Ambiguity fixing method, device, electronic device and automatic driving equipment

Abstract

The present disclosure provides an ambiguity fixing method, apparatus, electronic device, and autonomous driving device, relating to the field of autonomous driving technology, and in particular, to real-time dynamic positioning technology. The implementation scheme is: an ambiguity fixing method for real-time dynamic positioning, comprising: obtaining initial ambiguities associated with an initial satellite set and an initial variance-covariance matrix corresponding to the initial ambiguities; in response to determining that a first success rate test fails based on the initial ambiguities and the initial variance-covariance matrix, or determining that an initial ambiguity candidate value obtained via the initial ambiguities and the initial variance-covariance matrix is ​​incorrect, performing a hierarchical ambiguity fixing operation, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-process, and each level of ambiguity fixing sub-process includes a corresponding satellite elimination operation; and determining, based on the hierarchical ambiguity fixing operation, outputting a fixed solution or a floating-point solution for the updated ambiguity candidate value.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, and in particular to real-time kinematic (RTK) positioning technology, and specifically to an ambiguity fixing method, device, electronic device, computer-readable storage medium, computer program product, and autonomous driving device for real-time kinematic positioning. Background Art

[0002] In recent years, the Global Navigation Satellite System (GNSS) has been widely used in autonomous driving for positioning due to its all-weather, high-precision, and low-cost advantages. GNSS typically employs RTK algorithms, and achieving high-precision positioning with these algorithms requires the correct fixation of double-difference ambiguities. Effective ambiguity fixation methods are crucial for RTK algorithms and high-precision positioning of autonomous vehicles.

[0003] The approaches described in this section are not necessarily approaches that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any approach described in this section is prior art simply by virtue of its inclusion in this section. Similarly, unless otherwise indicated, the issues raised in this section should not be considered as having been recognized in any prior art. Summary of the Invention

[0004] The present disclosure provides an ambiguity fixing method, apparatus, electronic device, computer-readable storage medium, computer program product, and autonomous driving device for real-time dynamic positioning.

[0005] According to one aspect of the present disclosure, an ambiguity fixing method for real-time kinematic positioning is provided, comprising: obtaining initial ambiguities associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguities; in response to determining that a first success rate check fails based on the initial ambiguities and the initial variance-covariance matrix, or in response to determining that initial ambiguity candidate values ​​obtained via the initial ambiguities and the initial variance-covariance are incorrect, performing a hierarchical ambiguity fixing operation, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-process, and each level of ambiguity fixing sub-process includes a corresponding satellite rejection operation; and determining to output a fixed solution or a floating-point solution for an updated ambiguity candidate value according to the hierarchical ambiguity fixing operation.

[0006] According to one aspect of the present disclosure, an ambiguity fixing apparatus for real-time kinematic positioning is provided, comprising: an initial value acquisition module configured to acquire initial ambiguities associated with an initial satellite set and an initial variance-covariance matrix corresponding to the initial ambiguities; a hierarchical fixing module configured to, in response to determining that a first success rate check fails based on the initial ambiguities and the initial variance-covariance matrix, or in response to determining that initial ambiguity candidate values ​​obtained via the initial ambiguities and the initial variance-covariance matrix are incorrect, perform a hierarchical ambiguity fixing operation, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-process, and each level of ambiguity fixing sub-process includes a corresponding satellite rejection operation; and a result determination module configured to determine and output, according to the hierarchical ambiguity fixing operation, a fixed solution or a floating-point solution for the updated ambiguity candidate values.

[0007] According to one aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor executes the method described above.

[0008] According to one aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described above.

[0009] According to one aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method described above when executed by a processor.

[0010] According to one aspect of the present disclosure, an autonomous driving device is provided, including a controller configured to implement the method described above.

[0011] According to one or more embodiments of the present disclosure, the availability of RTK positioning can be improved, and the effectiveness of the positioning system for positioning the autonomous driving device can be enhanced.

[0012] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the description of the specification, they serve to explain exemplary implementation of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals designate similar, but not necessarily identical, elements.

[0014] Figure 1 A schematic diagram is shown of an exemplary system in which the various methods and apparatus described herein may be implemented according to an embodiment of the present disclosure.

[0015] Figure 2 A flowchart of a fuzzy fixation method according to an embodiment of the present disclosure is shown.

[0016] Figure 3 A flowchart of a fuzzy fixation method according to another embodiment of the present disclosure is shown.

[0017] Figure 4 A block diagram of a fuzzy fixing device according to an embodiment of the present disclosure is shown.

[0018] Figure 5 A block diagram of a fuzzy fixing device according to another embodiment of the present disclosure is shown.

[0019] Figure 6 A structural block diagram of an electronic device applicable to the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0020] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0022] The terms used in the descriptions of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0023] In related technologies, ambiguity fixation methods can involve both full ambiguity fixation and partial ambiguity fixation. In recent years, with the increasing number of satellite navigation constellations and the increasing number of available satellites, partial ambiguity fixation can be used in environments with poor observation quality, even when full ambiguity fixation is not possible. This improves satellite positioning availability. For partial ambiguity fixation, a specific ambiguity fixation method is often selected based on the specific scenario. However, this single approach presents a bottleneck in correctly selecting high-precision satellites, which may result in the inability to achieve partial ambiguity fixation.

[0024] In view of the above problems, according to one aspect of the present disclosure, a method for fixing ambiguity is provided. The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0025] Figure 1 FIG2 is a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes a motor vehicle 110 , a server 120 , and one or more communication networks 130 coupling the motor vehicle 110 to the server 120 .

[0026] In an embodiment of the present disclosure, the motor vehicle 110 may include a computing device according to an embodiment of the present disclosure and / or be configured to perform a method according to an embodiment of the present disclosure.

