Unmanned aerial vehicle radio communication positioning method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicle positioning, and provides an unmanned aerial vehicle radio communication positioning method and system, and the method comprises the following steps: obtaining target data when a first radio positioning signal of an unmanned aerial vehicle is in a first interference state; the first moment is the previous moment of the current moment; determining a position range of the unmanned aerial vehicle at the current moment according to the target data; determining whether the first radio positioning signal is in a second interference state according to the first radio positioning signal and the position range of the unmanned aerial vehicle; the signal interference degree corresponding to the second interference state is greater than the signal interference degree corresponding to the first interference state; and when the first radio positioning signal is in the second interference state, determining target position information of the unmanned aerial vehicle at the current moment according to the first radio positioning signal and the position range of the unmanned aerial vehicle. The accuracy of unmanned aerial vehicle positioning can be improved.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) positioning technology, and in particular to a UAV radio communication positioning method and system. Background Technology

[0002] In modern industrial production and infrastructure maintenance, drones are widely used for tasks such as power line inspection. To ensure the smooth execution of these tasks, drones require accurate and stable self-position information.

[0003] Traditional radio communication positioning methods perform well in open environments, but their positioning accuracy and stability are severely challenged when UAVs enter specific areas with complex electromagnetic environments and limited signal propagation paths. Particularly in industrial parks with numerous highly reflective metal structures, the multipath propagation effect of radio signals significantly interferes with the quality of positioning signals. Simultaneously, Global Navigation Satellite System (GNSS) signals may become ineffective due to obstruction, making it difficult for UAVs to obtain reliable position data, thus affecting their autonomous operation capabilities and mission execution efficiency. Summary of the Invention

[0004] This application provides a method and system for wireless communication positioning of unmanned aerial vehicles (UAVs), which can improve the accuracy of UAV positioning.

[0005] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, a method for unmanned aerial vehicle (UAV) radio communication positioning is provided, comprising the following steps: acquiring target data when the first radio positioning signal of the UAV is in a first interference state; the target data includes the first IMU data of the UAV at the current moment, the first environmental image of the UAV at the current moment, the first position information of the UAV at the first moment, and the second environmental image of the UAV at the first moment; the first moment is the previous moment of the current moment; determining the position range of the UAV at the current moment based on the target data; determining whether the first radio positioning signal is in a second interference state based on the first radio positioning signal and the position range of the UAV; the signal interference level corresponding to the second interference state is greater than the signal interference level corresponding to the first interference state; and determining the target position information of the UAV at the current moment based on the first radio positioning signal and the position range of the UAV when the first radio positioning signal is in the second interference state.

[0006] Optionally, before acquiring the target data, the method further includes: acquiring first location information and a first radio positioning signal; determining second location information of the UAV at the current moment based on the first radio positioning signal; determining a first moving speed of the UAV based on the first location information, the second location information, the current moment, and the first moment; and determining that the first radio positioning signal of the UAV is in a first interference state at the current moment if the first moving speed is greater than a first threshold, wherein the first threshold is the maximum moving speed of the UAV.

[0007] Optionally, the location range of the UAV at the current moment is determined based on the target data, including: determining the third location information of the UAV at the current moment based on the first IMU data and the first location information; determining the movement distance of the UAV based on the first environmental image and the second environmental image; and using a sphere with the third location information as the center and the movement distance as the radius as the location range.

[0008] Optionally, the third location information of the UAV at the current moment is determined based on the first IMU data and the first location information, including: loading an extended Kalman filter or an unscented Kalman filter; and processing the first IMU data and the first location information based on the extended Kalman filter or the unscented Kalman filter to obtain the third location information.

[0009] Optionally, determining the movement distance of the UAV based on the first environmental image and the second environmental image includes: determining the first pixel coordinates of the target pixel in the second environmental image; determining the second pixel coordinates of the target pixel in the first environmental image; and determining the movement distance based on the distance between the first pixel coordinates and the second pixel coordinates.

[0010] Optionally, determining whether the first radio positioning signal is in a second interference state based on the first radio positioning signal and the location range of the UAV includes: comparing the second location information with the location range to obtain a comparison result; if the comparison result indicates that the second location information is outside the location range, determining that the first radio positioning signal is in a second interference state; if the comparison result indicates that the second location information is within the location range, determining that the first radio positioning signal is not in a second interference state.

[0011] Optionally, determining the target location information of the UAV at the current moment based on the first radio positioning signal and the UAV's location range includes: obtaining a first weight value and a second weight value; the sum of the first weight value and the second weight value is 1, and the first weight value is less than the second weight value; determining the fourth location information by multiplying the first weight value by each coordinate value in the second location information; determining the fifth location information by multiplying the second weight value by each coordinate value in the third location information; the third location information is determined based on the first IMU data and the first location information; and determining the target location information by summing the corresponding coordinate values ​​of the fourth location information and the fifth location information.

[0012] Optionally, the drone is equipped with a barometric pressure sensor to acquire the drone's first IMU data, including: acquiring the drone's raw IMU data at the current moment, the barometric pressure sensor's first barometric pressure data at the current moment, and the barometric pressure sensor's second barometric pressure data at the first moment; and determining the first IMU data based on the raw IMU data, the first barometric pressure data, and the second barometric pressure data.

[0013] Optionally, determining the first IMU data based on the original IMU data, the first atmospheric pressure data, and the second atmospheric pressure data includes: obtaining a preset correspondence; the preset correspondence includes a one-to-one correspondence between multiple atmospheric pressure difference ranges and multiple atmospheric pressure adjustment coefficients; determining the absolute value of the difference between the first atmospheric pressure data and the second atmospheric pressure data as the atmospheric pressure difference; determining the atmospheric pressure adjustment coefficient corresponding to the atmospheric pressure difference range in the preset correspondence as the target atmospheric pressure adjustment coefficient; and determining the first IMU data based on the target atmospheric pressure adjustment coefficient and the original IMU data.

[0014] Secondly, a UAV radio communication positioning system is provided, comprising: an acquisition device and a processing device; the acquisition device is configured to acquire target data when a first radio positioning signal of the UAV is in a first interference state; the target data includes first IMU data of the UAV at the current moment, a first environmental image of the UAV at the current moment, first position information of the UAV at the first moment, and a second environmental image of the UAV at the first moment; the first moment is the previous moment of the current moment; the processing device is configured to determine the position range of the UAV at the current moment based on the target data; the processing device is further configured to determine whether the first radio positioning signal is in a second interference state based on the first radio positioning signal and the position range of the UAV; the signal interference level corresponding to the second interference state is greater than the signal interference level corresponding to the first interference state; the processing device is further configured to determine the target position information of the UAV at the current moment based on the first radio positioning signal and the position range of the UAV when the first radio positioning signal is in a second interference state.

