A drone cluster cooperative navigation system

Abstract

The application relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle cluster cooperative navigation system. The system comprises a data acquisition module, which is used for collecting flight data of each unmanned aerial vehicle in the unmanned aerial vehicle cluster in real time; a signal reliability evaluation module, which is used for determining the signal reliability of each unmanned aerial vehicle according to the flight data and in combination with unmanned aerial vehicle sensor performance parameters, so as to divide reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles; a reference selection module, which is used for analyzing the flight trajectory correlation between the reliable unmanned aerial vehicles and the unreliable unmanned aerial vehicles, and selecting a reference unmanned aerial vehicle for providing navigation support for each unreliable unmanned aerial vehicle according to the flight trajectory correlation; and a cooperative control module, which is used for fusing the navigation information of the reference unmanned aerial vehicle, obtaining the estimated state information of the unreliable unmanned aerial vehicle, and controlling the unreliable unmanned aerial vehicle to adjust the flight state, so as to realize unmanned aerial vehicle cluster cooperative navigation. The application realizes the improvement of the accuracy and robustness of unmanned aerial vehicle cluster cooperative navigation.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and more specifically to a UAV swarm cooperative navigation system. Background Technology

[0002] Unmanned aerial vehicle (UAV) swarm collaborative navigation refers to a technical system in which multiple UAVs work together to achieve high-precision positioning and trajectory control through "perception sharing, information interaction, and joint decision-making." As the tasks performed by UAVs become increasingly diverse and complex, the capabilities of a single UAV are insufficient to meet the demands. Multi-UAV collaborative operations offer advantages such as functional distribution, high system survivability, and high efficiency, enabling them to better accomplish mission objectives in complex environments. They are widely used in scenarios such as aerial performances, geographic mapping, and emergency rescue.

[0003] The prerequisite for multiple drones to successfully complete their missions is the availability of accurate navigation information. In aerial performance scenarios, since performances often take place in densely populated areas such as city squares and stadiums, the GNSS signals of drones are easily interfered with, causing each node in the cluster to be unable to obtain accurate time references. This makes it difficult to guarantee data consistency and coordination, thereby reducing the accuracy and robustness of the cluster's collaborative navigation. Summary of the Invention

[0004] To address the technical problem of low accuracy in cooperative navigation of unmanned aerial vehicle (UAV) swarms, the present invention aims to provide a UAV swarm cooperative navigation system, the specific technical solution of which is as follows:

[0005] In a first aspect, embodiments of the present invention provide a drone swarm cooperative navigation system, the system comprising:

[0006] The data acquisition module is used to collect flight data of each drone in the drone swarm in real time;

[0007] The signal reliability assessment module is used to determine the signal reliability of each UAV based on the flight data and the performance parameters of the UAV sensors, thereby classifying reliable UAVs into unreliable UAVs.

[0008] The reference selection module is used to analyze the correlation between the flight trajectories of unreliable UAVs and reliable UAVs, and select a reference UAV to provide navigation support for each unreliable UAV based on the correlation between the flight trajectories.

[0009] The collaborative control module is used to fuse the navigation information of the reference UAV, obtain the estimated state information of the unreliable UAV, and control the unreliable UAV to adjust its flight state according to the estimated state information, thereby realizing collaborative navigation of the UAV swarm.

[0010] In some embodiments, the flight data includes one or more of the following: the UAV's three-dimensional position coordinates, flight speed, actual flight path, planned flight path, and number of satellite signals received.

[0011] In some embodiments, determining the signal reliability of each drone includes:

[0012] Based on the flight data, determine the yaw-related parameters for each UAV within the preset reference time period;

[0013] Based on the number of satellite signals received by the UAV in the flight data, calculate the signal quality parameters of each UAV during the reference time period;

[0014] The sensor performance parameters of the UAV are obtained, and the yaw-related parameters, the signal quality parameters, and the sensor performance parameters are weighted and fused to obtain the signal reliability of the UAV.

[0015] In some embodiments, determining the yaw-related parameters of each UAV within a preset reference time period based on the flight data includes:

[0016] Extract the actual position coordinates and the position coordinates of the predetermined trajectory of each UAV within a preset reference time period from the flight data;

[0017] Calculate the first spatial distance between the actual position coordinates of the UAV and the predetermined trajectory position coordinates at each moment within the reference time period, and normalize the first spatial distance to obtain the yaw distance at each moment.

