Cooperative positioning configuration optimization method and device, electronic equipment and storage medium

Abstract

The application discloses a cooperative positioning configuration optimization method and device, electronic equipment and a storage medium, and particularly relates to the target positioning field. The method comprises the following steps: in the process of observing the observation position based on multiple unmanned aerial vehicles, the unmanned aerial vehicle positions of the multiple unmanned aerial vehicles and the unit vectors of the line-of-sight directions obtained by each unmanned aerial vehicle detecting the observation position are acquired; the initial estimation result of the observation position is determined according to the unit vectors and the unmanned aerial vehicle positions of each unmanned aerial vehicle; the observation constraint space of the initial estimation result is acquired, and multiple alternative configurations are determined from the observation constraint space; for each alternative configuration, the quality detection result of the alternative configuration is determined according to the unmanned aerial vehicle positions, the unit vectors of each unmanned aerial vehicle in the alternative configuration and the initial estimation result; and the target configuration is screened out from each alternative configuration according to the quality detection results of each alternative configuration. The application embodiment can improve the positioning accuracy.

Description

Technical Field

[0001] This invention relates to the field of target positioning, and more particularly to a cooperative positioning configuration optimization method, apparatus, electronic device, and storage medium. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are widely used in reconnaissance, surveillance, and emergency rescue missions. Target localization is one of the key fundamental issues in these missions. By acquiring relative information (such as direction and distance) between the UAV and the target through onboard sensors, the position of the target in the inertial coordinate system can be estimated, providing a basis for subsequent tracking, control, and decision-making.

[0003] Currently, existing multi-UAV cooperative localization methods typically rely on multi-sensor configurations and ideal observation models, which have limitations in terms of system cost, hardware complexity, and engineering implementation difficulty, especially unsuitable for small multi-rotor UAV swarms in close-range scenarios. Multimodal observation, to some extent, mitigates the geometric degradation problem under purely visual conditions, making it difficult to directly extend the relevant configuration design results to application scenarios that rely solely on orientation observation. Summary of the Invention

[0004] This invention provides a cooperative positioning configuration optimization method, apparatus, electronic device, and storage medium, which can improve positioning accuracy.

[0005] According to one aspect of the present invention, a cooperative positioning configuration optimization method is provided, the method comprising: During the observation of the observation position based on multiple drones, the drone positions of the multiple drones and the unit vector of the line of sight direction obtained by each drone at the detected observation position are obtained. Based on the unit vector of each UAV and the UAV position, the initial estimation result of the observation position is determined; Obtain the observation constraint space of the initial estimation result, and determine multiple candidate configurations from the observation constraint space. Each candidate configuration includes the spatial positions of multiple UAVs. For each of the candidate configurations, the quality inspection result of the candidate configuration is determined based on the UAV position, unit vector, and initial estimation result of each UAV in the candidate configuration; Based on the quality test results of each of the candidate configurations, the target configuration is selected from the candidate configurations.

[0006] According to one aspect of the present invention, a cooperative positioning configuration optimization device is provided, the device comprising: The line-of-sight vector determination module is used to obtain the drone positions of multiple drones and the unit vector of the line-of-sight direction obtained by each drone from the detected observation position during the observation process based on multiple drones. The position estimation module is used to determine the initial estimation result of the observation position based on the unit vector of each UAV and the position of the UAV; The configuration generation module is used to obtain the observation constraint space of the initial estimation result and determine multiple candidate configurations from the observation constraint space. Each candidate configuration includes the spatial positions of multiple UAVs. The quality inspection module determines the quality inspection result of each candidate configuration based on the UAV position, unit vector, and initial estimation result of each UAV in the candidate configuration. The configuration screening module is used to screen out the target configuration from the candidate configurations based on the quality test results of each candidate configuration.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the cooperative positioning configuration optimization method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the cooperative positioning configuration optimization method according to any embodiment of the present invention.

[0009] The technical solution of this invention involves using a UAV to observe the observation location and determine a unit vector to describe the true visual measurement direction; utilizing the intersection relationship of multiple UAV lines of sight to achieve direction-based positioning; determining multiple candidate configurations based on the initial estimation results obtained from positioning and the observation constraint space; determining the quality inspection results of each candidate configuration; and selecting the target configuration from the candidate configurations to optimize the positioning configuration of the UAV. This allows for configuration optimization based on the quality inspection results, thereby improving the accuracy and robustness of target positioning. It solves the problem of the lack of configuration optimization relying solely on direction observation in the prior art, and can improve the accuracy of positioning results.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0012] Figure 1 This is a flowchart of a cooperative positioning configuration optimization method provided by an embodiment of the present invention; Figure 2 This is a scene diagram of a cooperative positioning configuration optimization method provided by an embodiment of the present invention; Figure 3 This is a flowchart of another cooperative positioning configuration optimization method provided by an embodiment of the present invention; Figure 4 This is a schematic diagram of a cooperative positioning configuration optimization device according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device that implements the cooperative positioning configuration optimization method of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Figure 1This is a flowchart illustrating a cooperative positioning configuration optimization method provided by an embodiment of the present invention. This embodiment is applicable to situations where multiple UAVs are positioning at the same location. The method can be executed by a cooperative positioning configuration optimization device, which can be implemented in hardware and / or software.

[0016] See Figure 1 The cooperative positioning configuration optimization method shown includes: S101. During the observation of the observation position based on multiple UAVs, the positions of the multiple UAVs and the unit vector of the line of sight direction obtained by each UAV at the observation position are obtained.