[0015] The UAV radio communication positioning method disclosed in this application acquires target data, including first IMU data, first environmental image, first location information, and second environmental image, when the UAV's first radio positioning signal is in a first interference state. This method uses this target data to determine the UAV's current location range and, based on the first radio positioning signal and this location range, determines whether the first radio positioning signal is in a second interference state (i.e., a more severe interference state). After confirming that the signal is in a second interference state, the method further combines the first radio positioning signal and the UAV's location range to determine the UAV's target location information at the current moment.

[0016] Through the above technical solution, this application effectively solves the problem in the prior art where UAVs in areas with complex electromagnetic environments and limited signal propagation paths are susceptible to interference from multipath propagation effects, leading to decreased positioning accuracy and stability, and difficulty in obtaining reliable location data. Specifically, when radio positioning signals are interfered with, this application no longer relies solely on the interfered radio signals for positioning, but introduces multiple information sources such as IMU data and environmental images. Using IMU data and environmental images, the motion state and location range of the UAV can be estimated more accurately, providing more reliable constraints for subsequent positioning. When the degree of radio signal interference further intensifies, this application can identify this more severe interference state and, in this case, correct and optimize the final positioning result by fusing radio positioning signals and the location range determined based on multi-source information. This strategy of multi-source information fusion and graded interference judgment enables UAVs to obtain stable and high-precision location information even when facing challenges such as complex multipath propagation and signal blockage, significantly improving the autonomous operation capability and task execution efficiency of UAVs in tasks such as power line inspection, thereby enhancing the accuracy of UAV positioning. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a UAV radio communication positioning method provided in this application; Figure 2 A flowchart illustrating yet another UAV radio communication positioning method provided in this application; Figure 3 This is a schematic diagram of the architecture of a UAV radio communication positioning system provided in this application. Detailed Implementation

[0018] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application 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 this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0020] Traditional UAV radio communication positioning methods suffer from significant degradation in positioning accuracy and stability in complex electromagnetic environments and areas with limited signal propagation paths, such as industrial parks. Particularly in industrial parks with numerous highly reflective metal structures, the multipath propagation effect of radio signals significantly interferes with the quality of positioning signals. Furthermore, Global Navigation Satellite System (GNSS) signals may become ineffective due to obstruction, making it difficult for UAVs to obtain reliable position data and thus impacting their autonomous operation capabilities and mission execution efficiency.

[0021] In response, this application proposes a method for unmanned aerial vehicle (UAV) radio communication positioning, comprising the following steps: Under the condition that the first radio positioning signal of the UAV is in the first interference state, target data is acquired; the target data includes the first IMU data of the UAV at the current moment, the first environmental image of the UAV at the current moment, the first position information of the UAV at the first moment, and the second environmental image of the UAV at the first moment; the first moment is the moment before the current moment.

[0022] Determine the location range of the drone at the current moment based on the target data.

[0023] Based on the first radio positioning signal and the location range of the UAV, it is determined whether the first radio positioning signal is in a second interference state; the signal interference level corresponding to the second interference state is greater than the signal interference level corresponding to the first interference state.

[0024] When the first radio positioning signal is under a second interference state, the target location information of the UAV at the current moment is determined based on the first radio positioning signal and the UAV's location range.

[0025] This application effectively improves the positioning accuracy and stability of UAVs in complex electromagnetic environments by introducing multi-source data fusion and interference state classification processing mechanisms. By acquiring target data such as UAV IMU data, environmental images, and historical location information, the location range of the UAV can be estimated more accurately. Furthermore, classification processing based on the degree of interference with radio positioning signals ensures that reliable target location information can still be provided even under severe signal interference, effectively solving the problem of positioning failure in complex environments using traditional methods.

[0026] To better understand the UAV radio communication positioning method proposed in this application, the following will provide a detailed explanation of some key terms and implementation environments involved.

[0027] "Unmanned aerial vehicle" refers to an unmanned aerial vehicle controlled by radio remote control equipment or its own programs. It is usually equipped with various sensors and communication modules to perform specific tasks.

[0028] "First radio positioning signal" refers to the radio signal used by the UAV for its own positioning, such as signals from GNSS satellites, radio signals from ground base stations, or UWB (ultra-wideband) positioning signals.

[0029] "First interference state" and "second interference state" are levels used to describe the degree of interference to radio positioning signals. The interference level corresponding to the "second interference state" is greater than that corresponding to the "first interference state," indicating that the reliability of radio positioning signals is lower under the second interference state, requiring a more refined positioning strategy.

[0030] "IMU data" refers to the data output by the Inertial Measurement Unit, which typically includes information such as the angular velocity and acceleration of the UAV, and is used to estimate the attitude and motion state of the UAV.

[0031] "Environmental imagery" refers to image data of the surrounding environment acquired by cameras or other imaging sensors mounted on a drone, which can be used for technologies such as visual odometry or SLAM (simultaneous localization and mapping).

[0032] "First moment" and "current moment" represent two consecutive points in time, where the first moment is the moment before the current moment, used to describe the state of the drone at different points in time.

[0033] "Location information" refers to the spatial coordinates of a drone at a specific moment, such as latitude, longitude, and altitude.

[0034] "Location range" refers to an estimated area that includes the current true location of the drone, usually represented in the form of a sphere, ellipsoid, or polygon.

[0035] The implementation environment of this application is typically the area where the UAV performs its mission, such as power line inspection or industrial park patrol. These areas may contain complex electromagnetic environments, such as highly reflective metal structures and sources of electromagnetic interference, all of which can adversely affect radio positioning signals.

[0036] This application proposes a radio communication positioning method for unmanned aerial vehicles (UAVs). Its core lies in improving the positioning accuracy and stability of UAVs in complex electromagnetic environments through multi-source data fusion and hierarchical interference processing. Figure 1 As shown, the method includes the following steps: S101. Acquire target data when the first radio positioning signal of the UAV is under first interference.

[0037] The target data includes the UAV's first IMU data at the current moment, the UAV's first environmental image at the current moment, the UAV's first position information at the first moment, and the UAV's second environmental image at the first moment. The first moment is the moment before the current moment.

