CN113917506A - Ambiguity fixing method and device, electronic equipment and automatic driving equipment - Google Patents
Ambiguity fixing method and device, electronic equipment and automatic driving equipment Download PDFInfo
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
- G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method
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Abstract
The disclosure provides a method and a device for fixing ambiguity, electronic equipment and automatic driving equipment, relates to the technical field of automatic driving, and particularly relates to a real-time dynamic positioning technology. The implementation scheme is as follows: an ambiguity fixing method for real-time dynamic positioning, comprising: obtaining an initial ambiguity associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguity; in response to determining, based on the initial ambiguity and the initial variance-covariance matrix, that the first power test fails or that an initial ambiguity candidate value obtained via the initial ambiguity and the initial variance-covariance is incorrect, performing a hierarchical ambiguity fixing operation, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-flow, and each level of ambiguity fixing sub-flow includes a corresponding satellite culling operation; and determining to output a fixed solution or a floating solution with respect to the updated ambiguity candidate value according to the hierarchical ambiguity fixing operation.
Description
Technical Field
The present disclosure relates to the field of autopilot technology, and in particular to a real-time kinematic (RTK) positioning technology, and more particularly to an ambiguity fixing method, apparatus, electronic device, computer-readable storage medium, computer program product, and autopilot device for real-time kinematic positioning.
Background
In recent years, Global Navigation Satellite Systems (GNSS) have been widely used for positioning an autonomous device in autonomous driving because of their advantages of all weather, high accuracy, and low cost. GNSS typically employs an RTK algorithm, which is used to achieve high-precision positioning on the premise that double-difference ambiguities need to be fixed correctly. An effective ambiguity fixing method is crucial for RTK algorithms and high precision positioning of autonomous devices.
The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.
Disclosure of Invention
The present disclosure provides an ambiguity fixing method, apparatus, electronic device, computer readable storage medium, computer program product and autopilot device for real-time dynamic positioning.
According to an aspect of the present disclosure, there is provided an ambiguity fixing method for real-time dynamic positioning, including: obtaining an initial ambiguity associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguity; performing a hierarchical ambiguity fixing operation in response to determining that the first power-forming test fails based on the initial ambiguity and the initial variance-covariance matrix or in response to determining that an initial ambiguity candidate value obtained via the initial ambiguity and the initial variance-covariance is incorrect, wherein the hierarchical ambiguity fixing operation comprises at least one level of ambiguity fixing sub-flow, and each level of ambiguity fixing sub-flow comprises a corresponding satellite culling operation; and determining to output a fixed solution or a floating solution with respect to the updated ambiguity candidate value according to the hierarchical ambiguity fixing operation.
According to an aspect of the present disclosure, there is provided an ambiguity fixing apparatus for real-time dynamic positioning, comprising: an initial value acquisition module configured to acquire an initial ambiguity associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguity; a hierarchical fixing module configured to perform a hierarchical ambiguity fixing operation in response to determining that the first power test fails based on the initial ambiguity and the initial variance-covariance matrix or in response to determining that an initial ambiguity candidate value obtained via the initial ambiguity and the initial variance-covariance matrix is incorrect, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-flow, and each level of ambiguity fixing sub-flow includes a corresponding satellite culling operation; and a result determination module configured to determine to output a fixed solution or a floating solution with respect to the updated ambiguity candidate value according to the hierarchical ambiguity fixing operation.
According to an aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, which when executed by the at least one processor, cause the at least one processor to perform the method as described above.
According to an aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method as described above.
According to an aspect of the disclosure, a computer program product is provided, comprising a computer program, wherein the computer program realizes the method as described above when executed by a processor.
According to an aspect of the present disclosure, there is provided an autopilot device comprising a controller configured to implement the method as described above.
According to one or more embodiments of the present disclosure, the usability of RTK positioning can be improved and the effectiveness of a positioning system for positioning an autonomous device can be enhanced.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.
Fig. 1 illustrates a schematic diagram of an exemplary system in which various methods and apparatus described herein may be implemented, according to an embodiment of the present disclosure.
FIG. 2 shows a flow diagram of an ambiguity fixing method according to one embodiment of the present disclosure.
Fig. 3 shows a flow chart of an ambiguity fixing method according to another embodiment of the present disclosure.
FIG. 4 shows a block diagram of an ambiguity fixing apparatus according to one embodiment of the present disclosure.
Fig. 5 shows a block diagram of an ambiguity fixing apparatus according to another embodiment of the present disclosure.
Fig. 6 shows a block diagram of an electronic device to which the embodiments of the present disclosure can be applied.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.
The terminology used in the description of the various described examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.
In the related art, the ambiguity fixing method may involve full ambiguity fixing and partial ambiguity fixing. In recent years, with the increase of satellite navigation constellations, the number of available satellites is also increased, so that in an environment with poor observation quality, if the fixation of all ambiguities cannot be realized, the fixation of partial ambiguities can also be adopted, and the usability of satellite positioning is improved. For partial ambiguity fixing, an ambiguity fixing method is often selected for a specific scene. However, such a single approach has a bottleneck for correctly selecting a satellite with high accuracy, so that partial ambiguity fixing may not be achieved.
In view of the above, according to an aspect of the present disclosure, an ambiguity fixing method is provided. Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented, according to an embodiment of the present disclosure. Referring to fig. 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.
