Carrier phase cycle slip real-time detection and repair method of airborne GNSS receiver

Abstract

The invention discloses a carrier phase cycle slip real-time detection and repair method of an airborne GNSS receiver, and relates to the technical field of satellite navigation and positioning. The method comprises the following steps: acquiring GNSS signal data; calculating the phase variation of adjacent epochs, and constructing a cycle slip detection model; a multi-dimensional filtering method is adopted to carry out smoothing processing on carrier phase data, and measurement noise is reduced; correcting errors by using a non-difference method and a double-difference method in combination with satellite geometric information; repairing the detected cycle slip data by adopting a prediction method based on Kalman filtering or an autoregression model; and finally, recalculating a baseline solution result, and performing quality control to ensure that the repaired GNSS data meets the high-precision positioning requirement. The method can effectively improve the positioning precision of the airborne GNSS receiver in a high dynamic environment, enhances the integrity and reliability of data, and has important engineering application value.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation and positioning technology, and in particular to a method for real-time detection and repair of carrier phase cycle slips of an airborne GNSS receiver. Background Art

[0002] The Global Navigation Satellite System (GNSS) is widely used in aviation navigation, precision measurement, and scientific research. However, due to factors such as signal obstruction, ionospheric delay variations, and receiver hardware jitter, GNSS carrier phase observations are prone to sudden changes, known as carrier phase cycle slips. Cycle slips can reduce GNSS positioning accuracy and even cause solution failure. Therefore, real-time detection and correction of carrier phase cycle slips are necessary to minimize the impact on positioning accuracy.

[0003] Traditional cycle slip detection methods rely primarily on simple thresholding of phase mutations between single epochs, making them incapable of handling error accumulation in complex environments. Furthermore, traditional carrier phase cycle slip correction methods often rely on high-precision auxiliary data, such as reference stations or multi-frequency observations. This limits their applicability to airborne GNSS receivers, which cannot rely on fixed base stations. Summary of the Invention

[0004] To address these issues, this application proposes a method for real-time carrier phase cycle slip detection and repair in airborne GNSS receivers. This method addresses the technical issues that traditional cycle slip detection methods struggle to cope with error accumulation in complex environments and are unsuitable for airborne GNSS receivers. This method achieves efficient and robust cycle slip detection and repair.

[0005] In a first aspect, an embodiment of the present application provides a method for real-time detection and repair of carrier phase cycle slips of an airborne GNSS receiver, comprising: acquiring GNSS signal data in real time, the GNSS signal data including carrier phase observations; determining an initial phase change between adjacent epochs based on the carrier phase observations; A cycle slip detection model is constructed, and the initial phase change is input as an input parameter to the cycle slip detection model to perform preliminary cycle slip detection; the carrier phase observation value is smoothed, denoised and enhanced, and the error of the carrier phase observation value is corrected to obtain the corrected carrier phase observation value; based on the corrected carrier phase observation value, the corrected phase change is determined; the corrected phase change is input as an input parameter to the cycle slip detection model to obtain initial cycle slip data; the initial cycle slip data includes the satellite where the cycle slip occurs and the epoch where the cycle slip occurs; the initial cycle slip data is repaired to obtain cycle slip data, and the repaired carrier phase observation value is obtained based on the cycle slip data; the baseline solution result is re-determined based on the repaired carrier phase observation value, and the quality control of the repaired carrier phase observation value is performed.

[0006] With reference to the first aspect, in a possible implementation, the phase change includes: Where, represents the phase change between adjacent epochs, Indicates the k Carrier phase observations at the epoch, Indicates the k Carrier phase observation at epoch -1.

[0007] In combination with the first aspect, in a second possible implementation method, the preliminary cycle slip detection includes: setting a cycle slip threshold and calculating the mean and standard deviation of the initial phase change; preliminarily determining the epoch range where the cycle slip occurs based on the initial phase change, mean, standard deviation and cycle slip threshold; obtaining the mutation point based on wavelet transform; and verifying or supplementing the epoch range where the cycle slip occurs through the mutation point.

[0008] In combination with the second possible implementation method of the first aspect, in a third possible implementation method, the verification or supplementation of the epoch range where the cycle slip occurs based on the wavelet transform includes: calculating the local mutation characteristics of the initial phase change amount based on the wavelet transform to obtain the mutation point; based on the mutation point, verifying or supplementing the epoch range where the cycle slip occurs.

