CN117908053A - Ionosphere data extraction method and device, storage medium and electronic equipment - Google Patents
Ionosphere data extraction method and device, storage medium and electronic equipment Download PDFInfo
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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Abstract
The application provides an ionosphere data extraction method, an ionosphere data extraction device, a storage medium and electronic equipment; the method comprises the following steps: obtaining observation data, including pseudo-range observation quantity and phase observation quantity, constructing a phase observation equation related to the phase observation quantity, constructing an epoch differential model according to the phase observation equation, and constructing a first phase observation equation based on the epoch differential model; constructing a linear equation set of true error vectors of the phase observables according to a first phase observation equation, adding a constructed projection matrix to obtain the true error equation set, adding a quasi-observation norm minimum condition to the true error equation set to obtain a full rank equation set, solving the full rank equation set to remove the phase observables with coarse difference and cycle slip, determining a clock slip of a receiver, and repairing the clock slip to correct the corresponding phase observables; and solving to obtain ionosphere data by using the corrected phase observed quantity according to a constructed recursive formula of Kalman filtering.
Description
Technical Field
The embodiment of the application relates to the technical field of satellite communication, in particular to an ionosphere data extraction method, an ionosphere data extraction device, a storage medium and electronic equipment.
Background
In the related method for extracting ionosphere data by using observables of a satellite navigation system, high-precision correction data outside the satellite navigation system is often required to be introduced, so that the calculation of the ionosphere data is very inconvenient, and meanwhile, data such as real-time orbit or clock error and the like are often required to be accessed through a network, so that depending on an environment with a public network, the ionosphere data cannot be extracted in real time in an environment without the public network
Disclosure of Invention
In view of the above, the present application aims to provide a method, a device, a storage medium and an electronic apparatus for extracting ionosphere data.
Based on the above object, the present application provides a method for extracting ionosphere data, comprising:
Taking the position of a receiver as an observation position, enabling the receiver to acquire observation data of each satellite, wherein the observation data of each satellite comprises pseudo-range observation quantity and phase observation quantity corresponding to the satellite, and acquiring a correction number and broadcast ephemeris of each satellite;
For each phase observation quantity, constructing a phase observation equation related to the phase observation quantity, constructing an epoch differential model according to the phase observation equation, and constructing a first phase observation equation based on the epoch differential model;
Constructing a linear equation set of true error vectors of phase observance according to the first phase observation equation, adding a constructed projection matrix to obtain a true error equation set, adding quasi-observation norm minimum conditions to the true error equation set to obtain a full rank equation set, eliminating the phase observance with coarse difference and cycle slip by solving the full rank equation set, and determining a clock slip after clock slip of the receiver to correct the corresponding phase observance;
And solving to obtain ionosphere data by using the corrected phase observed quantity according to a constructed recursive formula of Kalman filtering.
Further, for each phase observance quantity, constructing a phase observation equation for the phase observance quantity includes:
The phase observation equation shown below is constructed,
Where S denotes the satellite system, S denotes the S-th satellite, k denotes the receiver, j denotes the frequency number,Representing the phase observations of satellite s and receiver k at the jth frequency,/>Representing the geometric distance of the satellite s from the antenna phase center of receiver k, δt k representing the receiver clock difference, δt s representing the satellite clock difference,/>Representing tropospheric delay,/>Represents ionospheric delay, α j represents the frequency ratio, and/>F j denotes the value of the j-th frequency, f 1 denotes the value of the 1 st frequency,/>Representing the hardware delay of the receiver at the jth frequency,/>Representing the hardware delay of the satellite at the j-th frequency,/>Representing the phase offset of the receiver at the j-th frequency,/>Represents the phase offset of the satellite at the jth frequency, lambda j represents the carrier wavelength at the jth frequency, N j represents the non-differential phase integer ambiguity at the jth frequency,/>Representing a modelable error,/>The phase observation noise of the jth frequency is represented, and c represents the speed of light.