[0018] The yaw time is determined based on the yaw distance, and consecutive adjacent yaw times are combined into a yaw time period;

[0019] For each yaw period, the second spatial distance between the actual position coordinates of the UAV at two adjacent moments is calculated, and the second spatial distance is normalized to obtain the position deviation of the next moment in the adjacent moments.

[0020] The number of times within each yaw period that the position deviation meets the preset jump characteristics is counted, and the proportion of the number of times that meet the preset jump characteristics to the total number of times in the yaw period is calculated to obtain the jump time proportion.

[0021] The yaw-related parameters include: the yaw distance, the position deviation, and the percentage of jump times.

[0022] In some embodiments, the process of distinguishing between reliable and unreliable drones is as follows:

[0023] The signal reliability value of each drone is compared with the preset signal reliability threshold;

[0024] If the signal reliability value of the drone is greater than the preset signal reliability threshold, the drone is determined to be a reliable drone; if the signal reliability value of the drone is less than or equal to the preset signal reliability threshold, the drone is determined to be an unreliable drone.

[0025] Record all judgment results, distinguish between reliable and unreliable drones in the drone cluster, and mark them.

[0026] In some embodiments, the analysis of the flight trajectory correlation between unreliable and reliable drones includes:

[0027] Obtain the distance sequence between unreliable drones and reliable drones within a preset time interval, and calculate the mean and variance of the distance sequence to obtain a stable distance value;

[0028] Obtain the flight path slope at each moment on the actual flight path of the unreliable UAV and the reliable UAV within the time interval, and determine the consistency of the positive and negative signs of the slopes at the same moment.

[0029] Obtain the velocity time series sequences of unreliable and reliable UAVs within the time interval, and calculate the similarity between their velocity sequences;

[0030] The distance stability value, the positive and negative consistency of the slopes of the two, and the similarity of the velocity sequence are weighted and fused to obtain the correlation of flight trajectories between unreliable and reliable drones.

[0031] In some embodiments, the system further includes:

[0032] The navigation reference value calculation module is used to calculate the navigation reference value data of each reliable UAV to each unreliable UAV by combining the signal reliability of reliable UAVs and the flight trajectory correlation between unreliable UAVs and reliable UAVs.

[0033] In some embodiments, selecting a reference UAV to provide navigation support for each unreliable UAV based on the flight trajectory correlation includes:

[0034] Retrieve the navigation reference value data;

[0035] For each unreliable UAV, collect navigation reference value data from all reliable UAVs corresponding to that unreliable UAV;

[0036] All navigation reference value data collected for the same unreliable drone are arranged in descending order of value;

[0037] The number of reference drones to be selected for each unreliable drone is preset, and reliable drones that rank high and whose number meets the preset setting are selected from the sorted navigation reference value data sequence.

[0038] The selected reliable drone is designated as the reference drone for the corresponding unreliable drone.

[0039] In some embodiments, fusing navigation information from a reference UAV to obtain estimated state information of an unreliable UAV includes:

[0040] Collect navigation information for each reference drone selected for the unreliable drone, and retrieve navigation reference value data for each reference drone relative to the corresponding unreliable drone;

[0041] Weights are assigned to the navigation information corresponding to each reference drone based on the navigation reference value data of each reference drone.

[0042] The navigation information of each reference UAV with predefined weights is input into a preset filtering algorithm for data fusion calculation.

[0043] By fusion calculation of data, estimated state information of unreliable drones is obtained.

[0044] In some embodiments, controlling unreliable UAVs to adjust their flight states based on the estimated state information to achieve UAV swarm cooperative navigation includes:

[0045] Acquire the current actual flight status data of unreliable drones, compare the estimated state information of unreliable drones with the current actual flight status data, and analyze the deviation between the two;

[0046] Based on the deviation analysis results and the preset flight requirements of the UAV swarm formation, flight status adjustment instructions are generated for unreliable UAVs, and the adjustment instructions are transmitted to the corresponding unreliable UAVs in real time.

[0047] In a second aspect, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the various possible implementations of the first aspect.

[0048] Thirdly, embodiments of the present invention provide a computer program product comprising: computer program code, which, when run on a computer, causes the computer to perform the method described in the first aspect or any possible implementation thereof.

[0049] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the various possible implementations of the first aspect.