In embodiments of the present disclosure, motor vehicle 110 may include a computing device and/or be configured to perform a method in accordance with embodiments of the present disclosure.
The server 120 may run one or more services or software applications that enable the implementation of methods in accordance with 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. In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user of motor vehicle 110 may, in turn, utilize one or more client applications to interact with server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.
The server 120 may include one or more general purpose computers, special purpose 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. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.
The computing units in 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.
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.
Network 130 may be any type of network known to those skilled in the art that may 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, 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, e.g., bluetooth, WiFi), and/or any combination of these and other networks.
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 database 150 may reside in various 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 may communicate with the server 120 via a network-based or dedicated connection. The database 150 may be of different types. In some embodiments, the database used by the server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.
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 application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.
The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.
Fig. 2 shows a flow chart of an ambiguity fixing method 200 according to an embodiment of the present disclosure. As shown in fig. 2, the method 200 may include the steps of:
s202, acquiring initial ambiguity associated with an initial satellite set and an initial variance-covariance matrix corresponding to the initial ambiguity;
s204, in response to determining that the first power test fails based on the initial ambiguity and the initial variance-covariance matrix or in response to determining that the initial ambiguity candidate value obtained via the initial ambiguity and the initial variance-covariance is incorrect, performing a hierarchical ambiguity fixing operation, wherein the hierarchical ambiguity fixing operation comprises at least one level of ambiguity fixing sub-process, and each level of ambiguity fixing sub-process comprises a corresponding satellite rejecting operation; and
s206, according to the hierarchical ambiguity fixing operation, determining to output a fixed solution or a floating solution of the updated ambiguity candidate value.
According to the ambiguity fixing method disclosed by the invention, the correct selection of the satellite is provided through the hierarchical ambiguity fixing operation, the selection of the satellite can be ensured to be meticulous by virtue of the step-by-step satellite removing operation, the corresponding satellite removing operation is carried out step by utilizing the multistage judgment logic, and the universal applicability is provided for the implementation under various environments, so that the precision improvement bottleneck caused by specifically selecting different ambiguity fixing methods is avoided, the usability of RTK positioning is improved, and the effectiveness of a positioning system for positioning the automatic driving equipment is effectively enhanced. In addition, by setting a corresponding predetermined condition (the first power verification fails or the initial ambiguity candidate value is incorrect) for entering the hierarchical ambiguity fixing operation, the overall operation flow can not be limited to the specific situations of various environments, and the universality of applying the overall operation flow in various environments is provided.
In step S202, floating double-differenced ambiguities associated with a number of satellites and their corresponding variance-covariance matrices, i.e., an initial ambiguity associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguities, may be filtered from satellite signals received at an autonomous device (e.g., an unmanned vehicle) and signals from a base station.
In step S204, a first power-up test may be performed to determine in advance a success rate size for achieving ambiguity fixing based on the initial ambiguity and the initial variance-covariance acquired in step S202. The first power-forming test may be a bootstrap success test.
If the success rate of implementing ambiguity fixing based on the initial ambiguity and the initial variance-covariance is relatively large, the ambiguity search operation (e.g., the LAMBDA (least squares ambiguity-reduced correlation squared error) algorithm) can be directly continued to obtain the initial ambiguity candidate value. In this case, if the initial ambiguity candidate value is correct, ambiguity fixing can be achieved directly. However, if the initial ambiguity candidate value is incorrect, a hierarchical ambiguity fixing operation according to the present disclosure may be performed.
In other words, the hierarchical ambiguity fixing operation according to the present disclosure may be performed when any of the following conditions is satisfied: i) responsive to determining that the first power-forming test fails based on the initial ambiguity and the initial variance-covariance matrix; ii) in response to determining that the initial ambiguity candidate obtained via the initial ambiguity and the initial variance-covariance is incorrect.
The reason for this is that if the first power-forming test fails or the initial ambiguity candidate value is incorrect, it indicates that further correct selection of satellites in the initial set of satellites is required to finally achieve ambiguity fixing. For example, in the case of a large number of satellites or some satellites with cycle slips and gross errors, the ambiguity candidate obtained after the ambiguity search operation often fails to be checked, i.e., is determined to be incorrect, and therefore, the satellite selection needs to be continued to realize ambiguity fixing.
For reference, the bootstrap success rate test can adopt the following calculation formula:
Qzz＝zTQaaz
wherein, Psuccess_rateIndicating the calculated success rate; phi represents a standard normal density probability function; qaaA variance-covariance matrix is represented,and QzzRepresenting the result obtained after the decorrelation; diRepresents QzzConditional variance D obtained after LD decompositionzThe ith diagonal element of (1). Since the calculation formula used for bootstrap success rate test is known in the art, the details thereof will not be described herein.
In the present disclosure, a passing threshold (e.g., 95%) with respect to the first power-up test may be set according to a practical application. If the success rate calculated by substituting the initial ambiguity and the initial variance-covariance matrix is less than the pass threshold, the first power-forming test may be considered failed. In this case, a hierarchical ambiguity fixing operation according to the present disclosure may be performed.
