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

Abstract

The present invention discloses an RTK positioning method, apparatus, equipment and storage medium based on composite Gaussian filtering, comprising the following steps: S1: using a GNSS / INS combination to calculate real-time status information of a carrier; S2: acquiring satellite observation data of a base station and a mobile station; S3: using triple-frequency pseudorange and carrier phase observations to construct a combined observation value, solving ultra-wide lane and wide lane ambiguities to obtain corrected pseudorange observation values; S4: using a GNSS / INS combination to calculate real-time status information of the carrier, triple-frequency pseudorange and triple-frequency carrier phase observations, and corrected pseudorange observation values to construct a system model; S5: performing outlier detection and elimination based on the standardized new information obtained in S4; S6: performing composite Gaussian filtering on the classification results in S5; and S7: subjecting a floating-point solution to integer constraints. The present invention can reduce the amount of calculation and improve positioning performance and accuracy in real-time positioning.

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 fields such as intelligent transportation and agricultural machinery. RTK technology achieves centimeter-level positioning accuracy by simultaneously receiving raw satellite observation data and acquiring differential data streams from base stations through wireless communication or the Internet (NTRIP). Specifically, the solution involves modeling double-difference carrier phase and double-difference pseudorange observations and employing a Kalman filter to resolve floating-point ambiguities. Subsequently, the LAMBDA algorithm is used to fix the carrier phase integer ambiguities, achieving high-precision positioning. However, in environments such as tree-lined roads and urban canyons, factors such as multipath effects and signal obstruction can reduce the quality of observation data, making it difficult for the filter to converge and even causing divergence.

[0003] In related technologies, Kalman filtering and its extensions are often used to design robust filters, using normalized prior information as a criterion for judging observation quality. Weights are adjusted through direct exclusion, linear weight reduction, and piecewise function weight reduction to reduce the Kalman gain and suppress the impact of abnormal observations on filter performance. However, as the observable frequency of existing global satellite systems increases and the number of observations multiplies, the inversion of high-dimensional matrices is extremely time-consuming and difficult to meet the needs of real-time computing. Furthermore, weight reduction applied to the entire measurement set ignores the differences between observations from different satellites, making it difficult to apply to positioning solutions in complex environments.

[0004] To overcome these shortcomings, this application proposes an RTK positioning method based on composite Gaussian filtering. This method leverages the multi-constellation and multi-frequency characteristics of modern navigation systems to construct combined observations to obtain corrected pseudorange observations. The results of the GNSS / INS loose combination mechanical arrangement serve as prior information to group the current satellite observations. Furthermore, a composite Gaussian filtering unit is used to fuse and update different measurement categories, improving 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 with 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 compound Gaussian filter, comprising the following steps:

[0007] S1: Use the GNSS / INS combination to calculate the real-time status information of the carrier, including carrier position, carrier attitude, position variance, and attitude variance;

[0008] S2: Acquire satellite observation data of the base station and the rover station, wherein the satellite observation data includes carrier observation values and pseudorange observation values of the triple-frequency carrier;

[0009] S3: Use the triple-frequency pseudorange and carrier phase observations to construct combined observations, resolve ultra-wide lane and wide lane ambiguities, and use the ultra-wide lane and wide lane ambiguities to obtain corrected pseudorange observations.

[0010] S4: Use the GNSS / INS combination in step S1 to solve the real-time status information of the carrier, the three-frequency pseudorange and three-frequency carrier phase observations, and the corrected pseudorange observations solved in S3 to build a system model, perform state prediction, and calculate new information and normalized new information.

[0011] S5: Detect and eliminate outliers based on the standardized new information obtained in S4, and classify the remaining observations to obtain classification results.

[0012] S6: Perform a composite Gaussian filter on the classification results from S5. Perform a sequential filter update on the observations of the first category (first category measurement). Fusion the observations of the second category (second category measurement) using a progressive Gaussian filter. Discard observations classified as the third category (third category measurement). Perform state fusion on the two to obtain a floating-point solution.

