A satellite DCB filter estimation method and system based on elastic power detection

Abstract

The present invention discloses a satellite DCB filter estimation method and system based on elastic power detection, belonging to the field of global satellite navigation technology. The method comprises: obtaining satellite signal carrier-to-noise ratio data; calculating GF combination observation values ​​based on the satellite observation data, and constructing a normal equation based on the GF combination observation values; calculating a satellite power detection index based on the satellite signal carrier-to-noise ratio data; adjusting the constraints on the satellite DCB parameters to be estimated in a parameter filter based on the satellite power detection index and a preset threshold to obtain process noise; predicting the initial state of the GNSS parameter estimation system at the current moment based on the process noise, the state of the GNSS parameter estimation system obtained at the previous moment, and the error covariance matrix; correcting the initial state of the GNSS parameter estimation system at the current moment to obtain the final state of the GNSS parameter estimation system at the current moment; and obtaining a satellite DCB filter estimation result using the GNSS parameter estimation system.

Description

Technical Field

[0001] The present invention relates to the technical field of global satellite navigation, and in particular to a satellite DCB filter estimation method and system based on elastic power detection. Background Art

[0002] The development of the Global Navigation Satellite System (GNSS) has resulted in a wealth of observational data that facilitates precise data processing, but this also presents the challenge of addressing signal bias. When GNSS receivers and satellites transmit and receive signals, signal delays occur during the matching and tracking process, which is the source of hardware delay bias. Differential code bias, the difference between the two signal delays, can result in errors of several meters. Therefore, its impact must be considered when processing precise GNSS data.

[0003] Generally speaking, differential code bias (DCB) is satellite-specific and receiver-specific, and remains stable over a period of time (e.g., a day). Based on these assumptions, some International GNSS Service (IGS) analysis centers provide single-day solutions for multi-frequency, multi-system DCBs, with daily average repeatability of 0.05-0.3ns. However, in reality, satellite replacements and flexible satellite power adjustments can affect DCB estimates, and these factors can occur at any time of the day. Current single-day solutions ignore these factors.

[0004] With the continuous development and upgrade of GNSS, many satellites have been equipped with programmable power output, also known as elastic power. This capability allows for convenient adjustment of satellite signal power, adjusting the boost amplitude and enhancing service coverage. Enabling or disabling elastic power significantly impacts GNSS signal strength and pseudorange measurements, and consequently, all GNSS applications using pseudoranges are affected to varying degrees. The average impact of elastic power on GPS intra-frequency differential code deviation is approximately 0.4 nanoseconds, and short-term deviation variations of BDS2 and BDS3 can reach tens of nanoseconds. Recently, amidst global tensions, GPS satellites have frequently used elastic power to enhance signal strength in localized areas, and its impact on differential code deviation has become increasingly frequent.

[0005] Therefore, there is an urgent need for a satellite DCB estimation method and system that can reflect the changes in elastic power in real time, detect the satellite elastic power adjustment process in time, and make corresponding adjustments to the DCB estimation process to avoid the shortcomings of the single-day DCB solution. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a satellite DCB filter estimation method and system based on elastic power detection, which can timely detect the process of satellite elastic power adjustment and make corresponding adjustments to the DCB estimation process to avoid the shortcomings of the single-day DCB solution scheme.

[0007] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0008] In one aspect, the present invention provides a satellite DCB filter estimation method based on elastic power detection, comprising:

[0009] Obtain satellite observation data and satellite signal carrier-to-noise ratio data;

[0010] Calculating GF combination observation values ​​according to the satellite observation data, and constructing a normal equation according to the GF combination observation values;

[0011] Calculating a satellite power detection index based on the satellite signal carrier-to-noise ratio data, and adjusting constraints on satellite DCB parameters to be estimated in a parameter filter based on the satellite power detection index and a preset threshold to obtain process noise;

[0012] The initial state of the GNSS parameter estimation system at the current moment is predicted based on the process noise, the state of the GNSS parameter estimation system at the previous moment, and the error covariance matrix. The initial state of the GNSS parameter estimation system at the current moment is corrected based on the satellite observation data at the current moment to obtain the final state of the GNSS parameter estimation system at the current moment.

[0013] A satellite DCB filter estimation result is obtained using a GNSS parameter estimation system; wherein the GNSS parameter estimation system includes a final state and a normal equation of the GNSS parameter estimation system at a current moment.