Additionally, for reference, the LAMBDA algorithm used for the ambiguity search operation may involve a set of optimal solutions and a set of suboptimal solutions that result in ambiguity candidates after the algorithm is executed. The difference between the two can then be judged according to the ratio test. If the difference between the two is small, it means that the two sets of solutions are incorrect; and if the difference between the two is large, the ambiguity candidate can be considered to be correct. The ambiguity candidate value is also the integer ambiguity obtained by the ambiguity search operation, and the integer ambiguity is used to update the baseline vector to output the fixed solution. Since the LAMBDA algorithm and ratio test are also known in the art, the details thereof will not be described herein.
A hierarchical ambiguity fix operation according to the present disclosure may include at least one level of ambiguity fix sub-flow, and each level of ambiguity fix sub-flow may include a respective satellite culling operation. That is, when it is determined that further correct selection of satellites in the initial satellite set is required by the above-mentioned predetermined condition (the first power verification fails or the initial ambiguity candidate value is incorrect), a hierarchical ambiguity fixing operation is employed to provide correct selection of satellites. Particularly, the satellite selection can be ensured to be carried out in detail by virtue of the step-by-step satellite rejection operation, so that the universal applicability can be provided for various environments.
In step S206, it may be desirable to finalize the output fix solution so that the satellite positioning accuracy may be on the order of several centimeters or less. Possible cases also include the case where only the output floating solution is determined, which means that the satellite positioning accuracy may be slightly worse than the fixed solution, i.e. on the order of tens of centimeters or more.
According to some embodiments, each level of ambiguity fixing sub-flow in the hierarchical ambiguity fixing operation may include: after performing the satellite culling operation, performing a second power-forming test, wherein it is determined whether the second power-forming test passes based on the updated ambiguities and the updated variance-covariance matrix obtained by the satellite culling operation.
The second power-forming test may be the same as the first power-forming test, i.e., may also be a bootstrapping success rate test.
In this way, because the corresponding satellite removing operation is executed in each stage of ambiguity fixing sub-process to obtain the updated ambiguity and the updated variance-covariance matrix, the success rate of ambiguity fixing can be judged according to the current ambiguity and the variance-covariance matrix, and the step-by-step or progressive satellite selection can be realized to improve the satellite selection accuracy.
According to some embodiments, in response to determining that the second power-up check fails, one of the following operations may be performed: when the current stage of ambiguity stationary sub-process is not the last stage of ambiguity stationary sub-process, executing the next stage of ambiguity stationary sub-process; and when the current stage of ambiguity fixed sub-process is the last stage of ambiguity fixed sub-process, determining to output a floating solution.
In this way, the probability of correctly selecting the satellite can be maximized through multiple rounds of ambiguity fixing sub-processes, and it is ensured that a fixing solution can be output as much as possible so as to ensure the positioning accuracy. In other words, the floating solution is output only when the output fixed solution cannot be realized in each stage of the ambiguity fixed sub-process.
According to some embodiments, in response to determining that the second power verification passes, the per-stage ambiguity fixing sub-flow may further include: performing a ambiguity search operation to obtain an optimal candidate solution and a suboptimal candidate solution with respect to the updated ambiguity candidate values; and checking whether the updated ambiguity candidate value is correct based on the optimal candidate solution and the suboptimal candidate solution.
Here, the ambiguity search operation may be the LAMBDA algorithm as described above, and accordingly, the method for verifying whether the updated ambiguity candidate values are correct may be a ratio verification as described above.
In this way, in each stage of ambiguity fixing sub-process, the radio inspection of the data level can be further performed after the bootstrapping success rate inspection of the model level, that is, two inspection processes are set before and after the ambiguity searching operation, so that the satellite selection can be gradually performed at different screening angles to improve the satellite selection accuracy.
According to some embodiments, in response to the updated ambiguity candidate value being verified as correct, the per-stage ambiguity fixing sub-flow may further include: it is determined whether the number of satellites remaining after performing the satellite culling operation is greater than or equal to a predetermined threshold.
Here, the predetermined threshold may be set to 4, that is, it is necessary to determine whether the number of remaining satellites is greater than or equal to 4. Because the satellite removing operation is performed in each stage of ambiguity fixing sub-process, the situation that the number of remaining satellites is insufficient so that the positioning accuracy cannot be ensured may be caused. Therefore, in each stage of ambiguity fixing sub-process, although a correct ambiguity candidate value is obtained through ambiguity searching operation, whether the number of remaining satellites meets the requirement of the minimum number of satellites for guaranteeing positioning accuracy can be further judged.
According to some embodiments, in response to determining that the number of remaining satellites is greater than or equal to the predetermined threshold, an output stationary solution may be determined; or in response to determining that the number of remaining satellites is less than the predetermined threshold, one of: when the current stage of ambiguity stationary sub-process is not the last stage of ambiguity stationary sub-process, executing the next stage of ambiguity stationary sub-process; and when the current stage of ambiguity fixed sub-process is the last stage of ambiguity fixed sub-process, determining to output a floating solution.
In this way, on the one hand, a fixed solution can be output deterministically, given that the minimum number of satellites required to guarantee positioning accuracy can be met; on the other hand, even in the event that the number of remaining satellites does not meet the minimum satellite number requirement for ensuring positioning accuracy, there are alternative alternatives, i.e., depending on whether the current stage ambiguity fix sub-procedure is the last stage, or the next stage ambiguity fix sub-procedure may be performed to enable satellite selection to be re-performed with additional satellite culling operations, or a float solution may be output instead.
