A High-Precision Positioning and Correction Method and System for Unmanned Aerial Vehicles Based on BeiDou Tri-Frequency Signals

Abstract

This application provides a method and system for high-precision positioning and correction of unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals. The method first receives a tri-frequency signal group transmitted by the BeiDou satellite navigation system. Then, utilizing the dispersion characteristics of the tri-frequency signal group propagating in the ionosphere, a real-time correction value for ionospheric delay error is generated. Next, multipath effect characteristic data caused by terrain undulations during agricultural surveying operations is acquired. Then, the multipath effect characteristic data is coupled with the real-time correction value to construct a dynamic error suppression model. Finally, the dynamic error suppression model is used to correct the original positioning trajectory of the UAV point by point to output a high-precision positioning result. The technical solution provided by this application not only effectively solves the combined interference problem of ionospheric delay and multipath effect, but also achieves high-precision positioning of UAVs in complex agricultural terrain environments, significantly improving the accuracy and environmental adaptability of the positioning results.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) navigation and positioning technology, and in particular to a high-precision positioning and correction method and system for UAVs based on BeiDou tri-frequency signals. Background Technology

[0002] In low-altitude complex terrain operations such as agricultural surveying, for example, when conducting agricultural remote sensing and surveying in hilly and mountainous terrain, the flight trajectory of UAVs is easily affected by the combined interference of ionospheric delay and multipath effect, which poses a severe challenge to positioning accuracy. There is an urgent need for highly reliable positioning correction technology that can suppress the above two types of errors at the same time.

[0003] Current technical solutions typically employ a combination of BeiDou dual-frequency signals and Kalman filtering for processing. This approach constructs an ionospheric delay correction model using dual-frequency observations and utilizes filtering algorithms to smooth the positioning trajectory, aiming to obtain a more stable positioning output.

[0004] However, the existing scheme has obvious drawbacks. First, the combination of dual-frequency signals is difficult to completely eliminate the influence of higher-order ionospheric errors, especially during periods of intense ionospheric activity. Second, the standard filtering algorithm is not adaptable to strong multipath reflection signals caused by terrain undulations, resulting in significant deviations in the corrected trajectory in complex terrain areas, which cannot meet the stringent requirements of agricultural high-precision surveying for absolute positioning accuracy. Summary of the Invention

[0005] This application provides a high-precision positioning and correction method and system for UAVs based on BeiDou tri-frequency signals, which solves the problems in the prior art that the combination of dual-frequency signals is difficult to completely eliminate higher-order ionospheric errors, and that the standard filtering algorithm is not adaptable to strong multipath reflection signals caused by terrain undulations.

[0006] Firstly, this application provides a high-precision positioning and correction method for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals, including:

[0007] Receives three-frequency signal groups transmitted by the BeiDou Navigation Satellite System;

[0008] By utilizing the dispersion characteristics of the three-frequency signal group as it propagates in the ionosphere, a real-time correction amount for the ionospheric delay error is generated.

[0009] To acquire multipath effect characteristic data caused by terrain undulations during agricultural surveying operations using drones;

[0010] The multipath effect feature data is coupled with the real-time correction amount to construct a dynamic error suppression model;

[0011] The dynamic error suppression model is used to correct the original positioning trajectory of the UAV point by point, so as to output the high-precision positioning result of the UAV.

[0012] Optionally, the system receives three frequency signal groups transmitted by the BeiDou Navigation Satellite System, including:

[0013] The drone simultaneously receives satellite signals at three frequencies via its onboard navigation receiver, including B1C, B2a, and B2b satellite signals.

[0014] Carrier stripping is performed on the satellite signals at each frequency to separate the corresponding signal components;

[0015] The signal components that have undergone carrier stripping are time-synchronized and aligned to output a time-synchronized tri-frequency signal group.

[0016] Optionally, by utilizing the dispersion characteristics of the three-frequency signal group propagating in the ionosphere, a real-time correction amount for the ionospheric delay error is generated, including:

[0017] Based on the difference in propagation speed between different frequency signals in the three-frequency signal group, a correspondence between frequency and ionospheric delay is established.

[0018] Calculate the first delay difference between the B1C satellite signal and the B2a satellite signal, and the second delay difference between the B1C satellite signal and the B2b satellite signal based on the correspondence.

