Unmanned aerial vehicle positioning method and apparatus, and unmanned aerial vehicle

Abstract

This invention discloses a method, apparatus, and drone for drone positioning. The method includes: acquiring raw positioning data of the drone and VRS data from a ground-based system; performing PPK calculation based on the raw positioning data and VRS data to obtain a first calculation result, which includes a fixed solution and a floating-point solution of the PPK; filtering the first calculation result to determine time periods in the first calculation result where the baseline length of an epoch is greater than a first preset threshold; recording the state matrix and variance matrix corresponding to the calculation of the fixed solution within the time period; performing PPP calculation based on the state matrix, variance matrix, and error parameters to obtain a second calculation result, which includes a fixed solution and a floating-point solution of the PPP; and determining the drone's positioning information based on the first and second calculation results. This technical solution can achieve high-precision positioning in areas without VRS service coverage, meeting the high-precision spatial location information requirements in drone inspection tasks.

Description

Technical Field

[0001] This invention relates to the field of positioning technology, and more specifically, to a method, apparatus, and drone positioning system for unmanned aerial vehicles (UAVs). Background Technology

[0002] With the widespread application of drones in fields such as power line inspection, pipeline monitoring, and topographic mapping, the requirements for drone positioning accuracy are increasing. Currently, most drones use RTK (Real-Time Kinematic) or VRS (Virtual Reference Station) services to obtain centimeter-level positioning results. However, scenarios such as power line inspection, pipeline monitoring, and topographic mapping are often located in remote areas with harsh geographical conditions. In some remote areas, mountainous regions, or areas with insufficient communication network coverage, RTK or VRS services cannot reliably provide differential data, resulting in the inability to calculate high-precision location information. Summary of the Invention

[0003] In view of the above problems, the purpose of this invention is to provide a drone positioning method, device and drone to overcome the shortcomings of the prior art.

[0004] According to one embodiment of the present invention, a method for locating a drone is provided, the method comprising:

[0005] Acquire raw positioning data of the UAV and VRS data of the ground-based system;

[0006] PPK is calculated based on the original positioning data and the VRS data to obtain a first calculation result, wherein the first calculation result includes a fixed solution and a floating-point solution of PPK;

[0007] The first solution result is filtered to determine the time period in the first solution result where the baseline length of the epoch is greater than the first preset threshold.

[0008] Record the state matrix and variance matrix corresponding to the fixed solution of PPK within the time period;

[0009] Based on the state matrix, the variance matrix, and the error parameters, PPP is calculated to obtain a second solution result; wherein, the error parameters are the error parameters of the satellite-based calculation obtained from the satellite-based server, and the second solution result includes a fixed solution and a floating-point solution of PPP;

[0010] The location information of the UAV is determined based on the first solution result and the second solution result.

[0011] In the above-described UAV positioning method, before performing PPK calculation based on the original positioning data and the VRS data to obtain the first calculation result, the method further includes:

[0012] The original positioning data is processed by single-point calculation to obtain the single-point positioning trajectory;

[0013] The baseline length for each epoch is calculated based on the original positioning data;

[0014] If the baseline length is greater than the second preset threshold or the VRS data meets the preset conditions, then the following steps are executed: “Perform PPK calculation based on the original positioning data and the VRS data to obtain a first calculation result, wherein the first calculation result includes a fixed solution and a floating-point solution of PPK” and all subsequent steps.

[0015] Otherwise, the single-point positioning trajectory will be used as the positioning information of the UAV.

[0016] In the above-described UAV positioning method, determining the UAV's positioning information based on the first calculation result and the second calculation result includes:

[0017] Determine the overlap between the first solution result and the second solution result;

[0018] The overlapping results are filtered according to accuracy to determine the positioning information of the UAV.

[0019] The above-mentioned drone positioning methods also include:

[0020] The location information of the UAV is output according to the time sequence.

[0021] In the above-described UAV positioning method, the step of performing PPP calculation based on the state matrix, the variance matrix, and the error parameters to obtain the second calculation result includes:

[0022] Using the state matrix and the variance matrix as initial values ​​for Kalman filtering, the initial values ​​are corrected based on the error parameters to obtain the second solution result.