[0009] In combination with the first aspect, in a fourth possible implementation method, the smoothing, denoising and enhancement of the carrier phase observation value includes: performing data smoothing, denoising and enhancement processing on the carrier phase observation value based on a multidimensional filtering method, including: predicting the current state of the carrier phase observation value through a conventional Kalman filtering method, and calculating the Kalman gain of the carrier phase observation value; smoothing the carrier phase observation value based on the current state and the Kalman gain; denoising the smoothed carrier phase observation value using a median filtering method; and performing signal enhancement on the denoised carrier phase observation value using a Weiner filter.

[0010] In combination with the first aspect, in a fifth possible implementation, the correction of the error of the carrier phase observation value; obtaining the corrected carrier phase observation value includes: calculating the first i Satellite No. k The carrier phase of the epoch is calculated based on the pseudorange observation equation. i Satellite No. k The pseudorange observation value of the epoch; the error range of the carrier phase is calculated using the error propagation formula; the error range is auxiliary corrected based on the pseudorange observation value to obtain the corrected carrier phase observation value.

[0011] In combination with the first aspect, in a sixth possible implementation, repairing the initial cycle slip data to obtain the cycle slip data includes: repairing the initial cycle slip data based on a regression prediction method, including: using a linear interpolation method to fill missing values in the initial cycle slip data to obtain the filled cycle slip data; establishing a dynamic repair model based on a recursive least squares algorithm, inputting the filled cycle slip data as an input parameter into the dynamic repair model, optimizing the repair result, and obtaining the cycle slip data.

[0012] In combination with the sixth possible implementation method of the first aspect, in a seventh possible implementation method, after obtaining the cycle slip data, it also includes: using the filled cycle slip data and the cycle slip data as training samples, inputting them into the long short-term memory network learning model for training; based on the trained long short-term memory network learning model, predicting and repairing the filled cycle slip data in other epochs to obtain the predicted and repaired cycle slip data.

[0013] In combination with the first aspect, in an eighth possible implementation method, the recalculation of the baseline solution result based on the repaired carrier phase observation value includes: determining the carrier phase of the repaired carrier phase observation value based on the carrier phase observation equation, and determining the pseudorange observation value of the repaired carrier phase observation value based on the pseudorange observation equation; constructing a double-difference observation equation based on the carrier phase and pseudorange observation values, and eliminating the common error terms; the common error terms include the receiver clock error and the satellite clock error; estimating the ambiguity parameters in the double-difference observation equation based on the least squares method; determining the baseline solution result based on the ambiguity parameters and the double-difference observation equation; the baseline solution result includes the baseline vector and the covariance matrix of the baseline vector; wherein the baseline vector represents the relative position relationship between the two airborne GNSS receivers, and the covariance matrix represents the accuracy of the baseline solution result.

[0014] In combination with the eighth possible implementation method of the first aspect, in a ninth possible implementation method, the quality control of the repaired carrier phase observation value includes: obtaining the residual of the pseudorange observation value based on the receiver clock error and pseudorange observation value in the repaired carrier phase observation value; determining the standard deviation based on the residual of the pseudorange observation value; calculating the reliability factor through the residual and standard deviation of the pseudorange observation value; obtaining the final positioning error based on the position solution accuracy; and using the residual, reliability factor and final positioning error to feed back to the repaired carrier phase observation value to control the quality of the repaired carrier phase observation value.

[0015] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: This paper proposes a real-time method for detecting and repairing carrier phase cycle slips in airborne GNSS receivers. This method targets carrier phase cycle slips caused by various factors (such as high-dynamic motion, ionospheric delay, and obstruction) in airborne environments. A complete set of cycle slip detection, data smoothing, error correction, cycle slip repair, and quality control methods are proposed to improve the continuity and accuracy of GNSS measurement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments of the present application or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 A flowchart of a method for real-time detection and repair of carrier phase cycle slips for an airborne GNSS receiver provided in an embodiment of the present application; Figure 2 A carrier phase variation curve diagram provided in an embodiment of the present application; Figure 3 A schematic diagram of cycle slip detection provided in an embodiment of the present application; Figure 4 This is a comparison graph before and after cycle slip repair provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] Figure 1 A flowchart of a method for real-time detection and repair of carrier phase cycle slips of an airborne GNSS receiver provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method provided in the embodiment of the present application includes steps S1 to S7.

