A discontinuous leap second detection method

Abstract

The present application relates to the technical field of satellite navigation signal processing, and particularly relates to a non-continuous epoch cycle slip detection method, comprising collecting observation data; judging whether the observation data produces an inter-epoch cycle slip, if the observation data produces an inter-epoch cycle slip, then executing the next step, otherwise performing sampling separation; interrupting the counting of the epochs of the observation data losing lock to obtain the lock loss time; using a data processing software to perform sampling separation on the observation data to obtain a sampling interval; judging whether the inter-epoch interval of the observation data or the lock loss time is greater than 1s, if the inter-epoch interval of the observation data or the lock loss time is greater than 1s, then executing the next step, otherwise performing multiple difference detection; using the sampling interval to use the Lagrange interpolation method to perform interpolation on the observation data to construct high sampling rate data; using the high difference method to perform multiple difference detection on the observation data or the high sampling rate data to judge whether there is a non-continuous epoch cycle slip, and obtaining a detection result, thereby solving the problem that the existing cycle slip processing method cannot detect non-continuous epoch cycle slip.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation signal processing technology, and in particular to a method for detecting cycle slips in discontinuous epochs. Background Technology

[0002] Cycle slips refer to jumps or interruptions in integer cycle counting caused by loss of satellite signal lock in carrier phase measurements using Global Navigation Satellite System (GNSS) technology. Accurately detecting and recovering cycle slips is a crucial and essential problem in carrier phase measurements. In achieving high-precision relative positioning using satellites, the correctness and effectiveness of cycle slip handling in carrier phase observations directly impact the final positioning result.

[0003] To address cycle slips caused by environmental disturbances affecting the receiver, it is necessary to detect cycle slips and repair or remove the corresponding observations to ensure data quality. Common cycle slip classifications include major and minor cycle slips, as well as continuous and discontinuous epoch cycle slips. Discontinuous epoch cycle slips arise from the uncertainty of environmental interference, resulting in cycle slips between consecutive epochs, as well as phase jumps occurring after satellite lock-on loss and reacquisition.

[0004] However, conventional single-frequency cycle slip processing methods, such as pseudorange-phase combination method, higher order difference method, and polynomial fitting method, are all based on the condition of continuous observation epochs, and lack the ability to detect cycle slips in non-continuous epochs. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting cycle slips in discontinuous epochs, which aims to solve the problem that existing cycle slip processing methods cannot detect cycle slips in discontinuous epochs.

[0006] To achieve the above objectives, the present invention provides a method for detecting cycle slips in discontinuous epochs, comprising the following steps:

[0007] S1 collects observation data;

[0008] S2 determines whether the observed data has generated an inter-epoch cycle slip. If an inter-epoch cycle slip has generated, then step S3 is executed. If no inter-epoch cycle slip has generated, then step S4 is executed.

[0009] S3 performs an interruption count on the epochs in which the observed data is lost, and obtains the time of loss of lock.

[0010] S4 uses data processing software to sample and separate the observed data to obtain the sampling interval;

[0011] S5 determines whether the epoch interval of the observed data or the unlock time is greater than 1s. If it is greater than 1s, then execute step S6; if it is less than 1s, then execute step S7.

[0012] S6 uses the sampling interval to interpolate the observed data using the Lagrange interpolation method to construct high sampling rate data;

[0013] S7 uses the high-order difference method to perform multiple difference detections on the observed data or the high sampling rate data to determine whether there are discontinuous epoch cycle slips and obtain the detection results.

[0014] The specific method for calculating the time of data loss by counting interruptions at each epoch of the observed data loss is as follows:

[0015] S31 calculates the difference between all two adjacent epochs in the observation data to obtain multiple interruption times;

[0016] S32 integrates the multiple interrupt times to obtain the lockout time.

[0017] The data processing software is TEQC data processing software.

[0018] The specific method for constructing high-sampling-rate data by interpolating the observed data using the Lagrange interpolation method at the sampling interval is as follows:

[0019] S61 Derives the basis functions for Lagrange linear interpolation;

[0020] S62 derives the corresponding interpolation polynomial based on the basis functions of the Lagrange linear interpolation;

[0021] S63 uses a polynomial fitting method to fit the observed data using the interpolation polynomial to obtain high sampling rate data.

[0022] In step S7, the difference detection is performed 4 times.

