A multi-frequency ambiguity robust solving method and device based on semi-compact combination, equipment and storage medium

Abstract

A multi-frequency ambiguity robust solving method and device based on semi-tight combination, equipment and storage medium, the method comprises the following steps: S1: using GNSS / INS combination to solve the real-time state information of the carrier; S2: acquiring satellite observation data of the reference station and the rover station; S3: constructing a multi-frequency ambiguity first solver, the first solver is obtained by a Kalman filter to obtain an ambiguity float solution; S4: constructing a multi-frequency ambiguity second solver, the second solver is obtained by multi-frequency observation combination to realize ambiguity hierarchical solution, to obtain double difference ambiguity of different frequency combination; S5: quality control is carried out on the integer ambiguity estimation value of the second solver; S6: using the second solver information, the state of the first solver is constrained, and the double difference ambiguity integer estimation value of all frequency bands is obtained; S7: quality control is carried out on the integer ambiguity estimation value of the first solver, and the solving result of the current epoch is obtained; S8: using the current epoch solving result to update the error of INS.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to a method, device, equipment and storage medium for robust multi-frequency ambiguity resolution based on semi-tight combination. Background Art

[0002] GNSS-RTK (Global Navigation Satellite System-Real-Time Kinematic) technology, with its high-precision positioning capabilities, has broad application prospects in surveying and mapping, unmanned driving, agriculture, and precision engineering. By utilizing the differential signal between a ground base station and a mobile terminal, the technology can provide real-time positioning with centimeter-level accuracy. However, while GNSS-RTK technology performs excellently under ideal conditions, it still faces numerous challenges when satellite signals are blocked or interfered with. This is particularly true in urban environments with tall buildings, forested areas, and areas with dense interference sources, where RTK filtering is prone to divergence. Signal multipath effects and interference sources (such as radio frequency interference) can increase pseudorange measurement errors, causing significant fluctuations in pseudorange positioning results.

[0003] Related technologies often use a geometrically dependent model in conjunction with a Kalman filter to iteratively solve floating-point ambiguities, followed by ambiguity fixation using the LAMBDA algorithm. Alternatively, a geometrically independent model is used in conjunction with multi-frequency observations to construct a linear combination, resolving ambiguities step by step through ultra-wide lanes, wide lanes, and narrow lanes. However, existing geometric models are computationally intensive and cannot fix the correct integer ambiguities if the floating-point ambiguities are biased. The accuracy and success rate of solutions without geometric models are strongly correlated with pseudorange noise, ionospheric noise, and multipath noise, resulting in low robustness in complex environments. Summary of the Invention

[0004] The present invention aims to overcome the above-mentioned shortcomings of the prior art and provide a method, device, equipment and storage medium for robust resolution of multi-frequency ambiguity based on semi-tight combination, so as to accurately and robustly fix the ambiguity and improve the baseline resolution fixation rate and accuracy.

[0005] This invention utilizes a dual-solver collaboration mechanism. The first solver implements floating-point estimation of non-combined single-difference ambiguities, while the second solver hierarchically fixes double-difference ambiguities based on multi-frequency combinations. Combined with a cross-solver constraint model, the results of ultra-widelane and widelane ambiguity fixation are fed back into the optimized single-difference solution, forming a closed-loop feedback loop. This significantly improves the ambiguity fixation success rate and positioning reliability in complex scenarios.

[0006] In a first aspect, an embodiment of the present invention provides a method for robustly resolving multi-frequency ambiguity based on semi-tight combination, 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, accelerometer bias, and gyroscope bias;

[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: Construct a multi-frequency ambiguity first solver, which passes through a Kalman filter to obtain a floating-point solution for the ambiguity;

[0010] S4: Construct a second multi-frequency ambiguity solver. The second solver implements ambiguity hierarchical resolution by combining multi-frequency observations to obtain double-difference ambiguities of different frequency combinations.

[0011] S5: Perform quality control on the integer ambiguity estimates of the second solver.

[0012] S6: Use the second solver information to constrain the state of the first solver and obtain double-difference ambiguity integer estimates for all frequency bands.

[0013] S7: Perform quality control on the integer ambiguity estimate of the first solver to obtain the solution result of the current epoch.

