A method for dynamic target tracking of multiple UAVs

Abstract

This application discloses a multi-UAV target dynamic tracking method, belonging to the field of UAV control. The method includes: all UAVs in a UAV swarm obtaining the tracked target; all UAVs in the swarm synchronizing the tracked target information; continuously acquiring the performance and actual motion information of the tracked target; UAVs not involved in tracking the target updating the actual motion information of the tracked target in real time to obtain an adjusted predicted trajectory; real-time updating the predicted trajectory of the tracked target; updating the initial tracking position of the UAVs not involved in tracking the target, and when they move to the updated initial tracking position and can lock onto the tracked target, they are adjusted to become UAVs involved in tracking the target; all UAVs involved in tracking the target acquiring the target's motion information to dynamically track the target. This method solves the problem of not being able to cover the entire process of high-precision target locking by multiple UAVs, thereby significantly improving the quality of multi-UAV target dynamic tracking.

Description

Technical Field

[0001] This application belongs to the field of unmanned aerial vehicle (UAV) control, specifically to a method for dynamic target tracking of multiple UAVs. Background Technology

[0002] Unmanned aerial vehicle (UAV) technology plays a crucial role in various fields, particularly in dynamic target tracking within complex urban spaces. To improve target tracking effectiveness, multi-UAV tracking methods are commonly employed. Current research in multi-UAV target dynamic tracking primarily focuses on how to group and continuously track multiple UAVs after all UAVs in the swarm have locked onto the target. It does not address the analysis of the process from UAV takeoff and target search to target locking within the swarm. In other words, the main problem is that the control scheme during the target dynamic tracking process of a UAV swarm, from the start of target search to the entire swarm locking onto the target, is often inefficient. Alternatively, relying solely on a ground-based control system to independently control all UAVs within the swarm results in low control efficiency, making it difficult to achieve full-process dynamic tracking in multi-UAV target dynamic tracking.

[0003] Therefore, how to achieve target dynamic tracking of multiple drones, from the moment a drone begins searching for a target to the moment all drones in the drone swarm can stably lock onto the target, thus realizing full-process target tracking of multiple drones, is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] To enable dynamic target tracking across multiple UAVs and achieve full-process tracking of the tracked target, this application discloses a dynamic target tracking method for multiple UAVs, specifically:

[0005] A target dynamic tracking method for multiple unmanned aerial vehicles (UAVs), the tracking method comprising:

[0006] All drones in the drone swarm work together to search for target information in order to obtain the tracked target;

[0007] All drones in the drone swarm are simultaneously tracked by the target information, reach the initial tracking position and lock onto the target, so as to obtain the drones participating in the tracking of the target;

[0008] The drone participating in tracking the target adjusts the orientation of its image acquisition device to continuously acquire performance information of the tracked target and continuously acquire actual motion information.

[0009] The drone that does not participate in tracking updates the actual movement information of the tracked target in real time. The tracked target's trajectory is predicted by either the drone that participates in tracking or the drone that does not participate in tracking, so as to obtain the adjusted predicted trajectory.

[0010] Based on the adjusted predicted trajectory, the predicted trajectory of the tracked target is updated in real time to obtain the updated trajectory of the tracked target.

[0011] Based on the updated tracked target's trajectory, the initial tracking position of the drone that is not participating in the tracking target is updated, and when it moves to the updated initial tracking position and can lock onto the tracked target, it is adjusted to become a drone participating in the tracking target.

[0012] All drones involved in tracking the target acquire the target's motion information and adjust their movement data based on the target's motion information to perform dynamic tracking of the target.

[0013] Optionally, all drones within the drone swarm jointly search for target information to obtain the tracked target, including:

[0014] The task distribution system of the drone swarm sends basic information of the tracked target to the corresponding drone swarm for the drone swarm to search for the tracked target;

[0015] After receiving the basic information of the tracked target, the drone swarm searches for target information in the corresponding airspace based on the image acquisition system and radar system;

[0016] After any drone in the drone swarm detects a suspected tracked target, the suspected tracked target is verified by image and track analysis.

[0017] When the suspected tracked target passes the image and movement verification, the suspected tracked target is confirmed as the tracked target.

[0018] Optionally, all drones within the drone cluster simultaneously track target information, reach the initial tracking position, and lock onto the tracked target, thus obtaining the drones participating in the target tracking, including:

[0019] After any drone in the drone swarm acquires the tracked target, it obtains the target's motion information.

[0020] The motion information of the tracked target is sent to other drones in the drone cluster to achieve synchronization of the tracked target's motion information among all drones in the drone cluster;

[0021] After a drone in the drone swarm that failed to find the tracked target obtains the movement information of the tracked target, it obtains its initial tracking position.

[0022] Based on the initial tracking position, the drone cluster failed to find the planned flight path of the drone tracking target to reach the initial tracking position;

[0023] When a drone in a drone swarm that failed to find the target arrives at the initial tracking position and successfully locks onto the target, the drone that failed to find the target is designated as a drone participating in the tracking; otherwise, it is designated as a drone not participating in the tracking.

[0024] Optionally, after a drone in the drone cluster that failed to find the tracked target obtains the movement information of the tracked target, it obtains an initial tracking position, including:

[0025] All drones in the drone swarm that failed to find the tracked target output the predicted trajectory of the tracked target based on the tracked target information, and obtain the predicted trajectory of the tracked target that appears most frequently, which is the initial predicted trajectory.

[0026] The location range of the drone that failed to find the tracked target and the initial predicted track spacing is not higher than the preset position spacing is obtained.

[0027] From the given location range, obtain the location of the drone that failed to find the tracked target and the location of the minimum distance between them, so as to obtain the initial tracking position.

[0028] Optionally, the UAV participating in tracking the target adjusts the orientation of its image acquisition device to continuously acquire performance information of the tracked target and continuously acquire actual motion information, including:

[0029] The drone participating in the tracking acquires the relative position and orientation of the tracked target;

[0030] The image acquisition device on the UAV participating in tracking the target is oriented towards the relative position direction, and the UAV is ensured to be at the center of the frame image acquired by the image acquisition device;

[0031] The drone participating in the tracking continuously adjusts its relative position to the tracked target in order to continuously acquire the tracked target's performance information and actual movement information.

[0032] Optionally, the non-target-tracking UAV updates the actual movement information of the tracked target in real time, and the tracked target's trajectory is predicted by either the target-tracking UAV or the non-target-tracking UAV to obtain an adjusted predicted trajectory, including:

[0033] Obtain the number of drones participating in tracking the target and the number of drones not participating in tracking within the drone swarm;

[0034] All drones involved in tracking the target acquire the real-time location of the target;

[0035] Based on the real-time position of the tracked target, obtain the motion information of the tracked target;

[0036] When the number of drones participating in tracking the target in a drone swarm is greater than the number of drones not participating in tracking the target, the drones participating in tracking the target jointly predict the track of the tracked target; otherwise, the drones not participating in tracking the target jointly predict the track of the tracked target.

[0037] Optionally, all the drones participating in tracking the target acquire the real-time location of the tracked target, including:

[0038] All the drones participating in the target tracking acquire the real-time location information of the target being tracked, and discard real-time location information with excessive deviation to obtain the correct real-time location;

[0039] The drones that are tracking targets and whose real-time location information is too large are identified and then converted into drones that are not tracking targets.

