Bistatic SAR unmanned aerial vehicle trajectory optimization method based on dynamic sequence solution

Abstract

The invention relates to a bistatic SAR unmanned aerial vehicle trajectory optimization method based on dynamic sequence solution, and belongs to the technical field of cluster guidance. Bistatic SAR imaging detection constraints, unmanned aerial vehicle dynamic characteristics and unmanned aerial vehicle flight control constraints are considered according to a known receiver flight trajectory with higher task priority; and according to the comprehensive resolution of the imaging scene of the detection target in each stage and the motion constraint of the unmanned aerial vehicle, establishing an unmanned aerial vehicle acceleration optimization model under the multi-constraint condition of each stage, dynamically planning an unmanned aerial vehicle trajectory optimization problem into a plurality of sub-problems, and carrying out dynamic solution to obtain an optimal solution of each sub-problem, according to the method, on one hand, the trajectory design of the bistatic SAR unmanned aerial vehicle under the multi-constraint condition is realized, and on the other hand, the bistatic SAR imaging detection capability and the bistatic SAR unmanned aerial vehicle flight trajectory of bistatic SAR system guidance control are met, and on the other hand, the operation efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of cluster guidance, and in particular to a dual-base SAR unmanned aerial vehicle trajectory optimization method based on dynamic sequence solution. Background Art

[0002] The dual-base SAR imaging detection technology, due to its separate transmission and reception, enables the receiver to have forward-looking passive imaging capabilities. At present, Chinese patent applications with publication numbers CN110187345B, CN103901430A and CN112698327A have conducted research and exploration on the design of dual-base forward-looking SAR UAV flight trajectory.

[0003] According to the gradient theory of imaging resolution, the geometric configuration of the bistatic SAR system trajectory has a direct impact on the imaging resolution capability of the bistatic SAR system. Therefore, when bistatic SAR is applied to actual projects, the problem of bistatic flight trajectory design must be solved first, especially the problem of UAV trajectory design. Because the receiver of the bistatic SAR system is generally used as the main executor of the mission, its own flight trajectory cannot be changed at will, or the priority of the receiver flight trajectory change is higher than that of bistatic SAR imaging detection. At this time, the imaging resolution capability of the bistatic SAR system mainly depends on the UAV of the bistatic SAR system.

[0004] Most of the bistatic SAR trajectory design methods in the prior art are static optimizations, and do not consider the constraints of guidance control. Therefore, the bistatic SAR configuration design methods in the prior art cannot meet the requirements of dynamic process trajectory design. Moreover, the bistatic SAR flight trajectory designed in the prior art to meet the flight control requirements does not consider the imaging area constraints and the initial configuration conditions in the corresponding constraints, which is defective. Summary of the invention

[0005] In view of the above problems, the present invention provides a dual-base SAR UAV trajectory optimization method based on dynamic sequence solution. According to the known flight trajectory of a receiver with a higher task priority, the dual-base SAR imaging detection constraints, the UAV dynamic characteristics and the UAV flight control constraints are considered. According to the comprehensive resolution of the imaging scene of the detection target at each stage and the UAV motion constraints, a UAV acceleration optimization model under multiple constraints at each stage is established. The UAV trajectory optimization problem is dynamically planned into multiple sub-problems, and dynamically solved to obtain the optimal solution of each sub-problem, that is, the optimal acceleration of the UAV at each moment. On the one hand, the dual-base SAR UAV trajectory design under multiple constraints is realized, and the dual-base SAR UAV flight trajectory that meets the dual-base SAR imaging detection capability and the dual-base SAR system guidance and control is satisfied. On the other hand, the computing efficiency is improved, and the practical application value of the dual-base SAR system is increased.

