Mobile device direction positioning method and system based on air-ground cooperation

Abstract

The invention provides a mobile device direction positioning method and system based on air-ground cooperation, and belongs to the technical field of positioning, and the method comprises the steps that an unmanned aerial vehicle receives a ground signal from a ground base station, carries out the fusion processing of the ground signal and the position information and attitude information of the unmanned aerial vehicle, and generates a fusion signal containing air-ground cooperation information; the target mobile device receives the fusion signal and preprocesses the fusion signal; and performing triangulation and attitude resolving processing according to the preprocessed fusion signal, and determining direction information of the target mobile equipment. The beneficial effects of the invention are that based on an air-ground cooperation mode, in combination with multi-source information provided by a ground base station and an unmanned aerial vehicle, and by applying triangulation and an attitude calculation algorithm, the problems of easy environmental interference, unstable signals, low data fusion efficiency and the like during direction positioning of mobile equipment in the prior art are solved; the direction information of the mobile equipment can be quickly and accurately obtained, and the requirements for high precision and real-time performance of direction positioning of the mobile equipment in different complex scenes are met.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, and in particular to a method and system for positioning the direction of a mobile device based on air-ground collaboration. Background Art

[0002] Accurately acquiring directional information from mobile devices is crucial in numerous applications. For example, in autonomous driving, vehicles require real-time and precise knowledge of their driving direction to enable accurate path planning and decision-making in complex road conditions. In smart logistics, mobile robots carrying goods within warehouses rely on accurate directional information to plan optimal routes and efficiently complete cargo handling tasks.

[0003] Existing mobile device orientation positioning technologies include methods based on single sensors and methods that directly acquire direction using satellite positioning systems. Single sensors, such as electronic compasses, are susceptible to interference from the surrounding electromagnetic environment. Measurement errors increase significantly in environments with numerous electronic devices or metal objects, leading to inaccurate direction determination. Furthermore, satellite positioning systems struggle to provide stable and accurate direction information in environments such as urban canyons and dense forests, where satellite signal obstruction and multipath effects degrade signal quality.

[0004] Although some technologies attempt to improve directional positioning through multi-sensor fusion, the data fusion algorithms between sensors are not optimized enough, the data processing process is complex and has poor real-time performance, and it is impossible to quickly and accurately determine the direction of the mobile device, making it difficult to meet the needs of application scenarios with high requirements for real-time and accuracy. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a mobile device direction positioning method based on air-ground collaboration; on the other hand, it also provides a mobile device direction positioning system based on air-ground collaboration.

[0006] The technical problem solved by the present invention can be achieved by adopting the following technical solutions:

[0007] A method for positioning a mobile device based on air-ground collaboration, comprising:

[0008] Step S1: While the UAV is flying along a predetermined route, it receives a ground signal containing the ground base station's location information and a timestamp from a ground base station, and fuses the ground signal with the UAV's location information and attitude information to generate a fused signal containing air-ground collaboration information.

[0009] Step S2: The target mobile device receives the fused signal sent from the UAV and pre-processes the fused signal to obtain a pre-processed fused signal;

[0010] Step S3: performing triangulation and attitude calculation based on the pre-processed fusion signal to determine the direction information of the target mobile device.

[0011] Preferably, step S3 includes:

[0012] Step S31, determining the location coordinate information of the target mobile device based on the location information of at least three ground base stations and the location information of the drone;

[0013] Step S32 , using the drone as a reference coordinate system, performing attitude calculation based on the drone attitude information and the position coordinate information of the target mobile device to determine the direction information of the target mobile device relative to the drone.

[0014] Preferably, after step S32, the following steps are further included:

[0015] Step S33 , correcting the direction information according to the timestamp information extracted from the fused signal to obtain corrected direction information of the target mobile device.

[0016] Preferably, the step S33 includes:

[0017] Step S331, determining a signal propagation delay time based on timestamp information extracted from the fused signal;

[0018] Step S332: determining a first displacement of the drone and a second displacement of the target mobile device within the signal propagation delay time based on the motion state information of the drone and the motion state information of the target mobile device;

[0019] Step S333: updating the attitude information of the drone and the position coordinate information of the target mobile device according to the first displacement of the drone and the second displacement of the target mobile device within the signal propagation delay time;

[0020] Step S334 : Repeat step S32 according to the updated drone attitude information and the position coordinate information of the target mobile device to obtain the corrected direction information of the target mobile device.

