Multi-system multi-frequency-point dynamic real-time positioning method, device and equipment and storage medium
By constructing and calculating the matrix form of the single-difference observation equation between stations, the problem of not fully utilizing multi-system and multi-frequency information in traditional GNSS positioning methods is solved, achieving fast and accurate positioning and clock error calculation, and simplifying the processing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-21
- Publication Date
- 2026-04-03
AI Technical Summary
Traditional GNSS positioning methods fail to fully utilize information from multiple systems and frequencies, resulting in ambiguity that is difficult to guarantee integer characteristics, complex processing, and inability to resolve receiver clock bias information.
By constructing the inter-station single-difference observation equation and converting it into matrix form, a new error matrix is constructed using the correction matrix of the unknown matrix, the approximation matrix, and the ambiguity matrix. The coordinate and clock error unknowns are solved by combining the least squares method and the integer least squares method.
It achieves fast and accurate multi-system, multi-frequency positioning, can solve receiver clock errors and maintain the integer characteristics of ambiguity, and simplifies the processing.
Smart Images
Figure CN121784795A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of GNSS positioning technology, specifically to a method, apparatus, equipment, and storage medium for dynamic real-time positioning of multiple systems and multiple frequencies. Background Technology
[0002] Global Navigation Satellite Systems (GNSS) include the Global Positioning System (GPS), the BeiDou Navigation Satellite System (BDS), the GLONASS system, and the Galileo satellite navigation system. Modernization of navigation satellites has progressed, with GPS now using 3 frequencies, BDS 2nd and 3rd generation using 8 frequencies, Galileo using 5 frequencies, and GLONASS gradually being updated to 3 frequencies. This multi-system, multi-frequency navigation architecture provides GNSS observations with more information, making Real-time Kinematic (RTK) positioning faster. Traditional RTK typically employs a dual-frequency ionospheric desaturation strategy or a dual-difference mode, failing to fully utilize information from all frequencies.
[0003] For multi-system GNSS, due to satellite system bias, the difference between satellites in each system cannot eliminate the system bias when establishing the double-difference observation equation. Only by performing double differences between satellites within the same system can the LAMBDA method be used to fix integer ambiguities. Once clock bias parameters are present, it is difficult to guarantee the integer properties of the ambiguities, which is not conducive to fast resolution.
[0004] Traditional double-difference solutions, while capable of calculating high-precision coordinates, eliminate the receiver clock bias unknown after double-difference, thus failing to obtain the receiver clock bias information (required for timing). On the other hand, double-difference ambiguity makes the processing more complex during algorithm software development due to frequent satellite switching issues. Double-difference also eliminates other parameters that people need to pay attention to. Furthermore, the carrier phases of multiple systems and multiple frequency points cannot be subtracted. Summary of the Invention
[0005] This invention provides a multi-system, multi-frequency dynamic real-time positioning method, device, equipment, and storage medium to solve the problem in the prior art that it is impossible to fully utilize the information of all frequency points for positioning.
[0006] In a first aspect, the present invention provides a multi-system, multi-frequency dynamic real-time positioning method, comprising: constructing an inter-station single-difference observation equation for each frequency point of each satellite based on observation data from two observation stations for each satellite, and converting the inter-station single-difference observations into a matrix form to characterize the relationship between an unknown matrix, an inter-station single-difference ambiguity matrix, and an error matrix, wherein the unknown matrix includes coordinate unknowns and clock error unknowns; calculating an approximate value matrix of the inter-station single-difference ambiguity matrix based on the single-difference pseudorange and single-difference carrier for each frequency point of each satellite from the two observation stations; and utilizing the unknown matrix and approximate value matrix... A new error matrix is constructed from the value matrix and the correction matrix of the inter-station single-difference ambiguity matrix. The normal equation of the new error matrix is calculated using the least squares method to obtain the floating-point solution of the ambiguity and the covariance matrix. The floating-point solution of the ambiguity and the covariance matrix are searched using the integer least squares method to obtain the integer solution of the single-difference ambiguity. The integer solution is used as the inter-station single-difference ambiguity matrix. Combined with the error matrix, the unknown matrix is calculated to obtain the coordinate correction and clock error between the two observation stations for each frequency point of each satellite. The positioning result is obtained by combining the coordinate correction and clock error between the two observation stations for each frequency point of each satellite.