[0020] Step S1: Acquire GNSS signal data in real time, wherein the GNSS signal data includes observation data such as carrier phase observation value, pseudorange, satellite azimuth angle, etc.

[0021] Step S2: Based on the carrier phase observation value, calculate the initial phase change between adjacent epochs.

[0022] Step S3: Construct a cycle slip detection model, and input the initial phase change as an input parameter into the cycle slip detection model to perform preliminary cycle slip detection.

[0023] Step S4: Smoothing, denoising and enhancing the carrier phase observation value, and correcting the error of the carrier phase observation value; obtaining the corrected carrier phase observation value, and calculating the corrected phase change based on the corrected carrier phase observation value.

[0024] Step S5: The corrected phase change is used as an input parameter to the cycle slip detection model to obtain initial cycle slip data; the initial cycle slip data includes the satellite where the cycle slip occurs and the epoch where the cycle slip occurs.

[0025] Step S6: Repair the initial cycle slip data to obtain cycle slip data, and obtain a repaired carrier phase observation value based on the cycle slip data.

[0026] Step S7: recalculate the baseline solution result based on the repaired carrier phase observation value, and perform quality control on the repaired carrier phase observation value.

[0027] This paper proposes a real-time detection and repair method for carrier phase cycle slips in airborne GNSS receivers. This method targets carrier phase cycle slips caused by various factors (such as high-dynamic motion, ionospheric delay, and obstruction) in airborne environments. It proposes a complete set of cycle slip detection, data smoothing, error correction, cycle slip repair, and quality control methods to improve the continuity and accuracy of GNSS measurement data.

[0028] Although this application provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on routine or non-creative work. The order of steps listed in this embodiment is only one way of executing the steps among many, and does not represent the only execution order. When an actual device or client product executes, the method shown in this embodiment or the accompanying drawings may be executed sequentially or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0029] It should be noted that in step S1, an airborne GNSS receiver receives GNSS signal data transmitted by satellites and performs frequency conversion, amplification, filtering, and digitization. GNSS signal data includes carrier phase observations, pseudoranges, satellite azimuths, and other observations. Airborne GNSS receivers include single-frequency receivers, multi-frequency receivers, and high-dynamic range receivers.

[0030] The signal receiving process includes signal capture and signal tracking.

[0031] Signal acquisition is used to identify visible satellites and initially lock onto their signals. The principle is as follows: the spread spectrum signal transmitted by the satellite consists of a pseudo-random noise code and a carrier wave. The receiver performs a correlation operation between the locally generated pseudo-random noise code and the received signal, and captures the signal after a successful match.

[0032] Signal tracking is used to continuously lock onto satellite signals to extract pseudorange and carrier phase observations. It primarily relies on a phase-locked loop (PLL) to track changes in carrier phase observations and a delay-locked loop (DLL) to track phase changes in pseudorange observations.

[0033] For example, the initial phase change mentioned in step S2 is calculated as follows: .

[0034] Where, represents the initial phase change between adjacent epochs, Indicates that the same satellite is in k The initial carrier phase observation value of the epoch, Indicates that the same satellite is in k The initial carrier phase observation value for the -1 epoch. The phase change can be used for high-precision positioning. It should be noted that the phase change between adjacent epochs represents the change in the carrier phase observation value between two consecutive time points of the same satellite, and the purpose is to detect cycle slips. In the embodiment of the present application, the initial phase change is only used for preliminary cycle slip detection (to determine the approximate epoch range in which the cycle slip occurs).

[0035] Before preliminary cycle slip detection, a cycle slip detection model needs to be constructed. In the cycle slip detection model, the initial phase change is used as the input parameter of the cycle slip detection model to perform preliminary cycle slip detection.

[0036] Specifically, the initial cycle slip detection process includes: Set the cycle slip threshold and calculate the mean and standard deviation of the initial phase change.

[0037] The calculation method of the mean value of the initial phase change includes: .

[0038] Where, μ represents the mean, N represents the number of epochs in the time window, Indicates the k -1 epoch and the k The initial phase change between epochs, that is, the initial phase change between two adjacent epochs.

[0039] The standard deviation of the initial phase change is calculated as follows: .

[0040] Where, Represents the square of the standard deviation. Represents standard deviation.

[0041] Based on the initial phase change, mean, standard deviation, and cycle slip threshold, the epoch range where the cycle slip occurs is preliminarily determined. Specifically, the following are included: .

