A real-time monitoring and positioning system and method for low-altitude unmanned aerial vehicles

Abstract

The application provides a real-time monitoring and positioning system and method for low-altitude unmanned aerial vehicles, and belongs to the technical field of real-time monitoring and positioning of low-altitude unmanned aerial vehicles. The system is realized by integrating a small-sized airborne device into an unmanned aerial vehicle. The small-sized airborne device integrates a GNSS module, an IMU module, a vision module, a 5G module and an embedded processor based on an ARM architecture. The hardware-level time synchronization of the GNSS module, the IMU module and the vision module is realized through a pulse-per-second signal output by a built-in GNSS board card. The ARM processor receives original observation data of each module in real time, and the three-dimensional position and attitude angle information of the device is solved in real time through embedded multi-source data fusion positioning software. The 5G communication module transmits the solving result to a remote monitoring and display terminal in real time.

Description

Technical Field

[0001] This invention relates to the field of real-time monitoring and positioning technology for low-altitude unmanned aerial vehicles (UAVs), and in particular to a real-time monitoring and positioning system and method for low-altitude UAVs. Background Technology

[0002] Currently, UAV positioning primarily relies on the Global Navigation Satellite System (GNSS). However, in complex low-altitude environments (such as urban canyons, dense forests, near-ground areas, and areas with electromagnetic interference), GNSS signals are susceptible to multipath effects, obstruction, non-line-of-sight propagation, and deception interference, leading to a sharp drop in positioning accuracy or loss of lock. Commonly used Real-Time Kinematic (RTK) positioning relies on ground-based reference stations, and its accuracy degrades significantly with distance. Inertial Navigation Systems (INS) can achieve short-term autonomous high-precision positioning, but error accumulation makes it difficult to guarantee high-precision positioning over long periods. Visual positioning is easily affected by lighting, texture, and dynamic targets, and is computationally complex.

[0003] Existing UAV positioning solutions primarily rely on GNSS (including RTK) for absolute positioning. However, in complex low-altitude environments (urban canyons, forests / dense forests, near the ground, areas with strong electromagnetic interference or spoofing, etc.), GNSS signals are susceptible to multipath effects, obstruction, non-line-of-sight (NLOS) propagation, and interference / spoofing, leading to severe degradation of pseudorange / carrier observations. This results in significantly reduced positioning accuracy, unstable positioning solutions, and even loss of lock. In particular, RTK positioning relies on ground reference stations and data links, which suffer from drawbacks such as reduced differential correction effectiveness due to increased baseline distance and solution degradation due to link interruptions. Consequently, it is difficult to guarantee continuous, stable, reliable, and high-precision positioning for UAVs in complex low-altitude scenarios.

[0004] Inertial navigation systems (INS) can provide high-frequency autonomous positioning / attitude information, but their integral drift is unavoidable. Errors accumulate over time, and position and heading errors increase rapidly during long-term operation. In the absence of external absolute observation constraints, it is difficult to maintain high accuracy and stability for a long period of time.

[0005] While visual / visual-inertial positioning can provide relative pose in GNSS-constrained scenarios, it is easily affected by changes in lighting, weak / repetitive textures, dynamic targets, motion blur, rain, and fog, causing feature extraction and matching to fail or degrade. At the same time, the algorithm is computationally complex, resource-intensive, and prone to introducing latency, affecting the stability of real-time closed-loop control and online positioning.

[0006] In existing solutions, there is often a lack of unified error models and consistency constraints among different sensors. When GNSS availability changes, problems such as abrupt mode switching, discontinuous state estimation, and uncontrollable error propagation often occur, affecting trajectory smoothness and flight control stability. Summary of the Invention

[0007] To address the above technical problems, this invention provides a real-time monitoring and positioning system for low-altitude unmanned aerial vehicles (UAVs), achieving multi-source data fusion positioning through the integration of miniaturized airborne equipment. The system utilizes a GNSS module, combined with BeiDou PPP-B2b and Galileo HAS (High Accuracy Service), to acquire raw GNSS data, broadcast ephemeris data, and precise orbital clock correction data in real time. It also incorporates an Inertial Measurement Unit (IMU) positioning module and a vision module to collect pose information. The system features a built-in ARM (Advanced RISC Machine) architecture processor and embeds a high-precision fusion positioning algorithm to calculate high-precision positioning results based on GNSS / INS / vision in real time. The position and attitude results are transmitted in real time to a monitoring and display terminal via a 5G module, achieving decimeter-level positioning of UAVs globally. This system will significantly improve the positioning accuracy, reliability, and continuity of UAVs in complex environments. The specific technical solution is as follows:

[0008] A real-time monitoring and positioning system for low-altitude unmanned aerial vehicles (UAVs) is implemented by integrating miniaturized airborne equipment into the UAV. This miniaturized airborne equipment integrates a GNSS module, an IMU module, a vision module, a 5G module, and an embedded processor based on an ARM architecture. Hardware-level time synchronization between the GNSS module, IMU module, and vision module is achieved through the pulse-per-second signal output by the built-in GNSS module. The processor receives raw observation data from each module in real time and calculates the device's three-dimensional position and attitude angle information in real time through embedded multi-source data fusion positioning. The 5G module transmits the calculation results to a remote monitoring and display terminal in real time. The ARM module integrates a HAS and PPP-B2b fusion positioning module and a multi-source sensor fusion positioning module. The HAS and PPP-B2b fusion positioning module is used to locate and obtain the approximate position of the UAV, determining whether to use HAS or PPP-B2b data based on the approximate position. The multi-source sensor fusion positioning module is used to generate precise position and attitude information for the device.

[0009] A real-time monitoring and positioning method for low-altitude unmanned aerial vehicles (UAVs) includes the following steps:

[0010] The GNSS module receives broadcast ephemeris, HAS correction data, and PPP-B2b correction data in real time. Through the precise ephemeris fusion module, the orbit clock error correction information from HAS and PPP-B2b services is used to generate precise ephemeris in real time. Subsequently, based on the raw GNSS observation data and the real-time generated precise ephemeris, the three-dimensional position coordinates, velocity, and receiver clock error of the equipment are used as parameters to be estimated to construct the raw observation equation. Further, a dual-frequency ionospheric desiccation combination processing mode is adopted to construct a combined observation equation. Finally, the commonly used Kalman filter parameter estimation method is used to solve for the unknown parameters to obtain the high-precision position parameters of the equipment and output the GNSS-based position information.

[0011] The IMU module first acquires raw velocity observation data from the three-axis accelerometer and raw attitude observation data from the three-axis gyroscope in real time. Then, it uses the position information output by GNSS to achieve initial alignment. Based on the initial alignment, the attitude reference of the IMU is determined. The attitude quaternion, velocity vector and position coordinates of the device are used as the state vector to be estimated. The state update equation is established, including velocity update, attitude update and position update. The real-time zero bias estimation parameters are compensated. Finally, the state vector is updated in real time through integration and the pose information based on the IMU is output.

[0012] The vision module first acquires visual observation data by acquiring environmental image sequences in real time. Then, based on the position information provided by the GNSS module, it calibrates the acquired parameters and preprocesses the images. Then, it uses the relative displacement vector and attitude change of the device as the state parameters to be estimated, establishes a motion estimation model, solves the state parameters, and outputs relative pose change information based on vision.

[0013] The multi-source sensor fusion positioning module first receives position information from the GNSS module, pose information from the IMU module, and relative pose change information from the vision module in real time. It uses the device's three-dimensional position coordinates, velocity vector, attitude angle, and IMU sensor zero bias as a unified state vector. It establishes multi-source observation equations using a loosely combined Kalman filter architecture and uses a coordinate system transformation module to uniformly transform the coordinates to the WGS84 coordinate system. Then, it performs multi-source data fusion through extended Kalman filtering iteration. The Kalman filter includes a prediction stage and an update stage. The prediction stage is mainly driven by IMU mechanical orchestration. The update stage fuses GNSS position observations, visual relative displacement observations, and IMU zero bias constraints to output the fused UAV pose information.

[0014] The drone's attitude information is transmitted to a remote monitoring and display terminal via a 5G module;

[0015] After receiving the data, the remote monitoring and display terminal overlays the map, superimposing the latitude and longitude of the drone to display its real-time location. At the same time, it visualizes the drone's attitude by displaying the drone's attitude through a 3D model.

