A Bistatic SAR UAV Trajectory Optimization Method Based on Dynamic Sequence Solution

Abstract

The present invention relates to a method for optimizing the trajectory of a bistatic SAR unmanned aerial vehicle (UAV) based on dynamic sequence solution, belonging to the field of cluster guidance technology. According to the known flight trajectory of a receiver with a higher mission priority, considering the imaging detection constraints of bistatic SAR, the dynamic characteristics of the UAV, and the flight control constraints of the UAV, based on the comprehensive resolution of the imaging scene of the detection target in each stage and the motion constraints of the UAV, an optimization model for the acceleration of the UAV under multiple constraints in each stage is established. The UAV trajectory optimization problem is dynamically programmed 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, it realizes the trajectory design of the bistatic SAR UAV under multiple constraints, and at the same time satisfies the flight trajectory of the bistatic SAR UAV with the imaging detection ability of bistatic SAR and the guidance control of the bistatic SAR system. On the other hand, it improves the operation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of cluster guidance, and particularly relates to a method for optimizing the trajectory of a bistatic SAR unmanned aerial vehicle based on dynamic sequence solution. Background Art

[0002] For the bistatic SAR imaging detection technology, due to the separated transceiver configuration, the receiver has the ability of forward-looking passive imaging. At present, the Chinese patent applications with publication numbers CN110187345B, CN103901430A, and CN112698327A have carried out research and exploration on the relevant design of the flight trajectory of a bistatic forward-looking SAR unmanned aerial vehicle.

[0003] According to the gradient theory of imaging resolution, the geometric configuration of the trajectory of a bistatic SAR system has a direct impact on the imaging resolution ability of the bistatic SAR system. Therefore, when applying the bistatic SAR to actual engineering, the problem of designing the bistatic flight trajectory needs to be solved first, especially the problem of designing the trajectory of an unmanned aerial vehicle. Because the receiver of the bistatic SAR system is generally the main task execution mechanism, its own flight trajectory cannot be changed arbitrarily, or the priority of changing the flight trajectory of the receiver is higher than that of the bistatic SAR imaging detection. At this time, the imaging resolution ability of the bistatic SAR system mainly depends on the unmanned aerial vehicle of the bistatic SAR system.

[0004] Most of the existing bistatic SAR trajectory design methods are static optimizations and do not consider the constraints of guidance and control. Therefore, the existing bistatic SAR configuration design methods cannot meet the requirements of dynamic process trajectory design. Moreover, for the bistatic SAR flight trajectories designed in the existing technology that meet the flight control requirements, the imaging area constraints and the initial configuration conditions are not considered in their corresponding constraint conditions, which has defects. Summary of the Invention

[0005] In view of the above problems, the present invention provides a method for optimizing the trajectory of a bistatic SAR unmanned aerial vehicle based on dynamic sequence solution. According to the known flight trajectory of a receiver with a higher task priority, considering the imaging detection constraints of the bistatic SAR, the dynamic characteristics of the unmanned aerial vehicle, and the flight control constraints of the unmanned aerial vehicle, an optimization model of the acceleration of the unmanned aerial vehicle under multiple constraints is established based on the comprehensive resolution of the imaging scene of the detection target in each stage and the motion constraints of the unmanned aerial vehicle. The problem of optimizing the trajectory of the unmanned aerial vehicle is dynamically programmed into multiple sub-problems and dynamically solved to obtain the optimal solution of each sub-problem, that is, the optimal acceleration of the unmanned aerial vehicle at each moment. On the one hand, the design of the trajectory of the bistatic SAR unmanned aerial vehicle under multiple constraints is realized, and the flight trajectory of the bistatic SAR unmanned aerial vehicle that meets both the imaging detection ability of the bistatic SAR and the guidance and control of the bistatic SAR system is obtained. On the other hand, the operation efficiency is improved, and the practical application value of the bistatic SAR system is increased.

