A method and device for cooperative positioning state optimization decision-making for moving targets

Abstract

The application discloses a cooperative positioning state optimization decision method and device for moving targets, relates to the application technical field of multi-unmanned aerial vehicle cooperative positioning planning decision, and constructs multiple solution spaces according to the unmanned aerial vehicle kinematics equation and multiple constraint conditions, conforms to actual positioning scenes, has good practical application value, and has strong scalability of the solution space construction method; different constraints can be set to adapt to various scenes; the optimization model established comprehensively considers optimization targets such as positioning accuracy, energy consumption, safety distance in the formation and communication distance, and can obtain more reasonable optimization results that meet cooperative motion characteristics. The application can make optimization decision according to the motion trajectory of the moving target, obtain the optimal positioning state of the multi-unmanned aerial vehicle under the actual cooperative positioning scene, and realizes sustained high-precision tracking and positioning of the moving target.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-unmanned aerial vehicle cooperative positioning planning decision application, in particular to a cooperative positioning state optimization decision method and device for dynamic targets. BACKGROUND

[0002] The infrared photoelectric detection system is an important target search and detection equipment, compared with the radar system, has the unique advantages of high angle measurement precision, passive stealth working state, low power consumption, small size, light weight and the like. However, the infrared photoelectric detection system can only obtain the azimuth information of the target, and needs to be measured multiple times to locate the target, while the multi-infrared sensor platform can complete the target positioning task through information sharing, and realize accurate measurement of real-time angle and distance information. Through cooperation, the relative position relationship between the multi-infrared sensor platform and the target can be established. In addition, in the information-based, intelligent and unmanned battlefield environment, the infrared photoelectric detection system can be carried on the unmanned aerial vehicle to realize the unmanned reconnaissance and positioning requirements, and has high military significance and engineering value.

[0003] When conducting air reconnaissance and positioning tasks for dynamic targets, a group of unmanned aerial vehicles can be used to form an anti-stealth detection network to cooperatively locate the target and transmit the target state information obtained by positioning to the rear long machine. However, due to the characteristics of the dynamic target, such as strong maneuverability and various movement forms, and the significant influence of the relative position relationship between the multi-unmanned aerial vehicle and the target on the cooperative positioning performance, a reasonable decision method must be designed to enable the unmanned aerial vehicle to continuously locate the dynamic target. SUMMARY

[0004] The purpose of the present application is to provide a cooperative positioning state optimization decision method and device for dynamic targets, which can make optimization decisions according to the motion trajectory of the dynamic target, obtain the optimal positioning state of the multi-unmanned aerial vehicle in the actual cooperative positioning scene, and realize continuous high-precision tracking and positioning of the dynamic target.

[0005] To achieve the above purpose, the present application provides the following scheme:

[0006] In a first aspect, the present application provides a cooperative positioning state optimization decision method for dynamic targets, comprising:

[0007] For each unmanned aerial vehicle in the multi-unmanned aerial vehicle formation, a kinematic model is constructed, and a solution space is determined based on constraint conditions, wherein the constraint conditions include a speed constraint of the unmanned aerial vehicle, an acceleration constraint of the unmanned aerial vehicle, a safety distance constraint between the unmanned aerial vehicle and a moving target, an acceleration change constraint of the unmanned aerial vehicle, and an angle change constraint of the unmanned aerial vehicle; the solution space is a set of a plurality of discrete position points, each of the position points includes motion state information of the unmanned aerial vehicle corresponding to a current time, and the motion state information includes a position, a speed, and an acceleration of the unmanned aerial vehicle.

[0008] An optimization decision model is established, wherein the optimization decision model includes a first objective function for calculating a positioning error of the moving target, a second objective function for calculating a flight energy consumption of the unmanned aerial vehicle, a third objective function for calculating a size relationship between a distance between the unmanned aerial vehicles and a safety distance threshold, and a fourth objective function for calculating a size relationship between a communication distance between the unmanned aerial vehicles and a communication distance threshold;

[0009] The solution space is substituted into the optimization decision model, and an optimization algorithm is used for solving, and when a result obtained by performing a weighted summation on a first function value, a second function value, a third function value, and a fourth function value is maximum, an optimal positioning state is obtained, wherein the first function value is a value solved by the first objective function, the second function value is a value solved by the second objective function, the third function value is a value solved by the third objective function, the fourth function value is a value solved by the fourth objective function, and the optimal positioning state includes an optimal positioning position point corresponding to each unmanned aerial vehicle and a formation overall formation.

[0010] In a second aspect, the present application provides a computer device, comprising a memory, a processor, a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the method for cooperative positioning state optimization decision of a moving target according to the first aspect.

[0011] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for cooperative positioning state optimization decision of a moving target according to the first aspect.

[0012] In a fourth aspect, the present application provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the method for cooperative positioning state optimization decision of a moving target according to the first aspect.

[0013] According to the embodiments of the present application, the following technical effects are achieved:

[0014] The application provides a cooperative positioning state optimization decision method and device for a moving target. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without any creative effort.

[0016] Figure 1 A flowchart of a cooperative positioning state optimization decision method for a moving target provided for the embodiment 1 of the present application is shown in the figure.