[0017] In this embodiment of the invention, multiple drones are used to locate the target. For example, there are three drones. The drone position can refer to the drone's coordinates. The observation position can refer to the position of the target observed by the drone. The line of sight direction refers to the direction from the observer to the target object, specifically the direction from the drone (drone position) to the observation position. The unit vector is a vector with a modulus of 1. Each drone is equipped with a monocular vision sensor, which can obtain the direction information of the target relative to the machine system under its known pose, but cannot directly measure the distance between the drone and the target. In this embodiment of the invention, each drone relies solely on direction observation for positioning. Each drone is equipped with a monocular camera and simultaneously observes the same ground target object in the local navigation coordinate system. Each drone uses its onboard visual perception module to obtain the pixel coordinates of the target in the image using a target detection algorithm. Combining the camera's intrinsic and extrinsic parameters and the drone's own pose information (self-localization module), the pixel coordinates are converted into a unit vector of the line of sight (LOS) in the local navigation coordinate system and the camera's optical center position. For the i-th drone, the optical center position of its onboard camera is o. i The unit direction vector of the target obtained by visual measurement is d. i ,||d i ||=1.

[0018] However, in practical applications, orientation measurement is inevitably affected by factors such as angular noise, occlusion, and false detections. Solutions relying solely on two UAVs exhibit significant ill-conditioning along the line-of-sight and are extremely sensitive to angular noise. Introducing a third UAV provides a third independent spatial ray constraint, ensuring a unique and stable solution to the least squares problem under the general configuration. Therefore, the three UAVs constitute the minimum robust configuration for orientation measurement cooperative localization.

[0019] In a specific instance, such as Figure 2 As shown, three drones (o1, o2, and o3) work together to observe a person on the ground (the actual observation location P). truth(To locate.) i Let be the position of the optical center of the i-th UAV camera in the local navigation coordinate system. After the three-UAV cooperative positioning configuration is determined, at time frame t, the i-th UAV obtains the pixel coordinates of its observation position in the image plane through visual detection. And using the camera intrinsic parameter matrix, it is mapped to the normalized line of sight (LOS) in the camera coordinate system. i : Combined with camera external parameters With UAV attitude R i (t) gives the unit vector d of the line of sight in the world coordinate system. i (t), the unit vector, represents the spatial direction information of the UAV (or camera) pointing towards the target, and is a unified observation for subsequent spatial rendezvous and cooperative estimation. The unit vector d can be calculated based on the following formula. i (t): orthogonal projection vector P i (t) can be calculated using the following formula: I is the identity matrix. The orthogonal projection vector P i (t) can refer to the vector formed by projecting the unit vector onto a plane perpendicular to the UAV's line-of-sight direction. For any initial estimation result p obj The orthogonal distance from the initial estimate to the unit vector of the LOS of the i-th (i-th UAV) can be expressed as: This represents the residual vector corresponding to the localization algorithm. It represents the shortest displacement from the target point to the line of sight, and its magnitude is the Euclidean distance from the point to the line.

[0020] S102. Determine the initial estimation result of the observation position based on the unit vector of each UAV and the position of the UAV.

[0021] The initial estimation result can refer to the positioning result obtained based on observations from multiple UAVs. In some embodiments, a linear triangulation algorithm can be used to calculate the initial estimation result. Specifically, the observation position should be located on a ray along a unit vector originating from the position of a certain UAV, thus obtaining the linear equation of that UAV. Based on the linear equations determined by multiple UAVs, the initial estimation result of the observation position is obtained. For example, for a group of three UAVs, the initial value p can be calculated based on the following formula. init In the formula: A and b are respectively: in, d is an antisymmetric matrix. i Let p be the antisymmetric matrix of the i-th UAV, where i = 1, 2, or 3. The initial value p can be... init Alternatively, the initial value can be updated iteratively by reprojecting the error to obtain the initial estimate p. obj .

[0022] S103. Obtain the observation constraint space of the initial estimation result, and determine multiple candidate configurations from the observation constraint space. Each candidate configuration includes the spatial positions of multiple UAVs.

[0023] The observation constraint space can refer to the distribution space of the UAV swarm determined based on the initial estimation results and the visual acquisition parameters of the UAVs. The alternative configuration can refer to the spatial distribution of each UAV. Configuration optimization is used to reduce the impact of different spatial distributions among UAVs on positioning. Alternative configurations can include the spatial positions of all UAVs, or they can include the spatial positions of some UAVs. Typically, an alternative configuration includes the spatial positions of at least three UAVs. In some embodiments, the total number of UAVs is three. The alternative configuration includes the spatial positions of these three UAVs.

[0024] In reality, when multiple drones are on the same straight line, there is no observation information in certain directions, resulting in blind spots in positioning. In order to reduce blind spots and improve positioning accuracy, it is necessary to increase the number of observation directions as much as possible to diversify the line of sight.

[0025] In some embodiments, the observation constraint space can be determined based on the constraint of the angle between the line-of-sight directions of multiple UAVs, and / or the constraint of the difference between the UAV's shooting range and the initial estimation result. The angle may include an angle in the horizontal plane (parallel to the ground) and / or an angle in the vertical plane (perpendicular to the ground).

[0026] Specifically, when two or more lines of sight are approximately collinear, the uncertainty of the observation position along the line of sight direction will be significantly amplified, leading to a sharp decrease in positioning accuracy. Furthermore, when the distances between each UAV and the observation position are on the same order of magnitude, the contributions of different viewpoints to the effective information are relatively balanced; however, if the distance differences are too large, the closer UAV will dominate the positioning results, weakening the advantages of multi-view collaboration. Additionally, when all UAVs are located in the same altitude plane, and the target altitude change is small or located near that plane, the components of each line of sight vector in the altitude direction will be highly correlated, causing the unit vectors of the line of sight directions of all UAVs to exhibit approximately the same trend of change in the vertical direction, resulting in a significant lack of effective information in the altitude direction.