[0014] Optionally, calculating the GF combined observation value based on the satellite observation data includes:

[0015] ;

[0016] in, represents the GF combination observation value; 、 They represent the pseudorange observation values ​​of the i-th signal and the j-th signal from the observation station r to the satellite s respectively; 、 Represent the frequencies of the i-th signal and the j-th signal respectively; represents the total electron content in the oblique direction between the observation station r and the satellite s; represents the speed of radio waves in a vacuum; 、 They represent the differential code bias of observation station r and satellite s respectively; Satellite receiver unit The difference in distortion deviation between two correlated signals.

[0017] Optionally, before constructing the normal equation based on the GF combined observations, the following is also included:

[0018] According to whether the components of the GF combination observation value are at the same frequency, it is judged whether the GF combination observation value needs to be corrected for ionospheric delay;

[0019] If the components of the GF combination observations are at the same frequency, no ionospheric delay correction is required;

[0020] If the components of the GF combination observations are not at the same frequency, the ionospheric delay correction is performed on the GF combination observations using the global ionosphere map and the ionospheric projection function.

[0021] Optionally, construct a normal equation based on the GF combined observations, including:

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] ;

[0027] ;

[0028] in, Indicates the DCB parameters to be estimated for the satellite; and represent the differential code bias vector of the observation station and the differential code bias vector of the satellite respectively; represents the n×n identity matrix; represents an n×1 unit vector; represents the m×m identity matrix; represents the m×1 identity matrix; represents the Kronecker operator; represents the total number of observation stations; represents the total number of satellites; T represents the transpose of the matrix; represents the differential code deviation of the nth observation station; represents the differential code bias of the mth satellite; Indicates the DCB parameters to be estimated for the mth satellite.

[0029] Optionally, calculating a satellite power detection index based on the satellite signal carrier-to-noise ratio data includes:

[0030] ;

[0031] in, Indicates the satellite power adjustment monitoring index; represents the total number of observation stations; 、 They represent the signal carrier-to-noise ratio of the satellite s received by the observation station r at the current moment and the moment before the current moment, respectively.

[0032] Optionally, according to the satellite power detection index and a preset threshold, adjusting the constraints on the satellite DCB parameters to be estimated in the parameter filter to obtain process noise includes:

[0033] If the satellite power detection index is greater than a preset threshold, the satellite power is adjusted, and the constraints on the satellite DCB parameters to be estimated in the parameter filter are relaxed to obtain process noise until the preset time range is reached; otherwise, the satellite power is not adjusted and the process noise remains unchanged; wherein the process noise is the constraints on the satellite DCB parameters to be estimated in the parameter filter.

[0034] Optionally, the acquisition of the GNSS parameter estimation system includes:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] in, represents the non-singular prior covariance matrix; Represents the state parameters of the GNSS parameter estimation system; represents the prior observation vector; represents the observation matrix; represents the observation vector.

[0040] Optionally, based on the process noise, the state of the GNSS parameter estimation system at the previous moment, and the error covariance matrix, an initial state of the GNSS parameter estimation system at the current moment is predicted, and based on the satellite observation data at the current moment, the state of the GNSS parameter estimation system at the current moment is corrected to obtain the final state of the GNSS parameter estimation system at the current moment, including:

[0041] Perform MP simulation based on process noise and obtain the dynamic residual equation:

[0042] ;

[0043] in, 、 They represent the dynamic parameters of the j+1th epoch and the dynamic parameters of the jth epoch respectively; represents the state transition matrix; represents the j-th epoch process noise, which satisfies:

[0044] ;

[0045] ;

[0046] Where, E( ) represents the mean; D( ) represents the variance; represents the k-th epoch process noise; represents a symmetric positive definite matrix; represents the upper triangular matrix after QR decomposition; represents the Kronecker operator;

[0047] use Normalize the process noise variance:

[0048] ;

[0049] The initial state of the GNSS parameter estimation system at the current moment can be obtained by using the normalized process noise and prior information:

[0050] ;

[0051] in, 、 、 、 、 They represent the submatrices of the upper triangular matrix after QR decomposition; Indicates the initial state of the GNSS parameter estimation system at the current moment; represents the j-th epoch observation vector after time update; represents the j+1th epoch prior observation vector after time update;

[0052] The initial state of the GNSS parameter estimation system at the current moment is calculated based on the satellite observation data at the current moment. Perform correction to obtain the final state of the GNSS parameter estimation system at the current moment .