According to some embodiments, in response to the updated ambiguity candidate value being verified as incorrect, one of the following operations may be performed: when the current stage of ambiguity stationary sub-process is not the last stage of ambiguity stationary sub-process, executing the next stage of ambiguity stationary sub-process; and when the current stage of ambiguity fixed sub-process is the last stage of ambiguity fixed sub-process, determining to output a floating solution.
In this way, even if a fixed solution cannot be output through the current stage of ambiguity stationary sub-flow, satellite selection can be re-performed by means of different satellite culling operations in other stages of ambiguity stationary sub-flows to try to output the fixed solution, or a floating solution can be output instead.
According to some embodiments, at least one of the hierarchical ambiguity fixing sub-processes may comprise a first-level ambiguity fixing sub-process, wherein the satellite culling operation performed in the first-level ambiguity fixing sub-process may comprise: culling at least one of satellites whose elevation angles do not satisfy a predetermined angle and newly observed satellites from the initial set of satellites to generate a first subset of satellites; and generating a first updated ambiguity and a first updated variance-covariance matrix for determining whether the second power-related test passes based on the first subset of satellites.
In the present disclosure, satellites that are most likely not to meet the positioning accuracy can be simply rejected first in an empirical manner in a first-stage ambiguity fixing sub-process. In general, satellites with elevation angles below 20 degrees may mean that a greater distance is required to reach the receiver, and therefore errors through the atmosphere may be greater, with the result that the positioning accuracy may be more affected. In addition, the new observed satellite may not be guaranteed to be accurate because it was acquired for the first time. Based on the above empirical determination, these satellites most likely not to satisfy the positioning accuracy may be first rejected for ambiguity fixing.
In addition, in the present disclosure, since each stage of ambiguity fixing sub-process may include performing a second power verification (such as bootstrapping success rate verification) after performing a satellite culling operation, when an updated ambiguity and an updated variance-covariance matrix are obtained through the satellite culling operation, the success rate of ambiguity fixing according to the current ambiguity and variance-covariance matrix may be determined accordingly, so that step-by-step or progressive satellite selection may be implemented to improve satellite selection accuracy.
According to some embodiments, the at least one stage of ambiguity fixing sub-flow in the hierarchical ambiguity fixing operation may further include a second stage of ambiguity fixing sub-flow, wherein the satellite culling operation performed in the second stage of ambiguity fixing sub-flow may include: rejecting satellites from the first subset of satellites according to an ambiguity resolution factor (ADOP) method to generate a second subset of satellites; and generating a second updated ambiguity and a second updated variance-covariance matrix for determining whether the second power-on test passed based on the second subset of satellites.
Because the first-stage ambiguity fixing sub-process is based on experience to remove the satellite, if the ambiguity fixing can not be realized through the first-stage ambiguity fixing sub-process, the satellite removing can be further carried out on the rest first satellite subsets again through an ADOP method in the second-stage ambiguity fixing sub-process.
For reference, the calculation formula of the ADOP method is
Wherein QaaA variance-covariance matrix is represented. Since the ADOP method is known in the art, the details thereof will not be described herein.
According to the ADOP method, on the basis of the remaining satellites (i.e., the first subset of satellites) after the satellite culling operation is performed by the first-stage ambiguity fixing sub-process, one satellite may be culled in sequence to obtain the corresponding calculation result of the corresponding satellite set, and the smallest calculation result among the results may be selected, which indicates that the satellite that is culled when calculating the ADOP should be culled.
In addition, similar to the first-stage ambiguity fixing sub-process, in the second-stage ambiguity fixing sub-process, under the condition that the updated ambiguity and the updated variance-covariance matrix are obtained through satellite removing operation, the success rate of ambiguity fixing can be judged according to the current ambiguity and the variance-covariance matrix, and therefore step-by-step or progressive satellite selection can be achieved to improve satellite selection accuracy.
According to some embodiments, the at least one stage of ambiguity fixing sub-flow in the hierarchical ambiguity fixing operation may further include a third stage of ambiguity fixing sub-flow, wherein the satellite culling operation performed in the third stage of ambiguity fixing sub-flow may include: according to the diagonal elements of the conditional variance after the initial variance-covariance matrix is subjected to decorrelation, removing satellites from the initial satellite set to generate a third satellite subset; and generating a third updated ambiguity and a third updated variance-covariance matrix for determining whether the second power-related test passes based on the third subset of satellites.
In the present disclosure, the ambiguity fixing required to be performed in the Z domain is set in the third-level ambiguity fixing sub-flow, so that the ambiguity fixing can be attempted by means of a relatively simpler satellite culling operation in the ambiguity fixing sub-flows of other levels first, and the start of the overall operation flow in a complicated calculation manner is avoided.
In addition, similar to the first and second stage ambiguity fixing sub-processes, in the third stage ambiguity fixing sub-process, under the condition that the updated ambiguity and the updated variance-covariance matrix are obtained through satellite removing operation, the success rate of ambiguity fixing can be judged according to the current ambiguity and the variance-covariance matrix, and therefore step-by-step or progressive satellite selection can be achieved to improve satellite selection accuracy.