[0027] In some embodiments, the constraints that the observation constraint space needs to satisfy include: (1) the line-of-sight angle is large enough that the approximately equal angle distribution in the horizontal plane can significantly reduce the ill-formation in the line-of-sight direction; (2) the distances of each UAV to the target should be on the same order of magnitude to avoid information contribution imbalance; (3) a moderate height difference is introduced to avoid the three UAVs being coplanar with the target, which would lead to information degradation in the vertical direction.

[0028] Based on the spatial configuration characteristic analysis and configuration quality assessment method of multi-UAV cooperative target localization, this paper formalizes the spatial configuration optimization problem of multi-UAVs into a configuration search problem with geometric constraints: under the premise of satisfying the configuration optimization design principle, by adjusting the UAV spatial configuration, the configuration quality index is optimized, thereby improving the geometric conditions and estimation stability of target localization.

[0029] S104. For each of the candidate configurations, determine the quality inspection result of the candidate configuration based on the UAV position, unit vector and the initial estimation result of each UAV in the candidate configuration.

[0030] In this process, a quality inspection result is determined for each candidate configuration. The quality inspection result is used to evaluate the merits of the candidate configurations. In some embodiments, the natural logarithm of the determinant of the Fisher Information Matrix (FIM) can be calculated based on the UAV position, unit vector, and initial estimation results of each UAV in the candidate configurations, and this result can be used as the quality inspection result. In some embodiments, the Geometric Dilution of Precision (GDOP) can be calculated based on the UAV position, unit vector, and initial estimation results of each UAV in the candidate configurations, and this result can also be used as the quality inspection result.

[0031] S105. Based on the quality inspection results of each of the candidate configurations, select the target configuration from the candidate configurations.

[0032] Among these options, the candidate configuration with the best quality test results is selected as the target configuration. For example, the candidate configuration with the largest FIM is selected as the target configuration. Similarly, the candidate configuration with the smallest GDOP is selected as the target configuration.

[0033] The technical solution of this invention involves using a UAV to observe the observation location and determine a unit vector to describe the true visual measurement direction; utilizing the intersection relationship of multiple UAV lines of sight to achieve direction-based positioning; determining multiple candidate configurations based on the initial estimation results obtained from positioning and the observation constraint space; determining the quality inspection results of each candidate configuration; and selecting the target configuration from the candidate configurations to optimize the positioning configuration of the UAV. This allows for configuration optimization based on the quality inspection results, thereby improving the accuracy and robustness of target positioning. It solves the problem of the lack of configuration optimization relying solely on direction observation in the prior art, and can improve the accuracy of positioning results.

[0034] In an optional embodiment, determining the initial estimation result of the observation position based on the unit vector and position of each UAV includes: calculating an initial value based on the unit vector and position of each UAV; calculating the reprojection error of each UAV based on the initial value and the unit vector of each UAV; and iteratively updating the initial value with the goal of minimizing the reprojection error of each UAV to obtain the initial estimation result of the observation position.

[0035] The estimated observation position can be calculated using a triangulation algorithm based on the unit vector and position of each UAV, yielding an initial value. The initial value can refer to the position obtained from the first estimation of the observation position. The initial value and a unit vector of one UAV determine the reprojection direction of that UAV's observation of the initial value. The line-of-sight direction is the true observation direction, and the reprojection error is the difference between the observation direction and the reprojection direction. A nonlinear least squares method can be used to solve for the estimated observation position, with the optimization objective being to minimize the reprojection error of each UAV.

[0036] In one example, at time frame t, the projection model of the camera corresponding to the i-th drone is: The reprojected pixel coordinates on the image plane of the i-th UAV can be represented as: : In the formula: This represents the depth component of the vector in the camera coordinate system.

[0037] Coordinates obtained by object detection algorithm The corresponding reprojection error is : In fact, This represents the residual vector corresponding to the localization algorithm. It represents the shortest displacement from the target point to the line of sight, and its magnitude is the Euclidean distance from the point to the line. and These represent the residuals corresponding to the two positioning algorithms, respectively. When the actual line of sight is completely consistent with the direction of the line of sight, the residual vector is zero; when measurement errors exist, this residual vector reflects the degree of geometric inconsistency between the observed position and the i-th line of sight.

[0038] Under the condition of multi-UAV cooperative observation, the following nonlinear weighted least squares optimization function can be constructed by minimizing the reprojection error of all views: The minimum reprojection error for each UAV can be defined as the minimum sum of the reprojection errors of all UAVs. When the sum of the reprojection errors converges or the rate of change is less than a preset threshold, the initial value iteration is completed, and the current initial value is determined as the initial estimate of the observation position.

[0039] It is evident that by minimizing the reprojection error to calculate the initial estimate of the observation position, the observation residual can be directly characterized in the image domain, making the pixel-level error modeling more intuitive. However, it is also quite sensitive to the calibration accuracy of camera intrinsic and extrinsic parameters, UAV attitude estimation error, and the selection of initial values, thus improving the accuracy of the initial estimate.

[0040] In an optional embodiment, after selecting the target configuration from the candidate configurations based on the quality detection results of each candidate configuration, the method further includes: controlling each UAV to adjust to the position corresponding to the target configuration, and observing the observation position to obtain an updated unit vector of the line of sight direction; and determining the target estimation result of the observation position based on the updated unit vector of each UAV and the position corresponding to the target configuration.