Further, constructing an epoch differential model according to the phase observation equation, and constructing a first phase observation equation based on the epoch differential model, including:
Constructing an epoch differential model between two epochs according to the phase observation equation,
Wherein Δ represents the amount of change between the t+1 epoch and the t epoch;
in the epoch differential model Neglecting, and pair/>After the taylor expansion is performed, the first order term is reserved, a first phase observation equation is obtained as shown below,
Wherein,Unit vector representing satellite s to receiver k direction, Δζ k represents receiver k displacement vector to be estimated,/>The satellite s motion-induced satellite distance change is denoted by Δcδt k, the receiver k-clock variation is denoted by Δcδt s, and the satellite k-clock variation is denoted by Δcδt s.
Further, constructing a linear equation set of true error vectors of the phase observance according to the first phase observation equation, and adding the constructed projection matrix to obtain the true error equation set, including:
In response to the number of observed satellites being greater than a preset satellite number threshold, constructing a truth vector representing a receiver displacement vector and receiver clock variation based on all observed satellites;
Constructing a linear equation set between the true error vector and the phase observed quantity superimposed true error vector according to the first phase observed equation, and representing the linear equation set as follows,
Wherein A represents a preset truth vector coefficient matrix, X represents a truth vector, X= [ delta ] ζ k Δcδtk ], L represents a phase observed quantity vector,A true error vector representing the phase observables;
The projection matrix is constructed as shown below,
R＝I-A(ATPA)-1ATP；
Wherein R represents the projection matrix, P represents the weight matrix of the phase observed quantity, and I represents the identity matrix;
multiplying the projection on both sides of the linear system of equations simultaneously, resulting in a true error system of equations as shown below,
Further, adding a quasi-observation norm minimum condition to the true error equation set to obtain a full rank equation set, including:
A quasi-observation norm minima condition is constructed as shown below,
Adopting quasi-calibration method, attaching the quasi-observation norm minimum condition to the true error equation set to obtain full rank equation set as shown below,
Wherein,Representing the coefficient matrix corresponding to the quasi-observation, and P k represents the weight matrix corresponding to the quasi-observation.
Further, rejecting phase observables with coarse differences and cycle slips by solving the full rank equation set, and determining a post-slip repair clock slip for the receiver, comprising:
Solving the full rank equation set to obtain the conjugate transpose of the true error vector as shown below,
Responding to the group division phenomenon of the conjugate transpose of the true error vector, and determining that the corresponding phase observed quantity has coarse difference and/or cycle slip;
The clock-skip value is determined according to the formula shown below,
Wherein Js represents a clock-skip value, round represents a rounding function, and k 2 represents a preset clock-skip threshold;
the phase observations corrected according to the repair formula shown below, to repair the clock-skip,
Further, according to a constructed recursive formula of Kalman filtering, obtaining ionosphere data by solving the corrected phase observed quantity, wherein the method comprises the following steps:
The corrected phase observed quantity is input into a recursive formula of robust Kalman filtering shown in the following to iterate so as to predict ionosphere data,
Wherein t represents time, X t,t-1 represents a one-step predicted value of the ionosphere data, M t,t-1 represents a variance covariance matrix of the one-step predicted value, K t represents a gain matrix, Θ t,t-1 represents a one-step transition matrix from time t-1 to time t, Q represents a variance matrix of a noise sequence of a satellite system composed of a satellite and a receiver, R represents a variance matrix of observed noise of the satellite system composed of the satellite and the receiver,A filtered estimate representing ionospheric data, M t representing a variance covariance matrix of the filtered estimate;
And outputting a filtered estimation of ionosphere data predicted at any moment in response to determining that a normalized residual of a pseudo-range observed quantity predicted at any moment in iteration is less than or equal to a preset first residual threshold and that a normalized residual of a phase observed quantity is less than or equal to the first residual threshold.
