Beidou water vapor ionosphere detection system calibration method, device and equipment

Abstract

The embodiment of the invention provides a Beidou water vapor ionosphere detection system calibration method, device and equipment, and is applied to the technical field of detection calibration. The method comprises the following steps: performing double-frequency combination observation according to observation values of any two frequency bands in Beidou multi-frequency observation data, and calculating residual information after first-order delay elimination of an ionized layer; performing optimization combination on the residual information based on a preset combination optimization rule to generate high-order residual information; calculating the moisture delay of the troposphere according to the meteorological parameters; performing filtering correction on the high-order residual error information and the wet delay based on a preset dynamic filtering model to obtain high-order residual error correction information and corrected wet delay; based on a preset navigation positioning equation, static term delay is calculated according to the corrected wet delay; the high-order residual error correction information is used as ionosphere delay, the correction wet delay and the static term delay are used as zenith troposphere delay, and the Beidou water vapor ionosphere detection system is calibrated. In conclusion, the precision of the Beidou system in water vapor and ionosphere detection can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, in particular to the field of detection and calibration technology, and specifically to a Beidou water vapor ionosphere detection system calibration method, device and equipment. Background Art

[0002] With the continuous development of the BeiDou Navigation Satellite System (BDS), accurate water vapor and ionosphere detection has become an important technical requirement in meteorology, communications, aerospace and other fields. To improve the accuracy of BeiDou water vapor and ionosphere detection, metrological calibration methods are particularly important.

[0003] The current metrological calibration method for the BeiDou water vapor and ionosphere detection system primarily eliminates first-order ionospheric delays through dual-frequency observations. This approach achieves over 95% elimination of first-order ionospheric delays by combining signals in the B1 / B2 bands. However, rapid variations in ionospheric electron content can affect tropospheric delay estimates through dual-frequency observation residuals, resulting in low accuracy for the BeiDou system in water vapor and ionosphere detection. Studies have shown that during peak solar activity (F10.7 > 200 sfu), wet delay estimation errors can reach ±4 mm, leading to biases exceeding ±1.5 mm in the inversion of atmospheric precipitable water. Summary of the Invention

[0004] The present disclosure provides a BeiDou water vapor ionosphere detection system calibration method, apparatus, equipment, and storage medium.

[0005] According to a first aspect of the present disclosure, a method for calibrating a BeiDou water vapor ionosphere detection system is provided. The method comprises:

[0006] Obtain Beidou multi-frequency observation data, meteorological parameters and carrier phase observations;

[0007] Perform dual-frequency combined observation based on the observation values ​​of any two frequency bands in the Beidou multi-frequency observation data, and calculate the residual information after the first-order ionospheric delay is eliminated;

[0008] Based on a preset combination optimization rule, the residual information is optimized and combined to generate high-order residual information;

[0009] calculating the tropospheric wet delay based on the meteorological parameters;

[0010] Based on a preset dynamic filtering model, filtering and correcting the high-order residual information and the wet delay to obtain high-order residual correction information and corrected wet delay;

[0011] Based on a preset navigation and positioning equation, calculating the static delay according to the corrected wet delay; the preset navigation and positioning equation is constructed according to the carrier phase observation;

[0012] The high-order residual correction information is used as the ionospheric delay, and the corrected wet delay and the static term delay are used as the zenith tropospheric delay to calibrate the Beidou water vapor ionosphere detection system.

[0013] According to the above aspects and any possible implementation, an implementation is further provided, wherein the Beidou multi-frequency observation data includes observation values ​​of the Beidou B1 frequency band, the B2 frequency band, and the B3 frequency band;

[0014] The residual information includes:

[0015] y=[y 12 ,y 13 ,y 23 ]

[0016]

[0017] x={x i =(f i ,p i ,L i )|i∈[1,3]}

[0018] Among them, y represents the residual information after the first-order delay of the ionosphere is eliminated, y 12 represents the dual-frequency combination observation results of B1 and B2 bands, y 13 represents the dual-frequency combination observation results of B1 and B3 bands, y 23 represents the dual-frequency combination observation results of B2 and B3 bands, x i represents the observation value of the i-th frequency band, f i is the frequency of the ith frequency band, p i Represents the observed value x i The pseudorange in L i Represents the observed value x i The carrier phase in .