[0017] Figure 2 A detailed flowchart of a cooperative positioning state optimization decision method for a moving target provided for the embodiment 1 of the present application is shown in the figure.

[0018] Figure 3 An initial solution space in the embodiment 1 of the present application is shown in the figure.

[0019] Figure 4 A safety distance constraint in the embodiment 1 of the present application is shown in the figure.

[0020] Figure 5 A heading angle in the embodiment 1 of the present application is shown in the figure.

[0021] Figure 6 A climbing angle in the embodiment 1 of the present application is shown in the figure.

[0022] Figure 7 A multi-aircraft AOA positioning principle in the embodiment 1 of the present application is shown in the figure.

[0023] Figure 8 A moving target motion trajectory in the embodiment 1 of the present application is shown in the figure.

[0024] Figure 9Fig. 1 is a schematic diagram of the speed change of a moving target in Embodiment 1 of the present application;

[0025] Figure 10 Fig. 2 is a schematic diagram of the simulation results of scenario 1 in Embodiment 1 of the present application;

[0026] Figure 11 Fig. 3 is a schematic diagram of the relative positioning error of scenario 1 in Embodiment 1 of the present application;

[0027] Figure 12 Fig. 4 is a schematic diagram of the simulation results of scenario 2 in Embodiment 1 of the present application, wherein, Figure 12 (a) is a standard view of the simulation results of scenario 2, Figure 12 (b) is a top view of the simulation results of scenario 2;

[0028] Figure 13 Fig. 5 is a schematic diagram of the relative positioning error of scenario 2 in Embodiment 1 of the present application;

[0029] Figure 14 Fig. 6 is a schematic diagram of the simulation results of scenario 3 in Embodiment 1 of the present application, wherein, Figure 14 (a) is a standard view of the simulation results of scenario 3, Figure 14 (b) is a top view of the simulation results of scenario 3;

[0030] Figure 15 Fig. 7 is a schematic diagram of the relative positioning error of scenario 3 in Embodiment 1 of the present application;

[0031] Figure 16 Fig. 8 is an internal structure diagram of a computer device. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0033] The purpose of the present application is to provide a cooperative positioning state optimization decision method and device for moving targets, aiming to make optimization decisions according to the movement trajectory of a moving target, to obtain the optimal positioning state of multiple unmanned aerial vehicles under actual cooperative positioning scenarios, and to realize continuous high-precision tracking and positioning of the moving target.

[0034] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0035] Embodiment 1

[0036] The embodiment mainly solves two technical problems: modeling the solution problem of the optimal cooperative positioning state into an optimization search problem in multiple finite sets, and designing an optimization decision method for the cooperative positioning state. Figure 1 As shown in the figure, the cooperative positioning state optimization decision method for the moving target in the embodiment includes:

[0037] S1: For each unmanned aerial vehicle in the multi-unmanned aerial vehicle formation, a kinematic model is constructed, and a solution space is determined based on constraint conditions, wherein the constraint conditions include a speed constraint of the unmanned aerial vehicle, an acceleration constraint of the unmanned aerial vehicle, a safety distance constraint of the unmanned aerial vehicle and the moving target, an acceleration change constraint of the unmanned aerial vehicle, and an angle change constraint of the unmanned aerial vehicle; the solution space is a set of multiple discrete position points, each position point includes motion state information of the unmanned aerial vehicle at the current time, and the motion state information includes the position, speed and acceleration of the unmanned aerial vehicle.

[0038] S1 specifically includes:

[0039] S11: For each unmanned aerial vehicle, a kinematic model is constructed, and a reachable region of each unmanned aerial vehicle is determined based on a limited maneuvering capability constraint condition, wherein the limited maneuvering capability constraint condition includes a speed constraint of the unmanned aerial vehicle and an acceleration constraint of the unmanned aerial vehicle.

[0040] S12: An initial solution space is determined according to all possible position points of the unmanned aerial vehicles in the reachable region.

[0041] S13: Based on a specific constraint condition, the position points in the initial solution space that do not satisfy the specific constraint condition are removed to obtain an optimization search solution space, wherein the specific constraint condition includes a safety distance constraint of the unmanned aerial vehicle and the moving target, an acceleration change constraint of the unmanned aerial vehicle, and an angle change constraint of the unmanned aerial vehicle.

[0042] S14: The optimization search solution space is taken as the final solution space.

[0043] S2: An optimization decision model is established, wherein the optimization decision model includes a first objective function for calculating a positioning error of the moving target, a second objective function for calculating a flight energy consumption of the unmanned aerial vehicle, a third objective function for calculating a size relationship between distances between the unmanned aerial vehicles and a safety distance threshold, and a fourth objective function for calculating a size relationship between communication distances between the unmanned aerial vehicles and a communication distance threshold.

[0044] S3: substituting the solution space into the optimization decision model and solving by using an optimization algorithm, and obtaining an optimal positioning state when a result obtained by weighted sum of a first function value, a second function value, a third function value and a fourth function value is maximum, wherein the first function value is a value solved by the first objective function, the second function value is a value solved by the second objective function, the third function value is a value solved by the third objective function, and the fourth function value is a value solved by the fourth objective function, and the optimal positioning state includes an optimal positioning position point corresponding to each of the UAVs and a formation overall array.