Based on the same inventive concept, the application also provides an ionosphere data extraction device, which comprises: the system comprises a data acquisition module, a first data processing module, a second data processing module and a settlement module;
The data acquisition module is configured to take the position of the receiver as an observation position, enable the receiver to acquire observation data of each satellite, enable the observation data of each satellite to comprise pseudo-range observation quantity and phase observation quantity corresponding to the satellite, and acquire correction number and broadcast ephemeris of each satellite;
The first data processing module is configured to construct, for each phase observation quantity, a phase observation equation about the phase observation quantity, construct an epoch differential model according to the phase observation equation, and construct a first phase observation equation based on the epoch differential model;
The second data processing module is configured to construct a linear equation set of true error vectors of phase observance according to the first phase observation equation, add the constructed projection matrix to obtain a true error equation set, add quasi-observation norm minimum conditions to the true error equation set to obtain a full rank equation set, remove the phase observance with coarse difference and cycle slip by solving the full rank equation set, and determine a clock slip to repair the clock slip after the clock slip of the receiver so as to correct the corresponding phase observance;
the resolving module is configured to obtain ionosphere data by utilizing the corrected phase observed quantity according to a constructed recursive formula of Kalman filtering.
Based on the same inventive concept, the application also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the extraction method of the ionosphere data according to any one of the above when executing the program.
Based on the same inventive concept, the present application also provides a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions for causing the computer to perform the method of extracting ionospheric data as described above.
From the above, it can be seen that the method, the device, the storage medium and the electronic equipment for extracting ionosphere data provided by the application construct a phase observation equation based on the acquired pseudo-range observance and the phase observance, obtain a first phase observation equation by combining with an epoch differential model, construct a linear equation set according to the first phase observation equation, obtain a true error equation by introducing a projection matrix into the linear equation set, determine a full rank equation set by comprehensively considering the minimum condition of quasi-observation norms in quasi-detection, remove the phase observance with coarse difference and cycle slip according to the minimum condition, repair clock slip, obtain the repaired phase observance, further utilize robust Kalman filtering to calculate the ionosphere data, and consider the consistency of ambiguity parameters and variances thereof in each iteration.
Drawings
In order to more clearly illustrate the technical solutions of the present application or related art, the drawings that are required to be used in the description of the embodiments or related art will be briefly described below, and it is apparent that the drawings in the following description are only embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort to those of ordinary skill in the art.
FIG. 1 is a flow chart of a method for ionosphere data extraction according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an ionosphere data extracting apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the application.
Detailed Description
The present application will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present application more apparent.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present application should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present application belongs. The terms "first," "second," and the like, as used in embodiments of the present application, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As described in the background section, the related ionosphere data extraction method is also difficult to meet the needs in practical production.
The applicant has found in the process of implementing the present application that the main problems of the related ionosphere data extraction method are: in the related manner of extracting ionosphere data by using the observed quantity of the satellite navigation system, high-precision correction data outside the satellite navigation system is often required to be introduced, so that the calculation of the ionosphere data is very inconvenient, and meanwhile, data such as a real-time track or clock error and the like are often required to be accessed through a network, so that depending on an environment with a public network, the ionosphere data cannot be extracted in real time in an environment without the public network.
Based on this, one or more embodiments of the present application provide a method of ionospheric data extraction.
In the embodiment of the application, a Beidou satellite navigation system can be used as a specific implementation scene, one or more satellites can be arranged in the satellite navigation system, the receiver can be used for acquiring relevant data of each satellite, and meanwhile, the correction of satellite orbit clock error and code deviation can be received.
In this embodiment, when each item of data of the satellite is observed, the observation position at which the observation is performed and the position of the receiver can be regarded as the same position.
In this embodiment, based on the implementation scenario described above, ionospheric data, such as ionospheric delay, is estimated in real time as a parameter to be estimated.
Embodiments of the present application are described in detail below with reference to the accompanying drawings.
Referring to fig. 1, the ionosphere data extraction method according to one embodiment of the present application includes the following steps:
Step S101, taking the position of a receiver as an observation position, enabling the receiver to acquire observation data of each satellite, wherein the observation data of each satellite comprises pseudo-range observation quantity and phase observation quantity corresponding to the satellite, and acquiring correction number and broadcast ephemeris of each satellite.
In an embodiment of the present application, the receiver may acquire observations of each satellite, where the observations may include phase observations (i.e., carrier phase observations) and pseudorange observations.
Further, at the same time, for an observable satellite, the receiver can also receive the broadcast ephemeris, the orbit clock correction and the satellite code deviation of the satellite, and store the data into the corresponding structure.