As described above, according to the ambiguity fixing method of the present disclosure, a correct selection of satellites is provided through a hierarchical ambiguity fixing operation, wherein a selection of satellites can be ensured to be performed in detail by means of a step-by-step satellite rejecting operation, and a corresponding satellite rejecting operation is performed step by using a multistage judgment logic, thereby providing a universal applicability for implementation in various environments, thereby avoiding a precision improvement bottleneck caused by specifically selecting different ambiguity fixing methods, further improving usability of RTK positioning, and effectively enhancing effectiveness of a positioning system for positioning an autonomous driving apparatus. In addition, by setting a corresponding predetermined condition (the first power verification fails or the initial ambiguity candidate value is incorrect) for entering the hierarchical ambiguity fixing operation, the overall operation flow can not be limited to the specific situations of various environments, and the universality of applying the overall operation flow in various environments is provided.
Fig. 3 shows a flow diagram of an ambiguity fixing method 300 according to another embodiment of the present disclosure.
As shown in FIG. 3, step 301 illustrates obtaining an initial ambiguity associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguity.
Step 302 illustrates a determination of whether a first power-on test (shown in fig. 3 as a bootstrapping success rate test) passed based on the initial ambiguity and the initial variance-covariance matrix, wherein in the event of a failure, a hierarchical ambiguity fixing operation 305 is entered. Step 303 shows obtaining an initial ambiguity candidate via the initial ambiguity and the initial variance-covariance (shown in fig. 3 as being subjected to the ambiguity search operation of the LAMBDA algorithm), and step 304 shows determining whether the initial ambiguity candidate is correct (shown in fig. 3 as being subjected to a ratio test), wherein, in the event of being incorrect, a hierarchical ambiguity fixing operation 305 is entered. That is, the predetermined condition for entering the hierarchical ambiguity fixing operation 305 (the first success rate check fails or the initial ambiguity candidate value is incorrect) is set by steps 302 and 304.
The hierarchical ambiguity fixing operation 305 may include a first level ambiguity fixing sub-flow 310, a second level ambiguity fixing sub-flow 320, and a third level ambiguity fixing sub-flow 330. Each level of ambiguity fixing sub-flow 310, 320, and 330 includes a respective satellite culling operation 311, 321, and 331.
Specifically, the satellite culling operation 311 in the first-stage ambiguity fixing sub-process 310 includes culling at least one of a satellite whose altitude angle does not satisfy the predetermined angle and a newly observed satellite from the initial set of satellites. The satellite culling operation 321 in the second-level ambiguity fixing sub-process 320 includes culling satellites from the first subset of satellites according to the ADOP method. Satellite culling 331 in the third level ambiguity fixing sub-process 330 includes culling satellites from the initial set of satellites according to the diagonal elements of the conditional variance after the initial variance-covariance matrix decorrelation.
According to the hierarchical ambiguity fix operation 305, the process may proceed to step 340 to output a fixed solution or to step 350 to output a floating solution.
Alternatively, if the result of step 304 is a determination that the initial ambiguity candidate is correct, the process may also proceed to step 340 to output a corresponding fixed solution.
As shown in FIG. 3, each stage of ambiguity fixing sub-flows 310, 320, and 330 may include second power tests 312, 322, and 332 (shown in FIG. 3 as bootstrap success rate tests) that are performed after the respective satellite culling operations 311, 321, and 331 are performed. In the first and second stage ambiguity fixing sub-flows 310 and 320, if it is determined that the second power tests 312, 322 fail, the process proceeds to the respective next stage ambiguity fixing sub-flows 320 and 330 to continue ambiguity fixing. While in the third stage ambiguity fix sub-flow 330 (which is the last stage ambiguity fix sub-flow), if it is determined that the second power check 332 fails, the process proceeds to step 350 to output a floating solution, which means that a fixed solution with the best precision is not finally obtained.
Further, as shown in FIG. 3, if the respective second power tests 312, 322, and 332 in the first, second, and third level ambiguity fixing sub-flows 310, 320, and 330 pass, the process proceeds to ambiguity search operations 313, 323, and 333 (shown as the LAMBDA algorithm in FIG. 3) to obtain the optimal candidate solution and the suboptimal candidate solution for the updated ambiguity candidate value, respectively, and proceeds to test steps 314, 324, and 334 (shown as the ratio test in FIG. 3) to test whether the updated ambiguity candidate value is correct.
In the checking steps 314, 324 and 334, if the updated ambiguity candidate value is checked as correct, it can be further determined whether the number of remaining satellites meets the minimum number requirement, i.e. whether the number of remaining satellites is greater than or equal to a predetermined threshold value. If the number of remaining satellites is greater than or equal to the predetermined threshold, the stationary solution may be deterministically output, i.e., the process proceeds to step 340. If the number of remaining satellites is less than the predetermined threshold, the process may proceed to the next stage ambiguity fixing sub-process 320 or 330 to perform ambiguity fixing again through other satellite culling operations, or in the third stage ambiguity fixing sub-process 330, the process may proceed to step 350 to output a floating solution, which also means that a fixed solution with the best precision is not finally obtained.