[0019] According to the above aspects and any possible implementation, a further implementation is provided, wherein the preset combination optimization rule includes:

[0020] Construct carrier phase combination observation function;

[0021] Constructing a frequency band weight optimization objective function according to the residual information;

[0022] Optimizing the frequency band weight of the carrier phase combination observation function according to the frequency band weight optimization objective function to obtain an optimized frequency band weight;

[0023] Calculate high-order residual information based on the optimized frequency band weights;

[0024] Wherein, the carrier phase combination observation function includes:

[0025] L=w1·L1+w2·L2+w3·L3

[0026] Wherein, L represents the carrier phase information that completely eliminates the first-order delay of the ionosphere, and the carrier phase information L is used as the high-order residual information, w1, w2, and w3 represent the weights of the B1 frequency band, B2 frequency band, and B3 frequency band to be optimized respectively;

[0027] The frequency band weight optimization objective function includes:

[0028]

[0029] w=[w1,w2,w3]

[0030] E1(w)=λ1(w1·(f1) 2 +w2·(f2) 2 +w3·(f3) 2 )

[0031] E2(w)=λ2(w1+w2+w3-1)

[0032] Wherein, w represents the frequency band weight vector composed of the weights of the B1 frequency band, B2 frequency band, and B3 frequency band to be optimized, E1(w) and E2(w) represent the introduced Lagrangian functions, and λ1 and λ2 represent the Lagrangian coefficients.

[0033] According to the above aspects and any possible implementation, there is further provided an implementation, wherein the meteorological parameters are obtained by converting atmospheric data using a drift ratio measurement method; the meteorological parameters include temperature and water vapor pressure, and the atmospheric data includes a temperature sequence and a water vapor pressure sequence under an atmospheric environment;

[0034] Calculating the tropospheric wet delay according to the meteorological parameters includes:

[0035]

[0036] Where F1 represents the moist delay of the troposphere, Tem represents the temperature, and E represents the water vapor pressure.

[0037] According to the above aspects and any possible implementation, there is further provided an implementation, wherein the preset dynamic filtering model includes a state vector construction module and a state transfer module;

[0038] The state vector construction module is used to convert the high-order residual information and the wet delay into a state vector; the state transfer module is used to iteratively transfer the state vector until a preset termination transfer requirement is met, thereby obtaining high-order residual correction information and corrected wet delay.

[0039] According to the above aspects and any possible implementation, further provided is an implementation, wherein the calculation of the static delay according to the corrected wet delay based on a preset navigation positioning equation includes:

[0040] Calculate tropospheric delay information based on preset navigation positioning equations;

[0041] Converting the tropospheric delay information into a corresponding mapping function;

[0042] Based on the mapping function, a static term delay is calculated according to the corrected wet delay.

[0043] According to the above aspects and any possible implementation, further provided is an implementation, wherein the preset navigation positioning equation includes:

[0044]

[0045] in, represents the pseudorange between the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna of the carrier phase observation, ρ represents the geometric distance between the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna, τ represents the sum of the offset and change of the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna, δ1 represents the hardware delay of the satellite, δ2 represents the hardware delay of the receiver, d1 represents the satellite clock error, d2 represents the receiver clock error, d3 represents the relativistic effect, α1 represents the wavelength of the satellite signal, α2 represents the phase wrapping, α3 represents the integer ambiguity, and c represents the speed of light. Indicates delay information. represents the ionospheric delay, and G represents the tropospheric delay information;

[0046] The mapping function includes:

[0047]

[0048] Among them, M1 represents the wet term projection function, M2 represents the static term projection function, M3 represents the angle projection function, θ1 represents the angle between the ground signal receiving antenna and the ground plane, θ2 represents the horizontal angle between the direction of the ground signal receiving antenna pointing to the satellite and the ground north direction, Grad1 represents the ground angle projection coefficient, and Grad2 represents the horizontal angle projection coefficient. represents the static delay, Indicates corrected wet delay.

[0049] According to a second aspect of the present disclosure, a BeiDou water vapor ionosphere detection system calibration device is provided. The device comprises:

[0050] Acquisition module, used to obtain Beidou multi-frequency observation data, meteorological parameters and carrier phase observations;

[0051] A calculation module is used to perform dual-frequency combined observation based on the observation values ​​of any two frequency bands in the Beidou multi-frequency observation data, and calculate the residual information after the first-order delay of the ionosphere is eliminated;

[0052] An optimization module, configured to optimize and combine the residual information based on a preset combination optimization rule to generate high-order residual information;

[0053] The calculation module is further used to calculate the tropospheric wet delay according to the meteorological parameters;

[0054] a correction module, configured to perform filtering correction on the high-order residual information and the wet delay based on a preset dynamic filtering model to obtain high-order residual correction information and corrected wet delay;

[0055] The calculation module is further configured to calculate the static delay according to the corrected wet delay based on a preset navigation and positioning equation; the preset navigation and positioning equation is constructed based on the carrier phase observation;

[0056] A calibration module is used to calibrate the Beidou water vapor ionosphere detection system by using the high-order residual correction information as the ionospheric delay, and using the corrected wet delay and the static term delay as the zenith tropospheric delay.