[0019] The first delay difference and the second delay difference are input into the ionospheric error mapping relationship to generate a delay correction amount for the current ionospheric state;

[0020] Based on the satellite geometry distribution at the location of the UAV receiver, the delay correction amount is adjusted by spatial geometric constraints to output the real-time correction amount for ionospheric delay error.

[0021] Optionally, data on the multipath effect characteristics caused by terrain undulations during agricultural mapping operations by UAVs can be acquired, including:

[0022] Acquire digital elevation model data of the UAV's flight area;

[0023] A three-dimensional terrain surface profile is constructed based on the elevation digital model data;

[0024] The distribution of satellite signal reflection intensity on different terrain surfaces was recorded using a signal receiving device mounted on a drone.

[0025] Analyze the correspondence between the reflection intensity distribution and the three-dimensional terrain surface contour, and extract the signal reflection feature patterns caused by terrain undulations;

[0026] Multipath effect feature data is generated based on the signal reflection feature pattern.

[0027] Optionally, the multipath effect feature data is coupled with the real-time correction amount to construct a dynamic error suppression model, including:

[0028] Establish an interaction table between multipath effect characteristic data and real-time correction values;

[0029] The compensation ratio between multipath error and ionospheric delay error under different terrain conditions is determined based on the interaction relationship table.

[0030] Dynamic weight allocation rules are generated based on the aforementioned compensation ratio relationship;

[0031] The dynamic weight allocation rule is applied to the fusion process of multi-path effect feature data and real-time correction amount to form an initial error suppression model;

[0032] The initial error suppression model is iteratively optimized to generate a dynamic error suppression model.

[0033] Optionally, the dynamic weight allocation rule is applied to the fusion process of multi-path effect feature data and real-time correction quantities to form an initial error suppression model, including:

[0034] According to the dynamic weight allocation rule, a first weight value is assigned to the multipath effect feature data, and a second weight value is assigned to the real-time correction amount.

[0035] The multipath effect feature data with the first weight value is weighted and combined with the real-time correction amount with the second weight value.

[0036] Establish an error compensation mapping relationship based on the weighted combination results;

[0037] An initial error suppression model is generated based on the error compensation mapping relationship.

[0038] Optionally, the original positioning trajectory of the UAV is corrected point by point using the dynamic error suppression model to output a high-precision positioning result for the UAV, including:

[0039] Obtain the raw location point sequence, including timestamps, recorded during the drone's flight;

[0040] Each original positioning point in the original positioning point sequence is input into the dynamic error suppression model to obtain the corresponding position adjustment amount;

[0041] The position adjustment amount is applied to the corresponding original positioning point in a preset time sequence to obtain the adjusted positioning point.

[0042] A corrected flight trajectory is generated based on the adjusted positioning points;

[0043] The corrected flight trajectory is used to output high-precision positioning results for the UAV.

[0044] Secondly, this application provides a high-precision positioning and correction system for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals, comprising:

[0045] The receiving module is used to receive the three-frequency signal groups transmitted by the BeiDou satellite navigation system;

[0046] The generation module is used to generate a real-time correction amount for the ionospheric delay error by utilizing the dispersion characteristics of the three-frequency signal group when it propagates in the ionosphere.

[0047] The acquisition module is used to acquire multipath effect feature data caused by terrain undulations during agricultural surveying operations by UAVs;

[0048] A coupling module is used to couple the multipath effect feature data with the real-time correction amount to construct a dynamic error suppression model;

[0049] The correction module is used to correct the original positioning trajectory of the UAV point by point using the dynamic error suppression model, so as to output the high-precision positioning result of the UAV.

[0050] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a high-precision positioning and correction method for unmanned aerial vehicles based on BeiDou tri-frequency signals as described in the first aspect above.

[0051] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a high-precision positioning and correction method for unmanned aerial vehicles based on BeiDou tri-frequency signals as described in the first aspect.

[0052] This application achieves coordinated processing of ionospheric delay error and multipath error by simultaneously utilizing the dispersion characteristics of BeiDou tri-frequency signals and agricultural terrain multipath effect feature data. By coupling multipath effect feature data with real-time ionospheric correction quantities to construct a dynamic error suppression model, this method can effectively cope with the combined interference of the two types of errors in low-altitude complex terrain environments, significantly improving the positioning reliability of UAVs in agricultural surveying scenarios.

