A ut1-utc hybrid differential prediction method

Abstract

A UT1-UTC mixed difference prediction method, comprising the following steps: obtaining UT1-UTC basic data and LOD basic data through a file or a network; removing the leap seconds and the tides of the UT1-UTC basic data to obtain UT1R-TAI basic data, and removing the tides of the LOD basic data to obtain basic data; calculating a first-order difference sequence of the UT1R-TAI basic data; calculating a sum sequence of the first-order difference sequence of the UT1R-TAI basic data and the basic data; calculating a predicted value of the first-order difference sequence of the UT1R-TAI basic data according to the sum sequence and a predicted value of the basic data, and calculating a predicted value of the UT1R-TAI basic data by using a difference recovery operator; and adding the leap seconds and the tides of the predicted value of the UT1R-TAI basic data to obtain a predicted value of the UT1-UTC basic data. The present application improves the accuracy of UT1-UTC prediction.

Description

Technical Field

[0001] This invention relates to the fields of geodesy and satellite navigation technology, and in particular to a UTI-UTC hybrid differential prediction method, which is mainly applicable to improving the accuracy of UTI-UTC prediction. Background Technology

[0002] Earth rotation parameters (ERPs) are essential for conversions between celestial bodies and the Earth reference system. However, data processing in space geodesy introduces time delays in acquiring these parameters. Therefore, ultra-short-term (UTC) predictions of ERPs are crucial for real-time applications related to reference frame conversion. Among these, UT1-UTC is the most challenging to determine in real-time estimation. Improving the performance of UT1-UTC ultra-short-term predictions (1–10 days) provides vital support for related real-time applications, such as rapid and ultra-rapid orbit determination for GNSS satellites.

[0003] Improving the accuracy of UT1-UTC ultra-short-term prediction has always been a focus of attention and research in the fields of geodesy and satellite navigation. Currently, methods for UT1-UTC ultra-short-term prediction primarily process the UT1-UTC data itself. For example, methods like LS+AR directly use UT1-UTC data for fitting and prediction, or LS+MAR supplement UT1-UTC data with information such as LOD (Level of Detail) to predict UT1-UTC. However, during the fitting process, these traditional methods inevitably introduce unmodeled errors into the UT1-UTC fitting residuals. Even using AR and MAR methods to process these fitting residuals cannot completely eliminate the influence of unmodeled errors, thus limiting the current prediction accuracy of UT1-UTC. Summary of the Invention

[0004] The purpose of this invention is to overcome the defects and problems of low forecast accuracy in the existing technology and to provide a UTI-UTC hybrid differential forecasting method with high forecast accuracy.

[0005] To achieve the above objectives, the technical solution of the present invention is: a UTI-UTC hybrid differential prediction method, which includes the following steps:

[0006] S1. Obtain UT1-UTC basic data and LOD basic data via file or network;

[0007] S2. Remove the jump seconds and tides from the UT1-UTC basic data to obtain the UT1R-TAI basic data, and remove the tides from the LOD basic data to obtain the LODR basic data;

[0008] S3. Calculate the first-order difference sequence of the UT1R-TAI basic data;

[0009] S4. Calculate the first-order difference sequence of the UT1R-TAI basic data and the sum sequence of the LODR basic data;

[0010] S5. Calculate the predicted value of the first-order difference sequence of the UT1R-TAI basic data based on the predicted values ​​of the sum sequence and LODR basic data, and use the differential recovery operator to calculate the predicted value of the UT1R-TAI basic data.

[0011] S6. Add the second jump and tide of the forecast value of UT1R-TAI basic data to obtain the forecast value of UT1-UTC basic data.

[0012] In step S3, the first-order difference sequence Δf of the UT1R-TAI basic data is calculated using the following formula. ut1 ;

[0013]

[0014] In the formula, Δf t ut1 f is the first difference of the UT1R-TAI basic data at time t. t ut1 For the UT1R-TAI base data at time t, The UT1R-TAI base data for time t-1.