[0040] Obtain the spatial coordinates of all the correct real-time locations, and calculate the average of the coordinates to obtain the real-time location of the tracked target.

[0041] Optionally, the step of updating the predicted trajectory of the tracked target in real time based on the adjusted predicted trajectory to obtain the updated tracked target trajectory includes:

[0042] The adjusted predicted trajectory is sent to all drones that are not involved in tracking the target;

[0043] All drones not involved in tracking the target are compared for the similarity between the new adjusted predicted trajectory acquired at the current moment and the adjusted predicted trajectory applied at the current time.

[0044] When the similarity is not lower than the preset similarity, the drones not participating in the tracking target move based on the adjusted predicted trajectory applied in the current time period; otherwise, the drones not participating in the tracking target replace the adjusted predicted trajectory applied in the current time period with a new adjusted predicted trajectory to obtain the updated tracked target trajectory.

[0045] Optionally, the step of updating the initial tracking position of a non-target-participating UAV based on the updated tracked target trajectory, and adjusting it to a target-participating UAV when it moves to the updated initial tracking position and can lock onto the tracked target, includes:

[0046] After all non-target-tracking drones obtain the updated tracked target's trajectory, they obtain the updated initial tracking position.

[0047] After the drone that is not involved in tracking arrives at the updated initial tracking position, it searches for and locks onto the tracked target, and verifies the correctness of the locked tracked target based on the continuously acquired performance information of the tracked target.

[0048] Once the tracked target is locked, drones that were not involved in tracking the target are adjusted to become drones involved in tracking the target.

[0049] Optionally, all UAVs participating in the target tracking acquire the target's motion information and adjust their movement data based on the target's motion information to perform dynamic tracking of the target, including:

[0050] All drones involved in tracking the target obtain the area they need to reach at the next flight moment based on the movement information of the tracked target;

[0051] Based on the area to be reached at the next flight time, acquire the flight data of the UAVs participating in the target tracking;

[0052] Based on the flight data, the flight attitude of the UAV participating in the tracking of the target is adjusted in order to perform dynamic tracking of the tracked target.

[0053] The beneficial effects of this application include:

[0054] 1. Achieved full-process dynamic target tracking. In the technical solution of this application, when a target needs to be tracked in a certain area, the UAV swarm starts running and searches for the target. After any UAV locks onto the target, all UAVs in the entire UAV swarm can dynamically track the target based on the information collected by that UAV. This achieves full-process dynamic target tracking by covering the entire process of multiple UAVs from the initial target search to the final target dynamic tracking.

[0055] 2. High-efficiency dynamic target tracking is achieved. In the technical solution of this application, all UAVs in the entire UAV cluster participate in the target search task, which fundamentally improves the target identification efficiency. Then, the target's motion information is directly collected, and information is synchronized within the UAV cluster based on this motion information. This allows all UAVs to plan their flight routes based on the synchronized information, directly reach the corresponding location, and lock onto the tracked target. This method can improve the efficiency of dynamic target tracking.

[0056] 3. High-precision dynamic target tracking is achieved. In the technical solution of this application, during the dynamic target tracking process, the UAVs involved in tracking the target are classified into those involved in tracking the target and those not involved in tracking the target. The information obtained by the UAVs involved in tracking the target is further verified. Based on the verification results, it is determined whether they have truly locked onto the target, thus ensuring that the UAVs correctly lock onto the target during the dynamic target tracking process. At the same time, the target prediction trajectory is determined based on the number of UAVs involved in tracking the target and those not involved in tracking the target, so as to fully guarantee the accuracy of the target prediction trajectory. Thus, high-precision dynamic target tracking is achieved. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the embodiments of this application or the prior art will be briefly introduced below. Obviously, the following description is only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings are used to provide a further understanding of this disclosure and constitute a part of the specification. They are used together with the following detailed description to explain this disclosure, but do not constitute a limitation of this disclosure. In the drawings:

[0058] Figure 1 A flowchart of a multi-UAV target dynamic tracking method provided in this application embodiment;

[0059] Figure 2 This is a schematic diagram of the UAV position range in a multi-UAV target dynamic tracking method provided in an embodiment of this application;

[0060] Figure 3 This is a schematic diagram illustrating the initial tracking position generation of a multi-UAV target dynamic tracking method provided in an embodiment of this application. Detailed Implementation

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Furthermore, in the embodiments of this application, "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0062] When multiple UAVs collaborate on dynamic target tracking, the entire process includes three stages: "task confirmation - target search - swarm follow". Among these, the target search process cannot guarantee that all UAVs can simultaneously find the target. Therefore, the target search is actually the most difficult and complex stage of the entire process. In particular, the tracked target is usually not hovering but in motion, which makes it even more difficult to guarantee that all UAVs in the UAV swarm can simultaneously search for and lock onto the target. This often results in a long time consumption from target search to swarm follow. In some cases, when some UAVs are under unified ground control, the target may be outside the ground tracking range, causing some UAVs in the swarm to be unable to search for and lock onto the target. This leads to excessive time consumption and low coordination control accuracy of UAVs during the target search to swarm follow process, resulting in poor dynamic target tracking quality for multiple UAVs.

[0063] To address the problems existing in the prior art, this application discloses a multi-UAV target dynamic tracking method, such as... Figure 1 The diagram shown is a flowchart of a multi-UAV target dynamic tracking method disclosed in an embodiment of this application. Specifically:

[0064] S110, all drones in the drone swarm, work together to search for target information in order to obtain the tracked target.

[0065] S120: All drones in the drone cluster are simultaneously tracked by the target information, reach the initial tracking position and lock onto the target, so as to obtain the drones participating in the tracking of the target.

[0066] S130. The UAV participating in tracking the target adjusts the orientation of the image acquisition device to continuously acquire the performance information of the tracked target and continuously acquire actual motion information.

[0067] S140. The drone that does not participate in tracking updates the actual movement information of the tracked target in real time. The drone that participates in tracking the target or the drone that does not participate in tracking the target predicts the track of the tracked target to obtain the adjusted predicted track.

[0068] S150. Based on the adjusted predicted trajectory, update the predicted trajectory of the tracked target in real time to obtain the updated trajectory of the tracked target.

[0069] S160. Based on the updated tracked target trajectory, the initial tracking position of the drone that is not participating in the tracking target is updated, and when it moves to the updated initial tracking position and can lock onto the tracked target, it is adjusted to become the drone participating in the tracking target.

[0070] S170. All UAVs participating in the tracking of the target acquire the movement information of the tracked target and adjust their movement data based on the movement information of the tracked target in order to perform dynamic tracking of the tracked target.

[0071] The beneficial effect of all the above steps is that it enables full-process coverage of target dynamic tracking for multiple UAVs, while improving the efficiency and accuracy of target dynamic tracking, and significantly enhancing the quality of target dynamic tracking for multiple UAVs.