[0006] The present invention provides a bistatic SAR UAV trajectory optimization method based on dynamic sequence solution, including:

[0007] Step S1, obtain the comprehensive resolution of the imaging scene of the detected target at the moment of the bistatic SAR system, and establish an UAV acceleration optimization model under multi-constraint conditions at the moment k ; k where k = 1, 2, 3 …K, K represents the total number of moments;

[0008] Step S2, based on the UAV acceleration optimization model under multi-constraint conditions at the moment k , obtain the first-order gradient k of the acceleration state vector of the UAV at the i -th iteration and the second-order Hessian matrix g k,i k,i G k,i ;

[0009] Step S3, based on the first-order gradient g k,i and the second-order Hessian matrix G k,i , obtain the acceleration state vector of the UAV at the k -th iteration; i+

[0010] Step S4, judge whether i is greater than or equal to I , I where represents the total number of iterations. If so, obtain the optimal acceleration of the UAV at the moment k . If not, let i = i + 1, and return to Step S2;

[0011] Step S5, traverse K moments to obtain the optimal acceleration of the UAV at each moment, which is characterized as the acceleration sequence of the bistatic SAR UAV trajectory;

[0012] Step S6, obtain the optimal bistatic SAR UAV trajectory based on the acceleration sequence of the bistatic SAR UAV trajectory.

[0013] k Optionally, the specific steps of obtaining the acceleration state vector of the UAV at the i+ -th iteration in Step S3 include:

[0014] Step S31, judge whether the first-order gradient g k,i is less than the preset convergence condition. If so, at the moment k , the acceleration state vector of the UAV at the k -th iterationi The acceleration state vector of the previous iteration is used as the moment k The UAV is at the i+ acceleration state vector of the first iteration, and proceeds to step S4;

[0015] If not, proceed to step S32;

[0016] Step S32: Based on the first-order gradient g k,i and the second-order Hessian matrix G k,i Construct the quadratic objective function of the acceleration state vector of the UAV at the moment k The UAV is at the i previous iteration, and obtain the corresponding minimum value x' k,i , and use the minimum value x' k,i to represent the acceleration direction of the UAV at the moment k The UAV is at the i previous iteration;

[0017] Step S33: Based on the acceleration direction of the UAV at the moment k The UAV is at the i previous iteration, obtain the iteration step size of the UAV at the moment k The UAV is at the i previous iteration, and update the acceleration state vector of the UAV at the moment k The UAV is at the i previous iteration, to obtain the updated acceleration state vector of the UAV at the moment k The UAV is at the i previous iteration, which is used as the acceleration state vector of the UAV at the moment k The UAV is at the i+ first iteration.

[0018] Optionally, the specific steps for obtaining the comprehensive resolution of the imaging scene of the detected target at the moment of the bistatic SAR system k include:

[0019] Respectively obtain the range resolution of each scene target point of the detected target at the moment k , the azimuth resolution of each grid point of the imaging scene of the detected target at the moment k , and the gradient angle between the range gradient and the Doppler gradient of each grid point of the imaging scene of the detected target at the moment k ; Based on the strictest detection criterion, obtain the comprehensive resolution of the imaging scene of the detected target at the moment k of the bistatic SAR system.

[0020] Optionally, the bistatic SAR system at the moment kThe synthetic resolution of the imaging scene of the detection target is expressed as:

[0021]

[0022] Wherein, represents the maximum range resolution of the imaging scene of the detection target at time k ; represents the synthetic resolution of the imaging scene of the detection target at time k ; represents the maximum azimuth resolution of the imaging scene of the detection target at time k ; represents the minimum gradient angle of the imaging scene of the detection target at time k .

[0023] Optionally, the specific steps for obtaining each scene target point of the detection target at time k include:

[0024] Select the scene center point at time k , and obtain the beam coverage range at time k based on the scene center point at time k , which is characterized as the imaging scene of the detection target at time k ;

[0025] Determine the scene grid interval at time k ;

[0026] Based on the scene grid interval at time k , perform grid division within the imaging scene of the detection target at time k to obtain multiple grid points of the imaging scene of the detection target at time k , which are characterized as multiple scene target points of the detection target at time k .

[0027] Optionally, the multiple constraint conditions in step S1 at time k include the motion constraint and imaging constraint conditions of the UAV at time k .

[0028] Optionally, the specific steps for obtaining the motion constraint of the UAV at time k include:

[0029] Determine the overload constraint of the UAV at time k ;

[0030] Based on the overload constraint of the UAV at time k , obtain the motion constraint of the UAV at time k .