[0023] This invention discloses a method for detecting discontinuous epoch cycle slips. The method involves: collecting observation data; determining whether the observation data generates inter-epoch cycle slips; if so, proceeding to the next step; if not, performing sampling separation; counting the interruptions of the epochs in which the observation data is unlocked to obtain the unlock time; using data processing software to sample and separate the observation data to obtain a sampling interval; determining whether the epoch interval of the observation data or the unlock time is greater than 1 second; if greater than 1 second, proceeding to the next step; if less than 1 second, performing multiple difference detections; using the sampling interval and Lagrange interpolation to interpolate the observation data to construct high-sampling-rate data; and using a high-order difference method to perform multiple difference detections on the observation data or the high-sampling-rate data to determine whether discontinuous epoch cycle slips exist, obtaining the detection result. This method achieves the detection of discontinuous epoch cycle slips and solves the problem that existing cycle slip processing methods cannot detect discontinuous epoch cycle slips. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a graph showing the results of high-order cycle slip detection at 1-second intervals.

[0026] Figure 2 This is a graph showing the results of high-order cycle slip detection at 10-second intervals.

[0027] Figure 3 This is a diagram showing the results of the improved high-order difference cycle slip detection after interpolation.

[0028] Figure 4 This is a block diagram illustrating the principle of a non-continuous epoch cycle slip detection method provided by the present invention.

[0029] Figure 5 This is a flowchart of a non-continuous epoch cycle slip detection method provided by the present invention. Detailed Implementation

[0030] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0031] Please see Figures 1 to 5 This invention provides a method for detecting cycle slips in discontinuous epochs, comprising the following steps:

[0032] S1 collects observation data;

[0033] S2 determines whether the observed data has generated an inter-epoch cycle slip. If an inter-epoch cycle slip has generated, then step S3 is executed. If no inter-epoch cycle slip has generated, then step S4 is executed.

[0034] S3 performs an interruption count on the epochs in which the observed data is lost, and obtains the time of loss of lock.

[0035] The specific method is as follows:

[0036] S31 calculates the difference between all two adjacent epochs in the observation data to obtain multiple interruption times;

[0037] Specifically, an interruption count is performed at the epoch where the data is lost. Let's assume that epoch is... The recaptured epoch is The time of loss of lock is .

[0038] S32 integrates the multiple interrupt times to obtain the lockout time.

[0039] S4 uses data processing software to sample and separate the observed data to obtain the sampling interval;

[0040] Specifically, the data processing software is TEQC data processing software.

[0041] The raw data was sampled and segmented using TEQC data processing software. After sampling:

[0042]

[0043] in, The epoch value before sampling. These are the epoch values ​​after sampling. The sampling interval is denoted as .

[0044] S5 determines whether the epoch interval of the observed data or the unlock time is greater than 1s. If it is greater than 1s, then execute step S6; if it is less than 1s, then execute step S7.

[0045] S6 uses the sampling interval to interpolate the observed data using the Lagrange interpolation method to construct high sampling rate data;

[0046] The specific method is as follows:

[0047] S61 Derives the basis functions for Lagrange linear interpolation;

[0048] Specifically, Lagrange interpolation:

[0049] Assuming a known function function table , The range of values ​​is Then there is a corresponding polynomial. , making

[0050]

[0051] In the known interval function values ​​within , Within this interval, there exists a linear interpolation polynomial. ,satisfy .

[0052] The basis functions for Lagrange linear interpolation are obtained by performing interpolation operations on the polynomial:

[0053]

[0054] in: , .

[0055] S62 derives the corresponding interpolation polynomial based on the basis functions of the Lagrange linear interpolation;

[0056] Specifically, the corresponding Lagrange interpolation polynomial is:

[0057]

[0058] S63 uses a polynomial fitting method to fit the observed data using the interpolation polynomial to obtain high sampling rate data.

[0059] Specifically, the principle of polynomial fitting:

[0060] Polynomial fitting is a method that utilizes the previous Calculation of each phase value The fitted value is then compared with the observed value, and the difference is taken as the cycle slip. The mathematical model is as follows:

[0061]

[0062] In the formula, For carrier phase observations, For fitting epochs. The initial fitting time is used. The coefficients are calculated using the least squares method. Based on the fitted residuals Calculate the mean square error :

[0063]

[0064] Finally, using the calculated polynomial coefficients, the phase value of the next epoch is derived.

[0065] S7 uses the high-order difference method to perform multiple difference detections on the observed data or the high sampling rate data to determine whether there are discontinuous epoch cycle slips and obtain the detection results.

[0066] Specifically, the difference detection was performed 4 times.