[0038] For example, a drone can be equipped with an inertial measurement unit (IMU) to collect its angular velocity and acceleration information in real time; this data constitutes the first IMU data. Simultaneously, the drone can carry one or more cameras to periodically capture images of its surrounding environment; these images are recorded as a first environmental image at the current moment and as a second environmental image at a later moment. The drone can also acquire its initial position information at any given moment through its own positioning system (such as a GNSS receiver or radio positioning module). This data can be synchronously collected and stored through the drone's internal data acquisition module.

[0039] S102. Determine the location range of the UAV at the current moment based on the acquired target data.

[0040] For example, using the first IMU data and the first position information at the first moment, an inertial navigation algorithm (such as dead reckoning) can be used to estimate the drone's approximate position at the current moment. Simultaneously, using the first and second environmental images, visual odometry can be used to estimate the drone's relative motion between the two moments, thus obtaining the distance traveled. Using the estimated approximate position as the center and the travel distance as the radius, a spherical region can be constructed as the range of the drone's position at the current moment. This range represents the possible area of ​​the drone's current position, and its size reflects the uncertainty of the positioning.

[0041] S103. Determine whether the first radio positioning signal is under a second interference state based on the first radio positioning signal and the location range of the UAV.

[0042] The signal interference level corresponding to the second interference state is greater than that corresponding to the first interference state.

[0043] For example, a current location of the UAV provided by a radio positioning system (e.g., a location calculated from GNSS signals) can be calculated using a first radio positioning signal. This radio positioning location is then compared to a previously determined range of UAV locations. If the radio positioning location falls outside this range, the first radio positioning signal is considered to be in a second interference state, indicating that the error of the radio positioning signal has exceeded an acceptable range and the signal interference level is high. Conversely, if the radio positioning location falls within the range, the first radio positioning signal is considered not to be in a second interference state.

[0044] S104. When the first radio positioning signal is in a second interference state, determine the target position information of the UAV at the current moment based on the first radio positioning signal and the UAV's position range.

[0045] For example, if the first radio positioning signal is determined to be in a second interference state, it indicates that the reliability of the radio positioning signal is low and cannot be directly used as the final positioning result. In this case, a fusion strategy can be adopted to weight and fuse the position information provided by the radio positioning signal with the position information estimated based on the IMU. A lower weight value can be assigned to the radio positioning signal, while a higher weight value can be assigned to the position information estimated based on the IMU. Through this weighted fusion, a more reliable target position information of the UAV at the current moment can be obtained, thus providing a relatively accurate positioning result even under severe radio signal interference.

[0046] The UAV radio communication positioning method proposed in this application acquires multi-source target data, including first IMU data, first environmental image, first position information at a first moment, and second environmental image, when the UAV's first radio positioning signal is under a first interference state. This data provides rich information for the subsequent positioning process. Then, based on this target data, the UAV's current position range can be determined; this range is an estimate of the UAV's current position, reflecting its possible location area. Next, by comparing the position calculated from the first radio positioning signal with this position range, it can be determined whether the first radio positioning signal is under a more severe second interference state. When the signal is indeed under a second interference state, it indicates that traditional radio positioning methods are unreliable. In this case, this application determines the UAV's target position information at the current moment through a fusion strategy based on the first radio positioning signal and the UAV's position range. This method effectively combines the advantages of inertial navigation, visual odometry, and radio positioning. When radio signals are interfered with, it can utilize other reliable data sources for auxiliary positioning, thereby significantly improving the positioning accuracy and stability of UAVs in complex electromagnetic environments.

[0047] Compared with traditional UAV radio communication positioning methods, this application has significant advantages and innovations. Traditional methods often struggle to provide stable and reliable positioning information in complex electromagnetic environments, such as multipath propagation caused by highly reflective metal structures in industrial parks and GNSS signal blockage, leading to decreased UAV operational efficiency and safety. The core innovation of this application lies in the introduction of a multi-source data fusion and hierarchical interference state processing mechanism. By acquiring target data such as UAV IMU data, environmental images, and historical location information, this application can more comprehensively perceive the UAV's motion state and surrounding environment. Based on this, this application can determine the UAV's current position range according to this multi-source data, providing a foundation for subsequent interference judgment and positioning fusion. More importantly, this application can intelligently determine whether the signal is in a more severe second interference state by comparing the first radio positioning signal with the position range. When the signal is in a second interference state, this application no longer blindly relies on the interfered radio signal, but instead uses a weighted fusion or optimization strategy by combining the radio positioning signal and the position range to determine the UAV's target position information at the current moment. This hierarchical processing and multi-source fusion strategy enables the application to provide relatively accurate and stable positioning results even under severe radio signal interference, effectively solving the problem of positioning failure in complex environments using traditional methods. For example, in power line inspection missions, when a UAV flies near a large substation or high-voltage tower, electromagnetic interference may cause GNSS signal loss. In this case, the application can utilize IMU data and visual image data to assist positioning, and optimize it by combining the interfered radio signals, thereby ensuring that the UAV can continue to accurately perform the inspection mission and avoid work interruption or safety risks caused by positioning failure. Therefore, the application significantly improves the robustness and reliability of UAV positioning in complex electromagnetic environments, and has significant technological advancements.

[0048] In one design, the method further includes, before acquiring the target data: Acquire first location information and first radio positioning signal; determine second location information of the UAV at the current moment based on the first radio positioning signal; determine first moving speed of the UAV based on the first location information, second location information, current moment and first moment; if the first moving speed is greater than a first threshold, determine that the first radio positioning signal of the UAV is in a first interference state at the current moment, where the first threshold is the maximum moving speed of the UAV.

[0049] Specifically, when a drone performs positioning operations, it first needs to acquire first location information and first radio positioning signal. The first location information can be understood as the known or estimated location of the drone at a given moment, while the first radio positioning signal is the radio positioning signal data received by the drone at the current moment. Further, based on the first radio positioning signal, the drone's second location information at the current moment can be preliminarily determined. The second location information is usually calculated directly based on the raw measurement results of the radio signals (e.g., satellite navigation systems such as GPS and BeiDou, or wireless local area network positioning systems such as UWB and Wi-Fi), and may be subject to certain errors due to environmental interference.

[0050] Based on this, by comparing the drone's position information at different times, its movement speed can be determined. Specifically, based on the first position information, the second position information, the current time, and the first time, the drone's displacement between the two times can be calculated, thereby determining the drone's first movement speed. The first movement speed reflects the drone's motion state over a short period of time. The first threshold is a preset critical speed value used to determine whether the drone's motion state might cause radio signal interference. When the drone's first movement speed is greater than the first threshold, that is, when the drone's first movement speed is greater than its maximum movement speed, it can be determined that the drone's first radio positioning signal is in a first interference state at the current time.