[0007] In one optional implementation, the normal equation of the new error matrix is calculated using the least squares method to obtain the ambiguity floating-point solution and the covariance matrix, including: constructing a priori weight matrix for the inter-station single-difference ambiguity matrix, where the elements in the priori weight matrix represent the weights of the phase observations at each frequency point of each satellite; constructing the normal equation of the new error matrix using the priori weight matrix; and calculating the normal equation using the least squares method to obtain the ambiguity floating-point solution and the covariance matrix.
[0008] In one optional implementation, constructing the prior weight matrix of the inter-station single-difference ambiguity matrix includes: selecting reference satellites corresponding to each frequency point from multiple satellites, setting the weight value of the phase observation value corresponding to the reference satellite at each frequency point as the first weight value, setting the weight value of the phase observation value of other satellites at each frequency point as the second weight value, wherein the first weight value is greater than the second weight value; and constructing the prior weight matrix based on the weight value of the phase observation value of each frequency point of each satellite.
[0009] In one alternative implementation, the approximation matrix is:
[0010] in, It is an approximate matrix. Indicates the satellite number, Indicates the frequency point number. for frequency and The satellite's corresponding single-difference pseudorange, for The wavelength of the frequency carrier. for frequency and The satellite's corresponding single-difference carrier, This is the floor function.
[0011] In one alternative implementation, the new error matrix is:
[0012]
[0013] in, For the new error matrix, For matrix The corresponding coefficient matrix, A matrix of unknowns. For the correction matrix The corresponding coefficient matrix, This is the inter-station single-difference ambiguity matrix. The constant term matrix calculated from the observed values and approximate coordinates. The correction matrix for the inter-station single-difference ambiguity matrix is... , It is an approximate matrix.
[0014] In one alternative implementation, the normal equation for the new error matrix is:
[0015] in, matrix of unknowns The corresponding coefficient matrix, For the correction matrix The corresponding coefficient matrix, This is the correction matrix for the inter-station single-difference ambiguity matrix. The prior weight matrix, , The constant term matrix calculated from the observed values and approximate coordinates. It is an approximate matrix.
[0016] In one alternative implementation, the ambiguity floating-point solution is:
[0017] The covariance matrix is:
[0018] in, The covariance matrix of the unknown matrix X, The covariance matrix between the unknown matrix X and the inter-station single-difference ambiguity matrix Y. The covariance matrix between the inter-station single-difference ambiguity matrix Y and the unknown matrix X, For the correction matrix The covariance matrix.
[0019] Secondly, the present invention provides a multi-system multi-frequency dynamic real-time positioning device, comprising: an error matrix construction module, used to construct the inter-station single-difference observation equation for each frequency point of each satellite based on the observation data of two observation stations for each frequency point of each satellite, and convert the inter-station single-difference observation into a matrix form, used to characterize the relationship between the unknown matrix, the inter-station single-difference ambiguity matrix and the error matrix, wherein the unknown matrix contains coordinate unknowns and clock error unknowns; The system comprises the following modules: an approximation matrix construction module for calculating the approximation matrix of the inter-station single-difference ambiguity matrix based on the single-difference pseudorange and single-difference carrier for each frequency point of each satellite from both observation stations; an error matrix update module for constructing a new error matrix using the unknown matrix, the approximation matrix, and the correction matrix of the inter-station single-difference ambiguity matrix; an error matrix calculation module for performing calculations on the normal equation of the new error matrix using the least squares method to obtain the floating-point solution of the ambiguity and the covariance matrix; an integer solution search module for searching the floating-point solution of the ambiguity and the covariance matrix using the integer least squares method to obtain the integer solution of the single-difference ambiguity; an unknown matrix calculation module for using the integer solution as the inter-station single-difference ambiguity matrix and combining it with the error matrix to calculate the unknown matrix, thereby obtaining the coordinate correction and clock error between the two observation stations for each frequency point of each satellite; and a positioning module for obtaining the positioning result by combining the coordinate correction and clock error between the two observation stations for each frequency point of each satellite.
