A partial ambiguity fixing method, apparatus and device

Abstract

The application discloses a partial ambiguity fixing method, device and equipment, and the method comprises the following steps: acquiring floating ambiguity estimates corresponding to multiple satellites; performing combination processing on the multiple floating ambiguity estimates to obtain multiple ambiguity subsets and subset information corresponding to the multiple ambiguity subsets; performing fixing and checking processing on the multiple ambiguity subsets and the multiple subset information to determine an integer ambiguity subset satisfying a preset checking condition; when the number of the integer ambiguity subsets is greater than or equal to a preset number threshold, performing subset reorganization processing on the integer ambiguity subsets to obtain reorganized integer ambiguity subsets; and determining a target ambiguity fixing solution based on the reorganized integer ambiguity subsets. The application can fix more satellites, improve the number of fixed satellites in an observation epoch, and thus can improve the ambiguity fixing solution rate of positioning calculation while improving the positioning accuracy of the fixing solution.

Description

Technical Field

[0001] This application relates to the technical field of solving fixed ambiguity solutions, and in particular to a method, apparatus and equipment for fixing partial ambiguity. Background Technology

[0002] When using the Global Navigation Satellite System (GNSS) for high-precision positioning, it is essential to use carrier phase observations for solution because the carrier wavelength is shorter than the symbol width of the ranging code. However, the phase detector in the receiver can only directly obtain less than one cycle when measuring the carrier phase, while the number of integer cycles remains unknown. Therefore, determining the integer ambiguity is a crucial step in achieving a fixed solution for precise point positioning. However, when carrier observations from some satellites are affected by observation noise, multipath effects, etc., cycle slips or unmodeled errors may occur, making it impossible to fix the integer ambiguity parameters of all satellites in a short time. Therefore, when solving for a fixed ambiguity solution, a partial ambiguity fixation method is usually adopted to avoid the problem of reduced fixation rate caused by interference with some satellite observations.

[0003] In some existing ambiguity fixing methods, after obtaining the floating-point ambiguity on the carrier observation, the floating-point ambiguity is sorted according to objective indicators of the observation, such as elevation angle, signal-to-noise ratio, and floating-point solution variance. Then, satellites with poor indicators are eliminated one by one to form a subset of ambiguity to be fixed and attempt to fix it until the quality check index of the fixed solution of the ambiguity subset reaches a preset threshold. Obviously, the following shortcomings exist: First, when selecting an ambiguity subset, because elevation angle, signal-to-noise ratio, and floating-point unambiguity variance are highly correlated, it is easy to select several satellites with higher elevation angles to enter the fixed ambiguity subset, regardless of whether the sorting is based on elevation angle, signal-to-noise ratio, or floating-point unambiguity variance. This will affect the satellite's geometric configuration and is not conducive to ambiguity fixation. Second, when fixing partial ambiguity, it is assumed that satellites with higher elevation angles have better observation quality. However, when they experience signal interference, they cannot be removed, which will affect the fixation of the entire ambiguity subset. Furthermore, satellites excluded from the fixed subset may not necessarily have problematic observations and may still be correctly fixed. Third, the ambiguity subsets generated by traditional partial ambiguity fixation methods are relatively homogeneous, resulting in a low fixation success rate, which cannot meet the high fixation rate requirements of high-precision navigation and positioning. Summary of the Invention

[0004] To address the aforementioned technical issues, this application discloses a partial ambiguity fixing method. By randomly combining all floating-point ambiguity estimates, the number of ambiguity subsets can be maximized. The integer ambiguity subsets obtained from fixing and verification are recombined to obtain recombined integer ambiguity subsets, which can effectively fix more satellites, increase the number of fixed satellites in the observation epoch, and thus significantly improve the ambiguity fixing rate of the positioning solution while improving the positioning accuracy of the fixed solution.

[0005] To achieve the above-mentioned objective, this application provides a method for partially fixing ambiguity, the method comprising:

[0006] Obtain floating-point ambiguity estimates for multiple satellites;

[0007] Multiple floating-point ambiguity estimates are combined to obtain multiple ambiguity subsets and their respective subset information;

[0008] The multiple ambiguity subsets and multiple subset information are fixed and checked to determine the integer ambiguity subsets that meet the preset check conditions.

[0009] If the number of integer ambiguity subsets is greater than or equal to a preset threshold, subset recombination is performed based on the integer ambiguity subsets to obtain recombined integer ambiguity subsets.

[0010] The target ambiguity fixed solution is determined based on the recombined integer ambiguity subset.

[0011] In some implementations, the step of combining multiple floating-point ambiguity estimates to obtain multiple ambiguity subsets and their corresponding subset information includes:

[0012] The total number of floating-point ambiguity estimates is determined based on the multiple floating-point ambiguity estimates.

[0013] Based on the total number and all natural numbers between the preset combination number and the total number, the multiple floating-point fuzziness estimates are iterated and recursively combined to obtain multiple fuzziness subsets and their respective subset information; the preset combination number is greater than or equal to the preset combination threshold and less than or equal to the total number.

[0014] The subset information includes ambiguity covariance matrix information; the process of fixing and checking the multiple ambiguity subsets and the multiple subset information to determine the integer ambiguity subsets that meet the preset check conditions includes:

[0015] Based on preset fixed configuration information, the multiple ambiguity subsets are fixed to obtain multiple initial integer ambiguity subsets;

[0016] Based on the multiple initial integer ambiguity subsets and multiple ambiguity covariance matrix information, the multiple initial integer ambiguity subsets are checked to determine the integer ambiguity subsets that meet the preset check conditions.