[0021] Preferably, the timestamp information includes a first timestamp when the ground base station sends the ground signal, a second timestamp when the drone receives the ground signal, a third timestamp when the drone sends the fused signal, and a fourth timestamp when the target mobile device receives the fused signal;

[0022] The signal propagation delay time is the sum of the ground signal propagation time and the fusion signal propagation time, wherein the ground signal propagation time is the difference between the second timestamp and the first timestamp, and the fusion signal propagation time is the difference between the fourth timestamp and the third timestamp.

[0023] Preferably, the step S3 further includes:

[0024] In step S41A, the UAV continuously receives ground signals sent from the ground base station during flight, updates the UAV position information and attitude information, generates a new fusion signal and sends it to the target mobile device. After the target mobile device receives the new fusion signal, steps S2-S3 are repeated to update the direction information of the target mobile device.

[0025] Preferably, the step S3 further includes:

[0026] Step S41B, detecting whether the direction information of the target mobile device determined in step S3 has a direction positioning error;

[0027] Step S42B: When it is detected that the direction positioning error exceeds a preset threshold, the calculation parameters and algorithm weights of triangulation and attitude solution are adjusted according to the direction positioning error to obtain optimized direction information of the target mobile device.

[0028] Preferably, determining whether there is a direction positioning error in step S41B includes:

[0029] Comparing the reference direction of the target mobile device with the direction information of the target mobile device determined in step S3; or

[0030] Comparing the direction information of the target mobile device determined in step S3 with the mean and standard deviation of the direction information of the historical positioning of the target mobile device; or

[0031] Cross-verification is performed based on the direction information of the plurality of mobile devices and the direction information of the target mobile device.

[0032] Preferably, the calculation parameters and algorithm weights adjusted in step S42B include:

[0033] A reference coordinate system constructed with the drone; and / or

[0034] The rotation matrix from the world coordinate system to the drone coordinate system; and / or

[0035] The distance weight coefficients between each ground base station and the target mobile device in the triangulation; and / or

[0036] An angle weight coefficient between the drone and the target mobile device in the attitude solution;

[0037] The algorithmic weights for the triangulation and pose solution.

[0038] On the other hand, a mobile device direction positioning system based on air-ground collaboration is also provided, which is characterized in that it is used to implement the mobile device direction positioning method based on air-ground collaboration as described above, including:

[0039] A ground base station group, comprising a plurality of ground base stations, each of which is used to send a ground signal including ground base station location information and a timestamp;

[0040] A drone swarm includes multiple drones, each of which is configured to fly along a predetermined route and receive a ground signal containing the ground base station's location information and a timestamp from a ground base station during flight. The ground signal is then fused with the drone's location information and attitude information to generate a fused signal containing air-ground collaboration information.

[0041] A mobile device, configured to receive the fused signal sent from the drone, and pre-process the fused signal to obtain a pre-processed fused signal;

[0042] The direction determination module is used to perform triangulation and posture calculation based on the pre-processed fusion signal to determine the direction information of the mobile device.

[0043] The advantages or beneficial effects of the technical solution of the present invention are:

[0044] The present invention is based on an air-ground collaborative approach, combines multi-source information provided by ground base stations and drones, and uses triangulation and attitude solution algorithms to achieve direction positioning. It solves the problems of susceptibility to environmental interference, signal instability, and low data fusion efficiency in the existing technology when locating the direction of mobile devices. It can quickly and accurately obtain the direction information of mobile devices, and meet the high-precision and real-time requirements for the direction positioning of mobile devices in different complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flow chart of a method for locating the direction of a mobile device based on air-ground collaboration in a preferred embodiment of the present invention;

[0046] Figure 2 A schematic diagram of a process for determining direction information in a preferred embodiment of the present invention;

[0047] Figure 3 A schematic diagram of a process for determining and correcting direction information in a preferred embodiment of the present invention;

[0048] Figure 4A schematic diagram of a flow chart of direction information correction in a preferred embodiment of the present invention;

[0049] Figure 5 A schematic diagram of a real-time updating process in a preferred embodiment of the present invention;

[0050] Figure 6 A schematic diagram of a process for directional positioning optimization in a preferred embodiment of the present invention;

[0051] Figure 7 This is a structural block diagram of a mobile device direction positioning system based on air-ground collaboration in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0054] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0055] See also Figure 1 In a preferred embodiment of the present invention, based on the above-mentioned problems existing in the prior art, a method for positioning the direction of a mobile device based on air-ground collaboration is provided, comprising:

[0056] Step S1: While the UAV is flying along a predetermined route, it receives a ground signal from a ground base station containing the base station's location information and timestamp, and fuses the ground signal with the UAV's location information and attitude information to generate a fused signal containing air-ground collaboration information.

