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GNSS single station double-frequency observation data cycle-slip detection and processing method based on fuzzy mathematics

A technology of cycle slip detection and fuzzy mathematics, which is applied in measuring devices, radio wave measurement systems, instruments, etc., can solve the problems that the threshold value of cycle slip detection is difficult to be determined reliably, achieve the effect of improving accuracy and reliability, and solving the problem that is difficult to reliably determine

Active Publication Date: 2016-12-07
WUHAN UNIV
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Problems solved by technology

The invention can effectively solve the problem that the cycle slip detection threshold is difficult to be reliably determined, thereby improving the accuracy and reliability of GNSS precision data processing

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  • GNSS single station double-frequency observation data cycle-slip detection and processing method based on fuzzy mathematics
  • GNSS single station double-frequency observation data cycle-slip detection and processing method based on fuzzy mathematics
  • GNSS single station double-frequency observation data cycle-slip detection and processing method based on fuzzy mathematics

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Embodiment Construction

[0051] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0052] please see figure 1 , a fuzzy mathematics-based GNSS single-station dual-frequency observation data cycle-slip detection and processing method provided by the present invention judges observation by determining the ionospheric delay variation between epochs and the membership degree of MW combination observation value variation between epochs Whether there is a cycle slip occurs in the value, and the weight reduction process is performed on the observation value that is difficult to determine whether there is a cycle slip. The present invention can eff...

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Abstract

The invention discloses a GNSS single station double-frequency observation data cycle-slip detection and processing method based on fuzzy mathematics. Whether there is a cycle slip in the observation value is determined by determining the membership of the variation of the ionospheric delay between the epochs and the variation of the MW combined observation value between the epochs, and the descending weight processing is performed on the observation value, in which the cycle slip is hard to determine. According to the invention, the problem that the threshold is difficult to reliably determine during the cycle slip detection data processing in the prior art can be effectively solved, and the GNSS precise data processing precision and reliability can be improved.

Description

technical field [0001] The invention belongs to the technical field of global navigation system precision data processing, relates to a cycle slip detection and processing method of GNSS single-station dual-frequency observation data, and specifically relates to a cycle-slip detection and processing of GNSS single-station dual-frequency observation data based on fuzzy mathematics method. Background technique [0002] At present, the cycle slip detection methods of GNSS (Global Navigation Satellite System) dual-frequency / multi-frequency data mainly include TurboEdit method, ionospheric residual method, MW (Melbourne-Wübbena) combination combined with ionospheric total electron content change rate, etc., but these Each method has its own limitations. The ionospheric residual method detects cycle slips based on the assumption that the ionosphere changes slowly. If the ionosphere is relatively active or the signal loses lock for a long time, the reliability of cycle slip detect...

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Application Information

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IPC IPC(8): G01S19/37
CPCG01S19/37
Inventor 邹璇陈亮宋伟伟冯瑾李宗楠唐卫明施闯
Owner WUHAN UNIV