[0050] The embodiments of the present invention have at least the following beneficial effects:

[0051] This invention analyzes the signal reliability of each UAV in real time, filters out several unreliable UAVs and reliable UAVs, and selects several reference UAVs for each unreliable UAV for cooperative navigation based on the correlation between the flight trajectories of reliable and unreliable UAVs. This avoids formation misalignment that affects the visual effect of the performance, reduces the flight safety risk caused by positioning inaccuracy, and significantly improves the accuracy and robustness of UAV swarm cooperative navigation. Attached Figure Description

[0052] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a system block diagram of a drone swarm cooperative navigation system provided in one embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of the structure of a computer device provided in one embodiment of the present invention. Detailed Implementation

[0055] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the UAV swarm cooperative navigation system proposed according to the present invention.

[0056] In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0057] In the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" means two or more.

[0058] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0060] The embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0061] The specific scheme of the UAV swarm cooperative navigation system provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0062] Example 1:

[0063] Please see Figure 1 The diagram illustrates a system block diagram of a drone swarm cooperative navigation system according to an embodiment of the present invention. The system includes the following modules:

[0064] This invention provides a collaborative navigation system for unmanned aerial vehicle (UAV) swarms, the system comprising:

[0065] The data acquisition module 10 is used to collect flight data of each drone in the drone swarm in real time.

[0066] The data acquisition module is the core unit of the system for obtaining basic flight status data of UAVs. Its core function is to collect flight data of each UAV in the swarm in real time, providing data support for subsequent signal reliability assessment and trajectory correlation analysis. This module acquires data through positioning sensors (such as Global Navigation Satellite System Receiver, GNSS), velocity sensors, attitude sensors, and satellite signal receiving modules mounted on the UAVs. It collects flight data from each UAV in the swarm at a preset acquisition frequency (e.g., once per second) to ensure real-time capture of changes in UAV flight status. The collected flight data specifically includes the UAV's three-dimensional position coordinates (covering altitude, longitude, and latitude, reflecting the UAV's real-time position in three-dimensional space), flight speed (including horizontal and vertical velocity components, reflecting the UAV's speed and direction), actual flight path (the path trajectory of the UAV during actual flight), planned flight path (the pre-planned flight path of the UAV, serving as a benchmark for judging yaw), and the number of satellite signals received (a key indicator reflecting GNSS positioning accuracy; a higher number usually indicates more reliable positioning). This data is transmitted in real time to the system's central processing unit for subsequent storage and analysis.

[0067] The signal reliability assessment module 11 is used to determine the signal reliability of each UAV based on the flight data and in combination with the UAV sensor performance parameters, thereby classifying reliable UAVs into unreliable UAVs.

[0068] Specifically, firstly, the actual position coordinates and the position coordinates of the predetermined trajectory of each UAV within a preset reference time period (the reference time period is the period from the current time to 60 sampling times before the current time, such as the first 60 seconds) are extracted from the flight data; then, the first spatial distance between the actual position coordinates of the UAV and the position coordinates of the predetermined trajectory at each moment within this time period is calculated, and the first spatial distance is normalized to obtain the yaw distance at each moment. ( This is used to quantify the deviation between the actual position of the drone and the predetermined trajectory; then, a preset yaw distance threshold (e.g., 0.2) is set. The time interval ≥0.2 is recorded as the yaw time. Consecutive adjacent yaw times are combined into a yaw time interval. For each yaw time interval, the second spatial distance between the actual position coordinates of the UAV at two adjacent times is calculated. This second spatial distance is then normalized to obtain the position deviation of the next time interval in the adjacent time interval. ( This reflects the instantaneous change in the drone's position; then, a preset position deviation threshold (e.g., 0.8) is set. Moments with a distance ≥0.8 are recorded as meeting the preset jump characteristics. The number of such moments within each yaw period is counted, and their proportion of the total number of moments in the yaw period is calculated to obtain the jump moment percentage. The yaw distance is then... Positional deviation The percentage of jump times, together with the yaw-related parameters, are collectively referred to as yaw-related parameters.

[0069] Furthermore, based on the number of satellite signals received by the UAV in the flight data, the signal quality parameters of each UAV within the reference time period are calculated. Specifically, a preset satellite number threshold (e.g., 4) is used to mark moments with more than 4 satellite signals received during yaw moments as 1, and vice versa as 0, forming a 0-1 sequence; consecutive adjacent "1" moments constitute a signal stable period, and vice versa is recorded as a signal unstable period.