[0023] In the above-described UAV positioning method, recording the state matrix and variance matrix corresponding to the fixed solution of PPK within the time period includes:

[0024] If no fixed solution is found within the time period, the time period is extended until a fixed solution for PPK is found and the extension is stopped.

[0025] Record the state matrix and variance matrix corresponding to the fixed solution of PPK within the time period.

[0026] According to one embodiment of the present invention, a drone positioning device is provided, the device comprising:

[0027] The acquisition module is used to acquire the raw positioning data of the UAV and the VRS data of the ground-based system;

[0028] The first calculation module is used to perform PPK calculation based on the original positioning data and the VRS data to obtain a first calculation result, wherein the first calculation result includes a fixed solution and a floating-point solution of PPK;

[0029] The filtering module is used to perform sub-filtering on the first solution result and determine the time period in the first solution result where the baseline length of the epoch is greater than a first preset threshold.

[0030] The recording module is used to record the state matrix and variance matrix corresponding to the calculation of the fixed solution of PPK within the time period.

[0031] The second solution module is used to perform PPP solution based on the state matrix, the variance matrix, and the error parameters to obtain a second solution result; wherein, the error parameters are the error parameters of the satellite-based solution obtained from the satellite-based server, and the second solution result includes a fixed solution and a floating-point solution of PPP;

[0032] The positioning information determination module is used to determine the positioning information of the UAV based on the first calculation result and the second calculation result.

[0033] According to one embodiment of the present invention, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0034] According to one embodiment of the present invention, a drone is provided, the drone including a memory and a processor, the memory for storing a computer program, and the processor running the computer program to enable the drone to perform the drone positioning method described above.

[0035] According to one embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores the computer program used in the above-described UAV.

[0036] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0037] This invention discloses a UAV positioning method, comprising: acquiring raw positioning data of the UAV and VRS data from a ground-based system; performing calculations based on the raw positioning data and the VRS data to obtain a first calculation result, wherein the first calculation result includes a fixed solution to facilitate determining the time period for satellite-based calculation based on the first calculation result; filtering the first calculation result to determine the time period in which the baseline length of the epoch in the first calculation result is greater than a first preset threshold; recording the state matrix and variance matrix corresponding to the fixed solution of the PPK within the time period; performing calculations based on the state matrix, the variance matrix, and error parameters to obtain a second calculation result; wherein the error parameters are error parameters of satellite-based calculation obtained from a satellite-based server; and determining the positioning information of the UAV based on the first calculation result and the second calculation result. The convergence speed of the calculation process for the second calculation result is accelerated by using the parameters of the first calculation result, and the robustness of the calculation is ensured by combining multiple calculation processes and results, thereby improving the positioning accuracy of the UAV.

[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0039] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope of protection of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 A flowchart illustrating a drone positioning method provided by an embodiment of the present invention is shown;

[0041] Figure 2 A flowchart illustrating another UAV positioning method provided by an embodiment of the present invention is shown;

[0042] Figure 3 A flowchart illustrating another UAV positioning method provided by an embodiment of the present invention is shown;

[0043] Figure 4 A schematic diagram of the structure of a drone positioning device provided in an embodiment of the present invention is shown. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0045] Example 1

[0046] Figure 1 The diagram shows a flowchart of a drone positioning method provided by an embodiment of the present invention.

[0047] The drone positioning method includes the following steps:

[0048] In step S110, the raw positioning data of the UAV and the VRS data of the ground system are acquired.

[0049] In this embodiment, the raw positioning data includes raw GNSS (Global Navigation Satellite System) measurement data.

[0050] Among them, the raw GNSS measurement data includes the raw observation data recorded by the satellite navigation receiver. This data contains relevant information about the satellite signals received by the receiver, such as the time of signal reception, and / or the satellite's position, and / or velocity, and / or the satellite's signal strength, and / or the satellite's code phase, and / or the satellite's Doppler, and / or the satellite's pseudorange, and / or the satellite's carrier phase, and / or the satellite's signal-to-noise ratio, and / or the satellite's pseudorange.

[0051] GNSS is a comprehensive satellite navigation system that encompasses satellite positioning systems from multiple countries and regions, including China's BeiDou, the United States' GPS (Global Positioning System), Russia's GLONASS, and Europe's Galileo system. Together, these systems form a global network for navigation, positioning, and timing services.