[0006] The present invention provides a dual-base SAR unmanned aerial vehicle trajectory optimization method based on dynamic sequence solution, comprising: Step S1: Obtaining the bistatic SAR system time k The comprehensive resolution of the imaging scene of the detection target and establish the moment k UAV acceleration optimization model under multiple constraints; k =1,2,3 …K, K Indicates the total number of moments; Step S2: based on time k UAV acceleration optimization model under multiple constraints to obtain the moment k Drones in the i The first-order gradient of the acceleration state vector of the iteration g k,i and the second-order Hessian matrix G k,i ; Step S3: Based on the first-order gradient g k,i and the second-order Hessian matrix G k,i Get the moment k Drones in the i+ The acceleration state vector of 1 iteration; Step S4: Determination i Is it greater than or equal to I , I Table total number of iterations, if yes, get the time k The optimal acceleration of the drone, if not, let i = i +1, return to step S2; Step S5: traverse K At each moment, the optimal acceleration of the UAV is obtained, which is represented as the acceleration sequence of the bistatic SAR UAV trajectory. Step S6: obtaining an optimal bistatic SAR UAV trajectory based on the acceleration sequence of the bistatic SAR UAV trajectory.

[0007] Optionally, the time of obtaining in step S3 is k Drones in the i+ The specific steps of the acceleration state vector of 1 iteration include: Step S31: Determine the first-order gradient g k,i Is it less than the preset convergence condition? If so, set the time k Drones in the i The acceleration state vector of the iteration is taken as the moment k Drones in the i+ The acceleration state vector of the first iteration goes to step S4; If not, proceed to step S32; Step S32: Based on the first-order gradient g k,i and the second-order Hessian matrix G k,i Build time k Drones in the i The quadratic objective function of the acceleration state vector of the iteration and obtain the corresponding minimum value x' k,i , the minimum value x' k,i Represented as a moment k Drones in the i The acceleration direction of the iteration; Step S33: based on time k Drones in the i The acceleration direction of the iteration is obtained at the moment k Drones in the i The iteration step size of the iteration, and the time k Drones in the i The acceleration state vector of the iteration is updated to obtain the moment k Drones in the i The updated acceleration state vector of the iteration is used as the moment k Drones in the i+ Acceleration state vector for iteration 1.

[0008] Optionally, obtain the bistatic SAR system time k The specific steps of detecting the comprehensive resolution of the imaging scene of the target include: Get the time separately k The distance resolution and time of each scene target point of the detection target k The azimuth resolution and time of each grid point in the imaging scene of the detection target k The gradient angle between the range gradient and the Doppler gradient of each grid point in the imaging scene of the detected target; based on the most stringent detection criteria, the dual-static SAR system moment k The comprehensive resolution of the imaging scene of the detection target.

[0009] Optionally, the bistatic SAR system time k The comprehensive resolution of the imaging scene of the detection target is expressed as:

[0010] in, Indicates time k The maximum distance resolution of the imaging scene of the detected target, Indicates time kThe comprehensive resolution of the imaging scene of the detection target, Indicates time k The maximum azimuth resolution of the imaging scene of the detected target, Indicates time k The minimum gradient angle of the imaging scene of the detection target.

[0011] Optionally, get the time k The specific steps of detecting each scene target point of the target include: Selected time k The center point of the scene, based on the time k The scene center point is obtained at the moment k The beam coverage range is represented by the time k Imaging scene of the detection target; Determine the moment k The scene layout interval; Based on time k The scene layout interval is at time k The imaging scene of the detection target is grid-divided to obtain the time k The multiple grid points of the imaging scene of the detection target are represented as time k Multiple scene target points of the detection target.

[0012] Optionally, the moment in step S1 k Multiple constraints including time k The UAV motion constraints and imaging constraints.

[0013] Optionally, get the time k The specific steps of UAV motion constraints include: Determine the moment k Overload constraints on drones; Based on time k The UAV's overload limit is obtained at the time k UAV motion constraints.