According to another aspect of the present disclosure, there is also provided an ambiguity fixing apparatus. Fig. 4 illustrates a block diagram of an ambiguity fixing apparatus 400 according to one embodiment of the present disclosure. As shown in fig. 4, the apparatus 400 may include:
an initial value acquisition module 402 configured to acquire an initial ambiguity associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguity;
a rank fixing module 404 configured to perform a rank ambiguity fixing operation in response to determining that the first power test fails based on the initial ambiguity and the initial variance-covariance matrix or in response to determining that an initial ambiguity candidate value obtained via the initial ambiguity and the initial variance-covariance matrix is incorrect, wherein the rank ambiguity fixing operation comprises at least one rank ambiguity fixing sub-flow, and each rank ambiguity fixing sub-flow comprises a corresponding satellite culling operation; and
a result determination module 406 configured to determine a fixed solution or a floating solution to output the updated ambiguity candidate values according to the hierarchical ambiguity fixing operation.
The operations performed by the modules 402, 404, and 406 correspond to the steps S202, S204, and S206 described with reference to fig. 2, and therefore details of various aspects thereof are not repeated.
Fig. 5 shows a block diagram of an ambiguity fixing apparatus 500 according to another embodiment of the present disclosure. Modules 502, 504, and 506 shown in FIG. 5 may correspond to modules 402, 404, and 406, respectively, shown in FIG. 4. In addition, the apparatus 504 may further include further 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.
According to some embodiments, the rank fixing module 504 may include, for each level of ambiguity fixing sub-flow: a success rate checking unit 5040 configured to perform a second power-forming check after performing the satellite culling operation, wherein it is determined whether the second power-forming check passes based on the updated ambiguity and the updated variance-covariance matrix obtained through the satellite culling operation.
According to some embodiments, the success rate check unit 5040 may include a first notification unit 5040-1, the first notification unit 5040-1 configured to: in response to determining that the second power verification fails, performing one of: when the current-level ambiguity stationary sub-flow is not the last-level ambiguity stationary sub-flow, the hierarchical stationary module 504 is notified to execute the next-level ambiguity stationary sub-flow; and when the current-level ambiguity stationary sub-flow is the last-level ambiguity stationary sub-flow, notify the output module 506 to output the floating solution.
According to some embodiments, the rank fixing module 504 may further include an ambiguity search unit 5042 for each rank of ambiguity fixing sub-flow, the ambiguity search unit 5042 may include: a result obtaining subunit 5042-1 configured to, in response to determining that the second power verification passes, perform an ambiguity search operation to obtain an optimal candidate solution and a suboptimal candidate solution with respect to the updated ambiguity candidate values; and a result checking subunit 5042-2 configured to check whether the updated ambiguity candidate values are correct based on the optimal candidate solution and the optimal candidate solution.
According to some embodiments, the ambiguity search unit 5042 may further include a second notification unit 5042-3, the second notification unit 5042-3 configured to: in response to the updated ambiguity candidate being verified as incorrect, performing one of the following operations: when the current-level ambiguity stationary sub-flow is not the last-level ambiguity stationary sub-flow, the hierarchical stationary module 504 is notified to execute the next-level ambiguity stationary sub-flow; and when the current-level ambiguity stationary sub-flow is the last-level ambiguity stationary sub-flow, notify the output module 506 to output the floating solution.
According to some embodiments, hierarchical fixing module 504 may include a first ambiguity fixing sub-module 5044 for the at least one level of ambiguity fixing sub-flow, wherein first ambiguity fixing sub-module 5044 includes, for satellite culling operations: a first satellite culling unit 5044-1 configured to cull at least one of satellites whose altitude angles do not satisfy a predetermined angle and newly observed satellites from the initial set of satellites to generate a first subset of satellites; and a first generating unit 5044-2 configured to generate a first updated ambiguity and a first updated variance-covariance matrix for determining whether the second power-up test passes based on the first subset of satellites.
According to some embodiments, hierarchical fixing module 504 may include a second ambiguity fixing sub-module 5046 for the at least one level of ambiguity fixing sub-flow, wherein the second ambiguity fixing sub-module 5046 includes, for satellite culling operations: a second satellite culling unit 5046-1 configured to cull satellites from the first subset of satellites according to the ADOP method to generate a second subset of satellites; and a second generating unit 5046-2 configured to generate a second updated ambiguity and a second updated variance-covariance matrix for determining whether the second power-on test passes based on the second subset of satellites.
According to some embodiments, hierarchical fixing module 504 may include a third ambiguity fixing sub-module 5048 for the at least one level of ambiguity fixing sub-flow, wherein third ambiguity fixing sub-module 5048 includes, for satellite culling operations: a third satellite culling unit 5048-1 configured to cull satellites from the initial set of satellites to generate a third subset of satellites according to diagonal elements of the conditional variance after the initial variance-covariance matrix decorrelation; and a third generating unit 5048-2 configured to generate a third updated ambiguity and a third updated variance-covariance matrix for determining whether the second power-forming test passes based on the third subset of satellites.
According to some embodiments, the hierarchical fixing module 504 may further include a remaining satellite determining unit 5043 for each level of ambiguity fixing sub-flow, the remaining satellite determining unit 5043 configured to: in response to the ambiguity candidate being verified as correct, it is determined whether the number of satellites remaining after performing the satellite culling operation is greater than or equal to a predetermined threshold.