[0017] In some implementations, the step of performing a check on the plurality of initial integer ambiguity subsets based on the plurality of initial integer ambiguity subsets and the plurality of ambiguity covariance matrix information to determine the integer ambiguity subsets that meet preset check conditions includes:

[0018] Based on the multiple initial integer ambiguity subsets and the multiple ambiguity covariance matrix information, a first detection kernel value corresponding to each of the initial integer ambiguity subsets is determined; the first detection kernel value includes an integer solution ratio value;

[0019] Based on the multiple fuzziness covariance matrix information, a second kernel value corresponding to each of the multiple fixed subsets of fuzziness is determined; the second kernel value includes a fuzziness accuracy attenuation factor value.

[0020] For each of the aforementioned ambiguity subsets, if the integer solution ratio is greater than a preset first check threshold and the ambiguity accuracy decay factor is less than a preset second check threshold, the initial integer ambiguity subset is determined as the integer ambiguity subset.

[0021] In some implementations, when the number of integer ambiguity subsets is greater than or equal to a preset threshold, performing subset recombination processing based on the integer ambiguity subsets to obtain recombined integer ambiguity subsets includes:

[0022] If the number of integer ambiguity subsets is equal to a preset number threshold, the integer ambiguity subsets are determined as recombined integer ambiguity subsets.

[0023] If the number of integer ambiguity subsets is greater than a preset threshold, adjacent integer ambiguity subsets are sequentially recombined to obtain recombined integer ambiguity subsets.

[0024] In some embodiments, when the number of integer ambiguity subsets exceeds a preset threshold, the process of sequentially recombining two adjacent integer ambiguity subsets to obtain a recombined integer ambiguity subset includes:

[0025] If the number of integer ambiguity subsets is greater than a preset threshold, a first integer ambiguity subset and a second integer ambiguity subset are obtained, and the first integer ambiguity subset and the second integer ambiguity subset are set adjacent to each other.

[0026] Obtain the first integer solution ratio corresponding to the first integer ambiguity subset and the second integer solution ratio corresponding to the second integer ambiguity subset;

[0027] Based on the first integer solution ratio and the second integer solution ratio, the first integer ambiguity subset and the second integer ambiguity subset are recombined to obtain the recombined integer ambiguity subset.

[0028] In some implementations, based on the first integer resolution ratio and the second integer resolution ratio, the first integer ambiguity subset and the second integer ambiguity subset are recombined to obtain the recombined integer ambiguity subset, including:

[0029] Obtain multiple first-integer ambiguities from the first integer ambiguity subset and multiple second-integer ambiguities from the second integer ambiguity subset;

[0030] If the first integer solution ratio is greater than the second integer solution ratio, a first target integer ambiguity corresponding to each of the plurality of second integer ambiguities is determined from the plurality of second integer ambiguities;

[0031] Based on the plurality of second integer ambiguities and at least one first target integer ambiguity, at least one second target integer ambiguity is determined, wherein the second target integer ambiguity is an integer ambiguity other than the first target integer ambiguity among the plurality of second integer ambiguities.

[0032] The plurality of first integer ambiguities and the at least one second target integer ambiguity are recombined to obtain the recombined integer ambiguity subset.

[0033] In some embodiments, the method further includes:

[0034] If the number of integer ambiguity subsets is less than a preset threshold, obtain the ambiguity floating-point solutions corresponding to the multiple floating-point ambiguity estimates;

[0035] The ambiguity floating-point solution is determined as the target localization solution.

[0036] This application also provides a partial ambiguity fixing device, the device comprising:

[0037] The acquisition module is used to obtain floating-point ambiguity estimates for multiple satellites.

[0038] The combination processing module is used to estimate and combine multiple floating-point ambiguities to obtain multiple ambiguity subsets and their respective subset information.

[0039] The first determining module is used to fix and check the multiple ambiguity subsets and multiple subset information to determine the integer ambiguity subsets that meet the preset check conditions.

[0040] The reorganization processing module is used to perform subset reorganization processing on the integer ambiguity subset when the number of the integer ambiguity subset is greater than or equal to a preset number threshold, so as to obtain a reorganized integer ambiguity subset.

[0041] The second determining module is used to determine the target ambiguity fixed solution based on the recombined integer ambiguity subset.

[0042] This application also provides a partial ambiguity fixing device, the device including a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the partial ambiguity fixing method as described above.

[0043] Implementing the embodiments of this application has the following beneficial effects:

[0044] The partial ambiguity fixing method of this application can maximize the number of ambiguity subsets by randomly combining all floating-point ambiguity estimates; and by reorganizing the integer ambiguity subsets obtained from fixing and checking, a reorganized integer ambiguity subset can be obtained, which can fully fix more satellites, increase the number of fixed satellites in the observation epoch, and thus significantly improve the ambiguity fixing rate of the positioning solution while improving the positioning accuracy of the fixed solution. Attached Figure Description

[0045] To more clearly illustrate the partial ambiguity fixing methods, apparatus, and devices described in this application, the accompanying drawings required for the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A schematic diagram of an implementation environment with partially fixed ambiguity provided for an embodiment of this application;

[0047] Figure 2 This application provides a flowchart illustrating a method for fixing partial ambiguity.