[0070] Calculate the signal quality parameters of the target UAV during the s-th yaw period. :

[0071]

[0072] in, This represents the number of moments in the longest stable signal period within the s-th yaw period. This represents the number of all times within the s-th yaw period. This represents the signal stability duration factor during the s-th yaw period. express The average number of satellites received by the target drone at all times within the corresponding time period. The larger the value, the higher the positioning accuracy.

[0073] Furthermore, the sensor performance parameters of the UAV are obtained (including IMU drift rate and performance coefficients corresponding to sensor operating states (normal / fault warning), which are obtained through the sensor fault detection module on the UAV); then, the stability of the target UAV's position change during the s-th yaw period is calculated using the following formula. :

[0074]

[0075] in, This represents the number of transition moments in the s-th yaw period. This represents the number of all times within the s-th yaw period. This indicates the frequency of sudden changes in the UAV's position during the s-th yaw period. This represents the yaw distance at the nth transition time within the s-th yaw time period. This represents the positional deviation at the nth transition time within the s-th yaw period. This indicates the degree of yaw change of the UAV during the s-th yaw period.

[0076] The signal reliability of the target UAV at the current moment is calculated using the following formula. :

[0077]

[0078] in, This indicates the number of time periods before the current time. This indicates the number of normal times within the reference time period at the current time. This indicates the total duration of the reference time period at the current moment. The percentage of time the target drone is in normal flight condition. The larger the value, the less likely the target drone will veer off course during flight, indicating better flight stability and a more reliable signal. This indicates the percentage of time during which the target drone's flight deviated from its intended path. The larger the value, the more severe the drone's flight yaw, the worse its flight quality, and the less reliable its signal. This indicates normalization processing. This represents the stability of the target UAV's position change during the s-th yaw time period. This represents the signal quality of the target UAV during the s-th yaw period.

[0079] When classifying reliable and unreliable drones, the signal reliability value of each drone is compared with a preset signal reliability threshold (e.g., 0.4). If the signal reliability value is >0.4, it is determined to be a reliable drone (its own positioning system is stable and can provide navigation reference); if the signal reliability value is ≤0.4, it is determined to be an unreliable drone (its own positioning system is faulty or severely degraded, and it needs to rely on other drones for navigation support). At the same time, all judgment results are recorded, and reliable and unreliable drones are marked for easy use by subsequent modules.

[0080] The reference selection module 12 is used to analyze the correlation between the flight trajectories of unreliable UAVs and reliable UAVs, and select a reference UAV to provide navigation support for each unreliable UAV based on the correlation between the flight trajectories.

[0081] Specifically, first, the distance sequence between the unreliable UAV (the b-th drone) and the reliable UAV (the c-th drone) within a preset time interval (the same as the reference time period mentioned above, for example, the first 60 seconds) is obtained. } Calculate the mean of the sequence. With variance mean Reflects the average distance and variance between the two machines Reflecting the degree of distance fluctuation, and The distance stability value is obtained by combining the results; then the flight synchronization of the two aircraft at the current moment is calculated. : , The larger the value, the more stable the distance between the two aircraft; then, the slope of the flight path at each moment on the actual flight path of the two aircraft within the reference time period is obtained. , .when , When both values ​​are negative or both are positive at time i, the label values ​​of the b-th unreliable drone and the c-th reliable drone at time i are compared. Record it as 1, otherwise record it as 0.1. Compare the label values ​​of the b-th unreliable drone and the c-th reliable drone at time i. Synchronization of flight between the b-th unreliable UAV and the c-th reliable UAV at time i. The product of these two values ​​is denoted as the consistency of the motion trends of the b-th unreliable UAV and the c-th reliable UAV at time i. Calculate the mean of the consistency of the motion trends of the b-th unreliable UAV and the c-th reliable UAV at all times within the reference time period. Then obtain the velocity time series of the two machines within the reference time period. }as well as{ } Calculate the DTW distance between the two sequences. , Reflecting speed similarity, the smaller the value, the closer the speeds are; finally, the correlation of flight trajectories is calculated using the following formula. :

[0082]

[0083] High correlation of flight trajectories means that the two drones move in the same direction. The parameters of the reliable drone, such as speed, heading angle, and turning angular velocity, are highly consistent with the requirements of the unreliable drone. The unreliable drone can directly refer to the parameters of the reliable drone to adjust its own movement, ensuring synchronization with the cluster and guaranteeing visual integrity and flight safety.