[0014] Optionally, time k UAV motion constraints and timing k The expressions of the UAV's overload constraints are:

[0015] in, For the moment k Synthetic aperture time, is the maximum synthetic aperture time; is the maximum heading speed of the UAV, For the moment k Drone heading speed, For the moment k The altitude at which the drone is flying; is the maximum flight altitude of the drone, For the moment k Drones in x The acceleration of the axis, For the moment k Drones in y The acceleration of the axis, For the moment k Drones in z The acceleration of the axis, For drones x The acceleration of the axis, For drones y The acceleration of the axis, For drones z The acceleration of the axis.

[0016] Optionally, time k Drones in the i The expression of the quadratic objective function of the acceleration state vector of the iteration is: F k,i

[0017] in, F k,i Indicates time k Drones in the i The quadratic objective function of the acceleration state vector of the iteration, Indicates time k The optimal acceleration state vector of the drone, For the moment k Drones in the i The acceleration state vector of the iteration, For the moment k Drones in the i The first-order gradient of the acceleration state vector at iteration, For the moment k Drones in the i The second-order Hessian matrix of the acceleration state vector of the iteration, (.) is the objective function, Indicates transpose.

[0018] Optionally, the time k Drones in the i+ The acceleration state vector of 1 iteration is expressed as: ; in, For the moment k Drones in thei+ The acceleration state vector of 1 iteration, For the moment k Drones in the i The acceleration state vector of the iteration, For the moment k Drones in the i The iteration step size is For the moment k Drones in the i The acceleration direction for the iteration.

[0019] Compared with the prior art, the present invention has at least the following beneficial effects: In the actual mission process, the present invention calculates the optimal UAV motion trajectory in real time online based on the existing receiver trajectory, so that the transmitter and the receiver can perform the best imaging coordination, and then obtain the optimal detection spatial resolution for the target area, which provides good detection information for the classification, identification and positioning of the target and the execution of precision-guided strikes, and improves the application capability of the dual-base SAR system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.

[0021] Figure 1 Schematic diagram of a shift space model of a receiver and a drone in an embodiment of the present invention; Figure 2 It is a schematic diagram of the process of bistatic SAR UAV trajectory optimization based on dynamic sequence solution in an embodiment of the present invention.

[0022] Reference numerals: 1. Grid points of the imaging scene; 2. Receiver; 3. Drone. DETAILED DESCRIPTION