[0013] S7: Constrain the floating-point solution to integers and perform quality control on the constrained integer solutions.

[0014] S8: Update the INS error using the current epoch solution.

[0015] Optionally, step S2 includes:

[0016] S201: Pre-process the data and perform pre-inspection based on the signal-to-noise ratio and cycle slip detection conditions.

[0017] S202: Mark the observations for which cycle slip information is detected.

[0018] Optionally, step S3 includes:

[0019] S301: Based on the multi-frequency observation values of different satellite systems, different ultra-wide lane and wide lane combination observation values are established.

[0020] S302: Solve the combined observations of different satellite systems and perform quality control on the floating-point solutions of the combined observations. Only the double-difference combined observations that meet the conditions are fixed. Only when the floating-point solution meets the decision probability is it fixed. Otherwise, it is considered that the error is too large or the noise is too high.

[0021] S303: After fixing the above combined observation, the double-difference pseudorange and the double-difference integer ambiguity of the combined observation are used to calculate the corrected double-difference pseudorange observation, that is, the precise double-difference pseudorange observation.

[0022] S304: giving different observation noises to the above different ultra-wide lane and wide lane observation values.

[0023] Optionally, step S4 includes:

[0024] S401: Perform kinematic estimation using the position and velocity information calculated by the GNSS solution of the previous epoch to obtain the satellite guidance prediction solution of the current epoch.

[0025] S402: Utilize the GNSS / INS loosely coupled system mechanical arrangement results to obtain the combined dead reckoning solution for the current epoch.

[0026] S403: Using the existing satellite guidance prediction solution and the integrated navigation solution, perform innovation and standardized innovation tests on the precise double-difference pseudorange observations of the combined observations in step S3.

[0027] S404: Using the satellite observation data of the base station and the mobile station, construct double-difference observation values.

[0028] S405: Using the existing satellite guidance prediction solution and the integrated navigation solution, the double difference observation value in step S404 is standardized.

[0029] Optionally, step S5 includes:

[0030] S501: Detect and remove outliers based on the normalized values corresponding to the predicted values in S4

[0031] S502: Cluster the remaining measurement innovations using K-means.

[0032] S503: Compare the absolute values of the three center points after K-means clustering, and classify the three clusters into the first category of measurement, the second category of measurement, and the third category of measurement in order of the absolute values of the center points from small to large.

[0033] Optionally, step S6 includes:

[0034] S601: For the first type of clustering, sort the measurements according to the absolute value of the new information from small to large, and update them one by one using sequential filtering.

[0035] S602: During the sequential update process in S601, after each update with new measurements, a standardized innovation check is performed. If the check threshold is met, the sequential update continues; if the check threshold is not met, the update process is rolled back to the previous update process, which is used as the sequential filtering result.

[0036] S603: For measurements classified as the second type, asymptotic Gaussian filtering is used to fuse the measurement information. Based on the set total number of asymptotic steps, the likelihood function is decomposed into multiple asymptotic likelihood functions. The corresponding measurement update process is also decomposed into individual asymptotic measurement updates. At each pseudo-time instant, the corresponding asymptotic likelihood functions are fused, and the system posterior state is solved through continuous iteration.

[0037] S604: For the progressive filtering process in S604, the first type of measurement is used to guide the progressive update process of the second type of measurement. When the cutoff condition is met, the progressive measurement update stops and the estimation result of the current pseudo-time is output as the progressive measurement update result.

[0038] S605: For the third type of clustering measurement, directly remove it.

[0039] S606: Perform state fusion on the above sequential results and the incremental measurement update results to obtain a floating-point solution

[0040] Optionally, step S7 includes:

[0041] S701: The state fusion result in S7 is corrected using the wide lane ambiguity in S3.

[0042] S702: Perform R-Test and post-test residual test on the floating-point solution. If the corresponding test values are met, it is considered that the fixation is successful.