[0016] An electronic device includes: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method.

[0017] A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the method described thereon.

[0018] The present invention has the following beneficial effects:

[0019] This invention integrates GNSS / IMU / visual / 5G / ARM processors into a miniaturized airborne integrated device to achieve hardware-level time synchronization and airborne real-time computation. It also introduces a fusion precision positioning and integrity adaptive switching mechanism between BeiDou PPP-B2b and Galileo HAS. Within and outside the PPP-B2b service range, it dynamically selects and fuses correction data from different constellations (PPP-B2b prioritizes GPS+BDS, with HAS supplementing Galileo; outside the range, HAS prioritizes GPS+Galileo, with PPP-B2b supplementing BDS). Simultaneously, it automatically switches to the next function in case of service interruption or data anomaly through integrity detection. Combined with airborne GNSS / INS / visual loose combination fusion positioning and 5G backhaul, it enables decimeter-level, continuous, and reliable positioning and remote real-time monitoring and display of UAVs in complex low-altitude environments.

[0020] This invention utilizes the built-in fusion positioning algorithm of the airborne ARM processor. When GNSS is available, it outputs precise GNSS positioning based on the combination of dual-frequency ionospheric de-sphericity and error model correction (ionosphere, troposphere, antenna phase center, etc.). Kalman filtering is used as an external observation to perform online correction of the IMU mechanical arrangement results. When GNSS degrades, the IMU provides high-frequency continuous pose updates to suppress INS drift and ensure positioning continuity.

[0021] This invention uses the relative pose information output by the vision module as a supplementary observation source for the fusion system. By using a loose combination strategy and a filtering framework to uniformly manage the introduction and weight allocation of visual observations, vision can improve the observability of positioning when GNSS is limited and prevent it from dominating the solution divergence when the environment is degraded. This improves the stability and availability in complex scenarios while ensuring airborne real-time performance.

[0022] This invention constructs a PPP-B2b service range coordinate boundary dataset and uses pseudorange single-point positioning to obtain approximate locations, implementing a strategy of "coverage discrimination - data priority selection - cross-constellation correction fusion". At the same time, an integrity detection module is set up to continuously monitor the status and data quality of HAS and PPP-B2b. When an anomaly occurs, it automatically switches to another available service, reducing the risk of positioning jumps and loss of lock caused by correction data interruption or anomalies, and improving positioning continuity and reliability.

[0023] This invention achieves multi-source fusion positioning by integrating miniaturized airborne equipment. The system utilizes a GNSS module to receive raw observation data and broadcast ephemeris in real time, and combines this with BeiDou PPP-B2b and Galileo satellite HAS services to obtain precise orbital clock correction data. Simultaneously, it integrates attitude information provided by an IMU module and pose data from a vision module, running an embedded fusion positioning algorithm on an ARM architecture processor to calculate GNSS / INS / vision data in real time, generating high-precision position and attitude results for the carrier. This result is transmitted via a 5G communication module to a ground monitoring and display terminal for real-time display and flight path comparison to identify system anomalies. The system possesses decimeter-level positioning capability globally, significantly improving the positioning performance of UAVs in complex environments. It maintains high-precision positioning even in environments where GNSS signals are blocked, providing strong technical support for the safe operation of low-altitude UAVs.

[0024] This invention integrates multi-source sensor information such as GNSS / RTK, INS, and vision, and employs unified estimation methods such as Kalman filtering and factor graph optimization to model and constrain the errors of each sensor online. This enables UAVs to achieve continuous, high-precision, and highly reliable positioning in complex low-altitude environments: providing absolute positioning and real-time correction of inertial drift when GNSS signal is good; seamlessly switching to INS / vision-dominated positioning when GNSS is blocked, multipath, interfered with, or briefly lost lock, suppressing rapid error divergence and maintaining smooth trajectory output; and rapidly converging and smoothly realigning after GNSS recovery. This significantly improves positioning robustness, anti-interference capability, and engineering availability, ensuring stable flight control and continuous mission execution. Attached Figure Description

[0025] Figure 1 This is a framework diagram of a real-time monitoring and positioning system for low-altitude unmanned aerial vehicles (UAVs) according to the present invention.