[0023] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0024] A specific embodiment of the present invention, as Figure 1-Figure 2 , discloses a dual-base SAR UAV trajectory optimization method based on dynamic sequence solution, and the specific implementation steps include: Step S1, obtaining the receiver trajectory in the bistatic SAR system, and establishing a shift space model of the receiver and the UAV based on the receiver trajectory; It can be understood that the UAV is a transmitter platform in a bistatic SAR system, and is an aircraft or mobile platform for carrying a transmitter of a bistatic SAR system; The bistatic SAR system is a bistatic synthetic aperture radar system, comprising a transmitter and a receiver; Preferably, the step S1 of establishing the shift space model of the receiver and the drone includes the following specific steps: The launch coordinate system is established with the power-on position of the receiver as the origin, the projection point of the power-on position of the receiver on the local horizontal plane as the origin of the launch coordinate system, and the heading direction of the projection speed of the power-on position of the receiver on the local horizontal plane as the direction of the launch coordinate system. x Axis, with the vertical direction of the local horizontal plane upward as the launch coordinate system y Axis, based on xyz The axis determines the launch coordinate system z axis; The detection target scene is located directly in front of the receiver, and the receiver looks directly forward to the detection target scene area; Step S2: k =1, when k =1 indicates the initial time; Step S3: Obtaining the time based on the shift space model of the receiver and the drone k Drone location 、 Speed ​​and acceleration; Optionally, time k The receiver’s location information is P rk =( x rk , y rk , z rk ), the speed information is V rk =( v rxk , v ryk , v rzk ) and acceleration information is A rk =( a rxk , a ryk , a rzk ),in, k For the moment k , x rk For the moment k Receiver in xThe coordinates of the axes, y rk For the moment k Receiver in y The coordinates of the axes, z rk For the moment k Receiver in z The coordinates of the axes, v rxk For the moment k Receiver in x The speed of the axis, v ryk For the moment k Receiver in y The speed of the axis, v rzk For the moment k Receiver in z The speed of the axis, a rxk For the moment k Receiver in x The acceleration of the axis, a ryk For the moment k Receiver in y The acceleration of the axis, a rzk For the moment k Receiver in z The acceleration of the axis; time k The location information of the drone is P tk =( x tk , y tk , z tk ), the speed information is V tk =( v txk , v tyk , v tzk ) and acceleration information is A tk =( a txk , a tyk , a tzk ),in, x tk For the moment k Drones in x Axis coordinates, y tk For the momentk Drones in y The coordinates of the axes, z tk For the moment k Drones in z The coordinates of the axes, v txk For the moment k Drones in x Axis speed, v tyk For the moment k Drones in y The speed of the axis, v tzk For the moment k Drones in z The speed of the axis, a txk For the k At this moment, the drone x The acceleration of the axis, a tyk For the k At this moment, the drone y The acceleration of the axis, a tzk For the k At this moment, the drone z The acceleration of the axis; Step S4: Select time k The center point of the scene, based on the time k The scene center point is obtained at the moment k The beam coverage range is represented by the time k Imaging scene of the detection target; Determine the moment k The scene layout interval; Based on time k The scene layout interval is at time k The imaging scene of the detection target is grid-divided to obtain the time k The multiple grid points of the imaging scene of the detection target are represented as time k Multiple scene target points of the detection target; Optionally, the time obtained in step S4 is k The specific steps of detecting multiple grid points of an imaging scene of a target include: Set the detection target scene level, the detection target is on the horizontal plane, and obtain the initial position information of the detection target: P p =( x p ,0, z p ), xp To detect the target x The initial coordinates of the axis, the detection target is y The axis coordinate is 0, z p To detect the target z The initial coordinates of the axes; The imaging scene of the detection target is grid-divided according to the preset imaging width. x Axis step and z The axis step lengths are △ x and z , each grid point of the imaging scene of the detection target The location information is P (e,j) =( x e ,0, z j ), the expression is: P (e,j) =( x e ,0, z j ) = ( x p +M· △ x ,0, z p +N· △ z ); in, e Imaging scene grid points for detecting targets x The index of the direction, j Imaging scene grid points for detecting targets y The index of the direction, M Indicates that the detection target is in x The offset of the axis, N Indicates that the detection target is in z Axis offset; x e The imaging scene grid points for detecting targets are x The coordinates of the axes, z j The imaging scene grid points for detecting targets are z The coordinates of the axes, x p The imaging scene grid points for detecting targets are x The starting coordinates of the axis, z p The imaging scene grid points for detecting targets are z The starting coordinate of the axis, △ x express xAxis step length, i.e. x Axis grid spacing, △ z express z Axis step size, i.e. the z-axis grid spacing.

[0025] Step S5: Determine time k Overload constraints on drones; Based on time k The UAV's overload limit is obtained at the time k UAV motion constraints; Optionally, time k UAV motion constraints and timing k The expressions of the UAV's overload constraints are:

[0026] in, For the moment k Synthetic aperture time, is the maximum synthetic aperture time; is the maximum heading speed of the UAV, For the moment k Drone heading speed, For the moment k The altitude at which the drone is flying; is the maximum flight altitude of the drone, For the moment k Drones in x The acceleration of the axis, For the moment k Drones in y The acceleration of the axis, For the moment k Drones in z The acceleration of the axis, For drones x The acceleration of the axis, For drones y The acceleration of the axis, For drones z The acceleration of the axis.

[0027] Furthermore, the specific steps for determining the UAV motion constraints include: k The relationship between the pitch, yaw and roll attitude of the drone and the launch coordinate system is k The overloads of the drone's body axial, upward, and lateral directions are transferred to the launch coordinate system and based on the time k The position and speed of the drone are obtained at time k The upper and lower bounds of the drone’s speed and position; Based on time kThe time when the upper and lower limits of the drone speed and the upper and lower limits of the position are established k Drone motion constraints, consisting of time k The drone's position and velocity are centered on a sphere.