[0057] A ground base station cluster is deployed on the ground, comprising multiple ground base stations. The ground base stations have highly accurate location coordinates and stable signal transmission capabilities. Furthermore, the relative positions of the individual ground base stations in the cluster are known.

[0058] At the same time, a drone swarm is deployed in the air, consisting of several drones. Each drone is equipped with positioning equipment and a signal transceiver. The drones fly along a predetermined route, acquiring real-time information about their position and attitude during flight.

[0059] During the flight of the drone, the ground base station will send a ground signal containing its own location information, timestamp, etc. to the drone. The drone receives the ground signal and fuses its own location information and attitude information with the received ground signal to generate a fusion signal containing air-ground collaborative information.

[0060] Exemplarily, the ground base station may include but is not limited to a home base station (e.g., home evolved NodeB, or home Node B, HNB), a 5G base station, such as a gNB base station or a TRP base station in a new radio (NR) system.

[0061] Step S2: The target mobile device receives the fused signal sent from the UAV and preprocesses the fused signal to obtain a preprocessed fused signal;

[0062] Exemplarily, mobile devices may include but are not limited to smart phones, desktop computers, tablet computers, laptops, vehicles, robots, augmented reality (AR) / virtual reality (VR) devices, smart wearable devices, and other types of electronic devices.

[0063] The target mobile device can be any mobile device equipped with a signal receiving module that can receive the fusion signal sent by the drone.

[0064] When the mobile device receives the fused signal, it first pre-processes the signal. The pre-processing includes but is not limited to removing noise interference from the signal, decoding the signal, and extracting key data such as the ground base station location information, drone location information and attitude information, and timestamp from the fused signal.

[0065] Step S3: performing triangulation and attitude calculation based on the pre-processed fusion signal to determine the direction information of the target mobile device.

[0066] Specifically, based on the data obtained after preprocessing, an algorithm combining triangulation and attitude solution is used to calculate the direction of the mobile device. Figure 2 As shown, the specific steps of the triangulation and attitude solution algorithm are as follows:

[0067] Step S31, determining the location coordinate information of the target mobile device based on the location information of at least three ground base stations and the location information of the drone;

[0068] In step S32 , the UAV is used as a reference coordinate system, and attitude calculation is performed based on the attitude information of the UAV and the position coordinate information of the target mobile device to determine the direction information of the target mobile device relative to the UAV.

[0069] Specifically, in this embodiment, based on the principle of triangulation, the position information of at least three ground base stations and the position information of the drone are used to calculate the geometric relationship of the mobile device relative to the spatial triangle formed by the ground base stations and the drone, and obtain the position coordinate information of the mobile device in the world coordinate system.

[0070] Next, a reference coordinate system is established based on the drone. In this reference coordinate system, the drone's current position is used as the origin, and the positive x-axis is set as the flight direction of the drone, the positive y-axis is perpendicular to the flight direction and pointing horizontally to the right, and the positive z-axis is vertically upward.

[0071] At the same time, determine the position coordinates of the mobile device in the world coordinate system (X m , Y m , Z m ) and the position coordinates of the drone in the world coordinate system (X u ,Y u ,Z u ), combined with the attitude information of the drone, such as pitch angle θ, yaw angle ψ, roll angle ), construct the rotation matrix R from the world coordinate system to the drone coordinate system.

[0072] The rotation matrix R can be obtained by multiplying the rotation matrices around the x, y, and z axes respectively, that is:

[0073]

[0074] in, The rotation matrix representing the angle of rotation around the x-axis;

[0075] R y (θ) represents the rotation matrix corresponding to the angle of rotation around the y-axis;

[0076] R z (ψ) represents the rotation matrix corresponding to the angle of rotation around the z-axis.

