GNSS/INS integrated navigation data quality control method

Abstract

The embodiment of the present invention discloses a GNSS / INS integrated navigation data quality control method, which includes: step 1: reading GNSS data; step 2: extracting signal-to-noise ratio / carrier-to-noise ratio statistics, satellite altitude angle statistics, and number of satellites and satellite signal number statistics, carrier phase cycle slip and lock loss statistics, and satellite observation residual statistics; Step 3: Determine the weight factor reflecting the GNSS observation environment; Step 4: According to the GNSS positioning mode and the GNSS observation environment The weight factor classification enters the data quality control algorithm; Step 5: Determine the classification threshold of each component of the satellite observation residual statistic; determine according to the weight factor reflecting the GNSS observation environment, the satellite observation residual statistic and the GNSS original position STD The value of the new GNSS position STD; Step 6: According to the actual distribution, set the STD threshold and remove the gross error. The invention makes full use of the residual statistic of the satellite observation value, effectively improves the consistency of the GNSS position solution STD and the real position error, and has a stable effect.

Description

technical field [0001] The invention relates to the technical field of navigation and positioning, in particular to a method for quality control of GNSS / INS integrated navigation data. Background technique [0002] In the filtering strategy of the combined algorithm of the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS), the R array is used as the measurement noise covariance matrix, which is mainly determined by the post-test variance of the GNSS positioning solution (below). It is called GNSS position solution standard deviation (Standard Deviation, STD) for assignment. The R array is one of the main factors affecting the accuracy of the GNSS / INS combined solution. The GNSS position solution STD needs to reflect the true error level of the GNSS position solution, which is consistent with the real position. The error has good consistency. This kind of problem can also be described as whether the GNSS position solution STD is accurate. [...

AI Technical Summary

Problems solved by technology

[0003] Affected by various factors, among the various existing GNSS algorithms, the consistency between the GNSS position solution STD and the true position error is poor

Especially in the occlusion environment, it is often impossible to reflect the true error level of the GNSS position solution, which leads to the introduction of a GNSS positioning solution with a superior STD but a large real error, which seriously affects the positioning quality of the GNSS / INS combined solution

[0004] Machine learning algorithms can better solve the above problems, but this solution is not feasible on embedded devices that have limited storage and computing resources and are sensitive to power consumption

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