[0053] Optionally, a GNSS parameter estimation system is used to obtain satellite DCB filter estimation results, including:

[0054] Perform QR decomposition on the GNSS parameter estimation system to obtain the satellite DCB filter estimation result, which is expressed as: ;

[0055] ;

[0056] Wherein, QR represents QR decomposition; represents the prior covariance matrix of the j-th epoch; represents the j-th epoch observation matrix; Represents the DCB filter estimation result of the j-th epoch satellite, and estimates the final state of the system at the current moment through GNSS parameters and the law equation is obtained; represents the j-th epoch prior observation vector; represents the j-th epoch observation vector; represents the upper triangular covariance matrix of the jth epoch after QR decomposition; represents the j-th epoch observation vector after QR decomposition; Represents the residual vector after solution.

[0057] On the other hand, the present invention provides a satellite DCB filter estimation system based on elastic power detection, comprising:

[0058] Data acquisition module, which acquires satellite observation data and satellite signal carrier-to-noise ratio data;

[0059] a normal equation construction module, calculating GF combination observation values ​​according to the satellite observation data, and constructing a normal equation according to the GF combination observation values;

[0060] a parameter constraint adjustment module for calculating a satellite power detection index based on the satellite signal carrier-to-noise ratio data, and adjusting the constraint on the satellite DCB parameter to be estimated in the parameter filter based on the satellite power detection index and a preset threshold to obtain process noise;

[0061] The time update module predicts the initial state of the GNSS parameter estimation system at the current moment based on the process noise, the state of the GNSS parameter estimation system at the previous moment, and the error covariance matrix;

[0062] The measurement update module corrects the initial state of the GNSS parameter estimation system at the current moment according to the satellite observation data at the current moment to obtain the final state of the GNSS parameter estimation system at the current moment;

[0063] The parameter solving module obtains the satellite DCB filter estimation result by using the GNSS parameter estimation system; wherein the GNSS parameter estimation system includes the final state and normal equation of the GNSS parameter estimation system at the current moment.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] The present invention adopts a filtering method rather than a least squares method to solve the satellite DCB. Satellite power adjustment detection is introduced in the satellite DCB solution process, and corresponding information is provided to adjust the parameter estimation process. The DCB estimation process can be adjusted accordingly in a timely manner during the satellite elastic power adjustment process, and a differential code deviation product with higher time resolution that is not affected by the elastic power is obtained, thereby avoiding the shortcomings of the single-day DCB solution solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 FIG2 is a flow chart of a satellite DCB filter estimation method based on elastic power detection according to an embodiment of the present invention;

[0067] Figure 2 FIG2 is a flow chart of another embodiment of a satellite DCB filter estimation method based on elastic power detection according to the present invention;

[0068] Figure 3 FIG. 1 is a time series diagram of a differential code deviation product in one embodiment of the prior art;

[0069] Figure 4 FIG. 1 shows a time series diagram of differential code deviation products in one embodiment of the present invention. DETAILED DESCRIPTION

[0070] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0071] The term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " generally indicates an "or" relationship between the related objects.

[0072] Example 1

[0073] like Figure 1As shown, this embodiment introduces a satellite DCB filtering estimation method based on elastic power detection, introduces satellite power detection into the GNSS parameter estimation system, and adopts a filtering method instead of a least squares method to obtain a differential code deviation product with higher time resolution that is not affected by elastic power, thereby avoiding the shortcomings of a single-day DCB solution.

[0074] The method specifically comprises the following steps:

[0075] Step 1: Obtain satellite observation data and satellite signal carrier-to-noise ratio data.

[0076] Step 2: Elastic power detection and noise parameter adjustment, specifically:

[0077] Using the observation data of the globally distributed GNSS reference stations, we construct the geometry-free (GF) combined observation values ​​and construct the normal equation, such as Figure 2 As shown, the GF combined observation value is calculated based on the satellite observation data, and the GF combined observation value Expressed as:

[0078] (1);

[0079] in, 、 They represent the pseudorange observation values ​​of the i-th signal and the j-th signal from the observation station r to the satellite s respectively; 、 Represent the frequencies of the i-th signal and the j-th signal respectively; represents the slant total electron content (STEC) between the observation station r and the satellite s; represents the speed of radio waves in a vacuum; 、 They represent the differential code bias of observation station r and satellite s respectively; Satellite receiver unit The difference in the distortion bias (SDB) of two related signals;

[0080] in, , , , 、 They represent the pseudorange hardware delay of the i-th signal and the j-th signal at the observation station respectively; 、 They represent the pseudorange hardware delay of the i-th signal and the j-th signal at the satellite end respectively; 、 Respectively represent the satellite receiver group The distortion deviation of the related i-th signal and j-th signal.