[0042] Where, Indicates the cycle slip threshold value set in advance. When the judgment condition of this formula is met, it means that the k A cycle slip occurred in the epoch.

[0043] In the embodiments of the present application, the use of wavelet transform to verify or supplement the epoch range where the cycle slip occurs specifically includes: Based on the wavelet transform, the local mutation characteristics of the initial phase change are calculated to obtain the mutation point. Specifically, it includes: .

[0044] Where, represents the wavelet transform function, represents the wavelet basis function, a Indicates scale, b represents the translation parameter, t Indicates the mutation point of the wavelet coefficient, that is, the time point of the epoch when the cycle slip occurs, Indicates t The carrier phase observation value at time .

[0045] Based on the mutation point, verify or supplement the epoch range where the cycle slip occurs.

[0046] In practical applications, the measurement of carrier phase observations presents two key issues: measurement noise and cycle slips. Measurement noise is high-frequency random noise caused by multipath effects, ionospheric disturbances, and receiver hardware errors. Cycle slips, on the other hand, are sudden changes in the carrier phase cycle, such as those caused by signal obstruction, which disrupt phase continuity and require timely detection and repair. Therefore, in subsequent calculations, multidimensional filtering methods are required to smooth, denoise, and enhance the carrier phase observations. Multidimensional filtering refers to the use of multiple dimensions of information, such as time, frequency, space, and satellite cluster distribution, to construct a filtering model and jointly process the original phase changes.

[0047] Specifically, the carrier phase observations are smoothed, denoised, and enhanced, including: Step 401: Predicting the current state of the carrier phase observation value based on conventional Kalman filtering, including: .

[0048] Where, Indicates the k The current state of the carrier phase observations at the epoch, i.e., the state prediction vector, A represents the state transition matrix, Indicates the k -1 epoch state prediction vector, B represents the control input matrix, represents the control input vector.

[0049] Step 402: Calculate the Kalman gain of the carrier phase observation value to smooth the carrier phase observation value between two adjacent epochs based on the current state and the Kalman gain, remove high-frequency noise, and retain the true trend of phase change. This includes: .

[0050] Where, Indicates the k The Kalman gain matrix for the epoch, H represents the observation matrix, T represents transpose, R represents the covariance of the observation noise, Indicates the k -1 epoch to k the covariance matrix of the predicted state at the epoch, , represents the process noise covariance, Indicates the k The covariance matrix of the predicted state for the -1 epoch.

[0051] Step 403: De-noise the smoothed carrier phase observations using a median filter. The median filter effectively suppresses noise interference by sorting the values in the time series data window and replacing the center value with the median value.

[0052] Step 404: Enhance the denoised carrier phase observations using Weiner filtering. Weiner filtering is based on the statistical characteristics of the signal and noise and minimizes the mean square error by optimizing the filter coefficients, thereby improving signal quality. This includes: .

[0053] Where, represents the carrier phase observation value after Weiner filtering, represents the power spectral density of the carrier phase observation, represents the power spectral density of the noise, represents the observed signal, , represents the frequency domain of the carrier phase observations, Represents the frequency domain of the noise.

[0054] In the embodiment of the present application, step S4 records the correction of the error of the carrier phase observation value; and obtains the corrected carrier phase observation value. Specifically, the error is corrected by the non-difference and double-difference methods. Among them, the non-difference method directly uses the original carrier phase observation value of a single satellite by a single receiver without performing differential processing. The double-difference method is to perform two differences on the carrier phase of the same group of satellites observed by two receivers (reference station and rover) to eliminate the single difference between the reference stations, the satellite clock error, the single difference between the satellites, and the receiver clock error. Specifically, it includes: Step 405: Calculate the first i Satellite No. k The carrier phase observation value of the epoch. The carrier phase observation equation includes: .

[0055] Where, Indicates the i Satellite No. k Carrier phase observations for the epoch. Indicates the i Satellite No. k The geometric distance of the epoch, represents the carrier wavelength, Indicates the i Satellite No. k The ambiguity parameters of the epoch, Indicates the i Satellite No. k The atmospheric delay of the epoch, Indicates the i Satellite No. k Observation error of the epoch.

[0056] Step 406: Calculate the first i Satellite No. k The pseudorange observation value of the epoch. The pseudorange observation equation includes: .