[0027] The server 120 may run one or more services or software applications that enable implementation of the methods according to the disclosed embodiments. In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. The user of the motor vehicle 110 may, in turn, utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0028] Server 120 may include one or more general-purpose computers, specialized server computers (e.g., PC (personal computer) servers, UNIX servers, terminal servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0029] The computing units in the server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

[0030] In some embodiments, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from motor vehicle 110. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of motor vehicle 110.

[0031] The network 130 may be any type of network known to those skilled in the art that can support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a satellite communication network, a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infrared network, a wireless network (including, for example, Bluetooth, WiFi), and / or any combination of these and other networks.

[0032] The system 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 150 may be used to store information such as audio files and video files. The databases 150 may reside in a variety of locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and communicate with the server 120 via a network-based or dedicated connection. The databases 150 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0033] In some embodiments, one or more of the databases 150 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0034] Motor vehicle 110 may include sensors 111 for sensing its surroundings. Sensors 111 may include one or more of the following: visual cameras, infrared cameras, ultrasonic sensors, millimeter-wave radar, and laser radar (LiDAR). Different sensors offer different detection accuracy and range. Cameras may be mounted on the front, rear, or other locations of the vehicle. Visual cameras can capture real-time information about the vehicle's interior and exterior and present it to the driver and / or passengers. Furthermore, by analyzing the images captured by the visual cameras, information such as traffic light indications, intersection conditions, and the operating status of other vehicles can be obtained. Infrared cameras can detect objects in night vision conditions. Ultrasonic sensors can be mounted on all sides of the vehicle, utilizing the strong directionality of ultrasonic waves to measure the distance of external objects from the vehicle. Millimeter-wave radars can be mounted on the front, rear, or other locations of the vehicle, utilizing the properties of electromagnetic waves to measure the distance of external objects from the vehicle. LiDARs can be mounted on the front, rear, or other locations of the vehicle, detecting object edges and shapes for object recognition and tracking. Due to the Doppler effect, radar devices can also measure changes in the speed of the vehicle and moving objects.

[0035] The motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module that can receive satellite positioning signals (e.g., Beidou, GPS, GLONASS, and GALILEO) from satellites 141 and generate coordinates based on these signals. The communication device 112 may also include a module for communicating with a mobile communication base station 142. The mobile communication network may implement any suitable communication technology, such as GSM / GPRS, CDMA, LTE, and other current or evolving wireless communication technologies (e.g., 5G technology). The communication device 112 may also have a vehicle-to-everything (V2X) module that is configured to implement vehicle-to-vehicle (V2V) communication with other vehicles 143 and vehicle-to-infrastructure (V2I) communication with infrastructure 144, for example. In addition, the communication device 112 may also include a module configured to communicate with a user terminal 145 (including but not limited to a smartphone, tablet computer, or wearable device such as a watch) via a wireless local area network or Bluetooth using the IEEE 802.11 standard, for example. Using the communication device 112, the motor vehicle 110 may also access the server 120 via the network 130.

[0036] The motor vehicle 110 may further include a control device 113. The control device 113 may include a processor that communicates with various types of computer-readable storage devices or media, such as a central processing unit (CPU) or a graphics processing unit (GPU), or other dedicated processors. The control device 113 may include an autonomous driving system for automatically controlling various actuators in the vehicle. The autonomous driving system is configured to control the powertrain, steering system, and braking system of the motor vehicle 110 via multiple actuators in response to inputs from multiple sensors 111 or other input devices to control acceleration, steering, and braking, respectively, without human intervention or limited human intervention. Some processing functions of the control device 113 may be implemented through cloud computing. For example, some processing may be performed using an on-board processor, while other processing may be performed using computing resources in the cloud. The control device 113 may be configured to execute the method according to the present disclosure. In addition, the control device 113 may be implemented as an example of a computing device on the motor vehicle side (client) according to the present disclosure.

[0037] Figure 1 The system 100 may be configured and operated in various ways to enable application of the various methods and apparatuses described in accordance with the present disclosure.

[0038] Figure 2FIG. 2 shows a flow chart of a fuzzy fixing method 200 according to an embodiment of the present disclosure. Figure 2 As shown, the method 200 may include the following steps:

[0039] S202, obtaining initial ambiguities associated with the initial satellite set and an initial variance-covariance matrix corresponding to the initial ambiguities;

[0040] S204: In response to determining that the first success rate check fails based on the initial ambiguities and the initial variance-covariance matrix, or in response to determining that the initial ambiguity candidate value obtained through the initial ambiguities and the initial variance-covariance matrix is ​​incorrect, performing a hierarchical ambiguity fixing operation, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-process, and each level of ambiguity fixing sub-process includes a corresponding satellite rejection operation; and

[0041] S206 , determining to output a fixed solution or a floating-point solution for the updated ambiguity candidate value according to the hierarchical ambiguity fixing operation.

[0042] According to the ambiguity fixing method disclosed herein, a hierarchical ambiguity fixing operation is used to ensure the correct selection of satellites. A step-by-step satellite elimination operation is used to ensure that satellite selection can be carried out in a detailed manner. The corresponding satellite elimination operation is performed step-by-step using multi-level judgment logic, providing universal applicability for implementation in various environments. This avoids the accuracy improvement bottleneck caused by the need to specifically select different ambiguity fixing methods, thereby improving the availability of RTK positioning and effectively enhancing the effectiveness of the positioning system used to locate autonomous driving equipment. Furthermore, by setting corresponding pre-judgment conditions for entering the hierarchical ambiguity fixing operation (failure of the first success rate test or incorrect initial ambiguity candidate value), the overall operation process is not limited to the specific circumstances of each environment, but rather provides a universal application of the overall operation process in various environments.