[0043] In a second aspect, an embodiment of the present invention provides an RTK positioning device based on a composite Gaussian filter, comprising: a posture recursive unit, a combined ambiguity solving unit, a composite Gaussian filtering unit, and a quality control unit; wherein:

[0044] Pose recursion unit: adopts GNSS / INS loose combined recursion, which can continuously provide high-frequency current pose information after dynamic alignment initialization, and is used as prior position information for the combined ambiguity resolution unit.

[0045] Combined ambiguity resolution unit: Multi-frequency information is used to construct ultra-wide lane combinations and wide lane combinations for ambiguity resolution.

[0046] Composite Gaussian filter unit: Utilizes the combined ambiguity and prior information to classify the observations, performs sequential filtering or progressive Gaussian filtering updates based on the differences in the observations, and guides the fusion of observations with larger new information.

[0047] Quality control unit: controls the quality of the ambiguity solved by the above-mentioned solving unit.

[0048] In a third aspect, an embodiment of the present invention provides an RTK positioning device based on composite Gaussian filtering, comprising: a memory and a processor, wherein the memory stores at least one instruction, 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.

[0049] In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, in which a computer program is stored. When the computer program is executed by a processor, all or part of the steps of the above-mentioned RTK positioning method based on composite Gaussian filtering are implemented.

[0050] The beneficial effects of the present invention are as follows:

[0051] The present invention proposes an RTK positioning method based on compound Gaussian filtering, which fixes the combined ambiguity and obtains the corrected pseudo-range observation value by fusing the GNSS / INS inertial recursion results and the combined ambiguity data. Based on the inertial recursion results and the corrected observation values, the prior information is detected, classified, and fused, thereby improving the success rate of ambiguity fixation and positioning reliability in complex scenarios. Through the compound Gaussian filtering fusion mechanism, the amount of calculation for inverting the multi-dimensional observation matrix can be reduced, and the available data can be integrated as much as possible to improve positioning performance and accuracy. Through the above content, the method of the present invention can reduce the amount of calculation compared with general methods, and improve positioning performance and accuracy in real-time positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the process of the present invention;

[0053] Figure 2 It is a structural schematic diagram of the device of the present invention;

[0054] Figure 3 It is a structural schematic diagram of the device of the present invention. DETAILED DESCRIPTION

[0055] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to the examples. The examples are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0056] The present invention describes a plurality of embodiments, but this description is exemplary rather than restrictive, and it is apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described herein. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0057] 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 may also be combined with any conventional features or elements to form a unique inventive solution. Any features or elements of any embodiment may 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 may be implemented individually or in any appropriate combination. Therefore, the embodiments are not subject to other limitations except for the limitations set forth in the appended claims and their equivalents. In addition, various modifications and changes may be made within the scope of the appended claims.

[0058] 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 rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As one of ordinary skill in the art will understand, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as limiting the claims. In addition, the claims to the method and / or process should not be limited to performing their steps in the order written, and those skilled in the art can readily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present invention.

[0059] It should be noted that, in the present invention, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0060] Example 1

[0061] See also Figure 1 As shown, an embodiment of the present invention provides an RTK positioning method based on compound Gaussian filtering, comprising the following steps:

[0062] S1: Use the GNSS / INS combination to calculate the real-time status information of the carrier, including carrier position, carrier velocity, carrier attitude, accelerometer bias, and gyroscope bias;

[0063] ;

[0064] described represents the position error, is the speed error, is the attitude error, and The GNSS / INS loosely combined error update algorithm is an existing technology for integrated navigation and is obvious to those skilled in the art, so it will not be described in detail.

[0065] S2: Acquire satellite observation data of the base station and the rover station, wherein the satellite observation data includes carrier observation values and pseudorange observation values of the triple-frequency carrier;

[0066] S201: Pre-process the data and perform pre-check based on the signal-to-noise ratio and cycle slip detection. The signal-to-noise ratio threshold is generally determined based on different receivers and the average signal-to-noise ratio, and is generally set between 32-36 dB. In this embodiment, the signal-to-noise ratio threshold is 36 dB.