[0072] The following will provide a detailed explanation of all the steps above:

[0073] As described in step S110, the purpose of this step is to ensure that when the drone swarm receives a task requiring dynamic target tracking, the entire drone swarm operates collaboratively to acquire the tracked target. Furthermore, to improve target search efficiency, all drones within the swarm are engaged in the target search task, and after any drone finds the target, that drone serves as the basis for subsequent technical implementation. Specifically:

[0074] S111 The task distribution system of the UAV swarm sends basic information of the tracked target to the corresponding UAV swarm for the purpose of tracking the target of the UAV swarm.

[0075] The purpose of this step is that when the ground distribution system of the drone swarm determines that the swarm needs to perform dynamic target tracking, the entire drone swarm will jointly search for the target and thus find it.

[0076] The mission distribution system includes ground systems, air platforms, and air communication relay platforms. These platforms can send target dynamic tracking mission instructions to the drone swarm.

[0077] The task instructions in the task distribution system of the drone swarm include basic information about the tracked target, such as its appearance, detected tracks, and type.

[0078] The task distribution system can also determine the images of the tracked target that can be obtained from various perspectives during the flight of the UAV. This makes it easier to compare images of the tracked target obtained from different angles during the dynamic tracking of targets by multiple UAVs, thus enabling faster identification of the tracked target.

[0079] S112. After receiving the basic information of the tracked target, the UAV cluster searches for target information in the corresponding airspace based on the image acquisition system and radar system.

[0080] The purpose of this step is to enable multiple UAVs to identify the tracked target in the corresponding airspace based on the received target information during target dynamic tracking in a multi-UAV system.

[0081] After the drone swarm receives the basic information of the tracked target, it directly obtains various basic information about the target.

[0082] In the process of dynamic target tracking of multiple drones, it is necessary to be able to send the obtained basic information to all drones in the drone cluster.

[0083] The task distribution system determines the airspace where the tracked target is located based on the motion and trajectory information of the tracked task.

[0084] In this system, multiple UAVs simultaneously search for the tracked target based on both image acquisition and radar systems. Specifically, radar can determine whether there is a detectable target in the corresponding airspace, and then the image acquisition system is used to assist in the target search. This mode is mainly used at night, while during the day, the image acquisition system can be used directly to search for the tracked target.

[0085] S113. After any drone in the drone swarm detects a suspected tracked target, the suspected tracked target is verified by image and track verification.

[0086] The purpose of this step is to identify the correct target in the dynamic target tracking process of multiple drones, where multiple objects may exist in the same airspace.

[0087] Two methods can be used: one is for the drone to send all the information it has obtained to the ground system after acquiring the suspected tracked target, and the other is for the drone to verify the information itself.

[0088] In the case of self-verification by the drone, the obtained information is used to photograph the tracked target, and the photographed results are directly compared with the image information sent by the task distribution system.

[0089] Among them, the acquisition of suspected tracked target tracks is compared with the tracks already obtained, or the tracks predicted based on the tracks obtained by the task distribution system are used for verification.

[0090] In the case of ground verification, the task distribution system only needs to send target dynamic tracking task instructions and airspace information to the UAV cluster to provide guidance for the UAV's target search direction.

[0091] The images and tracks of suspected tracked targets obtained by the drone are sent to the task distribution system, which then verifies the images and tracks.

[0092] S114. When the suspected tracked target passes the image and track verification, the suspected tracked target is determined to be the tracked target.

[0093] The purpose of this step is to identify the target being tracked.

[0094] In this process, both the image and the tracks of the suspected target are verified. Once both verifications are successful, the suspected target is confirmed as the target being tracked.

[0095] The weights of the analysis results for image and track verification can be determined based on nighttime and daytime. For example, at night, image comparison accuracy is usually lower, while track verification is more effective. In this case, the weight of track verification is 0.8, and that of image verification is 0.2. The opposite can be done during the day. The specific weight settings can be based on the experience of technical personnel or determined based on machine learning methods. No restrictions are imposed here.

[0096] The beneficial effect of step S110 is that all drones in the drone cluster simultaneously perform target search, and the obtained search results are further verified to obtain the accurate tracked target, thereby ensuring the accuracy of target identification.

[0097] As described in step S120, the purpose of this step is to synchronize the tracked target information directly throughout the entire drone swarm after any drone has detected the target, and then allow the drones within the swarm to reach the initial tracking position and lock onto the target. Specifically:

[0098] S121. After any UAV in the UAV cluster acquires the tracked target, it acquires the movement information of the tracked target.

[0099] The purpose of this step is to ensure that all drones in the drone swarm have lead time when performing target dynamic tracking, so that they can arrive at a specific position to wait for the tracked target before the tracked target arrives at a certain position. Therefore, it is obviously necessary to determine the preset track of the tracked target. To obtain the preset track, it is necessary to determine the motion information of the tracked target.

[0100] After obtaining the existing tracks, it is necessary to acquire the current motion information.

[0101] The so-called motion information includes the tracking target's direction of motion, acceleration, current velocity, and position.

[0102] All the obtained motion information can be measured using the radar device installed on the drone.

[0103] For motion information, the positional changes of the tracked target relative to the UAV are determined directly from the radar, thus obtaining all motion information.

[0104] Specifically, to determine the location of the tracked target, all drones that have locked onto the target need to work together using their radar devices to pinpoint the target's location.

[0105] The specific positioning method involves recognizing that all drones that have locked onto the target are essentially drones participating in the target tracking, and further expanding the range of drones participating in the target tracking based on these drones.

[0106] In this process, all UAVs participating in the target tracking measure the distance and relative position between the tracked target and the UAV using radar devices, determine the position of each UAV participating in the target tracking, and determine the radar measurement position of the tracked target that can be overlaid based on a spatial coordinate system. This position is the position of the tracked target.

[0107] In the case of multiple drones, if only one drone or a drone not participating in the tracking of the target is detected, the approximate location of the tracked target can only be determined based on the drone's own position and the relative position of the tracked target. In reality, this situation is extremely rare. Usually, at least three drones jointly search for and lock onto the tracked target, at which point three-point positioning can be achieved, thus obtaining the precise location of the tracked target.

[0108] S122. The motion information of the tracked target is sent to other drones in the drone cluster to achieve synchronization of the tracked target motion information among all drones in the drone cluster.

[0109] The purpose of this step is that, after obtaining the motion information of the tracked target, all UAVs need to predict the track of the tracked target directly based on their own computing units according to the obtained information, and then determine the initial tracking position based on the prediction results.

[0110] Once one or more drones have determined the operational information of the tracked target, this information is directly synchronized throughout the entire drone cluster.

[0111] S123. After a drone in the drone cluster that failed to find the tracked target obtains the movement information of the tracked target, it obtains the initial tracking position.

[0112] The purpose of this step is that, after obtaining the movement information of the tracked target, the drone that has failed to locate the target can determine its initial tracking position. Based on this position, the drone can then proceed to that location to wait for the tracked target. Specifically:

[0113] S1231. All drones in the drone cluster that failed to find the tracked target output the predicted trajectory of the tracked target based on the tracked target information, and obtain the predicted trajectory of the tracked target with the highest frequency of occurrence as the initial predicted trajectory.

[0114] The purpose of this step is to predict the tracked target's trajectory in the future, taking into account that the drones in the system may arrive at the target's potential location in advance during the initial target determination process.

