RTK positioning method and device based on composite Gaussian filtering, equipment and storage medium

Abstract

The invention discloses an RTK positioning method, device and equipment based on composite Gaussian filtering and a storage medium. The method comprises the following steps: S1, resolving carrier real-time state information by using a GNSS / INS (Global Navigation Satellite System / Inertial Navigation System) combination; s2, acquiring satellite observation data of the base station and the moving station; s3, constructing a combined observation value by using the three-frequency pseudo-range observation value and the carrier phase observation value, and solving ultra-wide lane and wide lane ambiguity to obtain a corrected pseudo-range observation value; s4, calculating carrier real-time state information, a three-frequency pseudo-range observation value, a three-frequency carrier phase observation value and a corrected pseudo-range observation value by using a GNSS / INS combination, and constructing a system model; s5, abnormal value detection and elimination are carried out based on the standardized information obtained in the S4; s6, performing composite Gaussian filtering on a classification result in S5; s7, performing integer constraint on the floating point solution; according to the invention, the calculation amount can be reduced, and the positioning performance and precision in real-time positioning are improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to an RTK positioning method, device, equipment and storage medium based on composite Gaussian filtering. Background Art

[0002] As a core technology in positioning and navigation, RTK is widely used in intelligent transportation, agricultural machinery and other fields. While receiving the original satellite observation data, the differential data stream of the base station is obtained through wireless communication or network (NTRIP), and RTK technology can achieve centimeter-level positioning accuracy. Specifically, the solution process mainly includes modeling the double-difference carrier phase and double-difference pseudo-range observations, and using the Kalman filter to solve the floating-point ambiguity. Subsequently, the LAMBDA algorithm is used to fix the integer ambiguity of the carrier phase to achieve high-precision positioning. However, in environments such as tree-lined roads and urban canyons, factors such as multipath effects and signal shielding interference will lead to reduced quality of observation data, making it difficult for the filter to converge, and even divergence.

[0003] In related technologies, Kalman filtering and its extended methods are often used to design robust filters, using normalized prior information as a criterion for judging the quality of observations. The weights are adjusted by direct exclusion, linear weight reduction, and piecewise function weight reduction to reduce the Kalman gain and suppress the impact of abnormal observations on the filtering performance. However, the observable frequency of the existing global satellite system has increased, and the number of observations has increased exponentially. The inversion operation of high-dimensional matrices is very time-consuming and difficult to meet the needs of real-time computing. On the other hand, the weight reduction for the overall measurement set ignores the differences between different satellite observations and is difficult to apply to positioning solutions in complex environments.

[0004] In order to overcome the above defects, this application proposes an RTK positioning method based on composite Gaussian filtering. The multi-constellation and multi-frequency characteristics of modern navigation systems are used to construct combined observation values ​​to obtain corrected pseudo-range observations, and the results of GNSS / INS loose combination mechanical arrangement are used as prior information to group the current satellite observation values; secondly, different measurement categories are fused and updated through composite Gaussian filtering units, which improves the positioning accuracy in complex environments. Summary of the invention

[0005] The present invention aims to overcome the above-mentioned shortcomings of the prior art and provide an RTK positioning method, device, equipment and storage medium of a composite Gaussian filter to accurately and robustly fix the ambiguity and improve the baseline solution fixation rate and accuracy.

[0006] In a first aspect, an embodiment of the present invention provides an RTK positioning method using a composite Gaussian filter, comprising the following steps: S1: Use the GNSS / INS integrated solution to calculate the real-time status information of the carrier, including the carrier position, carrier attitude, position variance, and attitude variance; S2: Obtain the satellite observation data of the reference station and the rover station, where the satellite observation data includes the carrier observations and pseudorange observations of three-frequency carriers; S3: Use the three-frequency pseudorange and carrier phase observations to construct combined observations, solve for the ultra-wide lane and wide lane ambiguities, and obtain the corrected pseudorange observations using the ultra-wide lane and wide lane ambiguities; S4: Use the GNSS / INS integrated solution of the carrier real-time status information in step S1, the three-frequency pseudorange and three-frequency carrier phase observations, and the corrected pseudorange observations solved in S3 to construct a system model, perform state prediction, and calculate the innovation and standardized innovation.