[0020] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the multi-system multi-frequency dynamic real-time positioning method described in the first aspect or any corresponding embodiment thereof.
[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the multi-system multi-frequency dynamic real-time positioning method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description
[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0023] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of the first type of dynamic real-time positioning method for multiple systems and multiple frequencies according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a multi-system, multi-frequency dynamic real-time positioning device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0025] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.
[0026] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.
[0027] As an optional application scenario of this invention, such as Figure 1 As shown, the system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.
[0028] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.
[0029] According to an embodiment of the present invention, a method for dynamic real-time positioning of multiple systems and multiple frequencies is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.
[0030] This embodiment provides a multi-system, multi-frequency dynamic real-time positioning method. Figure 2 This is a flowchart of a multi-system, multi-frequency dynamic real-time positioning method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Based on the observation data of each frequency point of each satellite from the two observation stations, construct the inter-station single-difference observation equation for each frequency point of each satellite, and convert the inter-station single-difference observation into matrix form to characterize the relationship between the unknown matrix, the inter-station single-difference ambiguity matrix and the error matrix. The unknown matrix contains coordinate unknowns and clock error unknowns.
[0031] This invention provides an embodiment for positioning using multi-frequency observation data from multiple systems, including satellite navigation systems such as GPS, BDS, GLONASS, and Galileo. Each satellite navigation system contains multiple satellites, and each satellite communicates using one or more frequencies.
[0032] Positioning can be achieved using data observed by two observation stations at the same frequency of the same satellite. However, there are discrepancies between the observation data obtained by different observation stations. In this embodiment of the invention, an inter-station single-difference observation equation is constructed for each frequency of each satellite, and the inter-station single-difference observation equations for all frequencies of all satellites are combined and converted into a matrix form.
[0033] In an optional embodiment, in high-precision GNSS carrier phase positioning, the pseudorange and carrier phase observation equations for a single epoch satellite s can be expressed as:
[0034] Among them, superscript Represents satellite number, subscript Represents the receiver number, subscript Represents the frequency point number. for j The wavelength of the frequency carrier. and Receiver exist Satellite received at frequency The pseudorange and phase observations, This represents the approximate station-to-satellite distance calculated from the receiver's approximate coordinates and the satellite's ephemeris. Indicates receiver To satellite The unit vector of direction can be calculated from the approximate coordinates of the station and the satellite coordinates. For receiver Coordinate correction amount, The speed of light in a vacuum and These represent receiver clock bias and satellite clock bias, respectively. For tropospheric mapping functions, For tropospheric zenith delay; the first The ionospheric delay conversion factor at a frequency point can be expressed as: (in and The first frequency point and (Frequency point corresponds to frequency) For the first-order ionospheric delay at the first frequency, For the first Integer ambiguity of frequency This refers to the pseudorange hardware delay deviation at the receiver end. This refers to the pseudorange hardware delay deviation at the satellite end; This refers to the phase hardware delay deviation at the receiver end; This is due to phase hardware delay deviation at the satellite end. and Let represent pseudorange and phase observation noise, respectively. The goal of RTK is to quickly solve for the coordinate difference between two stations. Generally, atmospheric delay error is ignored, and the inter-station single-difference phase observation error equation can be expressed as:
[0035] In the formula, For single-difference operators, it represents the receiver. , Difference between them For receiver and receiver The difference between the phase observations, For receiver Approximate coordinates and receiver The difference in theoretical station-satellite distance calculated using approximate coordinates, with a clock bias unknown set for the same frequency and the same global navigation satellite system. , For receiver and receiver Receiver clock bias between For single-difference ambiguity, single-difference phase noise This refers to the result of performing a single-difference operator operation on the phase observation noise. For example, if GPS and BDS respectively have... , One satellite, , If there are 1 frequency point, then the unknowns include: 3 coordinate unknowns. A degree of ambiguity, There are several unknown clock differences.