[0052] GNSS uses artificial satellites as navigation stations to provide all-weather, high-precision position, velocity, and time information for various vehicles on land, sea, in the air, and in space around the world. Therefore, GNSS is also known as a space-based positioning, navigation, and timing system.

[0053] UAVs are typically equipped with GNSS modules, which are used to receive and process raw GNSS measurement data from these satellite systems.

[0054] In this embodiment, a single-point positioning trajectory of the UAV is obtained by performing single-point calculation based on the raw GNSS measurement data. VRS data is then acquired based on the single-point positioning trajectory and the ground-based server in the ground-based system. The single-point positioning trajectory includes trajectory data formed by multiple single-point calculation results. These multiple single-point calculation results are obtained by performing single-point calculations using multiple received raw GNSS measurement data.

[0055] In this embodiment, the VRS data includes the base station coordinates of the VRS and / or the virtual observation values ​​of the VRS.

[0056] In this embodiment, the ground-based system, particularly the ground-based augmentation system, is an infrastructure that improves satellite positioning accuracy through differential technology. It can correct satellite signal errors and meet the high-precision positioning needs of various industries.

[0057] In this embodiment, the single-point solution involves the receiver locking onto only three satellites. In this case, clock errors and atmospheric errors cannot be eliminated, resulting in low positioning accuracy. Therefore, more accurate positioning requires combining VRS data with the single-point positioning trajectory.

[0058] In step S120, PPK calculation is performed based on the original positioning data and the VRS data to obtain the first calculation result.

[0059] In this embodiment, the original positioning data and the VRS data can be solved using PPK post-processing differential technology to obtain the first solution result.

[0060] PPK post-processing differential technology performs joint processing on the positioning data collected by two GPS receivers (base station receiver and rover receiver) after positioning observation, thereby calculating the coordinate position of the rover station at the corresponding time. There is no strict limitation on the distance between the base station and the rover station. Its advantages are high positioning accuracy, high operation efficiency, large operation radius, and ease of operation.

[0061] Specifically, GNSS satellites are observed synchronously using the same base station receiver and at least one rover receiver. This means the base station receiver maintains continuous observation, while the rover receiver, after initialization, moves to the next undetermined location. During this relocation, continuous tracking of the satellite is required to transfer integer ambiguities to the undetermined location. The synchronously received data from the base station receiver (VRS base station coordinates) and the rover receiver are linearly combined in a computer to form virtual observations (VRS virtual observations, such as carrier phase observations), determining the relative positions of each receiver. Finally, the known coordinates of the base station (raw positioning data) are introduced to obtain the three-dimensional coordinates of the rover (the first solution result).

[0062] In this embodiment, the first solution includes a fixed PPK solution. This fixed solution requires locking onto at least five satellites and refers to the PPK solution (narrow_int) after the carrier phase integer ambiguity has been fixed, with an accuracy ranging from centimeters to millimeters. Whenever carrier phase is used, carrier phase integer ambiguity is unavoidable. Normally, this is equivalent to superimposing a fixed integer on a distance measured in cycles. This ambiguity must be calculated to correctly utilize the phase for differential positioning; this process is called integer ambiguity resolution (AR). After successful PPK resolution, the correct integer ambiguity is obtained, which is called ambiguity fixing. The result obtained by differential positioning using the carrier phase with fixed ambiguity is the fixed solution.

[0063] In this embodiment, the first solution also includes the floating-point solution of PPK. The floating-point solution refers to a solution where the receiver has locked onto only four satellites, but the measurement result has a certain degree of uncertainty due to signal interference or other factors. The positioning accuracy of the floating-point solution is greater than 0.5 meters, and the horizontal and vertical errors are generally between 1 and 3 meters. The positioning accuracy of the floating-point solution is lower than that of the fixed solution.

[0064] In step S130, the first solution result is filtered to determine the time period in the first solution result where the baseline length of the epoch is greater than the first preset threshold.

[0065] In this embodiment, the baseline length of the epoch can be calculated by the baseline vector. The baseline vector can be calculated by using the differential observation values ​​formed by the synchronous observation data collected by two or more GPS receivers (such as base station receivers or mobile receivers) and the three-dimensional coordinate difference between the two receivers through parameter estimation.

[0066] A baseline vector is a vector that has both length and direction properties, while the baseline length is a scalar that only has length properties. Baseline vectors are primarily expressed in the form of coordinate differences in spatial rectangular coordinates.