[0048] Figure 3 A flowchart illustrating a method for determining first calibration sensing data provided in an embodiment of this application;

[0049] Figure 4 A flowchart illustrating a method for determining a subset of reconstructed integer ambiguities provided in an embodiment of this application;

[0050] Figure 5 A flowchart illustrating a specific integer ambiguity reconstruction method provided in this application embodiment;

[0051] Figures 6a-6b A schematic diagram of integer ambiguity reconstruction provided for an embodiment of this application;

[0052] Figure 7 A flowchart illustrating another method for fixing partial ambiguity provided in an embodiment of this application;

[0053] Figure 8 This is a schematic diagram of a partial ambiguity fixing device provided in an embodiment of this application;

[0054] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0056] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0057] Please see Figure 1 It illustrates a schematic diagram of the implementation environment provided in the embodiments of this application, which may include:

[0058] At least one terminal 01 and at least one server 02. The at least one terminal 01 and the at least one server 02 can communicate data via a network.

[0059] In an optional embodiment, terminal 01 may be the executor of a partial ambiguity fixing method. Terminal 01 may be, but is not limited to, electronic devices such as in-vehicle terminals, smartphones, desktop computers, tablets, laptops, smart speakers, digital assistants, augmented reality (AR) / virtual reality (VR) devices, and smart wearable devices. The operating system running on terminal 01 may include, but is not limited to, Android, iOS, Linux, Windows, and Unix.

[0060] Server 02 can provide terminal 01 with floating-point fuzzyness estimation, preset check conditions, and preset quantity thresholds. Optionally, server 02 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0061] Please refer to Figure 2 The diagram illustrates a flowchart of a partial ambiguity fixing method provided in this application. This specification provides the operational steps of the method described in the embodiments or flowchart, but based on conventional or non-inventive methods, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many steps and does not represent the only execution order. The partial ambiguity fixing method can be executed according to the method order shown in the embodiments or drawings. Specifically, as shown... Figure 2 As shown, the method includes:

[0062] S201, obtain floating-point ambiguity estimates for multiple satellites;

[0063] In this embodiment of the application, the floating-point ambiguity estimate can be the ambiguity estimate when the ambiguity is in a floating-point state; each satellite corresponds to at least one floating-point ambiguity estimate.

[0064] Optionally, carrier observation data and precise correction parameters from multiple satellites can be acquired; based on these parameters, precise corrections can be applied to the carrier observation data to obtain multiple carrier observation correction data; and a Kalman filter algorithm can be used to determine multiple floating-point ambiguity estimates from the carrier observation data and correction data. The carrier observation data measures the phase difference between the satellite carrier signal received by the GPS receiver and the reference carrier signal generated by the receiver oscillator. Precise correction parameters may include ionospheric delay data, tropospheric delay data, receiver clock bias data, and phase deviation data. The Kalman filter algorithm is a method that uses the state equations of a linear system to optimally estimate the system state using system input and output observation data. The multiple satellites can be any satellites whose carrier observation data can be received by the currently used receiver.

[0065] S202 combines multiple floating-point ambiguity estimates to obtain multiple ambiguity subsets and their corresponding subset information.

[0066] In this embodiment of the application, the ambiguity subset can be a set obtained by randomly combining multiple ambiguity estimates; the subset information can be the correlation information between each floating-point ambiguity in each subset; for example, the subset information can include ambiguity covariance matrix information.

[0067] Optionally, multiple floating-point fuzziness estimates can be iterated over and randomly combined to obtain multiple fuzziness subsets.

[0068] In some exemplary embodiments, the total number of floating-point ambiguity estimates can be determined based on the plurality of floating-point ambiguity estimates; based on the total number and all natural numbers between a preset combination number and the total number, the plurality of floating-point ambiguity estimates are iterated and recursively combined to obtain a plurality of ambiguity subsets and subset information corresponding to each of the plurality of ambiguity subsets; the preset combination number is greater than or equal to a preset combination threshold and less than or equal to the total number. The preset combination number can be a natural number greater than or equal to 1, preferably 4.

[0069] In one example, using the method of Model 1, multiple floating-point ambiguity estimates are randomly combined to obtain multiple subsets of ambiguity, as follows:

[0070] Model 1:

[0071] Where n represents the total number of floating-point ambiguity estimates; m represents the number of ambiguity subsets; and a represents the preset number of combinations, where a is a natural number greater than or equal to 1. This application uses a loop traversal method to maximize the number of ambiguity subsets, making it more likely to search for subsets that meet preset check conditions and thus obtain fixed solutions, thereby improving the ambiguity fixed solution rate of the positioning solution.

[0072] S203, fix and check multiple ambiguity subsets and multiple subset information to determine the integer ambiguity subset that meets the preset check conditions.

[0073] In this embodiment, the integer ambiguity subset may include multiple integer ambiguities; an integer ambiguity can refer to the integer unknown corresponding to the first observation value of the phase difference between the carrier phase and the reference phase during carrier phase measurement in Global Positioning System (GPS) technology. The integer ambiguity is the ambiguity obtained after fixing multiple floating-point ambiguity estimates within the ambiguity subset. The preset check condition may be that the integer solution ratio is greater than a first check threshold and the ambiguity accuracy attenuation factor value is less than a preset second check threshold.

[0074] Optionally, the plurality of ambiguity subsets can be fixed based on preset fixed configuration information to obtain a plurality of initial integer ambiguity subsets; further, based on the plurality of initial integer ambiguity subsets and a plurality of ambiguity covariance matrix information, the plurality of initial integer ambiguity subsets are checked to determine the integer ambiguity subsets that meet preset check conditions. The preset fixed configuration information may include the LAMBDA (Leastsquare AMBiguity Decorrelation Adjustment-LAMBDA) algorithm.

[0075] In some exemplary embodiments, for each initial integer ambiguity subset, at least one check value corresponding to the initial integer ambiguity subset can be determined based on the initial integer ambiguity subset and the ambiguity covariance matrix information; the initial integer ambiguity subset is checked based on the check value to determine whether the initial integer ambiguity subset meets the preset check conditions.