[0051] This application's solution effectively addresses the problem in the basic solution of failing to clearly identify the first radio positioning signal being in a first interference state by introducing a judgment on the drone's movement speed. Specifically, when a drone is in high-speed flight, its received radio positioning signal is highly susceptible to environmental factors (such as multipath effects, signal blockage, atmospheric refraction, etc.) and its own motion (such as Doppler shift), leading to a decrease in signal quality and positioning accuracy, i.e., entering the first interference state. By acquiring the drone's position information at different times (first position information and second position information) and combining it with the time difference (current time and first time) to calculate the drone's first movement speed, the drone's motion state can be objectively assessed. When the calculated first movement speed exceeds a preset first threshold, the system can intelligently determine that the current radio positioning signal is likely in a first interference state. This motion state-based judgment mechanism enables the system to proactively and promptly identify interference in the radio positioning signal, thus providing accurate triggering conditions for subsequently initiating more robust positioning strategies (such as positioning by combining IMU data and environmental images).

[0052] Through the above technical solution, this application can determine whether the first radio positioning signal of a UAV is in a first interference state, avoiding the passive perception or manual judgment of interference state in traditional methods. This judgment mechanism enables the UAV to switch to a more reliable positioning mode in a timely manner when the quality of the radio positioning signal may deteriorate, significantly improving the adaptability and robustness of the positioning method. Especially when the UAV operates in complex environments, this solution can effectively avoid the accumulation of positioning errors caused by radio signal interference, thereby ensuring the continuity and high accuracy of UAV positioning and improving the safety and efficiency of UAV mission execution.

[0053] In some preferred embodiments, a specific example is given below. Assume a drone is performing an inspection task. At a certain moment, the drone's first position information P1 is acquired at a first moment (e.g., t-1 seconds), and its second position information P2 at the current moment (e.g., t seconds) is preliminarily determined using radio positioning signals. By calculating the distance between P1 and P2 and dividing it by the time interval (t - (t-1)), the drone's first moving speed is obtained. For example, if the calculated first moving speed is 25 m / s, and the preset first threshold is 20 m / s, then since 25 m / s is greater than 20 m / s, the system will determine that the drone's first radio positioning signal is in a first interference state at the current moment. Once the first interference state is determined, the system will acquire target data (including first IMU data, first environmental image, etc.) and initiate the subsequent positioning process to ensure that the drone can still obtain accurate target position information even when the radio signal is interfered with.

[0054] In a design, such as Figure 2 As shown, S102 may specifically include the following steps: S201. Determine the third location information of the UAV at the current moment based on the first IMU data and the first location information.

[0055] Specifically, the first IMU data typically includes inertial measurement unit data such as the drone's acceleration and angular velocity, which reflects the drone's motion state over a short period. The first position information refers to the drone's known position at the first moment. By combining this data, the drone's trajectory from the first moment to the current moment can be estimated, thereby deducing the drone's third position information at the current moment. This third position information can be considered as the estimated position of the drone at the current moment based on inertial navigation.

[0056] S202. Determine the movement distance of the UAV based on the first environmental image and the second environmental image.

[0057] The first and second environmental images record the visual information of the environment in which the UAV is located at the current moment and at the first moment, respectively. By performing feature matching and visual odometry processing on these two images, the relative distance traveled by the UAV between these two moments can be accurately calculated. This distance reflects the actual displacement of the UAV between two consecutive moments.

[0058] S203. Use a sphere with its center as the third position information and its radius as the distance it has moved as the position range.

[0059] In practical applications, once the third position information based on inertial measurement and the movement distance based on vision are obtained, a sphere with the third position information as its center and the movement distance as its radius can be defined as the location range of the UAV at the current moment. This sphere represents the spatial region where the UAV may exist at the current moment.

[0060] This application's solution combines inertial measurement unit (IMU) data and visual environment images, comprehensively utilizing the advantages of two different types of sensors. Specifically, first IMU data and first position information are used to provide a preliminary estimate of the UAV's motion, forming a predicted position based on inertial navigation, i.e., third position information. Simultaneously, first and second environmental images, through visual odometry technology, provide a more accurate relative distance traveled by the UAV between two points in time. By using the predicted position from inertial navigation as the center and the distance traveled by visual measurement as the radius, the determined position range can effectively fuse information from both sensors, thus providing a relatively accurate and physically meaningful potential UAV position area even when radio positioning signals are interfered with. This combination effectively compensates for the limitations of a single sensor in specific environments, such as drift that may occur due to long-term integration of IMU data, and the instability of visual odometry when texture is missing or illumination changes drastically.

[0061] Through the above technical solution, this application provides a more accurate and robust method for determining the location range of a UAV. Compared to relying solely on single sensor data for location range estimation, this solution effectively constrains the potential location space of the UAV by fusing inertial measurement and visual information. Therefore, when subsequently determining whether the first radio positioning signal is in a second interference state, a more reliable location range can be used for comparison, thereby improving the accuracy of interference state determination. Furthermore, this method provides more accurate prior information for determining the UAV target location information under severe radio positioning signal interference (second interference state), contributing to improved accuracy and reliability of the final positioning result.

[0062] In one design, the process of determining the third location information of the UAV at the current moment based on the first IMU data and the first location information can be implemented in the following way.

[0063] The determination process includes: loading an extended Kalman filter or an unscented Kalman filter; and processing the first IMU data and the first location information based on the extended Kalman filter or the unscented Kalman filter to obtain the third location information.

[0064] Both the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are nonlinear filtering algorithms commonly used for state estimation problems. The EKF linearizes the nonlinear system and uses the Kalman filtering framework for state estimation. The Unscented Kalman Filter, on the other hand, uses the unscented transform to approximate the mean and covariance of the nonlinear function, avoiding linearization errors and typically exhibiting better performance under conditions of high nonlinearity.

[0065] Specifically, when determining the third position information of the UAV at the current moment, an extended Kalman filter or an unscented Kalman filter can be pre-loaded. This filter is configured to receive the UAV's first IMU data and first position information as input. The first IMU data typically includes sensor data from accelerometers, gyroscopes, etc., used to estimate the UAV's motion state; the first position information provides the UAV's known position at the first moment. By inputting this data into the loaded filter, the filter can estimate the UAV's motion state and position at the current moment based on its internal state prediction and update mechanism, thereby outputting more accurate third position information. This process aims to fuse data from different sensors to improve the accuracy and robustness of position estimation.