[0067] The baseline length can be calculated based on the Euclidean norm principle, for example... , where (x,y,z) represents the baseline vector.

[0068] In this embodiment, since the GNSS module acquires the UAV's raw positioning data and the ground-based system's VRS data in real time, it will obtain multiple first solution data. After determining the baseline length corresponding to each set of data acquired in real time (the UAV's raw positioning data and the ground-based system's VRS data), the multiple first solution results can be filtered. The filtering conditions include the baseline length being greater than a first preset threshold, determining the time information corresponding to the first solution results with a baseline length greater than the first preset threshold, and combining the time information corresponding to multiple first solution results with a baseline length greater than the first preset threshold to form a time period.

[0069] In this embodiment, the first preset threshold can be set by the user based on the positioning accuracy, for example, it can be set to 20km. In some other embodiments, the first preset threshold can also be automatically set according to the positioning accuracy, which is not limited here.

[0070] In step S140, the state matrix and variance matrix corresponding to the calculation of the fixed solution of PPK within the time period are recorded.

[0071] In this embodiment, when solving the fixed solution of the PPK, it is necessary to perform Kalman filtering on the original positioning data and the VRS data, and the state matrix and variance matrix are the parameters in the Kalman filter.

[0072] In this embodiment, if no fixed solution for PPK is found within the time period, the time period is expanded until a fixed solution for PPK is found, at which point the expansion stops. The state matrix and variance matrix corresponding to the calculation of the fixed solution for PPK within the time period are recorded. In this case, the fixed solution for PPK can be found by expanding the range of the time period, so that PPP can be calculated subsequently based on the state matrix and variance matrix corresponding to the fixed solution for PPK.

[0073] In step S150, PPP is calculated based on the state matrix, the variance matrix, and the error parameters to obtain a second calculation result.

[0074] In this embodiment, the state matrix, the variance matrix, and the error parameters can be solved using PPP solution technology to obtain a second solution result, thereby achieving high-precision positioning.

[0075] In this embodiment, the error parameters are error parameters obtained from the satellite-based server and calculated by the satellite-based server. The error parameters may include orbital errors, and / or clock errors, and / or ionospheric errors, etc.

[0076] Since the state matrix and variance matrix can be used as the initial values ​​of the filtering parameters in the PPP solution process, if the initial values ​​of these two matrices are accurate enough, the convergence time of the PPP solution process can be reduced.

[0077] Specifically, the state matrix and variance matrix can be used as the initial values ​​of the Kalman filter parameters in the PPP solution process. PPP solution is performed on the original positioning data and the VRS data of the ground system to obtain a second solution result. Error correction is applied to the second solution result based on the error parameters. This calculation process is repeated until the accuracy of the second solution result reaches the preset requirement, at which point the calculation process stops. In this embodiment, because the state matrix and variance matrix are sufficiently accurate, the number of repetitions is greatly reduced, thus shortening the convergence time of the PPP solution process.

[0078] In this embodiment, the second solution result includes the fixed solution of PPP. The fixed solution requires locking onto at least five satellites and refers to the PPP solution result (narrow_int) after the carrier phase integer ambiguity has been fixed, with an accuracy ranging from centimeters to millimeters. Whenever carrier phase is used, carrier phase integer ambiguity is unavoidable. Normally, this is equivalent to superimposing a fixed integer on a distance in units of cycles. This ambiguity must be calculated to correctly utilize the phase for differential positioning; this process is called integer ambiguity resolution (AR). After successful PPP resolution, the correct integer ambiguity is obtained, which is called ambiguity fixing. The result obtained by differential positioning using the carrier phase with fixed ambiguity is the fixed solution.

[0079] In this embodiment, the second solution also includes a floating-point solution for PPP. This floating-point solution refers to a solution where the receiver has locked onto only four satellites, but the measurement results are subject to certain uncertainties due to signal interference or other factors. The positioning accuracy of the floating-point solution is greater than 0.5 meters, with horizontal and vertical errors typically ranging from 1 to 3 meters. The positioning accuracy of the floating-point solution is lower than that of the fixed-point solution.

[0080] In step S160, the positioning information of the UAV is determined based on the first solution result and the second solution result.