[0057] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the program.

[0058] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method described above is implemented.

[0059] The embodiment of the present application provides a Beidou water vapor ionosphere detection system calibration method, which can obtain Beidou multi-frequency observation data, meteorological parameters and carrier phase observations; perform dual-frequency combined observations based on the observation values ​​of any two frequency bands in the Beidou multi-frequency observation data, and calculate the residual information after the first-order delay of the ionosphere is eliminated, so as to obtain the residual information by using the dual-frequency combined observation method; optimize and combine the residual information based on the preset combination optimization rules to generate high-order residual information, so as to optimize and combine the residual information using the multi-frequency combination optimization method to generate high-order residual information; calculate the wet delay of the troposphere based on the meteorological parameters; filter and correct the high-order residual information and wet delay based on the preset dynamic filtering model to obtain high-order residual correction information and corrected wet delay, so as to use the dynamic filtering model to correct the high-order residual information and wet delay. Real-time correction; based on the preset navigation positioning equation, the static term delay is calculated according to the corrected wet delay; the preset navigation positioning equation is constructed based on the carrier phase observation; the high-order residual correction information is used as the ionospheric delay, and the corrected wet delay and static term delay are used as the zenith tropospheric delay to calibrate the Beidou water vapor ionosphere detection system; based on this, the high-order residual information that suppresses the interference of multiple observation paths and eliminates the first-order ionospheric delay can be obtained according to the dual-frequency combination observation method and the multi-frequency combination optimization method, and then the high-order residual information and wet delay are filtered and corrected using the Kalman filtering method based on the dynamic coefficient to solve the influence of high-frequency residual information on the wet delay estimation, improve the accuracy of wet delay estimation, and further achieve the purpose of improving the accuracy of the Beidou system in water vapor and ionosphere detection by eliminating the error of correcting the total electron content of the ionosphere and environmental interference.

[0060] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present disclosure and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0062] Figure 1 A flowchart of a BeiDou water vapor ionosphere detection system calibration method according to an embodiment of the present disclosure is shown;

[0063] Figure 2 A block diagram of a BeiDou water vapor ionosphere detection system calibration device according to an embodiment of the present disclosure is shown;

[0064] Figure 3A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0065] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present disclosure.

[0066] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0067] In the present disclosure, high-order residual information that suppresses interference from multiple observation paths and eliminates the first-order ionospheric delay can be obtained based on the dual-frequency combination observation method and the multi-frequency combination optimization method. The high-order residual information and wet delay are then filtered and corrected using the Kalman filtering method based on the dynamic coefficient to solve the influence of high-frequency residual information on wet delay estimation, improve the accuracy of wet delay estimation, and further achieve the purpose of improving the accuracy of the Beidou system in water vapor and ionosphere detection by eliminating the error in correcting the total electron content of the ionosphere and environmental interference.

[0068] Figure 1 A flowchart of a BeiDou water vapor ionosphere detection system calibration method 100 according to an embodiment of the present disclosure is shown.

[0069] In block 110 , BeiDou multi-frequency observation data, meteorological parameters, and carrier phase observations are acquired.

[0070] In some embodiments, the Beidou multi-frequency observation data may be Beidou tri-frequency observation data, and the Beidou tri-frequency observation data may be acquired by using a Beidou tri-frequency receiver.

[0071] In some embodiments, the Beidou triple-frequency observation data may be observation values ​​of three frequency bands, which may be the Beidou B1 band, the B2 band, and the B3 band, respectively. That is, the Beidou multi-frequency observation data includes the observation values ​​of the Beidou B1 band, the B2 band, and the B3 band. The representation of the Beidou triple-frequency observation data is:

[0072] x={x i =(f i ,p i ,L i )|i∈[1,3]}

[0073] Among them, x represents BeiDou triple-frequency observation data, x i represents the observation value of the i-th frequency band, where the 1st to 3rd frequency bands are BeiDou B1, B2, and B3, respectively; f i is the frequency of the ith frequency band, p i Represents the observed value x i The pseudorange in L i Represents the observed value x i The carrier phase in .

[0074] Specifically, the frequencies of the Beidou B1 band, B2 band and B3 band are 1575.62 MHz, 1207.14 MHz and 1268.52 MHz respectively.

[0075] In some embodiments, meteorological parameters can be obtained by using sensors to collect atmospheric data and converting the atmospheric data into meteorological parameters using a drift ratio measurement method. The sampling rate of the sensor is 1 Hz, where the meteorological parameters include temperature and water vapor pressure, and the atmospheric data is a temperature sequence and a water vapor pressure sequence under the atmospheric environment.