[0045] The embodiment models the solving problem of the cooperative positioning optimal positioning state as an optimization search problem in multiple finite sets, and comprehensively considers multiple constraints and multiple optimization targets to obtain an optimal positioning state satisfying an actual cooperative positioning scene, thereby realizing continuous high-precision tracking positioning of a moving target.

[0046] To make the specific execution process of S1-S3 clearer to those skilled in the art, the following Figure 2 is specifically described.

[0047] (1) Establishing a UAV kinematics model, and designing an initial solution space based on a finite maneuverability constraint condition.

[0048] High-precision positioning requires a position relationship of the UAVs, and in an actual scene, due to dynamic constraints, it is difficult to achieve an optimal positioning state. Therefore, a reachable region of the UAVs is first constructed, corresponding to a solution space of an optimization problem, and reasonable design of the solution space can effectively improve optimization efficiency and ensure accuracy of an optimization result.

[0049] To obtain a multi-UAV solution space, first, a kinematics model of the UAVs is constructed. The embodiment focuses on optimal positioning positions and array relationships, and therefore a 3-DOF particle motion model is used to establish a kinematics model of the UAVs, without considering influences of environmental wind and air density at different altitudes. In addition, influences of the earth rotation are also not considered, and the gravitational acceleration is regarded as a constant value. Based on the above assumptions, a three-dimensional mass center kinematics equation is established according to a ground inertial coordinate system:

[0050]

[0051] wherein (x, y, z) is a position coordinate of the UAV; v is a velocity; ψ is a track angle, that is, a heading angle, representing an included angle between a projection of the velocity direction on a horizontal plane and an x-axis direction; is a derivative of (x, y, z), is a velocity in an x direction, a velocity in a y direction and a velocity in a z direction respectively; η is a track inclination angle, that is, a climb angle, representing an included angle between the velocity direction and a horizontal plane. After integrating both sides of equation (1) and discretizing based on time, the following equation is obtained:

[0052]

[0053]

[0054] wherein the position coordinates of the UAV at time t1 are the position coordinates of the UAV at time t2 are v n is the velocity at time t1 ; are the climb angles at times t1 and t2, respectively; are the heading angles at times t1 and t2, respectively;v max and a max are the maximum velocity and maximum acceleration, respectively; is the derivative of v, i.e., the acceleration;Δη max and Δψ max are the maximum changes in the climb angle and the heading angle, respectively, within Δt = t2 - t1.

[0055] In summary, the solution space is constructed according to the kinematic equation of formula (1). As shown in Figure 3 , taking the three-airport scenario as an example, the quadrilateral envelope is the reachable region of the two UAVs under the constraint of only considering the limited maneuvering capability (i.e., the velocity and acceleration constraints), i.e., the region that can be reached under the velocity constraint by using different accelerations within Δt, (V i , a i ) represents the velocity and acceleration of the UAV at the current position, and the range of the reachable region is related to the maximum values of the acceleration and velocity of the aircraft and the time, as shown in the following formula.

[0056] S i = f(a max , V max , Δt) (4);

[0057] wherein S i represents the reachable region of the i-th UAV; a max and V max are the maximum values of the acceleration and velocity of the i-th UAV. In the decision problem proposed in this embodiment, the multi-machine reachable domain corresponds to the multiple solution spaces of the optimization problem, and therefore, after the motion state of the UAV at the initial time t1 (position, velocity, and acceleration) is determined, an irregular initial solution space composed of multiple discrete position points can be constructed.

[0058] (2) The solution space is reconstructed based on multiple constraint conditions (i.e., the specific constraint conditions mentioned above), and a more reasonable searching solution space is obtained.

[0059] The initial solution space is obtained by calculating the flight reachable domain, which is established based on speed and acceleration constraints. However, in actual scenarios, there are three constraints: safety distance constraint from the moving target, acceleration change constraint, and angle change constraint (including climb angle and heading angle change constraint). Before designing the optimization problem, directly using the initial solution space cannot meet the specific constraint conditions, so it is crucial to reconstruct the initial solution space. By eliminating solutions that do not meet the constraint conditions, the size of the solution space can be effectively compressed, improving the efficiency of the optimization algorithm and better fitting the research scenario and research problem.

[0060] 1) Safety distance from the moving target.

[0061] During positioning, if the distance to the positioning target is too close, there is a security risk of being detected and attacked, so it is necessary to filter out the positions of unmanned aerial vehicles that do not meet the safety distance to improve the safety of cooperative flight. As shown in Figure 4 , a sphere is established with the moving target as the center and the safety distance d safe as the radius, and the points inside the sphere are removed.

[0062] The constraint expression is constructed, and the Euclidean distance is used to describe the position relationship between the unmanned aerial vehicle and the positioning target:

[0063]

[0064] where, is the position of the i-th unmanned aerial vehicle at t1(i = 1, 2,..., N); is the position of the moving target at t1.

[0065] 2) Acceleration change constraint.