[0026] Figure 2 A flowchart of a multi-source fusion positioning algorithm for unmanned aerial vehicles (UAVs). Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above objectives, this invention adopts the following technical solution.

[0028] like Figure 1 As shown, this invention provides a real-time monitoring and positioning system for low-altitude unmanned aerial vehicles (UAVs). It is implemented by integrating miniaturized airborne equipment into the UAV. This miniaturized airborne equipment integrates a GNSS module, an IMU module, a vision module, a 5G module, and an embedded processor based on an ARM architecture. Hardware-level time synchronization is achieved between the GNSS module, IMU module, and vision module through the pulse-per-second signal output by the built-in GNSS module. The ARM processor receives raw observation data from each module in real time and calculates the device's three-dimensional position and attitude angle information in real time using embedded multi-source data fusion positioning software. The 5G communication module transmits the calculation results to a remote monitoring and display terminal in real time.

[0029] The miniaturized airborne device incorporates a HAS and PPP-B2b fusion positioning module. Since HAS provides global orbit and clock correction data from GPS and Galileo systems, while PPP-B2b provides regional orbit and clock correction data from GPS and BDS satellites, a fusion scheme is adopted to leverage the advantages of both. However, broadcasting GPS satellite correction data from both systems introduces system redundancy. Therefore, the fusion positioning module first constructs a coordinate boundary dataset of the PPP-B2b service area. Then, it uses pseudorange single-point positioning to obtain the approximate location of the UAV. Based on this approximate location, if the UAV is within the PPP-B2b service area, it prioritizes using the PPP-B2b GPS and BDS correction data, fusing it with the Galileo correction data from HAS. If the UAV is outside the PPP-B2b service area, it uses the HAS GPS and Galileo correction data, fusing it with the BDS correction data from PPP-B2b. Meanwhile, the system has a built-in integrity detection module that continuously monitors the status and data quality of HAS and PPP-B2b services. Once an interruption or data anomaly is detected in either service, it will automatically switch to another available service to ensure the continuity of the location results.

[0030] The small unmanned aerial vehicle (UAV) is equipped with an ARM processor that integrates a multi-source sensor fusion positioning module. This module receives real-time GNSS raw observation data, broadcast ephemeris data, and real-time orbit clock correction data from HAS and PPP-B2b. First, it constructs the raw observation equations based on pseudorange and carrier phase, as shown in equations (1) and (2).

[0031] (1)

[0032] (2)

[0033] In the formula, s, r, and i represent the satellite, receiver, and signal frequency, respectively. Represents pseudorange observations. Represents the carrier phase observation value. Let c be the geometric distance between the satellite and the receiver, and c represent the speed of light (299,792,458 m / s). Indicates receiver clock bias. Indicates satellite clock bias, For ionospheric delay error, For tropospheric delay error, For wavelength, For integer ambiguity, This is pseudorange noise. This is carrier phase noise.

[0034] To eliminate the influence of the ionosphere, a dual-frequency ionosphere desiccation combination is used to construct the combined observation equation, as shown in formula (3):

[0035] (3)

[0036] (4)

[0037] In the formula, The combined pseudorange observation equations are as follows: The combined carrier phase observation equation is as follows: It is the square of the first frequency and the square of the second frequency. and The pseudorange observations are for the first frequency and the second frequency. and The carrier phase observations for the first frequency and the carrier phase observations for the second frequency are given. The geometric distance between the satellite and the receiver. The wavelength factor for the first frequency. and The integer ambiguity of the first frequency and the integer ambiguity of the second frequency.

[0038] In data processing, the tropospheric delay is corrected using the commonly used Saastamoinen model, and the antenna phase center deviation is corrected using the igs14.atx file. Then, the commonly used Kalman filter parameter estimation method is used to estimate the position of the airborne equipment in real time and output GNSS-based positioning information. At the same time, the IMU module continuously updates the position, attitude and velocity of the carrier through mechanical orchestration algorithms, as shown in formulas (5) to (7), to generate the corresponding IMU positioning and attitude results.