[0076] In some embodiments, the carrier phase observations may be acquired by using a receiver.

[0077] In some embodiments, the carrier phase observation includes the pseudorange between the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna observed by the carrier phase, the geometric distance between the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna, the sum of the offset and change of the phase center of the satellite signal transmitting antenna and the offset and change of the phase center of the ground signal receiving antenna, the hardware delay of the satellite, the hardware delay of the receiver, the satellite clock error, the receiver clock error, the relativistic effect, the wavelength of the satellite signal, the phase wrapping and the integer ambiguity.

[0078] In block 120 , dual-frequency combined observation is performed based on the observation values ​​of any two frequency bands in the BeiDou multi-frequency observation data, and residual information after the first-order ionospheric delay is eliminated is calculated.

[0079] In some embodiments, the residual information may represent an error in the total ionospheric electron content.

[0080] In some embodiments, the residual information includes:

[0081] y=[y 12 ,y 13 ,y 23 ]

[0082]

[0083] x={x i =(f i ,p i ,L i )|i∈[1,3]}

[0084] Among them, y represents the residual information after the first-order delay of the ionosphere is eliminated, y 12 represents the dual-frequency combination observation results of B1 and B2 bands, y 13 represents the dual-frequency combination observation results of B1 and B3 bands, y 23 represents the dual-frequency combination observation results of B2 and B3 bands, x i represents the observation value of the i-th frequency band, f i is the frequency of the ith frequency band, p i Represents the observed value x i The pseudorange in L i Represents the observed value x i The carrier phase in .

[0085] In block 130 , the residual information is optimized and combined based on a preset combination optimization rule to generate high-order residual information.

[0086] In some embodiments, the preset combination optimization rules can be set according to the actual needs of the user, such as a three-band combination optimization method.

[0087] In some embodiments, the preset combination optimization rules include:

[0088] Construct carrier phase combination observation function;

[0089] Construct the frequency band weight optimization objective function based on the residual information;

[0090] The frequency band weight of the carrier phase combination observation function is optimized according to the frequency band weight optimization objective function to obtain the optimized frequency band weight;

[0091] Calculate high-order residual information based on the optimized frequency band weights;

[0092] Among them, the carrier phase combination observation function includes:

[0093] L=w1·L1+w2·L2+w3·L3

[0094] Wherein, L represents the carrier phase information that completely eliminates the first-order delay of the ionosphere, and the carrier phase information L is used as the high-order residual information. w1, w2, and w3 represent the weights of the B1 frequency band, B2 frequency band, and B3 frequency band to be optimized respectively.

[0095] The objective function of band weight optimization includes:

[0096]

[0097] w=[w1,w2,w3]

[0098] E1(w)=λ1(w1·(f1) 2 +w2·(f2) 2 +w3·(f3) 2 )

[0099] E2(w)=λ2(w1+w2+w3-1)

[0100] Wherein, w represents the frequency band weight vector composed of the weights of the B1 frequency band, B2 frequency band, and B3 frequency band to be optimized, E1(w) and E2(w) represent the introduced Lagrangian functions, and λ1 and λ2 represent the Lagrangian coefficients.

[0101] In some embodiments, the constructed carrier phase combination observation function may be a carrier phase combination observation function of triple-frequency observation.

[0102] In some embodiments, the process of optimizing the combination of residual information using a three-frequency combination optimization method can specifically include: respectively deriving w1, w2, w3 and λ1, λ2 in the frequency band weight optimization objective function J(w), and setting the derivative results to 0, constructing 5 sets of equations, and setting the 5 sets of equations as the derivative result equation group; solving the derivative result equation group to obtain w1, w2, w3, and calculating the high-order residual information L.

[0103] At block 140 , the tropospheric wet delay is calculated based on the meteorological parameters.

[0104] In some embodiments, when converting atmospheric data to obtain meteorological parameters using the drift ratio measurement method, the conversion formula for the atmospheric data includes:

[0105]

[0106]

[0107] Among them, Tem h It represents the hth group of temperatures collected by the sensor, that is, the hth group of sequence values ​​in the temperature sequence under the atmospheric environment, E h It represents the hth group of water vapor pressure collected by the sensor, that is, the hth group of sequence values ​​in the water vapor pressure sequence under atmospheric conditions, h∈[1,H], where H represents the length of the sequence; Indicates Tem h The drift ratio measurement results are: Indicates the drift amount, Indicates E h Drift ratio measurement results; β1 represents the temperature attenuation coefficient, and β2 represents the water vapor pressure attenuation coefficient.

[0108] In some embodiments, calculating the tropospheric wet delay based on meteorological parameters specifically includes:

[0109]

[0110] Where F1 represents the moist delay of the troposphere, Tem represents the temperature, and E represents the water vapor pressure.