[0007] S5: Perform outlier detection and rejection based on the standardized innovation obtained in S4, and classify the remaining observations to obtain the classification results.

[0008] S6: Perform composite Gaussian filtering on the classification results in S5 above, perform sequential filtering update on the observations of the first category (the first type of measurement), fuse the observations of the second category (the second type of measurement) through the progressive Gaussian filtering method, and discard the observations of the third category (the third type of measurement) with the classification result of the third category. Perform state fusion on the two to obtain a floating-point solution.

[0009] S7: Perform integer constraint on the floating-point solution and perform quality control on the completed integer solution.

[0010] S8: Use the solution result of the current epoch to update the INS error.

[0011] Optionally, step S2 includes: S201: Perform pre-preprocessing on the data and perform pre-inspection according to the signal-to-noise ratio and cycle slip detection situation.

[0012] S202: Mark the observations with detected cycle slip information.

[0013] Optionally, step S3 includes: S301: Establish different ultra-wide lane and wide lane combined observations based on the multi-frequency observations of different satellite systems.

[0014] S302: Solve the combined observations of different satellite systems, perform quality control on the floating-point solution of the combined observations, and only fix the double-difference combined observations that meet the conditions. When the floating-point solution meets the decision probability, it is fixed; otherwise, it is considered that the error is too large or the noise is too large.

[0015] S303: After fixing the above combined observations, use the double-difference pseudorange of the combined observations and the double-difference integer ambiguity of the combined observations to calculate the corrected double-difference pseudorange observation, that is, the precise double-difference pseudorange observation.

[0016] S304: Assign different observation noises to the above different ultra-wide lane and wide lane observations.

[0017] Optionally, step S4 includes: S401: Use the position information and velocity information solved by GNSS in the previous epoch for kinematic estimation to obtain the satellite navigation prediction solution for the current epoch.

[0018] S402: Use the mechanical arrangement result of the GNSS / INS loose integration system to obtain the combined navigation calculation solution for the current epoch.

[0019] S403: Use the existing satellite navigation prediction solution and the combined navigation calculation solution to perform innovation and standardized innovation tests on the precise double-difference pseudorange observation value of the combined observations in step S3.

[0020] S404: Use the satellite observation data of the reference station and the rover station to construct double-difference observations.

[0021] S405: Use the existing satellite navigation prediction solution and the combined navigation calculation solution to perform innovation standardization on the double-difference observations in step S404.

[0022] Optionally, step S5 includes: S501: Based on the standardized innovation corresponding to the predicted value in S4, perform outlier detection and rejection. S502: Use K-means to cluster the innovation of the remaining measurements.

[0023] S503: Compare the absolute values of the three center points after K-means clustering, and classify the three clusters into the first type of measurement, the second type of measurement, and the third type of measurement in ascending order of the absolute value of the center point.

[0024] Optionally, step S6 includes: S601: For the measurement of the first type of clustering category, sort in ascending order of the absolute value of the innovation and perform sequential filtering for successive updates.

[0025] S602: During the sequential update process in S601, each time a new measurement is incorporated and updated, perform a standardized innovation test. If the test threshold is met, continue the sequential update; if the test threshold is not met, roll back to the previous update process as the sequential filtering result.

[0026] S603: For the second type of measurement in the clustering category, use progressive Gaussian filtering to fuse measurement information. Based on the set total number of progressive steps, decompose the likelihood function into multiple progressive likelihood functions, and the corresponding measurement update process is also decomposed into progressive measurement updates. At each pseudo-time, fuse the corresponding progressive likelihood functions and continuously iterate to solve the posterior state of the system.

[0027] S604: For the progressive filtering process in S604, use the first type of measurement to guide the progressive update process of the second type of measurement. When the cut-off condition is met, the progressive measurement update stops, and the estimation result at the current pseudo-time is output as the progressive measurement update result.

[0028] S605: For the third type of measurement in the clustering category, directly eliminate it.

[0029] S606: Perform state fusion on the above sequential result and the progressive measurement update result to obtain a floating-point solution. Optionally, step S7 includes: S701: For the state fusion result in S7, use the wide-lane ambiguity in S3 to correct it.

[0030] S702: Perform R-Test and post-fit residual tests on the floating-point solution. If the corresponding test values are met, it is considered that the fixation is successful.