[0043] In step S202, based on the satellite signals received at the autonomous driving device (such as an unmanned vehicle) and the signals from the base station, floating-point double-difference ambiguities associated with a certain number of satellites and their corresponding variance-covariance matrices, that is, initial ambiguities associated with the initial satellite set and the initial variance-covariance matrix corresponding to the initial ambiguities, can be calculated through filtering.

[0044] In step S204, a first success rate test may be performed to pre-determine the success rate of achieving ambiguity fixation based on the initial ambiguity and initial variance-covariance obtained in step S202. The first success rate test may be a bootstrapping success rate test.

[0045] If the success rate of ambiguity fixing based on the initial ambiguities and initial variance-covariance is relatively high, an ambiguity search operation (e.g., a LAMBDA (Least Squares Ambiguity Down-Dependent Adjustment) algorithm) can be directly performed to obtain initial ambiguity candidate values. In this case, if the initial ambiguity candidate values ​​are correct, ambiguity fixing can be directly performed. However, if the initial ambiguity candidate values ​​are incorrect, a hierarchical ambiguity fixing operation according to the present disclosure can be performed.

[0046] In other words, the hierarchical ambiguity fixing operation according to the present disclosure may be performed when any one of the following situations is met: i) in response to determining that the first success rate test fails based on the initial ambiguity and the initial variance-covariance matrix; ii) in response to determining that the initial ambiguity candidate value obtained via the initial ambiguity and the initial variance-covariance is incorrect.

[0047] The reason for this is that if the first success rate check fails or the initial ambiguity candidate values ​​are incorrect, it indicates that further satellite selection in the initial satellite set is required to ultimately achieve ambiguity fixation. For example, in the case of a large number of satellites or when some satellites contain cycle slips and gross errors, the ambiguity candidate values ​​obtained after the ambiguity search operation often fail the check and are therefore determined to be incorrect, requiring further satellite selection to achieve ambiguity fixation.

[0048] For reference, the bootstrapping success rate test can be calculated using the following formula:

[0049]

[0050] Q zz =z T Q aa z

[0051]

[0052] Among them, P success_rate represents the calculated success rate; Φ represents the standard normal density probability function; Q aa represents the variance-covariance matrix, and Q zz It represents the result obtained after descending correlation; d i Indicates Q zz The conditional variance D obtained after LD decomposition z Since the calculation formula used in the bootstrapping success rate test is well known in the art, its details are not repeated here.

[0053] In the present disclosure, a pass threshold (e.g., 95%) for the first success rate test can be set based on actual application conditions. If the success rate calculated by substituting the initial ambiguity and the initial variance-covariance matrix is ​​less than the pass threshold, the first success rate test can be considered to have failed. In this case, the hierarchical ambiguity fixing operation according to the present disclosure can be performed.

[0054] For reference, the LAMBDA algorithm used in the ambiguity search operation may involve obtaining a set of optimal solutions and a set of suboptimal solutions for candidate ambiguity values ​​after executing the algorithm. The difference between these two sets of solutions can then be determined based on a ratio test. If the difference is small, it means that the two sets of solutions are incorrect; however, if the difference is large, the candidate ambiguity values ​​can be considered correct. The candidate ambiguity values ​​are integer ambiguities obtained through the ambiguity search operation, and these integer ambiguities are used to update the baseline vector and output a fixed solution. Since the LAMBDA algorithm and ratio test are also known in the art, their details are not further described here.

[0055] The hierarchical ambiguity fixing operation disclosed herein can include at least one level of ambiguity fixing sub-process, and each level of ambiguity fixing sub-process can include a corresponding satellite elimination operation. Specifically, when the aforementioned pre-determination conditions (failure of the first success rate test or incorrect initial ambiguity candidate values) indicate the need for further correct satellite selection from the initial satellite set, the hierarchical ambiguity fixing operation is employed to ensure accurate satellite selection. In particular, the step-by-step satellite elimination operation ensures detailed satellite selection, thereby providing universal applicability in various environments.

[0056] In step S206, the ideal situation is to finally determine and output a fixed solution so that the satellite positioning accuracy can be on the order of a few centimeters or less. Possible situations also include determining and outputting only a floating-point solution, which means that the satellite positioning accuracy may be slightly lower than that of the fixed solution, that is, the satellite positioning accuracy is on the order of tens of centimeters or more.

[0057] According to some embodiments, each level of ambiguity fixing sub-process in the hierarchical ambiguity fixing operation may include: performing a second success rate check after performing a satellite removal operation, wherein whether the second success rate check passes is determined based on updated ambiguities and an updated variance-covariance matrix obtained through the satellite removal operation.

[0058] The second success rate test may be the same as the first success rate test, that is, it may also be a bootstrapping success rate test.

[0059] In this way, since the updated ambiguity and the updated variance-covariance matrix are obtained by performing the corresponding satellite elimination operation in each level of ambiguity fixation sub-process, the success rate of ambiguity fixation can be judged based on the current ambiguity and variance-covariance matrix, thereby enabling step-by-step or progressive satellite selection to improve the accuracy of satellite selection.

[0060] According to some embodiments, in response to determining that the second success rate check fails, one of the following operations may be performed: when the current level ambiguity fixing sub-routine is not the last level ambiguity fixing sub-routine, executing the next level ambiguity fixing sub-routine; and when the current level ambiguity fixing sub-routine is the last level ambiguity fixing sub-routine, determining an output floating-point solution.

[0061] In this way, multiple rounds of ambiguity fixing sub-processes can be used to maximize the probability of correctly selecting satellites, ensuring that fixed solutions are output as much as possible to ensure positioning accuracy. In other words, a floating point solution is only output when all ambiguity fixing sub-processes fail to output a fixed solution.