[0111] It should be noted that, from the above, the present disclosure also proposes a multi-band observation optimization method, which uses a dual-frequency combination observation method to generate residual information after the first-order delay of the ionosphere is eliminated, and combines the three-frequency combination optimization method to construct a carrier phase combination observation function of the three-frequency observation. The goal is to minimize the difference between the carrier phase and the residual information after the three-frequency weighting. The Lagrangian method is used to solve the function to obtain high-order residual information that suppresses the interference of multiple observation paths; then the sensor is used to collect atmospheric data, and the drift ratio measurement method is used to convert the atmospheric data into meteorological parameters. During the conversion process, the atmospheric data is corrected based on the drift ratio deviation of the atmospheric data collection to obtain more accurate meteorological parameters, and the meteorological parameters are converted into wet delays, so that the dynamic filtering model can be used to perform real-time corrections on the high-order residual information and wet delays, solve the influence of high-frequency residual information on wet delay estimation, and improve the accuracy of wet delay estimation.

[0112] In block 150 , filtering and correction are performed on the high-order residual information and the wet delay based on a preset dynamic filtering model to obtain high-order residual correction information and corrected wet delay.

[0113] In some embodiments, the preset dynamic filtering model can be set according to the actual needs of the user.

[0114] In some embodiments, the preset dynamic filtering model includes a state vector construction module and a state transfer module;

[0115] The state vector construction module is used to convert high-order residual information and wet delay into a state vector; the state transfer module is used to iteratively transfer the state vector until the preset termination transfer requirements are met, thereby obtaining high-order residual correction information and corrected wet delay.

[0116] In some embodiments, the process of using a dynamic filtering model to perform real-time correction on the high-order residual information L and the wet delay F1 may specifically include:

[0117] The state vector construction module converts the high-order residual information L and the wet delay F1 into a state vector R0:

[0118] R0=[L,F1] T

[0119] Where T represents transpose;

[0120] Initialize and generate the covariance matrix Q0;

[0121] The state transfer module iteratively transfers the state vector based on the covariance matrix, where the iterative transfer formula of the state vector is:

[0122]

[0123] R t+1|t =R t +U t (Ω t -ZR t|t-1 )

[0124]

[0125] U t =Q k Z T (ZQ k Z T +D k ) -1

[0126] D k =ψ t D k-1 +(1-ψ t )(Ω t -ZR t|t-1 )(Ω t -ZR t|t-1 ) T

[0127]

[0128] Q k =(IU t-1 Z)Q k-1

[0129] Among them, R t+1 Represents the t+1th iteration transfer result of the state vector, R t+1|t represents the t+1th iteration result of the state vector, serving as an intermediate variable in the t+1th iteration transfer process, Δ represents the preset control parameter, Represents the iteration result R t+1|t The corresponding noise parameter; U t Represents the gain coefficient of the state vector in the tth iteration process, Z=[1,0], Z represents the coefficient matrix, Ω t Iteration observation value of the t-th iteration result, Q k represents the t-th iteration result of the covariance matrix, I represents the identity matrix; ψ t represents the dynamic coefficient of the state vector during the tth iteration, D krepresents the observation noise covariance of the state vector during the tth iteration, Ω t -ZR t|t-1 It represents the iterative observation difference of the t-th iteration result, and std(t) represents the standard deviation of the t-group iterative observation difference. The preset termination transfer requirement is that when the change of the state vector is less than the preset change threshold, the iterative transfer of the state vector is terminated.

[0130] Among them, the high-order residual information L and the high-order residual correction information corresponding to the wet delay F1 are: Corrected wet delay is

[0131] It should be noted that, from the above, the present disclosure also proposes a dynamic filtering model, which performs filtering correction on high-order residual information and wet delay based on the Kalman filtering method, introduces gain coefficients and historical iteration results, generates observation noise covariance of historical iteration results, guides the update of state vectors, and then adaptively adjusts the filtering method, improves filtering accuracy, accelerates convergence speed, reduces the impact of ionospheric scintillation, proposes a dynamic coefficient based on observation difference, dynamically adjusts the gain amplitude based on the fluctuation of observation difference, and automatically reduces the dynamic coefficient when the ionospheric disturbance is severe, thereby enhancing the tracking capability of the state vector during the filtering process and quickly responding to ionospheric disturbances. In a stable environment, the dynamic coefficient is appropriately increased to improve noise suppression capability, improve filtering stability, and realize dynamic filtering correction processing that dynamically adapts to the ionospheric environment.

[0132] In block 160 , the static delay is calculated according to the corrected wet delay based on a preset navigation and positioning equation; the preset navigation and positioning equation is constructed based on the carrier phase observation.