[0077] Further, R y (θ) and R z The mathematical expression of (ψ) is as follows:

[0078]

[0079] Next, calculate the position vector v of the mobile device relative to the drone in the world coordinate system:

[0080] v=(X m -X u ,Y m -Y u ,Z m -Zu )

[0081] Then, the position vector v is used to transform the position coordinate information of the mobile device into the reference coordinate system based on the drone through the rotation matrix R, and the position vector v′ of the mobile device in the reference coordinate system is obtained:

[0082] v′=R×v.

[0083] At this time, the coordinates of v′ are expressed as (x′, y′, z′), which reflects the relative position of the mobile device in the reference coordinate system based on the drone.

[0084] In a reference coordinate system based on the drone, the direction information of the mobile device relative to the drone is calculated based on the coordinate relationship of the position vector.

[0085] The direction information includes a horizontal direction angle α and a vertical direction angle β.

[0086] The horizontal angle α can be calculated using the inverse tangent function:

[0087] α=arctan2(y′,x′)

[0088] The arctan2 function is a two-quadrant inverse tangent function that can accurately calculate the angle value in the horizontal direction according to the positive and negative values ​​of x′ and y′. Its value range is (-π,π].

[0089] The vertical angle β can also be calculated using the inverse tangent function:

[0090] β=arctan2(z′,x′ 2 +y′ 2 )

[0091] This formula combines the vector synthesis in the horizontal direction and the vector relationship in the vertical direction to accurately calculate the vertical angle of the mobile device relative to the drone, and its value range is (-2π, 2π].

[0092] The horizontal angle α and vertical angle β calculated by the above attitude solution algorithm determine the direction angle of the mobile device relative to the drone.

[0093] As a preferred embodiment, wherein Figure 3 As shown, after step S32, the following steps are further included:

[0094] Step S33 , correcting the direction information according to the timestamp information extracted from the fused signal to obtain corrected direction information of the target mobile device.

[0095] Specifically, taking into account factors such as signal propagation delay that may exist in the actual environment, the timestamp information in the fused signal is used to correct the calculation results of the above-mentioned attitude solution algorithm, and finally the accurate direction information of the mobile device is obtained.

[0096] The timestamp information includes the first timestamp tb1 when the ground base station sends the ground signal, the second timestamp tu1 when the drone receives the ground signal, the third timestamp tu2 when the drone sends the fusion signal, and the fourth timestamp tm when the target mobile device receives the fusion signal.

[0097] Then, the propagation time of the ground signal from the ground base station to the UAV is the ground signal propagation time Tbu, and the propagation time of the fused signal from the UAV to the mobile device is the fused signal propagation time Tum.

[0098] As a preferred embodiment, wherein Figure 4 As shown, step S33 includes:

[0099] Step S331, determining the signal propagation delay time based on the timestamp information extracted from the fused signal;

[0100] The signal propagation delay time is the sum of the ground signal propagation time and the fusion signal propagation time, that is, the total signal propagation delay time T=Tbu+Tum.

[0101] The ground signal propagation time Tbu is the difference between the second timestamp tu1 and the first timestamp tb1, that is, Tbu=tu1-tb1; the fused signal propagation time Tum is the difference between the fourth timestamp tm and the third timestamp tu2, that is, Tum=tm-tu2.

[0102] Step S332, determining a first displacement of the drone and a second displacement of the target mobile device within a signal propagation delay time based on the motion state information of the drone and the motion state information of the target mobile device;

[0103] The motion state information of the drone and mobile device includes but is not limited to speed and acceleration, which can be obtained through the sensors they carry. Let the velocity vector of the drone be v u =(v ux ,v uy ,v uz ), the velocity vector of the mobile device is v m =(v mx ,v my ,v mz );

[0104] Based on the velocity vectors of the drone and the mobile device, the displacements of the drone and the mobile device within the signal propagation delay time T can be calculated. The first displacement and the second displacement are expressed in vector form. The displacement vector of the drone Δu = (v ux T,v uy T,v uz T), the displacement vector of the mobile device Δm=(v mx T,v my T,v mz T).

[0105] Step S333: updating the drone attitude information and the position coordinate information of the target mobile device according to the first displacement of the drone and the second displacement of the target mobile device within the signal propagation delay time;

[0106] Among them, the drone position information extracted from the fusion signal is corrected according to the displacement of the drone and mobile device within the signal propagation delay time T.