[0066] When considering dynamic constraints, only the boundary constraints of speed and acceleration are considered, and the change of acceleration, i.e., the influence of jerk, is often ignored. Jerk represents the rate of change of acceleration, and a smaller jerk means that the acceleration changes more smoothly, which helps to avoid sudden changes or discontinuities in motion and provides more reasonable initial trajectory points for subsequent planning. When constructing the solution space, each potential solution has time and acceleration information, and the following constraint expression can be established:

[0067]

[0068] where, are the accelerations of the i-th unmanned aerial vehicle at t2 and t1, respectively; Je max is the maximum jerk.

[0069] 3) Angle change constraint.

[0070] Although the kinematic model does not consider the UAV's own attitude, it still needs to account for the constraints on the changes in heading and climb angles. The heading angle, also known as the track deflection angle, is the angle between the projection of the velocity direction onto the horizontal plane xOy and the Ox axis. UAVs typically change their heading by adjusting the angles of the rudder and aileron control surfaces. Due to inertia and maneuverability limitations, excessively large changes in heading angle cannot be achieved within a certain timeframe; therefore, it is necessary to construct constraints on the changes in heading angle. The heading angle model is as follows: Figure 5 As shown, the constraint can be expressed as:

[0071]

[0072] in, The velocities at different positions at different times are Δψ max This represents the maximum change in heading angle.

[0073] In addition to the heading angle, constraints on the climb angle also need to be considered. The climb angle, or track inclination angle, is the angle between the velocity direction and the horizontal plane (xOy). During the climb, the UAV is limited by engine performance, airfoil, and weather conditions, and the climb angle cannot change too much; otherwise, it will lose its climb rate and cause the UAV to stall. Figure 6 As shown, the constraint can be expressed as:

[0074]

[0075] By using angle constraints, a solution space that better reflects the motion characteristics of the UAV can be obtained, avoiding excessively drastic changes in heading and climb angles. By constructing these three constraints, the process of reconstructing and optimizing the solution space is realized, eliminating multi-UAV position points that do not meet the constraints, improving optimization efficiency while better suiting collaborative positioning scenarios.

[0076] (3) Establish an optimization decision-making model.

[0077] The expression for the optimization decision model is:

[0078] J fit =[J CR J d J s J con ]·λ(9);

[0079] Among them, J fit J is the value of the optimization decision model; CR J is the first function value, used to describe the positioning accuracy of the moving target; d The second function value is used to describe the kinetic energy consumption of the UAV; J s The third function value is used to determine whether the distance between drones meets the safe distance threshold within the formation; J conThe fourth function value is used to describe whether the communication distance between UAVs meets the communication distance threshold; λ is a weighting coefficient, λ = [λ CR ,λ d ,λ s ,λ con ] T , λ CR λ is the weighting coefficient for the first function value; d λ is the weighting coefficient for the second function value; s λ is the weighting coefficient for the third function value; con The weighting coefficients for the fourth function value.

[0080] In addition, the fitness function value during the optimization process is also calculated according to the above formula.

[0081] 1) Positioning accuracy calculation index J CR (That is, the first function value).

[0082] This embodiment uses an infrared photoelectric detection system as the positioning device for collaborative positioning. This system can detect and locate a single target at any angle. The infrared photoelectric detection system can only acquire the target's angle information. First, the angular relationship between the target and the UAV is obtained through multiple infrared sensor platforms. Then, the target position is calculated based on the relative positional relationship between the UAVs, thus constituting a collaborative positioning problem based on the Angle of Arrival (AOA).

[0083] like Figure 7 As shown, in a 3D scene, the basic principle of the AOA (Optical Angle of Arrival) localization method is to estimate the target's position by combining the azimuth angles measured by multiple drones. Assume that N drones participate in the localization, and the horizontal and vertical azimuth angles of the target relative to the drones measured by the N drones are θ. i and φ i From a geometric perspective, taking each drone as a starting point, the N rays formed by the estimated angles will intersect at a single point in space; this point is the target's location. The position coordinates of the N drones in three-dimensional space are u. i =[x i ,y i ,z i ] T The coordinates of the target are T = [x, i = 1, 2, ..., N]. T ,y T ,z T ] T Based on geometric relationships, the horizontal and vertical azimuth angles measured by each UAV are as follows:

[0084]

[0085] Consider the measurement noise in the measurement process, that is:

[0086]

[0087] where, represents the horizontal azimuth angle measurement value; θ i,r represents the horizontal azimuth angle true value, represents the vertical azimuth angle measurement value; φ i,r represents the vertical azimuth angle true value, n i and w i represents the angle measurement error, according to existing research, the angle measurement error mostly obeys Gaussian distribution.

