A collaborative target positioning method based on dual UAV data
Through the dual-drone collaborative target positioning method, the visible light passive sensor data of the dual-drone is used to generate lateral lines and calculate the shortest distance, and optimize the target positioning, which solves the problem of low positioning accuracy of traditional single drone and achieves higher positioning accuracy and stability.
Patent Information
- Application Number
- CN202411948628.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-27
- Publication Date
- 2025-04-08
AI Technical Summary
Traditional target positioning technology relies on multiple observations of a single large drone, and has problems such as low positioning accuracy, poor concealment and high economic costs, and is especially not suitable for small drone applications.
Using a collaborative target positioning method based on dual drone data, data is collected through visible light passive sensors of the first drone and the second drone, lateral lines are generated and the shortest distance is calculated, target positioning is optimized, and positioning accuracy is improved.
It significantly improves the accuracy and stability of target positioning, reduces the impact of errors caused by relying on a single measurement source, is suitable for small drones, and reduces the cost of drones.
Smart Images
Figure CN119780834A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of target positioning technology, and in particular to a collaborative target positioning method based on dual unmanned aerial vehicle data. Background Art
[0002] With the rapid development of science and technology, the application of UAVs in military and civilian fields has become more and more extensive and diversified. Traditional target positioning technology mainly relies on multiple observations by a single UAV. These observation methods include a combination of laser, visible light and infrared light, which is usually called three-in-one technology. However, this method has certain limitations. It is mainly suitable for large UAVs and is not friendly to small UAVs. In addition, the method of relying on large UAVs for multiple observations is easy to be exposed on the battlefield, which is not conducive to maintaining concealment, and it will also bring greater economic expenses. Summary of the invention
[0003] In order to facilitate target positioning of small UAVs, the present application provides a collaborative target positioning method based on dual UAV data.
[0004] The present application provides a collaborative target positioning method based on dual UAV data, which adopts the following technical solution: comprising the following steps:
[0005] Step S1: The first UAV and the second UAV fly, and a first visible light passive sensor on the first UAV and a second visible light passive sensor on the second UAV collect target positioning data;
[0006] Step S2, generating a first lateral line between the first UAV and the target world polar coordinates; generating a second lateral line between the second UAV and the target world polar coordinates;
[0007] Step S3: The world polar coordinates at the intersection of the first lateral line and the second lateral line are the preliminary positioning coordinates of the target;
[0008] Step S4, calculating a first shortest distance between the first lateral line and the actual position of the target; calculating a second shortest distance between the second lateral line and the actual position of the target;
[0009] Step S5, using the first shortest distance and the second shortest distance to obtain the target optimized positioning coordinates in the world polar coordinate system;
[0010] Step S6, using the target to optimize the positioning coordinates to obtain a positioning error value;
[0011] Step S7: if the positioning error value is greater than the preset accuracy value, the flight route of the second UAV is optimized and step S1 is performed; if the positioning error value is not greater than the preset accuracy value, the target optimized positioning coordinates are output as the target optimized positioning coordinates.
[0012] Preferably, the step S1 comprises the following steps:
[0013] Step S11, establishing a first UAV geographic location coordinate system; establishing a second UAV geographic location coordinate system;
[0014] Step S12: Obtain the coordinates of the first drone in the first drone geographic location coordinate system Get the coordinates of the second drone in the second drone's geographic location coordinate system
[0015] Preferably, step S2 comprises the following steps: the first drone coordinates Get the first lateral line between the target world polar coordinates; the second drone coordinates A second lateral line is obtained between the target world polar coordinates.
[0016] Preferably, step S3 comprises the following steps:
[0017] Step S31: Obtain the first lateral angle of the first lateral line in the world polar coordinate system in is the first pitch angle, θ1) is the first azimuth angle; obtain the second lateral angle of the second lateral line in the world polar coordinate system in is the second pitch angle, θ2 is the second azimuth angle;
[0018] Step S32: Setting the coordinate matrix
[0019] Get the first direction finding angle matrix
[0020] Get the second direction finding angle matrix
[0021] Step S33: Utilize The initial positioning coordinates of the target are obtained by solving T(x T ,y T ,z T ) T .