[0028] Step S6: Get time k The range resolution of each scene target point of the detected target is expressed as:

[0029] in, Indicates time k Imaging scene grid points of the detection target The distance gradient, For the moment k Imaging scene grid points of the detection target The range resolution is c is the speed of light; B r The bandwidth of the pulse signal transmitted for the drone's radar detection system.

[0030] Step S7: Get time k The azimuth resolution of each grid point in the imaging scene of the detection target is expressed as:

[0031] in, Indicates time k Imaging scene grid points for detecting targets The Doppler gradient of t is the synthetic aperture time of the radar, that is, the residence time of the beam in the imaging area, time k Imaging scene grid points of the detection target Azimuth resolution.

[0032] Step S8: Get time k The gradient angle between the range gradient and the Doppler gradient of each grid point in the imaging scene of the detection target is expressed as:

[0033] in, For the moment k Imaging scene grid points of the detection target The gradient angle between the range gradient and the Doppler gradient, acos is the inverse cosine function.

[0034] Step S9: Based on the most stringent detection criteria, obtain the bistatic SAR system time kThe comprehensive resolution of the imaging scene of the detection target , the expression is:

[0035] r rgk =max(max( r rg_k )); r ak =max(max( r a_k )); ψ k =min(min( ψ _k )); Among them, max(max( r rg_k )) indicates time k The maximum distance resolution of each grid point in the imaging scene of the detection target, max(max( r a_k )) indicates time k The maximum azimuth resolution of each grid point of the imaging scene of the detection target, min(min( ψ _k )) indicates time k The minimum gradient angle of each grid point in the imaging scene of the detection target, Indicates time k The maximum distance resolution of the imaging scene of the detected target, Indicates time k The comprehensive resolution of the imaging scene of the detection target, Indicates time k The maximum azimuth resolution of the imaging scene of the detected target, Indicates time k The minimum gradient angle of the imaging scene of the detection target, r rg_k Indicates time k The range resolution of the imaging scene grid points for detecting targets, r a_k Indicates time k The azimuth resolution of the imaging scene grid points of the detection target, ψ _k Indicates time k The minimum gradient angle of the imaging scene grid points for detecting the target.

[0036] Step S10: according to time k The comprehensive resolution of the imaging scene of the detection target ,time k Drone acceleration and time k The UAV motion constraints are established at the moment k The optimization model of UAV acceleration under multiple constraints is expressed as:

[0037] in, Indicates time k UAV acceleration optimization model under multiple constraints, i =1,2,3... I , I Indicates the total number of iteration steps; Furthermore, imaging constraints are also included, expressed as:

[0038] in, The preset range resolution of the imaging scene for detecting targets, The preset azimuth resolution of each grid point in the imaging scene of the detection target, Indicates time k The distance resolution of the imaging scene grid points of the detection target, Indicates time k The azimuth resolution of each grid point of the imaging scene of the detection target, Indicates time k The gradient angle of each grid point in the imaging scene of the detection target.

[0039] Step S11: Based on the sequential quadratic programming SQP, the time k The UAV acceleration optimization model under multiple constraints is solved to obtain the time k Optimal acceleration of drones; Step S12: Determination k Is it greater than or equal to K , K represents the total number of moments. If so, we get K The optimal acceleration of the UAV at a certain moment is represented by the acceleration sequence of the UAV trajectory. If not, let k = k +1, return to step S3; Step S13: obtaining an optimal UAV trajectory based on the acceleration sequence of the UAV trajectory.