[0088] In order to measure the positioning accuracy of the current positioning state when AOA positioning is adopted, Cramer-Rao Lower Bound (CRLB) is introduced as an index to measure the positioning accuracy of the current situation. The Cramer-Rao bound (CRB) determines a lower bound for the variance of any unbiased estimator. The variance of an unbiased estimator can only approach the CRB without limit, and cannot be lower than the CRB, so this bound can also be called CRLB, that is, Cramer-Rao lower bound. CRLB can be used to calculate the best estimation accuracy that can be obtained in unbiased estimation, so it is often used to calculate the best estimation accuracy that can be achieved in theory and evaluate the performance of parameter estimation method. The covariance matrix based on CRLB is as follows:

[0089]

[0090] where, t represents the vector to be estimated, that is, the target position information in three-dimensional space; is the estimated value of t; P represents the covariance matrix of the estimation error; C is the CRLB. F is the Fisher Information Matrix (FIM), which is used to measure the amount of information about the unknown parameters of the random variable contained in its own random distribution function.

[0091] Next, based on the AOA positioning principle and Cramer-Rao lower bound, the detailed expression of positioning accuracy is derived, and the optimal positioning state is analyzed.

[0092] The target information observed by the unmanned aerial vehicle is determined by formula (10), and the Jacobian matrix of the observation function corresponding to the i-th unmanned aerial vehicle can be expressed as:

[0093]

[0094] where, is the relative position vector of the i-th unmanned aerial vehicle and the moving target, and the observation error matrix of the i-th unmanned aerial vehicle is:

[0095]

[0096] Suppose the motion state equation and observation equation of the moving target are as follows:

[0097]

[0098] where F is a state transition matrix; h is an observation matrix. Based on this, the FIM of the positioning observation system can be expressed as:

[0099]

[0100] where Φ k,k-1 represents the Jacobian matrix of the target state transition function, and k represents the kth moment.

[0101] In the study of multi-vehicle optimal positioning state, only the current position of the target is considered, and it is assumed that the current position of the target is fixed. When N unmanned aerial vehicles observe a single target, the FIM can be expressed as:

[0102]

[0103] where i represents the ith unmanned aerial vehicle, H i represents the Jacobian matrix of the observation function of the ith unmanned aerial vehicle.

[0104] Substituting equation (10) and equation (13) into the above equation can express the FIM as:

[0105]

[0106]

[0107] According to the geometric relationship between the horizontal azimuth and the vertical azimuth in Figure 7 , it can be known that:

[0108]

[0109] to the FIM and the angle-related expression form:

[0110]

[0111]

[0112] After the positional relationship d i and D i between the unmanned aerial vehicle and the target, and the measurement standard deviation σ θ and σ φ of the azimuth are known, the value of the FIM is only related to the horizontal and vertical azimuths. Therefore, a suitable azimuth needs to be found to maximize the FIM, so as to obtain the highest positioning accuracy. However, for F NThe value of the matrix is not only one of the measurement criteria, so it is necessary to find a real scalar function as the objective function. The A-type optimal criterion is the most commonly used criterion in the three-dimensional AOA positioning scene, and its physical meaning is to minimize the variance of the average estimate, which is defined as minimizing the trace of the CRLB, so the A-type optimal criterion is adopted to measure the positioning accuracy in this embodiment. In the cooperative positioning scene corresponding to this embodiment, the positioning error is expressed based on the A-type optimal criterion to measure the positioning accuracy:

[0113]

[0114] wherein J CR is the absolute positioning error, i.e. the first function value, representing the size of the current positioning error, with the unit of m, and the smaller the positioning error, the higher the accuracy. In addition, as can be seen from equations (22) and (23), the factors affecting the positioning accuracy are mainly the distance between the UAV and the target and the azimuth angle formed with the target, and the azimuth angle can be reflected on the formation relationship, so the positioning accuracy can be improved by adjusting the distance between the UAV and the target and the multi-UAV formation.

[0115] (2) Flight energy consumption.

[0116] In the process of making optimal positioning state decisions, not only the positioning accuracy but also the energy consumption of the UAV flight should be considered. When making optimization decisions, the solution space at each time can be obtained, and a plurality of feasible positions of the UAVs can be obtained. Therefore, based on the position information, the Euclidean distance between the adjacent two time instants of the UAVs is taken as the measurement criterion of the flight energy consumption, and the expression of the second objective function is as follows:

[0117]

[0118] wherein, is the coordinate of the i-th UAV at t2 time instant, is the coordinate of the i-th UAV at t1 time instant, and N is the number of UAVs.

[0119] (3) Internal safety distance of formation.

[0120] In the process of cooperative flight, the internal safety distance of the formation needs to be considered, on the one hand to avoid collision, and on the other hand to provide more reasonable input for subsequent planning process. The objective function of the formation safety distance (i.e. the third objective function) is constructed as follows:

[0121]

[0122] wherein ΔP t,i = X t,i+1 -X t,i represents the relative position vector between the UAVs at t time instant, and L safeFor the formation safety distance threshold, formula (26) judges whether the safety distance constraint is met within the current formation, and calculates J according to the value exceeding the safety distance s .

[0123] (4) Communication distance.

[0124] In the process of cooperative flight, the effective communication distance between UAVs needs to be considered, so the cost function (i.e. the fourth objective function) describing the communication distance is established:

[0125]

[0126] Where ΔP t,i =X t,i+1 -X t,i is the relative position vector between UAVs at time t, L con is the maximum communication distance, formula (27) judges whether the distance between the current UAVs meets the communication distance requirement, and calculates J according to the value exceeding the communication distance con .