[0066] The proposed solution effectively fuses the first IMU data and first position information of a UAV by introducing an extended Kalman filter or an unscented Kalman filter. IMU data provides the relative motion information of the UAV over a short period, but is susceptible to noise and drift; while the first position information provides an absolute position reference at historical moments. As an optimal estimation algorithm, the Kalman filter iteratively estimates the system state through two stages: prediction and update. In the prediction stage, the current position of the UAV is predicted using IMU data; in the update stage, the prediction result is corrected by incorporating the first position information. For nonlinear systems, the extended Kalman filter linearizes the system using Taylor series expansion, while the unscented Kalman filter handles nonlinearity through the propagation of sigma points, thus capturing the statistical characteristics of nonlinear systems more accurately without explicit linearization. Therefore, through this data fusion and filtering process, sensor noise can be effectively suppressed, estimation errors reduced, and a more accurate and reliable third position information of the UAV at the current moment obtained.

[0067] By employing the aforementioned technical solutions and processing the first IMU data and first position information using an extended Kalman filter or an unscented Kalman filter, the estimation accuracy of the UAV's third position information at the current moment can be significantly improved. Compared to simple integration or linear interpolation methods, the Kalman filter algorithm can effectively handle sensor noise and system uncertainties, providing smoother and more accurate position estimation results. Especially under conditions of high-speed UAV movement or complex and changing external environments, this filtering process can enhance the robustness of position estimation, providing more reliable basic data for subsequent position range determination and final target position information determination, thereby improving the overall performance and reliability of the entire UAV radio communication positioning method.

[0068] In some embodiments described above in this application, a method for determining the location range of a UAV at a current moment based on target data is proposed, which involves determining the UAV's movement distance based on a first environmental image and a second environmental image. Specifically, this application further proposes steps for determining the UAV's movement distance including: Determine the first pixel coordinates of the target pixel in the second environmental image; determine the second pixel coordinates of the target pixel in the first environmental image; determine the movement distance based on the distance between the first pixel coordinates and the second pixel coordinates.

[0069] Specifically, when a drone moves, its onboard image acquisition device continuously acquires environmental images. The first and second environmental images are acquired at different times, with the first time being the time preceding the current time. To determine the distance the drone has traveled between these two times, one or more target pixels can first be identified in the second environmental image. Target pixels can be any point in the image with significant features, such as corner points, edge points, or texture feature points. After identifying the target pixels, their first pixel coordinates in the second environmental image can be determined. Subsequently, the point corresponding to the target pixel needs to be found in the first environmental image, and its second pixel coordinates in the first environmental image need to be determined. The search for corresponding points can be achieved using image matching algorithms, such as matching based on feature descriptors (e.g., SIFT, SURF, ORB), or tracking based on optical flow. Once the first and second pixel coordinates are determined, the distance the drone has traveled can be calculated based on the pixel distance between these two coordinates.

[0070] This application's solution analyzes environmental images acquired by a drone at different times and uses image processing technology to indirectly estimate the drone's movement distance. Specifically, by identifying and matching identical target pixels in two consecutive environmental images (i.e., the first environmental image and the second environmental image), the displacement of that target pixel on the image plane can be obtained. Since the movement of the drone in space causes changes in the relative positions of objects in its captured images, by calculating the distance between the pixel coordinates of the same target pixel in different image frames, and combining this with image sensor parameters (such as focal length, pixel size, etc.) and the drone's altitude information, the actual movement distance of the drone in space can be estimated. This method utilizes the basic principle of visual odometry, estimating camera motion by analyzing image sequences, thereby providing crucial movement distance information for drone positioning.

[0071] The above technical solution enables the precise determination of a drone's movement distance using environmental images acquired by its onboard visual sensors, thus providing reliable input for subsequent location range determination. This method, based on image feature matching and pixel distance calculation, avoids excessive reliance on other external sensors and improves the robustness and accuracy of drone autonomous positioning when radio positioning signals are interfered with. By accurately tracking and calculating the distance to target pixels in the image, errors that may exist in other positioning methods can be effectively compensated for, making the drone's location range estimation more accurate and thus improving the overall performance of drone radio communication positioning in complex environments.

[0072] In one design, the step of determining whether the first radio positioning signal is under a second interference state based on the first radio positioning signal and the location range of the UAV, as proposed in this application, includes: The second location information is compared with the location range to obtain a comparison result; if the comparison result indicates that the second location information is outside the location range, the first radio positioning signal is determined to be in a second interference state; if the comparison result indicates that the second location information is within the location range, the first radio positioning signal is determined not to be in a second interference state.

[0073] Specifically, the second location information refers to the UAV's current location information determined based on the first radio positioning signal, reflecting the radio positioning system's estimate of the UAV's location at that moment. The location range refers to the area where the UAV might exist at that moment, determined based on information such as the UAV's IMU data and environmental imagery; it represents an estimate of the UAV's location based on non-radio positioning methods. Comparing the second location information with the location range can be understood as evaluating the consistency between the radio positioning result and the estimated possible location area of ​​the UAV based on other sensors (such as IMU and visual sensors). The comparison result can indicate whether the second location information is within or outside the location range. For example, when the location range is defined as a sphere, the comparison can be made by calculating the distance from the second location information to the center of the sphere and comparing it to the radius of the sphere.

[0074] This application's solution addresses the aforementioned problem of inaccurate signal interference assessment by introducing a comparison mechanism between second location information and location range. When the first radio positioning signal of a UAV is in a first interference state, its positioning accuracy may already be affected. At this point, relying solely on the signal itself to determine whether the interference has further intensified to a second interference state may lead to misjudgment. By comparing the second location information obtained from the first radio positioning signal with the location range estimated by other independent sensors (such as IMU and visual sensors), a cross-validation mechanism can be provided. If the second location information significantly deviates from the reasonable location range estimated by other sensors, it strongly indicates that the interference level of the first radio positioning signal has further intensified, reaching a second interference state. Conversely, if the second location information remains within a reasonable location range, it indicates that although a first interference state exists, the signal has not yet deteriorated to a second interference state. This comparison mechanism utilizes the redundancy and complementarity of multi-source information, improving the accuracy and reliability of judging the interference state of radio positioning signals.

[0075] Through the above technical solution, this application can more accurately and reliably determine whether the first radio positioning signal of a UAV is under a second interference state. By comparing the radio positioning result with the position range estimated based on other sensor data, it effectively avoids misjudgments that may be caused by relying on a single radio signal source, especially when the signal quality is already compromised. This multi-source information fusion judgment method can promptly identify more severe signal interference situations, thus providing a solid foundation for adopting more appropriate positioning strategies (for example, in a second interference state, it may be necessary to adjust the positioning algorithm or rely more on non-radio positioning methods), thereby improving the positioning robustness and safety of the UAV in complex electromagnetic environments.