[0031] In a second aspect, an RTK positioning device based on composite Gaussian filtering according to an embodiment of the present invention includes: a pose recursion unit, a combined ambiguity solution unit, a composite Gaussian filtering unit, and a quality control unit; where: Pose recursion unit: Adopt GNSS / INS loose combination recursion, and be able to continuously provide high-frequency current pose information after dynamic alignment initialization as prior position information for the combined ambiguity solution unit to use.

[0032] Combined ambiguity solution unit: Construct ultra-wide-lane combinations and wide-lane combinations with multi-frequency information for ambiguity resolution.

[0033] Composite Gaussian filtering unit: Use the combined ambiguity and prior information to classify the observations, and perform sequential filtering or progressive Gaussian filtering updates according to the differences of the observations, and perform guided fusion on the observations with larger innovations.

[0034] Quality control unit: Control the quality of the ambiguity solved by the above solution units.

[0035] In a third aspect, an embodiment of the present invention provides an RTK positioning device based on composite Gaussian filtering, including: a memory and a processor. At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the aforementioned RTK positioning method based on composite Gaussian filtering.

[0036] In a fourth aspect, an embodiment of the present invention further provides a computer storage medium. A computer program is stored in the computer storage medium, and when the computer program is executed by a processor, all or part of the steps of the above RTK positioning method based on composite Gaussian filtering are implemented.

[0037] The beneficial effects of the present invention are as follows: The present invention proposes an RTK positioning method based on composite Gaussian filtering. By fusing GNSS / INS inertial recursion results and combined ambiguity data, the combined ambiguity is fixed and the corrected pseudorange observation is obtained. Based on the combination of inertial recursion results and corrected observations, the prior innovation is detected, classified, and fused, improving the success rate of ambiguity fixing and positioning reliability in complex scenarios. Through the composite Gaussian filtering fusion mechanism, the computational complexity of inverting the multi-dimensional observation matrix can be reduced, and as much available data as possible is fused to improve positioning performance and accuracy. Through the above content, the method of the present invention can reduce the computational complexity compared with general methods and improve the positioning performance and accuracy in real-time positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a schematic flow chart of the method of the present invention; Figure 2 is a schematic structural diagram of the device of the present invention; Figure 3 is a schematic structural diagram of the device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To deepen the understanding of the present invention, the following will further elaborate on the present invention in conjunction with embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation to the protection scope of the present invention.

[0040] The present invention describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those of ordinary skill in the art that there can be more embodiments and implementation schemes within the scope of the embodiments described in the present invention. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combination ways of the disclosed features are also possible. Unless specifically restricted, any feature or element of any embodiment can be combined with any other feature or element in any other embodiment, or can replace any other feature or element in any other embodiment.

[0041] The present invention includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The disclosed embodiments, features, and elements of the present invention can also be combined with any conventional features or elements to form a unique inventive solution. Any feature or element of any embodiment can also be combined with features or elements from other inventive solutions to form another unique inventive solution. Therefore, it should be understood that any feature shown and / or discussed in the present invention can be implemented alone or in any suitable combination. Therefore, the embodiments are not subject to other limitations except those made in accordance with the appended claims and their equivalents. In addition, various modifications and changes can be made within the scope of the appended claims.

[0042] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not depend on the specific order of the steps described herein, the method or process should not be limited to the specific order of steps described. As will be understood by those of ordinary skill in the art, other step orders are possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation on the claims. In addition, the claims directed to the method and / or process should not be limited to performing their steps in the order written, as those skilled in the art can readily understand that these orders can vary and still remain within the spirit and scope of the embodiments of the present invention.

[0043] It should be noted that in the present invention, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device.

[0044] Embodiment 1 See Figure 1 As shown, an RTK positioning method based on composite Gaussian filtering is provided in an embodiment of the present invention, including the following steps: S1: Using GNSS / INS combined solution to calculate the real-time state information of the carrier, including the carrier position, carrier speed, carrier attitude, accelerometer zero bias, and gyroscope zero bias; ; The represents the position error, is the speed error, is the attitude error, and respectively represent the accelerometer bias and the gyroscope bias. The GNSS / INS loosely coupled error update algorithm is an existing technology for integrated navigation and is obvious to those skilled in the art, so it will not be elaborated here.