[0127] (4) The particle swarm optimization algorithm is used to solve the optimization decision problem of finite discrete set.

[0128] The particle swarm optimization algorithm simulates group behavior, in which individuals represent points in a D-dimensional search space. A particle represents a potential solution. The basic process of the particle swarm optimization algorithm is as follows:

[0129] Initialize to set N particles in D-dimensional space for search, and the initial values of the position vector and the velocity vector are randomly generated. The position of the nth particle (1≤n≤N) represents a potential solution to the problem, so each particle can be represented as a D-dimensional vector:

[0130]

[0131] In addition to position information, each particle also has velocity information v n , based on which the position is constantly updated to realize the optimization process:

[0132]

[0133] The optimal position searched by the nth particle is called the individual optimal:

[0134]

[0135] The optimal position searched by the entire group is the global optimal, which is represented as:

[0136]

[0137] The position coordinates of the particles represent a current solution of the algorithm, and the position of the particles is updated by the velocity vector. The velocity vector of the particles determines the advancing direction of the particles, i.e., the optimization search direction. The position and velocity update strategy of the traditional particle swarm algorithm is as follows:

[0138]

[0139] wherein ω is an inertia weight factor, representing the influence degree of the velocity vector of the last generation of the particles on the current velocity vector, r1 and r2 are random numbers between 0 and 1, and c1 and c2 are learning factors, respectively representing the influence degree of the historical individual optimal position of the particles and the global optimal position on the position update of the particles.

[0140] In order to solve the decision model by using the PSO algorithm, the position information of the multiple machines at different times and the coding of the particles in the algorithm need to be combined. Each unmanned aerial vehicle has a corresponding reachable area at the current time, i.e., a solution space composed of discrete three-dimensional coordinate information. After the solution space is reconstructed, the position information in the solution space is not continuous, so based on the coding method of the discrete particle swarm algorithm, on the basis of the known solution space and target information, the position coordinates in the solution space are arranged from near to far according to the distance from the target, and the serial number corresponding to the coordinate value is taken as the particle coding. The dimension of the particle corresponds to the number of unmanned aerial vehicles, and the particle information of the particle swarm can be represented as:

[0141]

[0142] wherein P all represents all particle information, N is the total number of particles in the population, P i is the information of the i-th particle, X d is the position information of the d-th unmanned aerial vehicle, and D is the number of unmanned aerial vehicles, also corresponding to the dimension information of each particle.

[0143] The collaborative positioning state optimization decision method for moving targets proposed in this embodiment models the solution problem of the optimal positioning state of collaborative positioning as an optimization search problem in multiple finite sets, and comprehensively considers multiple constraints and multiple optimization targets to obtain an optimal positioning state satisfying the actual collaborative positioning scene:

[0144] (1) A multiple solution space is constructed according to the kinematic equation of the unmanned aerial vehicle and multiple constraint conditions, which conforms to the actual positioning scene and has good practical application value, and the constructed solution space method has strong scalability. By setting different constraints, multiple scenes can be matched.

[0145] (2) A multi-objective optimization model is established, which comprehensively considers optimization targets such as positioning accuracy, energy consumption, and safety distance in the formation, and can obtain more reasonable optimization results conforming to the characteristics of collaborative motion.

[0146] (3) According to the target motion position optimization decision, the continuous tracking and positioning process of the moving target can be realized.

[0147] To verify the effectiveness of the cooperative positioning decision method for moving targets constructed in this embodiment, multiple groups of scenarios are designed for simulation experiments. First, a moving target needs to be constructed, such as Figure 8 As shown in the figure, the target takes off from the horizontal plane, flies horizontally and then climbs to an altitude of 3000 m. In order to better reflect the target maneuvering characteristics, only the climbing part is selected as the target motion trajectory in the simulation experiment. As shown in the figure Figure 9 The target speed is 400 m / s. The maximum speed of the unmanned aerial vehicle V max = 300 m / s, the maximum acceleration a max = 15 m / s 2 , and the safety distance for positioning the target is set to 500 m, and the communication distance is set to 10 km. Let the angle error of the infrared sensor follow the Gaussian distribution with mean 0 and standard deviation σ θ = σ φ = 0.1°, and the single positioning target can be detected and positioned in any angular direction. In addition, in order to measure the positioning performance of the multi-vehicle system at different times, the relative positioning error is defined as:

[0148]

[0149] Where J is the Cramer-Rao lower bound of the positioning error, in m, J r is the relative positioning error, the smaller the relative positioning error, the higher the positioning accuracy, and L is the distance between the center of the multi-vehicle array and the target.

[0150] (1) Scene 1

[0151] A three-vehicle positioning scenario is constructed. The target motion trajectory with a time length of 20 s is selected, and the optimal position decision is made every 2 s. The simulation parameters are shown in Table 1.