[0039] (5)

[0040] (6)

[0041] (7)

[0042] In the formula, , , These are the error state vectors for attitude, velocity, and position, respectively. This is the Earth's angular velocity vector in the n-frame (navigation coordinate system); The direction cosine matrix for transforming the b-frame (carrier coordinate system) to the n-frame; The angular velocity vector of the n-system; It is the angular velocity vector; It is the coordinate transformation matrix from the b-system to the n-system; This refers to the measurement error of the gyroscope. The angle of inaccuracy; express The error, express The error, express The error, It is a force vector. It is the error of the force. and Let be the velocity vector and its differential in the n-system. This represents the gravity error term. The differential of the position vector in the n-system. ,in Indicates latitude, Indicates the error in longitude. This indicates the error in latitude.

[0043] The vision module calculates relative position information based on vision through image acquisition, image preprocessing, and feature matching. Finally, the fusion positioning module uses a loosely combined fusion strategy to comprehensively process data from GNSS, IMU, and the vision module, generating accurate position and attitude information of the device in real time.

[0044] The small drone has a built-in 5G communication module, which can transmit the position and attitude results calculated in real time by the multi-source fusion positioning module to the ground monitoring and display terminal. After receiving the positioning results, the terminal immediately displays them on a map and compares the real-time position with the preset flight path coordinates to monitor whether the drone's flight status deviates from the expected value, thereby determining whether there are any anomalies. Specific Implementation

[0045] The miniaturized airborne equipment is the core carrier of the system. This equipment integrates a GNSS module, an IMU module, a vision module, a 5G communication module, and an ARM architecture processing system to achieve hardware-level time synchronization and multi-source data acquisition. The specific steps are as follows:

[0046] (1) Composition of miniaturized airborne equipment hardware modules:

[0047] The system includes a GNSS module (supporting multi-frequency observation data from four global systems: BeiDou, GPS, Galileo, and GLONASS), an IMU module (containing a three-axis accelerometer and a three-axis gyroscope), and a vision module (visual camera with a resolution of no less than 1920). 1080), 5G communication modules and ARM architecture processors are integrated into a size ≤100mm. 80mm Electrical connections are achieved through a PCB board within a 50mm thick aluminum heat sink housing, ensuring a weight of ≤500g.

[0048] (2) Multi-source sensor time synchronization:

[0049] The pulse-per-second (PPS) signal (accuracy ≤ 10ns) output by the GNSS module is used to synchronize the IMU module and the vision module through hardware circuitry. After receiving the PPS signal, the IMU module will trigger the accelerometer and gyroscope to sample at a sampling rate of 100Hz. After receiving the PPS signal, the vision module will synchronously acquire images and add time stamps to the images.

[0050] Steps for HAS and PPP-B2b integrated positioning:

[0051] This step provides technical support for high-precision global positioning of UAVs by integrating precise orbital clock correction data from BeiDou PPP-B2b and Galileo HAS services. The specific steps are as follows:

[0052] (1) Synthesis based on PPP-B2b and HAS precise ephemeris:

[0053] Using a GNSS board, orbit and clock correction data from PPP-B2b and HAS can be received in real time. However, the correction data is in binary data stream format, so the binary data first needs to be parsed into usable orbit and clock correction data. Then, combined with broadcast ephemeris, a precise ephemeris based on PPP-B2b and HAS can be generated in real time.

[0054] (2) Service selection and data fusion:

[0055] The system first establishes a PPP-B2b service range coordinate boundary dataset based on the PPP-B2b service area (signals from some PPP-B2b satellites can be received outside the PPP-B2b dataset boundary, but the accuracy is difficult to guarantee). Then, using raw GNSS observation data and broadcast ephemeris, the system calculates the approximate coordinates of the UAV using a pseudorange single-point positioning method. Finally, the system compares the approximate position coordinates of the UAV with the PPP-B2b service boundary dataset to determine whether the UAV is within the PPP-B2b service range. If the UAV is within the service range of PPP-B2b, the GPS and BDS correction data of PPP-B2b (with higher regional service accuracy) are used first, and the Galileo correction data of HAS (supplementing global satellite coverage) is fused to construct a multi-system joint precise ephemeris. During data processing, the weights between systems are as shown in formula (8), and the weight of the Galileo system is reduced. If the UAV is outside the service range of PPP-B2b, the GPS and Galileo correction data of HAS are used, and the BDS correction data of PPP-B2b (supplementing Beidou satellite coverage) is fused to construct a multi-system joint precise ephemeris. The weights between systems are as shown in formula (8), and the weight of the BDS satellite system is reduced. At the same time, the device has a built-in service integrity detection module to monitor the integrity of the correction data in real time. If the correction data is interrupted or a system abnormality is detected, it will automatically switch to another available service (such as switching to HAS service when PPP-B2b is abnormal) to ensure the continuity of positioning.