[0067] S202: Mark the observations where cycle slip information is detected. Methods such as MW combination and GF combination are used. The GNSS / INS loose combination error update algorithm is an existing technology for integrated navigation and is obvious to those skilled in the art, so it will not be described in detail.

[0068] For example, the L1 / L2 / L5 frequency bands of GPS and the E1 / E5 / E6 frequency bands of Galileo. For the convenience of description, this embodiment takes BDS as an example. The three signal frequency bands are , , .

[0069] A short baseline geometric model is constructed to resolve ambiguity, and the double-difference observation equation is expressed as:

[0070] ;

[0071] described represents the reference star, Indicates participating stars, Indicates a mobile station; Indicates the base station; Indicates the signal frequency band involved in the calculation; and represent double-difference carrier phase observations and double-difference pseudorange observations respectively; is the Euclidean distance after double difference; Indicates the signal wavelength of the corresponding frequency band; and denote the inter-station single-difference ambiguities of the reference star and the participating stars, respectively; and represent the double-difference measurement noise of carrier phase and pseudorange respectively.

[0072] S3: Use the triple-frequency pseudorange and carrier phase observations to construct combined observations, resolve ultra-wide lane and wide lane ambiguities, and use the ultra-wide lane and wide lane ambiguities to obtain corrected pseudorange observations.

[0073] S301: Establish different ultra-wide lane and wide lane combination observation values according to different satellite systems and frequencies.

[0074] S302: Solve the combined observation values of different satellite systems and perform quality control on the combined observation value floating point solution, fixing only the double difference combined observation values that meet the conditions. Otherwise, it is considered that the error is too large or the noise is too large.

[0075] S303: After fixing the above combined observation, the double-difference pseudorange and the double-difference integer ambiguity of the combined observation are used to calculate the corrected double-difference pseudorange observation, that is, the precise double-difference pseudorange observation.

[0076] S304: Different observation noises are applied to the different ultra-wide lane and wide lane observation values. This is a common technique in this field and will not be described in detail.

[0077] For example, the wavelength and ambiguity of the triple-frequency combination observation are shown as follows:

[0078] ;

[0079] described is the combination coefficient, represents the speed of light, The ultra-wide lane ambiguity is solved using the geometry-free model described below, and the wide lane ambiguity is solved based on the ultra-wide lane ambiguity;

[0080] ;

[0081] described and is the wavelength and ambiguity corresponding to the ultra-wide lane combination, and its subscript is the corresponding ultra-wide lane combination coefficient. and are the wavelength and ambiguity corresponding to the wide lane combination, respectively, and their subscripts are the corresponding wide lane combination coefficients; and are the noise of the carrier phase and pseudorange observation of the ultra-wide lane combination, and are the noise of the carrier phase and pseudorange observation in the wide-lane combination, respectively. This solution process is a common technique in this field and will not be described in detail here.

[0082] Perform quality control on the fixed solution of the combined observations, based on the decision probability (fixed to the nearest integer) determines whether to fix, the It is expressed as follows:

[0083] ;

[0084] described is the combined ambiguity estimate, is its standard deviation, For the closest Integer value of Confidence Level The general value is 0.1, 0.05 or 0.01. The value is 0.01. If Greater than , then the wide lane double difference ambiguity fixation is successful, otherwise it fails.

[0085] After fixing the above combined observation values, the double-difference carrier phase is inversely solved using the wide-lane double-difference pseudorange and wide-lane double-difference integer ambiguity, and is used as the precise double-difference pseudorange observation, corresponding to Precise double-difference pseudorange of frequency It is expressed as follows:

[0086]

[0087] S4: Use the GNSS / INS combination in step S1 to solve the real-time status information of the carrier, the three-frequency pseudorange and three-frequency carrier phase observations, and the corrected pseudorange observations solved in S3 to build a system model, perform state prediction, and calculate new information and normalized new information.

[0088] S401: Perform kinematic estimation using the position and velocity information calculated by the GNSS solution of the previous epoch to obtain the satellite guidance prediction solution of the current epoch.