[0053] Furthermore, the adjustment values ​​generated by the dynamic error suppression model are sequentially applied to each original positioning point, achieving refined processing of the flight trajectory. This time-series-based point-by-point correction method maintains the continuity and stability of the trajectory, ultimately outputting positioning results with high accuracy and consistency, meeting the stringent requirements of agricultural surveying for absolute positioning accuracy.

[0054] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart of a high-precision positioning and correction method for unmanned aerial vehicles based on BeiDou tri-frequency signals provided in this application is shown;

[0057] Figure 2 A schematic diagram of the structure of a high-precision positioning and correction system for unmanned aerial vehicles based on BeiDou tri-frequency signals provided in this application is shown.

[0058] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0060] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0061] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] Figure 1This application provides a flowchart of a high-precision positioning and correction method for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals, as shown in the flowchart. Figure 1 As shown, the method includes:

[0063] Step 101: Receive the three-frequency signal group transmitted by the BeiDou Navigation Satellite System.

[0064] Optionally, step 101 may specifically include the following steps:

[0065] Step 1011: Simultaneously receive satellite signals at three frequencies via the navigation receiver on the UAV, wherein the three frequencies include: B1C satellite signal, B2a satellite signal and B2b satellite signal;

[0066] Step 1012: Carrier stripping is performed on the satellite signal at each frequency to separate the corresponding signal components;

[0067] Step 1013: Time synchronization alignment is performed on the signal components that have undergone carrier stripping to output a time-synchronized tri-frequency signal group.

[0068] In the above scheme, the BeiDou Navigation Satellite System is a global satellite navigation system built by China, which provides positioning services by transmitting radio signals to the ground through multiple satellites; the three-frequency signal group refers to the set of satellite signals that simultaneously contain three different frequencies: B1C, B2a, and B2b; carrier stripping is a signal processing technique used to remove the high-frequency carrier component from the satellite signals and extract the baseband signal containing navigation information; the signal component after carrier stripping refers to the low-frequency signal containing navigation data and ranging information obtained after removing the carrier; the time-synchronized three-frequency signal group refers to the set of signals that have the same time reference for the three frequencies through time synchronization alignment processing.

[0069] In this scheme, firstly, in step 1011, the navigation receiver on the UAV simultaneously receives satellite signals at three frequencies: B1C, B2a, and B2b. This receiver includes a multi-band antenna and RF front-end circuitry, enabling parallel reception of signals from different frequency bands. Secondly, in step 1012, carrier stripping is performed on the satellite signals of each frequency. Phase-locked loop (PLL) technology is used to track and remove the high-frequency carrier component from the signal, separating the baseband signal component containing navigation messages and pseudocode information. Finally, in step 1013, the processed signal components of the three frequencies are sent to the time synchronization unit. By comparing the time stamps of each signal component, an interpolation algorithm is used to adjust the time deviation, outputting a three-frequency signal group with a unified time reference.

[0070] For example, in UAV mapping operations in farmland area A, a B-type multi-band navigation receiver is used to simultaneously capture the B1C, B2a, and B2b signals transmitted by the BeiDou system. The receiver first receives the radio frequency signals of the three frequency bands simultaneously through its built-in tri-band antenna array. Then, it performs carrier stripping processing on each frequency signal through three parallel processing channels. Finally, a high-precision clock synchronization module is used to time-align the output signals of the three channels, generating a time-synchronized tri-band signal group for subsequent processing.

[0071] This scheme achieves synchronous acquisition and processing of satellite signals at three frequencies through parallel reception and multi-channel processing technology, ensuring that the multi-frequency signal data required for subsequent error correction has a high degree of temporal consistency and integrity, and providing a reliable data foundation for high-precision positioning.

[0072] Step 102: Utilize the dispersion characteristics of the three-frequency signal group as it propagates in the ionosphere to generate a real-time correction amount for the ionospheric delay error.

[0073] Optionally, step 102 may specifically include the following steps:

[0074] Step 1021: Based on the difference in propagation speed between different frequency signals in the three-frequency signal group, establish the correspondence between frequency and ionospheric delay.