[0081] In this embodiment, the first solution result and the second solution result can be listed, and one of the solution results can be selected as the positioning information of the UAV based on the positioning accuracy.

[0082] The technical solution of this embodiment can achieve high-precision positioning of UAVs by using the first and second calculation results when the VRS service cannot stably provide differential data. This ensures that centimeter- to sub-meter level positioning accuracy can still be achieved in areas without VRS service coverage, thus meeting the high-precision requirements for spatial location information in UAV inspection missions.

[0083] Figure 2 A flowchart illustrating another UAV positioning method provided by an embodiment of the present invention is shown.

[0084] The drone positioning method includes the following steps:

[0085] In step S210, the raw positioning data of the UAV and the VRS data of the ground system are acquired.

[0086] This step is the same as step S110, and will not be described again here.

[0087] In step S220, the original positioning data is processed by single-point calculation to obtain the single-point positioning trajectory.

[0088] In this embodiment, the single-point solution includes the receiver locking onto only three satellites. At this time, clock errors and atmospheric errors cannot be eliminated, resulting in low positioning accuracy.

[0089] To improve the real-time performance of positioning, this embodiment also requires a judgment process. If the positioning accuracy of the single-point positioning trajectory is considered to meet the requirements, the single-point positioning trajectory can be directly used as the positioning information of the UAV. Otherwise, more accurate positioning needs to be achieved by combining VRS data with the single-point positioning trajectory.

[0090] In step S230, the baseline length of each epoch is calculated based on the original positioning data.

[0091] In this embodiment, the accuracy of a single-point positioning trajectory can be determined by the baseline length of the epoch. The calculation scheme for the baseline length can refer to the scheme in step S130, and will not be repeated here.

[0092] In step S240, it is determined that the baseline length is greater than the second preset threshold or the VRS data meets the preset conditions.

[0093] In this embodiment, the user can manually set the value of the second preset threshold, for example, the second preset threshold can be set to 30km. In some other embodiments, the value of the second preset threshold can also be automatically set based on the positioning accuracy, which is not limited here.

[0094] In this embodiment, when the drone has a long single inspection route or is in a remote area, the VRS system may only cover a part of the area of ​​a single drone flight. It is generally believed that if VRS data is not collected or the VRS data is incomplete, it is impossible to accurately locate the drone using VRS. In this case, it is necessary to combine VRS data (other drone flights, or the area where VRS data can be obtained for a single drone flight) on the basis of the single-point positioning trajectory to achieve more accurate positioning.

[0095] In this embodiment, if the baseline length is greater than the second preset threshold or the VRS data meets the preset conditions (no VRS data was collected or the VRS data is incomplete), then proceed to step S260; if the baseline length is less than or equal to the second preset threshold, or the VRS data does not meet the preset conditions, then it is considered that the accuracy of the single-point positioning trajectory also meets the requirements, and then proceed to step S250.

[0096] In step S250, the single-point positioning trajectory is used as the positioning information of the UAV.

[0097] In step S260, PPK calculation is performed based on the original positioning data and the VRS data to obtain the first calculation result.

[0098] This step is the same as step S120, and will not be described again here.

[0099] In step S270, the first solution result is filtered to determine the time period in the first solution result where the baseline length of the epoch is greater than the first preset threshold.

[0100] This step is the same as step S130, and will not be described again here.

[0101] In step S280, the state matrix and variance matrix corresponding to the calculation of the fixed solution of PPK within the time period are recorded.

[0102] This step is the same as step S140, and will not be repeated here.

[0103] In step S290, PPP is calculated based on the state matrix, the variance matrix, and the error parameters to obtain a second calculation result.

[0104] This step is the same as step S150, and will not be described again here.

[0105] In step S300, the positioning information of the UAV is determined based on the first solution result and the second solution result.

[0106] This step is the same as step S160, and will not be described again here.

[0107] The technical solution of this embodiment can eliminate the need for subsequent calculations when the accuracy of the single-point positioning trajectory meets the requirements. This greatly improves the real-time performance of UAV positioning while maintaining positioning accuracy.

[0108] Figure 3 The diagram shows a flowchart of another UAV positioning method provided by an embodiment of the present invention.

[0109] The drone positioning method includes the following steps:

[0110] In step S410, the raw positioning data of the UAV and the VRS data of the ground system are acquired.