According to some embodiments, the remaining satellites determination unit 5043 may include a second notification unit 5043-1, the 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 output module 506 to output a fixation solution; or in response to determining that the number of remaining satellites is less than the predetermined threshold, performing one of: when the current-level ambiguity stationary sub-flow is not the last-level ambiguity stationary sub-flow, the hierarchical stationary module 504 is notified to execute the next-level ambiguity stationary sub-flow; and when the current-level ambiguity stationary sub-flow is the last-level ambiguity stationary sub-flow, notify the output module 506 to output the floating solution.
According to another aspect of the present disclosure, there is also provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method as described above.
According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program, wherein the computer program realizes the method as described above when executed by a processor.
According to another aspect of the present disclosure, there is also provided an autopilot device comprising a controller configured to implement the method as described above.
According to another aspect of the present disclosure, there is also provided an electronic device comprising at least one processor; and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, which when executed by the at least one processor, cause the at least one processor to perform the method as described above.
Referring to fig. 6, a block diagram of a structure of an electronic device 600, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may 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 meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 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. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation 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 bus 604.
A number of 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. Input unit 606 may be any type of device capable of inputting information to device 600, and input unit 606 may receive inputAnd generating key signal inputs related to user settings and/or function controls of the electronic device, and may include, but are not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 607 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 608 may 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 may 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 bluetoothTMDevices, 1302.11 devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 executes the respective methods and processes described above, such as the ambiguity fixing method. For example, in some embodiments, the ambiguity fixing method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the ambiguity fixing method described above may be performed. Alternatively, in other embodiments, the calculation unit 601 may be configured to perform the ambiguity fixing method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here 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 a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, 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 may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, 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: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, 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 with equivalent elements that appear after the present disclosure.
Claims (22)
1. An ambiguity fixing method for real-time dynamic positioning, comprising:
obtaining an initial ambiguity associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguity;
performing a hierarchical ambiguity fixing operation in response to determining that a first power test fails based on the initial ambiguity and the initial variance-covariance matrix or in response to determining that an initial ambiguity candidate value obtained via the initial ambiguity and the initial variance-covariance matrix is incorrect, wherein the hierarchical ambiguity fixing operation comprises at least one level ambiguity fixing sub-flow, and each level ambiguity fixing sub-flow comprises a respective satellite culling operation; and
determining to output a fixed solution or a floating solution with respect to the updated ambiguity candidate values according to the hierarchical ambiguity fixing operation.
2. The method of claim 1, wherein the per-level ambiguity fixing sub-process comprises:
after performing the satellite culling operation, performing a second power-strike test, wherein it is determined whether the second power-strike test passes based on the updated ambiguities and the updated variance-covariance matrix obtained by the satellite culling operation.
3. The method of claim 2, wherein, in response to determining that the second power-reducing test fails, performing one of:
when the current stage of ambiguity stationary sub-process is not the last stage of ambiguity stationary sub-process, executing the next stage of ambiguity stationary sub-process; and
and when the current stage of ambiguity fixed sub-process is the last stage of ambiguity fixed sub-process, determining to output the floating solution.
4. The method of claim 2, wherein in response to determining that the second power verification passes, the per-stage ambiguity-fixing sub-routine further comprises:
performing a ambiguity search operation to obtain an optimal candidate solution and a suboptimal candidate solution for the updated ambiguity candidate values; and
verifying whether the updated ambiguity candidate value is correct based on the optimal candidate solution and the suboptimal candidate solution.
5. The method of claim 4, wherein in response to the updated ambiguity candidate value being verified as correct, the per-stage ambiguity-fixing sub-flow further comprises:
determining whether a number of satellites remaining after performing the satellite culling operation is greater than or equal to a predetermined threshold.
6. The method of claim 5, wherein:
determining to output the fixed solution in response to determining that the number of remaining satellites is greater than or equal to the predetermined threshold; or
In response to determining that the number of remaining satellites is less than the predetermined threshold, performing one of:
when the current stage of ambiguity stationary sub-process is not the last stage of ambiguity stationary sub-process, executing the next stage of ambiguity stationary sub-process; and
and when the current stage of ambiguity fixed sub-process is the last stage of ambiguity fixed sub-process, determining to output the floating solution.
7. The method of claim 4, wherein, in response to the updated ambiguity candidate value being verified as incorrect, performing one of:
when the current stage of ambiguity stationary sub-process is not the last stage of ambiguity stationary sub-process, executing the next stage of ambiguity stationary sub-process; and
and when the current stage of ambiguity fixed sub-process is the last stage of ambiguity fixed sub-process, determining to output the floating solution.
8. The method of any of claims 2 to 7, wherein the at least one level of ambiguity fixing sub-process comprises a first level of ambiguity fixing sub-process, wherein the satellite culling operations performed in the first level of ambiguity fixing sub-process comprise:
culling at least one of satellites whose elevation angles do not meet a predetermined angle and newly observed satellites from the initial set of satellites to generate a first subset of satellites; and
generating a first updated ambiguity and a first updated variance-covariance matrix for determining whether the second power-related test passes based on the first subset of satellites.
9. The method of claim 8, wherein the at least one level of ambiguity fixing operations further comprises a second level of ambiguity fixing sub-process, wherein the satellite culling operations performed in the second level of ambiguity fixing sub-process comprise:
removing satellites from the first satellite subset according to a ambiguity precision factor method to generate a second satellite subset; and
generating a second updated ambiguity and a second updated variance-covariance matrix for determining whether the second power-on test passed based on the second subset of satellites.