[0045] S2: Obtain the satellite observation data of the reference station and the rover station, where the satellite observation data includes the carrier observations and pseudorange observations of three-frequency carriers; S201: Preprocess the data and perform pre-check according to the signal-to-noise ratio and cycle slip detection. The signal-to-noise ratio threshold is generally determined according to different receivers and the average signal-to-noise ratio, and the general value is (32 - 36 dB). In this embodiment, the signal-to-noise ratio threshold is taken as 36 dB.

[0046] S202: Mark the observables with detected cycle slip information. Such as methods like MW combination, GF combination, etc. The GNSS / INS loosely coupled error update algorithm is an existing technology for integrated navigation and is obvious to those skilled in the art, so it will not be elaborated here.

[0047] Exemplarily, such as the L1 / L2 / L5 frequency bands of GPS and the E1 / E5 / E6 frequency bands of Galileo. For the convenience of description, in this embodiment, BDS is taken as an example, and the three signal frequency bands are respectively , , .

[0048] Construct a short baseline geometric model to solve the ambiguity, and the double-difference observation equation is expressed as: ; The represents the reference satellite, represents the participating satellite, represents the rover station; represents the reference station; represents the signal frequency band participating in the calculation; and respectively represent the double-difference carrier phase observation value and the double-difference pseudorange observation value; is the Euclidean distance after double-differencing; represents the signal wavelength of the corresponding frequency band; and respectively represent the inter-station single-difference ambiguities of the reference satellite and the participating satellite; and respectively represent the double-difference measurement noises of the carrier phase and the pseudorange.

[0049] S3: Using triple-frequency pseudorange and carrier phase observations, construct combined observations, solve for ultra-wide-lane and wide-lane ambiguities, and obtain corrected pseudorange observations using the ultra-wide-lane and wide-lane ambiguities; S301: Establish different ultra-wide-lane and wide-lane combined observations according to different satellite systems and the number of frequencies.

[0050] S302: Solve the combined observations of different satellite systems, and perform quality control on the floating-point solutions of the combined observations, only fixing the double-difference combined observations that meet the conditions. When the floating-point solution meets the decision probability is met, then it is fixed; otherwise, it is considered that the error or noise is too large.

[0051] S303: After fixing the above-mentioned combined observations, use the double-difference pseudorange of the combined observations and the double-difference integer ambiguity of the combined observations to calculate the corrected double-difference pseudorange observation, that is, the precise double-difference pseudorange observable.

[0052] S304: Assign different observation noises to the above different ultra-wide-lane and wide-lane observations. This is a commonly used technique in the art, so it will not be elaborated here; Exemplarily, the wavelengths and ambiguities of the triple-frequency combined observations are shown in the following formula: ; The is the combination coefficient, represents the speed of light, are the ambiguities corresponding to the respective frequencies. Solve for the ultra-wide-lane ambiguity using the geometric-free model described below, and solve for the wide-lane ambiguity based on the ultra-wide-lane ambiguity; ; The and are the wavelength and ambiguity corresponding to the ultra-wide-lane combination, and their subscripts are the corresponding ultra-wide-lane combination coefficients, and are the wavelength and ambiguity corresponding to the wide-lane combination, and their subscripts are the corresponding wide-lane combination coefficients; and are the noises of the carrier phase and pseudorange observations in the ultra-wide-lane combination respectively, and are the noises of the carrier phase and pseudorange observations in the wide-lane combination respectively. This solving process is a common technique in the art, so it will not be elaborated here.

[0053] Perform quality control on the fixed solution of the combined observations, and decide whether to fix according to the decision probability (fixing to the closest integer), the is expressed as follows: ; The is the combined ambiguity estimate value, is its standard deviation, is the integer value closest to . The confidence level generally takes values of 0.1, 0.05 or 0.01. In this embodiment, takes the value of 0.01. If is greater than , then the wide-lane double-difference ambiguity is successfully fixed, otherwise it fails.

[0054] After fixing the above combined observation values, using the wide-lane double-difference pseudorange and wide-lane double-difference integer ambiguity, the double-difference carrier phase is inversely solved and used as the precise double-difference pseudorange observation, corresponding to the precise double-difference pseudorange of the frequency is expressed as follows: S4: Using the GNSS / INS combined solution in step S1 to calculate the real-time state information of the carrier, the triple-frequency pseudorange and triple-frequency carrier phase observations, and the corrected pseudorange observations solved in S3, construct a system model, perform state prediction and calculate the innovation and standardized innovation.