[0056] (8)

[0057] Steps for achieving precise positioning through multi-source data fusion:

[0058] Multi-source data fusion positioning mainly involves fusing observation data from the GNSS module, data from the IMU module, and data from the vision module for positioning. For detailed procedures, please refer to [link / reference needed]. Figure 2 :

[0059] The GNSS module first utilizes broadcast ephemeris data, HAS correction data, and PPP-B2b correction data received in real time from the GNSS board. Through a precise ephemeris fusion module, it generates precise ephemeris in real time by incorporating orbit clock correction information from HAS and PPP-B2b services. Subsequently, based on the raw GNSS observation data and the real-time generated precise ephemeris, the device's three-dimensional position coordinates, velocity, and receiver clock error are used as parameters to be estimated to construct the original observation equations. To eliminate the influence of ionospheric delay errors, a dual-frequency ionospheric de-escalation combined processing mode is further adopted to construct combined observation equations. Finally, the commonly used Kalman filter parameter estimation method is used to solve for the unknown parameters, obtaining the device's high-precision position parameters and outputting GNSS-based position information.

[0060] The IMU module first acquires raw velocity observation data from the three-axis accelerometer and raw attitude observation data from the three-axis gyroscope in real time. Then, it uses the position information output by GNSS to achieve initial alignment. Based on the initial alignment, the IMU's attitude reference is determined. The device's attitude quaternion, velocity vector, and position coordinates are used as the state vector to be estimated. A mechanical orchestration algorithm for strapdown inertial navigation systems is used to establish state update equations (including velocity update, attitude update, and position update). To suppress the accumulation of sensor errors (such as gyroscope bias and accelerometer bias), the real-time bias estimation parameters are compensated. Finally, the state vector is updated in real time through integral calculations (velocity updates need to compensate for gravitational acceleration and Earth's rotation effects; attitude updates use quaternion differential equations; and position updates are obtained by velocity integration), outputting IMU-based pose information (position, attitude, and velocity).

[0061] The vision module first acquires visual observation data by acquiring real-time environmental image sequences (resolution ≥ 1920×1080, frame rate ≥ 30fps) via a camera. Then, based on the location information provided by GNSS, the camera parameters are calibrated. To eliminate dynamic environmental interference (such as changes in illumination and motion blur), the images are preprocessed. Next, the relative displacement vector and attitude change of the device are used as the estimated state parameters. Feature processing and matching methods are applied to establish a motion estimation model. Finally, the state parameters are solved using multi-view geometry principles, outputting vision-based relative pose change information (displacement accuracy ≤ 0.1m, attitude accuracy ≤ 0.5°).

[0062] The multi-source data fusion positioning module first receives position information from the GNSS module, pose information from the IMU module, and relative pose change information from the vision module in real time. Then, it uses the device's three-dimensional position coordinates, velocity vector, attitude angle, and IMU sensor zero bias as a unified state vector and establishes multi-source observation equations using a loosely combined Kalman filter architecture. To eliminate spatiotemporal reference differences in multi-source data (such as inconsistent coordinate systems), a coordinate system transformation module is first used to uniformly transform the coordinates to the WGS84 coordinate system. Then, multi-source data fusion is performed through an iterative extended Kalman filter. The Kalman filter includes a prediction phase and an update phase. The prediction phase is mainly driven by IMU mechanical orchestration, while the update phase fuses GNSS position observations, visual relative displacement observations, and IMU zero bias constraints, outputting the fused global high-precision pose information of the device.