[0107] The corrected drone position coordinates are:

[0108] (X u′ ,Y u′ ,Z u′ )=(X u +v ux T,Y u +v uy T,Z u +v uz T)

[0109] The corrected position coordinates of the mobile device are:

[0110] (X m′ ,Y m′ ,Z m′ )=(X m +v mx T,Y m +v my T,Z m +v mz T).

[0111] Step S334 : Repeat step S32 based on the updated drone attitude information and the position coordinate information of the target mobile device to obtain the corrected direction information of the target mobile device.

[0112] Specifically, based on the corrected position coordinates of the drone and mobile device, the angular direction of the mobile device relative to the drone is recalculated according to the process of establishing a coordinate system, coordinate conversion, and angle calculation in step S32 to obtain a new horizontal angle α′ and vertical angle β′.

[0113] The newly obtained horizontal angle α′ and vertical angle β′ are used to replace the previously uncorrected angle values, thereby obtaining a direction angle of the mobile device that is more in line with the actual situation and improving the accuracy of direction positioning.

[0114] The mobile device direction positioning method of the present invention is based on an air-ground collaborative approach, which solves the problems existing in the prior art in positioning the direction of mobile devices, such as susceptibility to environmental interference, unstable signals, and low data fusion efficiency. It can quickly and accurately obtain the direction information of the mobile device, and meet the high-precision and real-time requirements for the direction positioning of the mobile device in different complex scenarios.

[0115] As a preferred embodiment, wherein Figure 5 As shown, after step S3, the following steps are also included:

[0116] In step S41A, the UAV continuously receives ground signals sent from the ground base station during flight, updates the UAV's position information and attitude information, generates a new fusion signal and sends it to the target mobile device. After the target mobile device receives the new fusion signal, it repeats steps S2-S3 to update the direction information of the target mobile device.

[0117] Specifically, in this embodiment, the drone continuously collects ground signals transmitted by the ground base station during flight, continuously updates its own position and attitude information, and generates a new fused signal that is transmitted to the mobile device. After receiving the new fused signal, the mobile device repeats the signal preprocessing and direction positioning calculation steps S2-S3 above, achieving real-time updates of the mobile device's direction information.

[0118] As a preferred embodiment, wherein Figure 6 As shown, after step S3, the following steps are also included:

[0119] Step S41B, detecting whether the direction information of the target mobile device determined in step S3 contains a direction positioning error;

[0120] Step S42B: When it is detected that the direction positioning error exceeds a preset threshold, the calculation parameters and algorithm weights of triangulation and attitude solution are adjusted according to the direction positioning error to obtain optimized direction information of the target mobile device.

[0121] Specifically, in this embodiment, data analysis is performed based on multiple direction positioning results. If it is found that the direction positioning error exceeds the set threshold, the calculation parameters and algorithm weights are automatically adjusted to optimize the direction positioning results, thereby further improving the accuracy and stability of direction positioning.

[0122] As a preferred embodiment, determining whether there is a direction positioning error in step S41B includes:

[0123] The reference direction of the target mobile device is compared with the direction information of the target mobile device determined in step S3.

[0124] Specifically, under known environmental conditions, a high-precision positioning device or authoritative positioning system is used in advance. For example, a high-precision differential GPS system can be used in open areas, or a high-precision inertial navigation system combined with fixed landmarks can be used indoors to determine the precise direction of the mobile device as a reference direction. Each time the mobile device's direction information is calculated using the method of the present invention, it is compared with the reference direction.

[0125] For example, assuming the reference direction is due east, that is, the angle of due east is 0°, the direction angle calculated by the method of the present invention is 5°, and the difference between the two is 5°, which is the direction positioning error of this time.

[0126] As another preferred embodiment, determining whether there is a direction positioning error in step S41B includes:

[0127] The mean and standard deviation of the direction information of the historical positioning of the target mobile device are compared with the direction information of the target mobile device determined in step S3.

[0128] To further evaluate the accuracy of positioning results, error estimation can be performed based on historical data. Specifically, historical data generated by multiple positioning attempts of the same mobile device under similar environmental conditions is collected. The mean and standard deviation of the directional information in this historical data are calculated. The mean represents the average level of directional positioning of mobile devices under similar environments; the standard deviation reflects the degree of dispersion relative to the mean.

[0129] Each time the direction information of the mobile device is calculated by the method of the present invention, the difference between the direction information and the mean of the historical data is calculated, and the degree of error is determined in combination with the standard deviation.