[0041] In this process, the UAV re-observes the observation position at the location specified by the target configuration. In fact, throughout the entire process of the cooperative localization configuration optimization method in this embodiment, the UAV performs two observations: once when calculating the initial estimation result, and once after the target configuration calculation is complete, when it is at the location corresponding to the target configuration, it re-observes and estimates the observation position. The new unit vector and new UAV position observed by the UAV at the new UAV position are obtained, a new estimate is calculated, and this new estimate is used as the target estimation result for the observation position. The target estimation result is the positioning result with improved accuracy after configuration optimization.

[0042] In some embodiments, the control device can send control commands via an interface to the flight control systems of the corresponding UAVs, based on the positions (coordinates) of each UAV in the target configuration. The UAV swarm then collaboratively flies to the new position, completing the online adjustment of the spatial configuration. Under the new configuration with better geometric conditions, visual observation data is collected again. Using the updated, high-quality observation data, a high-precision positioning algorithm (such as the aforementioned reprojection error-based positioning) is employed to calculate the observed position, obtaining the target estimation result for the observed position. This significantly improves positioning accuracy and realizes a closed-loop online configuration control mechanism encompassing perception, evaluation, optimization, and control.

[0043] It is evident that by re-observing and repositioning based on the target configuration, configuration quality assessment and rapid search results can be directly integrated into the flight control system, enabling dynamic, closed-loop, and real-time adjustments to multiple unmanned configurations and improving positioning optimization efficiency.

[0044] Figure 3 This is a flowchart illustrating a cooperative positioning configuration optimization method provided by an embodiment of the present invention. Based on the above embodiments, this embodiment of the present invention specifies the acquisition of the observation constraint space of the initial estimation result as follows: determining the horizontal constraint range based on the initial estimation result and the performance information of each UAV; acquiring non-coplanar height constraints and non-collinear line-of-sight angle constraints; determining the height constraint range based on the horizontal distance range, non-coplanar height constraints, and non-collinear line-of-sight angle constraints; and determining the observation constraint space of the initial estimation result based on the horizontal constraint range and the height constraint range. It should be noted that parts not detailed in this embodiment of the present invention can be found in the descriptions of other embodiments.

[0045] See Figure 3 The cooperative positioning configuration optimization method shown includes: S301. During the observation of the observation position based on multiple UAVs, the positions of the multiple UAVs and the unit vector of the line of sight direction obtained by each UAV at the observation position are acquired.

[0046] S302. Determine the initial estimation result of the observation position based on the unit vector of each UAV and the position of the UAV.

[0047] S303. Based on the initial estimation results and the performance information of each UAV, determine the horizontal constraint range and the altitude constraint range.

[0048] For applications that rely solely on visual direction observation at close range, the principles of configuration optimization design can be summarized into the following set of constraints: Among them, C θ To constrain the horizontal line-of-sight angle of the drone, C RFor the constraint of the horizontal distance of the UAV, C z Constraints on the horizontal altitude of drones.

[0049] (1) Horizontal line of sight angle constraint To avoid geometric degradation caused by near-collinearity of multiple lines of sight, the angle between the lines of sight of the three UAVs in the horizontal projection plane must satisfy a minimum angle constraint, where the horizontal line of sight angle θ between the i-th UAV and the j-th UAV is... ij satisfy: Experiments have shown that, for a cooperative positioning scenario involving three drones, better geometric conditions can be obtained when the three lines of sight are approximately evenly distributed in the horizontal plane, such as when the included angle is close to 120°.

[0050] (2) Horizontal distance scale constraints The horizontal projection distance of the UAV-target is limited to the minimum safe flight distance and the effective field of view of the camera in close-range application scenarios to avoid accuracy degradation caused by observations that are too close or too far away. The horizontal distance of the i-th UAV satisfies: (3) Height non-coplanar constraint By introducing height distribution constraints, the UAVs are ensured to be non-coplanarly distributed in three-dimensional space, thereby enhancing the observability of three-dimensional positioning. The height distance of the i-th UAV satisfies: The above constraints together constitute a set of feasible configurations that satisfy the principles of configuration optimization design.

[0051] The performance information of the UAV can refer to the performance information of the sensor hardware used by the UAV for observation. For example, the performance information of the UAV includes hardware parameters such as camera parameters. Based on the performance information of the UAV, the maximum and minimum values ​​of horizontal and vertical distances are determined. The horizontal constraint range can refer to the range of coordinate values ​​on a horizontal plane parallel to the ground. The vertical constraint range can refer to the range of coordinate values ​​on a vertical plane perpendicular to the ground. The horizontal and vertical constraint ranges can be understood as the search boundaries of the alternative configurations.

[0052] Based on the target estimation results and the maximum and minimum values ​​of the horizontal distance, the range of values ​​for the UAV's horizontal coordinates can be determined [R]. min ,R max [ ], and serves as the horizontal constraint range. Wherein, the minimum horizontal distance R min This can refer to the minimum safe flight distance of a drone, which can be determined based on the drone's obstacle avoidance capabilities and the camera's minimum field of view to ensure complete target imaging. The maximum horizontal distance R... maxIt can refer to the maximum effective viewing distance within the camera's effective field of view, which can be determined based on camera resolution, target size, and reliable identification distance using detection algorithms.

[0053] Based on the target estimation results and the maximum and minimum values ​​of altitude and distance, the range of values ​​for the UAV's altitude coordinates can be determined [z]. min , z max [and serve as the height constraint range. Minimum height distance z] min This could refer to the minimum safe flight altitude of a drone, which can be determined based on scenarios where collisions with ground obstacles are unlikely. Maximum altitude-distance z max It can refer to the maximum height within the effective field of view of the camera, which can be determined based on the minimum field of view of the camera to ensure complete imaging of the target.

