocation for multi-measurements of GNSS time transfer were analyzed, and a federated Kalman filter was designed and applied to fuse multi-GNSS measurements combined with the standard-deviation-allocated weight. Tests with real data showed that the proposed approach can reduce the noise level to well below about 250 ps for short averaging times.

摘要

相对于单个全球导航卫星系统 (GNSS) 测量值，即来自单个 GNSS 系统、单个 GNSS 码和单个 GNSS 接收机的测量值，用于时间传递的多 GNSS 测量值可以提高可靠性并提供更好的短期稳定性。先前的研究对不同的 GNSS 系统或不同的 GNSS 时间传递接收机应用了相等的权重，这在一定程度上揭示了两种或更多种 GNSS 测量值组合带来的额外短期稳定性的提高。在本研究中，分析了 GNSS 时间传递多测量值的不同权重分配的影响，并设计了联邦卡尔曼滤波器来融合多 GNSS 测量值，并结合标准偏差分配权重。使用实际数据进行的测试表明，所提出的方法可以将噪声水平降低到短平均时间低于约 250 ps 以下。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/c6f4988a88e1/sensors-23-05328-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/05f8dce10562/sensors-23-05328-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/16c5a6544a53/sensors-23-05328-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/59dc025c48f8/sensors-23-05328-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/e288139f5d36/sensors-23-05328-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/c48821e1a4fe/sensors-23-05328-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/0ac10b240d41/sensors-23-05328-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/ff3dbe6e616e/sensors-23-05328-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/29c4b3c7780d/sensors-23-05328-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/c6f4988a88e1/sensors-23-05328-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/05f8dce10562/sensors-23-05328-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/16c5a6544a53/sensors-23-05328-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/59dc025c48f8/sensors-23-05328-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/e288139f5d36/sensors-23-05328-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/c48821e1a4fe/sensors-23-05328-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/0ac10b240d41/sensors-23-05328-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/ff3dbe6e616e/sensors-23-05328-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/29c4b3c7780d/sensors-23-05328-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2b3/10256004/c6f4988a88e1/sensors-23-05328-g009.jpg

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