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

[0015] Optionally, step S1 includes:

[0016] S101: The initial heading information of the GNSS / INS loose combination is provided by RTK-assisted dynamic alignment or directly by dual-antenna RTK.

[0017] S102: Perform inertial mechanics on the acceleration and angular velocity data collected by the inertial measurement unit (IMU) to obtain the attitude, velocity, and position solutions of the carrier in the east, north, and sky directions;

[0018] Optionally, step S3 includes:

[0019] S301: Select a reference star, perform inter-station single difference and inter-satellite single difference processing on the original observation data, and generate double-difference observations;

[0020] S302: Construct a first Kalman filter state vector, where the state vector includes the receiver's three-dimensional position, three-dimensional velocity, and inter-station single-difference ambiguity parameters for each frequency point;

[0021] S303: Establishing observation equations based on double-difference pseudorange observations and double-difference carrier phase observations, executing state prediction and measurement update processes, and obtaining state variables under ambiguity floating-point solutions;

[0022] Optionally, step S4 includes:

[0023] S401: Based on the double-difference carrier phase measurement value and the linear combination of the three-frequency signal, an ultra-wide lane linear combination is established, and the ultra-wide lane ambiguity fixation resolution is performed;

[0024] S402: Establishing a wide lane linear combination and performing wide lane ambiguity fixation resolution based on the double-difference pseudorange combination measurement value after the ultra-wide lane ambiguity fixation resolution;

[0025] S403: Establishing a narrow lane linear combination, and using the MLAMBDA algorithm to resolve the narrow lane ambiguity based on the double-difference pseudorange combination measurement value after the wide lane ambiguity is fixed;

[0026] Optionally, step S6 includes:

[0027] S601: Establishing a cross-solver constraint model: The double-difference ambiguities of the ultra-wide lane and wide lane obtained in S4 are used as virtual observation values, and the single-difference ambiguity state vector in S3 is converted into double-difference ultra-wide lane and wide lane ambiguities, and a linear constraint equation is constructed.

[0028] S602: Hierarchical Sequential Ambiguity Constraints: Linear constraint equations are constructed and fixed step by step using the combined double-difference ambiguities with longer wavelengths fixed in S4. Only one combined ambiguity is used for constraint in each iteration. If multiple ambiguities were fixed in S4, multiple constraints are applied. The ambiguities are resolved using the MLAMBDA algorithm.

[0029] In a second aspect, an embodiment of the present invention provides a multi-frequency ambiguity robust solution device based on semi-tight combination, comprising: a posture recursive unit, a first ambiguity solution unit, a second ambiguity solution unit, and a quality control unit; wherein:

[0030] Posture recursion unit: adopts GNSS / INS loose combined recursion, which can continuously provide high-frequency current posture information after dynamic alignment initialization, and is used as prior position information for the first ambiguity solver and the second ambiguity solver.

[0031] The first ambiguity solving unit: constructs state variables as position, velocity, and inter-station single-difference ambiguity, and performs non-frequency combination ambiguity solving.

[0032] The second ambiguity solving unit: uses multi-frequency information to construct ultra-wide lane combination, wide lane combination, and narrow lane ambiguity for solution.

[0033] Quality control unit: controls the quality of the ambiguity solved by the above-mentioned solving unit.

[0034] In a third aspect, an embodiment of the present invention provides a multi-frequency ambiguity robust resolution device based on semi-compact combination, 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 multi-frequency ambiguity robust resolution method based on semi-compact combination.

[0035] In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned ambiguity resolution method is implemented.

[0036] The beneficial effects of the present invention are as follows:

[0037] The present invention proposes a robust multi-frequency ambiguity resolution method based on semi-tight combination. By integrating GNSS / INS observation data with a multi-frequency ambiguity hierarchical resolution architecture, the ambiguity fixation success rate and positioning reliability in complex scenarios are significantly improved. Through the dual-solver collaboration mechanism, the first solver realizes the estimation of the floating-point solution of non-combined single-difference ambiguity, and the second solver fixes the double-difference ambiguity based on the multi-frequency combination hierarchical fixation. Combined with the cross-solver constraint model, the ultra-wide lane wide lane ambiguity fixation result is fed back to optimize the single-difference solution to form a closed-loop feedback. Using a semi-tight combination architecture, the INS high-frequency posture information is used to provide high-precision prior constraints for ambiguity resolution, effectively suppressing the impact of interference such as multipath and cycle slips on carrier phase observations. Through the above content, the method of the present invention can improve the positioning robustness and fixed solution ratio in real-time positioning compared with general methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of the process of the present invention;