[0055] S401: Use the position information and velocity information solved by GNSS in the previous epoch for kinematic estimation to obtain the satellite navigation prediction solution for the current epoch.

[0056] S402: Use the mechanical arrangement result of the GNSS / INS loose combination system to obtain the combined navigation calculation solution for the current epoch. This is a commonly used technology in the art, so it will not be elaborated here.

[0057] S403: Use the existing satellite navigation prediction solution and combined navigation solution to perform innovation and standardized innovation tests on the precise double-difference pseudorange observations of the combined observations in step S3.

[0058] S404: Use the satellite observation data of the reference station and the rover station to construct double-difference observations.

[0059] S405: Use the existing satellite navigation prediction solution and combined navigation solution to perform innovation standardization on the double-difference observations in step S404.

[0060] Exemplarily, the system model is modeled as follows: ; Among them, is the RTK real-time state, including the carrier position and ambiguity; includes the carrier phase measurement and the corrected pseudorange measurement described in S3; is the non-linear measurement function; and are the process noise and the measurement noise respectively, and their covariances are respectively expressed as and .

[0061] Combined with the state prediction process of the Kalman filter, the state information of the carrier at the current moment is obtained: ; For the system formula (10), perform a Taylor expansion at : ; The said represents the prior estimation error, which is defined as . Ignoring the high-order terms, we can obtain: ; In the said formula represents the prior innovation; .

[0062] The normalized prior innovation is expressed as follows: ; S5: Based on the standardized innovation obtained in the said S4, perform outlier detection and rejection, and classify the remaining observations to obtain the classification result; S501: Based on the standardized innovation corresponding to the prediction value in the said S4, perform outlier detection and rejection; S502: Use K-means to cluster the innovation of the remaining measurements. The clustering parameter takes the value of 3; S503: Compare the absolute values of the three center points after K-means clustering, and classify the three clusters in ascending order of the absolute values of the center points as the first type of measurement , the second type of measurement , and the third type of measurement .

[0063] Based on the standardized innovation obtained in the said S4, perform outlier detection, that is, the double-difference observations of will be rejected. The threshold usually takes the value of 2.5 - 8.0. In this embodiment, the value is 4.5.

[0064] Furthermore, based on the K-means clustering method, cluster the remaining measurements. The clustering category is set to , and according to the absolute value size of the clustering center point in the cluster after clustering, classify them in ascending order as the first type of measurement , the second type of measurement , and the third type of measurement .

[0065] S6: Perform composite Gaussian filtering on the classification results in S5 above, perform sequential filtering update on the observation values of the first category, fuse the observation values of the second category through the progressive Gaussian filtering method, and discard the observations with the classification result of the third category. Perform state fusion on the two to obtain the floating-point solution.

[0066] S601: For the measurements with the clustering category of the first category, sort them in ascending order of the absolute value of the innovation, and perform sequential filtering update one by one.

[0067] S602: During the sequential update process in S601, after each incorporation of a new measurement and update, perform standardized innovation test. The test threshold is usually set to 1.0 - 1.5, and in this embodiment, it is taken as 1.5. If the test threshold is satisfied, continue with the sequential update; if the test threshold is not satisfied, roll back to the previous update process as the sequential filtering result.

[0068] S603: For the measurements with the clustering category of the second category, use the progressive Gaussian filtering to fuse the measurement information. Based on the set total number of progressive steps, decompose the likelihood function into multiple progressive likelihood functions, and the corresponding measurement update process is also decomposed into progressive measurement updates. At each pseudo-time, fuse the corresponding progressive likelihood functions and continuously iterate to solve the posterior state of the system.

[0069] S604: For the progressive filtering process in S604, use the measurements of the first category to guide the progressive update process of the measurements of the second category. When is satisfied, the progressive measurement update stops, and the progressive measurement update result at the pseudo-time is output. .

[0070] S605: For the measurements with the clustering category of the third category, directly eliminate them.