Step S102, for each phase observed quantity, constructing a phase observation equation related to the phase observed quantity, constructing an epoch differential model according to the phase observation equation, and constructing a first phase observation equation based on the epoch differential model.
In the embodiment of the application, based on the obtained phase observance, the obtained phase observance can be preprocessed to control the data quality.
In this embodiment, preprocessing of the data may include coarse rejection, cycle slip detection, clock slip detection and repair of the receiver, and so on.
Specifically, from the observed phase observance, a phase observation equation as shown below can be constructed:
where S denotes the satellite system, S denotes the S-th satellite, k denotes the k-th receiver, j denotes the frequency number, Representing the phase observations of satellite s and receiver k at the jth frequency,/>Representing the geometric distance of the satellite s from the antenna phase center of receiver k, δt k representing the receiver clock difference, δt s representing the satellite clock difference,/>Representing tropospheric delay,/>Represents ionospheric delay, α j represents the frequency ratio, and/>F j denotes the value of the j-th frequency, f 1 denotes the value of the 1 st frequency,/>Representing the hardware delay of the receiver at the jth frequency,/>Representing the hardware delay of the satellite at the j-th frequency,/>Representing the phase offset of the receiver at the j-th frequency,/>Represents the phase offset of the satellite at the jth frequency, lambda j represents the carrier wavelength at the jth frequency, N j represents the non-differential phase integer ambiguity at the jth frequency,/>Representing a modelable error,/>The phase observation noise of the jth frequency is represented, and c represents the speed of light.
In this embodiment, the modelable errors include antenna phase centre correction, antenna phase wrapping, relativistic effects, tidal correction, etc., and are considered to have been corrected into observations using a pre-set empirical model.
In this embodiment, for any two epochs, based on the above-constructed phase observation equation, an epoch differential model as follows may be formed:
where Δ represents the amount of change between t+1 epochs and t epochs, and may specifically be the increment between epochs.
Further, for high frequency data, since the interval time is short, the epoch differential model isCan ignore urgency and pair/>Taylor expansion is performed, and after expansion, a first-order term is reserved, and can be expressed as follows:
further, the above expressed formula may be regarded as the first phase observation equation.
Wherein,Unit vector representing satellite s to receiver k direction, Δζ k represents receiver k displacement vector to be estimated,/>The satellite s motion-induced satellite distance change is denoted by Δcδt k, the receiver k-clock variation is denoted by Δcδt s, and the satellite k-clock variation is denoted by Δcδt s.
Further, the aboveAnd deltac deltat s can be calculated using the precise orbit correction and broadcast ephemeris.
It can be seen that the first observation equation in this embodiment specifically describes: the relationship between the differential observations between the carrier phase high frequency epochs and the receiver displacement and receiver clock variation.
Step S103, constructing a linear equation set of true error vectors of the phase observables according to the first phase observation equation, adding the constructed projection matrix to obtain the true error equation set, adding quasi-observation norm minimum conditions to the true error equation set to obtain a full rank equation set, eliminating the phase observables with coarse differences and cycle slips by solving the full rank equation set, determining the clock slip of the receiver, and repairing the clock slip after the clock slip so as to correct the corresponding phase observables.
In an embodiment of the present application, when the number of observable satellites is sufficient, a linear equation set may be established based on the first observation equation constructed as described above, and a full rank equation set in quasi-verification may be constructed therefrom to process coarse differences, cycle slips and clock hops.
Specifically, a threshold of the number of satellites may be preset, and the threshold of the number of satellites may be, for example, 4 satellites, and when the number of satellites that can be observed is greater than 4, a system of linear equations may be constructed.
Wherein the receiver displacement vector to be estimated and the receiver clock variation can be combined into a true value vector as follows:
X＝[Δξk Δcδtk]
Where X represents the truth vector.
Based on this, a linear equation set shown below can be constructed using the true value vector based on the first phase observation equation described above:
wherein A represents a preset true value vector coefficient matrix, L represents a phase observed quantity vector, Representing the true error vector of the phase observables.
It can be seen that the above-described system of linear equations specifically describes the relationship between the phase observations, after the true error vector, and the true value vector.
Further, a projection matrix may be introduced for a system of linear equations to obtain a system of true error equations.