[0039] Figure 2 It is a structural schematic diagram of the device of the present invention;

[0040] Figure 3 It is a structural schematic diagram of the device of the present invention. DETAILED DESCRIPTION

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] Example 1

[0047] Traditional geometrically dependent models, such as the MLAMBDA algorithm, can achieve good positioning results in open areas. However, as the number and frequency of signals increase, the number of available observations increases exponentially, leading to a geometrically increased computational load, making robust and fast resolution impossible. Partial ambiguity algorithms are currently commonly used, reducing the number of observations involved in the solution by selecting a subset, thereby reducing the computational load. However, the selection of subsets significantly impacts positioning results, especially in non-open areas. Multipath and non-line-of-sight signals can introduce significant errors in the carrier and pseudorange, resulting in inaccurate calculated floating-point ambiguities that cannot be correctly fixed by the MLAMBDA algorithm. This further affects and deviates from other correct ambiguities, degrading filter performance and preventing them from being fixed. Furthermore, frequent cycle slips and satellite signal interruptions complicate the calculation and filtering of multi-epoch data. Geometrically independent models use satellites to independently fix their own ambiguities. This independent ambiguity fixation process prevents the failure of one satellite to fix the ambiguities of other satellites from affecting their fixation. Compared to geometrically dependent algorithms, geometrically independent algorithms offer advantages in these scenarios. However, the errors introduced in non-open scenes are directly reflected in the fixed ambiguities of geometrically independent algorithms, severely impacting the success rate of ambiguity fixation. Stringent quality control algorithms are required to ensure the quality of ambiguity fixation using geometrically independent algorithms and effectively integrate them with geometrically dependent algorithms to achieve accurate and robust estimation. Currently available algorithms do not address these issues. Therefore, accurately and reliably identifying and removing these incorrectly fixed ambiguities and integrating them with geometrically dependent algorithms are key issues that urgently need to be addressed to achieve robust estimation.

[0048] To address the computationally intensive nature of geometrically dependent algorithms and the erroneous fixation of ambiguities using geometrically independent algorithms in modern GNSS positioning systems, a method was designed that first resolves floating-point ambiguities using a geometrically dependent model, then uses a geometrically independent model to resolve double-difference ambiguities to constrain the floating-point ambiguities of the geometrically dependent model. Finally, the double-difference ambiguities of the geometrically dependent model are fixed to resolve the original ambiguities. Building on the traditional geometrically dependent and geometrically independent models, a constraint and fusion method was introduced. Furthermore, by using the results of the loosely combined recursive method as a priori position, the correct ambiguity resolution is guaranteed even in weak signal scenarios. This reduces the computational complexity and solution time of the geometrically dependent model and the error rate of the ambiguity resolution using the geometrically independent model. This improves the robustness and robustness of ambiguity fixation and positioning results.

[0049] See also Figure 1 As shown, an embodiment of the present invention provides a method for robustly resolving multi-frequency ambiguity based on semi-tight combination, comprising the following steps:

[0050] S1: Use the GNSS / INS combination to calculate the real-time status information of the carrier, including carrier position, carrier attitude, accelerometer bias, and gyroscope bias;

[0051] For example, this embodiment may adopt a 15-dimensional state vector model for loose combination, and its state vector is recorded as:

[0052]

[0053] in, is the three-dimensional attitude error; δv is the three-dimensional velocity error; δp is the three-dimensional position error; is the estimated value of the three-axis accelerometer bias; is the estimated value of the three-axis gyro bias.

[0054] The GNSS / INS loose combination 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.

[0055] Optionally, the S1 includes:

[0056] S101: The initial heading information of the GNSS / INS loose combination is provided by RTK-assisted dynamic alignment or directly by dual-antenna RTK.