[0031] Optionally, at timek Drone motion constraints and moments k The expressions for the overload constraints of the drone are as follows:

[0032]

[0033] Among them, is the moment k synthetic aperture time, is the maximum synthetic aperture time; is the maximum heading speed of the drone, is the moment k the heading speed of the drone, is the moment k the flight altitude of the drone; is the maximum flight altitude of the drone, is the moment k the drone at x the acceleration on the is the moment k the drone at y the acceleration on the is the moment k the drone at z the acceleration on the is the acceleration of the drone on the x axis, is the acceleration of the drone on the y axis, is the acceleration of the drone on the z axis.

[0034] Optionally, the expression for the quadratic objective function of the acceleration state vector of the drone at the moment k in the i th iteration is:

[0035] F k,i

[0036] Among them, F k,i represents the quadratic objective function of the acceleration state vector of the drone at the moment k in the i th iteration, represents the optimal acceleration state vector of the drone at the moment k , is the moment k the acceleration state vector of the drone in the i th iteration, is the moment k the acceleration state vector of the drone in the i th iteration, and​ At time k The second - order Hessian matrix of the acceleration state vector of the UAV at the i th iteration, (.) is the objective function, represents the transpose.

[0037] Optionally, at the time k The acceleration state vector of the UAV at the i+ 1st iteration has the expression:

[0038] ;

[0039] Where is the acceleration state vector of the UAV at the time k at the i+ 1st iteration, is the acceleration state vector of the UAV at the time k at the i th iteration, is the iteration step size of the UAV at the time k at the i th iteration, is the acceleration direction of the UAV at the time k at the i th iteration.

[0040] Compared with the prior art, the present invention has at least the following beneficial effects:

[0041] During the actual mission process of the present invention, based on the existing receiver trajectory, the optimal UAV motion trajectory is solved online in real - time, enabling the best imaging cooperation between the transmitter and the receiver, thereby obtaining the optimal detection spatial resolution for the target area, providing good detection information for the classification, recognition, positioning of the target and the execution of precision - guided strikes, and improving the application ability of the bistatic SAR system in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings are only for the purpose of illustrating specific embodiments and are not considered to be a limitation of the present invention.

[0043] Figure 1 is a schematic diagram of the shift - varying space model of the receiver and the UAV in the embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of the flow of the bistatic SAR UAV trajectory optimization based on dynamic sequence solution in the embodiment of the present invention.

[0045] Reference Signs:

[0046] 1. Each grid point of the imaging scene; 2. Receiver; 3. UAV. Detailed implementation manners

[0047] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners. It should be noted that, without conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other. In addition, the present invention may be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0048] A specific embodiment of the present invention, such as Figure 1 - Figure 2 , discloses a bistatic SAR UAV trajectory optimization method based on dynamic sequence solution, and the specific implementation steps include:

[0049] Step S1: Obtain the receiver trajectory in the bistatic SAR system, and establish a time-varying space model of the receiver and the UAV based on the receiver trajectory;

[0050] It can be understood that the UAV is the transmitter platform in the bistatic SAR system, which is an aircraft or mobile platform for carrying the transmitter of the bistatic SAR system;

[0051] The bistatic SAR system is a bistatic synthetic aperture radar system, including a transmitter and a receiver;

[0052] Preferably, the specific steps of establishing the time-varying space model of the receiver and the UAV in step S1 include:

[0053] Establish a launch coordinate system with the receiver's power-on position as the origin, with the projection point of the receiver's power-on position on the local horizontal plane as the coordinate origin of the launch coordinate system, and with the heading of the projection speed direction of the receiver's power-on position speed on the local horizontal plane as the x axis of the launch coordinate system, and with the direction perpendicular to the local horizontal plane upward as the y axis of the launch coordinate system, and determine the xy axis of the launch coordinate system based on the z axis;

[0054] The detection target scene is located directly in front of the receiver, and the receiver directly forward views the detection target scene area;

[0055] Step S2: Let k = 1. When k = 1, it represents the initial moment;