Specifically, a projection matrix is constructed as follows:
R＝I-A(ATPA)-1ATP；
Wherein R represents a projection matrix, P represents a weight matrix of a phase observed quantity, and I represents an identity matrix.
Based on the above, the projection matrix can be multiplied by two sides of the above linear equation set formula equal sign at the same time to obtain the true error equation set as follows:
Based on the true error equation set, the data can be processed by a quasi-calibration method with the addition of quasi-observation norm minim condition.
Specifically, quasi-observation norm minima conditions are constructed as follows:
based on this, the quasi-observation norm minim condition is added to the true error equation set described above, resulting in a full rank equation set as shown below,
Wherein,Representing the coefficient matrix corresponding to the quasi-observation, and P k represents the weight matrix corresponding to the quasi-observation.
Based on the above, the full rank equation set can be solved, and the following results:
wherein, Representing the conjugate transpose of the true error vector.
Further, whenWhen clustering phenomena occur, e.g. one or more/>The values of (2) are greater than the values of the other major partsIf the value of (c) is large, the corresponding phase observation vector L may be considered to include a coarse difference or cycle slip, and further, the phase observation amount corresponding to the phase observation vector L may be eliminated.
Further, a test quantity formula as shown below may be constructed to detect a clock-skip of the receiver: iΩ t(s)|＝|Ds(t)-Ds(t-1)-(λ·φs(t)-λ·φ(t-1))|>k1 =0.001·c
Wherein Ω t(s) represents the check-up quantity of the satellite s at epoch t, D represents the pseudo-range observed quantity, λ represents the wavelength of the phase observed quantity, and k 1 represents the preset check-up threshold.
Based on this, for any epoch, it can be considered that there is a millimeter-scale cycle slip if and only if the test amounts corresponding to all observable satellites satisfy the test amount formula described above.
Based on this, the cycle slip value can be determined according to the following formula:
Where Js represents a clock-skip value in milliseconds, round represents a rounding function, and k 2 represents a preset clock-skip threshold.
In this embodiment, a looser clock-skip threshold setting may be considered more advantageous in maintaining its continuity in data processing, for example, the clock-skip threshold may be 0.05.
Based on this, in order to maintain the pseudo-range clock difference reference and also to consider the continuity of the ambiguity parameter in the estimation, when the existence of a clock jump is detected, the corresponding phase observed quantity can be corrected to repair the clock jump so as to ensure the consistency of the phase observed quantity and the pseudo-range observed quantity.
Specifically, the phase observance amount, which can be corrected according to the repair formula shown below:
wherein, Indicating the corrected phase observations.
And step S104, according to a constructed recursive formula of Kalman filtering, solving by using the corrected phase observed quantity to obtain ionosphere data.
In an embodiment of the present application, based on the corrected phase observables, robust Kalman (Kalman) filtering may be used to solve the ionospheric data.
Specifically, a recursive formula of robust Kalman filtering can be constructed as follows:
Wherein t represents time, X t,t-1 represents one-step predicted value of ionosphere data, M t,t-1 represents variance covariance matrix of one-step predicted value, K t represents gain matrix, Θ t,t-1 represents one-step transition matrix from time t-1 to time t, Q represents variance matrix of noise sequence of satellite system formed by satellite and receiver, R represents variance matrix of observed noise of satellite system formed by satellite and receiver, Representing filtered estimates of ionospheric data, M t representing a variance covariance matrix of the filtered estimates.
In this embodiment, for convenience of description, the time denoted by t is denoted by t in combination with the foregoing description, and thus, t may refer to different epochs in this embodiment.
In this embodiment, the ionospheric data may be ionospheric delay or the like.
Further, the fixed weights for the phase observations and the pseudo-range observations may be constructed as follows:
Where W i represents the weight corresponding to element L i in the phase observation vector L, Representing the corresponding normalized residuals, k a and k b are constants, each of which may be, for example: k a =1.0 to 1.5, k b =2.0 to 3.0, and k a as the first residual threshold and k b as the second residual threshold.