[0084] Furthermore, the navigation reference value of the c-th reliable UAV for the b-th unreliable UAV is calculated using the following formula. :

[0085]

[0086] in, This indicates the signal reliability of the c-th reliable UAV at the current moment. This represents the trajectory correlation between the b-th unreliable UAV and the c-th reliable UAV at the current moment. This indicates normalization processing.

[0087] in, As Adjustment coefficient, The larger the value, the more satellites the c-th reliable drone receives at the current moment, resulting in higher positioning accuracy and more reliable navigation support for the b-th unreliable drone, further enhancing the overall collaborative capability of the drone swarm.

[0088] Retrieve all navigation reference value data output by the navigation reference value calculation module; for each unreliable UAV, collect the navigation reference value data of all reliable UAVs corresponding to it (i.e., the data between the unreliable UAV and each reliable UAV). Then, all navigation reference value data corresponding to the same unreliable drone are sorted in descending order of value to form a navigation reference value sequence; the number of reference drones selected for each unreliable drone is preset (e.g., 5, which can be adjusted according to the cluster size); reliable drones with the highest ranking and the number that meets the preset setting (e.g., the top 5) are selected from the sorted navigation reference value sequence; finally, the selected reliable drones are determined as the reference drones for the corresponding unreliable drones, completing the selection and allocation of reference drones.

[0089] The collaborative control module 13 is used to fuse the navigation information of the reference UAV, obtain the estimated state information of the unreliable UAV, and control the unreliable UAV to adjust its flight state according to the estimated state information, thereby realizing collaborative navigation of the UAV cluster.

[0090] Specifically, the navigation information of each reference UAV selected for the unreliable UAV is first collected. This navigation information includes the reference UAV's GNSS coordinates (3D position data), flight attitude (roll angle, pitch angle, yaw angle), flight speed (horizontal and vertical velocity components), and relative distance (spatial distance between the two aircraft) and relative attitude (attitude difference between the two aircraft) between the reference UAV and the corresponding unreliable UAV. Then, the navigation reference value of each reference UAV for the corresponding unreliable UAV is retrieved. ,according to The normalized value is used to set the weight of the navigation information of the reference UAV. The larger the value, the greater the weight, and the higher its contribution to estimating state information.

[0091] Then, based on the size of the UAV swarm and data requirements, a preset filtering algorithm is selected (for example, when the swarm size is less than 50 UAVs and the real-time requirements of the data are high, the weighted Kalman filter algorithm is selected; when the swarm size is greater than or equal to 50 UAVs and the requirements of data fault tolerance are high, the distributed consensus filter algorithm is selected). The navigation information of each reference UAV with set weights is input into the selected filtering algorithm for data fusion calculation. Through iterative calculation of the filtering algorithm, the estimated state information of the unreliable UAV is finally obtained. This estimated state information includes position estimation information (corrected three-dimensional position coordinates), attitude estimation information (corrected flight attitude parameters), and velocity estimation information (corrected flight velocity parameters).

[0092] Furthermore, the system acquires the current actual flight status data of the unreliable UAV, compares the estimated status information with the current actual flight status data, and analyzes the deviation between the two (such as position deviation, attitude deviation, and speed deviation). Based on the deviation analysis results and the preset flight requirements of the UAV swarm formation (such as preset formation trajectory, attitude standard, and speed range), the system generates flight status adjustment commands for the unreliable UAV. These commands include position correction commands (used to correct position deviation), attitude adjustment commands (used to adjust flight attitude), and speed calibration commands (used to calibrate flight speed).

[0093] The adjustment command is transmitted in real time to the corresponding unreliable UAV. After receiving the adjustment command, the flight control system of the unreliable UAV adjusts its own flight parameters according to the command content, and performs position correction, attitude adjustment and speed calibration operations. The cooperative control module monitors the flight status of the unreliable UAV after adjustment in real time, obtains new actual flight status data, and compares it with the estimated status information to determine whether the deviation is within the preset allowable range (e.g., ±0.3 meters in altitude, ±0.2 meters / second in horizontal speed, etc., which can be set according to the application scenario). If the deviation is within the preset allowable range, it is confirmed that the unreliable UAV has completed the flight status adjustment. If the deviation exceeds the preset allowable range, the flight status adjustment process, such as deviation analysis, command generation and transmission, and status adjustment steps, is repeated until the deviation meets the requirements.