[0036] In an alternative embodiment, the inter-station single-difference observation equation is converted into matrix form to obtain:
[0037] in, It is an unknown matrix containing coordinate unknowns and clock error unknowns. for The corresponding coefficient matrix, This is the inter-station single-difference ambiguity matrix. for The corresponding coefficient matrix, For example, the constant term is calculated from the observed values and coordinate approximations. It is obtained by subtracting the theoretically calculated value from the observed value. This is the error matrix of single-difference phase observations between stations.
[0038] The normal equation for the above error matrix is:
[0039] Solving the above normal equations yields the matrix. Inter-station single-difference ambiguity matrix floating-point solution and its covariance matrix :
[0040] Theoretically, the inter-station single-difference ambiguity matrix is an integer matrix, which can be derived from... After searching using the integer least squares method to obtain integer solutions for the ambiguity, these solutions are then substituted back into the matrix calculation formula derived from the inter-station single-difference observation equation to calculate the unknown matrix. However, due to the unknown matrix There is a clock error unknown, which is coupled with the inter-station single-difference ambiguity. The least squares method cannot obtain an integer solution. Therefore, in this embodiment of the invention, steps S202-S206 are performed to calculate the unknown matrix. Thus, the coordinate unknowns and clock difference unknowns are obtained.
[0041] Step S202: Calculate the approximate value matrix of the inter-station single-difference ambiguity matrix based on the single-difference pseudorange and single-difference carrier for each frequency point of each satellite by the two observation stations.
[0042] In an optional embodiment, the approximation matrix is:
[0043] in, It is an approximate matrix. Indicates the satellite number, Indicates the frequency point number. for frequency and The satellite's corresponding single-difference pseudorange, for The wavelength of the frequency carrier. for frequency and The satellite's corresponding single-difference carrier, This is the floor function.
[0044] Step S203: Construct a new error matrix using the correction matrix of the unknown matrix, the approximation matrix, and the inter-station single-difference ambiguity matrix.
[0045] In an optional embodiment, the newly constructed error matrix is:
[0046]
[0047] in, For the new error matrix, For matrix The corresponding coefficient matrix, A matrix of unknowns. For the correction matrix The corresponding coefficient matrix, This is the inter-station single-difference ambiguity matrix. The constant term matrix is calculated for the observed values and approximate coordinates, where the observed values are obtained through observation and the approximate coordinates are obtained through pseudorange positioning or are given in advance. This is the correction matrix for the inter-station single-difference ambiguity matrix. The correction matrix represents the correction relative to the initial value, i.e., it is the least-squares floating-point solution. , It is an approximate matrix.
[0048] Step S204: Perform calculations on the normal equation of the new error matrix using the least squares method to obtain the floating-point solution of the ambiguity and the covariance matrix.
[0049] In an optional embodiment, step S204 specifically includes: Step a1: Construct the prior weight matrix of the inter-station single-difference ambiguity matrix. The elements in the prior weight matrix represent the weights of the phase observations at each frequency point of each satellite.
[0050] In an optional embodiment, the inter-station single-difference ambiguity matrix Prior weight matrix for:
[0051] in, Serial Number , The weights for the phase observations of each satellite and each frequency point. and These represent the number of satellites in the first GNSS system and the second GNSS system, respectively. and These represent the number of frequency points for the first GNSS system and the second GNSS system, respectively.
[0052] In an optional embodiment, when determining the value of each element in the prior weight matrix, reference satellites corresponding to each frequency point can be selected from multiple satellites. The weight value of the phase observation value corresponding to the reference satellite at each frequency point is set as the first weight value, and the weight value of the phase observation value of other satellites at each frequency point is set as the second weight value. The first weight value is greater than the second weight value. The prior weight matrix is constructed based on the weight value of the phase observation value of each frequency point of each satellite.
[0053] For example, if the first satellite at each frequency point of each GNSS system is selected as the reference satellite, then, when At that time, take , When taking other values, .