[0076] In some preferred embodiments, a specific example is given below. Assume that the UAV's first radio positioning signal is in a first interference state at the current moment. At this time, the UAV's second location information at the current moment has been determined based on the first radio positioning signal, for example, coordinates (X_radio, Y_radio, Z_radio). Simultaneously, based on first IMU data and environmental imagery, the system also determines the UAV's current location range, for example, a sphere with radius R centered at the third location information (X_imu, Y_imu, Z_imu). To determine whether the first radio positioning signal is in a second interference state, the second location information is compared with the sphere's location range. Specifically, the Euclidean distance D from the second location information (X_radio, Y_radio, Z_radio) to the sphere's center (X_imu, Y_imu, Z_imu) can be calculated. If the calculated distance D is greater than the radius R, i.e., D>R, the comparison result indicates that the second location information is outside the location range, and the first radio positioning signal is determined to be in a second interference state. This means that a significant discrepancy exists between the radio positioning results and the estimates based on IMU and vision, indicating that the interference level of the radio signal is already very severe. Conversely, if the distance D is less than or equal to the radius R, i.e., D ≤ R, the comparison result indicates that the second location information is within the location range, and it is determined that the first radio positioning signal is not under the second interference state. In this case, although the first interference state exists, the radio positioning result is still within a reasonable range, indicating that the interference has not yet reached a more severe level. Through this intuitive and quantitative comparison method, the system can effectively assess the interference level of the radio signal and adjust subsequent positioning strategies accordingly.

[0077] In one design, the present application proposes determining the target location information of the UAV at the current moment based on the first radio positioning signal and the location range of the UAV, including: Obtain the first weight value and the second weight value; the sum of the first weight value and the second weight value is 1, and the first weight value is less than the second weight value; determine the fourth position information by multiplying the first weight value by each coordinate value in the second position information; determine the fifth position information by multiplying the second weight value by each coordinate value in the third position information; the third position information is determined based on the first IMU data and the first position information; determine the target position information by summing the corresponding coordinate values ​​of the fourth position information and the fifth position information.

[0078] Specifically, when determining the target location information of the UAV at the current moment, a first weight value and a second weight value are first obtained. The sum of the first and second weight values ​​is set to 1, and the first weight value is set to be less than the second weight value. This indicates that in the subsequent weighted fusion, the location information corresponding to the second weight value will have a larger weight. The second location information is the UAV's location information at the current moment determined based on the first radio positioning signal, while the third location information is the UAV's location information at the current moment determined based on the first IMU data and the first location information. Specifically, the fourth location information is obtained by multiplying the first weight value by each coordinate value in the second location information. For example, if the second location information is three-dimensional coordinates (x2, y2, z2), then the fourth location information will be (w1*x2, w1*y2, w1*z2), where w1 is the first weight value. Similarly, the fifth location information is obtained by multiplying the second weight value by each coordinate value in the third location information. For example, if the third position information is three-dimensional coordinates (x3, y3, z3), then the fifth position information will be (w2*x3, w2*y3, w2*z3), where w2 is the second weight value. Ultimately, the target position information is determined as the sum of the corresponding coordinate values ​​of the fourth and fifth position information. For example, the target position information will be (w1*x2 + w2*x3, w1*y2 + w2*y3, w1*z2 + w2*z3). The third position information can be understood as the UAV position calculated using inertial measurement unit (IMU) data and the previous moment's position information. It typically has good short-term accuracy and continuity, but may have cumulative errors. The second position information is the position directly calculated from radio positioning signals. Its accuracy is greatly affected by signal interference, but it may provide a good absolute position in the absence of interference or with slight interference.

[0079] This application's solution effectively addresses the problem of insufficient positioning accuracy from a single data source when the first radio positioning signal is under second interference. Specifically, when the first radio positioning signal is under second interference, the directly calculated second position information may contain significant errors. While the third position information, calculated based on IMU data and the previous moment's position information, exhibits good short-term continuity and relative accuracy, it may experience long-term drift. By setting a first weight value lower than the second weight value—that is, assigning greater weight to the third position information calculated from IMU data—the impact of interfered radio positioning signals on the final target position information can be effectively reduced. Simultaneously, the short-term high accuracy of IMU data is utilized to correct the instantaneous errors of the radio positioning signal, thus enabling a more stable and reliable UAV target position information even under severe signal interference.

[0080] Through the above technical solution, this application can comprehensively utilize the location information calculated from the radio positioning signal and IMU data when the first radio positioning signal of the UAV is in a second interference state. By reasonably allocating weights, especially when the radio signal is severely interfered with, giving greater weight to the IMU's position calculation, the impact of a single radio positioning signal error on the final positioning result is effectively reduced. This significantly improves the positioning accuracy and robustness of the UAV in complex interference environments, ensuring the safe flight and mission execution capabilities of the UAV under adverse communication conditions.

[0081] In some preferred embodiments, it is assumed that the first radio positioning signal of the UAV at the current moment is in a second interference state. At this time, there may be a large deviation in the second position information determined by the first radio positioning signal, for example, (10.5, 20.3, 5.1) meters. At the same time, the third position information deduced based on the first IMU data and the first position information at the first moment may be (10.0, 20.0, 5.0) meters, and this position information is relatively more stable. In order to fuse these two kinds of information and reduce the influence of interference, the first weight value w1 can be set to 0.3, and the second weight value w2 can be set to 0.7 (satisfying w1 + w2 = 1 and w1 < w2). Specifically, the fourth position information will be calculated as: (0.3 * 10.5, 0.3 * 20.3, 0.3 * 5.1) = (3.15, 6.09, 1.53). The fifth position information will be calculated as: (0.7 * 10.0, 0.7 * 20.0, 0.7 * 5.0) = (7.0, 14.0, 3.5). Finally, the target position information of the UAV at the current moment will be determined as the sum of the corresponding coordinate values of the fourth position information and the fifth position information: (3.15 + 7.0, 6.09 + 14.0, 1.53 + 3.5) = (10.15, 20.09, 5.03) meters. Through this weighted fusion method, even if the radio positioning signal is interfered, the finally determined target position information can be closer to the real position, thereby improving the accuracy and reliability of positioning.