[0133] In some embodiments, the calculation of the static delay based on the corrected wet delay based on the preset navigation positioning equation specifically includes:

[0134] Calculate tropospheric delay information based on preset navigation positioning equations;

[0135] Convert the tropospheric delay information into a corresponding mapping function;

[0136] Based on the mapping function, the static delay is calculated according to the corrected wet delay.

[0137] In some embodiments, the preset navigation and positioning equation includes:

[0138]

[0139] in, represents the pseudorange between the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna of the carrier phase observation, ρ represents the geometric distance between the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna, τ represents the sum of the offset and change of the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna, δ1 represents the hardware delay of the satellite, δ2 represents the hardware delay of the receiver, d1 represents the satellite clock error, d2 represents the receiver clock error, d3 represents the relativistic effect, α1 represents the wavelength of the satellite signal, α2 represents the phase wrapping, α3 represents the integer ambiguity, and c represents the speed of light. Indicates delay information. represents the ionospheric delay, and G represents the tropospheric delay information;

[0140] The above mapping functions include:

[0141]

[0142] Among them, M1 represents the wet term projection function, M2 represents the static term projection function, M3 represents the angle projection function, θ1 represents the angle between the ground signal receiving antenna and the ground plane, θ2 represents the horizontal angle between the direction of the ground signal receiving antenna pointing to the satellite and the ground north direction, Grad1 represents the ground angle projection coefficient, and Grad2 represents the horizontal angle projection coefficient. represents the static delay, Indicates corrected wet delay.

[0143] In some embodiments, the mapping function can be solved to obtain the static delay Will correct wet delay and static delay As the zenith tropospheric delay F2:

[0144] It should be noted that, in summary, the present disclosure also proposes a delay calculation method, which calculates the tropospheric delay information based on the navigation positioning equation, converts the tropospheric delay information into a mapping function, uses the corrected wet delay to calculate the static term delay, solves the mapping function, obtains the static term delay, and uses the corrected wet delay and the static term delay as the zenith tropospheric delay F2; based on this, the zenith tropospheric delay F2 and the ionospheric delay can be used later. The BeiDou water vapor ionospheric sounding system is calibrated to obtain accurate pseudorange and carrier phase observations for precise point positioning and real-time kinematic positioning. The system also monitors atmospheric precipitable water (PW) and weather using corrected wet delay. If the ionospheric delay exceeds a preset threshold, it indicates a space weather event, such as a solar flare or geomagnetic storm. The PW can be calculated based on the corrected wet delay.

[0145] In box 170, the high-order residual correction information is used as the ionospheric delay, and the corrected wet delay and static term delay are used as the zenith tropospheric delay to calibrate the Beidou water vapor ionosphere detection system.

[0146] In some embodiments, the Beidou water vapor ionosphere detection system may include a satellite observation station, a data processing center, a Beidou satellite and a receiver. The satellite observation station is used to collect satellite signals transmitted by the Beidou satellite and perform carrier observation on the satellite signal transmitting antenna. The data processing center is used to process the satellite signal collection results into Beidou three-frequency observation data and process the carrier observation results into carrier phase observation quantities. The Beidou satellite includes a satellite signal transmitting antenna for transmitting satellite signals and monitoring the water vapor content and ionosphere state in the atmosphere. The receiver is a Beidou three-frequency receiver, including a ground signal receiving antenna for receiving satellite signals.

[0147] In some embodiments, the wet delay can also be corrected based on Calculate the atmospheric precipitable water P:

[0148]

[0149]

[0150] Where q1 represents the density of water, q2 represents the water vapor constant, and q3, q6, and q7 are the atmospheric refractive index coefficients. Specifically, q3 is 375.201±0.76×10 3 K 2 hPa -1 , q6 is 71.2455±1.3K·hPa -1 , q7 is 77.643±0.0094K·hPa -1 ; q4 is the weighted average temperature of the atmosphere, and q5 is the intermediate parameter.