[0062] According to some embodiments, in response to determining that the second success rate test passes, the per-level ambiguity fixing sub-process may further include: performing an ambiguity search operation to obtain an optimal candidate solution and a suboptimal candidate solution with respect to the updated ambiguity candidate value; and verifying whether the updated ambiguity candidate value is correct based on the optimal candidate solution and the suboptimal candidate solution.

[0063] Here, the ambiguity search operation may be the LAMBDA algorithm as described above, and accordingly, the method for verifying whether the updated ambiguity candidate value is correct may be the ratio test as described above.

[0064] In this way, in each ambiguity fixing sub-process, a data-level ratio test can be further performed after the model-level bootstrapping success rate test. That is, two test processes are set before and after the ambiguity search operation, so that satellite selection can be gradually performed with different screening angles to improve the accuracy of satellite selection.

[0065] According to some embodiments, in response to the updated ambiguity candidate value being verified as correct, the per-level ambiguity fixing subroutine may further include: determining whether the number of satellites remaining after performing the satellite elimination operation is greater than or equal to a predetermined threshold.

[0066] Here, the predetermined threshold can be set to 4, meaning it is necessary to determine whether the number of remaining satellites is greater than or equal to 4. Since satellite elimination is performed in each ambiguity-fixing sub-process, it is possible that insufficient satellites remain, making it impossible to guarantee positioning accuracy. Therefore, in each ambiguity-fixing sub-process, even if the correct candidate ambiguity value is obtained through the ambiguity search operation, it is still necessary to further determine whether the number of remaining satellites meets the minimum number of satellites required to guarantee positioning accuracy.

[0067] According to some embodiments, in response to determining that the number of remaining satellites is greater than or equal to the predetermined threshold, determining to output a fixed solution may be performed; or in response to determining that the number of remaining satellites is less than the predetermined threshold, one of the following operations may be performed: when a current level ambiguity fixing subroutine is not a last level ambiguity fixing subroutine, executing a next level ambiguity fixing subroutine; and when the current level ambiguity fixing subroutine is the last level ambiguity fixing subroutine, determining to output a float solution.

[0068] In this way, on the one hand, a fixed solution can be deterministically output when the minimum number of satellites required to ensure positioning accuracy is met; on the other hand, even if the number of remaining satellites does not meet the minimum number of satellites required to ensure positioning accuracy, there are alternative options available, that is, depending on whether the current first-level ambiguity fixing sub-process is the last level, either the next-level ambiguity fixing sub-process can be executed to enable satellite selection to be re-performed using another satellite elimination operation, or a floating-point solution can be output instead.

[0069] According to some embodiments, in response to an updated ambiguity candidate value being verified as incorrect, one of the following operations may be performed: when a current level ambiguity fixing subroutine is not a last level ambiguity fixing subroutine, performing a next level ambiguity fixing subroutine; and when the current level ambiguity fixing subroutine is the last level ambiguity fixing subroutine, determining an output float solution.

[0070] In this way, even if a fixed solution cannot be output through the current first-level ambiguity fixing sub-process, satellite selection can be re-performed by using different satellite elimination operations in other levels of ambiguity fixing sub-processes to try to output a fixed solution, or a floating-point solution can be output instead.

[0071] According to some embodiments, at least one level of ambiguity fixing sub-routine in the hierarchical ambiguity fixing operation may include a first level of ambiguity fixing sub-routine, wherein the satellite elimination operation performed in the first level of ambiguity fixing sub-routine may include: eliminating at least one of satellites having elevation angles that do not satisfy a predetermined angle and newly observed satellites from an initial satellite set to generate a first satellite subset; and generating, based on the first satellite subset, first updated ambiguities and a first updated variance-covariance matrix for determining whether a second success rate check is passed.

[0072] In this disclosure, satellites most likely to fail to meet positioning accuracy can be empirically eliminated in the first-level ambiguity fixation subprocess. Generally speaking, satellites with elevation angles below 20 degrees may require a greater distance to reach the receiver, resulting in greater atmospheric errors and a potentially significant impact on positioning accuracy. Furthermore, newly observed satellites, being acquired for the first time, may not guarantee accuracy. Based on this empirical assessment, these satellites most likely to fail to meet positioning accuracy can be eliminated before ambiguity fixation is performed.

[0073] In addition, in the present disclosure, since each level of ambiguity fixation sub-process can include performing a second success rate test (such as a bootstrapping success rate test) after performing the satellite removal operation, when the updated ambiguity and the updated variance-covariance matrix are obtained through the satellite removal operation, the success rate of ambiguity fixation can be judged based on the current ambiguity and variance-covariance matrix, thereby enabling step-by-step or progressive satellite selection to improve the accuracy of satellite selection.

[0074] According to some embodiments, at least one level ambiguity fixing sub-routine in the hierarchical ambiguity fixing operation may further include a second level ambiguity fixing sub-routine, wherein the satellite elimination operation performed in the second level ambiguity fixing sub-routine may include: eliminating satellites from the first satellite subset according to an Ambiguity Dilution of Precision (ADOP) method to generate a second satellite subset; and generating, based on the second satellite subset, second updated ambiguities and a second updated variance-covariance matrix for determining whether a second success rate check is passed.

[0075] Since the first-level ambiguity fixation sub-process is based on experience to eliminate satellites, if the ambiguity fixation cannot be achieved through the first-level ambiguity fixation sub-process, the ADOP method in the second-level ambiguity fixation sub-process can be further used to eliminate satellites from the remaining first satellite subset.

[0076] For reference, the calculation formula of the ADOP method is where Q aaDenotes the variance-covariance matrix. Since the ADOP method is well known in the art, its details are not repeated here.