[0082] In some of the above embodiments of the present application, when obtaining the first IMU data of the UAV, it may only rely on the original output of the inertial measurement unit. However, when the UAV is performing a flight mission, especially in a scenario with highly frequent altitude changes, the original IMU data may be affected by environmental factors such as air pressure changes, resulting in a decrease in its accuracy, and further affecting the accuracy of the position estimation based on this IMU data.

[0083] In response to this, the present application further proposes a radio communication positioning method for the above UAV. Among them, a barometric pressure sensor is configured on the UAV, and obtaining the first IMU data of the UAV includes: Obtaining the original IMU data of the UAV at the current moment, the first barometric pressure data of the barometric pressure sensor at the current moment, and the second barometric pressure data of the barometric pressure sensor at the first moment; determining the first IMU data according to the original IMU data, the first barometric pressure data, and the second barometric pressure data.

[0084] Specifically, a barometric pressure sensor is a device that measures ambient atmospheric pressure, and its measurement results can be correlated with the altitude of the drone. Raw IMU data refers to the unprocessed accelerometer and gyroscope data directly output by the inertial measurement unit. The first barometric pressure data is the pressure value collected by the barometric pressure sensor at the current moment, while the second barometric pressure data is the pressure value collected by the barometric pressure sensor at the previous moment (i.e., the moment before the current moment). By acquiring the pressure data at these two moments, the pressure change of the drone between the two moments can be calculated, and thus the altitude change information can be inferred. Determining the first IMU data based on the raw IMU data, the first barometric pressure data, and the second barometric pressure data involves fusing the raw IMU data with the altitude change information provided by the barometric pressure sensor to correct or compensate for possible errors in the raw IMU data, thereby obtaining more accurate and reliable first IMU data.

[0085] The solution proposed in this application effectively solves the aforementioned problems by configuring a barometric pressure sensor on the UAV and processing the raw IMU data using its measurement data. Specifically, the barometric pressure sensor provides accurate barometric pressure data. By comparing the first barometric pressure data at the current moment with the second barometric pressure data at the first moment, the barometric pressure change of the UAV between the two moments can be obtained, thereby inferring altitude change information. This altitude change information is used to correct or compensate for possible vertical errors in the raw IMU data. For example, by fusing the barometric pressure data with the raw IMU data, the vertical velocity and position of the UAV can be estimated more accurately, resulting in more accurate first IMU data. Therefore, the reliability of the IMU data can be guaranteed even when the UAV undergoes significant altitude changes.

[0086] Through the above technical solution, this application can acquire first IMU data corrected or compensated for barometric pressure data, significantly improving the accuracy and reliability of IMU data, especially when the UAV is performing flight missions with large altitude changes. Compared with solutions that rely solely on raw IMU data, this application can provide more accurate inertial measurement information, thus providing a more solid data foundation for subsequent location range determination and final target location information determination, effectively improving the overall accuracy and robustness of the UAV radio communication positioning method.

[0087] In one design, the present application further proposes a method for determining first IMU data based on raw IMU data, first atmospheric pressure data, and second atmospheric pressure data, which includes: Obtain a preset correspondence; the preset correspondence includes a one-to-one correspondence between multiple pressure difference ranges and multiple pressure adjustment coefficients; determine the absolute value of the difference between the first pressure data and the second pressure data as the pressure difference; determine the pressure adjustment coefficient corresponding to the pressure difference range in the preset correspondence as the target pressure adjustment coefficient; determine the first IMU data based on the target pressure adjustment coefficient and the original IMU data.

[0088] Specifically, "obtaining a preset correspondence" refers to the system pre-storing or loading a mapping table or function that associates different ranges of pressure difference with corresponding pressure adjustment coefficients. For example, based on empirical data or simulation results, the pressure difference can be divided into several intervals, and an adjustment coefficient that most effectively corrects IMU data can be set for each interval. The purpose is to provide a flexible and adaptive parameter basis for subsequent IMU data correction.

[0089] Specifically, "determining the absolute value of the difference between the first and second air pressure data as the air pressure difference value" refers to calculating the numerical difference between the current air pressure data and the previous air pressure data, and taking its absolute value. This air pressure difference value reflects the intensity of the vertical altitude change or airflow disturbance experienced by the UAV between the two moments.

[0090] In practical applications, "determining the pressure adjustment coefficient corresponding to the pressure difference range in the preset correspondence as the target pressure adjustment coefficient" means finding the range to which the calculated pressure difference belongs in the preset correspondence and extracting the corresponding pressure adjustment coefficient. For example, if the pressure difference falls within the range of "5Pa-20Pa", then the pressure adjustment coefficient corresponding to that range is selected. The purpose is to dynamically select the most appropriate correction intensity based on the actual magnitude of the pressure change.

[0091] Furthermore, "determining the first IMU data based on the target pressure adjustment factor and the raw IMU data" refers to applying the determined target pressure adjustment factor to the raw IMU data to correct or weight the raw IMU data, thereby obtaining more accurate first IMU data. For example, this can be accomplished by multiplying or adding a correction amount related to the target pressure adjustment factor to the raw IMU data.

[0092] This application's solution achieves adaptive correction of raw IMU data by establishing a preset correspondence between air pressure difference and air pressure adjustment coefficient. Specifically, when the UAV is at different altitudes or experiences different airflow disturbances, the first and second air pressure data acquired by the air pressure sensor will produce different air pressure differences. By matching this air pressure difference with multiple preset air pressure difference ranges, the target air pressure adjustment coefficient most suitable for the current air pressure change can be accurately selected. Therefore, the raw IMU data can be finely corrected according to the actual degree of air pressure change, avoiding errors that may be caused by a fixed adjustment coefficient and ensuring the accuracy of IMU data in dynamic environments.

[0093] The above technical solution enables more refined and adaptive correction of the raw IMU data based on actual air pressure changes. This significantly improves the accuracy and reliability of the initial IMU data, especially in scenarios where the drone experiences rapid ascents and descents or encounters complex airflow causing drastic air pressure changes. Compared to simple air pressure data correction methods, this solution more effectively suppresses IMU data drift and errors caused by air pressure changes, thus providing more accurate basic data for subsequent drone positioning and improving the robustness and accuracy of the overall positioning method.