[0081] According to whether the components of the GF combination observation value are at the same frequency, it is judged whether the GF combination observation value needs to be corrected for ionospheric delay;

[0082] If the components of the GF combination observations are at the same frequency, that is, , then there is no need to perform ionospheric delay correction, and the first term of formula (1) can be completely eliminated;

[0083] If the components of the GF combination observations are not at the same frequency, that is, , the Global Ionospheric Map (GIM) and the ionospheric projection function are used to perform ionospheric delay correction on the GF combined observations, namely:

[0084] (2);

[0085] in, Indicates that only the differential code bias of the observation station and satellite is included.

[0086] Repeat until all observation data from all observation stations are combined. The normal equation is constructed based on the GF combined observation values. Assuming there are n observation stations and m satellites are observed, the normal equation is expressed as:

[0087] (3);

[0088] (4);

[0089] (5);

[0090] in, Indicates the DCB parameters to be estimated for the satellite; represents the n×n identity matrix; represents an n×1 unit vector; represents the m×m identity matrix; represents the m×1 identity matrix; represents the Kronecker operator; T represents the transpose of the matrix; 、 denote the differential code bias vector of the observation station and the differential code bias vector of the satellite, respectively. , , , represents the differential code deviation of the nth observation station, represents the differential code bias of the mth satellite, Indicates the DCB parameters to be estimated for the mth satellite.

[0091] Since the observation station DCB and satellite DCB are linearly related, the matrix is ​​deficient and a constraint condition as shown in formula (5) needs to be added to separate the two.

[0092] The satellite signal carrier-to-noise ratio data obtained from the globally distributed GNSS base stations are used to perform satellite elastic power adjustment detection, and the process noise in the parameter filter is adjusted accordingly, such as Figure 2 As shown, the satellite power detection index is calculated based on the satellite signal carrier-to-noise ratio data to confirm the time and type of satellite power adjustment. Assuming there are n observation stations, the satellite power detection index Expressed as:

[0093] (6);

[0094] in, 、 They represent the signal carrier-to-noise ratio of the satellite s received by the observation station r at the current moment and the moment before the current moment, respectively.

[0095] If the satellite power detection index is greater than a preset threshold, the satellite power is adjusted, and the constraints on the satellite DCB parameters to be estimated in the parameter filter are relaxed to obtain process noise until the preset time range is reached; otherwise, the satellite power is not adjusted and the process noise remains unchanged; wherein the process noise is the constraints on the satellite DCB parameters to be estimated in the parameter filter.

[0096] Step 3: Filter and estimate satellite DCB parameters, specifically:

[0097] like Figure 2 As shown in the figure, filter solution includes time update and measurement update. Time update and measurement update are the two core steps of filtering theory. Time update predicts the state of the parameters at the current moment by combining the state of the parameters at the previous moment, and measurement update uses the current measurement value to correct the state at the current moment.

[0098] The GNSS parameter estimation system is expressed as:

[0099] (7);

[0100] Among them, the first formula represents the initial state information, and the second formula represents the standardized residual observation equation. represents the non-singular prior covariance matrix, including time-invariant parameters; Represents the state parameters of the GNSS parameter estimation system; represents the prior observation vector; represents the observation matrix; represents the observation vector.

[0101] in, (8);

[0102] (9);

[0103] (10).

[0104] Time update: Using process noise, the satellite DCB parameters to be estimated are treated as p-type parameters in the time domain. A first-order Markov process (MP) simulation is performed to obtain the dynamic residual equation. The satellite DCB parameters to be estimated are the parameters to be estimated in the parametric filter. The dynamic residual equation is expressed as:

[0105] (11);

[0106] in, 、 They represent the dynamic parameters of the j+1th epoch and the dynamic parameters of the jth epoch respectively; represents the state transition matrix; represents the j-th epoch process noise, which satisfies:

[0107] (12);

[0108] (13);

[0109] Where, E( ) represents the mean; D( ) represents the variance; represents the k-th epoch process noise; represents a symmetric positive definite matrix; represents the upper triangular matrix after QR decomposition; represents the Kronecker operator; the superscript -1 represents inversion, and the superscript T represents matrix transposition;