[0111] This step is the same as step S110, and will not be described again here.

[0112] In step S420, PPK calculation is performed based on the original positioning data and the VRS data to obtain the first calculation result.

[0113] This step is the same as step S120, and will not be described again here.

[0114] In step S430, the first solution result is filtered to determine the time period in the first solution result where the baseline length of the epoch is greater than the first preset threshold.

[0115] This step is the same as step S130, and will not be described again here.

[0116] In step S440, the state matrix and variance matrix corresponding to the calculation of the fixed solution of PPK within the time period are recorded.

[0117] This step is the same as step S140, and will not be repeated here.

[0118] In step S450, PPP is calculated based on the state matrix, the variance matrix, and the error parameters to obtain a second calculation result.

[0119] This step is the same as step S150, and will not be described again here.

[0120] In step S460, the overlap between the first solution result and the second solution result is determined.

[0121] In this embodiment, multiple solution results may exist simultaneously within the same time period. For example, a first solution result and a second solution result may exist simultaneously. Alternatively, the first solution result may include both a fixed solution and a floating-point solution for PPK, and the second solution result may include both a fixed solution and a floating-point solution for PPP. Therefore, to further improve positioning accuracy, the first and second solution results occurring within the same time period are treated as overlapping results for subsequent filtering.

[0122] In step S470, the overlapping results are filtered according to accuracy to determine the positioning information of the UAV.

[0123] In this embodiment, the overlapping results can be filtered according to accuracy to further improve the positioning accuracy.

[0124] In this embodiment, the filtering order of various solution results can be defined according to the accuracy level, such as: fixed solution of PPK, fixed solution of PPP, floating-point solution of PPK, floating-point solution of PPP, single-point solution of PPK (arranged from high to low accuracy). The overlapping results can be filtered according to the above arrangement order, and the solution result with the highest accuracy is used as the positioning information of the current time.

[0125] For example, if at a certain time, both the first and second solutions are calculated simultaneously, with the first solution including a fixed PPK solution and a floating-point PPK solution, and the second solution including a fixed PPP solution and a floating-point PPP solution, then according to the above filtering order, the positioning accuracy of the fixed PPK solution > the positioning accuracy of the fixed PPP solution > the positioning accuracy of the floating-point PPK solution > the positioning accuracy of the floating-point PPP solution. Therefore, the fixed PPK solution in the first solution can be used as the positioning information of the UAV at that time.

[0126] The technical solution in this embodiment further improves the positioning accuracy of the UAV by filtering multiple calculation results that exist at the same time.

[0127] In all the above embodiments, after determining the drone's location information, the drone's location information can also be output in a time sequence so that the user can determine the drone's location in real time.

[0128] Figure 4 A schematic diagram of a drone positioning device according to an embodiment of the present invention is shown. This drone positioning device 500 corresponds to the drone positioning method in Embodiment 1, and the drone positioning method in Embodiment 1 is also applicable to this drone positioning device 500, which will not be described again here.

[0129] The UAV positioning device 500 includes an acquisition module 510, a first calculation module 520, a filtering module 530, a recording module 540, a second calculation module 550, and a positioning information determination module 560.

[0130] The acquisition module 510 is used to acquire the raw positioning data of the UAV and the VRS data of the ground system.

[0131] The first calculation module 520 is used to perform PPK calculation based on the original positioning data and the VRS data to obtain a first calculation result, wherein the first calculation result includes a fixed solution and a floating-point solution of PPK.

[0132] The filtering module 530 is used to perform sub-filtering on the first solution result and determine the time period in the first solution result where the baseline length of the epoch is greater than a first preset threshold.

[0133] The recording module 540 is used to record the state matrix and variance matrix corresponding to the fixed solution of PPK during the time period.

[0134] The second solution module 550 is used to perform PPP solution based on the state matrix, the variance matrix, and the error parameters to obtain a second solution result; wherein, the error parameters are the error parameters of the satellite-based solution obtained from the satellite-based server, and the second solution result includes a fixed solution and a floating-point solution of PPP.

[0135] The positioning information determination module 560 is used to determine the positioning information of the UAV based on the first calculation result and the second calculation result.