[0022] Preferably, step S4 comprises the following steps:
[0023] Calculate the first shortest distance
[0024] in, is the unit direction vector of the first direction finding line in the world coordinate system, Identity Matrix
[0025] Calculate the second shortest distance
[0026] in, is the unit direction vector of the second direction-finding line in the world coordinate system,
[0027] Identity Matrix
[0028] Preferably, step S5 comprises the following steps:
[0029] Calculate the target optimization positioning coordinates in, Identity Matrix
[0030] Preferably, step S6 comprises the following steps:
[0031] Step S61: Setting a first lateral angle There is a first error Where i = 1;
[0032] Set the second lateral angle There is a second error Where i = 2;
[0033] Step S62: Obtain error matrix
[0034] Step S63: Calculate the positioning error value
[0035] in, M ij (1≤i≤3,1≤j≤4) is the error matrix M 3×4 Elements of is the azimuth variance.
[0036] Preferably, the azimuth variance in step S63 is a variance calculated after the first UAV measures the same positioning target multiple times at the same position to obtain multiple azimuths.
[0037] In summary, the present application includes at least one of the following beneficial technical effects:
[0038] 1. In the process of target positioning, the azimuth angles measured by the first UAV and the second UAV are first mapped into three-dimensional space, and then the positioning of the target position is optimized by minimizing the measurement error, making full use of the measurement redundancy and information complementarity of the first UAV and the second UAV, significantly improving the positioning accuracy. This method can effectively reduce the error impact that may be caused by relying on a single measurement source. Finally, a more stable and accurate target positioning is achieved in a dynamically changing and complex environment.
[0039] 2. The first UAV is only equipped with the first visible light passive sensor, and the second UAV is only equipped with the second visible light passive sensor. Since it only uses visible light passive sensors, it is suitable for small UAVs to locate targets, thereby reducing the cost of UAVs;
[0040] 3. Due to the first lateral angle and the second lateral angle There are errors in the measurement process, which causes the first lateral line and the second lateral line to be out of plane. Therefore, by calculating the first shortest distance and the second shortest distance, the scenario in which the first direction-finding line and the second lateral line are out of plane and cannot intersect in the three-dimensional coordinate system is solved, thereby obtaining more accurate target positioning and improving the positioning accuracy and stability of the UAV in three-dimensional space. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] Figure 1 This is a flow chart of a collaborative target positioning method based on dual UAV data in Example 1 of the present application.
[0042] Figure 2 This is a schematic diagram of the collaborative positioning model of the first drone and the second drone in Example 1 of the present application.
[0043] Figure 3 This is a schematic diagram of the collaborative positioning model of the first drone and the second drone in Example 2 of the present application.
[0044] Figure 4 is the target positioning error and R in Example 2 of the present application GDOP Schematic diagram of comparison of changing trends.
[0045] Figure 5 This is a schematic diagram of the collaborative positioning model of the first drone and the second drone in Example 3 of the present application.
[0046] Figure 6 is r in Example 3 of the present application v With R GDOP Schematic diagram of comparison of changing trends.
[0047] Figure 7 is q and R in Example 3 of the present application GDOP Schematic diagram of comparison of changing trends.
[0048] Figure 8 is α and R in Example 3 of the present application GDOP Schematic diagram of comparison of changing trends.
[0049] Fig. 9 is β and R in Example 3 of the present application GDOP Schematic diagram of comparison of changing trends. DETAILED DESCRIPTION
[0050] The following is combined with Figure 1-9 This application is described in further detail.
[0051] Example 1, reference Figure 1 and Figure 2 , an embodiment of the present application discloses a collaborative target positioning method based on dual UAV data, comprising the following steps.