[0115] After obtaining the motion information of the tracked target, the position of the tracked target at each time point contained in the motion information, as well as the speed, acceleration, orientation and other information at the corresponding position, can be directly obtained to obtain the motion information of the tracked target.

[0116] After obtaining the existing motion information of the tracked target, the motion information can be directly set as the corresponding motion curve, and the slope, position and other information of the motion curve can be obtained directly to obtain the existing motion data.

[0117] In this process, based on the motion information at each time point, the spatial location points reached by the tracked target in the next period of time are determined, and the predicted trajectory of the tracked target is obtained based on the available results.

[0118] The above steps describe the method for predicting the trajectory of each drone. Every drone that fails to find the tracked target is identified using this method, and a corresponding predicted trajectory is generated.

[0119] In this process, the frequency of predicted tracks of all drones that failed to find the tracked target is statistically analyzed. The predicted track with the highest frequency is taken as the highest accuracy predicted track that can be obtained and used to directly obtain the initial predicted trajectory.

[0120] S1232. Obtain the location range of the drone that failed to find the tracked target and the drone that failed to find the tracked target when the initial predicted track distance is not higher than the preset location distance.

[0121] The purpose of this step is to, after determining the initial predicted trajectory, infer the optimal position that the drone needs to reach in the subsequent operation of the entire system based on that trajectory.

[0122] After obtaining the initial predicted trajectory, the distance between the current types of drones and the initial predicted trajectory is analyzed.

[0123] Specifically, when the distance between the drone and the initial predicted track exceeds the set relative position distance, it is considered that the drone needs to fly towards the initial predicted track.

[0124] The preset relative spacing can be set freely, or the electronic fence range parameters of the drone can be used directly.

[0125] In this step, we essentially obtain a positional interval that follows the initial predicted trajectory, such as... Figure 2 The diagram shown is a schematic of the drone position range in a multi-drone target dynamic tracking method provided in this application embodiment. The dashed line represents the drone position range, which is a cylindrical shape. The arrow indicates the extreme position of the range when the drone reaches the position range.

[0126] S1233. From the position range, obtain the position of the drone that failed to find the tracked target and the position of the minimum distance between the tracked target and the target, so as to obtain the initial tracking position.

[0127] The purpose of this step is to finalize the initial tracking position.

[0128] This includes obtaining the possible flight routes for drones that failed to locate the tracked target within their arrival location range.

[0129] Among them, the specific location of the drone within the location range during its flight path when the tracked target could not be found is obviously a predictive result.

[0130] This involves determining the specific location of the UAV within a given location range and the distance to the tracked target. The initial tracking position is then obtained by finding the position with the smallest such distance. For example... Figure 3 The diagram shown illustrates the initial tracking position generation in a multi-UAV target dynamic tracking method according to an embodiment of this application. The initial tracking position refers to one of the selectable initial tracking positions based on the initial predicted trajectory. Upon reaching this initial tracking position, multiple selectable flight paths are possible. Figure 3 The available flight paths and initial tracking positions are clearly not fully shown in the text.

[0131] In some embodiments, during the initial tracking position process, it is necessary to determine the respective positions of the two drones within the same time period based on the flight data of the drone that failed to find the tracked target and the motion information of the tracked target, and continuously obtain the distance between the two drones. When ensuring that the distance between the two drones is minimized within the range of the location of the drone that failed to find the tracked target, the motion data of the drone that failed to find the tracked target at each time point is obtained.

[0132] S124. Based on the initial tracking position, the drone swarm fails to find the planned flight trajectory of the drone that is being tracked to reach the initial tracking position.

[0133] The purpose of this step is to determine the flight path of the drone that failed to find the tracked target after obtaining the initial tracking position, so that it can reach the initial tracking position within a specified time.

[0134] After determining the initial tracking position of the drone that failed to find the tracked target, the available path between the current position and the initial tracking position is obtained.

[0135] Among these methods, the applicable motion parameters of drones that failed to find the tracked target are obtained and adjusted, and the path with the shortest time to reach the initial tracking position is obtained.

[0136] This also allows for the analysis of the time taken to reach the initial tracking position via different paths, and the selection of the available path with the shortest time yields the planned flight path of the UAV that failed to find the tracked target.

[0137] Among them, drones that failed to find the tracked target flew according to the planned flight path and obtained the initial tracking position.

[0138] S125. When a drone in the drone swarm that failed to find the tracked target arrives at the initial tracking position and successfully locks onto the tracked target, the drone that failed to find the tracked target is set as a drone participating in the tracking of the target; otherwise, it is set as a drone not participating in the tracking of the target.

[0139] The purpose of this step is to determine whether a drone that has failed to find the target can become a drone participating in the tracking after reaching the initial tracking position. This method is essentially based on prediction data when the initial tracking position is determined. However, the motion information of some tracked targets is complex and they intend to get rid of the tracking. Therefore, this prediction method is likely to have a large deviation.

[0140] If a drone that fails to locate the tracked target reaches the initial tracking position, it will obviously participate in the target search and lock-on process. Therefore, it is necessary to determine whether the drone can correctly lock onto the tracked target.

[0141] When a drone that fails to locate the target reaches the initial tracking position, it performs a locking operation. If the target can be successfully locked, the drone that failed to locate the target becomes a drone participating in the tracking.

[0142] Among them, if a drone that fails to find the tracked target reaches the initial tracking position, the tracked target may not fly according to the initially predicted trajectory due to changes in the position of the tracked target or other changes in motion information. In this case, it is obviously impossible to lock onto the tracked target. In this situation, the drone that fails to find the tracked target is identified as a drone that does not participate in the tracking.

[0143] It should be noted that "the drone that failed to find the tracked target" is merely a term used in this application. When it is translated into "the drone that participated in the tracking of the target," it is clear that it did not fail to find the tracked target, but rather that it had found and locked onto it. However, for drones that did not participate in the tracking of the target, it is clear that they failed to find the tracked target.

[0144] The beneficial effect of step S120 is that, during the operation of a drone that fails to find the target being tracked, the initial tracking position of such drone is further determined, and the target being tracked is locked. Then, according to this method, drones can be divided into drones that participate in tracking the target and drones that do not participate in tracking the target, thereby realizing the differentiation of the functions and performance of drones.

[0145] As described in step S130, the purpose of this step is that, during the operation of the drone participating in the target tracking, once the target has been locked, the drone can continuously acquire image information of the target to achieve dynamic tracking. Simultaneously, it can determine motion information to continuously update the target's motion information and thus update the target's predicted trajectory. Specifically:

[0146] S131, The UAV participating in tracking the target acquires the relative position and direction of the tracked target.

[0147] The purpose of this step is to obtain the relative position of the drone and the tracked target at various time points during the flight of the drone involved in tracking the target, so that the orientation of the image acquisition device can be adjusted according to the relative position information.

[0148] Among them, the location of the tracked target and the location of the drone participating in the tracking are determined directly based on the radar of the drone participating in the tracking.

[0149] The relative position and direction of the drones involved in tracking the target are determined based on their locations and the location of the target being tracked.