[0063] The drone's attitude information is transmitted to the real-time positioning monitoring and display terminal via a 5G communication module, enabling visualized anomaly monitoring. High-precision pose information output by the multi-source data fusion positioning module is transmitted in real-time to the real-time positioning monitoring and display terminal in JSON format via the 5G communication module, with a transmission speed ≥100Mbps and latency ≤20ms. An example data format is shown below.

[0064]

[0065]

[0066] ;

[0067] Formatting instructions: lat represents latitude in degrees, lon represents longitude in degrees, alt represents altitude in meters, roll represents roll angle in degrees, pitch represents pitch angle in degrees, and yaw represents yaw angle in degrees. This represents the east-west velocity in the navigation coordinate system, expressed in meters per second. This indicates the speed in the north-south direction in navigation coordinates, expressed in meters per second. This indicates the velocity in the zenith direction in the navigation coordinate system, in meters per second. GNSS, IMU, and SLAM represent the working status of the positioning module, and can be 0 or 1. 1 indicates that the module is working normally and the data is valid, while 0 indicates that the module is malfunctioning.

[0068] After receiving the data, the real-time positioning monitoring and display terminal performs map overlay, superimposing the drone's latitude and longitude onto the Amap map at a scale of 1:500 to display the drone's real-time location. Simultaneously, it visualizes the drone's attitude using a 3D model.

[0069] The route comparison and anomaly monitoring module is optional. If the user terminal has imported the preset drone route coordinates, this module will be automatically called to calculate the horizontal and vertical errors between the drone's real-time position and the preset route, and to make anomaly judgments. When the deviation exceeds the set threshold, the terminal will trigger an alarm, and the abnormal location will be marked in red on the map.

[0070] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0071] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0074] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0075] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A real-time monitoring and positioning system for low-altitude unmanned aerial vehicles (UAVs), characterized in that, This is achieved by integrating miniaturized airborne equipment into drones. The miniaturized airborne equipment integrates a GNSS module, an IMU module, a vision module, a 5G module, and an embedded processor based on the ARM architecture. Hardware-level time synchronization between the GNSS module, the IMU module, and the vision module is achieved through the pulse-per-second signal output by the built-in GNSS module. The processor receives raw observation data from each module in real time and calculates the device's three-dimensional position and attitude angle information in real time through embedded multi-source data fusion positioning. The 5G module transmits the calculation results to the remote monitoring and display terminal in real time. The ARM has built-in HAS and PPP-B2b fusion positioning module and multi-source sensor fusion positioning module. The HAS and PPP-B2b fusion positioning module is used to locate and obtain the approximate position of the UAV and determines whether to use HAS or PPP-B2b data based on the approximate position. The multi-source sensor fusion positioning module is used to generate the device's precise position and attitude information.

2. The real-time monitoring and positioning system for low-altitude unmanned aerial vehicles according to claim 1, characterized in that, In the HAS and PPP-B2b fusion positioning module, HAS provides global orbit and clock correction data from GPS and Galileo systems, while PPP-B2b provides regional orbit and clock correction data from GPS and BDS satellites. First, a coordinate boundary dataset of the PPP-B2b service area is constructed. Then, pseudorange single-point positioning is used to obtain the approximate location of the UAV. Based on the approximate location, if the UAV is within the PPP-B2b service area, the clock correction data from GPS and BDS satellites provided by PPP-B2b is prioritized, and the clock correction data from Galileo system provided by HAS is fused. If the UAV is outside the PPP-B2b service area, the clock correction data from GPS and Galileo system provided by HAS is used, and the clock correction data from BDS satellites provided by PPP-B2b is fused.

3. A real-time monitoring and positioning system for low-altitude unmanned aerial vehicles according to claim 1, characterized in that, The multi-source sensor fusion positioning module receives raw GNSS observation data, broadcast ephemeris data, and real-time orbit clock correction data from HAS and PPP-B2b in real time. It constructs the raw observation equation using a dual-frequency ionospheric de-escalation combination and performs model corrections for ionospheric delay, tropospheric delay, and antenna phase center deviation. Then, it uses a Kalman filter solution strategy to estimate the position of the airborne equipment in real time and outputs GNSS-based positioning information. Meanwhile, the IMU module continuously updates the position, attitude, and velocity of the carrier through a mechanical orchestration algorithm, generating corresponding IMU positioning and attitude results. The vision module calculates the relative position information based on vision through image acquisition, image preprocessing, and feature matching. Finally, a loosely combined fusion strategy is used to comprehensively process the data from the GNSS module, IMU module, and vision module to generate accurate position and attitude information of the equipment in real time.