[0094] In some preferred embodiments, the preset correspondence can be configured as follows: when the pressure difference is less than 5 Pa, the pressure adjustment coefficient is 0.1; when the pressure difference is between 5 Pa and 20 Pa, the pressure adjustment coefficient is 0.5; and when the pressure difference is greater than 20 Pa, the pressure adjustment coefficient is 1.0. Assume that at a certain moment, the first pressure data of the UAV is 1000 hPa, and the second pressure data at the same moment is 990 hPa. The absolute value of the pressure difference is 10 hPa. According to the preset correspondence, 10 hPa falls within the range of 5 Pa to 20 Pa, therefore the target pressure adjustment coefficient is determined to be 0.5. Subsequently, this target pressure adjustment coefficient of 0.5 will be applied to the original IMU data to determine the final first IMU data. This segmented adjustment strategy makes the correction of IMU data more flexible and accurate, and can better adapt to different degrees of pressure changes.

[0095] A specific embodiment of this application also discloses a UAV radio communication positioning system, including: an acquisition device and a processing device; The acquisition device is used to acquire target data when the first radio positioning signal of the UAV is under a first interference state; the target data includes the first IMU data of the UAV at the current time, the first environmental image of the UAV at the current time, the first position information of the UAV at the first time, and the second environmental image of the UAV at the first time; the first time is the time before the current time; Processing unit, used to determine the current location range of the UAV based on target data; The processing device is further configured to determine, based on the first radio positioning signal and the location range of the UAV, whether the first radio positioning signal is in a second interference state; the signal interference level corresponding to the second interference state is greater than the signal interference level corresponding to the first interference state. The processing device is also configured to determine the target location information of the UAV at the current moment based on the first radio positioning signal and the location range of the UAV when the first radio positioning signal is under a second interference state.

[0096] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for radio communication positioning of unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: When the first radio positioning signal of the UAV is under first interference, target data is acquired; the target data includes the first IMU data of the UAV at the current time, the first environmental image of the UAV at the current time, the first position information of the UAV at the first time, and the second environmental image of the UAV at the first time; the first time is the time before the current time; Determine the location range of the UAV at the current moment based on the target data; Based on the first radio positioning signal and the location range of the UAV, it is determined whether the first radio positioning signal is in a second interference state; the signal interference level corresponding to the second interference state is greater than the signal interference level corresponding to the first interference state. When the first radio positioning signal is under a second interference state, the target location information of the UAV at the current time is determined based on the first radio positioning signal and the location range of the UAV.

2. The UAV radio communication positioning method according to claim 1, characterized in that, Before acquiring the target data, the method further includes: Obtain the first location information and the first radio positioning signal; The second location information of the UAV at the current moment is determined based on the first radio positioning signal; The first moving speed of the drone is determined based on the first location information, the second location information, the current time, and the first time. If the first moving speed is greater than the first threshold, it is determined that the first radio positioning signal of the UAV is in the first interference state at the current moment, and the first threshold is the maximum moving speed of the UAV.

3. The UAV radio communication positioning method according to claim 1, characterized in that, Determining the location range of the UAV at the current moment based on the target data includes: The third location information of the UAV at the current moment is determined based on the first IMU data and the first location information; The movement distance of the UAV is determined based on the first environmental image and the second environmental image; The position range is defined as a sphere with its center at the third position information and its radius equal to the distance traveled.

4. The UAV radio communication positioning method according to claim 3, characterized in that, Determining the third location information of the UAV at the current moment based on the first IMU data and the first location information includes: Load an extended Kalman filter or an unscented Kalman filter; The first IMU data and the first location information are processed based on the extended Kalman filter or the unscented Kalman filter to obtain the third location information.

5. The UAV radio communication positioning method according to claim 3, characterized in that, Determining the movement distance of the UAV based on the first environmental image and the second environmental image includes: Determine the first pixel coordinates of the target pixel in the second environmental image; Determine the second pixel coordinates of the target pixel in the first environmental image; The movement distance is determined based on the distance between the first pixel coordinate point and the second pixel coordinate point.

6. The UAV radio communication positioning method according to claim 2, characterized in that, Determining whether the first radio positioning signal is under a second interference state based on the first radio positioning signal and the location range of the UAV includes: The second location information is compared with the location range to obtain a comparison result; If the comparison result indicates that the second location information is outside the location range, it is determined that the first radio positioning signal is in a second interference state; If the comparison result indicates that the second location information is within the location range, it is determined that the first radio positioning signal is not in a second interference state.

7. The UAV radio communication positioning method according to claim 2, characterized in that, Determining the target location information of the UAV at the current time based on the first radio positioning signal and the location range of the UAV includes: Obtain a first weight value and a second weight value; the sum of the first weight value and the second weight value is 1, and the first weight value is less than the second weight value; The product of the first weight value and each coordinate value in the second location information is determined as the fourth location information; The product of the second weight value and each coordinate value in the third location information is used to determine the fifth location information; the third location information is determined based on the first IMU data and the first location information. The sum of the corresponding coordinate values ​​of the fourth location information and the fifth location information is determined as the target location information.

8. A method for unmanned aerial vehicle (UAV) radio communication positioning according to any one of claims 1-7, characterized in that, The drone is equipped with a barometric pressure sensor to acquire the drone's first IMU data, including: Acquire the raw IMU data of the UAV at the current time, the first air pressure data of the barometer at the current time, and the second air pressure data of the barometer at the first time; The first IMU data is determined based on the original IMU data, the first air pressure data, and the second air pressure data.

9. A UAV radio communication positioning method according to claim 8, characterized in that, Determining the first IMU data based on the original IMU data, the first air pressure data, and the second air pressure data includes: Obtain a preset correspondence; the preset correspondence includes a one-to-one correspondence between multiple pressure difference ranges and multiple pressure adjustment coefficients; The absolute value of the difference between the first air pressure data and the second air pressure data is determined as the air pressure difference value; The pressure adjustment coefficient corresponding to the pressure difference range in the preset correspondence is determined as the target pressure adjustment coefficient. The first IMU data is determined based on the target pressure adjustment coefficient and the original IMU data.

10. A radio communication positioning system for unmanned aerial vehicles (UAVs), characterized in that, include: Acquisition device and processing device; The acquisition device is used to acquire target data when the first radio positioning signal of the UAV is in a first interference state; The target data includes the first IMU data of the UAV at the current moment, the first environmental image of the UAV at the current moment, the first location information of the UAV at the first moment, and the second environmental image of the UAV at the first moment; the first moment is the previous moment of the current moment; The processing device is used to determine the location range of the UAV at the current moment based on the target data; The processing device is further configured to determine, based on the first radio positioning signal and the location range of the UAV, whether the first radio positioning signal is in a second interference state; the signal interference level corresponding to the second interference state is greater than the signal interference level corresponding to the first interference state. The processing device is further configured to determine the target location information of the UAV at the current moment based on the first radio positioning signal and the location range of the UAV when the first radio positioning signal is under a second interference state.

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