[0052] Step S1, a first UAV and a second UAV fly, a first visible light passive sensor on the first UAV and a second visible light passive sensor on the second UAV collect target positioning data, and step S1 includes the following steps.
[0053] Step S11, establishing a first UAV geographic location coordinate system; establishing a second UAV geographic location coordinate system;
[0054] Step S12: Obtain the coordinates of the first drone in the first drone geographic location coordinate system Get the coordinates of the second drone in the second drone's geographic location coordinate system
[0055] Step S2: First drone coordinates Get the first lateral line between the target world polar coordinates; the second drone coordinates A second lateral line is obtained between the target world polar coordinates.
[0056] Step S3: The world polar coordinates at the intersection of the first lateral line and the second lateral line are the preliminary positioning coordinates of the target. Step S3 includes the following steps:
[0057] Step S31: Obtain the first lateral angle of the first lateral line in the world polar coordinate system in is the first pitch angle, θ1 is the first azimuth angle; obtain the second lateral angle of the second lateral line in the world polar coordinate system in is the second pitch angle, θ2 is the second azimuth angle;
[0058] Step S32: Setting the coordinate matrix
[0059] Get the first direction finding angle matrix
[0060] Get the second direction finding angle matrix
[0061] Step S33: Through geometric relationship Get GX T =H,
[0062] use The initial positioning coordinates of the target are obtained by solving T(x T ,y T ,z T ) T .
[0063] Step S4: Calculate the first shortest distance between the first lateral line and the actual position of the target.
[0064] First shortest distance
[0065] in, is the unit direction vector of the first direction finding line in the world coordinate system,
[0066] Calculate the second shortest distance between the second lateral line and the actual position of the target,
[0067] The second shortest distance
[0068] in, is the unit direction vector of the second direction-finding line in the world coordinate system,
[0069] The first shortest distance and the second shortest distance are derived by the following steps.
[0070] The first step is to set the point closest to point T on the first direction finding line to be Where i = 1; let the unit direction vector of the first direction finding line in the world coordinate system be Where i = 1; let the point closest to point T on the second direction finding line be Where i = 2; let the unit direction vector of the first direction finding line in the world coordinate system be Where i = 2;
[0071] Step 2: The point on the first direction finding line closest to point T The coordinates can be expressed as: where λ i For vector The modulus of i = 1; then the point closest to point T on the second direction finding line The coordinates can be expressed as: where λ i For vector The modulus of , i = 2;
[0072] Step 3: Minimize the value of λ i The objective function is: Find λ i For the objective function δ i 2 The optimal solution of
[0073] Step 4: Order because is a unit vector, we can get and Therefore, when When Closest to target T;
[0074] Step 5: Arrange and get the first shortest distance
[0075] in,
[0076] Calculate the second shortest distance between the second lateral line and the actual position of the target,
[0077] The second shortest distance
[0078] in,
[0079] Step S5: Use the first shortest distance and the second shortest distance to obtain the target optimization positioning coordinates in the world polar coordinate system.
[0080] in,
[0081] The target optimized positioning coordinates are derived by the following steps.
[0082] Step 1: Accumulate all δ i 2 Find the optimal solution for T, that is, minimize the objective function with respect to T:
[0083] The second step is to find the optimal solution of T to the objective function Φ, let
[0084] Step 3: Get the stationary point coordinates:
[0085] Step 3. Since the Hessian matrix of the objective function Φ is positive definite, it can be concluded that in three-dimensional space, It is the minimum point of the objective function Φ, that is, the optimal target position estimation point;
[0086] Step 4: Order Then transform
[0087] Step S6, using the target optimized positioning coordinates to obtain a positioning error value, step S6 includes the following steps: Step S61, setting a first lateral angle There is a first error Where i = 1;
[0088] Set the second lateral angle There is a second error Where i=2.
[0089] Step S62: Obtain error matrix Error matrix M 3×4 It is derived from the following steps.