[0075] Step 1022: Calculate the first delay difference between the B1C satellite signal and the B2a satellite signal, and the second delay difference between the B1C satellite signal and the B2b satellite signal, based on the correspondence.

[0076] Step 1023: Input the first delay difference and the second delay difference into the ionospheric error mapping relationship to generate a delay correction amount for the current ionospheric state;

[0077] Step 1024: Based on the satellite geometric distribution at the location of the UAV receiver, the delay correction amount is adjusted by spatial geometric constraints to output the real-time correction amount of the ionospheric delay error.

[0078] In the above scheme, dispersion characteristics refer to the physical phenomenon that electromagnetic waves of different frequencies propagate at different speeds in the ionosphere; the real-time correction amount of ionospheric delay error refers to the correction value used to compensate for the time delay deviation generated when the signal passes through the ionosphere; the propagation speed difference describes the speed difference of three frequency signals, B1C, B2a, and B2b, when they are transmitted in the ionosphere; the correspondence between frequency and ionospheric delay amount is the association rule between different frequency signals and corresponding delay levels established by a mathematical model; the first delay difference and the second delay difference represent the measured propagation time difference between signals B1C and B2a, and between signals B1C and B2b, respectively; the ionospheric error mapping relationship is the conversion rule between the pre-established delay difference and the actual ionospheric delay correction amount; the delay correction amount for the current ionospheric state is a specific compensation value calculated based on the real-time ionospheric conditions; the satellite geometric distribution at the location refers to the azimuth structure of each satellite in space as observed from the UAV receiver position; the spatial geometric constraint adjustment is a precision optimization process of the delay correction amount based on the satellite distribution geometric characteristics.

[0079] In this scheme, firstly, step 1021 establishes a correspondence model between frequency and ionospheric delay based on the propagation speed differences between different frequency signals in the three-frequency signal group using a polynomial fitting algorithm. Secondly, step 1022 calculates the first delay difference between the propagation times of signals B1C and B2a, and the second delay difference between the propagation times of signals B1C and B2b, respectively, according to the established correspondence model. Then, step 1023 inputs the calculated first and second delay differences into a pre-calibrated ionospheric error mapping database, and generates a preliminary delay correction amount for the current ionospheric state through table lookup interpolation. Finally, step 1024 combines the satellite geometric distribution observed in real time by the UAV receiver, and uses a weighted least squares algorithm to adjust the preliminary delay correction amount with spatial geometric constraints, outputting a real-time ionospheric delay error correction amount optimized for spatial characteristics.

[0080] Following the specific implementation of the previous step, the obtained time-synchronized three-frequency signal group is used in UAV mapping operations in the farmland area of ​​location A. First, the frequency difference analysis module is used to calculate the propagation delay differences between the three frequency signals, establishing a frequency-delay correspondence model. Then, the delay differences B1C-B2a and B1C-B2b are calculated using the difference calculation unit. These differences are then input into a database containing pre-stored correction values ​​corresponding to different ionospheric states to query the current optimal correction value. Finally, combined with the spatial geometric distribution configuration of the six satellites currently received by the UAV, the correction value is optimized using a geometric accuracy factor to generate the final real-time correction value suitable for the current ionospheric delay error conditions and satellite geometric layout.

[0081] This scheme achieves high-precision real-time estimation and compensation of ionospheric delay error through multi-frequency signal difference analysis and satellite geometric constraint optimization, which significantly improves the accuracy and reliability of UAV positioning in complex ionospheric environments and provides a precise ionospheric error correction basis for subsequent multipath error joint processing.

[0082] Step 103: Obtain feature data of multipath effect caused by terrain undulation during agricultural surveying operations using UAVs.

[0083] Optionally, step 103 may specifically include the following steps:

[0084] Step 1031: Obtain the elevation digital model data of the UAV flight area;

[0085] Step 1032: Construct a three-dimensional terrain surface profile based on the elevation digital model data;

[0086] Step 1033: Record the distribution of satellite signal reflection intensity on different terrain surfaces using the signal receiving device mounted on the UAV;

[0087] Step 1034: Analyze the correspondence between the reflection intensity distribution and the three-dimensional terrain surface contour, and extract the signal reflection feature pattern caused by terrain undulation;

[0088] Step 1035: Generate multipath effect feature data based on the signal reflection feature pattern.