[0094] The flight status adjustment process described above is executed one by one for each unreliable drone in the drone swarm. This ensures that each unreliable drone corrects its flight status based on the estimated status information. Simultaneously, the formation flight status of the entire swarm is monitored in real time to ensure that both reliable drones and the adjusted unreliable drones fly along the preset formation trajectory, ultimately achieving cooperative navigation of the drone swarm. It should be noted that executing the flight status adjustment process is also equivalent to executing the reference selection module 12 and the cooperative control module 13.

[0095] It should be noted that the device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above.

[0096] Figure 2 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. For example, as shown... Figure 2 As shown, the computer device 20 includes: a memory 21, a processor 22, and a computer program 23 stored in the memory 21 and running on the processor 22, wherein when the processor 22 executes the computer program 23, the computer device can execute any UAV swarm cooperative navigation system described above.

[0097] Furthermore, embodiments of the present invention also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute the UAV swarm cooperative navigation system provided in the embodiments of the present invention.

[0098] In this embodiment of the invention, the device can be divided into functional modules according to the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and is only a logical functional division. In actual implementation, there may be other division methods.

[0099] It should be understood that the apparatus provided in this embodiment of the invention is used to execute the above-described UAV swarm cooperative navigation system, and therefore can achieve the same effect as the above-described implementation method.

[0100] When using integrated units, the device may include a processing module and a storage module. When applied to a device, the processing module can be used to control and manage the device's operations. The storage module can be used to support the device in executing program code, etc. The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits as described in this disclosure. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of Digital Signal Processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0101] In addition, the device provided in the embodiments of the present invention may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the UAV swarm cooperative navigation system provided in the above embodiments.

[0102] This invention also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the aforementioned method steps to implement the UAV swarm cooperative navigation system provided in the above embodiments.

[0103] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the UAV swarm collaborative navigation system provided in the above embodiments.

[0104] In this invention, the apparatus, computer-readable storage medium, computer program product, or chip provided in the embodiments are all used to execute the corresponding methods described above. Therefore, the beneficial effects they achieve can be referred to the beneficial effects in the corresponding methods described above, and will not be repeated here. Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways.

[0105] The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0106] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0107] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0109] The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A UAV swarm cooperative navigation system, characterized in that, The system comprises the following modules: a data acquisition module for acquiring flight data of each unmanned aerial vehicle in the unmanned aerial vehicle cluster in real time; a signal reliability evaluation module for determining the signal reliability of each unmanned aerial vehicle according to the flight data and in combination with the sensor performance parameters of the unmanned aerial vehicle, so as to divide reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles; wherein the method for obtaining the signal reliability comprises: determining the yaw-related parameters of each unmanned aerial vehicle in a preset reference period according to the flight data; calculating the signal quality parameters of each unmanned aerial vehicle in the reference period according to the number of satellite signals received by the unmanned aerial vehicle in the flight data; obtaining the sensor performance parameters of the unmanned aerial vehicle, and weighting and fusing the yaw-related parameters, the signal quality parameters and the sensor performance parameters to obtain the signal reliability of the unmanned aerial vehicle; wherein the method for determining the yaw-related parameters of each unmanned aerial vehicle in a preset reference period comprises: extracting the actual position coordinates of each unmanned aerial vehicle and the position coordinates of the predetermined trajectory in the preset reference period from the flight data; calculating the first spatial distance between the actual position coordinates of the unmanned aerial vehicle and the position coordinates of the predetermined trajectory at each time in the reference period, and performing normalization processing on the first spatial distance to obtain the yaw distance at each time; determining the yaw time according to the yaw distance, and integrating the continuously adjacent yaw times into a yaw period; for each yaw period, calculating the second spatial distance between the actual position coordinates of the unmanned aerial vehicle at adjacent two times, and performing normalization processing on the second spatial distance to obtain the position deviation of the latter time in the adjacent times; counting the number of times that the position deviation meets the preset jump feature in each yaw period, calculating the proportion of the number of times that meet the preset jump feature to the total number of times in the yaw period, and obtaining the proportion of the jump time; the yaw-related parameters include the yaw distance, the position deviation and the proportion of the jump time; a reference selection module for analyzing the flight trajectory correlation between the unreliable unmanned aerial vehicles and the reliable unmanned aerial vehicles, and selecting a reference unmanned aerial vehicle for providing navigation support for each unreliable unmanned aerial vehicle according to the flight trajectory correlation; a cooperative control module for fusing the navigation information of the reference unmanned aerial vehicle, obtaining the estimated state information of the unreliable unmanned aerial vehicle, and controlling the unreliable unmanned aerial vehicle to adjust the flight state according to the estimated state information, so as to realize cooperative navigation of the unmanned aerial vehicle cluster.