[0057] Where, Indicates the i Satellite No. k The pseudorange observation value of the epoch, c represents the speed of light, Indicates that the receiver is i The receiver clock error when receiving signals from a satellite.

[0058] Step 407: Calculate the error range of the carrier phase using the error propagation formula. Specifically including: .

[0059] Where, represents the standard deviation of the carrier phase error, D represents the design matrix, represents the error covariance matrix, T Indicates transpose.

[0060] Step 408: Establish a joint observation model and use pseudorange observations to assist in correcting the error range to obtain corrected carrier phase observations. This includes: .

[0061] Where, Represents the auxiliary correction result, where Indicates the correction change of the carrier phase, Indicates the correction change of the pseudorange observation value. represents the design matrix of the carrier phase, represents the design matrix of pseudorange observations, x represents the state vector, represents the observation noise matrix, where v represents the weight parameter, Indicates the i Satellite No. k Weight parameter for pseudorange observations at an epoch.

[0062] Based on the auxiliary correction result, the error range of the carrier phase observation value is corrected to obtain the corrected carrier phase observation value.

[0063] After obtaining the corrected carrier phase observation value, the corrected phase variation is calculated by the method described in step S2.

[0064] After obtaining the corrected phase variation, the corrected phase variation is used as the input parameter of the cycle slip detection model to obtain the initial cycle slip data, wherein the initial cycle slip data includes the satellite where the cycle slip occurs. i and the epoch where the cycle slip occurs k .

[0065] The initial cycle slip data is repaired to obtain cycle slip data, and a repaired carrier phase observation value is obtained based on the cycle slip data.

[0066] Specifically, the process of repairing the initial cycle slip data includes a prediction method based on a Kalman filter or an autoregressive model to repair the initial cycle slip data. The specific process is as follows: Linear interpolation is used to fill the missing values in the initial cycle slip data to obtain filled cycle slip data to restore the continuity of the initial cycle slip data. This includes: .

[0067] Where, Indicates in k Carrier phase observations at the epoch, Indicates in k -1 epoch carrier phase observations, Indicates in k Carrier phase observation at epoch +1.

[0068] A dynamic repair model is established through a recursive least squares algorithm, and the filled cycle slip data is input into the dynamic repair model as an input parameter to optimize the repair result for the initial cycle slip data and obtain the cycle slip data.

[0069] .

[0070] Where, Indicates in k The state estimate vector for the epoch, Indicates in k -1 epoch state estimate vector, represents the Kalman gain matrix, represents the observation vector, Indicates in k -1 epoch observation matrix.

[0071] The dynamic repair model gradually optimizes the repair results of the initial cycle slip data through iterative processing, thereby improving the data accuracy of the cycle slip data.

[0072] After obtaining the repaired cycle slip data, the method also includes selecting a long short-term memory network learning model (LSTM model), and using the filled cycle slip data and the cycle slip data as training samples, and inputting them into the long short-term memory network learning model for training.

[0073] Based on the trained long short-term memory network learning model, the filled cycle slip data in other epochs are predicted and repaired to obtain the predicted and repaired cycle slip data. Specifically, it includes: .

[0074] Where, y It represents the cycle slip data after prediction and repair. f is the prediction function of the machine learning model, x is the input feature vector (i.e., state vector).

[0075] At the same time, the recalculation of the baseline solution result based on the repaired carrier phase observation value recorded in step S7 of the embodiment of the present application specifically includes: Step 701: Calculate the carrier phase of the repaired carrier phase observation value based on the carrier phase observation equation, and calculate the pseudorange observation value of the repaired carrier phase observation value based on the pseudorange observation equation.

[0076] Step 702: Based on the carrier phase and pseudorange observation values, a double-difference observation equation is constructed, the carrier phase and pseudorange observation values are double-differenced, and the common error terms are eliminated; the common error terms include the receiver clock error and the satellite clock error.

[0077] Step 703: Estimate the ambiguity parameters in the double-difference observation equation based on the least squares method or other estimation methods.

[0078] Step 704: Calculate the baseline solution result based on the ambiguity parameters and the double-difference observation equation. The baseline solution result includes a baseline vector and a covariance matrix of the baseline vector. The baseline vector represents the relative position relationship between the two airborne GNSS receivers, and the covariance matrix represents the accuracy of the baseline solution result.

[0079] In the embodiment of the present application, step S7 records the quality control of the repaired carrier phase observation value, including: Step 705: Based on the receiver clock error in the carrier phase and the pseudorange observation value, obtain the residual of the pseudorange observation value; calculate the standard deviation based on the residual. The residual calculation process includes: .