[0151] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0152] It can obtain Beidou multi-frequency observation data, meteorological parameters and carrier phase observations; perform dual-frequency combined observations based on the observation values ​​of any two frequency bands in the Beidou multi-frequency observation data, and calculate the residual information after the first-order delay of the ionosphere is eliminated, so that the residual information can be calculated using the dual-frequency combined observation method; based on the preset combination optimization rules, the residual information is optimized and combined to generate high-order residual information, so that the residual information can be optimized and combined using the multi-frequency combination optimization method to generate high-order residual information; calculate the wet delay of the troposphere according to the meteorological parameters; based on the preset dynamic filtering model, the high-order residual information and wet delay are filtered and corrected to obtain high-order residual correction information and corrected wet delay, so that the high-order residual information and wet delay can be corrected in real time using the dynamic filtering model; based on the preset navigation positioning method The static term delay is calculated according to the corrected wet delay; the preset navigation positioning equation is constructed according to the carrier phase observation; the high-order residual correction information is used as the ionospheric delay, and the corrected wet delay and the static term delay are used as the zenith tropospheric delay to calibrate the Beidou water vapor ionosphere detection system; based on this, the high-order residual information that suppresses the interference of multiple observation paths and eliminates the first-order ionospheric delay can be obtained according to the dual-frequency combination observation method and the multi-frequency combination optimization method, and then the high-order residual information and wet delay are filtered and corrected using the Kalman filtering method based on the dynamic coefficient, so as to solve the influence of high-frequency residual information on the wet delay estimation, improve the wet delay estimation accuracy, and further achieve the purpose of improving the accuracy of the Beidou system in water vapor and ionosphere detection by eliminating the error of correcting the total electron content of the ionosphere and environmental interference.

[0153] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0154] The above is an introduction to the method embodiment. The following is a further explanation of the solution disclosed in the present disclosure through an apparatus embodiment.

[0155] Figure 2 FIG. 2 shows a block diagram of a BeiDou water vapor ionosphere detection system calibration device 200 according to an embodiment of the present disclosure. Figure 2 As shown, the device 200 includes:

[0156] Acquisition module 210, used to obtain Beidou multi-frequency observation data, meteorological parameters and carrier phase observations;

[0157] The calculation module 220 is used to perform dual-frequency combined observation based on the observation values ​​of any two frequency bands in the Beidou multi-frequency observation data, and calculate the residual information after the first-order ionospheric delay is eliminated;

[0158] An optimization module 230 is configured to optimize and combine the residual information based on a preset combination optimization rule to generate high-order residual information;

[0159] The calculation module 220 is further configured to calculate the tropospheric wet delay based on the meteorological parameters;

[0160] A correction module 240 is configured to filter and correct the high-order residual information and wet delay based on a preset dynamic filtering model to obtain high-order residual correction information and corrected wet delay;

[0161] The calculation module 220 is further configured to calculate the static delay based on the corrected wet delay based on a preset navigation and positioning equation; the preset navigation and positioning equation is constructed based on the carrier phase observation;

[0162] The calibration module 250 is used to calibrate the BeiDou water vapor ionosphere detection system by using the high-order residual correction information as the ionospheric delay and the corrected wet delay and static term delay as the zenith tropospheric delay.

[0163] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0164] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0165] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0166] Figure 3 A block diagram of an exemplary electronic device 300 capable of implementing embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0167] The electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a ROM 302 or a computer program loaded from a storage unit 308 into a RAM 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An I / O interface 305 is also connected to the bus 304.

[0168] Multiple components in the electronic device 300 are connected to the I / O interface 305, including an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0169] The computing unit 301 may be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 308.

[0170] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0171] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0172] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0173] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0174] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0175] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0176] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0177] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0178] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A BeiDou water vapor ionosphere detection system calibration method, characterized in that: include: Obtain Beidou multi-frequency observation data, meteorological parameters and carrier phase observations; Perform dual-frequency combined observation based on the observation values ​​of any two frequency bands in the Beidou multi-frequency observation data, and calculate the residual information after the first-order ionospheric delay is eliminated; Based on a preset combination optimization rule, the residual information is optimized and combined to generate high-order residual information; calculating the tropospheric wet delay based on the meteorological parameters; Based on a preset dynamic filtering model, filtering and correcting the high-order residual information and the wet delay to obtain high-order residual correction information and corrected wet delay; Based on a preset navigation positioning equation, calculating the static delay according to the corrected wet delay; The preset navigation positioning equation is constructed according to the carrier phase observation; The high-order residual correction information is used as the ionospheric delay, and the corrected wet delay and the static term delay are used as the zenith tropospheric delay to calibrate the Beidou water vapor ionosphere detection system.

2. The method according to claim 1, characterized in that The Beidou multi-frequency observation data includes the observation values ​​of the Beidou B1 frequency band, B2 frequency band and B3 frequency band; The residual information includes: and=[and 12 ,and 13 ,and 23 ] x={x i =(f i ,p i ,L i )|i∈[1,3]} Among them, y represents the residual information after the first-order delay of the ionosphere is eliminated, y 12 represents the dual-frequency combination observation results of B1 and B2 bands, y 13 represents the dual-frequency combination observation results of B1 and B3 bands, y 23 represents the dual-frequency combination observation results of B2 and B3 bands, x i represents the observation value of the i-th frequency band, f i is the frequency of the ith frequency band, p i Represents the observed value x i The pseudorange in L i Represents the observed value x i The carrier phase in .