Based on the above, according to the above weight, the filtered estimated value of the ionosphere data of each iteration can be predicted in each iteration, the phase observed quantity and the pseudo-range observed quantity corresponding to the filtered estimated value are determined, and the corresponding normalized residual is utilized to process the corresponding pseudo-range observed quantity and/or the phase observed quantity.
Specifically, for the phase observed quantity and the pseudo-range observed quantity, when the normalized residual error corresponding to the phase observed quantity is smaller than or equal to the first residual error threshold value, the phase observed quantity is considered to have no cycle slip, the ambiguity parameter is not required to be reset, and the corresponding weight is not required to be reduced.
Furthermore, at the same time, when the normalized residual error corresponding to the pseudo-range observed quantity is smaller than or equal to the first residual error threshold value, the weight is not required to be reduced, and the variance corresponding to the ambiguity parameter is not required to be processed.
Based on this, a filtered estimate of the corresponding ionospheric data may be output.
In some other embodiments, when the normalized residual corresponding to the phase observed quantity is greater than the first residual threshold, then it is considered that cycle slip occurs and the ambiguity parameter needs to be reset without the corresponding weights being de-weighted.
Further, when the normalized residual error corresponding to the observed quantity of the pseudo range is greater than the first residual error threshold, the variance corresponding to the ambiguity parameter may be appropriately enlarged in addition to the weight reduction corresponding to the normalized residual error, and the specific magnification may be empirically set, for example, may be enlarged by 2.5 times.
Based on this, the next iteration may be performed again after the above-described ambiguity parameter and weight reduction process is performed.
It can be seen that, according to the ionosphere data extraction method of the embodiment of the application, a phase observation equation is constructed based on the acquired pseudo-range observed quantity and the acquired phase observed quantity, a first phase observation equation is obtained by combining with an epoch differential model, a linear equation set is constructed according to the first phase observation equation, a true error equation is obtained by introducing a projection matrix into the linear equation set, a full rank equation set is determined by comprehensively considering the quasi-observation norm minimum condition in quasi-detection, the phase observed quantity with coarse difference and cycle slip is eliminated according to the full rank equation set, clock slip is repaired, the repaired phase observed quantity is obtained, the solution of the ionosphere data is further carried out by utilizing anti-difference kalman filtering, and the consistency of ambiguity parameters and variances thereof is considered in each iteration.
It should be noted that, the method of the embodiment of the present application may be performed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the method of an embodiment of the present application, the devices interacting with each other to complete the method.
It should be noted that the foregoing describes some embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Based on the same inventive concept, the embodiment of the application also provides an ionosphere data extraction device corresponding to the method of any embodiment.
Referring to fig. 2, the ionosphere data extraction apparatus includes: a data acquisition module 201, a first data processing module 202, a second data processing module 203, and a settlement module 204;
The data obtaining module 201 is configured to take a position of a receiver as an observation position, make the receiver obtain observation data of each satellite, wherein the observation data of each satellite includes pseudo-range observation quantity and phase observation quantity corresponding to the satellite, and obtain a correction number and a broadcast ephemeris of each satellite;
The first data processing module 202 is configured to construct, for each phase observance quantity, a phase observation equation about the phase observance quantity, construct an epoch differential model according to the phase observation equation, and construct a first phase observation equation based on the epoch differential model;
The second data processing module 203 is configured to construct a linear equation set of true error vectors of the phase observables according to the first phase observation equation, add the constructed projection matrix to obtain a true error equation set, attach quasi-observation norm minimum conditions to the true error equation set to obtain a full rank equation set, remove the phase observables with coarse differences and cycle slips by solving the full rank equation set, and determine a clock slip to repair the clock slip after the clock slip of the receiver so as to correct the corresponding phase observables;
The resolving module 204 is configured to obtain ionospheric data by resolving the corrected phase observed quantity according to a constructed recursive formula of kalman filtering.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing an embodiment of the present application.
The device of the foregoing embodiment is configured to implement the corresponding ionosphere data extraction method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, corresponding to the method of any embodiment, the embodiment of the application further provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to implement the method for extracting ionosphere data according to any embodiment.
Fig. 3 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided by the embodiments of the present application.