[0136] Another embodiment of the present invention provides a drone, the drone including a memory and a processor, the memory for storing a computer program, the processor running the computer program to enable the drone to perform the functions of the modules in the above-described drone positioning method or drone positioning device.

[0137] The memory may include a stored program area and a stored data area. The stored program area may store the operating system, applications required for at least one function, etc.; the stored data area may store data created based on the use of the computer device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0138] This invention also provides a computer storage medium for storing the drone positioning method used in the aforementioned drone.

[0139] This invention also provides a computer program product, which includes a computer program / instructions that, when executed by a processor, implement the steps of the above-described UAV positioning method.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, as an alternative implementation, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0141] In addition, the functional modules or units in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0142] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned computer-readable storage medium can include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0143] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for locating unmanned aerial vehicles (UAVs), characterized in that, The method includes: Acquire raw positioning data of the UAV and VRS data of the ground-based system; PPK is calculated based on the original positioning data and the VRS data to obtain a first calculation result, wherein the first calculation result includes a fixed solution and a floating-point solution of PPK; The first solution result is filtered to determine the time period in the first solution result where the baseline length of the epoch is greater than the first preset threshold. Record the state matrix and variance matrix corresponding to the fixed solution of PPK within the time period; Based on the state matrix, the variance matrix, and the error parameters, PPP is calculated to obtain a second solution result; wherein, the error parameters are the error parameters of the satellite-based calculation obtained from the satellite-based server, and the second solution result includes a fixed solution and a floating-point solution of PPP; The location information of the UAV is determined based on the first solution result and the second solution result.

2. The UAV positioning method according to claim 1, characterized in that, Before performing PPK calculation based on the original positioning data and the VRS data to obtain the first calculation result, the method further includes: The original positioning data is processed by single-point calculation to obtain the single-point positioning trajectory; The baseline length for each epoch is calculated based on the original positioning data; If the baseline length is greater than the second preset threshold or the VRS data meets the preset conditions, then the following steps are executed: "Calculate PPK based on the original positioning data and the VRS data to obtain a first calculation result, wherein the first calculation result includes a fixed solution and a floating-point solution of PPK" and all subsequent steps. Otherwise, the single-point positioning trajectory will be used as the positioning information of the UAV.

3. The UAV positioning method according to claim 1, characterized in that, The step of determining the location information of the UAV based on the first solution result and the second solution result includes: Determine the overlap between the first solution result and the second solution result; The overlapping results are filtered according to accuracy to determine the positioning information of the UAV.

4. The UAV positioning method according to claim 1, characterized in that, Also includes: The location information of the UAV is output according to the time sequence.

5. The UAV positioning method according to claim 1, characterized in that, The second solution result obtained by performing PPP calculation based on the state matrix, the variance matrix, and the error parameters includes: Using the state matrix and the variance matrix as initial values ​​for Kalman filtering, the initial values ​​are corrected based on the error parameters to obtain the second solution result.

6. The UAV positioning method according to claim 1, characterized in that, The state matrix and variance matrix corresponding to the fixed solution of PPK within the time period are recorded, including: If no fixed solution is found within the time period, the time period is extended until a fixed solution for PPK is found and the extension is stopped. Record the state matrix and variance matrix corresponding to the fixed solution of PPK within the time period.

7. A drone positioning device, characterized in that, The device includes: The acquisition module is used to acquire the raw positioning data of the UAV and the VRS data of the ground-based system; The first calculation module is used to perform PPK calculation based on the original positioning data and the VRS data to obtain a first calculation result, wherein the first calculation result includes a fixed solution and a floating-point solution of PPK; The filtering module is used to perform sub-filtering on the first solution result and determine the time period in the first solution result where the baseline length of the epoch is greater than a first preset threshold. The recording module is used to record the state matrix and variance matrix corresponding to the calculation of the fixed solution of PPK within the time period. The second solution module is used to perform PPP solution based on the state matrix, the variance matrix, and the error parameters to obtain a second solution result; wherein, the error parameters are the error parameters of the satellite-based solution obtained from the satellite-based server, and the second solution result includes a fixed solution and a floating-point solution of PPP; The positioning information determination module is used to determine the positioning information of the UAV based on the first calculation result and the second calculation result.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1-6.

9. A drone, characterized in that, The drone includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to enable the drone to perform the drone positioning method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores the computer program used in the UAV of claim 9.

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