[0090] The first step is to find the partial derivative of the first error and the second error:
[0091] Step 2: The differential form of the target optimization positioning coordinates relative to the first lateral angle and the second lateral angle is:
[0092]
[0093] Step S63: Calculate the positioning error value
[0094] in, M ij (1≤i≤3,1≤j≤4) is the error matrix M 3×4 Elements of is the azimuth variance, which is the variance calculated after the first UAV measures the same positioning target multiple times at the same position to obtain multiple azimuths.
[0095] Step S7: if the positioning error value is greater than the preset accuracy value, the flight route of the second UAV is optimized and step S1 is performed; if the positioning error value is not greater than the preset accuracy value, the target optimized positioning coordinates are output as the target optimized positioning coordinates.
[0096] Example 2, reference Figure 3 and Figure 4 , set the geographic location coordinate system of the first UAV to the world coordinate system, and set the coordinates in the geographic location coordinate system of the second UAV to point P(0,b,0), where b is the baseline distance between the two UAVs. Assume that the azimuth measurement error σθ =0.1°, high and low angle measurement error The coordinates of the target point in the geographic location coordinate system are (3km, 3km, -4km), 3km≤b≤10.5km. The baseline distance b is changed, and the target positioning error value of 1000 Monte Carlo simulations is compared with R GDOP The change law of the target positioning error value and R GDOP The results of the change trend are similar to those of GDOP It is feasible to describe the positioning error of the target point.
[0097] Example 3, reference Figure 5 , set the geographic coordinate system of the first drone to the world coordinate system, and set the coordinates in the geographic coordinate system of the second drone to point P(0,b,0), where b is the baseline distance between the two drones. The distance CT between the target and the baseline is r v , the distances between the target and the two drones are r1 and r1 respectively, and the ratio of r1 to r1 is q. The triangular inclined plane O formed by the first drone, the second drone and the target position W PT and Plane X W O W Y W The angle between the first UAV, the second UAV and the target position is α, and the intersection angle between the first UAV, the second UAV and the target position is β.
[0098] therefore, in It can be seen that the factors affecting the positioning accuracy of the target position are r v , q, α and β.
[0099] refer to Figure 5 and Figure 6 , when the ratio q of r1 and r2 remains unchanged, the first UAV, the second UAV and the target point are at any r v The triangular planes formed by the following are similar and in the same plane, so we can get From this analysis, we can see that The value of r v When α=64.28°, β=36°, σ θ =0.1°, let q be 0.6, 1.0, 1.4 respectively, and we get R GDOP With r v It is a monotonically increasing linear relationship, so when r v The smaller the value, the higher the positioning accuracy of the target point. GDOP The smaller.
[0100] refer to Figure 5 and Figure 7 , when the distance between the target point and the dual-machine baseline is r vWhen the value of q is unchanged and only the value of q changes, the parameter values in the simulation scenario are set to α = 64.28°, β = 36°, σ θ =0.1°, let r v The target positions set by any q value are all on the same straight line, and the straight line is parallel to the dual-machine baseline. When q = 1, the target point positioning accuracy is the highest, and R GDOP minimum; when the q value deviates from 1, the target point positioning accuracy is lower, and R GDOP The bigger.
[0101] refer to Figure 5 and Figure 8 When the angle β between the target point and the two machines is determined, the actual position of the target point is considered to be set at a circle with C as the center, r v is the radius, O W Y W The space circle with r as the central axis. v =5km,σ θ =0.1°. The positioning errors generated by the first lateral angle and the second lateral angle are symmetrical about the planes formed by the three coordinate axes, so α is set within the range of (0,90°). Let β be 41.8°, 47.5°, 53.3°, 59.0°. The larger the angle α between the inclined plane formed by the first UAV, the second UAV and the target point and the horizontal plane, the higher the positioning accuracy of the target point, and R GDOP The smaller.