[0110] use Normalize the process noise variance:

[0111] (14);

[0112] Using the normalized process noise and prior information, we can obtain the initial state and error covariance matrix of the GNSS parameter estimation system at the current moment:

[0113] (15);

[0114] in, represents the dynamic parameter information matrix of the jth epoch; represents the time-invariant parameter information matrix of the j-th epoch; represents the j-th epoch observation vector after QR decomposition; Indicates the initial state of the GNSS parameter estimation system at the current moment;

[0115] QR decomposition can uniquely decompose a full-rank matrix into an orthogonal matrix and an upper triangular matrix. Performing QR decomposition on the left-hand matrix in Equation (15) yields Equation (16):

[0116] (16);

[0117] in, represents the orthogonal matrix obtained by QR decomposition; 、 、 、 、 They represent the submatrices of the upper triangular matrix after QR decomposition, where Contains epoch dynamic parameter information, 、 Contains epoch dynamic parameter information, 、 Contains time-invariant parameter information;

[0118] Using the properties of orthogonal matrices , that is, the inverse matrix of the orthogonal matrix is ​​its transposed matrix, then multiply the left and right sides of formula (16) by Then we can get the following formula (17):

[0119] (17);

[0120] in, represents the j-th epoch observation vector after time update; Represents the j+1th epoch prior observation vector after time update.

[0121] Measurement update: Estimate the initial state of the GNSS system at the current moment based on the satellite observation data at the current moment Perform correction to obtain the final state of the GNSS parameter estimation system at the current moment .

[0122] Parameter adjustment: Use the GNSS parameter estimation system to obtain the satellite DCB filter estimation results, specifically:

[0123] The GNSS parameter estimation system includes a final state of the GNSS parameter estimation system at a current moment and a normal equation constructed based on GF combined observation values;

[0124] Measurement update Performing QR decomposition on the above equation (7), i.e. the GNSS parameter estimation system, yields:

[0125] (18);

[0126] Then you can use Calculate unknown parameters, is the residual vector after solution; QR represents QR decomposition; represents the prior covariance matrix of the j-th epoch; represents the j-th epoch observation matrix; Represents the j-th epoch satellite DCB parameter vector to be estimated, that is, the j-th epoch satellite DCB filter estimation result, and estimates the final state of the system at the current moment through GNSS parameter estimation and the law equation is obtained; represents the j-th epoch prior observation vector; represents the j-th epoch observation vector; Represents the upper triangular covariance matrix of the j-th epoch after QR decomposition.

[0127] By repeatedly iterating time updates and measurement updates, the GNSS parameter estimation system is subjected to QR decomposition to obtain the satellite DCB filter estimation result, thus achieving filtering.

[0128] This embodiment detects the change of satellite elastic power in real time, adjusts the process noise of DCB parameter estimation in real time, and solves the differential code deviation product with higher time resolution that is not affected by elastic power, so as to avoid the shortcomings of the single-day DCB solution.

[0129] Example 2

[0130] Based on Example 1, this example introduces an experimental example of a satellite DCB filter estimation method based on elastic power detection, which specifically includes the following steps:

[0131] like Figure 3 As shown in the figure, the intra-frequency DCB value for a single-day solution varies with the duration of flexible power mode activation. The longer the activation time, the larger the differential code deviation. This does not reflect the actual DCB value at a specific time of day, and it is not advisable to use the DCB of one day to replace the DCB of another day for GNSS data processing.

[0132] like Figure 3 As shown, the blue and orange lines represent the products of CAS and DLR respectively; the black line represents the percentage of time in a day when elastic power is enabled; Figure 3From top left to bottom right, the time series of G01-C1C-C1W, G06-C1C-C2W, G10-C2W-C2X, and G25-C2W-C2L of CAS and DLR in 2023 are represented.

[0133] Figure 4 This figure shows the C1C-C1W differential code bias time series from April 24 to April 29, 2023, using the method of this embodiment. During this period, elastic power was enabled throughout the day. After elastic power was enabled, the satellite's differential code bias remained stable for several days until elastic power was disabled a few days later. This effectively and promptly reflects the impact of satellite power adjustments on satellite DCB estimation.