[0152] Table 1 Initial conditions of unmanned aerial vehicles in scene 1

[0153] Drone Initial position (m) Initial velocity (m / s) Initial velocity direction 1 (21000,3200,3000) 250 Pointing towards the target 2 (20500,3000,3200) 250 Pointing towards the target 3 (20000,2800,3000) 250 Pointing towards the target

[0154] The initial array of the three unmanned aerial vehicles is a regular triangle, and the simulation results are shown in Figure 10 and Figure 11As shown, the positions of the multiple drones at 10 time points are presented, along with the formation relationships and movement trends of the drones at different times. Simulation results show that the proposed decision-making method can stably converge to the optimal positioning state in the current solution space in a three-drone positioning scenario, thus maintaining positioning accuracy during flight. The relative positioning error shows a trend of first decreasing and then increasing. This is because before 10 seconds, the target is still in level flight, and the four drones effectively improve positioning accuracy by adjusting their distance from the target and their formation relationships. However, after 10 seconds, the target begins to climb and maneuver. Since the target flies at a speed exceeding the drones' movement capabilities, it is difficult for the multiple drones to quickly adjust their flight states within a limited time, thus increasing the positioning error. Furthermore, in AOA positioning, positioning accuracy is closely related to the horizontal azimuth, vertical azimuth, and the distance between the drones and the target. The angular relationships reflect the multi-drone formation. Therefore, based on the simulation results of scenario 1, further verification of the proposed decision-making method's ability to adjust formation and distance is needed.

[0155] (2) Scene 2

[0156] To verify whether the decision-making method is based on the positioning principle and has the ability to adjust the distance between the UAV and the target, a three-UAV positioning scenario was set up. The initial velocity direction of the three UAVs is pointing in the X-axis direction, and the initial conditions of the UAVs are shown in Table 2:

[0157] Table 2 Initial Conditions for Drones in Scenario 2

[0158] Drone Initial position (m) Initial velocity (m / s) Initial velocity direction 1 (21000,3200,3000) 250 X-axis direction 2 (20500,3000,3200) 250 X-axis direction 3 (20000,2800,3000) 250 X-axis direction

[0159] Simulation results are as follows Figure 12 and Figure 13 As shown, the decision-making method significantly adjusted the velocity and direction of the three UAVs, guiding them towards the target to reduce the distance between the UAVs and the target, thus effectively improving positioning accuracy. Therefore, it can be verified that the decision-making method can effectively adjust the distance between the target and the UAVs, and that the positioning accuracy gradually improves as the distance to the target decreases, consistent with the AOA positioning principle.

[0160] (3) Scene 3

[0161] Simulation analysis in Scenario 2 shows that the proposed decision-making method has good adjustment capabilities for the speed and direction of the UAVs and their distance from the target. Building on this, it is necessary to verify the effectiveness of the decision-making method in adjusting formation. Therefore, in Scenario 3, the initial positions of the three UAVs are set to be relatively close to the target to highlight the formation adjustment capability. The initial conditions for the UAVs are shown in Table 3:

[0162] Table 3 Initial Conditions for Drones in Scenario 3

[0163] Drone Initial position (m) Initial velocity (m / s) Initial velocity direction 1 (20000,500,2500) 250 X-axis direction 2 (20000,0,4000) 250 X-axis direction 3 (20000,-500,2500) 250 X-axis direction

[0164] The simulation results are shown in FIG. 6, FIG. 7 and FIG. 8. Figure 14 and Figure 15 As shown in FIG. 6, FIG. 7 and FIG. 8, the relative positioning error of the whole process is maintained below 0.3%. The positioning error shows a trend of first rising and then falling, because the target has stronger maneuverability than the UAV, the distance between the UAV and the target gradually increases before the decision-making period, and the formation adjustment is not completed, so the positioning accuracy first decreases, and after 12s, the positioning formation adjustment is completed, the positioning error gradually decreases, and the positioning accuracy is improved. In addition, due to the constraint of the safety distance, the decision-making method can only improve the positioning accuracy by adjusting the formation, therefore, the simulation experiment can verify that the proposed decision-making method has the ability to adjust the formation to improve the positioning accuracy.

[0165] The simulation results of the above three scenes can verify that the decision-making method constructed in the embodiment can obtain the optimal positioning position and formation relationship of multiple aircrafts for the target maneuvering, so as to realize the continuous tracking and positioning process.

[0166] Embodiment 2

[0167] A computer device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executes the computer program to realize the state optimization decision-making method for cooperative positioning of a moving target in embodiment 1.

[0168] Embodiment 3

[0169] A computer readable storage medium, having a computer program stored thereon, the computer program is executed by a processor to realize the state optimization decision-making method for cooperative positioning of a moving target in embodiment 1.

[0170] Embodiment 4

[0171] A computer program product, comprising a computer program, the computer program is executed by a processor to realize the state optimization decision-making method for cooperative positioning of a moving target in embodiment 1.

[0172] Embodiment 5

[0173] A computer device, which can be a database, and its internal structure diagram can be as shown in FIG. 9. Figure 16As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the to-be-processed transaction. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor to implement the object-oriented cooperative positioning state optimization decision method in embodiment 1.

[0174] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program guiding relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. In the embodiments provided by the present application, any reference to the memory, database or other medium can include at least one of the non-volatile and volatile memories. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of the relational database and the non-relational database. The non-relational database can include a distributed database based on blockchain, etc., and is not limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and is not limited thereto.