[0077] According to the ADOP method, based on the remaining satellites (i.e., the first satellite subset) after the first-level ambiguity fixing subprocess performs the satellite elimination operation, one satellite can be eliminated in turn to obtain the corresponding calculation results of the corresponding satellite set, and the smallest calculation result among these results can be selected, which means that the satellite eliminated when calculating ADOP should be eliminated.

[0078] In addition, similar to the first-level ambiguity fixation sub-process, in the second-level ambiguity fixation sub-process, when the updated ambiguity and the updated variance-covariance matrix are obtained through the satellite elimination operation, the success rate of ambiguity fixation can be judged based on the current ambiguity and variance-covariance matrix, thereby realizing step-by-step or progressive satellite selection to improve the accuracy of satellite selection.

[0079] According to some embodiments, at least one level ambiguity fixing sub-routine in the hierarchical ambiguity fixing operation may further include a third level ambiguity fixing sub-routine, wherein the satellite elimination operation performed in the third level ambiguity fixing sub-routine may include: eliminating satellites from the initial satellite set to generate a third satellite subset according to diagonal elements of the conditional variance after down-correlating the initial variance-covariance matrix; and generating third updated ambiguities and a third updated variance-covariance matrix based on the third satellite subset for determining whether the second success rate check passes.

[0080] In the present disclosure, the ambiguity fixation required in the Z domain is set in the third-level ambiguity fixation sub-process, thereby first attempting to perform ambiguity fixation with the help of relatively simpler satellite rejection operations in other levels of ambiguity fixation sub-processes, thereby avoiding starting the overall operation process with complex calculations.

[0081] In addition, similar to the first and second level ambiguity fixation sub-processes, in the third level ambiguity fixation sub-process, when the updated ambiguity and updated variance-covariance matrix are obtained through the satellite elimination operation, the success rate of ambiguity fixation can be judged based on the current ambiguity and variance-covariance matrix, thereby realizing step-by-step or progressive satellite selection to improve the accuracy of satellite selection.

[0082] As described above, the ambiguity fixing method disclosed herein provides accurate satellite selection through a hierarchical ambiguity fixing operation. A step-by-step satellite elimination operation is employed to ensure detailed satellite selection. Multi-level judgment logic is utilized to perform corresponding satellite elimination operations step by step, providing universal applicability for implementation in various environments. This avoids the accuracy bottlenecks caused by the need to specifically select different ambiguity fixing methods, thereby improving the usability of RTK positioning and effectively enhancing the effectiveness of positioning systems used to locate autonomous driving equipment. Furthermore, by setting corresponding pre-judgment conditions for entering the hierarchical ambiguity fixing operation (failure of the first success rate test or incorrect initial ambiguity candidate values), the overall operational process is not limited to the specific circumstances of each environment, but rather provides universal applicability for the overall operational process to be applied in various environments.

[0083] Figure 3 FIG. 3 is a flowchart of a fuzzy fixing method 300 according to another embodiment of the present disclosure.

[0084] like Figure 3 As shown, step 301 shows obtaining initial ambiguities associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguities.

[0085] Step 302 shows determining a first success rate test ( Figure 3 In the figure, it is shown as the bootstrapping success rate test) whether it passes, wherein, if it fails, it enters the hierarchical ambiguity fixing operation 305. Step 303 shows that the initial ambiguity candidate value ( Figure 3 The ambiguity search operation is shown in FIG as a LAMBDA algorithm, and step 304 shows whether the initial ambiguity candidate value is correct ( Figure 3 In the example shown in FIG3 , the first success ratio test is shown as having passed the ratio test. If the first success ratio test is incorrect, the process proceeds to the hierarchical ambiguity fixing operation 305. That is, the pre-judgment conditions for entering the hierarchical ambiguity fixing operation 305 are set through steps 302 and 304 (the first success ratio test fails or the initial ambiguity candidate value is incorrect).

[0086] The hierarchical ambiguity fixing operation 305 may include a first-level ambiguity fixing sub-process 310, a second-level ambiguity fixing sub-process 320, and a third-level ambiguity fixing sub-process 330. Each level of ambiguity fixing sub-process 310, 320, and 330 includes corresponding satellite rejection operations 311, 321, and 331.

[0087] Specifically, the satellite removal operation 311 in the first-level ambiguity fixing subroutine 310 includes removing at least one of satellites whose elevation angles do not meet a predetermined angle and newly observed satellites from the initial satellite set. The satellite removal operation 321 in the second-level ambiguity fixing subroutine 320 includes removing satellites from the first satellite subset according to the ADOP method. The satellite removal operation 331 in the third-level ambiguity fixing subroutine 330 includes removing satellites from the initial satellite set based on the diagonal elements of the conditional variance after the down-correlation of the initial variance-covariance matrix.

[0088] According to the hierarchical ambiguity fixing operation 305, the process may proceed to step 340 to output a fixed solution, or proceed to step 350 to output a float solution.

[0089] In addition, if the result of step 304 is that the initial ambiguity candidate value is determined to be correct, the process may also proceed to step 340 to output the corresponding fixed solution.

[0090] like Figure 3 As shown, each level of ambiguity fixing sub-process 310, 320 and 330 may include a second success rate check 312, 322 and 332 ( Figure 3 ( denoted as a bootstrapping success rate test in FIG. 2 ). In the first and second level ambiguity fixing subroutines 310 and 320, if it is determined that the second success rate test 312 and 322 have failed, the process proceeds to the corresponding next-level ambiguity fixing subroutines 320 and 330 to continue ambiguity fixing. In the third level ambiguity fixing subroutines 330 (the final level ambiguity fixing subroutine), if it is determined that the second success rate test 332 has failed, the process proceeds to step 350 to output a floating-point solution, which means that the most accurate fixed solution was ultimately not obtained.