[0080] Where, R is the residual, P is the pseudorange observation value, c is the speed of light, is the receiver clock error, when the residual R The smaller the value, the higher the data quality.

[0081] Step 706: Calculate the reliability factor using the residual and standard deviation, including: .

[0082] Where, F is the reliability factor, r n For the n The residual of the observation, is the standard deviation of the residuals. The lower the value of the reliability factor, the more reliable the data point.

[0083] Step 707: Obtain the final positioning error based on the position solution accuracy.

[0084] .

[0085] Where, e represents the final positioning error, x Indicates the horizontal axis coordinate obtained by the solution, y Indicates the solved vertical axis coordinate, z Indicates the vertical axis coordinate obtained by the solution, x true represents the real horizontal axis coordinate, y true represents the true vertical axis coordinate, z true Indicates the true vertical axis coordinate.

[0086] Step 707 is used to verify whether the progress of the positioning result meets the requirements.

[0087] Step 708: Perform quality control on the repaired carrier phase observation value using the residual, reliability factor and final positioning error.

[0088] Figures 2 to 4 This is a comparison diagram of the curves provided in the embodiments of the present application. Figure 2 The graph is a curve diagram of the carrier phase observation value, which shows the curve comparison of the carrier phase observation value under normal conditions and when a cycle slip occurs. Figure 2In the diagram, Carrier Phase Change Curve (Normal vs Cycle Slip) is represented by the curve of carrier phase observation value (normal and cycle slip), and the horizontal axis Time (Epoch Index) is represented by time (epoch). The vertical axis Carrier Phase Change is represented by carrier phase. Normal Phase Change is the normal phase change in Chinese, Phase Change with Cycle Slips is the phase change when a cycle slip occurs in Chinese, and Cycle Slip is the cycle slip in Chinese.

[0089] Figure 3 This is a schematic diagram of cycle slip detection, which shows the detection effect under different thresholds. Figure 3 In the figure, Cycle Slip Detection under Different Thresholds is represented by cycle slip detection under different thresholds. The horizontal axis Time (Epoch Index) is represented by time, i.e. epoch. The vertical axis Carrier Phase Change is represented by the phase change of the carrier. Carrier Phase Change (with Cycle Slips) is the phase change of the carrier at which a cycle slip occurs. Low Threshold (High False Alarm) is the low threshold value, which has a high alarm sensitivity. Optional Threshold (Best Detection) is the threshold value of the optimal detection point. High Threshold (High Miss Rate) is the high threshold value, which has a high miss detection rate. Cycle SlipPoint indicates a cycle slip.

[0090] Figure 4 This is a comparison curve before and after cycle slip repair, which is mainly used to demonstrate the smoothing effect of Kalman filtering. Figure 4 In the figure, Cycle Slip Correction: Before and After Kalman Filtering is represented by cycle slip correction before and after Kalman filtering. The horizontal axis Time (Epoch Index) is represented by time, i.e. epoch. The vertical axis Carrier Phase Change is represented by the phase change of the carrier. Before Correction (With Cycle Slips) is the cycle slip effect before Kalman filtering. After Correction (Kalman Filtering) is the cycle slip effect after Kalman filtering. Cycle Slip is the cycle slip in Chinese.

[0091] Through the description of the above implementation methods, it can be seen that those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, or can be embodied through the implementation process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or certain parts of the embodiments.

[0092] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. All or part of this application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, mobile communication terminals, multi-processor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0093] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A method for real-time detection and repair of carrier phase cycle slips in an airborne GNSS receiver, characterized in that: include: Acquiring GNSS signal data in real time, wherein the GNSS signal data includes carrier phase observations; Based on the carrier phase observations, the initial phase change between adjacent epochs is determined; Construct a cycle slip detection model, and input the initial phase change as an input parameter into the cycle slip detection model to perform preliminary cycle slip detection; Smoothing, denoising and enhancing the carrier phase observation value, and correcting the error of the carrier phase observation value to obtain a corrected carrier phase observation value; determining a corrected phase change based on the corrected carrier phase observation value; The corrected phase change is used as an input parameter to the cycle slip detection model to obtain initial cycle slip data; The initial cycle slip data includes the satellite where the cycle slip occurs and the epoch where the cycle slip occurs; Repairing the initial cycle slip data to obtain cycle slip data, and obtaining a repaired carrier phase observation value based on the cycle slip data; The baseline solution result is re-determined based on the repaired carrier phase observation value, and the quality control of the repaired carrier phase observation value is performed.