[0054] In this embodiment of the invention, a reference satellite is selected for each frequency point of the GNSS system and a strong constraint is applied, that is, the weight is set to relative infinity. For example, the weight can be set to... The weights of other satellites are taken to be relatively infinitesimal; for example, the weights can be set to... There are a total of One reference star.
[0055] In this embodiment of the invention, the weight of the ambiguity approximation value of the reference star is defined as relatively infinite, and the ambiguity approximation values of other satellites are defined as relatively infinitesimal. Since a strong constraint is applied to the reference star, the ambiguity value of the reference star calculated by the weighted least squares method remains unchanged, that is, it is an integer approximation value, which maintains the integer characteristic. This achieves the effect of using the double difference observation equation, while retaining the function of solving the receiver clock error.
[0056] Step a2: Construct the normal equation of the new error matrix using the prior weight matrix.
[0057] In an alternative embodiment, the normal equation for the new error matrix is:
[0058] in, matrix of unknowns The corresponding coefficient matrix, For the correction matrix The corresponding coefficient matrix, This is the correction matrix for the inter-station single-difference ambiguity matrix. The prior weight matrix, , The constant term matrix calculated from the observed values and approximate coordinates. It is an approximate matrix.
[0059] Step a3: Perform calculations on the normal equations using the least squares method to obtain the ambiguity floating-point solution and the covariance matrix.
[0060] Let matrix Z contain matrix X and the ambiguity correction matrix. The new fuzzy floating-point solution is obtained by using the least squares method. and its covariance matrix :
[0061]
[0062]
[0063] in, The covariance matrix of the unknown matrix X, The covariance matrix between the unknown matrix X and the inter-station single-difference ambiguity matrix Y. The covariance matrix between the inter-station single-difference ambiguity matrix Y and the unknown matrix X, For the correction matrix The covariance matrix.
[0064] Step S205: Search the floating-point solution of ambiguity and the covariance matrix using the integer least squares method to obtain the integer solution of single-difference ambiguity.
[0065] Step S206: The integer solution is used as the inter-station single-difference ambiguity matrix. The unknown matrix is calculated using the relationship between the unknown matrix, the inter-station single-difference ambiguity matrix and the error matrix to obtain the coordinate correction and clock error between the two observation stations for each frequency point of each satellite.
[0066] In one possible embodiment, the integer solution can be substituted into the formula as the inter-station single-difference ambiguity matrix Y. In the process, the least squares method is used to solve for X to obtain the X that minimizes the error matrix V, thereby obtaining the coordinate correction and clock error in X.
[0067] Step S207: Combine the coordinate correction and clock difference between the two observation stations for each frequency point of each satellite to obtain the positioning result.
[0068] In one alternative embodiment, after calculating the coordinate correction, the final accurate coordinates can be obtained by combining the coordinate correction with relevant techniques.
[0069] The multi-system, multi-frequency dynamic real-time positioning method provided in this invention constructs an inter-station single-difference observation equation for each frequency point of each satellite based on the observation data of two observation stations for each satellite. This equation is then converted into a matrix form representing the relationship between the unknown matrix, the inter-station single-difference ambiguity matrix, and the error matrix. Considering the presence of clock error unknowns in the unknown matrix, and the coupling relationship between clock error unknowns and inter-station single-difference ambiguity, the traditional least squares method cannot obtain integer solutions for the inter-station single-difference ambiguity matrix. Therefore, this invention constructs an approximation matrix of the inter-station single-difference ambiguity matrix and uses this approximation matrix to construct a new error matrix. The normal equation of the new error matrix is then calculated using the least squares method to obtain the floating-point solution for ambiguity and the covariance matrix. The floating-point solution for ambiguity and the covariance matrix are then searched using integer least squares to obtain integer solutions for the single-difference ambiguity. Finally, the integer solutions are used as the inter-station single-difference ambiguity matrix to solve the unknown matrix, obtaining the coordinate unknowns and clock error unknowns. This enables positioning based on the coordinate unknowns and clock error unknowns. As can be seen, by setting clock bias unknowns for different systems and frequencies, the embodiments of the present invention can uniformly and systematically process all observation data from multiple systems and frequencies, achieving the effect of fully utilizing the observation values. By imposing constraints on the ambiguity of each satellite at each frequency point, the shortcomings of least squares search when considering clock bias unknowns are overcome, which cannot obtain integer solutions for ambiguity, thus ensuring rapid GNSS positioning.