[0057] S102: Perform inertial mechanics on the acceleration and angular velocity data collected by the inertial measurement unit (IMU) to obtain the attitude, velocity, and position solutions of the carrier in the east, north, and sky directions;

[0058] 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;

[0059] For example, for the convenience of description, this embodiment derives BDS as an example, and the three signals are B1 (1561.098 MHz), B2 (1207.14 MHz), and B3 (1268.52 MHz).

[0060] A short baseline geometric model is constructed to resolve ambiguity. The observation equation after single difference between stations is:

[0061]

[0062] For the convenience of description, the double difference operator is ignored; i represents the signal involved in the calculation; j represents the reference satellite, k represents the participating satellite, r represents the mobile station; b represents the reference station; represents the double-difference carrier phase observation value; represents the double-difference pseudorange observation value; is the geometric distance after double difference; λ i is the wavelength of the corresponding signal; and They represent the inter-station single-difference ambiguity of the corresponding satellites; Φ and p denote the double-difference observation noise of carrier phase and pseudorange, respectively.

[0063] S3: Construct a multi-frequency ambiguity first solver, which passes through a Kalman filter to obtain a floating-point solution for the ambiguity;

[0064] For example, this embodiment uses an extended Kalman filter (EKF) for filtering and solving, and its modeling is as follows:

[0065]

[0066] The variables in the formula are explained as follows:

[0067] described and P k Represent the estimated state vector and covariance matrix at time k respectively; (+) and () represent the estimated value after update and the estimated value before update respectively; K k represents the Kalman gain matrix; y k represents the observation value obtained from the sensor or system; h(x) represents the measurement model of the state vector; H(x) represents the Jacobian matrix; I represents the identity matrix; R k represents the noise matrix; F k represents the state transfer matrix; Q k represents the system noise matrix; Represents the change of a variable from epoch k to k+1.

[0068] For example, in this embodiment, the default is to perform modeling under a short baseline condition, so the corresponding tropospheric and ionospheric errors are considered to be almost completely eliminated, and the corresponding receiver clock error and satellite clock error are completely eliminated through double difference.

[0069]

[0070] The r r represents the location parameter of the receiver; B1, B2, and B3 represent the single-difference carrier phase observation values ​​of the corresponding signals.

[0071]

[0072] The y is the established measurement matrix; Φ i 、P i represent the double-difference carrier phase and double-difference pseudorange measurements of the corresponding signal i, respectively.

[0073]

[0074] The h(x) is the measurement model of the state vector; are the measurement models of the carrier phase and pseudorange corresponding to signal i, respectively.

[0075]

[0076] H(x) is the Jacobian matrix; D represents the single-difference to double-difference conversion matrix; E represents the satellite line of sight vector; λ i represents the carrier wavelength corresponding to signal i.

[0077]

[0078] The RΦ ,i and R P,i Respectively represent the observation value noise of the carrier phase and pseudorange of the corresponding signal pair; is the measurement standard deviation of the carrier phase and pseudorange observations of the corresponding signal; D represents the single-difference to double-difference conversion matrix; E represents the satellite line of sight vector; λ i represents the carrier wavelength corresponding to signal i.

[0079]

[0080] The hΦ ,i 、h P,i is the carrier and pseudorange measurement model corresponding to the i signal. The other symbols have been described above and will not be described here again.

[0081] Through the above model, the corresponding floating-point solution is obtained.

[0082] Optionally, the S3 includes:

[0083] S301: Select a reference star, perform inter-station single difference and inter-satellite single difference processing on the original observation data, and generate double-difference observations;

[0084] S302: Construct a first Kalman filter state vector, where the state vector includes the receiver's three-dimensional position, three-dimensional velocity, and inter-station single-difference ambiguity parameters for each frequency point;

[0085] S303: Establishing observation equations based on double-difference pseudorange observations and double-difference carrier phase observations, executing state prediction and measurement update processes, and obtaining state variables under ambiguity floating-point solutions;

[0086] S4: Construct a second multi-frequency ambiguity solver. The second solver implements ambiguity hierarchical resolution by combining multi-frequency observations to obtain double-difference ambiguities of different frequency combinations.

[0087] In the pseudorange double-difference observations, multipath error is difficult to be directly modeled and is generally treated as observation noise. The multipath error has a weak correlation between different signals. The multipath error can be detected and weakened by combining multi-frequency double-difference pseudorange observations. The multi-frequency combination double-difference pseudorange observation value is:

[0088]

[0089] described is the average of the pseudorange double difference observations of the three signals.