[0054] S304. Obtain non-coplanar height constraints and non-collinear line-of-sight angle constraints.

[0055] Among them, the non-coplanar height constraint refers to the constraint that the UAVs are distributed non-coplanarly in three-dimensional space, which can enhance the observability of three-dimensional positioning. The non-collinear line-of-sight angle constraint refers to the constraint that the line-of-sight angle between each UAV in the horizontal projection plane must meet the minimum angle constraint, which can reduce the geometric degradation caused by multiple lines of sight being nearly collinear.

[0056] S305. Determine the observation constraint space of the initial estimation result based on the horizontal constraint range, the height constraint range, the non-coplanar height constraint, and the non-collinear line-of-sight angle constraint.

[0057] Specifically, the two-dimensional position range of a single UAV can be determined based on the horizontal and height constraints; the distribution range among UAVs can be determined based on non-coplanar height constraints and non-collinear line-of-sight angle constraints. By combining the horizontal, height, non-coplanar height, and non-collinear line-of-sight angle constraints, the observation constraint space can be determined from both the dimensions of a single UAV and the dimensions of multiple UAVs working together.

[0058] In some embodiments, based on the content of the horizontal line-of-sight angle constraint, i.e., when the three lines of sight are approximately uniformly distributed in the horizontal plane, such as when the angle is close to 120°, better geometric conditions can be obtained. The minimum value of the horizontal line-of-sight angle is 360° / n, where n is the number of UAVs. To improve the configuration search efficiency, the horizontal line-of-sight angle can be directly determined as 360° / n, which can reduce the dimensionality of the three-dimensional configuration optimization to a two-dimensional horizontal plane, simplifying the configuration search process. For the cooperative localization scenario of three UAVs, the horizontal line-of-sight angle can be directly determined as 120°. For the cooperative localization scenario of four UAVs, the horizontal line-of-sight angle can be directly determined as 90°. Determining the horizontal line-of-sight angle according to the horizontal line-of-sight angle constraint fixes the horizontal line-of-sight angle to a single value, directly eliminating one degree of freedom. A three-dimensional space can be determined based on the horizontal line-of-sight angle, the horizontal constraint range, and the height constraint range, and used as the observation constraint space. Combining the horizontal constraint range and the height constraint range, along with the fixed horizontal line-of-sight angle, a complete three-dimensional observation constraint space can be uniquely determined.

[0059] S306. Determine multiple alternative configurations from the observation constraint space, where each alternative configuration includes the spatial positions of multiple UAVs.

[0060] S307. For each of the candidate configurations, determine the quality inspection result of the candidate configuration based on the UAV position, unit vector and the initial estimation result of each UAV in the candidate configuration.

[0061] S308. Based on the quality inspection results of each of the candidate configurations, select the target configuration from the candidate configurations.

[0062] The technical solution of this invention determines the two-dimensional position range of a single UAV based on horizontal and height constraints; and determines the distribution range among UAVs based on non-coplanar height constraints and non-collinear line-of-sight angle constraints. By combining horizontal, height, non-coplanar height, and non-collinear line-of-sight angle constraints, the observation constraint space can be determined from both the dimensions of a single UAV and the dimensions of multiple UAVs cooperating. This ensures that the observation constraint space is a feasible domain and that the configuration options for a safe and coordinated UAV swarm are available. Furthermore, based on the observation constraint space, infeasible configurations are filtered out, improving the feasibility and reliability of the optimized configuration.

[0063] In an optional embodiment, determining multiple candidate configurations from the observation constraint space includes: selecting multiple key discrete values ​​from the horizontal constraint range and the height constraint range of the observation constraint space to form the positions of multiple UAVs; and arranging and combining the positions of the multiple UAVs according to the non-coplanar height constraint and the non-collinear line-of-sight angle constraint to form multiple candidate configurations.

[0064] Here, key discrete values ​​can refer to representative discrete values. Endpoint values, mean, median, and interval sampling values ​​are selected from the height and horizontal constraint ranges of the observation constraint space, respectively, as key discrete values. For example, if the height constraint range is [1,6], 3.5 can be chosen as the key discrete value. The key discrete values ​​selected from the height and horizontal constraint ranges are arranged and combined to form at least one two-dimensional position coordinate system in a vertical and horizontal plane, which serves as the UAV's position. The two-dimensional position coordinates of each UAV are arranged and combined to form at least one UAV group. For each UAV group, based on non-coplanar height constraints and non-collinear line-of-sight angle constraints, the three-dimensional position coordinates of each UAV in the group are determined. Based on the three-dimensional position coordinates of the UAVs in that group, a candidate configuration is formed.

[0065] In some embodiments, determining the three-dimensional position coordinates of each UAV in a UAV group based on non-coplanar height constraints and non-collinear line-of-sight angle constraints can be achieved by determining a uniformly distributed angle based on the non-collinear line-of-sight angle constraint, and then determining the three-dimensional position coordinates of each UAV based on the angle and its two-dimensional position coordinates. Simultaneously, for UAVs with the same altitude within the same group, a moderate altitude difference is introduced to ensure that the non-coplanar height constraint is met; specifically, the altitudes of UAVs with the same altitude within the same group are fine-tuned.

[0066] In this configuration search, multiple key discrete values ​​within the height and horizontal constraints of the observation constraint space determine the positions of multiple UAVs. Instead of performing a dense grid search across the entire observation constraint space, a representative sampling strategy is employed. A small number of representative discrete values ​​are selected for each of the height and horizontal constraints within the observation constraint space; for example, 5m, 10m, and 15m are selected within the 4-15m range, and 1m, 3.5m, and 6m are selected within the 1-6m range. These selected discrete values ​​are then permuted and combined to obtain multiple combinations. For each combination, combining non-coplanar height constraints and non-collinear line-of-sight angle constraints, the three-dimensional coordinates of each UAV in each combination are determined, thus defining the spatial distribution of a UAV swarm and serving as candidate configurations.