[0150] S132. The image acquisition device on the UAV participating in tracking the target is oriented towards the relative position direction, and the UAV is ensured to be at the center of the frame image acquired by the image acquisition device.

[0151] The purpose of this step is to obtain the orientation of the image acquisition device after acquiring the relative positions of the drone involved in tracking the target and the target being tracked, so as to ensure that the image acquisition device is facing the target being tracked.

[0152] Among them, the gimbal device of the image acquisition device of the drone participating in tracking the target will be adjusted to adjust the orientation of the image acquisition device.

[0153] Among them, the image acquisition device of the drone participating in the tracking target must be oriented so that it coincides with the relative position direction of the drone participating in the tracking target and the tracked target, so as to ensure that the image acquisition device can capture the tracked target.

[0154] Among them, the image acquisition device of the UAV participating in tracking the target must ensure that the tracked target is centered in the image obtained by the image acquisition device.

[0155] In the specific tracking process, the drone participating in the tracking target can be in a tracking and circling flight state with the tracked target, thereby obtaining the performance information of the tracked target from different perspectives.

[0156] S133. The UAV participating in tracking the target continuously adjusts its relative position to the target in order to continuously acquire the target's performance information and actual movement information.

[0157] The purpose of this step is to continuously acquire images of the target being tracked during the flight of the drone involved in tracking the target, in order to extract performance information, and at the same time determine the actual motion information of the target being tracked based on the relative position.

[0158] During the flight of the drone involved in tracking the target, its image acquisition device is constantly adjusted in orientation to ensure that the image acquisition device is always facing the tracked target.

[0159] For image acquisition devices, results can also be obtained by shooting video.

[0160] For image acquisition devices, the orientation of the image acquisition device can be finely adjusted according to the time interval between different frames, so as to ensure that the image of the tracked target obtained by the UAV participating in tracking the target can be kept in the center of the image.

[0161] The process involves continuously acquiring the relative positions of the drones participating in the tracking and the tracked target, as well as obtaining the relative position information of adjacent time points and the position information of the drones participating in the tracking, in order to determine the actual movement information of the tracked target.

[0162] During the operation of the drone involved in tracking the target, it is necessary to continuously track the actual movement information of the target in order to obtain accurate results. The more actual movement information there is, the higher the accuracy of subsequent trajectory prediction.

[0163] The beneficial effect of step S130 is that, during the operation of the image acquisition device in the UAV participating in tracking the target, the image acquisition device can be kept facing the tracked target continuously, so that the tracked target can be kept in the center of the image to achieve continuous tracking of the tracked target. At the same time, it can also continuously acquire the actual motion information of the tracked target, thereby providing more data basis for predictive trajectory calculation.

[0164] As described in step S140, the purpose of this step is to synchronize the actual motion information obtained by the drones participating in tracking the target with the information obtained by the drones not participating in tracking the target. Then, the drones not participating in tracking the target can adjust their predicted trajectories based on the obtained actual motion information. Specifically:

[0165] S141. Obtain the number of drones participating in tracking the target and the number of drones not participating in tracking the target within the drone cluster.

[0166] The purpose of this step is to find that within a drone swarm, the more drones involved in tracking the target, the higher the accuracy of the motion signals they obtain, and the more direct the data they receive, the higher the accuracy of the predicted trajectory. Therefore, by comparing the number of drones of the two types, we can lay the foundation for subsequent analysis.

[0167] This includes obtaining the number of drones within the drone cluster.

[0168] Once the drones involved in tracking the target are identified, the remaining drones are those not involved in tracking the target, and the number of each type of drone is determined.

[0169] Within a drone swarm, after locking onto the target, information tags are set for each drone participating in the target tracking process directly within the control system and / or the drone swarm. The number of drones participating in the target tracking is determined by counting the number of information tags.

[0170] S142. All the drones participating in the tracking of the target acquire the real-time location of the tracked target.

[0171] The purpose of this step is to ensure the quality of the predicted trajectory adjustment for the tracked target, which obviously requires ensuring the positioning accuracy of the tracked target to obtain an accurate real-time position. Specifically:

[0172] S1421. All the UAVs participating in the tracking of the target acquire the real-time location information of the tracked target and discard the real-time location information with excessive deviation in order to obtain the correct real-time location.

[0173] The purpose of this step is to ensure that all drones participating in the target tracking obtain the real-time location information of the target directly based on their own onboard sensors. This is to ensure that the obtained location information of the target is accurate, even if there is a large deviation or misidentification of the target's location information.

[0174] In this process, all drones involved in tracking the target obtain the relative positions of the drone itself and the target being tracked based on sensors such as radar.

[0175] Among them, all types of drones currently available have self-positioning capabilities, so drones involved in tracking targets can obtain their current location.

[0176] In this process, the drone participating in the target tracking obtains the real-time position of the target being tracked based on its current position and the relative position between the drone participating in the target tracking and the target being tracked.

[0177] Specifically, the real-time positions of all tracked targets are compared using spatial coordinate systems to determine the deviation of each coordinate. The calculation equation is as follows:

[0178] ,

[0179] Among them, D xk D yk and D zk This represents the deviation between the x, y, and z coordinates of the k-th real-time position and all real-time positions; x k y k and z k This represents the x, y, and z coordinates of the k-th real-time position; x ky k and z i Let x, y, and z represent the x, y, and z coordinates of the i-th real-time location; i represents the index of the real-time location; j represents the total number of indexes of the real-time location; k ∈ [1, j], and k is a natural number.

[0180] Among them, D xk D yk and D zk If any one of the values ​​is significantly higher than all other values, or exceeds the preset deviation, then the real-time position is directly considered to be an incorrect real-time position, and the drone participating in tracking the target corresponding to that real-time position has actually locked onto the wrong tracked target.

[0181] S1422. Obtain the UAVs participating in the tracking target corresponding to the real-time location information of the excessively large deviation, and convert them into UAVs not participating in the tracking target.

[0182] The purpose of this step is to further identify the types of drones within the drone swarm, in order to determine which drones are involved in tracking the target and which are not.

[0183] This involves acquiring real-time location information with excessive deviation, and simultaneously acquiring the drones involved in tracking the target corresponding to that real-time location information.

[0184] If the real-time position deviation collected by the drone is too high, it can be clearly determined that the real-time position information collected by the drone participating in the tracking of the target is incorrect, and the drone is tracking the wrong target.

[0185] Specifically, for drones whose real-time location information has excessive deviation and which are participating in the tracking of the wrong target, the drones that are participating in the tracking of the wrong target will be adjusted to be drones that are not participating in the tracking of the target.

[0186] S1423. Obtain the spatial coordinates of all the correct real-time positions, and calculate the average value of the coordinates to obtain the real-time position of the tracked target.

[0187] The purpose of this step is to determine the spatial coordinates of the correct real-time location after eliminating erroneous real-time location information, and then to determine the real-time location of the tracked target that can be obtained.

[0188] Specifically, for the obtained correct real-time position spatial coordinates, the mean value of each coordinate value in the spatial coordinates is obtained as the real-time position coordinate value of the tracked target.

[0189] After obtaining all the coordinate values, they are constructed into a single coordinate system to obtain the real-time position of the tracked target.