The memory 1020 may be implemented in the form of ROM (read only memory), RAM (Random Access Memory ), static storage, dynamic storage, etc. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present application are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown in the figure) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary for implementing the embodiments of the present application, and not all the components shown in the drawings.
The device of the foregoing embodiment is configured to implement the corresponding ionosphere data extraction method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, the present application also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the ionospheric data extraction method according to any of the embodiments above, corresponding to any of the embodiments above.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The storage medium of the foregoing embodiments stores computer instructions for causing the computer to execute the method for extracting ionosphere data according to any one of the foregoing embodiments, and has the advantages of the corresponding method embodiments, which are not described herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the application (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the application, the steps may be implemented in any order and there are many other variations of the different aspects of the embodiments of the application as described above, which are not provided in detail for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure embodiments of the present application. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring embodiments of the present application, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present application are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The embodiments of the application are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalents, improvements and the like, which are within the spirit and principles of the embodiments of the application, are intended to be included within the scope of the application.
Claims (10)
1. A method of ionospheric data extraction, comprising:
Taking the position of a receiver as an observation position, enabling the receiver to acquire observation data of each satellite, wherein the observation data of each satellite comprises pseudo-range observation quantity and phase observation quantity corresponding to the satellite, and acquiring a correction number and broadcast ephemeris of each satellite;
For each phase observation quantity, constructing a phase observation equation related to the phase observation quantity, constructing an epoch differential model according to the phase observation equation, and constructing a first phase observation equation based on the epoch differential model;
Constructing a linear equation set of true error vectors of phase observance according to the first phase observation equation, adding a constructed projection matrix to obtain a true error equation set, adding quasi-observation norm minimum conditions to the true error equation set to obtain a full rank equation set, eliminating the phase observance with coarse difference and cycle slip by solving the full rank equation set, and determining a clock slip after clock slip of the receiver to correct the corresponding phase observance;
And solving to obtain ionosphere data by using the corrected phase observed quantity according to a constructed recursive formula of Kalman filtering.
2. The method of claim 1, wherein for each phase observance quantity, constructing a phase observance equation for that phase observance quantity comprises:
The phase observation equation shown below is constructed,
Where S denotes the satellite system, S denotes the S-th satellite, k denotes the receiver, j denotes the frequency number,Representing the phase observations of satellite s and receiver k at the jth frequency,/>Representing the geometric distance of the satellite s from the antenna phase center of receiver k, δt k representing the receiver clock difference, δt s representing the satellite clock difference,/>Representing tropospheric delay,/>Represents ionospheric delay, α j represents the frequency ratio, and/>F j denotes the value of the j-th frequency, f 1 denotes the value of the 1 st frequency,/>Representing the hardware delay of the receiver at the jth frequency,/>Representing the hardware delay of the satellite at the j-th frequency,/>Representing the phase offset of the receiver at the j-th frequency,/>Represents the phase offset of the satellite at the jth frequency, lambda j represents the carrier wavelength at the jth frequency, N j represents the non-differential phase integer ambiguity at the jth frequency,/>Representing a modelable error,/>The phase observation noise of the jth frequency is represented, and c represents the speed of light.
3. The method of claim 1, wherein constructing an epoch differential model from the phase-observation equation and constructing a first phase-observation equation based on the epoch differential model comprises:
Constructing an epoch differential model between two epochs according to the phase observation equation,
Wherein Δ represents the amount of change between the t+1 epoch and the t epoch;
in the epoch differential model Neglecting, and pair/>After the taylor expansion is performed, the first order term is reserved, a first phase observation equation is obtained as shown below,
Wherein,A unit vector representing the direction of satellite s to receiver k, deltaxi k represents the receiver k displacement vector to be estimated,The satellite s motion-induced satellite distance change is denoted by Δcδt k, the receiver k-clock variation is denoted by Δcδt s, and the satellite k-clock variation is denoted by Δcδt s.