[0015] In step S4, the first-order difference sequence Δf of the UT1R-TAI basic data ut1 and the sequence of LODR basic data for:

[0016]

[0017] Step S5 specifically includes the following steps:

[0018] S51, Computation and Sequence Forecast value at time t+1 Predict(LODR) is the predicted value of the LODR base data at time t+1. t+1 ;

[0019] S52. Calculate the first-order difference sequence Δf of the UT1R-TAI basic data. ut1 Forecast value at time t+1

[0020] S53. Calculate the predicted value of UT1R-TAI basic data at time t+1 using the differential recovery operator.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] This invention provides an ultra-short-term Earth rotation parameter prediction method. By utilizing the high-precision observation characteristics of LOD data and mining the sum of LOD and first-order difference UT1-UTC data, common unmodeled errors in the fitting residuals can be eliminated. By predicting LOD and the sum of predicted LOD and first-order difference UT1-UTC, a high-accuracy UT1-UTC hybrid difference prediction method is indirectly provided, thus significantly improving the accuracy of UT1-UTC prediction. Therefore, this invention improves the accuracy of UT1-UTC prediction. Attached Figure Description

[0023] Figure 1 This is a flowchart of a UTI-UTC hybrid differential prediction method according to the present invention.

[0024] Figure 2 This is a flowchart of the prediction of ultra-short-term Earth rotation parameters using the traditional LS / WLS+MAR method and the traditional LS / WLS+AR method.

[0025] Figure 3 This is a schematic diagram showing the prediction error results of the present invention and the traditional method. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] See Figure 1 A UT1-UTC hybrid differential prediction method, comprising the following steps:

[0028] S1. Obtain UT1-UTC basic data and LOD basic data via file or network;

[0029] S2. Remove the jump seconds and tides from the UT1-UTC basic data to obtain the UT1R-TAI basic data, and remove the tides from the LOD basic data to obtain the LODR basic data;

[0030] S3. Calculate the first-order difference sequence of the UT1R-TAI basic data;

[0031] S4. Calculate the first-order difference sequence of the UT1R-TAI basic data and the sum sequence of the LODR basic data;

[0032] S5. Calculate the predicted value of the first-order difference sequence of the UT1R-TAI basic data based on the predicted values ​​of the sum sequence and LODR basic data, and use the differential recovery operator to calculate the predicted value of the UT1R-TAI basic data.

[0033] S6. Add the second jump and tide of the forecast value of UT1R-TAI basic data to obtain the forecast value of UT1-UTC basic data.

[0034] In step S3, the first-order difference sequence Δf of the UT1R-TAI basic data is calculated using the following formula. ut1 ;

[0035]

[0036] In the formula, Δf t ut1 f is the first difference of the UT1R-TAI basic data at time t. t ut1 For the UT1R-TAI base data at time t, The UT1R-TAI base data for time t-1.

[0037] In step S4, the first-order difference sequence Δf of the UT1R-TAI basic data ut1 and the sequence of LODR basic data for:

[0038]

[0039] Step S5 specifically includes the following steps:

[0040] S51, Computation and Sequence Forecast value at time t+1 Predict(LODR) is the predicted value of the LODR base data at time t+1. t+1 ;

[0041] S52. Calculate the first-order difference sequence Δf of the UT1R-TAI basic data. ut1 Forecast value at time t+1

[0042] S53. Calculate the predicted value of UT1R-TAI basic data at time t+1 using the differential recovery operator.

[0043] The principle of this invention is explained as follows:

[0044] On the one hand, as defined by UT1-UTC and LOD, LOD and first-difference UT1-UTC are strongly correlated, and negatively strongly correlated; moreover, the fitting residuals of LOD contain the same unmodeled errors as those in the fitting residuals of first-difference UT1-UTC, only with opposite signs. On the other hand, among existing UT1-UTC and LOD observational data, LOD has higher observational accuracy than UT1-UTC. Therefore, fully exploring and utilizing this information will help improve the current forecast accuracy of UT1-UTC.

[0045] Compared with traditional LS+AR and LS+MAR techniques, this design takes a completely different approach. Instead of focusing on processing the UT1-UTC data itself, it utilizes the high-precision observation characteristics of LOD data and mines the data of the sum of LOD and first-order difference UT1-UTC to eliminate common unmodeled errors in the fitting residuals. By forecasting LOD and the sum of forecasted LOD and first-order difference UT1-UTC, it indirectly provides a UT1-UTC hybrid difference forecasting method with high forecast accuracy.