[0130] For example, if the difference between the calculated mobile device direction information and the mean of historical data exceeds a certain multiple of the standard deviation (such as 2 times the standard deviation), it can be considered that there is a large error in the direction positioning.

[0131] As another preferred embodiment, determining whether there is a direction positioning error in step S41B includes:

[0132] Cross-verification is performed based on the direction information of the plurality of mobile devices and the direction information of the target mobile device.

[0133] Specifically, multiple positioning devices of the same or different types are deployed near the mobile device, and the mobile device's orientation is simultaneously calculated. The mobile device's orientation information calculated by the method of the present invention is compared with the calculation results of other devices. If the difference between the calculated results of multiple devices exceeds a certain range (e.g., more than 10°), a directional positioning error is determined. Through multi-device cross-validation, the accuracy of the positioning results can be comprehensively judged to determine the directional positioning error.

[0134] As a preferred embodiment, the calculation parameters and algorithm weights adjusted in step S42B include:

[0135] A reference coordinate system established by the drone; and / or

[0136] The rotation matrix from the world coordinate system to the drone coordinate system; and / or

[0137] The distance weight coefficients between each ground base station and the target mobile device in the triangulation measurement; and / or

[0138] The angle weight coefficient between the UAV and the target mobile device in attitude solution;

[0139] Algorithmic weights for triangulation and pose solving.

[0140] Specifically, in this embodiment, the calculation parameter adjustment includes coordinate system related parameters and coefficients in the calculation process.

[0141] To be more specific, the directional positioning error is mainly reflected in the horizontal direction or vertical direction, and the relevant parameters when establishing the coordinate system can be adjusted. For example, if the horizontal error is found to be large, it may be that there is a deviation in the setting of the drone's flight direction as the positive direction of the x-axis. At this time, the angle between the drone's flight direction and the positive direction of the x-axis can be fine-tuned according to the direction and size of the error. At the same time, the angle parameters in the rotation matrix (pitch angle θ, yaw angle ψ, roll angle ) is used for correction, and these angle values ​​are appropriately increased or decreased according to the error situation, and the rotation matrix is ​​rebuilt to optimize the coordinate transformation process and reduce the error.

[0142] Specifically, in the process of triangulating the approximate position coordinates and angle calculations, some coefficients involved (such as the distance weight coefficient, the angle weight coefficient, etc.) can be adjusted according to the error situation. When it is found that the overall positioning result is biased towards a certain direction, the value of the correlation coefficient can be increased or decreased to change the proportion of each calculation factor in the result. For example, if the calculated direction angle is generally small, the value of the correlation coefficient in the vertical direction angle calculation can be appropriately increased to increase the vertical direction angle calculation result, thereby adjusting the overall direction angle calculation result.

[0143] Algorithm weight adjustment includes dynamic weight allocation. The method of the present invention involves the integration of multiple algorithms such as triangulation and attitude solution, and the weight of each algorithm in the final result can be dynamically adjusted according to the error situation. When it is found that the direction positioning error is large in the horizontal direction and is mainly caused by the triangulation algorithm, the weight of the triangulation algorithm in the overall calculation can be reduced, and the weight of the attitude solution algorithm can be increased accordingly. For example, the original weight of the triangulation algorithm is 0.6, and the weight of the attitude solution algorithm is 0.4. According to the error situation, the weight of the triangulation algorithm can be adjusted to 0.4 and the weight of the attitude solution algorithm can be adjusted to 0.6, and the direction angle calculation can be re-performed to reduce the error.

[0144] More specifically, machine learning algorithms can be used to train large amounts of data containing directional positioning errors and corresponding calculation parameters and algorithm weights. Through training, a relationship model is established between errors, calculation parameters, and algorithm weights. When a new directional positioning error occurs, the directional positioning error data is input into the relationship model, and the model automatically outputs optimized calculation parameters and algorithm weights. For example, using a neural network model, historical error data and corresponding calculation parameters and algorithm weights are used as training samples. After multiple training sessions, the neural network can accurately predict the combination of calculation parameters and algorithm weights that can reduce the error based on the input error data, achieving automatic optimization and adjustment.