[0176] Any combination of the technical features of the above embodiments can be made, and in order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0177] The principles and implementation modes of the present application are described by using specific examples in this paper, and the above-mentioned examples are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for cooperative positioning state optimization decision of moving target, characterized in that, The method comprises: For each unmanned aerial vehicle in the multi-unmanned aerial vehicle formation, a kinematic model is constructed, and based on constraint conditions, a solution space is determined, wherein the constraint conditions comprise a speed constraint of the unmanned aerial vehicle, an acceleration constraint of the unmanned aerial vehicle, a safety distance constraint of the unmanned aerial vehicle and the moving target, an acceleration change constraint of the unmanned aerial vehicle, and an angle change constraint of the unmanned aerial vehicle; the solution space is a set of a plurality of discrete position points, each position point comprising motion state information of the unmanned aerial vehicle corresponding to a current time, and the motion state information comprises a position, a speed, and an acceleration of the unmanned aerial vehicle; An optimization decision model is established, wherein the optimization decision model comprises a first objective function for calculating a positioning error of the moving target, a second objective function for calculating a flight energy consumption of the unmanned aerial vehicle, a third objective function for calculating a size relationship between a distance between the unmanned aerial vehicles and a safety distance threshold, and a fourth objective function for calculating a size relationship between a communication distance between the unmanned aerial vehicles and a communication distance threshold; The solution space is substituted into the optimization decision model, and an optimization algorithm is used for solving, and when a result obtained by weighted summation between a first function value, a second function value, a third function value, and a fourth function value is maximum, an optimal positioning state is obtained, wherein the first function value is a value solved by the first objective function, the second function value is a value solved by the second objective function, the third function value is a value solved by the third objective function, the fourth function value is a value solved by the fourth objective function, and the optimal positioning state comprises an optimal positioning position point corresponding to each unmanned aerial vehicle and a formation overall formation.

2. The method of claim 1, wherein, For each unmanned aerial vehicle in the multi-unmanned aerial vehicle formation, a kinematic model is constructed, and based on constraint conditions, a solution space is determined, specifically comprising: For each unmanned aerial vehicle, a kinematic model is constructed, and based on a limited maneuvering capability constraint condition, a reachable region of each unmanned aerial vehicle is determined, wherein the limited maneuvering capability constraint condition comprises a speed constraint of the unmanned aerial vehicle and an acceleration constraint of the unmanned aerial vehicle; According to all possible position points of the unmanned aerial vehicles in the reachable region, an initial solution space is determined; Based on a specific constraint condition, the position points in the initial solution space that do not satisfy the specific constraint condition are eliminated, and an optimization search solution space is obtained, wherein the specific constraint condition comprises a safety distance constraint of the unmanned aerial vehicle and the moving target, an acceleration change constraint of the unmanned aerial vehicle, and an angle change constraint of the unmanned aerial vehicle; The optimization search solution space is taken as a final solution space.

3. The method of claim 1, wherein, An expression of the optimization decision model is: J fit = [J CR , J d , J s , J con ] · λ; wherein J fit is the value of the optimization decision model; J CR is the first function value; J d is the second function value; J s is the third function value; J con is the fourth function value; λ is a weight coefficient, λ = [λ CR , λ d , λ s , λ con ] T , λ CR is the weight coefficient of the first function value; λ d is the weight coefficient of the second function value; λ s is the weight coefficient of the third function value; λ con is the weight coefficient of the fourth function value.

4. The method of claim 1, wherein, A calculation process of the first function value specifically comprises: According to an azimuth angle between each unmanned aerial vehicle and the moving target, an AOA positioning method is used to position the moving target; A positioning error of the moving target is calculated according to a Cramer-Rao lower bound, and the first function value is obtained.

5. The method of claim 4, wherein, An expression of the first objective function is: wherein J CR is the first function value; tr() is the trace of a matrix; F N is the information matrix; CRLB is the Cramer-Rao Lower Bound; N is the total number of UAVs; i is the i-th UAV; θ i is the horizontal azimuth angle of the i-th UAV to the moving target; φ i is the vertical azimuth angle of the i-th UAV to the moving target; d i is the relative position relationship of the i-th UAV and the moving target in the OXY plane; D i is the relative position relationship of the i-th UAV and the moving target in the three-dimensional space; is the standard deviation of the horizontal azimuth angle of the i-th UAV to the moving target; is the standard deviation of the vertical azimuth angle of the i-th UAV to the moving target.

6. The method of claim 1, wherein, A calculation process of the second function value specifically comprises: According to the position points of the UAV at adjacent time points, a Euclidean distance is calculated; The Euclidean distance is taken as the second function value.

7. The method of claim 1, wherein, The optimization algorithm is a particle swarm optimization algorithm.

8. A computer apparatus comprising: A memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the dynamic target-oriented cooperative positioning state optimization decision method in any one of claims 1-7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the dynamic target-oriented cooperative positioning state optimization decision method in any one of claims 1-7.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the dynamic target-oriented cooperative positioning state optimization decision method in any one of claims 1-7.

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