2. The method according to claim 1, characterized in that Determining the initial phase change between adjacent epochs includes: ； Where, represents the initial phase change between adjacent epochs, Indicates the k Carrier phase observations at the epoch, Indicates the k Carrier phase observation value at epoch -1.

3. The method according to claim 1, characterized in that The preliminary cycle slip detection includes: Set the cycle slip threshold and calculate the mean and standard deviation of the initial phase change; Based on the initial phase change, mean, standard deviation and cycle slip threshold, the epoch range where the cycle slip occurs is preliminarily determined; The mutation point is obtained based on wavelet transform; through the mutation point, the epoch range where the cycle slip occurs is verified or supplemented.

4. The method according to claim 3, characterized in that The verification or supplementation of the epoch range where the cycle slip occurs based on the wavelet transform includes: Based on wavelet transform, the local mutation characteristics of the initial phase change are calculated to obtain the mutation point; Based on the mutation point, verify or supplement the epoch range where the cycle slip occurs.

5. The method according to claim 1, wherein The carrier phase observation value is smoothed, denoised and enhanced, including: The carrier phase observations are smoothed, denoised, and enhanced based on a multidimensional filtering method, including: The current state of the carrier phase observation value is predicted by the conventional Kalman filter method, and the Kalman gain of the carrier phase observation value is calculated; Smoothing the carrier phase observations based on the current state and Kalman gain; The median filter method is used to denoise the smoothed carrier phase observations; The Weiner filter is used to enhance the signal of the denoised carrier phase observations.

6. The method according to claim 1, characterized in that The method of correcting the error of the carrier phase observation value to obtain the corrected carrier phase observation value includes: Based on the carrier phase observation equation, the i Satellite No. k The carrier phase of the epoch; Based on the pseudo-range observation equation, the i Satellite No. k Pseudorange observations of the epoch; Calculate the error range of carrier phase using the error propagation formula; The error range is corrected based on the pseudorange observation value to obtain the corrected carrier phase observation value.

7. The method according to claim 1, characterized in that The repairing of the initial cycle slip data to obtain the cycle slip data includes: Based on the regression prediction method, the initial cycle slip data is repaired, including: Linear interpolation is used to fill the missing values in the initial cycle slip data to obtain filled cycle slip data; A dynamic repair model is established based on the recursive least squares algorithm. The cycle slip filling data is input into the dynamic repair model as an input parameter to optimize the repair results and obtain the cycle slip data.

8. The method according to claim 7, characterized in that After obtaining the cycle slip data, it also includes: The filled cycle slip data and the cycle slip data are used as training samples and input into the long short-term memory network learning model for training; Based on the trained long short-term memory network learning model, the filled cycle slip data in other epochs are predicted and repaired to obtain the predicted and repaired cycle slip data.

9. The method according to claim 1, characterized in that The recalculating the baseline solution result based on the repaired carrier phase observation value includes: Determine the carrier phase of the repaired carrier phase observation value based on the carrier phase observation equation, and determine the pseudorange observation value of the repaired carrier phase observation value based on the pseudorange observation equation; Based on the carrier phase and pseudorange observations, a double-difference observation equation is constructed and the common error terms are eliminated; the common error terms include the receiver clock error and the satellite clock error; Estimate the ambiguity parameters in the double-difference observation equation based on the least squares method; The baseline solution result is determined based on the ambiguity parameters and the double-difference observation equation. The baseline solution result includes the baseline vector and the covariance matrix of the baseline vector. The baseline vector represents the relative position relationship between the two airborne GNSS receivers, and the covariance matrix represents the accuracy of the baseline solution result.

10. The method according to claim 9, characterized in that The quality control of the repaired carrier phase observation value includes: Based on the receiver clock error and pseudorange observation value in the repaired carrier phase observation value, the residual of the pseudorange observation value is obtained; Determine the standard deviation based on the residuals of the pseudorange observations; The reliability factor is calculated by the residual and standard deviation of the pseudorange observations; The final positioning error is obtained based on the position solution accuracy; The residual, reliability factor and final positioning error are fed back to the repaired carrier phase observation value to control the quality of the repaired carrier phase observation value.

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