4. The method according to claim 1, wherein constructing a linear equation set of true error vectors of phase observables from the first phase observation equation and adding the constructed projection matrix to obtain the true error equation set includes:
In response to the number of observed satellites being greater than a preset satellite number threshold, constructing a truth vector representing a receiver displacement vector and receiver clock variation based on all observed satellites;
Constructing a linear equation set between the true error vector and the phase observed quantity superimposed true error vector according to the first phase observed equation, and representing the linear equation set as follows,
Wherein A represents a preset truth vector coefficient matrix, X represents a truth vector, X= [ delta ] ζ k Δcδtk ], L represents a phase observed quantity vector,A true error vector representing the phase observables;
The projection matrix is constructed as shown below,
R＝I-A(ATPA)-1ATP；
Wherein R represents the projection matrix, P represents the weight matrix of the phase observed quantity, and I represents the identity matrix;
multiplying the projection on both sides of the linear system of equations simultaneously, resulting in a true error system of equations as shown below,
5. The method of claim 1, wherein said appending quasi-observation norm minima conditions to said set of true error equations yields a set of full rank equations, comprising:
A quasi-observation norm minima condition is constructed as shown below,
Adopting quasi-calibration method, attaching the quasi-observation norm minimum condition to the true error equation set to obtain full rank equation set as shown below,
Wherein,Representing the coefficient matrix corresponding to the quasi-observation, and P k represents the weight matrix corresponding to the quasi-observation.
6. The method of claim 1, wherein said rejecting phase observations that are subject to coarse differences and cycle slips by solving the full set of rank equations and determining a post-clock-slip repair clock slip for the receiver comprises:
Solving the full rank equation set to obtain the conjugate transpose of the true error vector as shown below,
Responding to the group division phenomenon of the conjugate transpose of the true error vector, and determining that the corresponding phase observed quantity has coarse difference and/or cycle slip;
The clock-skip value is determined according to the formula shown below,
Wherein Js represents a clock-skip value, round represents a rounding function, and k 2 represents a preset clock-skip threshold;
the phase observations corrected according to the repair formula shown below, to repair the clock-skip,
7. The method of claim 1, wherein solving for ionospheric data using the corrected phase observations in accordance with the constructed kalman filter recurrence formula comprises:
The corrected phase observed quantity is input into a recursive formula of robust Kalman filtering shown in the following to iterate so as to predict ionosphere data,
Wherein t represents time, X t,t-1 represents a one-step predicted value of the ionosphere data, M t,t-1 represents a variance covariance matrix of the one-step predicted value, K t represents a gain matrix, Θ t,t-1 represents a one-step transition matrix from time t-1 to time t, Q represents a variance matrix of a noise sequence of a satellite system composed of a satellite and a receiver, R represents a variance matrix of observed noise of the satellite system composed of the satellite and the receiver,A filtered estimate representing ionospheric data, M t representing a variance covariance matrix of the filtered estimate;
And outputting a filtered estimation of ionosphere data predicted at any moment in response to determining that a normalized residual of a pseudo-range observed quantity predicted at any moment in iteration is less than or equal to a preset first residual threshold and that a normalized residual of a phase observed quantity is less than or equal to the first residual threshold.
8. An ionosphere data extraction apparatus, comprising: the system comprises a data acquisition module, a first data processing module, a second data processing module and a settlement module;
The data acquisition module is configured to take the position of the receiver as an observation position, enable the receiver to acquire observation data of each satellite, enable the observation data of each satellite to comprise pseudo-range observation quantity and phase observation quantity corresponding to the satellite, and acquire correction number and broadcast ephemeris of each satellite;
The first data processing module is configured to construct, for each phase observation quantity, a phase observation equation about the phase observation quantity, construct an epoch differential model according to the phase observation equation, and construct a first phase observation equation based on the epoch differential model;
The second data processing module is configured to construct a linear equation set of true error vectors of phase observance according to the first phase observation equation, add the constructed projection matrix to obtain a true error equation set, add quasi-observation norm minimum conditions to the true error equation set to obtain a full rank equation set, remove the phase observance with coarse difference and cycle slip by solving the full rank equation set, and determine a clock slip to repair the clock slip after the clock slip of the receiver so as to correct the corresponding phase observance;
the resolving module is configured to obtain ionosphere data by utilizing the corrected phase observed quantity according to a constructed recursive formula of Kalman filtering.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, wherein the processor implements the method of any one of claims 1 to 7 when executing the computer program.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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