4. A real-time monitoring and positioning system for low-altitude unmanned aerial vehicles according to claim 1, characterized in that, The GNSS module supports multi-frequency observation data from four global systems: BeiDou, GPS, Galileo, and GLONASS.

5. A real-time monitoring and positioning system for low-altitude unmanned aerial vehicles according to claim 1, characterized in that, The IMU module uses a three-axis accelerometer or a three-axis gyroscope.

6. A real-time monitoring and positioning system for low-altitude unmanned aerial vehicles according to claim 1, characterized in that, The vision module uses a vision camera.

7. A real-time monitoring and positioning system for low-altitude unmanned aerial vehicles according to claim 3, characterized in that, The 5G module transmits the pose information output by the multi-source sensor fusion positioning module to the ground display terminal in real time in JSON format.

8. A real-time monitoring and positioning method for low-altitude unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: The GNSS module receives broadcast ephemeris, HAS correction data, and PPP-B2b correction data in real time. Through the precise ephemeris fusion module, the orbit clock error correction information from HAS and PPP-B2b services is used to generate precise ephemeris in real time. Subsequently, based on the raw GNSS observation data and the real-time generated precise ephemeris, the three-dimensional position coordinates, velocity, and receiver clock error of the equipment are used as parameters to be estimated to construct the raw observation equation. Further, a dual-frequency ionospheric desiccation combination processing mode is adopted to construct a combined observation equation. Finally, the commonly used Kalman filter parameter estimation method is used to solve for the unknown parameters to obtain the high-precision position parameters of the equipment and output the GNSS-based position information. The IMU module first acquires raw velocity observation data from the three-axis accelerometer and raw attitude observation data from the three-axis gyroscope in real time. Then, it uses the position information output by GNSS to achieve initial alignment. Based on the initial alignment, the attitude reference of the IMU is determined. The attitude quaternion, velocity vector and position coordinates of the device are used as the state vector to be estimated. The state update equation is established, including velocity update, attitude update and position update. The real-time zero bias estimation parameters are compensated. Finally, the state vector is updated in real time through integration and the pose information based on the IMU is output. The vision module first acquires visual observation data by acquiring environmental image sequences in real time. Then, based on the position information provided by the GNSS module, it calibrates the acquired parameters and preprocesses the images. Then, it uses the relative displacement vector and attitude change of the device as the state parameters to be estimated, establishes a motion estimation model, solves the state parameters, and outputs relative pose change information based on vision. The multi-source sensor fusion positioning module first receives position information from the GNSS module, pose information from the IMU module, and relative pose change information from the vision module in real time. It uses the device's three-dimensional position coordinates, velocity vector, attitude angle, and IMU sensor zero bias as a unified state vector. It establishes multi-source observation equations using a loosely combined Kalman filter architecture and uses a coordinate system transformation module to uniformly transform the coordinates to the WGS84 coordinate system. Then, it performs multi-source data fusion through extended Kalman filtering iteration. The Kalman filter includes a prediction stage and an update stage. The prediction stage is mainly driven by IMU mechanical orchestration. The update stage fuses GNSS position observations, visual relative displacement observations, and IMU zero bias constraints to output the fused UAV pose information. The drone's attitude information is transmitted to a remote monitoring and display terminal via a 5G module; After receiving the data, the remote monitoring and display terminal overlays the map, superimposing the latitude and longitude of the drone to display its real-time location. At the same time, it visualizes the drone's attitude by displaying the drone's attitude through a 3D model.

9. An electronic device, characterized in that, include: One or more processors; A memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the real-time monitoring and positioning method for low-altitude unmanned aerial vehicles as described in claim 8.

10. A computer-readable storage medium, characterized in that, It stores executable instructions, which, when executed by a processor, enable the processor to implement the real-time monitoring and positioning method for low-altitude unmanned aerial vehicles as described in claim 8.

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