[0089] S402: Utilize the GNSS / INS loosely coupled system mechanical arrangement results to obtain the combined dead reckoning solution for the current epoch. This is a common technique in the art and will not be described in detail here.

[0090] S403: Using the existing satellite guidance prediction solution and the integrated navigation solution, perform innovation and standardized innovation tests on the precise double-difference pseudorange observations of the combined observations in step S3.

[0091] S404: Using the satellite observation data of the base station and the mobile station, construct double-difference observation values.

[0092] S405: Using the existing satellite guidance prediction solution and the integrated navigation solution, the double difference observation value in step S404 is standardized.

[0093] For example, the system model is modeled as follows:

[0094] ;

[0095] in, RTK real-time status, including carrier position and ambiguity; Including carrier phase measurement and the corrected pseudorange measurement described in S3; is a nonlinear measurement function; and are process noise and measurement noise respectively, and their covariances are expressed as and .

[0096] Combined with the state prediction process of Kalman filtering, the state information of the carrier at the current moment is obtained:

[0097] ;

[0098] For the system formula (10), Taylor expansion:

[0099] ;

[0100] described Represents the prior estimation error, defined as Ignoring higher-order terms, we can obtain:

[0101] ;

[0102] In the formula Indicates a priori new information; .

[0103] The normalized prior innovation is expressed as follows:

[0104] ;

[0105] S5: Detect and remove outliers based on the standardized new information obtained in S4, and classify the remaining observations to obtain classification results;

[0106] S501: Detect and remove outliers based on the normalized values corresponding to the predicted values in S4;

[0107] S502: Use K-means to cluster the remaining measurement information. Clustering parameters The value is 3;

[0108] S503: Compare the absolute values of the three center points after K-means clustering, and classify the three clusters into the first category of measurement according to the order of the absolute values of the center points from small to large. , the second type of measurement , the third type of measurement .

[0109] Based on the standardized new information obtained in S4 Perform outlier detection, i.e. Double difference observations will be removed. Threshold The value is usually 2.5-8.0. In this embodiment, the value is 4.5.

[0110] Furthermore, the remaining measurements are clustered based on the K-means clustering method. The clustering categories are set as According to the absolute value of the cluster center point in the cluster after clustering, the clusters are classified into the first category in order from small to large. , the second type of measurement , the third type of measurement .

[0111] S6: Perform a composite Gaussian filter on the classification results in S5 above, perform a sequential filter update on the observations of the first category, fuse the observations of the second category using the progressive Gaussian filter method, and discard the observations classified as the third category. Perform state fusion on the two to obtain a floating-point solution.

[0112] S601: For the measurements of the first cluster category, sort them from small to large according to the absolute value of the new information, and update them one by one using sequential filtering.

[0113] S602: During the sequential update process in S601, after each update incorporating new measurements, a standardized innovation check is performed. The check threshold is typically set between 1.0 and 1.5, but in this embodiment, 1.5 is used. If the check threshold is met, the sequential update continues; if not, the update process reverts to the previous update, which serves as the sequential filtering result.

[0114] S603: For measurements classified as the second cluster, asymptotic Gaussian filtering is used to fuse the measurement information. Based on the set total number of asymptotic steps, the likelihood function is decomposed into multiple asymptotic likelihood functions. The corresponding measurement update process is also decomposed into individual asymptotic measurement updates. At each pseudo-time instant, the corresponding asymptotic likelihood functions are fused, and the system posterior state is solved through continuous iteration.

[0115] S604: For the progressive filtering process in S604, the first type of measurement is used to guide the progressive update process of the second type of measurement. When the first type of measurement is satisfied, When the progressive measurement update stops, the pseudo time is output Progressive measurement update results .

[0116] S605: Directly eliminate the measurements whose clustering category is the third category.

[0117] S606: Perform state fusion on the above sequential results and the incremental measurement update results to obtain a floating-point solution

[0118] For example, the sequential measurement update is expressed as follows:

[0119] Assume that there is If a measurement is classified as a first-class measurement, then after sorting, the sequential measurement updates are as follows:

[0120] ;

[0121] described represents the state to be estimated based on the first type of measurement, express In the first type of measurement at the moment, measurements that are updated sequentially, and . represents the probability density function, For the first type of measurement until A collection of measurements at a moment in time.