[0076] S204, if the number of integer ambiguity subsets is greater than or equal to a preset threshold, perform subset recombination processing based on the integer ambiguity subsets to obtain recombined integer ambiguity subsets.

[0077] In this embodiment of the application, the preset quantity threshold can be pre-set, for example, it can be 1.

[0078] Optionally, the number of integer ambiguities in the recombined integer ambiguity subset is equal to the total number of floating-point ambiguity estimates or equal to all natural numbers between the preset number of combinations and the total number of floating-point ambiguities.

[0079] In some exemplary embodiments, when the number of integer ambiguity subsets is equal to a preset number threshold, the integer ambiguity subsets can be determined as recombined integer ambiguity subsets.

[0080] In one example, taking a preset quantity threshold of 1 as an example, when the quantity of the whole-week fuzziness subset is equal to 1, the whole-week fuzziness subset itself can be directly determined as the recombined whole-week fuzziness subset.

[0081] In some other exemplary embodiments, when the number of integer ambiguity subsets is greater than a preset number threshold, adjacent two integer ambiguity subsets can be recombined sequentially to obtain recombined integer ambiguity subsets.

[0082] In one example, taking a preset quantity threshold of 1 as an example, if the number of integer ambiguity subsets is greater than 1, such as when the number of integer ambiguity subsets is equal to 2, the two integer ambiguity subsets can be recombined to obtain a recombined integer ambiguity subset.

[0083] S205, Determine the fixed solution of target ambiguity based on the reorganized integer ambiguity subset.

[0084] In the embodiments of this application, the fixed ambiguity solution can be a positioning solution where the ambiguity is in a fixed state, and the fixed solution can be an integer solution.

[0085] Optionally, multiple integer ambiguities from the recombined integer ambiguity subset can be substituted into the preset observation equation to obtain a fixed solution for the target ambiguity.

[0086] In this embodiment, by randomly combining all floating-point ambiguity estimates, the number of ambiguity subsets can be maximized; by recombining the fixed and checked integer ambiguity subsets, a recombined integer ambiguity subset can be obtained, which can fully fix more satellites, increase the number of fixed satellites in the observation epoch, and thus significantly improve the ambiguity fixed solution rate of the positioning solution while improving the positioning accuracy of the fixed solution.

[0087] In some exemplary embodiments, such as Figure 3 The diagram shown is a flowchart illustrating a method for determining an integer ambiguity subset provided in an embodiment of this application; the details are as follows.

[0088] S301, determine the first kernel value to be checked for each of the initial integer ambiguity subsets based on multiple initial integer ambiguity subsets and multiple ambiguity covariance matrix information; the first kernel value to be checked includes the integer solution ratio.

[0089] In this embodiment, the first check value can be an indicator used for ambiguity quality checking. For example, it can include the integer solution ratio. The integer solution ratio can be the ratio of the second-best integer solution to the best integer solution of the ambiguity.

[0090] Optionally, for each initial integer ambiguity subset, the ambiguity estimate corresponding to that initial integer ambiguity subset can be obtained, and the integer solution ratio corresponding to that initial integer ambiguity subset can be determined based on the ambiguity estimate and the ambiguity covariance matrix information corresponding to that initial integer ambiguity subset.

[0091] In some exemplary embodiments, for each initial integer ambiguity subset, the ambiguity estimate, the best integer solution, and the second-best integer solution of the initial integer ambiguity can be obtained; the ratio of integer solutions corresponding to the initial integer ambiguity subset is determined based on the ambiguity estimate, the best integer solution, the second-best integer solution, and the ambiguity covariance matrix information corresponding to the initial integer ambiguity subset.

[0092] In one example, Model 2 can be used to determine the ratio of integer solutions;

[0093] Model 2:

[0094] Where Ratio represents the ratio of integer solutions; Estimating ambiguity; The optimal integer solution; It is a suboptimal integer solution; Let be the ambiguity covariance matrix.

[0095] S302, determine the second kernel value corresponding to each of the multiple fixed subsets of ambiguity based on the information of multiple ambiguity covariance matrices; the second kernel value to be detected includes the ambiguity accuracy attenuation factor value.

[0096] In this embodiment, the second check value can be an indicator used for ambiguity quality checking. For example, it can include a ambiguity accuracy attenuation factor value. The ambiguity accuracy attenuation factor value can be an indicator of ambiguity position quality.

[0097] Optionally, Model 3 can be used to determine the ambiguity accuracy attenuation factor value;

[0098] Model 3:

[0099] Wherein, ADOP represents the ambiguity accuracy attenuation factor value; is the ambiguity covariance matrix; n is the total number of ambiguity estimates.

[0100] S303, for each of the aforementioned ambiguity subsets, if the integer solution ratio is greater than a preset first check threshold and the ambiguity accuracy attenuation factor is less than a preset second check threshold, the initial integer ambiguity subset is determined as the integer ambiguity subset.

[0101] In this embodiment, the integer ambiguity subset can be any initial integer ambiguity subset from multiple initial integer ambiguity subsets where the ratio of integer solutions is greater than a preset first check threshold and the ambiguity accuracy attenuation factor is less than a preset second check threshold. The multiple initial integer ambiguity subsets may include one or more integer ambiguity subsets. Alternatively, they may not include any integer ambiguity subsets.

[0102] In this embodiment, the present application performs a dual check on the ambiguity subset by combining the ambiguity accuracy attenuation factor value and the integer solution ratio; this can more comprehensively reflect the overall ambiguity quality in the subset, making the obtained integer ambiguity more accurate, thereby improving the accuracy of the fixed solution obtained in subsequent calculations.