[0067] The principle of the higher-order difference method is to perform multiple subtractions on carrier phase observations across multiple epochs. The difference between adjacent observations is called the first-order difference, and the difference between adjacent first-order differences is called the second-order difference, and so on, to obtain the multiple subtractions. Because the ground receiver is continuously locked to the satellite, the carrier phase observations exhibit a continuous curve. After multiple subtractions between epochs, the value of the higher-order difference will approach zero. Therefore, this characteristic can be used for cycle slip detection. If the higher-order difference does not approach zero after multiple subtractions, it indicates that a cycle slip has occurred in the carrier phase. The higher-order difference method can effectively amplify cycle slips, improving the accuracy of cycle slip detection. The multiple subtractions are represented as follows:

[0068] First difference: (1)

[0069] Quadratic difference: (2)

[0070] Cubic difference: (3)

[0071] in, For phase value, It is an epoch number.

[0072] To verify the feasibility of improving the higher-order difference method for cycle slip detection, single-frequency, cycle-slip-free observation data, totaling 3300 epochs, was used with a sampling interval of 1 second. To simulate a discontinuous epoch, carrier observation data from epochs 50 to 60 were deleted without adding cycle slips. The data detection results are as follows: Figure 1 ; Figure 1 The results of high-order cycle slip detection at 1-second intervals are shown (continuous epoch data on the top side and non-continuous epoch data on the bottom side).

[0073] The sampling interval is initially set at 10 seconds, and a cycle slip of size 5 cycles is added at epoch 61. The data is then processed a second time, changing the sampling interval to 10 seconds and re-applying Lagrange interpolation for fourth-order interpolation. The detection results at the 10-second interval and after interpolation are shown below. Figure 2 , 3 ; Figure 2 Results of high-order cycle slip detection at 10-second intervals; Figure 3 Interpolation improves the detection results of high-order cycle slips;

[0074] Depend on Figure 1 , 2 As shown in section 3, the traditional high-order difference algorithm generates a "peak" similar to cycle slips after receiver reacquisition, making it impossible to detect cycle slips in discontinuous epochs. The improved algorithm overcomes the influence of discontinuous epochs, effectively detects cycle slips in reacquisition epochs, and does not affect the normal detection of other continuous epochs, thus effectively solving the problem of cycle slips in discontinuous epochs.

[0075] The above-disclosed method for detecting non-continuous epoch cycles is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. Those skilled in the art will understand that implementing all or part of the above-described embodiments and making equivalent changes in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A non-continuous epoch cycle slip detection method, characterized in that, The method comprises the following steps: S1: collecting observation data; S2: judging whether the observation data generates an epoch-to-epoch cycle slip, if yes, executing step S3, if not, executing step S4; S3: interrupting counting of epochs of the observation data losing lock to obtain a losing lock time; S4: sampling and separating the observation data by using data processing software to obtain a sampling interval; S5: judging whether an epoch interval of the observation data or the losing lock time is greater than 1s, if yes, executing step S6, if not, executing step S7; S6: using the sampling interval to interpolate the observation data by using a Lagrange interpolation method to construct high sampling rate data; S7: using a high difference method to detect a plurality of differences of the observation data or the high sampling rate data to judge whether there is a non-continuous epoch cycle slip to obtain a detection result; The specific method for using the sampling interval to interpolate the observation data by using the Lagrange interpolation method to construct high sampling rate data is: S61: deriving a base function of Lagrange linear interpolation, specifically, the Lagrange interpolation method is: Assuming a known function function table , The range of values ​​is Then there is a corresponding polynomial. , making ； function values in a known interval , In this interval, there exists a linear interpolation polynomial that satisfies ,​ performing interpolation operation on a polynomial to obtain the base function of Lagrange linear interpolation: ； wherein: , ; S62: deriving a corresponding interpolation polynomial based on the base function of Lagrange linear interpolation, specifically, the corresponding Lagrange interpolation polynomial is: ； S63: using a polynomial fitting method to fit the observation data by using the interpolation polynomial to obtain high sampling rate data, specifically, the polynomial fitting principle is: Polynomial fitting method is based on the previous Calculation of each phase value The fitted value is then compared with the observed value, and the difference is taken as the cycle slip. The mathematical model is as follows: ； wherein is the carrier phase observation, is the fitting epoch, is the initial fitting time, the coefficients are calculated using the least squares method , the residuals after fitting the mean error is calculated : ； Finally, the polynomial coefficients are calculated to derive a phase value of a next epoch.

2. The non-continuous epoch cycle slip detection method according to claim 1, wherein The specific method for interrupting counting of epochs of the observation data losing lock to obtain a losing lock time is: S31: calculating a difference value of all adjacent two epochs in the observation data to obtain a plurality of interruption times; S32: integrating the plurality of interruption times to obtain a losing lock time.

3. The non-continuous epoch cycle slip detection method according to claim 2, wherein The data processing software is TEQC data processing software.

4. The non-continuous epoch cycle slip detection method according to claim 3, wherein The number of difference detections in step S7 is 4.

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