[0056] Step S3: Obtain the position k , speed and acceleration of the UAV at time 、 based on the time-varying space model of the receiver and the UAV;

[0057] Optionally, at timek The position information of the receiver is P rk = ( x rk , y rk , z rk ), the velocity information is V rk = ( v rxk , v ryk , v rzk ), and the acceleration information is A rk = ( a rxk , a ryk , a rzk ), where k is the moment k , x rk is the coordinate of the receiver on the k axis at the moment x , y rk is the coordinate of the receiver on the k axis at the moment y , z rk is the coordinate of the receiver on the k axis at the moment z , v rxk is the velocity of the receiver on the k axis at the moment x , v ryk is the velocity of the receiver on the k axis at the moment y , v rzk is the velocity of the receiver on the k axis at the moment z , a rxk is the acceleration of the receiver on the k axis at the moment x , a ryk is the acceleration of the receiver on the k axis at the moment y , a rzk is the acceleration of the receiver on the k axis at the moment z ;

[0058] The momentk The position information of the UAV is P tk = ( x tk , y tk , z tk ), the speed information is V tk = ( v txk , v tyk , v tzk ), and the acceleration information is A tk = ( a txk , a tyk , a tzk ), where x tk is the moment k the UAV's coordinate on the x axis, y tk is the moment k the UAV's coordinate on the y axis, z tk is the moment k the UAV's coordinate on the z axis, v txk is the moment k the UAV's speed on the x axis, v tyk is the moment k the UAV's speed on the y axis, v tzk is the moment k the UAV's speed on the z axis, a txk is the acceleration of the UAV on the k axis at the x th moment, a tyk is the acceleration of the UAV on the k axis at the y th moment, a tzk is the acceleration of the UAV on the k axis at the z th moment;

[0059] Step S4. Select the moment kThe scene center point, based on the moment k The moment when the scene center point is obtained k The beam coverage range, characterized as the moment k The imaging scene of the detection target;

[0060] Determine the moment k The scene layout interval;

[0061] Based on the moment k The scene layout interval in the moment k Perform grid division within the imaging scene of the detection target to obtain the moment k Multiple grid points of the imaging scene of the detection target, characterized as the moment k Multiple scene target points of the detection target;

[0062] Optionally, the specific steps of obtaining the multiple grid points of the imaging scene of the detection target in step S4 include: k

[0063] Set the detection target scene level, the detection target is in the horizontal plane, and obtain the initial position information of the detection target as P p = ([[]] x p , 0, z p ) x p is the initial coordinate of the detection target on the x axis, the coordinate of the detection target on the y axis is 0, z p (e,j) is the initial coordinate of the detection target on the z axis;

[0064] Perform grid division on the imaging scene of the detection target according to the preset imaging width. The step sizes of the x axis and the z axis are respectively △ x and △ z . The position information of each grid point of the imaging scene of the detection target is P (e,j) = ([[]] x e , 0, z j )

[0065] P (e,j) = ([[]] x e , 0, z j ) = ([[]]x p +M· △ x ,0, z p +N· △ z );

[0066] Among them, e is the index of the imaging scene grid point of the detection target in the x direction, j is the index of the imaging scene grid point of the detection target in the y direction, M represents the offset of the detection target on the x axis, N represents the offset of the detection target on the z axis; x e is the coordinate of the imaging scene grid point of the detection target on the x axis, z j is the coordinate of the imaging scene grid point of the detection target on the z axis, x p is the starting coordinate of the imaging scene grid point of the detection target on the x axis, z p is the starting coordinate of the imaging scene grid point of the detection target on the z axis, △ x represents x axis step size, that is x axis grid spacing, △ z represents z axis step size, that is, the z-axis grid spacing.

[0067] Step S5, determine the overload constraint of the UAV at time k ;

[0068] Based on the overload constraint of the UAV at time k obtain the motion constraint of the UAV at time k ;

[0069] Optionally, the motion constraint of the UAV at time k and the overload constraint of the UAV at time k The expressions of the operation are respectively:

[0070]

[0071] Among them, is the synthetic aperture time at time k , is the maximum synthetic aperture time; is the maximum heading speed of the UAV, is the moment k heading speed of the UAV, is the moment k flight altitude of the UAV; is the maximum flight altitude of the UAV, is the moment k acceleration of the UAV in the x axis, is the moment k acceleration of the UAV in the y axis, is the moment k acceleration of the UAV in the z axis, is the acceleration of the UAV in the x axis, is the acceleration of the UAV in the y axis, is the acceleration of the UAV in the z axis.