[0103] In some exemplary embodiments, such as Figure 4 The diagram shown is a flowchart illustrating a method for determining a subset of recombined integer ambiguities provided in an embodiment of this application; the details are as follows.

[0104] S401, if the number of integer ambiguity subsets is greater than a preset threshold, obtain the first integer ambiguity subset and the second integer ambiguity subset, and set the first integer ambiguity subset and the second integer ambiguity subset adjacent to each other.

[0105] In the embodiments of this application, the first integer ambiguity subset and the second integer ambiguity subset can be any two adjacent subsets of the integer ambiguity subset that satisfy the preset check conditions.

[0106] Optionally, taking a preset quantity threshold of 1 as an example, if the number of integer ambiguity subsets is greater than the preset quantity threshold, it can be determined that at least two integer ambiguity subsets are included. Furthermore, two adjacent integer ambiguity subsets can be obtained sequentially to reorganize the two adjacent integer ambiguity subsets.

[0107] S402, obtain the first integer solution ratio corresponding to the first integer ambiguity subset and the second integer solution ratio corresponding to the second integer ambiguity subset.

[0108] S403, based on the first integer solution ratio and the second integer solution ratio, the first integer ambiguity subset and the second integer ambiguity subset are recombined to obtain the recombined integer ambiguity subset.

[0109] In the embodiments of this application, the recombined integer ambiguity subset may include at least all integer ambiguities in the first integer ambiguity subset, or at least all integer ambiguities in the second integer ambiguity subset.

[0110] Optionally, the first integer ambiguity subset and the second integer ambiguity subset can be reorganized according to the relationship between the first integer solution ratio and the second integer solution ratio.

[0111] If the ratio of the first integer solutions is greater than the ratio of the second integer solutions, the recombined integer ambiguity subset may include at least all integer ambiguities in the first integer ambiguity subset. Furthermore, it may also include at least one integer ambiguity from the second integer ambiguity subset.

[0112] If the ratio of the second integer solutions is greater than the ratio of the first integer solutions, the recombined integer ambiguity subset may include at least all integer ambiguities in the second integer ambiguity subset. Furthermore, it may also include at least one integer ambiguity from the first integer ambiguity subset.

[0113] Optionally, if a third integer ambiguity subset exists, the third integer ambiguity subset is set adjacent to the second integer ambiguity subset; the recombined integer ambiguity subset obtained by recombining the first integer ambiguity subset and the second integer ambiguity subset can be recombined with the third integer ambiguity subset; until every integer ambiguity subset that meets the preset check conditions is traversed.

[0114] In this embodiment, this application compares all integer ambiguity subsets that meet preset check conditions using check indices, selects the integer ambiguities from the subset with more favorable indices, and reassembles the selected integer ambiguities into a new recombined integer ambiguity subset. This method can maximize the fixation of more satellites, increase the number of fixed satellites in the observation epoch, and improve the positioning accuracy of fixed solutions.

[0115] In some exemplary embodiments, such as Figure 5 The diagram shown is a flowchart illustrating a specific integer ambiguity reconstruction method provided in an embodiment of this application; the details are as follows.

[0116] S501, obtain multiple first-week ambiguities in the first-week ambiguity subset and multiple second-week ambiguities in the second-week ambiguity subset;

[0117] In the embodiments of this application, each integer ambiguity subset may include multiple integer ambiguities.

[0118] S502, if the first integer solution ratio is greater than the second integer solution ratio, determine the first target integer ambiguity corresponding to each of the multiple second integer ambiguities from the multiple second integer ambiguities.

[0119] In this embodiment, the plurality of second integer ambiguities include a first target integer ambiguity and a second target integer ambiguity; the number of the first target integer ambiguity and the second target integer ambiguity is equal to the number of the second integer ambiguities. The first target integer ambiguity can be an ambiguity corresponding to any first integer ambiguity.

[0120] In an exemplary embodiment, taking an example where the total number of floating-point ambiguity estimates is 6 and the preset number of combinations is 4, the first integer ambiguity subset may include four first integer ambiguities; the second integer ambiguity subset may include four second integer ambiguities. For example, the four first integer ambiguities include: including N 1,K N 2,K N 4,K N 6，K The four second-round ambiguities include: N 1,K-1 N 3,K-1 N 5,K-1 N 6，K-1 The integer ambiguity of the first target is then N. 1,K-1 and N 6，K-1 .

[0121] S503, based on multiple second integer ambiguities and at least one first target integer ambiguity, determine at least one second target integer ambiguity, wherein the second target integer ambiguity is an integer ambiguity other than the first target integer ambiguity among the multiple second integer ambiguities.

[0122] In this embodiment of the application, the second integer ambiguity can be any ambiguity that does not correspond to the first integer ambiguity.

[0123] In an exemplary embodiment, taking an example where the total number of floating-point ambiguity estimates is 6 and the preset number of combinations is 4, the first integer ambiguity subset may include four first integer ambiguities; the second integer ambiguity subset may include four second integer ambiguities. For example, the four first integer ambiguities include: including N 1,K N 2,K N 4,K N 6，K The four second-round ambiguities include: N 1,K-1 N 3,K-1 N 5,K-1 N 6，K-1 The integer ambiguity of the second objective is then N. 3,K-1 and N 5,K-1 .

[0124] S504, reorganize the plurality of first integer ambiguities and at least one second target integer ambiguity to obtain a reorganized integer ambiguity subset.

[0125] In this embodiment of the application, a subset obtained by directly combining multiple first integer ambiguities and at least one second target integer ambiguity can be determined as a recombined integer ambiguity subset.