[0091] In addition, if Figure 3 As shown, if the corresponding second success rate tests 312, 322, and 332 in the first, second, and third level ambiguity fixing subroutines 310, 320, and 330 pass, the process proceeds to the ambiguity search operations 313, 323, and 333, respectively. Figure 3 LAMBDA algorithm) to obtain the optimal candidate solution and the suboptimal candidate solution for the updated ambiguity candidate value, and proceed to the test steps 314, 324 and 334 ( Figure 3 The updated ambiguity candidate value is correct.

[0092] In test steps 314, 324, and 334, if the updated candidate ambiguity values ​​are verified to be correct, it is further determined whether the number of remaining satellites meets the minimum number requirement, that is, whether the number of remaining satellites is greater than or equal to a predetermined threshold. If the number of remaining satellites is greater than or equal to the predetermined threshold, a fixed solution can be deterministically output, and the process proceeds to step 340. If the number of remaining satellites is less than the predetermined threshold, the process proceeds to the next-level ambiguity fixing sub-process 320 or 330 to re-fix the ambiguities through additional satellite elimination operations, or in the third-level ambiguity fixing sub-process 330, the process proceeds to step 350 to output a floating-point solution, which also means that the most accurate fixed solution was ultimately not obtained.

[0093] According to another aspect of the present disclosure, a fuzzy fixation device is also provided. Figure 4 FIG. 4 shows a block diagram of a fuzzy fixing device 400 according to an embodiment of the present disclosure. Figure 4 As shown, the apparatus 400 may include:

[0094] An initial value acquisition module 402 is configured to acquire initial ambiguities associated with an initial satellite set and an initial variance-covariance matrix corresponding to the initial ambiguities;

[0095] a hierarchical fixing module 404 configured to, in response to determining that a first success rate check fails based on the initial ambiguities and the initial variance-covariance matrix, or in response to determining that an initial ambiguity candidate value obtained via the initial ambiguities and the initial variance-covariance matrix is ​​incorrect, perform a hierarchical ambiguity fixing operation, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-process, and each level of ambiguity fixing sub-process includes a corresponding satellite rejection operation; and

[0096] The result determination module 406 is configured to determine and output a fixed solution or a floating-point solution regarding the updated ambiguity candidate value according to the hierarchical ambiguity fixing operation.

[0097] The operations performed by the above modules 402, 404 and 406 are similar to those of the reference Figure 2 The described steps S202, S204, and S206 correspond to each other, so the details of each aspect are not repeated.

[0098] Figure 5 FIG. 5 shows a block diagram of a ambiguity fixing device 500 according to another embodiment of the present disclosure. Figure 5 The modules 502, 504 and 506 shown may correspond to Figure 4Modules 402, 404 and 406 are shown. In addition, the apparatus 504 may further include functional modules 5040, 5042, 5043, 5044, 5046 and 5048, and these modules may further include further sub-functional modules, as will be described in detail below.

[0099] According to some embodiments, the hierarchical fixing module 504 may include, for each level of ambiguity fixing sub-process: a success rate verification unit 5040 configured to perform a second success rate verification after performing the satellite removal operation, wherein whether the second success rate verification passes is determined based on the updated ambiguity and the updated variance-covariance matrix obtained through the satellite removal operation.

[0100] According to some embodiments, the success rate verification unit 5040 may include a first notification unit 5040-1, which is configured to: in response to determining that the second success rate test fails, perform one of the following operations: when the current level ambiguity fixing sub-process is not the last level ambiguity fixing sub-process, notify the hierarchical fixing module 504 to execute the next level ambiguity fixing sub-process; and when the current level ambiguity fixing sub-process is the last level ambiguity fixing sub-process, notify the output module 506 to output a floating-point solution.

[0101] According to some embodiments, the hierarchical fixing module 504 may further include an ambiguity search unit 5042 for each level of ambiguity fixing sub-process, and the ambiguity search unit 5042 may include: a result acquisition sub-unit 5042-1, configured to, in response to determining that the second success rate test is passed, perform an ambiguity search operation to obtain an optimal candidate solution and a suboptimal candidate solution with respect to the updated ambiguity candidate value; and a result verification sub-unit 5042-2, configured to verify whether the updated ambiguity candidate value is correct based on the optimal candidate solution and the suboptimal candidate solution.

[0102] According to some embodiments, the ambiguity search unit 5042 may further include a second notification unit 5042-3, which is configured to: in response to the updated ambiguity candidate value being verified as incorrect, perform one of the following operations: when the current level ambiguity fixing sub-process is not the last level ambiguity fixing sub-process, notify the hierarchical fixing module 504 to execute the next level ambiguity fixing sub-process; and when the current level ambiguity fixing sub-process is the last level ambiguity fixing sub-process, notify the output module 506 to output a floating-point solution.

[0103] According to some embodiments, the hierarchical fixing module 504 may include a first ambiguity fixing submodule 5044 for the at least one level ambiguity fixing sub-process, wherein the first ambiguity fixing submodule 5044 includes, for a satellite elimination operation: a first satellite elimination unit 5044-1 configured to eliminate at least one of satellites having elevation angles that do not satisfy a predetermined angle and newly observed satellites from an initial satellite set to generate a first satellite subset; and a first generation unit 5044-2 configured to generate, based on the first satellite subset, first updated ambiguities and a first updated variance-covariance matrix for determining whether a second success rate check is passed.