[0070] This embodiment also provides a multi-system, multi-frequency dynamic real-time positioning device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.
[0071] This embodiment provides a multi-system, multi-frequency dynamic real-time positioning device, such as... Figure 3 As shown, it includes: The error matrix construction module 301 is used to construct the inter-station single-difference observation equation for each frequency point of each satellite based on the observation data of the two observation stations for each satellite, and convert the inter-station single-difference observation into matrix form to characterize the relationship between the unknown matrix, the inter-station single-difference ambiguity matrix and the error matrix. The unknown matrix contains coordinate unknowns and clock error unknowns. The approximation matrix construction module 302 is used to calculate the approximation matrix of the inter-station single-difference ambiguity matrix based on the single-difference pseudorange and single-difference carrier for each frequency point of each satellite by the two observation stations; Error matrix update module 303 is used to construct a new error matrix using the correction matrix of the unknown matrix, the approximation matrix and the inter-station single difference ambiguity matrix; Error matrix calculation module 304 is used to perform calculations on the normal equation of the new error matrix according to the least squares method to obtain the ambiguity floating-point solution and the covariance matrix; The integer solution search module 305 is used to search the floating-point solution of ambiguity and the covariance matrix using the integer least squares method to obtain the integer solution of the single-difference ambiguity. The unknown matrix calculation module 306 is used to use the integer solution as the inter-station single difference ambiguity matrix, and combine it with the error matrix to calculate the unknown matrix, so as to obtain the coordinate correction and clock difference between the two observation stations for each frequency point of each satellite; The positioning module 307 is used to obtain the positioning result by combining the coordinate correction and clock difference between the two observation stations for each frequency point of each satellite.
[0072] The multi-system, multi-frequency dynamic real-time positioning device provided in this embodiment of the invention can execute the multi-system, multi-frequency dynamic real-time positioning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.
[0073] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.
[0074] The following is a detailed reference. Figure 4 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.
[0075] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.
[0076] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the multi-system multi-frequency dynamic real-time positioning method of the embodiments of the present invention.
[0077] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.
[0078] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the multi-system, multi-frequency dynamic real-time positioning method shown in the above embodiments is implemented.
[0079] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.
[0080] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.
Claims
1. A multi-system, multi-frequency dynamic real-time positioning method, characterized in that, include: Based on the observation data of each satellite at each frequency point from the two observation stations, the inter-station single-difference observation equation for each frequency point of each satellite is constructed, and the inter-station single-difference observation is converted into matrix form to characterize the relationship between the unknown matrix, the inter-station single-difference ambiguity matrix and the error matrix. The unknown matrix contains coordinate unknowns and clock error unknowns. The approximate value matrix of the inter-station single-difference ambiguity matrix is calculated based on the single-difference pseudorange and single-difference carrier of each frequency point for each satellite by the two observation stations; A new error matrix is constructed using the unknown matrix, the approximation matrix, and the correction matrix of the inter-station single-difference ambiguity matrix; The normal equation of the new error matrix is calculated using the least squares method to obtain the ambiguity floating-point solution and the covariance matrix. The floating-point solution of the ambiguity and the covariance matrix are searched using the integer least squares method to obtain the integer solution of the single-difference ambiguity; The integer solution is used as the inter-station single-difference ambiguity matrix. The unknown matrix is calculated using the relationship between the unknown matrix, the inter-station single-difference ambiguity matrix and the error matrix to obtain the coordinate correction and clock difference between the two observation stations for each frequency point of each satellite. The positioning result is obtained by combining the coordinate correction and clock difference between the two observation stations for each frequency point of each satellite.