[0126] In one exemplary embodiment, such as Figures 6a-6b The diagram shown is a schematic representation of integer ambiguity reconstruction provided in an embodiment of this application, as detailed below.

[0127] The subset of ambiguity in the first integer cycle is taken as subset k, and the subset of ambiguity in the second integer cycle is taken as subset k-1;

[0128] When the ratio of the first integer solutions corresponding to subset k is greater than the ratio of the second integer solutions corresponding to subset k-1, that is, when Ratio k >Ratio k-1 In such cases, the reorganization method is as follows: Figure 6a As shown;

[0129] When the ratio of the first integer solutions corresponding to subset k is less than the ratio of the second integer solutions corresponding to subset k-1, that is, when Ratio k <Ratio k-1 In such cases, the reorganization method is as follows: Figure 6b As shown.

[0130] In this embodiment, by comparing the ratio of integer solutions corresponding to two adjacent integer ambiguity subsets, this application can select the integer ambiguity in the integer ambiguity subset with better indicators; and by forming a recombined integer ambiguity subset based on multiple integer ambiguities with better indicators, the positioning accuracy of the fixed ambiguity solution can be further improved.

[0131] In some exemplary embodiments, such as Figure 7 The diagram shown is a flowchart of another partial ambiguity fixing method provided in an embodiment of this application.

[0132] S701, obtains floating-point ambiguity estimates for multiple satellites.

[0133] S702 combines multiple floating-point ambiguity estimates to obtain multiple ambiguity subsets and their corresponding subset information.

[0134] S703 fixes and checks multiple ambiguity subsets and multiple subset information to determine the integer ambiguity subset that meets the preset check conditions.

[0135] S704: When the number of integer ambiguity subsets is less than a preset threshold, obtain the ambiguity floating-point solutions corresponding to multiple floating-point ambiguity estimates.

[0136] In this embodiment of the application, taking a preset quantity threshold of 1 as an example, if the number of integer ambiguity subsets is less than the preset quantity threshold, it can be characterized that the integer ambiguity subsets that satisfy the preset check conditions are zero. An ambiguity floating-point solution can refer to the positioning solution when the ambiguity is in a floating-point state; at this time, the positioning solution can be a decimal solution.

[0137] Optionally, the method for obtaining the ambiguity floating-point solution is the same as the method for obtaining the floating-point ambiguity estimate; multiple satellite carrier observation data and precise correction parameters can be obtained; multiple carrier observation data are precisely corrected based on multiple precise correction parameters to obtain multiple carrier observation correction data; multiple floating-point ambiguity estimates and the corresponding ambiguity floating-point solutions are determined based on the multiple carrier observation data and multiple carrier observation correction data using the Kalman filter algorithm.

[0138] S705 determines the ambiguity floating-point solution as the target localization solution.

[0139] In the embodiments of this application, the ambiguity localization solution can refer to the result obtained by resolving the ambiguity.

[0140] Optionally, if there is no integer ambiguity subset that meets the preset check conditions, the ambiguity floating-point solutions corresponding to multiple floating-point ambiguity estimates can be directly output.

[0141] In this embodiment, the present application directly determines the floating-point ambiguity solution as the target localization solution when there is no integer ambiguity subset that meets the preset check conditions; thus ending the calculation process of the fixed solution, unnecessary data processing can be avoided and costs can be saved.

[0142] This application also provides a partial ambiguity fixing device, such as... Figure 8 As shown, this is a schematic diagram of a partial ambiguity fixing device provided in an embodiment of this application; specifically, the device includes:

[0143] The acquisition module 801 is used to acquire floating-point ambiguity estimates for multiple satellites.

[0144] The combination processing module 802 is used to estimate and combine multiple floating-point ambiguities to obtain multiple ambiguity subsets and their respective subset information.

[0145] The first determining module 803 is used to fix and check the multiple ambiguity subsets and multiple subset information to determine the integer ambiguity subsets that meet the preset check conditions.

[0146] The recombination processing module 804 is used to perform subset recombination processing on the integer ambiguity subset when the number of integer ambiguity subsets is greater than or equal to a preset number threshold, to obtain a recombined integer ambiguity subset.

[0147] The second determining module 805 is used to determine the target ambiguity fixed solution based on the recombined integer ambiguity subset.

[0148] In this embodiment of the application, the combination processing module 802 includes:

[0149] The first determining unit is used to determine the total number of floating-point ambiguity estimates based on the plurality of floating-point ambiguity estimates;

[0150] The combination processing unit is used to perform recursive combination processing on the multiple floating-point fuzziness estimates based on the total number and all natural numbers between the preset combination number and the total number, to obtain multiple fuzziness subsets and the subset information corresponding to each of the multiple fuzziness subsets; the preset combination number is greater than or equal to the preset combination threshold and less than or equal to the total number.

[0151] In this embodiment of the application, the subset information includes ambiguity covariance matrix information; the first determining module 803 includes:

[0152] The second determining unit is used to perform fixed processing on the plurality of ambiguity subsets based on preset fixed configuration information to obtain a plurality of initial integer ambiguity subsets.

[0153] The third determining unit is used to perform a check on the multiple initial integer ambiguity subsets based on the multiple initial integer ambiguity subsets and multiple ambiguity covariance matrix information, and determine the integer ambiguity subsets that meet the preset check conditions.

[0154] In this embodiment of the application, the third determining unit includes:

[0155] The first determining subunit is used to determine the first kernel value to be detected for each of the multiple initial integer ambiguity subsets and the multiple ambiguity covariance matrix information; the first kernel value to be detected includes an integer solution ratio.