[0067] As can be seen, by optimizing and reducing the dimensionality of the three-dimensional configuration to a two-dimensional horizontal plane, and by selecting representative discrete values ​​for each dimension in the horizontal and height dimensions instead of traversing with a fixed step size, and by generating a small-scale candidate configuration set in combination with non-coplanar height constraints and non-collinear line-of-sight angle constraints, the complex three-dimensional continuous space search is reduced in dimensionality and discretized. Selecting representative values ​​can reduce the search for unnecessary configurations and improve real-time performance.

[0068] In an optional embodiment, determining the quality inspection result of the candidate configuration based on the UAV position, unit vector, and initial estimation result of each UAV in the candidate configuration includes: calculating the reprojection error of each UAV in the candidate configuration based on the UAV position, unit vector, and initial estimation result; determining the weight of each UAV in the candidate configuration based on the reprojection error; determining the orthogonal projection vector of each UAV in the candidate configuration based on the unit vector; and determining the quality inspection result of the candidate configuration based on the weight, orthogonal projection vector, and UAV position.

[0069] The reprojection error of the UAVs describes their contribution to the cooperative localization result, i.e., the initial estimation result, and thus determines the weight of the UAVs. For each UAV, the reprojection error is the deviation between the updated initial estimation result, projected back onto the image plane, and the actual detected pixel coordinates. The orthogonal projection vector can be the vector formed by projecting a unit vector onto a plane perpendicular to the UAV's line-of-sight direction. The UAV's position is used to determine the distance between the UAV and the observation position by combining the true value of the observation position. Based on the orthogonal projection vector and the UAV's position, the degree to which the UAV provides geometric constraints can be determined. Combined with the UAV weights, the geometric constraint capability provided by the spatial distribution of the UAV swarm can be determined. Based on the geometric constraints of the spatial distribution of the UAV swarm, the quality detection results of the corresponding candidate configurations can be determined.

[0070] In some embodiments, the reprojection error This serves as an indicator of the reliability of the observation. A smaller error indicates a more reliable observation. Therefore, an observation weight w is defined for the LOS of each UAV. i The weight w can be calculated using the following formula. i : Where e0 is the reference scale for the reprojection error on the confidence of the observation, which is set according to the camera parameters. For example, it can be set to 8 pixels according to the camera parameters. Let be the reprojection error corresponding to the i-th UAV. When When the weight is close to 1; when As the weight increases, the weight gradually decreases, thus achieving adaptive weight reduction for high-error observations.

[0071] It is evident that by determining the weight of each UAV in the candidate configuration through the reprojection error, the contribution of the UAV to the positioning result can be determined. Then, based on the weight of each UAV in the candidate configuration, the orthogonal projection vector, and the UAV position, the quality inspection result of the candidate configuration can be determined. Mapping the UAV position in three-dimensional space to a low-dimensional orthogonal subspace effectively eliminates redundant degrees of freedom, reduces the computational complexity of configuration evaluation, and reflects the observability of the observation position in different directions, which can enhance the interpretability and reliability of the quality inspection result.

[0072] In an optional embodiment, determining the quality inspection result of the candidate configuration based on the weights, orthogonal projection vectors, and positions of each UAV in the candidate configuration includes: calculating the information matrix of each UAV in the candidate configuration based on the UAV positions, orthogonal projection vectors, and the initial estimation result; calculating a weighted matrix based on the weights and information matrices of each UAV in the candidate configuration; and determining the quality inspection result of the candidate configuration based on the logarithmic determinant of the weighted matrix.

[0073] Specifically, an information matrix can be calculated based on the orthogonal projection vectors in the candidate configurations and the UAV positions. Finally, the quality inspection results are obtained by weighting and transforming the information matrices of each UAV. The information matrix can refer to a matrix in the observation data that includes position parameter information.

[0074] The information matrix F of the i-th UAV can be calculated based on the following formula. i : Where, σ i The uncertainty in the orientation measurement of the i-th UAV is represented by a small-angle Gaussian noise. R represents the distance R from the drone to the observation location. i P i σ is an orthogonal projection vector. i The inherent angular error variance of the drone sensor system is fixed and varies depending on the type of drone and the sensor. In this embodiment of the invention, all drones and their onboard sensors in the drone swarm are identical, meaning that the variance σ for each drone is the same. i All are equal. σ i It can be obtained through calibration experiments. Statistically, σ... i The average angular deviation of the UAV swarm pointing at the target object is represented, which already includes attitude estimation error, camera installation error and some image processing error.

[0075] In one example, a drone can perform multiple observations of a calibration board or beacon with known coordinates at a known location (e.g., using a high-precision differential GPS or motion capture system to provide ground truth). The line-of-sight vector d calculated from each observation is recorded. meas Based on the true positions of the drone and the target, the true line-of-sight vector d is calculated. truth Calculate the angle between two vectors. Collect a large amount of experimental data and calculate these angles. The standard deviation (std) is used to obtain σ. i .

[0076] The information matrices of each UAV are weighted according to their respective weights to obtain the weighted matrix F: Where F is the Weighted Fisher Information Matrix (FIM). The quality inspection result J can be calculated using the following formula. FIM : J FIM This comprehensively reflects the geometric constraints provided by the current multi-aircraft spatial distribution. The larger the value, the better the configuration and the lower the uncertainty of target positioning; the smaller the value, the worse the configuration and the higher the uncertainty of target positioning.