[0089] In the above scheme, multipath effect characteristic data refers to the set of characteristic parameters that record the interference signals generated after satellite signals are reflected by terrain; elevation digital model data is gridded terrain data that records the elevation of the earth's surface in a digital way; three-dimensional terrain surface contour is a three-dimensional morphological model of the earth's surface reconstructed through elevation data; the signal receiving device records satellite signals by collecting the intensity information of direct and reflected satellite signals through dedicated equipment; the reflection intensity distribution of different terrain surfaces is a distribution map describing the change in energy intensity of the signal after reflection on various terrain surfaces; and the signal reflection characteristic pattern is a combination of regular reflection characteristics extracted from the reflected signal.

[0090] In this scheme, firstly, step 1031 uses a terrain mapping system mounted on a UAV to acquire gridded digital elevation model data of the flight area, which includes altitude values ​​at regularly spaced points. Secondly, step 1032 uses a surface reconstruction algorithm to convert the digital elevation model data into a continuous three-dimensional terrain surface profile, generating a digital surface model containing terrain features such as slope and aspect. Then, step 1033 uses a multi-antenna signal receiving device to simultaneously record the signal intensity data of direct satellite signals and signals reflected by the terrain, forming a reflection intensity distribution map of different terrain surfaces. Next, step 1034 analyzes the spatial correspondence between the reflection intensity distribution and the three-dimensional terrain surface profile, and uses a pattern recognition method to extract specific signal reflection feature patterns caused by terrain undulations. Finally, step 1035 digitizes the extracted signal reflection feature patterns to generate a feature dataset describing the characteristics of multipath effects.

[0091] Following the specific implementation of the previous step, after completing the ionospheric correction, the pre-stored 10-meter resolution digital elevation model data for the region is first used to construct a three-dimensional terrain model including features such as ridges and valleys using a surface generation algorithm. Subsequently, a multi-polarized antenna system is activated to continuously record the changes in reflection intensity of the B2a signal on different slope aspects of the terrain surface. The data processing unit performs a comparative analysis between the reflection intensity data and the three-dimensional terrain model, identifying the characteristic pattern of strong reflection in the southern slope area and weak reflection in the northern slope area, ultimately generating a multipath effect feature dataset containing terrain-related reflection characteristics.

[0092] This solution integrates terrain elevation data with real-time signal reflection characteristics to achieve accurate feature extraction of multipath effects caused by terrain. It establishes a correlation model between terrain features and signal reflection, providing a reliable multipath interference feature data foundation for the subsequent construction of a comprehensive error suppression model, and significantly improving the anti-interference capability of UAV positioning in complex terrain environments.

[0093] Step 104: Couple the multipath effect feature data with the real-time correction amount to construct a dynamic error suppression model.

[0094] Optionally, step 104 may specifically include the following steps:

[0095] Step 1041: Establish an interaction table between multipath effect characteristic data and real-time correction quantities;

[0096] Step 1042: Determine the compensation ratio between multipath error and ionospheric delay error under different terrain conditions based on the interaction relationship table;

[0097] Step 1043: Generate dynamic weight allocation rules based on the compensation ratio relationship;

[0098] Step 1044: Apply the dynamic weight allocation rule to the fusion process of multipath effect feature data and real-time correction amount to form an initial error suppression model;

[0099] Step 1044 may specifically include the following steps:

[0100] According to the dynamic weight allocation rule, a first weight value is assigned to the multipath effect feature data, and a second weight value is assigned to the real-time correction value. The multipath effect feature data with the first weight value and the real-time correction value with the second weight value are weighted and combined. An error compensation mapping relationship is established based on the weighted combination result, and an initial error suppression model is generated based on the error compensation mapping relationship.

[0101] Step 1045: Iteratively optimize the initial error suppression model to generate a dynamic error suppression model.