[0156] The second determining subunit is used to determine the second kernel value to be detected for each of the multiple fixed subsets of ambiguity based on the multiple ambiguity covariance matrix information; the second kernel value to be detected includes the ambiguity accuracy attenuation factor value.

[0157] The third determining subunit is used to determine the initial integer ambiguity subset as the integer ambiguity subset for each ambiguity subset, provided that the integer solution ratio is greater than a preset first check threshold and the ambiguity accuracy decay factor is less than a preset second check threshold.

[0158] In this embodiment of the application, the recombination processing module 804 includes:

[0159] The first reorganization processing unit is used to determine the integer ambiguity subset as a reorganized integer ambiguity subset when the number of integer ambiguity subsets is equal to a preset number threshold.

[0160] The second reorganization processing unit is used to sequentially reorganize two adjacent integer ambiguity subsets when the number of integer ambiguity subsets is greater than a preset number threshold, so as to obtain reorganized integer ambiguity subsets.

[0161] In this embodiment of the application, the second recombination processing unit includes:

[0162] The first acquisition subunit is used to acquire a first integer ambiguity subset and a second integer ambiguity subset when the number of integer ambiguity subsets is greater than a preset number threshold. The first integer ambiguity subset and the second integer ambiguity subset are arranged adjacent to each other.

[0163] The second acquisition subunit is used to acquire the first integer solution ratio value corresponding to the first integer ambiguity subset and the second integer solution ratio value corresponding to the second integer ambiguity subset;

[0164] The recombination processing subunit is used to recombinate the first integer ambiguity subset and the second integer ambiguity subset based on the first integer solution ratio and the second integer solution ratio to obtain the recombined integer ambiguity subset.

[0165] In this embodiment of the application, the recombination processing subunit includes:

[0166] The acquisition submodule is used to acquire multiple first integer ambiguities in the first integer ambiguity subset and multiple second integer ambiguities in the second integer ambiguity subset.

[0167] The first determining submodule is used to determine, when the first integer solution ratio is greater than the second integer solution ratio, a first target integer ambiguity corresponding to each of the plurality of second integer ambiguities;

[0168] The second determining submodule is used to determine at least one second target integer ambiguity based on the plurality of second integer ambiguities and at least one first target integer ambiguity, wherein the second target integer ambiguity is an integer ambiguity other than the first target integer ambiguity among the plurality of second integer ambiguities.

[0169] The recombination processing submodule is used to recombine the plurality of first integer ambiguities and the at least one second target integer ambiguity to obtain the recombined integer ambiguity subset.

[0170] In this embodiment of the application, it also includes:

[0171] The floating-point solution acquisition module is used to acquire the fuzzy floating-point solution corresponding to the multiple floating-point fuzzy estimates when the number of integer fuzzy subsets is less than a preset number threshold.

[0172] The third determining module is used to determine the ambiguity floating-point solution as the target positioning solution.

[0173] It should be noted that the apparatus and method embodiments described in the device embodiments are based on the same inventive concept.

[0174] This application provides a partial ambiguity fixing device, which includes a processor and a memory. The memory stores at least one instruction or at least one program. The processor loads and executes the at least one instruction or at least one program to implement the partial ambiguity fixing method as described in the above method embodiments.

[0175] Furthermore, Figure 9 A schematic diagram of the hardware structure of an electronic device for implementing the partial ambiguity fixing method provided in the embodiments of this application is shown. The electronic device may participate in or include the partial ambiguity fixing device provided in the embodiments of this application. Figure 9 As shown, the electronic device 90 may include one or more processors 902 (processor 902 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 904 for storing data, and a transmission device 906 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 9 The structure shown is for illustrative purposes only and does not limit the structure of the electronic device described above. For example, the electronic device 90 may also include... Figure 9 The diagram shows more or fewer components, or has a different configuration than that shown.

[0176] It should be noted that the aforementioned one or more processors 902 and / or other partial ambiguity fixing circuitry are generally referred to herein as "partial ambiguity fixing circuitry." This partial ambiguity fixing circuitry can be wholly or partially embodied in software, hardware, firmware, or any other combination thereof. Furthermore, the partial ambiguity fixing circuitry can be a single, independent processing module, or wholly or partially integrated into any other element within the electronic device 90 (or mobile device). As involved in the embodiments of this application, this partial ambiguity fixing circuitry serves as a processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0177] The memory 904 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the partial ambiguity fixing method described in the embodiments of this application. The processor 902 executes various functional applications and partial ambiguity fixing by running the software programs and modules stored in the memory 904, thereby realizing the aforementioned partial ambiguity fixing method. The memory 904 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 904 may further include memory remotely located relative to the processor 902, and these remote memories can be connected to the electronic device 90 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0178] The transmission device 906 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the electronic device 90. In one example, the transmission device 906 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In one embodiment, the transmission device 906 may be a radio frequency (RF) module for wireless communication with the Internet.

[0179] The display can be, for example, a touchscreen liquid crystal display (LCD), which allows users to interact with the user interface of an electronic device (or mobile device).

[0180] Embodiments of this application also provide a computer-readable storage medium, which can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a partial ambiguity fixing method in the method embodiments. The at least one instruction or the at least one program is loaded and executed by the processor to implement the partial ambiguity fixing method provided in the above method embodiments.