[0104] According to some embodiments, the hierarchical fixing module 504 may include a second ambiguity fixing submodule 5046 for the at least one first-level ambiguity fixing sub-process, wherein the second ambiguity fixing submodule 5046 includes, for a satellite elimination operation, a second satellite elimination unit 5046-1 configured to eliminate satellites from the first satellite subset according to the ADOP method to generate a second satellite subset; and a second generation unit 5046-2 configured to generate, based on the second satellite subset, second updated ambiguities and a second updated variance-covariance matrix for determining whether a second success rate check is passed.

[0105] According to some embodiments, the hierarchical fixing module 504 may include a third ambiguity fixing submodule 5048 for the at least one first-level ambiguity fixing sub-process, wherein the third ambiguity fixing submodule 5048 includes, for a satellite elimination operation, a third satellite elimination unit 5048-1 configured to eliminate satellites from the initial satellite set according to diagonal elements of the conditional variance after down-correlating the initial variance-covariance matrix to generate a third satellite subset; and a third generation unit 5048-2 configured to generate, based on the third satellite subset, third updated ambiguities and a third updated variance-covariance matrix for determining whether the second success rate check passes.

[0106] According to some embodiments, the hierarchical fixing module 504 may further include a remaining satellite determination unit 5043 for each level of ambiguity fixing sub-process, wherein the remaining satellite determination unit 5043 is configured to: in response to the ambiguity candidate value being verified as correct, determine whether the number of satellites remaining after performing the satellite elimination operation is greater than or equal to a predetermined threshold.

[0107] According to some embodiments, the remaining satellite determination unit 5043 may include a second notification unit 5043-1, configured to: in response to determining that the number of remaining satellites is greater than or equal to the predetermined threshold, notify the output module 506 to output a fixed solution; or in response to determining that the number of remaining satellites is less than the predetermined threshold, perform one of the following operations: when the current first-level ambiguity fixing subroutine is not the last-level ambiguity fixing subroutine, notify the hierarchical fixing module 504 to execute the next-level ambiguity fixing subroutine; and when the current first-level ambiguity fixing subroutine is the last-level ambiguity fixing subroutine, notify the output module 506 to output a float solution.

[0108] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is further provided, wherein the computer instructions are used to enable the computer to execute the method described above.

[0109] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein the computer program implements the method described above when executed by a processor.

[0110] According to another aspect of the present disclosure, an autonomous driving device is provided, including a controller configured to implement the method described above.

[0111] According to another aspect of the present disclosure, an electronic device is also provided, comprising at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor executes the method as described above.

[0112] refer to Figure 6 , a block diagram of an electronic device 600 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0113] like Figure 6As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the device 600 can also be stored in the RAM 603. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0114] Multiple components in the device 600 are connected to the I / O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 can be any type of device that can input information to the device 600. The input unit 606 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone and / or a remote control. The output unit 607 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator and / or a printer. The storage unit 608 can include but is not limited to a magnetic disk, an optical disk. The communication unit 609 allows the device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as Bluetooth TM devices, 1302.11 devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0115] Computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing unit 601 performs the various methods and processes described above, such as the ambiguity fixing method. For example, in some embodiments, the ambiguity fixing method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by computing unit 601, one or more steps of the ambiguity fixing method described above can be performed. Alternatively, in other embodiments, computing unit 601 can be configured to perform the ambiguity fixing method in any other suitable manner (e.g., via firmware).

[0116] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0117] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0118] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0119] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0120] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0121] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0122] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0123] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. In addition, the steps may be performed in an order different from that described in this disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after this disclosure.

Claims

1. A method for fixing ambiguity for real-time dynamic positioning, comprising: Obtaining initial ambiguities associated with the initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguities; In response to determining that a first success rate check fails based on the initial ambiguities and the initial variance-covariance matrix, or in response to determining that an initial ambiguity candidate value obtained through the initial ambiguities and the initial variance-covariance matrix is ​​incorrect, performing a hierarchical ambiguity fixing operation, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-process, and each level of ambiguity fixing sub-process includes a corresponding satellite rejection operation, so as to implement progressive satellite selection through the at least one level of ambiguity fixing sub-process, wherein: In a first-level ambiguity fixing sub-process in the at least one-level ambiguity fixing sub-process: Eliminating at least one of satellites whose elevation angles do not satisfy a predetermined angle and newly observed satellites from the initial satellite set to generate a first satellite subset; generating first updated ambiguities and a first updated variance-covariance matrix for determining whether a second success rate check passes based on the first subset of satellites; and In a second-level ambiguity fixing sub-procedure in the at least one-level ambiguity fixing sub-procedure: Eliminate satellites from the first satellite subset according to an ambiguity dilution of precision method to generate a second satellite subset; generating second updated ambiguities and a second updated variance-covariance matrix based on the second subset of satellites for determining whether the second success rate check passes; In the sub-process of fixing the ambiguity at each level, in response to determining that the second success rate test passes based on the corresponding updated ambiguity and the corresponding updated variance-covariance matrix, a process for determining whether to output a fixed solution is entered.

2. The method according to claim 1, wherein In the second-level ambiguity fixing sub-process, in response to determining that the second success rate test fails, performing one of the following operations: When the current level ambiguity fixing sub-process is not the last level ambiguity fixing sub-process, executing the next level ambiguity fixing sub-process; and When the current level ambiguity fixing sub-process is the last level ambiguity fixing sub-process, an output floating-point solution is determined.

3. The method according to claim 1, wherein In the ambiguity fixing sub-process at each level, the process for determining whether to output a fixed solution includes: performing an ambiguity search operation to obtain an optimal candidate solution and a suboptimal candidate solution with respect to the corresponding updated ambiguity candidate values; and Whether the corresponding updated ambiguity candidate value is correct is checked based on the optimal candidate solution and the suboptimal candidate solution.