[0072] Furthermore, the specific steps to determine the motion constraint conditions of the UAV include: Based on the pitch, yaw, and roll attitude relationships between the UAV and the launch coordinate system at the moment k transfer the overloads in the body axial direction, body upward direction, and body lateral direction of the UAV at the moment k to the launch coordinate system, and based on the position and speed of the UAV at the moment k obtain the upper and lower bounds of the UAV speed range and the upper and lower bounds of the position range at the moment k respectively;

[0073] Based on the upper and lower bounds of the UAV speed range and the upper and lower bounds of the position range at the moment k establish the motion constraints of the UAV at the moment k to form a sphere centered on the position and speed of the UAV at the moment k of the UAV.

[0074] Step S6, obtain the range resolution in the distance direction of each scene target point of the detection target at the moment k , and the expression is:

[0075]

[0076] where, represents the distance gradient of the imaging scene grid point k of the detection target at the moment , is the moment k range resolution in the distance direction of the imaging scene grid point of the detection target, c is the speed of light; Br is the bandwidth of the pulse signal transmitted by the radar detection system of the drone.

[0077] Step S7: Obtain the k azimuth resolution of each grid point of the imaging scene of the detected target at time

[0078]

[0079] where represents the Doppler gradient of the grid point k of the imaging scene of the detected target at time ; △ t is the synthetic aperture time of the radar, that is, the dwell time of the beam in the imaging area, and k is the azimuth resolution of the grid point of the imaging scene of the detected target at time

[0080] Step S8: Obtain the gradient angle between the range gradient and the Doppler gradient of each grid point of the imaging scene of the detected target at time k as follows:

[0081]

[0082] where is the gradient angle between the range gradient and the Doppler gradient of the grid point k of the imaging scene of the detected target at time , and acos is the inverse cosine function.

[0083] Step S9: Based on the strictest detection criterion, obtain the comprehensive resolution k of the imaging scene of the detected target at time of the bistatic SAR system, expressed as:

[0084]

[0085] ρ rgk = max(max( ρ rg_k ));

[0086] ρ ak = max(max( ρ a_k ));

[0087] ψ k = min(min( ψ _k ));

[0088] Among them, max(max( ρ rg_k )) represents the maximum range resolution of each grid point of the imaging scene of the detection target at time k , max(max( ρ a_k )) represents the maximum azimuth resolution of each grid point of the imaging scene of the detection target at time k , min(min( ψ _k )) represents the minimum gradient angle of each grid point of the imaging scene of the detection target at time k , represents the maximum range resolution of the imaging scene of the detection target at time k , represents the comprehensive resolution of the imaging scene of the detection target at time k , represents the maximum azimuth resolution of the imaging scene of the detection target at time k , represents the minimum gradient angle of the imaging scene of the detection target at time k , ρ rg_k represents the range resolution of the grid point of the imaging scene of the detection target at time k , ρ a_k represents the azimuth resolution of the grid point of the imaging scene of the detection target at time k , ψ _k represents the minimum gradient angle of the grid point of the imaging scene of the detection target at time k .

[0089] Step S10, based on the comprehensive resolution of the imaging scene of the detection target at time k , the acceleration of the UAV at time , and the motion constraint of the UAV at time k , establish an optimization model for the acceleration of the UAV under multi-constraint conditions at time k , and the expression is: k Among them,

[0090]

[0091] where represents the optimization model for the acceleration of the UAV under multi-constraint conditions at time k , i = 1, 2, 3... I , I represents the total number of iterations;

[0092] Further, it also includes imaging constraint conditions, and the expression is:

[0093]

[0094] where is the preset distance resolution of the imaging scene of the detection target, is the preset azimuth resolution of each grid point of the imaging scene of the detection target, represents the moment k the distance resolution of the grid point of the imaging scene of the detection target, represents the moment k the azimuth resolution of each grid point of the imaging scene of the detection target, represents the moment k the gradient angle between each grid point of the imaging scene of the detection target.