[0077] It is evident that by calculating the information matrix to obtain the quality inspection results, the amount and direction of information can be preserved, enabling the anisotropic information distribution to be characterized. This allows the configuration to constrain unknown parameters more efficiently under limited observation conditions, providing information for configuration selection. It shifts from geometric distance-driven to information gain-driven approaches, achieving a quantitative assessment of system observability and parameter estimation accuracy, thereby improving the precision and accuracy of the quality inspection results.

[0078] Figure 4 This is a schematic diagram of a cooperative positioning configuration optimization device provided in an embodiment of the present invention. This embodiment of the present invention is applicable to situations where multiple UAVs are positioning at the same location. The device can execute a cooperative positioning configuration optimization method and can be implemented in hardware and / or software. The device can be configured in a server.

[0079] See Figure 4 The cooperative positioning configuration optimization device shown includes: The line-of-sight vector determination module 401 is used to obtain the drone positions of multiple drones and the unit vector of the line-of-sight direction obtained by each drone from the detected observation position during the process of observing the observation position based on multiple drones. The position estimation module 402 is used to determine the initial estimation result of the observation position based on the unit vector of each UAV and the position of the UAV; Configuration generation module 403 is used to obtain the observation constraint space of the initial estimation result and determine multiple candidate configurations from the observation constraint space. Each candidate configuration includes the spatial positions of multiple UAVs. The quality inspection module 404 determines the quality inspection result of each candidate configuration based on the UAV position, unit vector, and initial estimation result of each UAV in the candidate configuration. The configuration screening module 405 is used to screen out the target configuration from the candidate configurations based on the quality test results of each candidate configuration.

[0080] The technical solution of this invention involves using a UAV to observe the observation location and determine a unit vector to describe the true visual measurement direction; utilizing the intersection relationship of multiple UAV lines of sight to achieve direction-based positioning; determining multiple candidate configurations based on the initial estimation results obtained from positioning and the observation constraint space; determining the quality inspection results of each candidate configuration; and selecting the target configuration from the candidate configurations to optimize the positioning configuration of the UAV. This allows for configuration optimization based on the quality inspection results, thereby improving the accuracy and robustness of target positioning. It solves the problem of the lack of configuration optimization relying solely on direction observation in the prior art, and can improve the accuracy of positioning results.

[0081] Optionally, configuration generation module 403 is specifically used for: Based on the initial estimation results and the performance information of each UAV, the horizontal constraint range and the altitude constraint range are determined; Obtain non-coplanar height constraints and non-collinear line-of-sight angle constraints; The observation constraint space of the initial estimation result is determined based on the horizontal constraint range, the height constraint range, the non-coplanar height constraint, and the non-collinear line-of-sight angle constraint.

[0082] Optionally, configuration generation module 403 is specifically used for: Multiple key discrete values ​​are selected from the horizontal and vertical constraint ranges of the observation constraint space to form the positions of multiple UAVs; Based on the non-coplanar height constraint and the non-collinear line-of-sight angle constraint, multiple alternative configurations are formed by arranging and combining the positions of multiple UAVs.

[0083] Optional, the quality inspection module 404 is specifically used for: Based on the UAV position, unit vector, and initial estimation results of each UAV in the candidate configurations, calculate the reprojection error of each UAV in the candidate configurations; The weight of each UAV in the candidate configuration is determined based on the reprojection error of each UAV in the candidate configuration. The orthogonal projection vector of each UAV in the candidate configuration is determined based on the unit vector of each UAV in the candidate configuration. The quality inspection results of the candidate configurations are determined based on the weights, orthogonal projection vectors, and positions of each UAV in the candidate configurations.

[0084] Optional, the quality inspection module 404 is specifically used for: Based on the UAV position, orthogonal projection vector, and initial estimation results of each UAV in the candidate configurations, calculate the information matrix of each UAV in the candidate configurations; Calculate the weighting matrix based on the weights and information matrices of each UAV in the candidate configurations; The quality inspection results of the candidate configurations are determined based on the logarithmic determinant of the weighting matrix.

[0085] Optional, the position estimation module 402 is specifically used for: Calculate the initial values ​​based on the unit vector and position of each UAV; Based on the initial values ​​and the unit vector of each UAV, calculate the reprojection error of each UAV; With the goal of minimizing the reprojection error of each UAV, the initial value is iteratively updated to obtain the initial estimate of the observation position.

[0086] Optionally, the cooperative positioning configuration optimization device also includes: Optimize the observation module for: After selecting the target configuration from the candidate configurations based on the quality inspection results of each candidate configuration, each UAV is controlled to adjust to the position corresponding to the target configuration, and the observation position is observed to obtain the updated unit vector of the line of sight direction. The target estimation result at the observation position is determined based on the updated unit vector of each UAV and the position corresponding to the target configuration.

[0087] The acquisition, storage, and application of data involved in the technical solutions of this invention comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0088] The cooperative positioning configuration optimization device provided in the embodiments of the present invention can execute the cooperative positioning configuration optimization method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0089] Figure 5A schematic diagram of the structure of an electronic device 500 that can be used to implement an embodiment of the present invention is shown.

[0090] like Figure 5 As shown, the electronic device 500 includes at least one processor 501 and a memory, such as a read-only memory 502 or a random access memory 503, communicatively connected to the at least one processor 501. The memory stores computer programs executable by the at least one processor. The processor 501 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 502 or loaded from storage unit 508 into the random access memory 503. The random access memory 503 can also store various programs and data required for the operation of the electronic device 500. The processor 501, read-only memory 502, and random access memory 503 are interconnected via a bus 504. An input / output interface 505 is also connected to the bus 504.