[0190] S143. Based on the real-time position of the tracked target, obtain the motion information of the tracked target.

[0191] The purpose of this step is to obtain a more accurate predicted trajectory. Obviously, it is necessary to obtain the current location based on the real-time location information already obtained, and then obtain motion information based on this information in order to obtain a new predicted trajectory based on the motion information.

[0192] Among them, the real-time locations of all tracked targets that have been obtained are directly used to construct their trajectory curves.

[0193] Based on the obtained track curves and the motion information of each point on each track curve, the position of the tracked target in the next period of time is predicted.

[0194] Among them, for the location information of the tracked target, motion data such as velocity, acceleration, and direction can be directly calculated based on two real-time positions at adjacent time points to obtain motion information.

[0195] S144. When the number of drones participating in tracking the target in the drone swarm is greater than the number of drones not participating in tracking the target, the drones participating in tracking the target jointly predict the track of the tracked target; otherwise, the drones not participating in tracking the target jointly predict the track of the tracked target.

[0196] The purpose of this step is that, for the drones involved in tracking the target, there are two tasks: one is to continuously track the target, and the other is to acquire the motion information of the target. As can be seen from the technical solution described above, the determination of the real-time position needs to be based on the measurement results of multiple drones involved in tracking the target. The more drones involved in tracking the target, the higher the accuracy of the real-time position. Therefore, this step can obtain the accurate track of the target based on the distribution of the number of drones involved in tracking the target and drones not involved in tracking the target.

[0197] In cases where the number of drones involved in tracking a target exceeds the number of drones not involved in tracking a target, the target trajectory prediction for the tracked target is performed by the drones involved in tracking the target.

[0198] Specifically, when the number of drones participating in tracking the target is less than the number of drones not participating in tracking the target, the target trajectory prediction for the tracked target is performed by the drones not participating in tracking the target.

[0199] In the case where the trajectory prediction is performed by a drone that is not involved in tracking the target, all drones that are not involved in tracking the target need to acquire the real-time location information they have obtained and, based on the method in step S120, convert themselves into drones involved in tracking the target.

[0200] The reason for adopting this method is that the tasks performed by the drones involved in tracking the target include dynamic tracking of the target and determination of its real-time position, which is relatively complex. The tasks of the drones not involved in tracking the target are less, and they do not need to perform dynamic tracking of the target. If the number of drones involved in tracking is small, their main task is to stably lock onto the target and avoid losing the target. Considering the limited computing resources of a single drone, if the calculation of the predicted trajectory is also required, at least three tasks need to be completed. When the computing power of a single drone is low, the drone is prone to losing the target. However, as the number of drones involved in tracking the target increases, the drone group involved in tracking the target has a higher redundancy in handling the task loss problem. It can allow a certain number of drones to lose the target, so as to improve the processing accuracy of the prediction results by directly using all the real-time position information obtained by the drones involved in tracking the target.

[0201] In this process, the trajectory of the tracked target is predicted by the corresponding type of UAV. For the trajectory of the UAV participating in tracking the target, the parameters in the trajectory can be averaged after all the UAVs participating in tracking the target are obtained, and the adjusted predicted trajectory is obtained. For the trajectory obtained by the group of UAVs not participating in tracking the target, it is necessary to obtain the deviation between the trajectory equations, select all trajectory equations with a deviation lower than the preset value, and then average the parameters in the equations to obtain the adjusted trajectory equation.

[0202] The beneficial effect of step S140 is that it analyzes the specific types of drones involved in the calculation during the process of generating and adjusting the predicted trajectory of the drone, thereby ensuring the prediction accuracy and maintaining the operational stability of all drones in the entire drone cluster.

[0203] As described in step S150, the purpose of this step is to consider that the tracked target may actively adjust its flight attitude and parameters during operation, which would lead to a difference between the predicted trajectory and the actual flight trajectory of the tracked target. Therefore, it is necessary to update the predicted trajectory of the tracked target. Specifically:

[0204] S151. The adjusted predicted trajectory is sent to all drones that are not involved in tracking the target.

[0205] The purpose of this step is that, after obtaining the adjusted trajectory equation, the main function of which is to enable drones that are not involved in tracking the target to become drones that are involved in tracking the target, this information needs to be sent to all drones that are not involved in tracking the target.

[0206] After obtaining the adjusted predicted trajectory, the obtained trajectory is directly sent and transmitted within the drone that is not involved in tracking the target.

[0207] If the adjusted predicted trajectory is generated by a drone participating in the tracking of the target, the drone participating in the tracking of the target can directly send the adjusted predicted trajectory to drones not participating in the tracking of the target.

[0208] If the adjusted predicted trajectory is generated by a drone that is not involved in tracking, then for drones that generate predicted trajectories with excessively high deviations within the group, their own predicted trajectory data is unusable and is replaced by the adjusted predicted trajectory.

[0209] S152. All drones not involved in tracking the target compare the similarity between the new adjusted predicted trajectory acquired at the current moment and the adjusted predicted trajectory applied at the current time period.

[0210] The purpose of this step is to obtain a direct comparison between the predicted trajectory and the actual trajectory of the drone that is not involved in tracking the target, that is, to obtain the similarity. If the similarity is high, it means that the accuracy of the predicted trajectory is high, or that the tracked target is moving according to the predicted trajectory, thus laying the foundation for subsequent motion parameter adjustment.

[0211] For drones that are not involved in tracking, they will continuously acquire adjusted predicted trajectories. If the motion information of drones that are not involved in tracking is immediately adjusted after each acquisition of a new adjusted predicted trajectory, the adjustment frequency of the motion information of drones that are not involved in tracking will be too high, which will significantly reduce control efficiency. Therefore, it is necessary to perform prediction trajectory similarity between two consecutive moments.

[0212] This allows for direct comparison of all coefficients in the predicted trajectory equations obtained at two adjacent time points, and the similarity of all coefficients can be obtained.

[0213] In some embodiments, the predicted real-time position of the tracked target at the next moment in the adjusted predicted trajectory equation applied in the current time period can be directly obtained, along with the predicted real-time position of the currently obtained adjusted predicted trajectory at the next moment. The similarity between the spatial coordinates of the two predicted real-time positions is then considered the similarity between the adjusted predicted trajectory at the current moment and the adjusted predicted trajectory applied in the current time period. It is important to note that this method is based solely on the next moment, not on a comparison of the entire process.

[0214] S153. When the similarity is not lower than the preset similarity, the drone that is not participating in the tracking of the target moves based on the adjusted predicted trajectory applied in the current time period. Otherwise, the drone that is not participating in the tracking of the target replaces the adjusted predicted trajectory applied in the current time period with a new adjusted predicted trajectory to obtain the updated tracked target trajectory.

[0215] The purpose of this step is to analyze, based on the obtained similarity analysis results, whether the drones not involved in tracking need to further adjust their motion information based on the adjusted predicted trajectory obtained at the current moment.

[0216] The preset similarity can be set according to the accuracy requirements or by other methods, and this application does not limit it.