3. The method according to claim 2, characterized in that The preset combination optimization rules include: Construct carrier phase combination observation function; Constructing a frequency band weight optimization objective function according to the residual information; Optimizing the frequency band weight of the carrier phase combination observation function according to the frequency band weight optimization objective function to obtain an optimized frequency band weight; Calculate high-order residual information based on the optimized frequency band weights; Wherein, the carrier phase combination observation function includes: L=w1·L1+w2·L2+w3·L3 Wherein, L represents the carrier phase information that completely eliminates the first-order delay of the ionosphere, and the carrier phase information L is used as the high-order residual information, w1, w2, and w3 represent the weights of the B1 frequency band, B2 frequency band, and B3 frequency band to be optimized respectively; The frequency band weight optimization objective function includes: w=[w1,w2,w3] <h2 style=";text-align:left;direction:ltr">E1(w) = λ1(w1·(f1)<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +w2·(f2)<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +w3·(f3)<h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> ) E2(w)=λ2(w1+w2+w3-1) Wherein, w represents the frequency band weight vector composed of the weights of the B1 frequency band, B2 frequency band, and B3 frequency band to be optimized, E1(w) and E2(w) represent the introduced Lagrangian functions, and λ1 and λ2 represent the Lagrangian coefficients.

4. The method according to claim 1, wherein The meteorological parameters are obtained by converting atmospheric data using a drift ratio measurement method; the meteorological parameters include temperature and water vapor pressure, and the atmospheric data includes a temperature sequence and a water vapor pressure sequence under atmospheric conditions; Calculating the tropospheric wet delay according to the meteorological parameters includes: Where F1 represents the moist delay of the troposphere, Tem represents the temperature, and E represents the water vapor pressure.

5. The method according to claim 1, wherein The preset dynamic filtering model includes a state vector building module and a state transfer module; The state vector construction module is used to convert the high-order residual information and the wet delay into a state vector; the state transfer module is used to iteratively transfer the state vector until a preset termination transfer requirement is met, thereby obtaining high-order residual correction information and corrected wet delay.

6. The method according to claim 1, characterized in that The calculating of the static delay based on the corrected wet delay based on the preset navigation positioning equation includes: Calculate tropospheric delay information based on preset navigation positioning equations; Converting the tropospheric delay information into a corresponding mapping function; Based on the mapping function, a static term delay is calculated according to the corrected wet delay.

7. The method according to claim 6, characterized in that The preset navigation and positioning equation includes: in, represents the pseudorange between the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna of the carrier phase observation, ρ represents the geometric distance between the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna, τ represents the sum of the offset and change of the phase center of the satellite signal transmitting antenna and the phase center of the ground signal receiving antenna, δ1 represents the hardware delay of the satellite, δ2 represents the hardware delay of the receiver, d1 represents the satellite clock error, d2 represents the receiver clock error, d3 represents the relativistic effect, α1 represents the wavelength of the satellite signal, α2 represents the phase wrapping, α3 represents the integer ambiguity, c represents the speed of light, and θ represents the delay information. represents the ionospheric delay, and G represents the tropospheric delay information; The mapping function includes: Among them, M1 represents the wet term projection function, M2 represents the static term projection function, M3 represents the angle projection function, θ1 represents the angle between the ground signal receiving antenna and the ground plane, θ2 represents the horizontal angle between the direction of the ground signal receiving antenna pointing to the satellite and the ground north direction, Grad1 represents the ground angle projection coefficient, and Grad2 represents the horizontal angle projection coefficient. represents the static delay, Indicates corrected wet delay.

8. A BeiDou water vapor ionosphere detection system calibration device, characterized in that: include: Acquisition module, used to obtain Beidou multi-frequency observation data, meteorological parameters and carrier phase observations; A calculation module is used to perform dual-frequency combined observation based on the observation values ​​of any two frequency bands in the Beidou multi-frequency observation data, and calculate the residual information after the first-order delay of the ionosphere is eliminated; An optimization module, configured to optimize and combine the residual information based on a preset combination optimization rule to generate high-order residual information; The calculation module is further used to calculate the tropospheric wet delay according to the meteorological parameters; a correction module, configured to perform filtering correction on the high-order residual information and the wet delay based on a preset dynamic filtering model to obtain high-order residual correction information and corrected wet delay; The calculation module is further configured to calculate the static delay according to the corrected wet delay based on a preset navigation and positioning equation; the preset navigation and positioning equation is constructed based on the carrier phase observation; A calibration module is used to calibrate the Beidou water vapor ionosphere detection system by using the high-order residual correction information as the ionospheric delay, and using the corrected wet delay and the static term delay as the zenith tropospheric delay.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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