2. The UAV swarm cooperative navigation system of claim 1, wherein, The flight data includes one or more of the three-dimensional position coordinates, flight speed, actual route, planned route and number of satellite signals received by the unmanned aerial vehicle.

3. The UAV swarm cooperative navigation system of claim 1, wherein, The process of dividing reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles comprises: comparing the signal reliability value of each unmanned aerial vehicle with a preset signal reliability threshold value; if the signal reliability value of the unmanned aerial vehicle is greater than the preset signal reliability threshold value, the unmanned aerial vehicle is determined to be a reliable unmanned aerial vehicle; if the signal reliability value of the unmanned aerial vehicle is less than or equal to the preset signal reliability threshold value, the unmanned aerial vehicle is determined to be an unreliable unmanned aerial vehicle; recording all determination results, distinguishing reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles in the unmanned aerial vehicle cluster and marking.

4. The UAV swarm cooperative navigation system of claim 1, wherein, The analysis of the flight trajectory correlation between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle comprises: a distance sequence between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle in a preset time interval is obtained, and a mean value and a variance of the distance sequence are calculated to obtain a distance stability value; a slope of an actual flight path of the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle at each time in the time interval is obtained, and a positive-negative consistency of the slopes at the same time is determined; a speed time sequence of the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle in the time interval is obtained, and a speed sequence similarity of the two is calculated; the distance stability value, the positive-negative consistency of the slopes and the speed sequence similarity are weighted and fused to obtain the flight trajectory correlation between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle.

5. The UAV swarm cooperative navigation system of claim 1, wherein, The system further comprises: a navigation reference value calculation module configured to calculate, in combination with a signal reliability of the reliable unmanned aerial vehicle and the flight trajectory correlation between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle, a navigation reference value data of each reliable unmanned aerial vehicle for each unreliable unmanned aerial vehicle.

6. The UAV swarm cooperative navigation system of claim 5, wherein, The selection of a reference unmanned aerial vehicle providing navigation support for each unreliable unmanned aerial vehicle according to the flight trajectory correlation comprises: the navigation reference value data is called; for each unreliable unmanned aerial vehicle, navigation reference value data corresponding to the unreliable unmanned aerial vehicle is collected from all reliable unmanned aerial vehicles; all navigation reference value data corresponding to the same unreliable unmanned aerial vehicle is arranged in descending order of value; a number of reference unmanned aerial vehicles to be selected for each unreliable unmanned aerial vehicle is preset, and reference unmanned aerial vehicles with a high ranking and a number meeting the preset are selected from the arranged navigation reference value data sequence; the selected reference unmanned aerial vehicles are determined as the reference unmanned aerial vehicles corresponding to the unreliable unmanned aerial vehicles.

7. The UAV swarm cooperative navigation system of claim 5, wherein, The fusion of navigation information of the reference unmanned aerial vehicles and the obtaining of estimated state information of the unreliable unmanned aerial vehicle comprise: navigation information of each reference unmanned aerial vehicle selected for the unreliable unmanned aerial vehicle is collected, and navigation reference value data of each reference unmanned aerial vehicle for the corresponding unreliable unmanned aerial vehicle is called; a weight of navigation information of each reference unmanned aerial vehicle corresponding to the reference unmanned aerial vehicle is set according to the navigation reference value data of the reference unmanned aerial vehicle; the navigation information of each reference unmanned aerial vehicle with the set weight is input into a preset filtering algorithm for data fusion calculation; estimated state information of the unreliable unmanned aerial vehicle is obtained through the data fusion calculation.

8. The UAV swarm cooperative navigation system of claim 1, wherein, The control of the unreliable unmanned aerial vehicle to adjust a flight state according to the estimated state information, thereby realizing the cooperative navigation of the unmanned aerial vehicle cluster, comprises: current actual flight state data of the unreliable unmanned aerial vehicle is obtained, and the estimated state information of the unreliable unmanned aerial vehicle is compared with the current actual flight state data to analyze a deviation therebetween; a flight state adjustment instruction for the unreliable unmanned aerial vehicle is generated according to a deviation analysis result and a preset flight requirement of the unmanned aerial vehicle cluster formation, and the adjustment instruction is transmitted to the corresponding unreliable unmanned aerial vehicle in real time.

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