[0217] When the obtained similarity is found to be no less than the preset similarity, it is considered that the adjusted predicted trajectory applied at the current moment has high accuracy, and the UAV that is not involved in tracking has already flown according to the predicted trajectory. Directly using the predicted trajectory can reduce the attitude adjustment frequency of the UAV that is not involved in tracking, thereby simplifying the control system.

[0218] If the obtained similarity is found to be lower than the preset similarity, the predicted trajectory received by the drone that is not involved in tracking the target needs to be adjusted so that the subsequent task can be completed according to the new adjusted predicted trajectory.

[0219] The reason for adopting this method is that, in essence, for drones that are not tracking targets, after obtaining the predicted trajectory, they will fly towards the predicted trajectory. Moreover, the adjusted predicted trajectory is also generated in real time. If the flight state is constantly adjusted according to the newly received adjusted predicted trajectory, the computational resources consumed will be too high, and it will be easy to make extremely complex maneuvers, which will increase the motion energy consumption of drones that are not involved in tracking targets.

[0220] The beneficial effect of step S150 is that the predicted trajectory is updated within the drone swarm that is not involved in tracking the target, and it is also determined whether the adjusted predicted trajectory used by the drone that is not involved in tracking the target needs to be adjusted. This is to avoid placing excessive demands on the control system of the drone that is not involved in tracking the target, and to avoid excessive resource consumption for the drone motion information adjustment process during flight.

[0221] As described in step S160, the purpose of this step is to determine whether a drone that is not currently tracking the target can be converted into a drone that is actively tracking the target, thereby ensuring that more drones participate in tracking the target within the entire drone swarm, thus achieving continuous and dynamic tracking of the target. Specifically:

[0222] S161. After all non-target-participating UAVs obtain the updated tracked target's trajectory, they obtain the updated initial tracking position.

[0223] The purpose of this step is to adjust the initial tracking position after the drone that is not involved in tracking the target has acquired the updated track of the tracked target, thereby obtaining the updated initial tracking position.

[0224] The method for this step is the same as that for step S120, and will not be described again here.

[0225] S162. After the drone that is not involved in tracking reaches the updated initial tracking position, it performs target search and locks onto the tracked target, and verifies the correctness of the locked tracked target based on the performance information of the tracked target that is continuously acquired.

[0226] The purpose of this step is that once the drone that is not involved in tracking reaches the updated initial tracking position, it can perform target search and lock on at the corresponding location according to this processing method.

[0227] In this process, after a drone that is not involved in tracking reaches the updated initial tracking position, it scans the surrounding area using radar and image acquisition systems to identify all the information obtained.

[0228] Among them, based on all the information obtained and the performance information of the tracked target acquired by the drones involved in tracking the target, the accuracy of the obtained tracked target is analyzed.

[0229] Once a valid target information is found, the drones that are not involved in tracking the target will directly lock onto that target.

[0230] S163. After the tracked target is locked, the drone that was not involved in tracking the target is adjusted to be involved in tracking the target.

[0231] The purpose of this step is to adjust a drone that is not involved in tracking a target to one that is involved in tracking a target.

[0232] If a drone that is not involved in tracking successfully searches for and locks onto the tracked target and passes the verification, then the tracked target obtained by the drone that is not involved in tracking is considered to be correct. This indicates that the drone that is not involved in tracking has correctly locked onto the tracked target.

[0233] Specifically, once a drone that was not involved in tracking has locked onto the correct target, it will be directly converted into a drone involved in tracking.

[0234] The beneficial effect of step S160 is that it ensures that in the process of identifying and judging the tracked target, the drones that are not involved in tracking the target can be adjusted to be involved in tracking the target only after the correct tracked target is identified. Even when increasing the number and proportion of drones involved in tracking the target in the drone cluster, the correctness of the adjustment of drones involved in tracking the target can still be guaranteed.

[0235] As described in step S170, the purpose of this step is to dynamically track the target using a swarm of drones. Specifically:

[0236] S171. All UAVs participating in tracking the target obtain the area they need to reach at the next flight moment based on the movement information of the tracked target.

[0237] The purpose of this step is to allow the drones involved in tracking the target to predict the area they need to reach at the next flight moment based on the operational status of the target being tracked, thereby making maneuvers in advance.

[0238] In this scenario, the distance between the drone tracking the target and the target being tracked remains the same.

[0239] In addition to maintaining the same spacing, the system also acquires motion information of the tracked target and predicts the location of the tracked information at the next moment.

[0240] Among them, after obtaining the location of the tracked information at the next moment, the position of the drone participating in the tracking target is determined, provided that the distance between the drone participating in the tracking target and the tracked target is the same.

[0241] Among them, selecting a location from the possible positions of the drone participating in tracking the target will determine the area that the drone participating in tracking the target needs to reach in the next flight moment.

[0242] S172. Based on the area to be reached at the next flight time, obtain the flight data of the UAV participating in tracking the target.

[0243] The purpose of this step is to analyze the flight data of the drones involved in tracking the target over a period of time by analyzing the area that needs to be reached at the next flight time.

[0244] This involves determining the area to be reached at the next flight time to analyze the route of that area and the current location of the drones involved in tracking the target.

[0245] Based on the obtained route, the flight data of the drones participating in tracking the target is directly adjusted, thereby controlling the drone's flight according to the flight data.

[0246] S173. Based on the flight data, adjust the flight attitude of the UAV participating in the tracking target to perform dynamic tracking of the tracked target.

[0247] The purpose of this step is to adjust the flight attitude of the UAV involved in tracking the target based on the obtained flight data, thereby adjusting the flight path according to the flight attitude.

[0248] After acquiring the flight data, the flight attitude of the drone participating in the tracking target can be determined.

[0249] In this process, based on the flight attitude data of the UAV participating in the target tracking, the control system of the UAV participating in the target tracking issues corresponding control commands to adjust the flight attitude according to the issued commands.

[0250] Among these methods, the flight data and attitude of the drones involved in tracking the target are continuously adjusted to achieve dynamic tracking and monitoring of the tracked target.

[0251] The beneficial effect of step S170 is that the flight attitude and data of the UAV participating in tracking the target can be adjusted in real time and dynamically, thereby achieving dynamic tracking of the tracked target based on the adjustment results.

[0252] The beneficial effects of this application include:

[0253] 1. Achieved full-process dynamic target tracking. In the technical solution of this application, when a target needs to be tracked in a certain area, the UAV swarm starts running and searches for the target. After any UAV locks onto the target, all UAVs in the entire UAV swarm can dynamically track the target based on the information collected by that UAV. This achieves full-process dynamic target tracking by covering the entire process of multiple UAVs from the initial target search to the final target dynamic tracking.

[0254] 2. High-efficiency dynamic target tracking is achieved. In the technical solution of this application, all UAVs in the entire UAV cluster participate in the target search task, which fundamentally improves the target identification efficiency. Then, the target's motion information is directly collected, and information is synchronized within the UAV cluster based on this motion information. This allows all UAVs to plan their flight routes based on the synchronized information, directly reach the corresponding location, and lock onto the tracked target. This method can improve the efficiency of dynamic target tracking.