[0122] By sequential measurement updates, the corresponding and its covariance matrix .

[0123] Similar to S4, in the sequential update process, and It is expressed as follows:

[0124] ;

[0125] Similar to the above formula (14), the posterior innovation corresponding to each sequential update can be expressed as follows:

[0126] ;

[0127] Among them, the Indicates the The posterior estimation error of the sequential update is defined as .

[0128] Furthermore, the standardized innovation is expressed as follows:

[0129] ;

[0130] in, No. The standardized new information corresponding to the update of individual quantity sequencing, for The covariance matrix of .

[0131] For each sequential update result, Conduct standardized testing. , then continue to integrate into the If the measurement is not satisfied, the output As Sequential results of moments .

[0132] Exemplarily, the progressive measurement update is expressed as follows:

[0133] ;

[0134] described represents the state to be estimated based on the second type of measurement, For the second type of measurement until A collection of measurements at a moment in time. represents the asymptotic pseudo-time constant, where and , . Indicates the Progress is made step by step, is the total number of progressive steps. represents the asymptotic likelihood function, which is expressed as follows:

[0135] ;

[0136] Combined with the linearization process shown in formula (14), the likelihood function is further written as follows:

[0137] ;

[0138] Similarly, the corresponding and its corresponding covariance matrix .

[0139] Furthermore, the first type of measurement is used to guide the execution or cessation of the progressive measurement update, if , then stop the asymptotic measurement update and output the asymptotic posterior estimate As a result of incremental measurement updates .

[0140] After completing the sequential and progressive measurement updates, the system state is updated using the following formula: and Perform global fusion to obtain carrier state information and its covariance matrix:

[0141] ;

[0142] described Indicates the system global status information, represents its corresponding covariance matrix.

[0143] S7: Integer constraints are applied to floating-point solutions, and quality control is performed on the integer solutions after the constraints are completed.

[0144] S701: The state fusion result in S7 is corrected using the wide lane ambiguity in S3. and represent the modified system state and covariance matrix respectively.

[0145] S702: Furthermore, the LAMBDA algorithm is used to apply integer constraints to the above results to obtain a fixed solution, and its reliability is verified using the R-Test. The R-Test threshold is typically 2 to 3, and in this embodiment is set to 3. Ambiguity fixation based on the LAMBDA algorithm is a common technique in integrated navigation and is readily apparent to those skilled in the art, so this will not be described in detail.

[0146] Exemplary correction factor It is expressed by the following formula:

[0147] ;

[0148] in, is the wide lane ambiguity in S3, represents the floating point solution of the ambiguity in S6. The new information vector is used to 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 described in detail.

[0149] S8: Use the current epoch solution to update the INS error. This is a common technique in the field of integrated navigation and will not be described in detail.

[0150] Example 2

[0151] See also Figure 2 As shown, this embodiment provides an RTK positioning device based on composite Gaussian filtering.

[0152] It includes: posture recursion unit, combined ambiguity solving unit, compound Gaussian filtering unit, and quality control unit; among which:

[0153] Pose recursion unit: Using GNSS / INS loose combination recursion, combined with the solved corrected pseudorange, it can continuously provide high-frequency current pose information after dynamic alignment initialization, which is used as prior position information for the composite Gaussian filter unit.

[0154] Combined ambiguity resolution unit: Multi-frequency information is used to construct ultra-wide lane combinations and wide lane combinations for resolution. The calculated precise pseudo-range information is provided to the position and posture recursion unit.

[0155] Composite Gaussian filter unit: Utilizes the prior information derived from inertial navigation to classify the observations, adopts different sequential or progressive filtering methods for different categories of observations, uses measurements with smaller innovations to guide the fusion of measurements with larger innovations, and finally performs state fusion on the results of different categories.

[0156] Quality control unit: controls the quality of the ambiguity solved by the above-mentioned solving unit.