[0181] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0182] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0183] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0184] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and electronic device embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0185] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0186] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for fixing partial ambiguity, characterized in that, The method includes: Obtain floating-point ambiguity estimates for multiple satellites; Multiple floating-point ambiguity estimates are combined to obtain multiple ambiguity subsets and their respective subset information; The multiple ambiguity subsets and multiple subset information are fixed and checked to determine the integer ambiguity subsets that meet the preset check conditions. If the number of integer ambiguity subsets is equal to a preset number threshold, the integer ambiguity subsets are determined as recombined integer ambiguity subsets. If the number of integer ambiguity subsets is greater than the preset number threshold, a first integer ambiguity subset and a second integer ambiguity subset are obtained, and the first integer ambiguity subset and the second integer ambiguity subset are set adjacent to each other. Obtain the first integer solution ratio corresponding to the first integer ambiguity subset and the second integer solution ratio corresponding to the second integer ambiguity subset; Obtain multiple first-integer ambiguities from the first integer ambiguity subset and multiple second-integer ambiguities from the second integer ambiguity subset; If the first integer solution ratio is greater than the second integer solution ratio, a first target integer ambiguity corresponding to each of the plurality of second integer ambiguities is determined from the plurality of second integer ambiguities; Based on the plurality of second integer ambiguities and at least one first target integer ambiguity, at least one second target integer ambiguity is determined, wherein the second target integer ambiguity is an integer ambiguity other than the first target integer ambiguity among the plurality of second integer ambiguities. The plurality of first integer ambiguities and the at least one second target integer ambiguity are recombined to obtain the recombined integer ambiguity subset; The target ambiguity fixed solution is determined based on the recombined integer ambiguity subset.

2. The partial ambiguity fixing method according to claim 1, characterized in that, The process of combining multiple floating-point ambiguity estimates to obtain multiple ambiguity subsets and their corresponding subset information includes: The total number of floating-point ambiguity estimates is determined based on the multiple floating-point ambiguity estimates. Based on the total number and all natural numbers between the preset combination number and the total number, the multiple floating-point fuzziness estimates are iterated and recursively combined to obtain multiple fuzziness subsets and their respective subset information; the preset combination number is greater than or equal to the preset combination threshold and less than or equal to the total number.

3. The partial ambiguity fixing method according to claim 1, characterized in that, The subset information includes ambiguity covariance matrix information; the process of fixing and checking the multiple ambiguity subsets and the multiple subset information to determine the integer ambiguity subsets that meet the preset check conditions includes: Based on preset fixed configuration information, the multiple ambiguity subsets are fixed to obtain multiple initial integer ambiguity subsets; Based on the multiple initial integer ambiguity subsets and multiple ambiguity covariance matrix information, the multiple initial integer ambiguity subsets are checked to determine the integer ambiguity subsets that meet the preset check conditions.

4. The partial ambiguity fixing method according to claim 3, characterized in that, The step of performing a check on the multiple initial integer ambiguity subsets based on the multiple initial integer ambiguity subsets and multiple ambiguity covariance matrix information, and determining the integer ambiguity subsets that meet preset check conditions, includes: Based on the multiple initial integer ambiguity subsets and the multiple ambiguity covariance matrix information, a first detection kernel value corresponding to each of the initial integer ambiguity subsets is determined; the first detection kernel value includes an integer solution ratio value; Based on the multiple fuzziness covariance matrix information, a second kernel value corresponding to each of the multiple fuzziness subsets is determined; the second kernel value includes a fuzziness accuracy attenuation factor value. For each of the aforementioned ambiguity subsets, if the integer solution ratio is greater than a preset first check threshold and the ambiguity accuracy decay factor is less than a preset second check threshold, the initial integer ambiguity subset is determined as the integer ambiguity subset.

5. The partial ambiguity fixing method according to claim 1, characterized in that, The method further includes: If the number of integer ambiguity subsets is less than a preset threshold, obtain the ambiguity floating-point solutions corresponding to the multiple floating-point ambiguity estimates; The ambiguity floating-point solution is determined as the target localization solution.

6. A partial ambiguity fixing device, characterized in that, The device includes: The acquisition module is used to obtain floating-point ambiguity estimates for multiple satellites. The combination processing module is used to estimate and combine multiple floating-point ambiguities to obtain multiple ambiguity subsets and their respective subset information. The first determining module is used to fix and check the multiple ambiguity subsets and multiple subset information to determine the integer ambiguity subsets that meet the preset check conditions. The reorganization processing module is configured to: determine the integer ambiguity subset as a reorganized integer ambiguity subset when the number of integer ambiguity subsets equals a preset threshold; and, when the number of integer ambiguity subsets exceeds the preset threshold, obtain a first integer ambiguity subset and a second integer ambiguity subset, wherein the first integer ambiguity subset and the second integer ambiguity subset are arranged adjacently; obtain a first integer resolution ratio corresponding to the first integer ambiguity subset and a second integer resolution ratio corresponding to the second integer ambiguity subset; and obtain multiple first integer ambiguities in the first integer ambiguity subset and multiple integer ambiguities in the second integer ambiguity subset. Multiple second integer ambiguities; when the first integer solution ratio is greater than the second integer solution ratio, determine the first target integer ambiguity corresponding to each of the multiple first integer ambiguities from the multiple second integer ambiguities; based on the multiple second integer ambiguities and at least one first target integer ambiguity, determine at least one second target integer ambiguity, wherein the second target integer ambiguity is an integer ambiguity other than the first target integer ambiguity among the multiple second integer ambiguities; reassemble the multiple first integer ambiguities and the at least one second target integer ambiguity to obtain the reassembled integer ambiguity subset; The second determining module is used to determine the target ambiguity fixed solution based on the recombined integer ambiguity subset.

7. A partial ambiguity fixing device, characterized in that, The device includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the partial ambiguity fixing method as described in any one of claims 1 to 5.

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