[0102] In the above scheme, the dynamic error suppression model refers to an error compensation mathematical model that can adaptively adjust with changes in the environment; the interaction relationship table is a corresponding table recording the influence relationship between multipath effects and real-time correction quantities; the compensation ratio relationship is a proportional parameter describing the required compensation degree of the two error sources under different conditions; the dynamic weight allocation rule is a calculation rule for allocating compensation weights to different error sources according to real-time environmental conditions; the initial error suppression model is a basic error compensation model generated through preliminary fusion processing; the first weight value and the second weight value are the weight coefficients assigned to the multipath effect feature data and the real-time correction quantity, respectively; the error compensation mapping relationship is a function mapping describing the correspondence between input error features and output compensation quantities.

[0103] In this scheme, firstly, step 1041 establishes an interaction table between multipath effect characteristic data and real-time correction quantities through historical data analysis. This table records the degree of mutual influence between the two error sources under different terrain and ionospheric conditions. Secondly, step 1042 uses regression analysis based on the interaction table to determine the compensation ratio between multipath error and ionospheric delay error under different terrain conditions, forming a correspondence rule between environmental parameters and compensation ratios. Then, step 1043 designs a dynamic weight allocation algorithm based on the compensation ratio relationship, generating a dynamic weight allocation rule that can automatically adjust the weight coefficients according to real-time environmental parameters. Next, step 1044 applies the dynamic weight allocation rule to the fusion process of multipath effect characteristic data and real-time correction quantities: firstly, a first weight value is assigned to the multipath effect characteristic data according to the current environmental parameters, and a second weight value is assigned to the real-time correction quantity; secondly, the weighted two types of data are linearly combined; then, a correspondence between error characteristics and compensation quantities is established based on the combination result; and an initial error suppression model is generated based on this correspondence. Finally, in step 1045, the initial error suppression model is iteratively optimized using the gradient descent algorithm. By continuously adjusting the model parameters, the error compensation accuracy is gradually improved, and a high-precision dynamic error suppression model is finally generated.

[0104] Following the specific implementation of the previous step, based on the generated multipath effect feature dataset, the UAV navigation system first calls the pre-stored error interaction table. According to the terrain features of strong reflection on the south slope and weak reflection on the north slope, and the current ionospheric state, the multipath error compensation weight is determined to be 0.7, and the ionospheric error compensation weight is determined to be 0.3. The system then assigns a first weight value of 0.7 to the terrain-related multipath effect feature data and a second weight value of 0.3 to the real-time correction amount of the ionospheric delay error generated in step 102. Through weighted fusion, an initial error suppression model for hilly terrain is generated. Finally, a parameter optimization algorithm is used to iteratively adjust the model for 20 rounds, forming a dynamic error suppression model that can adapt to changes in the terrain of Hilly Region B.

[0105] This scheme establishes a collaborative processing mechanism for multiple error sources, thereby jointly suppressing ionospheric delay and multipath effects. It also constructs a dynamic error compensation model that can adapt to environmental changes, providing core processing capabilities for high-precision positioning of UAVs in complex environments and significantly improving the positioning stability and reliability in agricultural terrain undulation areas.

[0106] Step 105: The original positioning trajectory of the UAV is corrected point by point using the dynamic error suppression model to output the high-precision positioning result of the UAV.

[0107] Optionally, step 105 may specifically include the following steps:

[0108] Step 1051: Obtain the original positioning point sequence containing timestamps recorded during the drone's flight;

[0109] Step 1052: Input each original positioning point in the original positioning point sequence into the dynamic error suppression model to obtain the corresponding position adjustment amount;

[0110] Step 1053: Apply the position adjustment amount to the corresponding original positioning point in sequence according to the preset time order to obtain the adjusted positioning point.

[0111] Step 1054: Generate the corrected flight trajectory based on the adjusted positioning points;

[0112] Step 1055: Output high-precision positioning results for the UAV based on the corrected flight trajectory.

[0113] In the above scheme, the original positioning trajectory refers to the flight path data obtained by the initial calculation of the UAV navigation system without error correction; the high-precision positioning result of the UAV is the precise position information output after error correction; the original positioning point sequence containing timestamps is a set of original position points with time stamps arranged in chronological order; the corresponding position adjustment amount is the coordinate correction value calculated for each original positioning point; the positioning point after adjustment is the new coordinate point after the position adjustment amount correction; the corrected flight trajectory is the precise flight path formed by connecting all the corrected positioning points.