[0134] Example 3

[0135] Based on the same inventive concept as Example 1, this embodiment introduces a satellite DCB filter estimation system based on elastic power detection, including:

[0136] Data acquisition module, which acquires satellite observation data and satellite signal carrier-to-noise ratio data;

[0137] a normal equation construction module, calculating GF combination observation values ​​according to the satellite observation data, and constructing a normal equation according to the GF combination observation values;

[0138] a parameter constraint adjustment module for calculating a satellite power detection index based on the satellite signal carrier-to-noise ratio data, and adjusting the constraints of the satellite DCB parameter to be estimated in the parameter filter based on the satellite power detection index and a preset threshold to obtain process noise;

[0139] The time update module predicts the initial state of the GNSS parameter estimation system at the current moment based on the process noise, the state of the GNSS parameter estimation system at the previous moment, and the error covariance matrix;

[0140] The measurement update module corrects the initial state of the GNSS parameter estimation system at the current moment according to the satellite observation data at the current moment to obtain the final state of the GNSS parameter estimation system at the current moment;

[0141] The parameter solving module obtains the satellite DCB filter estimation result by using the GNSS parameter estimation system; wherein the GNSS parameter estimation system includes the final state and normal equation of the GNSS parameter estimation system at the current moment.

[0142] The specific functional implementation of each of the above modules can be found in the relevant content of the method in Example 1 and will not be elaborated on here.

[0143] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0144] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0147] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A satellite DCB filter estimation method based on elastic power detection, characterized in that: include: Obtain satellite observation data and satellite signal carrier-to-noise ratio data; Calculating GF combination observation values ​​according to the satellite observation data, and constructing a normal equation according to the GF combination observation values; Calculating a satellite power detection index based on the satellite signal carrier-to-noise ratio data, and adjusting constraints on satellite DCB parameters to be estimated in a parameter filter based on the satellite power detection index and a preset threshold to obtain process noise; The initial state of the GNSS parameter estimation system at the current moment is predicted based on the process noise, the state of the GNSS parameter estimation system at the previous moment, and the error covariance matrix. The initial state of the GNSS parameter estimation system at the current moment is corrected based on the satellite observation data at the current moment to obtain the final state of the GNSS parameter estimation system at the current moment. A satellite DCB filter estimation result is obtained using a GNSS parameter estimation system; wherein the GNSS parameter estimation system includes a final state and a normal equation of the GNSS parameter estimation system at a current moment.

2. The satellite DCB filter estimation method based on elastic power detection according to claim 1, characterized in that: Calculating GF combined observation values ​​according to the satellite observation data includes: ； in, represents the GF combination observation value; 、 They represent the pseudorange observation values ​​of the i-th signal and the j-th signal from the observation station r to the satellite s respectively; 、 Represent the frequencies of the i-th signal and the j-th signal respectively; represents the total electron content in the oblique direction between the observation station r and the satellite s; represents the speed of radio waves in a vacuum; 、 They represent the differential code bias of observation station r and satellite s respectively; Satellite receiver unit The difference in distortion deviation between two correlated signals.

3. The satellite DCB filter estimation method based on elastic power detection according to claim 1, characterized in that: Before constructing the normal equation based on the GF combined observations, it also includes: According to whether the components of the GF combination observation value are at the same frequency, it is judged whether the GF combination observation value needs to be corrected for ionospheric delay; If the components of the GF combination observations are at the same frequency, no ionospheric delay correction is required; If the components of the GF combination observations are not at the same frequency, the ionospheric delay correction is performed on the GF combination observations using the global ionosphere map and the ionospheric projection function.

4. The satellite DCB filter estimation method based on elastic power detection according to claim 1, characterized in that: The normal equation is constructed based on the GF combined observations, including: ； ； ； ； ； ； in, Indicates the DCB parameters to be estimated for the satellite; and represent the differential code bias vector of the observation station and the differential code bias vector of the satellite respectively; represents the n×n identity matrix; represents an n×1 unit vector; represents the m×m identity matrix; represents the m×1 identity matrix; represents the Kronecker operator; represents the total number of observation stations; represents the total number of satellites; T represents the transpose of the matrix; represents the differential code deviation of the nth observation station; represents the differential code bias of the mth satellite; Indicates the DCB parameters to be estimated for the mth satellite.

5. The satellite DCB filter estimation method based on elastic power detection according to claim 1, characterized in that: Calculating a satellite power detection index according to the satellite signal carrier-to-noise ratio data, including: ； in, Indicates the satellite power adjustment monitoring index; represents the total number of observation stations; 、 They represent the signal carrier-to-noise ratio of the satellite s received by the observation station r at the current moment and the moment before the current moment, respectively.