[0102] refer to Figure 5 and Fig. 9 , let α be 29.91°, 41.37°, 52.83°. When β is closer to 55°, the target point positioning accuracy is higher, and R GDOP The smaller the value, the lower the positioning accuracy of the target point. GDOP The bigger.
[0103] In summary, when R GDOP The smaller it is, the lower the positioning accuracy of the target point. GDOP The larger the value, the higher the positioning accuracy of the target point. GDOP The numerical value of can determine the positioning accuracy of the target point.
[0104] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.
Claims
1. A collaborative target positioning method based on dual UAV data, characterized by: The following steps are involved: Step S1: The first UAV and the second UAV fly, and a first visible light passive sensor on the first UAV and a second visible light passive sensor on the second UAV collect target positioning data; Step S2, generating a first lateral line between the first UAV and the target world polar coordinates; generating a second lateral line between the second UAV and the target world polar coordinates; Step S3: The world polar coordinates at the intersection of the first lateral line and the second lateral line are the preliminary positioning coordinates of the target; Step S4, calculating a first shortest distance between the first lateral line and the actual position of the target; calculating a second shortest distance between the second lateral line and the actual location of the target; Step S5, using the first shortest distance and the second shortest distance to obtain the target optimized positioning coordinates in the world polar coordinate system; Step S6, using the target to optimize the positioning coordinates to obtain a positioning error value; Step S7: if the positioning error value is greater than the preset accuracy value, the flight route of the second UAV is optimized and step S1 is performed; if the positioning error value is not greater than the preset accuracy value, the target optimized positioning coordinates are output as the target optimized positioning coordinates.
2. The collaborative target positioning method based on dual UAV data according to claim 1 is characterized in that: The step S1 comprises the following steps: Step S11, establishing a first UAV geographic location coordinate system; establishing a second UAV geographic location coordinate system; Step S12: Obtain the coordinates of the first drone in the first drone geographic location coordinate system Get the coordinates of the second drone in the second drone's geographic location coordinate system 3. The collaborative target positioning method based on dual UAV data according to claim 2 is characterized in that: The step S2 includes the following steps: the first drone coordinates Get the first lateral line between the target world polar coordinates; the second drone coordinates A second lateral line is obtained between the target world polar coordinates.
4. The collaborative target positioning method based on dual UAV data according to claim 1 is characterized in that: The step S3 comprises the following steps: Step S31: Obtain the first lateral angle of the first lateral line in the world polar coordinate system in is the first pitch angle, θ1 is the first azimuth angle; obtain the second lateral angle of the second lateral line in the world polar coordinate system in is the second pitch angle, θ2 is the second azimuth angle; Step S32: Setting the coordinate matrix Get the first direction finding angle matrix Get the second direction finding angle matrix Step S33: Utilize The initial positioning coordinates of the target are obtained by solving T(x T ,y T ,z T ) T .
5. The collaborative target positioning method based on dual UAV data according to claim 4 is characterized in that: The step S4 comprises the following steps: Calculate the first shortest distance in, is the unit direction vector of the first direction finding line in the world coordinate system, i=1, identity matrix Calculate the second shortest distance in, is the unit direction vector of the second direction-finding line in the world coordinate system, i=2, identity matrix 6. The collaborative target positioning method based on dual UAV data according to claim 5 is characterized in that: The step S5 comprises the following steps: Calculate the target optimization positioning coordinates in, Identity Matrix 7. The collaborative target positioning method based on dual UAV data according to claim 6 is characterized in that: The step S6 comprises the following steps: Step S61: Setting a first lateral angle There is a first error Where i = 1; Set the second lateral angle There is a second error Where i = 2; Step S62: Obtain error matrix Step S63: Calculate positioning error in, M ij (1≤i≤3,1≤j≤4) is the error matrix M 3×4 Elements of is the azimuth variance.
8. The method for collaborative target positioning based on dual UAV data according to claim 7, characterized in that: The azimuth variance in step S63 is the variance calculated after the first UAV measures the same positioning target multiple times at the same position to obtain multiple azimuths.