[0091] Multiple components in electronic device 500 are connected to input / output interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0092] Processor 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 501 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 501 performs the various methods and processes described above, such as cooperative localization configuration optimization methods.

[0093] In some embodiments, the cooperative positioning configuration optimization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 500 via read-only memory 502 and / or communication unit 509. When the computer program is loaded into random access memory 503 and executed by processor 501, one or more steps of the cooperative positioning configuration optimization method described above may be performed. Alternatively, in other embodiments, processor 501 may be configured to perform the cooperative positioning configuration optimization method by any other suitable means (e.g., by means of firmware).

[0094] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), systems-on-a-chip (SoCs), complex programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0097] To provide interaction with the user, the systems and techniques described herein can be implemented on an operational detection device. This call response processing device includes: a display device (e.g., a cathode ray tube or liquid crystal display monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the call response processing device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0098] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0099] A computing system can include target user terminals and servers. Target user terminals and servers are generally geographically separated and typically interact via communication networks. The relationship between target user terminals and servers is created by computer programs running on the respective computers and establishing a target user-server relationship between them. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and virtual private servers, such as high management difficulty and weak business scalability.

[0100] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0101] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A cooperative positioning configuration optimization method, characterized in that, include: During the observation of the observation position based on multiple drones, the drone positions of the multiple drones and the unit vector of the line of sight direction obtained by each drone at the detected observation position are obtained. Based on the unit vector of each UAV and the UAV position, the initial estimation result of the observation position is determined; Obtain the observation constraint space of the initial estimation result, and determine multiple candidate configurations from the observation constraint space. Each candidate configuration includes the spatial positions of multiple UAVs. For each of the candidate configurations, the quality inspection result of the candidate configuration is determined based on the UAV position, unit vector, and initial estimation result of each UAV in the candidate configuration; Based on the quality test results of each of the candidate configurations, the target configuration is selected from the candidate configurations.

2. The method according to claim 1, characterized in that, The observation constraint space for obtaining the initial estimation result includes: Based on the initial estimation results and the performance information of each UAV, the horizontal constraint range and the altitude constraint range are determined; Obtain non-coplanar height constraints and non-collinear line-of-sight angle constraints; The observation constraint space of the initial estimation result is determined based on the horizontal constraint range, the height constraint range, the non-coplanar height constraint, and the non-collinear line-of-sight angle constraint.

3. The method according to claim 2, characterized in that, The process of determining multiple candidate configurations from the observation constraint space includes: Multiple key discrete values ​​are selected from the horizontal and vertical constraint ranges of the observation constraint space to form the positions of multiple UAVs; Based on the non-coplanar height constraint and the non-collinear line-of-sight angle constraint, multiple alternative configurations are formed by arranging and combining the positions of multiple UAVs.

4. The method according to claim 1, characterized in that, The step of determining the quality inspection result of the candidate configuration based on the UAV position, unit vector, and initial estimation result of each UAV in the candidate configuration includes: Based on the UAV position, unit vector, and initial estimation results of each UAV in the candidate configurations, calculate the reprojection error of each UAV in the candidate configurations; The weight of each UAV in the candidate configuration is determined based on the reprojection error of each UAV in the candidate configuration. The orthogonal projection vector of each UAV in the candidate configuration is determined based on the unit vector of each UAV in the candidate configuration. The quality inspection results of the candidate configurations are determined based on the weights, orthogonal projection vectors, and positions of each UAV in the candidate configurations.

5. The method according to claim 4, characterized in that, The step of determining the quality inspection result of the candidate configuration based on the weights, orthogonal projection vectors, and positions of each UAV in the candidate configuration includes: Based on the UAV position, orthogonal projection vector, and initial estimation results of each UAV in the candidate configurations, calculate the information matrix of each UAV in the candidate configurations; Calculate the weighting matrix based on the weights and information matrices of each UAV in the candidate configurations; The quality inspection results of the candidate configurations are determined based on the logarithmic determinant of the weighting matrix.

6. The method according to claim 1, characterized in that, The initial estimation result for determining the observation position based on the unit vector and position of each UAV includes: Calculate the initial values ​​based on the unit vector and position of each UAV; Based on the initial values ​​and the unit vector of each UAV, calculate the reprojection error of each UAV; With the goal of minimizing the reprojection error of each UAV, the initial value is iteratively updated to obtain the initial estimate of the observation position.

7. The method according to claim 1, characterized in that, After selecting the target configuration from the candidate configurations based on the quality inspection results of each candidate configuration, the process further includes: Control each of the aforementioned UAVs to adjust to the position corresponding to the target configuration, and observe the observation position to obtain the updated unit vector of the line of sight direction; The target estimation result at the observation position is determined based on the updated unit vector of each UAV and the position corresponding to the target configuration.

8. A cooperative positioning configuration optimization device, characterized in that, The device includes: The line-of-sight vector determination module is used to obtain the drone positions of multiple drones and the unit vector of the line-of-sight direction obtained by each drone from the detected observation position during the observation process based on multiple drones. The position estimation module is used to determine the initial estimation result of the observation position based on the unit vector of each UAV and the position of the UAV; The configuration generation module is used to obtain the observation constraint space of the initial estimation result and determine multiple candidate configurations from the observation constraint space. Each candidate configuration includes the spatial positions of multiple UAVs. The quality inspection module determines the quality inspection result of each candidate configuration based on the UAV position, unit vector, and initial estimation result of each UAV in the candidate configuration. The configuration screening module is used to screen out the target configuration from the candidate configurations based on the quality test results of each candidate configuration.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the cooperative positioning configuration optimization method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the cooperative positioning configuration optimization method according to any one of claims 1-7.

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