[0090]

[0091] The N (0,-1,1) is the combined double difference ambiguity of the second signal and the third signal.

[0092]

[0093] described is the high-precision double-difference pseudorange observation value after fixing the double-difference ambiguity; (0,-1,1) is the carrier phase wavelength of the combination of the second signal and the third signal; Φ (0,-1,1) is the double-difference carrier phase observation value of the combination of the second signal and the third signal; N (0,-1,1) It is the double-difference integer ambiguity of the combination of the second signal and the third signal, also known as the ultra-wide lane ambiguity.

[0094]

[0095] The N (1,-1,0) is the combined double-difference ambiguity of the first signal and the second signal, also known as the wide-lane ambiguity.

[0096]

[0097] The MLAMBDA algorithm can be used to obtain the final double difference geometric distance and the solution of the original ambiguity.

[0098] Optionally, the S4 includes:

[0099] S401: Based on the double-difference carrier phase measurement value and the linear combination of the three-frequency signal, an ultra-wide lane linear combination is established, and the ultra-wide lane ambiguity fixation resolution is performed;

[0100] S402: Establishing a wide lane linear combination and performing wide lane ambiguity fixation resolution based on the double-difference pseudorange combination measurement value after the ultra-wide lane ambiguity fixation resolution;

[0101] S403: Establishing a narrow lane linear combination, and using the MLAMBDA algorithm to resolve the narrow lane ambiguity based on the double-difference pseudorange combination measurement value after the wide lane ambiguity is fixed;

[0102] S5: Perform quality control on the integer ambiguity estimates of the second solver.

[0103] In short-baseline scenarios, the ionosphere and troposphere are assumed to be completely eliminated by double-difference. However, double-difference cannot completely eliminate the effects of an active ionosphere. Complex scenarios also introduce multipath errors, and pseudorange and carrier observation noise increases. In the aforementioned models, double-difference noise in pseudorange and carrier phase is introduced, causing interference with the ambiguities directly solved by the second solver and resulting in fixed errors in the solved integer ambiguities. Because ambiguities are resolved step by step, this phenomenon can lead to errors in the next-level ambiguity resolution, thus affecting the mutual constraints between the two models, necessitating strict quality control.

[0104] The ultra-wide lane, wide lane, and original ambiguities obtained at S4 are graded for quality control. First, their double-difference ambiguities are brought back to their double-difference carrier observations to obtain their residual v.

[0105] Exemplarily, the ultra-wide lane ambiguity double difference residual is used as the object of quality control.

[0106] v (0,-1,1) =λ (0,-1,1) ×(Φ (0,-1,1) -N (0,-1,1) ) (twenty four)

[0107] The quartile method is used for outlier detection. A secondary judgment is introduced for the residuals identified as outliers. The outlier is subtracted from its mean and its absolute value is determined to determine whether it exceeds the threshold, thus avoiding misjudgment by the quartile method. The threshold for the secondary judgment is related to the carrier wavelength and is generally taken from its wavelength length (0.1-0.8). In this example, 0.35 is used.

[0108] Furthermore, we try to fix the wide lane and narrow lane ambiguity.

[0109] Furthermore, if the number of outliers exceeds 40% of the total number of points, the double-difference pseudo-range residuals are obtained by recursive position calculation using INS. Iterate.

[0110] S6: Use the second solver information to constrain the state of the first solver and obtain double-difference ambiguity integer estimates for all frequency bands.

[0111] For example, the double difference residual of ultra-wide lane ambiguity is used as the constraint object to construct the single and double difference transfer matrix of ultra-wide lane constraint:

[0112]

[0113] described The ultra-wide lane ambiguity is the combination of the floating-point solutions of the first solver for the multi-frequency ambiguity being sought; is the single-difference ambiguity of the carrier phase of satellite j on signal i.

[0114]

[0115] The v (0,1,1) is the difference between the second solver's ultra-widelane integer ambiguity and the first solver's ultra-widelane floating-point ambiguity.