[0145] See also Figure 7 In a preferred embodiment of the present invention, a mobile device direction positioning system based on air-ground collaboration is provided, which is characterized in that it is used to implement the mobile device direction positioning method based on air-ground collaboration as described above, including:

[0146] A ground base station group 1, comprising a plurality of ground base stations, each of which is configured to transmit a ground signal including location information and a timestamp of the ground base station;

[0147] UAV swarm 2 includes multiple UAVs, each of which is used to fly according to a predetermined route and receive a ground signal containing the ground base station's location information and a timestamp from the ground base station during flight. The ground signal is then fused with the UAV's location information and attitude information to generate a fused signal containing air-ground collaboration information.

[0148] The mobile device 3 is configured to receive the fused signal sent from the UAV, pre-process the fused signal, and obtain a pre-processed fused signal;

[0149] The direction determination module 4 is used to perform triangulation and posture calculation based on the pre-processed fusion signal to determine the direction information of the mobile device.

[0150] Specifically, this embodiment utilizes air-ground collaboration, combining multi-source information from ground base stations and drones, and employing triangulation and attitude calculation algorithms to effectively overcome the limitations of single-sensor or satellite positioning. In complex environments, such as urban high-rises or forested areas, this invention can improve directional positioning accuracy by 40%-60% compared to traditional methods, providing more precise directional information for a variety of applications.

[0151] The collaborative working mode of the ground base station and drone reduces the impact of environmental interference on a single signal source. Even in the presence of poor satellite signals or severe ground electromagnetic interference, stable signal transmission and information fusion between the ground base station and drone ensure the reliability of mobile device positioning, significantly enhancing the system's adaptability in complex environments.

[0152] The drone continuously updates and transmits fused signals, which are received and processed in real time by the mobile device, enabling rapid updates of directional information. The system also automatically optimizes calculation parameters and algorithms based on positioning errors, eliminating the need for human intervention. While ensuring real-time performance, it continuously improves directional positioning accuracy, meeting the demands of applications such as autonomous driving and intelligent logistics, which place extremely high demands on real-time performance and accuracy.

[0153] In urban road autonomous driving scenarios, multiple ground base stations are deployed at appropriate intervals around the road to ensure their fixed positions and precise coordinate measurements. Multiple drones are deployed to fly at a predetermined altitude above the road according to a predetermined route. As the autonomous vehicle drives on the road, the vehicle's signal receiving module receives the fused signal transmitted by the drone. After pre-processing the signal, the vehicle uses triangulation and attitude calculation algorithms, combining information from the ground base stations and drones, to quickly calculate the vehicle's current heading. The drone continuously flies and updates the signal, and the vehicle receives the new signal and updates its heading in real time. Simultaneously, the system automatically optimizes the calculation process based on positioning errors, ensuring that the vehicle always obtains the correct heading. This assists with precise path planning and driving decisions, improving the safety and reliability of autonomous driving.

[0154] In a smart logistics scenario, multiple ground base stations are installed in different corners of a large warehouse. Drones are deployed to fly over the warehouse. While handling goods, mobile robots within the warehouse receive fused signals from the drones through their own signal receiving devices. After preprocessing the signals, the mobile robots apply the directional positioning calculation method of the present invention to determine their orientation relative to the warehouse floor and the drones. The drones continuously update their signals, and the mobile robots acquire new directional information in real time, allowing them to accurately adjust their movement within the warehouse, efficiently completing cargo handling tasks, avoiding path deviations and collision risks caused by directional misjudgments, and improving the operational efficiency of smart logistics.

[0155] The above are only preferred embodiments of the present invention and do not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the contents of this specification and illustrations should be included in the protection scope of the present invention.

Claims

1. A method for positioning the direction of a mobile device based on air-ground collaboration, characterized in that: include: Step S1: While the UAV is flying along a predetermined route, it receives a ground signal containing the ground base station's location information and a timestamp from a ground base station, and fuses the ground signal with the UAV's location information and attitude information to generate a fused signal containing air-ground collaboration information. Step S2: The target mobile device receives the fused signal sent from the UAV and pre-processes the fused signal to obtain a pre-processed fused signal; Step S3: performing triangulation and attitude calculation based on the pre-processed fusion signal to determine the direction information of the target mobile device.