[0255] 3. High-precision dynamic target tracking is achieved. In the technical solution of this application, the dynamic target tracking process is divided into UAVs participating in the tracking and UAVs not participating in the tracking. The information obtained by the UAVs participating in the tracking is further verified. Based on the verification results, it is determined whether they have truly locked onto the tracked target, thus ensuring that the UAVs correctly lock onto the target during the dynamic target tracking process. At the same time, the target prediction trajectory is determined based on the number of UAVs participating in the tracking and UAVs not participating in the tracking, so as to fully guarantee the accuracy of the target prediction trajectory. Thus, high-precision dynamic target tracking is achieved.

[0256] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to computer program instructions. The aforementioned computer program can be stored in a non-volatile storage medium, and when executed, it performs the steps of the above method embodiments. Alternatively, if the integrated unit of the present invention is implemented as a software functional module and sold or used as an independent product, it can also be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention.

[0257] The above description is merely a specific embodiment of the present invention, but the scope of protection 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 scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-UAV target dynamic tracking method, characterized in that, The tracking method includes: All drones in the drone swarm work together to search for target information in order to obtain the tracked target; After any drone in the drone swarm acquires the tracked target, it obtains the target's motion information. The motion information of the tracked target is sent to other drones in the drone cluster to achieve synchronization of the tracked target's motion information among all drones in the drone cluster; After a drone in the drone swarm that failed to find the tracked target acquires the movement information of the tracked target, it predicts the track of the tracked target based on the movement information and obtains the initial tracking position based on the prediction result. Based on the initial tracking position, the drone cluster failed to find the planned flight path of the drone tracking target to reach the initial tracking position; When a drone in a drone swarm that failed to find the target arrives at the initial tracking position and successfully locks onto the target, the drone that failed to find the target is designated as a drone participating in the tracking; otherwise, it is designated as a drone not participating in the tracking. The drone participating in tracking the target adjusts the orientation of its image acquisition device to continuously acquire performance information of the tracked target and continuously acquire actual motion information. The drones that are not involved in tracking the target update the actual movement information of the tracked target in real time, and obtain the number of drones involved in tracking the target and the number of drones not involved in tracking the target within the drone cluster; All drones involved in tracking the target acquire the real-time location of the target; Based on the real-time position of the tracked target, obtain the motion information of the tracked target; When the number of drones participating in tracking the target in a drone swarm is greater than the number of drones not participating in tracking the target, the drones participating in tracking the target jointly predict the track of the tracked target; otherwise, the drones not participating in tracking the target jointly predict the track of the tracked target, thus obtaining the adjusted predicted track. Based on the adjusted predicted trajectory, the predicted trajectory of the tracked target is updated in real time to obtain the updated trajectory of the tracked target. Based on the updated tracked target's trajectory, when a drone that is not participating in the tracking of the target updates its initial tracking position and moves to the updated initial tracking position and is able to lock onto the tracked target, it is adjusted to become a drone participating in the tracking of the target. All drones involved in tracking the target acquire the target's motion information and adjust their movement data based on this information to dynamically track the target. 2.The method of claim 1, wherein, All drones within the drone swarm jointly search for target information to obtain the tracked target, including: The task distribution system of the drone swarm sends basic information of the tracked target to the corresponding drone swarm for the drone swarm to search for the tracked target; After receiving the basic information of the tracked target, the drone swarm searches for target information in the corresponding airspace based on the image acquisition system and radar system; After any drone in the drone swarm detects a suspected tracked target, it performs image and track verification on the suspected tracked target. When the suspected tracked target passes the image and movement verification, the suspected tracked target is confirmed as the tracked target. 3.The method of claim 1, wherein, After a drone in the drone swarm that failed to locate the tracked target acquires the target's motion information, it predicts the target's trajectory based on the motion information and obtains the initial tracking position based on the prediction result, including: All drones in the drone swarm that failed to find the tracked target output the predicted trajectory of the tracked target based on the tracked target information, and obtain the predicted trajectory of the tracked target that appears most frequently, which is the initial predicted trajectory. The location range of the drone that failed to find the tracked target and the initial predicted track spacing is not higher than the preset position spacing is obtained. From the given location range, obtain the location of the drone that failed to find the tracked target and the location of the minimum distance between them, so as to obtain the initial tracking position. 4.The method of claim 1, wherein, The drone participating in the target tracking adjusts the orientation of its image acquisition device to continuously acquire performance information of the tracked target and continuously acquire actual motion information, including: The drone participating in the tracking acquires the relative position and orientation of the tracked target; The image acquisition device on the UAV participating in tracking the target is oriented towards the relative position direction, and the UAV is ensured to be at the center of the frame image acquired by the image acquisition device; The drone participating in the tracking continuously adjusts its relative position to the tracked target in order to continuously acquire the tracked target's performance information and actual movement information.

5. The method of claim 1, wherein, The drones participating in the target tracking acquire the real-time location of the tracked target, including: All the drones participating in the target tracking acquire the real-time location information of the target being tracked, and discard real-time location information with excessive deviation to obtain the correct real-time location; The drones that are tracking targets and whose real-time location information is too large are identified and then converted into drones that are not tracking targets. Obtain the spatial coordinates of all the correct real-time locations, and calculate the average of the coordinates to obtain the real-time location of the tracked target.

6. The target dynamic tracking method for multiple unmanned aerial vehicles according to claim 1, characterized in that, The step of updating the predicted trajectory of the tracked target in real time based on the adjusted predicted trajectory to obtain the updated trajectory of the tracked target includes: The adjusted predicted trajectory is sent to all drones that are not involved in tracking the target; All drones not involved in tracking the target are compared for the similarity between the new adjusted predicted trajectory acquired at the current moment and the adjusted predicted trajectory applied at the current time. When the similarity is not lower than the preset similarity, the drones not participating in the tracking target move based on the adjusted predicted trajectory applied in the current time period; otherwise, the drones not participating in the tracking target replace the adjusted predicted trajectory applied in the current time period with a new adjusted predicted trajectory to obtain the updated tracked target trajectory.

7. A target dynamic tracking method for multiple unmanned aerial vehicles according to claim 1, characterized in that, When a drone that is not involved in tracking updates its initial tracking position based on the updated tracked target's trajectory, and moves to the updated initial tracking position while being able to lock onto the tracked target, the drone that is then adjusted to participate in tracking the target includes: After all non-target-tracking drones obtain the updated tracked target's trajectory, they obtain the updated initial tracking position. After the drone that is not involved in tracking arrives at the updated initial tracking position, it searches for and locks onto the tracked target, and verifies the correctness of the locked tracked target based on the continuously acquired performance information of the tracked target. Once the tracked target is locked, drones that were not involved in tracking the target are adjusted to become drones involved in tracking the target.

8. A target dynamic tracking method for multiple unmanned aerial vehicles according to claim 1, characterized in that, All UAVs participating in the target tracking acquire the target's motion information and adjust their movement data based on this information to perform dynamic tracking of the target, including: All drones involved in tracking the target obtain the area they need to reach at the next flight moment based on the movement information of the tracked target; Based on the area to be reached at the next flight time, acquire the flight data of the UAVs participating in the target tracking; Based on the flight data, the flight attitude of the UAV participating in the tracking of the target is adjusted in order to perform dynamic tracking of the tracked target.

Images