[0116]

[0117] The R (0,1,1) For the corresponding v (0,1,1) The measurement noise matrix of var (0,1,1) is the noise of the ultra-wide lane integer ambiguity of the second solver, and its value range is generally (0.05-0.4) wavelengths. In this embodiment, it is 0.2 wavelengths.

[0118] Furthermore, the measurement update is performed through the above formula to complete the constraint of the second solver on the floating-point ambiguity of the first solver.

[0119] Optionally, the S6 includes:

[0120] S601: Establishing a cross-solver constraint model: The double-difference ambiguities of the ultra-wide lane and wide lane obtained in S4 are used as virtual observation values, and the single-difference ambiguity state vector in S3 is converted into double-difference ultra-wide lane and wide lane ambiguities, and a linear constraint equation is constructed.

[0121] S602: Hierarchical Sequential Ambiguity Constraints: Linear constraint equations are constructed and fixed step by step using the combined double-difference ambiguities with longer wavelengths fixed in S4. Only one combined ambiguity is used for constraint in each iteration. If multiple ambiguities were fixed in S4, multiple constraints are applied. The ambiguities are resolved using the MLAMBDA algorithm.

[0122] S7: Perform quality control on the integer ambiguity estimate of the first solver to obtain the solution result of the current epoch.

[0123] Furthermore, the aforementioned ambiguity is subjected to an R-Test, with a threshold value generally ranging from 2 to 3, and in this embodiment, 3. If the R-Test fails, the carrier phase ambiguity residuals are clustered using the DBSCAN method, with a neighborhood radius generally ranging from 0.1 to 1 times the wavelength, and a minimum number of points generally based on 0.3 to 0.7 times the number of valid observations involved in the calculation. In this embodiment, the neighborhood radius is 0.3 times the wavelength, and the minimum number of points is 0.4 times the number of valid observations.

[0124] S8: Further, the error of INS is updated using the solution result of the current epoch.

[0125] 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.

[0126] Example 2

[0127] See also Figure 2 As shown, this embodiment provides a multi-frequency ambiguity robust solution device based on semi-tight combination, including: a posture recursive unit, a first ambiguity solution unit, a second ambiguity solution unit, and a quality control unit; wherein:

[0128] Posture recursion unit: adopts GNSS / INS loose combined recursion, which can continuously provide high-frequency current posture information after dynamic alignment initialization, and is used as prior position information for the first ambiguity solver and the second ambiguity solver.

[0129] The first ambiguity solving unit: constructs state variables as position, velocity, and inter-station single-difference ambiguity, and performs non-frequency combination ambiguity solving.

[0130] The second ambiguity solving unit: uses multi-frequency information to construct ultra-wide lane combination, wide lane combination, and narrow lane ambiguity for solution.

[0131] Quality control unit: controls the quality of the ambiguity solved by the above-mentioned solving unit.

[0132] 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.

[0133] 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 A multi-frequency ambiguity robust solution based on semi-compact combination is shown and runs on the device.

[0134] Example 3

[0135] An embodiment of the present invention provides a filtering and resolving device for robust multi-frequency ambiguity resolution based on semi-tight combining, 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 the at least one instruction is loaded and executed by the processor to implement a robust multi-frequency ambiguity resolution method based on semi-tight combining according to embodiment 1.

[0136] 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.

[0137] 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), or 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.

[0138] The memory can be used to store computer programs and / or modules. The processor implements 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 mainly includes a program storage area and a data storage area. The program storage area can store an operating system and at least one application required for a function (such as video playback, image playback, etc.). The data storage area can store data generated by the use of the device (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, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital card (SD), a flash card (Flash Card), at least one disk storage device, a flash memory device, or other solid-state storage device.

[0139] Example 4

[0140] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for robustly resolving multi-frequency ambiguity based on semi-tight combination described in Embodiment 1 can be implemented.