[0071] S606: Perform state fusion on the above sequential result and the progressive measurement update result to obtain the floating-point solution. Exemplarily, the sequential measurement update is expressed as follows: Assume that there are measurements classified as the first-category measurements in S5. After sorting, the sequential measurement update is as follows: ; The represents the state to be estimated based on the first-category measurements, represents at the -th measurement for sequential update among the first-category measurements at the moment, and represents the probability density function, For the first type of measurement up to the set of measurements at time

[0072] Through sequential measurement update, calculate the corresponding and its covariance matrix .

[0073] Similar to the above S4, during the sequential update process, and are expressed as follows: ; Similar to the above formula (14), the posterior innovation corresponding to each sequential update can be expressed as follows: ; wherein, the represents the posterior estimation error of the th sequential update, defined as .

[0074] Furthermore, the normalized innovation is expressed as follows: ; wherein, the th normalized innovation corresponding to the sequential update of the measurement sequence, is 's covariance matrix.

[0075] For each sequential update result, perform a normalization test on the . If it satisfies , then continue to incorporate the th measurement for update. If not, output as the time sequential result .

[0076] Exemplarily, the progressive measurement update is expressed as follows: ; the represents the state to be estimated based on the second type of measurement, is the set of measurements of the second type up to time. represents the progressive pseudo-time constant, where and , . represents the th step progressive step size, is the total number of progressive steps. represents the progressive likelihood function, expressed as follows: ; Combined with the linearization process shown in the formula (14), the likelihood function is further written as follows: ; Similarly, through the aforementioned incremental measurement update, calculate the corresponding and its corresponding covariance matrix .

[0077] Furthermore, use the first type of measurement to guide the execution or stop of the incremental measurement update. If , then stop the incremental measurement update and output the incremental posterior estimate as the result of the incremental measurement update .

[0078] After completing the sequential and incremental measurement updates, use the following formula to perform global fusion on the system state and to obtain the vehicle state information and its covariance matrix: ; The represents the system global state information, and represents its corresponding covariance matrix.

[0079] S7: Perform integer constraint on the floating-point solution and perform quality control on the completed integer solution.

[0080] S701: For the state fusion result in S7, use the wide-lane ambiguity in S3 to correct it. and respectively represent the corrected system state and covariance matrix.

[0081] S702: Further, use the LAMBDA algorithm to perform integer constraint on the above results to obtain the fixed solution, and use the R-Test to check its reliability. The R-Test threshold is usually 2 - 3, and it is set to 3 in this embodiment. The ambiguity fixation based on the LAMBDA algorithm is a common technique for integrated navigation and is obvious to those skilled in the art, so it will not be elaborated here.

[0082] Exemplarily, the correction factor is represented by the following formula: ; where is the wide-lane ambiguity in S3, and represents the floating-point solution of the ambiguity obtained in S6. Taking as the innovation vector, perform the extended Kalman filter update process to obtain the corrected floating-point solution and its corresponding covariance matrix and , the extended Kalman filter is a commonly used technology in the navigation field, so it will not be elaborated here.

[0083] S8: Use the solution result of the current epoch to update the errors of the INS. This is a commonly used technology in the field of integrated navigation, so it will not be elaborated here.

[0084] Embodiment 2 Refer to Figure 2 As shown, this embodiment provides an RTK positioning device based on composite Gaussian filtering, including: a pose recursion unit, a combined ambiguity resolution unit, a composite Gaussian filtering unit, and a quality control unit; where: Pose recursion unit: Adopt GNSS / INS loose combination recursion, combine the solved corrected pseudorange, and can continuously provide high-frequency current pose information after dynamic alignment initialization, as the prior position information for the composite Gaussian filtering unit to use.

[0085] Combined ambiguity resolution unit: Solve by constructing ultra-wide lane combinations and wide lane combinations with multi-frequency information. The solved accurate pseudorange information is provided to the pose recursion unit.

[0086] Composite Gaussian filtering unit: Use the prior information deduced by inertial navigation to classify the observations, adopt different sequential or progressive filtering methods for different categories of observations, use the measurement with smaller innovation to guide the fusion of the measurement with larger innovation, and finally perform state fusion on the results of different categories.

[0087] Quality control unit: Control the ambiguity quality solved by the above-mentioned solving units.

[0088] The embodiment of the present invention provides an RTK positioning device with composite Gaussian filtering, including: a memory and a processor. At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the aforementioned RTK positioning method with composite Gaussian filtering.