[0095] Step S11: Solve the UAV acceleration optimization model under multiple constraint conditions for the moment k based on sequential quadratic programming (SQP), and obtain the optimal acceleration of the UAV at the moment k ;

[0096] Step S12: Judge whether k is greater than or equal to K , K represents the total number of moments. If so, obtain the optimal accelerations of the UAV at K moments, which are characterized as the acceleration sequence of the UAV trajectory. If not, let k = k +1, and return to step S3;

[0097] Step S13: Obtain the optimal UAV trajectory based on the acceleration sequence of the UAV trajectory.

[0098] Optionally, the specific steps of obtaining the optimal acceleration of the UAV at the moment k in step S11 include:

[0099] Step S11-1: Let i =1. When i =1, it indicates the initial iteration;

[0100] Step S11-2: Obtain the first-order gradient k and the second-order Hessian matrix G i of the acceleration state vector of the UAV at the moment g k,i in the k,i th iteration;

[0101] Step S11-3: Judge the first-order gradient gk,i Whether it is less than the preset convergence condition. If so, at the moment k The UAV at the i acceleration state vector at the th iteration is k the optimal acceleration state vector of the UAV at the moment , then at the moment k the UAV at the i acceleration state vector at the k th iteration is used as the acceleration state vector of the UAV at the i+ 1st iteration, and go to step S11-8;

[0102] If not, go to the next step;

[0103] Step S11-4: Based on the first-order gradient g k k,i and the second-order Hessian matrix G k,i Construct the quadratic objective function of the acceleration state vector of the UAV at the moment k The UAV at the i th iteration, and the expression is:

[0104] F k,i

[0105] where F k,i represents the quadratic objective function of the acceleration state vector of the UAV at the moment k The UAV at the i th iteration, represents the optimal acceleration state vector of the UAV at the moment k , is the acceleration state vector of the UAV at the moment k The UAV at the i th iteration, including the acceleration values of the UAV at the moment k The UAV at the i th iteration in the x , y and z directions respectively, is the first-order gradient of the acceleration state vector of the UAV at the moment k The UAV at the i th iteration, is the second-order Hessian matrix of the acceleration state vector of the UAV at the moment k The UAV at the i th iteration, (.) is the objective function.

[0106] Step S11-5: Obtain the moment k The UAV at thei The minimum value of the quadratic objective function of the acceleration state vector in the x' k,i ith iteration x' k,i is characterized as the acceleration direction of the UAV at time k in the i ith iteration;

[0107] Step S11-6: Based on the acceleration direction of the UAV at time k in the i ith iteration , use the one-dimensional line search method to obtain the iteration step size of the UAV at time k in the i ith iteration; ;

[0108] Step S11-7: Based on the iteration step size of the UAV at time k in the i ith iteration and the acceleration direction of the UAV at time k in the i ith iteration, update the acceleration state vector of the UAV at time k in the i ith iteration to obtain the updated acceleration state vector of the UAV at time k in the i ith iteration, which is characterized as the acceleration state vector of the UAV at time k in the i+ first iteration, and the expression is:

[0109] ;

[0110] where is the acceleration state vector of the UAV at time k in the i+ first iteration,

[0111] Step S11-8: Judge whether i is greater than or equal to I , I where k represents the total number of iteration steps. If so, obtain the optimal acceleration of the UAV at time i = i +1 and return to Step S11-2.

[0112] It can be understood that the optimal acceleration of the UAV at time k is , where is the optimal k of the UAV at time xDirectional acceleration is the optimal k directional acceleration of the drone at time y Directional acceleration is the optimal k directional acceleration of the drone at time z Directional acceleration.

[0113] As described above, it is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art 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 includes the following steps: 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+ 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: 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.

3. 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.

4. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 2 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.

5. 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.

6. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 5 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.

7. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 6 is characterized in that: time k UAV motion constraints and timing 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.

8. 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.

9. The bistatic SAR UAV trajectory optimization method based on dynamic sequence solution according to claim 2 is 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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