[0157] An embodiment of the present invention provides an RTK positioning device with a compound Gaussian filter, comprising: a memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the aforementioned RTK positioning method with a compound Gaussian filter.

[0158] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0159] The apparatus provided in the above embodiment can be implemented in the form of a computer program. The computer program can be used in Figure 3 The RTK positioning device shown is based on composite Gaussian filtering.

[0160] Example 3. An embodiment of the present invention provides an RTK positioning device based on composite Gaussian filtering, comprising: a memory, a processor, a serial interface, and a parallel interface connected via a system bus, wherein at least one instruction is stored in the memory, and at least one instruction is loaded and executed by the processor to implement an RTK positioning method based on composite Gaussian filtering in Example 1.

[0161] 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 will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0162] A processor can be a CPU, other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of a computer device, connecting all parts of the entire computer device through various interfaces and circuits.

[0163] The memory can be used to store computer programs and / or modules. The processor implements the various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory primarily includes a program storage area and a data storage area. The program storage area can store the operating system and at least one application required for a function (such as video playback or image playback). The data storage area can store data generated by device use (such as video data or image data). Furthermore, the memory can include high-speed random access memory (RAM) or non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital card (SD), a flash card, at least one disk storage device, a flash memory device, or other solid-state storage device.

[0164] Example 4: This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it can implement the RTK positioning method based on composite Gaussian filtering described in Example 1.

[0165] The embodiments of the present invention can complete all or part of the process through computer program instructions and related hardware. The computer program can be stored in a computer-readable storage medium and, when executed by a processor, implements the various steps of the above-mentioned 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 may include any entity or device that can carry 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), electric 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, electric carrier signal and telecommunication signal are not included.

[0166] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, servers, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage) containing computer-usable program code.

[0167] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0168] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0169] Finally, it should be noted that the above descriptions are merely 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 aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. The RTK positioning method based on compound Gaussian filtering is characterized by: The following steps are involved: S1: Use the GNSS / INS combination to calculate the real-time status information of the carrier, including carrier position, carrier attitude, position variance, and attitude variance; S2: Acquire satellite observation data from the base station and rover, where the satellite observation data includes triple-frequency carrier observation values and pseudorange observation values; S3: Using the triple-frequency carrier observations and pseudorange observations, a combined observation is constructed to resolve the ultra-wide lane ambiguity and wide lane ambiguity. The corrected pseudorange observations are obtained using the ultra-wide lane ambiguity and wide lane ambiguity. S4: Use the GNSS / INS combination in step S1 to calculate the real-time status information of the carrier, the triple-frequency pseudorange and triple-frequency carrier phase observations, and the corrected pseudorange observations solved in S3 to build a system model, perform state prediction, and calculate innovation and normalized innovation; S5: Detect and remove outliers based on the standardized new information obtained in S4, and classify the remaining observations to obtain classification results, including first-category measurements, second-category measurements, and third-category measurements; S6: Perform compound Gaussian filtering on the classification results in S5, perform sequential filtering and updating on the first type of measurement, fuse the second type of measurement using the progressive Gaussian filtering method, discard the classification results as the third type of measurement, perform state fusion on the two, and obtain a floating-point solution; S7: constrain the floating-point solution to integers and perform quality control on the constrained integer solutions; S8: Update the INS error using the current epoch solution.

2. The RTK positioning method based on compound Gaussian filtering according to claim 1, characterized in that: The specific steps of S2 are as follows: S201: Pre-process the data and perform a pre-check based on the signal-to-noise ratio and cycle slip detection conditions; S202: Mark the observations for which cycle slip information is detected.

3. The RTK positioning method based on compound Gaussian filtering according to claim 1, characterized in that: The specific steps of step 3 are as follows: S301: Establish different ultra-wide lane and wide lane combination observation values based on multi-frequency observation values of different satellite systems; S302: Solve the combined observation values of different satellite systems and perform quality control on the combined observation value floating point solution. Fix the combined observation value that meets the conditions, that is, when the floating point solution meets the decision probability When the S303: After fixing the above combined observation values, the double-difference pseudorange of the combined observation values and the double-difference integer ambiguity of the combined observation values are used to calculate the corrected double-difference pseudorange observation, i.e., the precise double-difference pseudorange observation value; S304: giving different observation noises to the above different ultra-wide lane and wide lane observation values.