[0089] It should be noted that those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described device and each unit can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated here.

[0090] The device provided in the above embodiment can be implemented in the form of a computer program, and the computer program can run on an RTK positioning device based on composite Gaussian filtering as Figure 3 shown.

[0091] Embodiment 3. An RTK positioning device based on composite Gaussian filtering provided by an embodiment of the present invention includes: a memory, a processor, a serial interface, and a parallel interface connected through a system bus. At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement an RTK positioning method based on composite Gaussian filtering in Embodiment 1.

[0092] Among them, the serial interface and the parallel interface are used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0093] The processor can be a CPU, or other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or any conventional processor. The processor is the control center of the computer device and connects various parts of the entire computer device through various interfaces and lines.

[0094] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area, where: the program storage area can store an operating system and application programs required for at least one function (such as video playback, image playback, etc.). The data storage area can store data created due to device use (such as video data, image data, etc.). In addition, the memory can include high-speed random access memory (RAM), and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one magnetic disk storage device, flash device, or other solid-state storage devices.

[0095] Example 4 provides a computer-readable storage medium storing a computer program which, when executed by a processor, can implement the RTK positioning method based on composite Gaussian filtering described in Example 1.

[0096] In the embodiments of the present invention, all or part of the processes can be completed by computer program instructions related to hardware. The computer program can be stored in a computer-readable storage medium and, when executed by the processor, implement each step of the above method. The computer program includes computer program code, and the code form can be source code, object code, executable file or intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, such as: recording medium USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content of the computer-readable storage medium may be adjusted according to the laws and patent practices of different jurisdictions. For example, in some regions, electrical carrier signals and telecommunication signals are not included.

[0097] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, system, server or computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0098] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems) and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the specified functions in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0099] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or system comprising a series of elements not only includes those elements but also other elements not expressly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or system comprising such element.

[0100] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An RTK positioning method based on compound Gaussian filtering, characterized in that It includes the following steps: S1: Use GNSS / INS combined solution to calculate the real-time state information of the carrier, including carrier position, carrier attitude, position variance, and attitude variance; S2: Obtain the satellite observation data of the reference station and the rover station. Among them, the satellite observation data includes triple-frequency carrier observations and pseudorange observations; S3: Use the triple-frequency carrier observations and pseudorange observations to construct combined observations, solve the ultra-wide-lane ambiguity and wide-lane ambiguity, and obtain the corrected pseudorange observations using the ultra-wide-lane ambiguity and wide-lane ambiguity; S4: Use the GNSS / INS combined solution in step S1 to calculate the real-time state information of the carrier, the triple-frequency pseudorange and triple-frequency carrier phase observations, and the corrected pseudorange observations solved in S3 to construct a system model, perform state prediction, and calculate the innovation and standardized innovation; S5: Based on the standardized innovation obtained in S4, perform outlier detection and rejection, and classify the remaining observations to obtain the classification results, including the first type of measurement, the second type of measurement, and the third type of measurement; S6: Perform composite Gaussian filtering on the classification results in S5 above, perform sequential filtering update on the first type of measurement, fuse the second type of measurement through the progressive Gaussian filtering method, discard the classification result as the third type of measurement, and perform state fusion on the two to obtain a floating-point solution; S7: Perform integer constraint on the floating-point solution and perform quality control on the completed integer solution; S8: Use the solution result of the current epoch to update the INS error.

2. The RTK positioning method based on composite Gaussian filtering according to claim 1, wherein, The specific steps of S2 are as follows: S201: Preprocess the data and perform pre-inspection according to the signal-to-noise ratio and cycle slip detection; S202: Mark the observables with detected cycle slip information.

3. The RTK positioning method based on compound Gaussian filtering according to claim 1, wherein The specific steps of step 3 are as follows: S301: According to the multi-frequency observations of different satellite systems, establish different ultra-wide-lane and wide-lane combined observations; S302: Solve the combined observations of different satellite systems, perform quality control on the floating-point solutions of the combined observations, and fix the combined observations that meet the conditions, that is, when the floating-point solution meets the decision probability at that time, perform fixation; S303: After fixing the above combined observations, use the combined observation double-difference pseudorange and the combined observation double-difference integer ambiguity to calculate the corrected double-difference pseudorange observation, that is, the accurate double-difference pseudorange observable; S304: Give different observation noises to the above different ultra-wide-lane and wide-lane observations.