[0040] Optionally, the time of obtaining in step S11 is k The specific steps for optimal acceleration of the drone include: Step S11-1: i =1, when i=1, it indicates the first iteration; Step S11-2: Get time k Drones in the i The acceleration state vector of the iteration The first-order gradient g k,i and the second-order Hessian matrix G k,i ; Step S11-3: Determine the first-order gradient g k,i Is it less than the preset convergence condition? If so, then the moment k Drones in the i The acceleration state vector of the iteration For the moment k The optimal acceleration state vector of the drone , the moment k Drones in the i The acceleration state vector of the iteration is taken as the moment k Drones in the i+ Acceleration state vector for 1 iteration , proceed to step S11-8; If not, proceed to the next step; Step S11-4: Based on the first-order gradient g k,i and the second-order Hessian matrix G k,i Build time k Drones in the i The quadratic objective function of the acceleration state vector of the iteration is expressed as: F k,i

[0041] in, F k,i Indicates time k Drones in the i The quadratic objective function of the acceleration state vector of the iteration, Indicates time k The optimal acceleration state vector of the drone, For the moment k Drones in the i The acceleration state vector of the iteration, including the time k Drones in the i The iterations are respectively x , y and z The acceleration value of the direction, For the moment k Drones in the iThe first-order gradient of the acceleration state vector at iteration, For the moment k Drones in the i The second-order Hessian matrix of the acceleration state vector of the iteration, (.) is the objective function.

[0042] Step S11-5: Get time k Drones in the i The minimum value of the quadratic objective function of the acceleration state vector of the iteration x' k,i , the minimum value x' k,i Represented as a moment k Drones in the i The acceleration direction of the iteration; Step S11-6: Based on time k Drones in the i The acceleration direction of the iteration , using a one-dimensional line search method to obtain the time k Drones in the i The iteration step size ; Step S11-7: Based on time k Drones in the i The iteration step size and time k Drones in the i The acceleration direction of the iteration is relative to the time k Drones in the i The acceleration state vector of the iteration is updated to obtain the moment k Drones in the i The updated acceleration state vector of the iteration is represented by the time k Drones in the i+ The acceleration state vector of 1 iteration is expressed as: ; in, For the moment k Drones in the i+ The acceleration state vector of 1 iteration, Step S11-8: Determination i Is it greater than or equal to I , I Represents the total number of iteration steps. If so, we get the time k The optimal acceleration of the drone, if not, let i = i +1 Return to step S11-2.

[0043] Understandably, the moment k Optimal acceleration for drones ,in, For the moment k Best drone x Directional acceleration, For the moment k Best drone y Directional acceleration, For the moment k Best drone z Direction acceleration.

[0044] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A bistatic SAR UAV trajectory optimization method based on dynamic sequence solution, characterized in that: include: Step S1: Obtaining the bistatic SAR system time k The comprehensive resolution of the imaging scene of the detection target and establish the moment k UAV acceleration optimization model under multiple constraints; k =1,2,3 ...K, K Indicates the total number of moments; Step S2: based on time k UAV acceleration optimization model under multiple constraints to obtain the moment k Drones in the i The first-order gradient of the acceleration state vector of the iteration g k,i and the second-order Hessian matrix G k,i ; Step S3: Based on the first-order gradient g k,i and the second-order Hessian matrix G k,i Get the moment k Drones in the i+ The acceleration state vector of 1 iteration; Step S4: Determination i Is it greater than or equal to I , I Table total number of iterations, if yes, get the time k The optimal acceleration of the drone, if not, let i = i +1, return to step S2; Step S5: traverse K At each moment, the optimal acceleration of the UAV is obtained, which is represented as the acceleration sequence of the bistatic SAR UAV trajectory. Step S6: obtaining an optimal bistatic SAR UAV trajectory based on the acceleration sequence of the bistatic SAR UAV trajectory.

2. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 1 is characterized in that: Step S3: obtaining the time k Drones in the i+ The specific steps of the acceleration state vector of 1 iteration include: Step S31: Determine the first-order gradient g k,i Is it less than the preset convergence condition? If so, set the time k Drones in the i The acceleration state vector of the iteration is taken as the moment k Drones in the i+ The acceleration state vector of the first iteration goes to step S4; If not, proceed to step S32; Step S32: Based on the first-order gradient g k,i and the second-order Hessian matrix G k,i Build time k Drones in the i The quadratic objective function of the acceleration state vector of the iteration and obtain the corresponding minimum value x' k,i , the minimum value x' k,i Represented as a moment k Drones in the i The acceleration direction of the iteration; Step S33: based on time k Drones in the i The acceleration direction of the iteration is obtained at the moment k Drones in the i The iteration step size of the iteration, and the time k Drones in the i The acceleration state vector of the iteration is updated to obtain the moment k Drones in the i The updated acceleration state vector of the iteration is used as the moment k Drones in the i+ Acceleration state vector for iteration 1.

3. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 1 is characterized in that: Get the bistatic SAR system time k The specific steps of detecting the comprehensive resolution of the imaging scene of the target include: Get the time separately k The distance resolution and time of each scene target point of the detection target k The azimuth resolution and time of each grid point in the imaging scene of the detection target k The gradient angle between the range gradient and the Doppler gradient of each grid point in the imaging scene of the detected target; based on the most stringent detection criteria, the dual-static SAR system moment k The comprehensive resolution of the imaging scene of the detection target.

4. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 1 is characterized in that: The bistatic SAR system moment k The comprehensive resolution of the imaging scene of the detection target is expressed as: in, Indicates time k The maximum distance resolution of the imaging scene of the detected target, Indicates time k The comprehensive resolution of the imaging scene of the detection target, Indicates time k The maximum azimuth resolution of the imaging scene of the detected target, Indicates time k The minimum gradient angle of the imaging scene of the detection target.

5. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 3 is characterized in that: Get the moment k The specific steps of detecting each scene target point of the target include: Selected time k The center point of the scene, based on the time k The scene center point is obtained at the moment k The beam coverage range is represented by the time k Imaging scene of the detection target; Determine the moment k The scene layout interval; Based on time k The scene layout interval is at time k The imaging scene of the detection target is grid-divided to obtain the time k The multiple grid points of the imaging scene of the detection target are represented as time k Multiple scene target points of the detection target.

6. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 1 is characterized in that: Step S1: k Multiple constraints including time k The UAV motion constraints and imaging constraints.

7. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 6 is characterized in that: Get the moment k The specific steps of UAV motion constraints include: Determine the moment k Overload constraints on drones; Based on time k The UAV's overload limit is obtained at the time k UAV motion constraints.

8. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 7 is characterized in that: time k UAV motion constraints and moments k The expressions of the UAV's overload constraints are: in, For the moment k Synthetic aperture time, is the maximum synthetic aperture time; is the maximum heading speed of the UAV, For the moment k Drone heading speed, For the moment k The altitude at which the drone is flying; is the maximum flight altitude of the drone, For the moment k Drones in x The acceleration of the axis, For the moment k Drones in y The acceleration of the axis, For the moment k Drones in z The acceleration of the axis, For drones x The acceleration of the axis, For drones y The acceleration of the axis, For drones z The acceleration of the axis.

9. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 2 is characterized in that: time k Drones in the i The expression of the quadratic objective function of the acceleration state vector of the iteration is: F k,i in, F k,i Indicates time k Drones in the i The quadratic objective function of the acceleration state vector of the iteration, Indicates time k The optimal acceleration state vector of the drone, For the moment k Drones in the i The acceleration state vector of the iteration, For the moment k Drones in the i The first-order gradient of the acceleration state vector at iteration, For the moment k Drones in the i The second-order Hessian matrix of the acceleration state vector of the iteration, (.) is the objective function, Indicates transpose.

10. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 2, characterized in that: The moment k Drones in the i+ The acceleration state vector of 1 iteration is expressed as: ； in, For the moment k Drones in the i+ The acceleration state vector of 1 iteration, For the moment k Drones in the i The acceleration state vector of the iteration, For the moment k Drones in the i The iteration step size is For the moment k Drones in the i The acceleration direction for the iteration.

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