[0141] 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 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 signals and telecommunication signals are not included.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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. A robust multi-frequency ambiguity resolution method based on semi-tight combination, comprising the following steps: S1: Use the GNSS / INS combination to calculate the real-time status information of the carrier, including carrier position, carrier attitude, accelerometer bias, and gyroscope bias; 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; S3: Construct a multi-frequency ambiguity first solver, which passes through a Kalman filter to obtain a floating-point solution for the ambiguity; S4: Construct a second multi-frequency ambiguity solver. The second solver implements ambiguity hierarchical resolution by combining multi-frequency observations to obtain double-difference ambiguities of different frequency combinations. S5: performing quality control on the integer ambiguity estimates of the second solver; S6: Using the second solver information, constrain the state of the first solver to obtain double-difference ambiguity integer estimates for all frequency bands; S7: Perform quality control on the integer ambiguity estimate of the first solver to obtain the solution result of the current epoch; S8: Update the INS error using the current epoch solution.

2. The method for robust multi-frequency ambiguity resolution based on semi-tight combination according to claim 1, characterized in that: Step S1 includes: S101: The initial heading information of the GNSS / INS loose combination is provided by RTK-assisted dynamic alignment or directly by dual-antenna RTK; S102: The acceleration and angular velocity data collected by the inertial measurement unit (IMU) are arranged using inertial mechanics to obtain the attitude, velocity, and position solutions of the carrier in the east, north, and sky directions.

3. The method for robust multi-frequency ambiguity resolution based on semi-tight combination according to claim 1, wherein: Step S3 includes: S301: Select a reference star, perform inter-station single difference and inter-satellite single difference processing on the original observation data, and generate double-difference observations; S302: Construct a first Kalman filter state vector, where the state vector includes the receiver's three-dimensional position, three-dimensional velocity, and inter-station single-difference ambiguity parameters for each frequency point; S303: Establish observation equations based on double-difference pseudorange observations and double-difference carrier phase observations, execute state prediction and measurement update processes, and obtain state variables under ambiguity floating-point solutions.

4. The method for robust multi-frequency ambiguity resolution based on semi-tight combination according to claim 1, wherein: Step S4 includes: S401: Based on the double-difference carrier phase measurement value and the linear combination of the three-frequency signal, an ultra-wide lane linear combination is established, and the ultra-wide lane ambiguity fixation resolution is performed; S402: Establishing a wide lane linear combination and performing wide lane ambiguity fixation resolution based on the double-difference pseudorange combination measurement value after the ultra-wide lane ambiguity fixation resolution; S403: Establish a narrow lane linear combination, and use the MLAMBDA algorithm to resolve the narrow lane ambiguity based on the double-difference pseudorange combination measurement value after the wide lane ambiguity is fixed.

5. The method for robust multi-frequency ambiguity resolution based on semi-tight combination according to claim 1, wherein: Step S6 includes: S601: Establishing a cross-solver constraint model: The double-difference ambiguities of the ultra-wide lane and wide lane obtained in S4 are used as virtual observation values, and the single-difference ambiguity state vector in S3 is converted into double-difference ultra-wide lane and wide lane ambiguities, and a linear constraint equation is constructed. S602: Hierarchical sequential ambiguity constraint: The linear constraint equations are constructed and fixed step by step from the combined double-difference ambiguities with longer wavelengths fixed in S4. Only one combined ambiguity is used for constraint in one iteration. If S4 fixes multiple levels of ambiguity, multiple constraints are applied. The ambiguity is resolved using the MLAMBDA algorithm.

6. A device for robust multi-frequency ambiguity resolution based on semi-tight combining, implementing a method for robust multi-frequency ambiguity resolution based on semi-tight combining according to any one of claims 1 to 5, the device comprising: Pose recursion unit, ambiguity first solution unit, ambiguity second solution unit, quality control unit; wherein: 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 first ambiguity solver and the second ambiguity solver; The first ambiguity solving unit: constructs the state variables as position, velocity, and inter-station single-difference ambiguity, and performs non-inter-frequency combined ambiguity solving; The second ambiguity solving unit uses multi-frequency information to construct ultra-wide lane combinations, wide lane combinations, and narrow lane ambiguities for solution; Quality control unit: controls the quality of the ambiguity solved by the above-mentioned solving unit.

7. A robust multi-frequency ambiguity resolution device based on semi-tight combination, 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 a multi-frequency ambiguity robust resolution method based on semi-tight combination according to one of claims 1 to 5.

8. A computer storage medium storing a computer program, wherein the computer storage medium stores a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for robust multi-frequency ambiguity resolution based on semi-compact combination according to any one of claims 1 to 5.

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