[0114] In this scheme, firstly, in step 1051, the UAV navigation recording system acquires a sequence of original positioning points, including timestamps, recorded chronologically during flight. This sequence contains the latitude and longitude coordinates of each positioning point and its corresponding timestamp. Secondly, in step 1052, each original positioning point, its corresponding timestamp, and environmental parameters are input into a dynamic error suppression model. The model outputs a corresponding position adjustment based on the terrain features and ionospheric conditions of the current point. Then, in step 1053, each position adjustment is applied sequentially to the corresponding original positioning point, and a coordinate transformation algorithm is used to calculate the corrected new coordinate points. Next, in step 1054, all the corrected positioning points are connected chronologically, and a path smoothing algorithm is used to generate a continuous and stable corrected flight trajectory. Finally, in step 1055, the corrected flight trajectory is combined with the timestamp information to output a high-precision UAV positioning result containing accurate position coordinates and time information.

[0115] Following the specific implementation of the previous step, in the dynamic error suppression model generated in the hilly area B, the UAV system first reads 500 original positioning points with timestamps recorded during the flight. These positioning points are then sequentially input into the dynamic error suppression model, which outputs different position adjustments based on the south / north slope terrain features of each point. The system applies corresponding coordinate corrections to each original positioning point; for example, the coordinates of positioning point number 120 are corrected from (X1, Y1, Z1) to (X1+ΔX, Y1+ΔY, Z1+ΔZ). Connecting all the corrected positioning points forms a smooth flight trajectory, ultimately outputting a high-precision UAV positioning result that meets the accuracy requirements of agricultural surveying.

[0116] This scheme achieves refined processing of the original positioning data through point-by-point error correction and trajectory reconstruction, effectively eliminating positioning deviations caused by ionospheric delay and multipath effects, and outputting flight trajectory data with high accuracy and consistency, meeting the stringent requirements of agricultural high-precision surveying for position accuracy.

[0117] Figure 2 This application provides a schematic diagram of the structure of a high-precision positioning and correction system for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals, as shown below. Figure 2 As shown, the system includes:

[0118] Receiver module 21 is used to receive the three-frequency signal group transmitted by the BeiDou satellite navigation system;

[0119] The generation module 22 is used to generate a real-time correction amount for the ionospheric delay error by utilizing the dispersion characteristics of the three-frequency signal group when it propagates in the ionosphere.

[0120] Module 23 is used to acquire feature data of multipath effects caused by terrain undulations during agricultural surveying operations by UAVs;

[0121] The coupling module 24 is used to couple the multipath effect feature data with the real-time correction amount to construct a dynamic error suppression model;

[0122] The correction module 25 is used to correct the original positioning trajectory of the UAV point by point using the dynamic error suppression model, so as to output the high-precision positioning result of the UAV.

[0123] Figure 2 The aforementioned high-precision positioning and correction system for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals can perform... Figure 1The implementation principle and technical effects of the high-precision positioning and correction method for UAVs based on BeiDou tri-frequency signals described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the high-precision positioning and correction system for UAVs based on BeiDou tri-frequency signals in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0124] In one possible design, Figure 2 The high-precision positioning and correction system for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals, as shown in the embodiment, can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0125] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0126] The processing component 32 is used for the above Figure 1 The embodiment describes a high-precision positioning and correction method for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals.

[0127] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0128] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0129] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0130] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0131] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0132] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0133] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a high-precision positioning and correction method for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals.

[0134] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0135] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0136] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A high-precision positioning and correction method for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals, characterized in that, include: Receives three-frequency signal groups transmitted by the BeiDou Navigation Satellite System; By utilizing the dispersion characteristics of the three-frequency signal group as it propagates in the ionosphere, a real-time correction amount for the ionospheric delay error is generated. To acquire multipath effect characteristic data caused by terrain undulations during agricultural surveying operations using drones; The multipath effect feature data is coupled with the real-time correction amount to construct a dynamic error suppression model; The dynamic error suppression model is used to correct the original positioning trajectory of the UAV point by point, so as to output the high-precision positioning result of the UAV.

2. The method according to claim 1, characterized in that, Receiving the three-frequency signal group transmitted by the BeiDou Navigation Satellite System, including: The drone simultaneously receives satellite signals at three frequencies via its onboard navigation receiver, including B1C, B2a, and B2b satellite signals. Carrier stripping is performed on the satellite signals at each frequency to separate the corresponding signal components; The signal components that have undergone carrier stripping are time-synchronized and aligned to output a time-synchronized tri-frequency signal group.