2. The method according to claim 1, characterized in that, The step of performing the normal equation of the new error matrix using the least squares method to obtain the ambiguity floating-point solution and the covariance matrix includes: Construct an a priori weight matrix for the inter-station single-difference ambiguity matrix, wherein the elements in the a priori weight matrix represent the weights of the phase observations at each frequency point for each satellite; The normal equation for the new error matrix is constructed using the prior weight matrix; The normal equations are calculated using the least squares method to obtain the ambiguity floating-point solution and the covariance matrix.
3. The method according to claim 2, characterized in that, The prior weight matrix for constructing the inter-station single-difference ambiguity matrix includes: Among multiple satellites, reference satellites corresponding to each frequency point are selected, and the weight value of the phase observation value corresponding to the reference satellite at each frequency point is set as the first weight value, and the weight value of the phase observation value of other satellites at each frequency point is set as the second weight value. The first weight value is greater than the second weight value. The prior weight matrix is constructed based on the weight values of the phase observations at each frequency point of each satellite.
4. The method according to claim 1, characterized in that, The approximation matrix is: in, This is an approximate matrix. Indicates the satellite number, Indicates the frequency point number. for frequency and The satellite's corresponding single-difference pseudorange for The wavelength of the frequency carrier. for frequency and The satellite's corresponding single-difference carrier, This is the floor function.
5. The method according to claim 2, characterized in that, The new error matrix is: in, For the new error matrix, For matrix The corresponding coefficient matrix, A matrix of unknowns. For the correction matrix The corresponding coefficient matrix, This is the inter-station single-difference ambiguity matrix. The constant term matrix calculated from the observed values and approximate coordinates. The correction matrix for the inter-station single-difference ambiguity matrix is... , It is an approximate matrix.
6. The method according to claim 5, characterized in that, The normal equation for the new error matrix is: in, For the unknown matrix The corresponding coefficient matrix, For the correction matrix The corresponding coefficient matrix, This is the correction matrix for the inter-station single-difference ambiguity matrix. The prior weight matrix, , The constant term matrix calculated from the observed values and coordinate approximations.
7. The method according to claim 6, characterized in that, The ambiguity floating-point solution is: The covariance matrix is: in, The covariance matrix of the unknown matrix X, The covariance matrix between the unknown matrix X and the inter-station single-difference ambiguity matrix Y. The covariance matrix between the inter-station single-difference ambiguity matrix Y and the unknown matrix X, For the correction matrix The covariance matrix.
8. A multi-system, multi-frequency dynamic real-time positioning device, characterized in that, include: The error matrix construction module is used to construct the inter-station single-difference observation equation for each frequency point of each satellite based on the observation data of the two observation stations for each satellite, and convert the inter-station single-difference observation into matrix form to characterize the relationship between the unknown matrix, the inter-station single-difference ambiguity matrix and the error matrix. The unknown matrix contains coordinate unknowns and clock error unknowns. An approximation matrix construction module is used to calculate the approximation matrix of the inter-station single-difference ambiguity matrix based on the single-difference pseudorange and single-difference carrier for each frequency point of each satellite by the two observation stations; The error matrix update module is used to construct a new error matrix using the unknown matrix, the approximation matrix, and the correction matrix of the inter-station single-difference ambiguity matrix. The error matrix calculation module is used to perform calculations on the normal equation of the new error matrix according to the least squares method to obtain the ambiguity floating-point solution and the covariance matrix. The integer solution search module is used to search the floating-point solution of the ambiguity and the covariance matrix using the integer least squares method to obtain the integer solution of the single-difference ambiguity. The unknown matrix calculation module is used to take the integer solution as the inter-station single-difference ambiguity matrix, and calculate the unknown matrix using the relationship between the unknown matrix, the inter-station single-difference ambiguity matrix and the error matrix to obtain the coordinate correction and clock difference between the two observation stations for each frequency point of each satellite. The positioning module is used to combine the coordinate corrections and clock errors of the two observation stations for each frequency point of each satellite to obtain the positioning result.
9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the multi-system, multi-frequency dynamic real-time positioning method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the multi-system, multi-frequency dynamic real-time positioning method according to any one of claims 1 to 7.