[0046] Example:

[0047] See Figure 1 A UT1-UTC hybrid differential prediction method, comprising the following steps:

[0048] S1. Obtain UT1-UTC basic data and LOD basic data via file or network;

[0049] S2. Remove the jump seconds and tides from the UT1-UTC basic data to obtain the UT1R-TAI basic data, and remove the tides from the LOD basic data to obtain the LODR basic data;

[0050] S3. Calculate the first-order difference sequence of the UT1R-TAI basic data;

[0051] The first-order difference sequence Δf of the UT1R-TAI basic data is calculated using the following formula. ut1 ;

[0052]

[0053] In the formula, Δf t ut1 f is the first difference of the UT1R-TAI basic data at time t. t ut1 For the UT1R-TAI base data at time t, The UT1R-TAI base data for time t-1;

[0054] S4. Calculate the first-order difference sequence of the UT1R-TAI basic data and the sum sequence of the LODR basic data;

[0055] The first-order difference sequence Δf of the UT1R-TAI basic data ut1 and the sequence of LODR basic data for:

[0056]

[0057] S5. Calculate the predicted value of the first-order difference sequence of the UT1R-TAI basic data based on the predicted values ​​of the sum sequence and LODR basic data, and calculate the predicted value of the UT1R-TAI basic data using the differential recovery operator; specifically including the following steps:

[0058] S51. Calculate and sequence according to AR method. Forecast value at time t+1 The predicted value of the LODR baseline data at time t+1 is calculated using conventional forecasting methods, such as the LS+AR method. t+1 ;

[0059] S52. Calculate the first-order difference sequence Δf of the UT1R-TAI basic data. ut1 Forecast value at time t+1

[0060] S53. Calculate the predicted value of UT1R-TAI basic data at time t+1 using the differential recovery operator.

[0061] S6. Add the second jump and tide of the forecast value of UT1R-TAI basic data to obtain the forecast value of UT1-UTC basic data.

[0062] The above method can be extended to UT1-UTC ultra-short-term / short-term / medium-term / long-term forecasts.

[0063] See Figure 3This embodiment presents the statistical results from 210 forecasts, each forecasting 1 to 10 days in advance. Here, MAE represents the mean absolute error of the UT1-UTC forecast, LS+AR and LS+MAR represent the forecast error results of traditional methods, and the present invention represents the forecast error results of this design. Therefore, the results show that the UT1-UTC ultra-short-term forecast results obtained by the UT1-UTC hybrid differential forecasting method of this design are significantly improved in forecast accuracy compared to the traditional LS+AR and LS+MAR methods. Since there are various weighting methods for WLS, and theoretically, LS can be considered a special type of WLS, the comparison between this design and the traditional WLS+AR / WLS+MAR methods will not be repeated here.

Claims

1. A UTI-UTC hybrid differential prediction method, characterized in that, The method includes the following steps: S1. Obtain UT1-UTC basic data and LOD basic data via file or network; S2. Remove the jump seconds and tides from the UT1-UTC base data to obtain the UT1R-TAI base data, and remove the tides from the LOD base data to obtain... Basic data; S3. Calculate the first-order difference sequence of the UT1R-TAI basic data; S4. Calculate the first-order difference sequence of the UT1R-TAI basic data and The basic data and sequences; S5, based on the sequence and The predicted values ​​of the UT1R-TAI basic data are calculated by using the first-order difference sequence of the basic data, and the predicted values ​​of the UT1R-TAI basic data are calculated using the difference recovery operator; specifically, the following steps are included: S51, Computation and Sequence In time Forecast value and Basic data in time Forecast value ; S52. Calculate the first-order difference sequence of the UT1R-TAI basic data. In time Forecast value : ； S53. Calculate the UT1R-TAI basic data in time using the differential recovery operator. Forecast value : ； S6. Add the second jump and tide of the forecast value of UT1R-TAI basic data to obtain the forecast value of UT1-UTC basic data.

2. The UTI-UTC hybrid differential prediction method according to claim 1, characterized in that: In step S3, the first-order difference sequence of the UT1R-TAI basic data is calculated using the following formula. ; ； In the formula, For UT1R-TAI basic data in time The first difference value, For time UT1R-TAI basic data, For time UT1R-TAI base data.

3. The UTI-UTC hybrid differential prediction method according to claim 2, characterized in that: In step S4, the first-order difference sequence of the UT1R-TAI basic data and Basic data and sequences for: 。

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