10. The method of claim 9, wherein the at least one level of ambiguity fixing operation further comprises a third level of ambiguity fixing sub-process, wherein the satellite culling operation performed in the third level of ambiguity fixing sub-process comprises:
according to the diagonal elements of the conditional variance after the initial variance-covariance matrix is subjected to decorrelation, removing satellites from the initial satellite set to generate a third satellite subset; and
generating a third updated ambiguity and a third updated variance-covariance matrix for determining whether the second power-related test passes based on the third subset of satellites.
11. An ambiguity fixing apparatus for real-time dynamic positioning, comprising:
an initial value acquisition module configured to acquire an initial ambiguity associated with an initial set of satellites and an initial variance-covariance matrix corresponding to the initial ambiguity;
a hierarchical fixing module configured to perform a hierarchical ambiguity fixing operation in response to determining that a first power test fails based on the initial ambiguity and the initial variance-covariance matrix or in response to determining that an initial ambiguity candidate value obtained via the initial ambiguity and the initial variance-covariance matrix is incorrect, wherein the hierarchical ambiguity fixing operation includes at least one level of ambiguity fixing sub-flow, and each level of ambiguity fixing sub-flow includes a corresponding satellite culling operation; and
a result determination module configured to determine a fixed solution or a floating solution to output a candidate value for an updated ambiguity according to the hierarchical ambiguity fixing operation.
12. The apparatus of claim 11, wherein the hierarchical fixing module comprises, for the per-level ambiguity fixing sub-procedure:
a success rate checking unit configured to perform a second power-forming check after performing the satellite culling operation, wherein it is determined whether the second power-forming check passes based on the updated ambiguity and the updated variance-covariance matrix obtained through the satellite culling operation.
13. The apparatus of claim 12, wherein the success rate checking unit comprises a first notification unit configured to: in response to determining that the second power verification fails, performing one of:
when the current stage of ambiguity fixed sub-flow is not the last stage of ambiguity fixed sub-flow, informing the grading fixed module to execute the next stage of ambiguity fixed sub-flow; and
and when the current stage of ambiguity fixed sub-process is the last stage of ambiguity fixed sub-process, informing the output module to output the floating solution.
14. The apparatus of claim 12, wherein the hierarchical fixing module further comprises, for the per-level ambiguity fixing sub-flow, an ambiguity search unit comprising:
a result obtaining subunit configured to, in response to determining that the second power verification passes, perform an ambiguity search operation to obtain an optimal candidate solution and a suboptimal candidate solution with respect to the updated ambiguity candidate values; and
a result checking subunit configured to check whether the updated ambiguity candidate value is correct based on the optimal candidate solution and the suboptimal candidate solution.
15. The apparatus of claim 14, wherein the ambiguity search unit further comprises a second notification unit configured to: in response to the updated ambiguity candidate value being verified as incorrect, performing one of the following:
when the current stage of ambiguity fixed sub-flow is not the last stage of ambiguity fixed sub-flow, informing the grading fixed module to execute the next stage of ambiguity fixed sub-flow; and
and when the current stage of ambiguity fixed sub-process is the last stage of ambiguity fixed sub-process, informing the output module to output the floating solution.
16. The apparatus of any of claims 12 to 15, wherein the hierarchical fixing module comprises a first ambiguity fixing sub-module for the at least one level ambiguity fixing sub-process, wherein the first ambiguity fixing sub-module comprises, for the satellite culling operation:
a first satellite culling unit configured to cull at least one of satellites whose altitude angles do not satisfy a predetermined angle and newly observed satellites from the initial set of satellites to generate a first subset of satellites; and
a first generating unit configured to generate a first updated ambiguity and a first updated variance-covariance matrix for determining whether the second power-up test passes based on the first subset of satellites.
17. The apparatus of claim 16, wherein the hierarchical fixing module comprises a second ambiguity fixing sub-module for the at least one level ambiguity fixing sub-process, wherein the second ambiguity fixing sub-module comprises, for the satellite culling operation:
a second satellite culling unit configured to cull satellites from the first subset of satellites according to a ambiguity precision factor method to generate a second subset of satellites; and
a second generation unit configured to generate a second updated ambiguity and a second updated variance-covariance matrix for determining whether the second power-up test passes based on the second subset of satellites.
18. The apparatus of claim 17, wherein the hierarchical fixing module comprises a third ambiguity fixing sub-module for the at least one level ambiguity fixing sub-process, wherein the third ambiguity fixing sub-module comprises, for the satellite culling operation:
a third satellite culling unit configured to cull satellites from the initial set of satellites to generate a third subset of satellites according to diagonal elements of the conditional variance after the initial variance-covariance matrix decorrelation; and
a third generating unit configured to generate a third updated ambiguity and a third updated variance-covariance matrix for determining whether the second power-up test passes based on the third subset of satellites.
19. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 10.
20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1 to 10.
21. A computer program product comprising a computer program, wherein the computer program realizes the method according to any one of claims 1 to 10 when executed by a processor.
22. An autopilot device comprising: a controller configured to implement the method of any one of claims 1 to 10.
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| CN117452462B (en) * | 2023-12-22 | 2024-03-29 | 华芯拓远(天津)科技有限公司 | Model and data combined partial ambiguity fixing method |
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