3. The method according to claim 1, characterized in that, Utilizing the dispersion characteristics of the three-frequency signal group propagating in the ionosphere, a real-time correction amount for the ionospheric delay error is generated, including: Based on the difference in propagation speed between different frequency signals in the three-frequency signal group, a correspondence between frequency and ionospheric delay is established. Calculate the first delay difference between the B1C satellite signal and the B2a satellite signal, and the second delay difference between the B1C satellite signal and the B2b satellite signal based on the correspondence. The first delay difference and the second delay difference are input into the ionospheric error mapping relationship to generate a delay correction amount for the current ionospheric state; Based on the satellite geometry distribution at the location of the UAV receiver, the delay correction amount is adjusted by spatial geometric constraints to output the real-time correction amount for ionospheric delay error.

4. The method according to claim 1, characterized in that, Acquire multipath effect characteristic data caused by terrain undulations during UAV agricultural surveying operations, including: Acquire digital elevation model data of the UAV's flight area; A three-dimensional terrain surface profile is constructed based on the elevation digital model data; The distribution of satellite signal reflection intensity on different terrain surfaces was recorded using a signal receiving device mounted on a drone. Analyze the correspondence between the reflection intensity distribution and the three-dimensional terrain surface contour, and extract the signal reflection feature patterns caused by terrain undulations; Multipath effect feature data is generated based on the signal reflection feature pattern.

5. The method according to claim 1, characterized in that, The multipath effect feature data is coupled with the real-time correction amount to construct a dynamic error suppression model, including: Establish an interaction table between multipath effect characteristic data and real-time correction values; The compensation ratio between multipath error and ionospheric delay error under different terrain conditions is determined based on the interaction relationship table. Dynamic weight allocation rules are generated based on the aforementioned compensation ratio relationship; The dynamic weight allocation rule is applied to the fusion process of multi-path effect feature data and real-time correction amount to form an initial error suppression model; The initial error suppression model is iteratively optimized to generate a dynamic error suppression model.

6. The method according to claim 5, characterized in that, The dynamic weight allocation rule is applied to the fusion process of multi-path effect feature data and real-time correction quantities to form an initial error suppression model, including: According to the dynamic weight allocation rule, a first weight value is assigned to the multipath effect feature data, and a second weight value is assigned to the real-time correction amount. The multipath effect feature data with the first weight value is weighted and combined with the real-time correction amount with the second weight value. Establish an error compensation mapping relationship based on the weighted combination results; An initial error suppression model is generated based on the error compensation mapping relationship.

7. The method according to claim 1, characterized in that, The dynamic error suppression model is used to correct the original positioning trajectory of the UAV point by point to output a high-precision positioning result for the UAV, including: Obtain the raw location point sequence, including timestamps, recorded during the drone's flight; Each original positioning point in the original positioning point sequence is input into the dynamic error suppression model to obtain the corresponding position adjustment amount; The position adjustment amount is applied to the corresponding original positioning point in a preset time sequence to obtain the adjusted positioning point. A corrected flight trajectory is generated based on the adjusted positioning points; The corrected flight trajectory is used to output high-precision positioning results for the UAV.

8. A high-precision positioning and correction system for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals, characterized in that, include: The receiving module is used to receive the three-frequency signal groups transmitted by the BeiDou satellite navigation system; The generation module is used to generate a real-time correction amount for the ionospheric delay error by utilizing the dispersion characteristics of the three-frequency signal group when it propagates in the ionosphere. The acquisition module is used to acquire multipath effect feature data caused by terrain undulations during UAV agricultural surveying operations; A coupling module is used to couple the multipath effect feature data with the real-time correction amount to construct a dynamic error suppression model; The correction module is used to correct the original positioning trajectory of the UAV point by point using the dynamic error suppression model, so as to output the high-precision positioning result of the UAV.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the high-precision positioning and correction method for UAVs based on BeiDou tri-frequency signals as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a high-precision positioning and correction method for unmanned aerial vehicles (UAVs) based on BeiDou tri-frequency signals as described in any one of claims 1 to 7.

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