4. The RTK positioning method based on composite Gaussian filtering according to claim 1, wherein The specific steps of step 4 are as follows: S401: Use the position information and velocity information calculated by GNSS in the previous epoch for kinematic estimation to obtain the satellite navigation prediction solution of the current epoch; S402: Use the mechanical arrangement result of the GNSS / INS loose combination system to obtain the combined navigation calculation solution of the current epoch; S403: Use the existing satellite navigation prediction solution and the combined navigation solution to perform innovation and standardized innovation tests on the accurate double-difference pseudorange observations of the combined observations in step S3; S404: Use the satellite observation data of the reference station and the rover station to construct double-difference observations by differencing the inter-station observations and inter-satellite observations; S405: Use the existing satellite navigation prediction solution and the combined navigation solution to perform innovation standardization on the double-difference observations in step S404.

5. The RTK positioning method based on compound Gaussian filtering according to claim 1, wherein The specific steps of step 5 are as follows: S501: Based on the standardized innovation corresponding to the predicted value in S4, perform outlier detection and rejection; S502: Cluster the innovation of the remaining measurements using K-means; S503: Compare the absolute values of the three centroids after K-means clustering. Classify the three clusters as the first type of measurement, the second type of measurement, and the third type of measurement in ascending order of the absolute values of the centroids.

6. The RTK positioning method based on compound Gaussian filtering according to claim 1, wherein The specific steps of step 6 are as follows: S601: For the first type of measurement in the clustering category, sort in ascending order of the absolute value of the innovation, and use sequential filtering to update sequentially; S602: During the sequential update process in S601, after each incorporation of a new measurement and update, perform a standardized innovation test; If the test threshold is met, continue with the sequential update; If the test threshold is not met, roll back to the previous update process as the sequential filtering result; S603: For the second type of measurement in the clustering category, use progressive Gaussian filtering to fuse the measurement information; based on the set total number of progressive steps, decompose the likelihood function into multiple progressive likelihood functions, and the corresponding measurement update process is also decomposed into progressive measurement updates. At each pseudo-time, fuse the corresponding progressive likelihood functions and iterate continuously to solve the posterior state of the system; S604: For the progressive filtering process in S604, use the first type of measurement to guide the progressive update process of the second type of measurement. When the cut-off condition is met, the progressive measurement update stops, and the estimated result at the current pseudo-time is output as the progressive measurement update result; S605: For the third type of measurement in the clustering category, directly eliminate it; S606: Perform state fusion on the above sequential result and the progressive measurement update result to obtain a floating-point solution.

7. The RTK positioning method based on compound Gaussian filtering according to claim 1, wherein The specific steps of step 7 are as follows: S701: For the state fusion result in S7, correct it using the wide-lane ambiguity in S3; S702: Perform R-Test and post-fit residual tests on the floating-point solution. If the corresponding test values are met, it is considered that the fixation is successful.

8. The RTK positioning device based on compound Gaussian filtering is characterized in that, Including: A pose recursion unit, a combined ambiguity solution unit, a composite Gaussian filtering unit, and a quality control unit; where: Pose recursion unit: Adopt GNSS / INS loose combination recursion, and be able to continuously provide high-frequency current pose information after dynamic alignment initialization, serving as prior position information for the combined ambiguity solution unit; Combined ambiguity solution unit: Construct ultra-wide-lane combinations and wide-lane combinations using multi-frequency information for ambiguity resolution; Composite Gaussian filtering unit: Use the combined ambiguity and prior information to classify the observations, and perform sequential filtering or progressive Gaussian filtering updates according to the differences in the observations, and perform guided fusion on the observations with larger innovations; Quality control unit: Control the quality of the ambiguity solved by the above solution units.

9. The RTK positioning device based on compound Gaussian filtering is characterized in that, Including: A memory and a processor. At least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement a RTK positioning method based on composite Gaussian filtering according to any one of claims 1-7.

10. A computer storage medium, characterized in that, A computer program is stored in the computer storage medium, and when the computer program is executed by the processor, it implements the RTK positioning method based on composite Gaussian filtering according to any one of claims 1-7.

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