6. The satellite DCB filter estimation method based on elastic power detection according to claim 1, characterized in that: According to the satellite power detection index and the preset threshold, the constraints on the satellite DCB parameters to be estimated in the parameter filter are adjusted to obtain process noise, including: If the satellite power detection index is greater than a preset threshold, the satellite power is adjusted, and the constraints on the satellite DCB parameters to be estimated in the parameter filter are relaxed to obtain process noise until the preset time range is reached; otherwise, the satellite power is not adjusted and the process noise remains unchanged; wherein the process noise is the constraints on the satellite DCB parameters to be estimated in the parameter filter.

7. The satellite DCB filter estimation method based on elastic power detection according to claim 4, characterized in that: The acquisition of the GNSS parameter estimation system includes: ； ； ； ； in, represents the non-singular prior covariance matrix; Represents the state parameters of the GNSS parameter estimation system; represents the prior observation vector; represents the observation matrix; represents the observation vector.

8. The satellite DCB filter estimation method based on elastic power detection according to claim 1, characterized in that: Based on the process noise, the state of the GNSS parameter estimation system at the previous moment, and the error covariance matrix, the initial state of the GNSS parameter estimation system at the current moment is predicted. The initial state of the GNSS parameter estimation system at the current moment is corrected according to the satellite observation data at the current moment to obtain the final state of the GNSS parameter estimation system at the current moment, including: Perform MP simulation based on process noise and obtain the dynamic residual equation: ； in, 、 They represent the dynamic parameters of the j+1th epoch and the dynamic parameters of the jth epoch respectively; represents the state transition matrix; represents the j-th epoch process noise, which satisfies: ； ； Where, E( ) represents the mean; D( ) represents the variance; represents the k-th epoch process noise; represents a symmetric positive definite matrix; represents the upper triangular matrix after QR decomposition; represents the Kronecker operator; use Normalize the process noise variance: ； The initial state of the GNSS parameter estimation system at the current moment can be obtained by using the normalized process noise and prior information: ； in, 、 、 、 、 They represent the submatrices of the upper triangular matrix after QR decomposition; Indicates the initial state of the GNSS parameter estimation system at the current moment; represents the j-th epoch observation vector after time update; represents the j+1th epoch prior observation vector after time update; The initial state of the GNSS parameter estimation system at the current moment is calculated based on the satellite observation data at the current moment. Perform correction to obtain the final state of the GNSS parameter estimation system at the current moment .

9. The satellite DCB filter estimation method based on elastic power detection according to claim 1, characterized in that: The satellite DCB filter estimation results are obtained using the GNSS parameter estimation system, including: Perform QR decomposition on the GNSS parameter estimation system to obtain the satellite DCB filter estimation result, which is expressed as: ； ； Wherein, QR represents QR decomposition; represents the prior covariance matrix of the j-th epoch; represents the j-th epoch observation matrix; Represents the DCB filter estimation result of the j-th epoch satellite, and estimates the final state of the system at the current moment through GNSS parameters and the law equation is obtained; represents the j-th epoch prior observation vector; represents the j-th epoch observation vector; represents the upper triangular covariance matrix of the jth epoch after QR decomposition; represents the j-th epoch observation vector after QR decomposition; Represents the residual vector after solution.

10. A satellite DCB filter estimation system based on elastic power detection, characterized in that: include: Data acquisition module, which acquires satellite observation data and satellite signal carrier-to-noise ratio data; a normal equation construction module, calculating GF combination observation values ​​according to the satellite observation data, and constructing a normal equation according to the GF combination observation values; a parameter constraint adjustment module for calculating a satellite power detection index based on the satellite signal carrier-to-noise ratio data, and adjusting the constraint on the satellite DCB parameter to be estimated in the parameter filter based on the satellite power detection index and a preset threshold to obtain process noise; The time update module predicts the initial state of the GNSS parameter estimation system at the current moment based on the process noise, the state of the GNSS parameter estimation system at the previous moment, and the error covariance matrix; The measurement update module corrects the initial state of the GNSS parameter estimation system at the current moment according to the satellite observation data at the current moment to obtain the final state of the GNSS parameter estimation system at the current moment; The parameter solving module obtains the satellite DCB filter estimation result by using the GNSS parameter estimation system; wherein the GNSS parameter estimation system includes the final state and normal equation of the GNSS parameter estimation system at the current moment.

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