4. The method according to claim 3, wherein: In response to the corresponding updated ambiguity candidate value being verified as correct, the process for determining whether to output a fixed solution further includes: It is determined whether the number of satellites remaining after performing the satellite elimination operation is greater than or equal to a predetermined threshold.

5. The method according to claim 4, wherein: In response to determining that the number of remaining satellites is greater than or equal to the predetermined threshold, determining to output the fixed solution; or In response to determining that the number of remaining satellites is less than the predetermined threshold, performing one of the following operations: When the current level ambiguity fixing sub-process is not the last level ambiguity fixing sub-process, executing the next level ambiguity fixing sub-process; and When the current level ambiguity fixing sub-process is the last level ambiguity fixing sub-process, an output floating-point solution is determined.

6. The method according to claim 3, wherein: In response to the corresponding updated ambiguity candidate value being verified as incorrect, performing one of the following operations: When the current level ambiguity fixing sub-process is not the last level ambiguity fixing sub-process, executing the next level ambiguity fixing sub-process; and When the current level ambiguity fixing sub-process is the last level ambiguity fixing sub-process, an output floating-point solution is determined.

7. The method according to any one of claims 1 to 6, wherein After the second-level ambiguity fixing sub-process, the at least one-level ambiguity fixing operation further includes a third-level ambiguity fixing sub-process, wherein the satellite rejection operation performed in the third-level ambiguity fixing sub-process includes: Eliminate satellites from the initial satellite set according to diagonal elements of the conditional variance after down-correlating the initial variance-covariance matrix to generate a third satellite subset; and Third updated ambiguities and a third updated variance-covariance matrix for determining whether the second success rate check passes are generated based on the third satellite subset.

8. An ambiguity fixing device for real-time dynamic positioning, comprising: An initial value acquisition module is configured to acquire initial ambiguities associated with an initial satellite set and an initial variance-covariance matrix corresponding to the initial ambiguities; A hierarchical fixing module is configured to, in response to determining that a first success rate check fails based on the initial ambiguities and the initial variance-covariance matrix, or in response to determining that an initial ambiguity candidate value obtained via the initial ambiguities and the initial variance-covariance matrix is ​​incorrect, perform a hierarchical ambiguity fixing operation, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-process, and each level of ambiguity fixing sub-process includes a corresponding satellite rejection operation, so as to implement progressive satellite selection through the at least one level of ambiguity fixing sub-process, wherein: In a first ambiguity fixing submodule of a first-level ambiguity fixing sub-process in the at least one-level ambiguity fixing sub-process: a first satellite elimination unit configured to eliminate at least one of satellites whose elevation angles do not satisfy a predetermined angle and newly observed satellites from the initial satellite set to generate a first satellite subset; a first generating unit configured to generate, based on the first satellite subset, first updated ambiguities and a first updated variance-covariance matrix for determining whether a second success rate check is passed; and In a second ambiguity fixing submodule of a second-level ambiguity fixing sub-procedure in the at least one first-level ambiguity fixing sub-procedure: a second satellite elimination unit, configured to eliminate satellites from the first satellite subset according to an ambiguity dilution of precision method to generate a second satellite subset; a second generating unit configured to generate, based on the second satellite subset, second updated ambiguities and a second updated variance-covariance matrix for determining whether the second success rate check is passed; and In the sub-process of fixing the ambiguity at each level, in response to determining that the second success rate test passes based on the corresponding updated ambiguity and the corresponding updated variance-covariance matrix, a process for determining whether to output a fixed solution is entered.

9. The device according to claim 8, wherein In the second-level ambiguity fixing sub-process, in response to determining that the second success rate test fails, performing one of the following operations: When the current level ambiguity fixing sub-process is not the last level ambiguity fixing sub-process, the hierarchical fixing module is configured to execute the next level ambiguity fixing sub-process; and When the current level ambiguity fixing sub-process is the last level ambiguity fixing sub-process, the output module outputs a floating-point solution.

10. The device according to claim 8, wherein In the ambiguity fixing sub-process at each level, the process for determining whether to output a fixed solution includes an ambiguity search unit, which includes: a result acquisition subunit configured to perform an ambiguity search operation to obtain an optimal candidate solution and a suboptimal candidate solution with respect to corresponding updated ambiguity candidate values; and The result verification subunit is configured to verify whether the corresponding updated ambiguity candidate value is correct based on the optimal candidate solution and the suboptimal candidate solution.

11. The device according to claim 10, wherein In response to the corresponding updated ambiguity candidate value being verified as incorrect, performing one of the following operations: When the current level ambiguity fixing sub-process is not the last level ambiguity fixing sub-process, executing the next level ambiguity fixing sub-process; and When the current level ambiguity fixing sub-process is the last level ambiguity fixing sub-process, the output module outputs a floating-point solution.

12. The device according to any one of claims 8 to 11, wherein After the second-level ambiguity fixing submodule, the hierarchical fixing module further includes a third ambiguity fixing submodule for a third ambiguity fixing subprocess in the at least one first-level ambiguity fixing subprocess, wherein the third ambiguity fixing submodule performs the satellite rejection operation including: a third satellite elimination unit configured to eliminate satellites from the initial satellite set to generate a third satellite subset according to diagonal elements of the conditional variance after down-correlating the initial variance-covariance matrix; and A third generating unit is configured to generate, based on the third satellite subset, third updated ambiguities and a third updated variance-covariance matrix for determining whether the second success rate check passes.

13. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

15. A computer program product comprising a computer program, wherein The computer program implements the method according to any one of claims 1 to 7 when executed by a processor.

16. An autonomous driving device comprising: A controller configured to implement the method according to any one of claims 1 to 7.

Images