4. The RTK positioning method based on compound Gaussian filtering according to claim 1, characterized in that: The specific steps of step 4 are as follows: S401: Perform kinematic estimation using the position and velocity information calculated by the GNSS solution of the previous epoch to obtain the satellite guidance prediction solution of the current epoch; S402: Obtaining the combined dead reckoning solution for the current epoch using the GNSS / INS loosely coupled system mechanical arrangement results; S403: Using the existing satellite guidance prediction solution and the integrated navigation solution, perform innovation and standardized innovation tests on the precise double-difference pseudorange observations of the combined observations in step S3; S404: Using the satellite observation data of the base station and the rover station, a double-difference observation value is constructed by subtracting the inter-station observation value and the inter-satellite observation value; S405: Using the existing satellite guidance prediction solution and the integrated navigation solution, the double difference observation value in step S404 is standardized.

5. The RTK positioning method based on compound Gaussian filtering according to claim 1, characterized in that: The specific steps of step 5 are as follows: S501: Detect and remove outliers based on the normalized values corresponding to the predicted values in S4; S502: clustering the remaining measurement innovations using K-means; S503: Compare the absolute values of the three center points after K-means clustering, and classify the three clusters into the first category of measurement, the second category of measurement, and the third category of measurement in order of the absolute values of the center points from small to large.

6. The RTK positioning method based on compound Gaussian filtering according to claim 1, characterized in that: The specific steps of step 6 are as follows: S601: For the first type of clustering, sort the measurements according to the absolute value of the new information from small to large, and update them one by one using sequential filtering; S602: During the sequential update process of S601, after each new measurement is incorporated and updated, a standardized new information check is performed; If the test threshold is met, the sequential update continues; If the test threshold is not met, it will fall back to the last update process as the sequential filtering result; S603: For the second type of clustered measurements, measurement information is fused using progressive Gaussian filtering. Based on the set total number of progressive steps, the likelihood function is decomposed into multiple progressive likelihood functions. The corresponding measurement update process is also decomposed into progressive measurement updates. The corresponding progressive likelihood functions are fused at each pseudo-time, and the system posterior state is solved through continuous iteration. S604: For the progressive filtering process in S604, the first type of measurement is used to guide the progressive update process of the second type of measurement. When a cutoff 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. S605: directly remove the third type of measurement. S606: Perform state fusion on the above sequential results and the progressive measurement update results to obtain a floating-point solution.

7. The RTK positioning method based on compound Gaussian filtering according to claim 1, characterized in that: The specific steps of step 7 are as follows: S701: For the state fusion result in S7, use the wide lane ambiguity in S3 to correct it; S702: Perform R-Test and post-test residual test 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 composite Gaussian filtering is characterized by: include: Pose recursion unit, combined ambiguity solving unit, compound Gaussian filtering unit, quality control unit; among which: Position and attitude recursion unit: adopts GNSS / INS loose combined recursion, which can continuously provide high-frequency current position and attitude information after dynamic alignment initialization, and is used as prior position information for the combined ambiguity resolution unit; Combined ambiguity resolution unit: Multi-frequency information is used to construct ultra-wide lane combinations and wide lane combinations for ambiguity resolution; Composite Gaussian filter unit: Utilizes combined ambiguity and prior information to classify observations, performs sequential filtering or progressive Gaussian filtering updates based on the differences in observations, and guides the fusion of observations with greater innovation. Quality control unit: controls the quality of the ambiguity solved by the above-mentioned solving unit.

9. RTK positioning equipment based on composite Gaussian filtering, characterized in that: include: A memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement an RTK positioning method based on composite Gaussian filtering as described in any one of claims 1-7.

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

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