2. The method for mobile device direction positioning based on air-ground collaboration according to claim 1, characterized in that: The step S3 comprises: Step S31, determining the location coordinate information of the target mobile device based on the location information of at least three ground base stations and the location information of the drone; Step S32 , using the drone as a reference coordinate system, performing attitude calculation based on the drone attitude information and the position coordinate information of the target mobile device to determine the direction information of the target mobile device relative to the drone.

3. The method for mobile device direction positioning based on air-ground collaboration according to claim 2, characterized in that: After step S32, the following steps are further included: Step S33 , correcting the direction information according to the timestamp information extracted from the fused signal to obtain corrected direction information of the target mobile device.

4. The method for locating the direction of a target mobile device based on air-ground collaboration according to claim 3 is characterized in that: The step S33 includes: Step S331, determining a signal propagation delay time based on timestamp information extracted from the fused signal; Step S332: determining a first displacement of the drone and a second displacement of the target mobile device within the signal propagation delay time based on the motion state information of the drone and the motion state information of the target mobile device; Step S333: updating the attitude information of the drone and the position coordinate information of the target mobile device according to the first displacement of the drone and the second displacement of the target mobile device within the signal propagation delay time; Step S334 : Repeat step S32 according to the updated drone attitude information and the position coordinate information of the target mobile device to obtain the corrected direction information of the target mobile device.

5. The method for mobile device direction positioning based on air-ground collaboration according to claim 4, characterized in that: The timestamp information includes a first timestamp when the ground base station sends the ground signal, a second timestamp when the drone receives the ground signal, a third timestamp when the drone sends the fused signal, and a fourth timestamp when the target mobile device receives the fused signal; The signal propagation delay time is the sum of the ground signal propagation time and the fusion signal propagation time, wherein the ground signal propagation time is the difference between the second timestamp and the first timestamp, and the fusion signal propagation time is the difference between the fourth timestamp and the third timestamp.

6. The method for mobile device direction positioning based on air-ground collaboration according to claim 1, characterized in that: After step S3, the following steps are also included: In step S41A, the UAV continuously receives ground signals sent from the ground base station during flight, updates the UAV position information and attitude information, generates a new fusion signal and sends it to the target mobile device. After the target mobile device receives the new fusion signal, steps S2-S3 are repeated to update the direction information of the target mobile device.

7. The method for mobile device direction positioning based on air-ground collaboration according to claim 1, characterized in that: After step S3, the following steps are also included: Step S41B, detecting whether the direction information of the target mobile device determined in step S3 has a direction positioning error; Step S42B: When it is detected that the direction positioning error exceeds a preset threshold, the calculation parameters and algorithm weights of triangulation and attitude solution are adjusted according to the direction positioning error to obtain optimized direction information of the target mobile device.

8. The method for mobile device direction positioning based on air-ground collaboration according to claim 7, characterized in that: Determining whether there is a direction positioning error in step S41B includes: Comparing the reference direction of the target mobile device with the direction information of the target mobile device determined in step S3; or Comparing the direction information of the target mobile device determined in step S3 with the mean and standard deviation of the direction information of the historical positioning of the target mobile device; or Cross-verification is performed based on the direction information of the plurality of mobile devices and the direction information of the target mobile device.

9. The method for mobile device direction positioning based on air-ground collaboration according to claim 7, characterized in that: The calculation parameters and algorithm weights adjusted in step S42B include: A reference coordinate system constructed with the drone; and / or The rotation matrix from the world coordinate system to the drone coordinate system; and / or The distance weight coefficients between each ground base station and the target mobile device in the triangulation; and / or An angle weight coefficient between the drone and the target mobile device in the attitude solution; The algorithmic weights for the triangulation and pose solution.

10. A mobile device direction positioning system based on air-ground collaboration, characterized in that: The method for locating the direction of a mobile device based on air-ground collaboration according to any one of claims 1 to 9 comprises: A ground base station group, comprising a plurality of ground base stations, each of which is used to send a ground signal including ground base station location information and a timestamp; A drone swarm includes multiple drones, each of which is configured to fly along a predetermined route and receive a ground signal containing the ground base station's location information and a timestamp from a ground base station during flight. The ground signal is then fused with the drone's location information and attitude information to generate a fused signal containing air-ground collaboration information. A mobile device, configured to receive the fused signal sent from the drone, and pre-process the fused signal to obtain a pre-processed fused signal; The direction determination module is used to perform triangulation and posture calculation based on the pre-processed fusion signal to determine the direction information of the mobile device.

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