Unmanned aerial vehicle flight path planning method and system under flight path deception interference background

Abstract

The invention provides an unmanned aerial vehicle flight path planning method and system under a flight path deception interference background, and belongs to the field of unmanned aerial vehicle flight path planning, and the method comprises the steps: setting an unmanned aerial vehicle flight speed range, an unmanned aerial vehicle flight height, an unmanned aerial vehicle maximum turning rate and an unmanned aerial vehicle maximum climbing angle, and building constraint conditions; weighting the total energy consumption of unmanned aerial vehicle motion, the total channel transmission loss of communication signals between the unmanned aerial vehicles and the radar discovery probability, and constructing an evaluation function model; and generating a false track of deception jamming according to a task demand, selecting a distance ratio coefficient as a particle of PSO, outputting an optimal fitness value and a corresponding particle position in combination with a constraint condition, and obtaining an unmanned aerial vehicle track planning result. The radar discovery rate and the transmission loss of the obtained unmanned aerial vehicle flight path planning result are optimal, and preconditions and reliable guarantee are provided for implementation of flight path deception interference.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) trajectory planning, and more specifically, relates to a method and system for unmanned aerial vehicle (UAV) trajectory planning under the background of trajectory deception and interference. Background Art

[0002] The trajectory planning of jamming UAVs is the basis for the implementation of trajectory deception jamming tasks by UAV swarms, and it affects the jamming effect to a certain extent. When planning the trajectory of jamming UAVs, it is necessary to ensure that the kinematic constraints are met and to consider the energy consumption and safety of the UAVs themselves. The more comprehensive the index factors considered, the better the comprehensive performance of the planned trajectory, and the more conducive it is to the completion of the jamming task.

[0003] The prior art proposes an improved whale algorithm to optimize the multi-machine collaborative deception jamming model in the context of multi-machine collaborative track deception jamming of networked radar systems. However, the cost function only considers the number of false target tracks generated, and does not provide the actual flight reference trajectory of the drone. There is also an existing technology that takes the minimum drone energy consumption and the minimum track curvature as the optimization goals, and obtains two different optimal control models, but the model components are simple and general, and some key parameter settings are not derived by formulas. The comprehensive performance of the planned track is low, and the solution is relatively complex. Summary of the invention

[0004] In view of the defects of the prior art, the purpose of the present invention is to provide a method and system for UAV trajectory planning under the background of track deception interference, aiming to solve the problem that the existing interference UAV trajectory planning method does not comprehensively construct the optimization objective when constructing the cost function and does not provide a clear parameter setting method, resulting in a more complex model solution and poor accuracy.

[0005] To achieve the above object, the present invention provides a method for UAV track planning under the background of track deception interference, comprising the following steps:

[0006] Generate deceptive and jamming false tracks according to mission requirements, select distance ratio coefficients as PSO particles, iteratively update particle evolution speed and position in combination with constraints, calculate fitness values, individual particle extreme values, and group extreme values ​​after each iteration until the maximum number of evolutions is reached, output the optimal fitness value and the corresponding particle position, and obtain the UAV track planning results;

[0007] The distance ratio coefficient is the ratio of the distance from the UAV to the radar to the distance from the false target to the radar; each particle represents an array constructed by a distance ratio coefficient corresponding to each UAV; the fitness value is the solution value of the evaluation function model;

[0008] Among them, the three points of false target, drone and radar are on the same line, and multiple jamming drones interfere with multiple radars at the same time. Each drone generates a false signal, and multiple false signals merge into one point in space to form a false target;

[0009] Among them, the UAV flight speed range, UAV flight altitude, UAV maximum turning rate and UAV maximum climbing angle are set to establish constraint conditions;

[0010] The method for constructing the evaluation function model includes the following steps:

[0011] A. Obtain the total energy consumption of the UAV movement by establishing the energy consumption from the drop point to the starting point of the track and the energy consumption expression of the flight according to the planned track; and assuming that the communication signal of the UAV implementing the jammer propagates in free space, construct the total channel transmission loss of the communication signal between UAVs; and construct the probability of being detected by radar;

[0012] B. Weighted processing is performed on the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the probability of being detected by radar to construct an evaluation function model.

[0013] Further preferably, the energy consumption from the delivery point to the starting point of the track includes uniform motion energy consumption, hovering energy consumption and altitude change energy consumption; the energy consumption of flying according to the planned track includes uniform motion energy consumption, altitude change energy consumption and turning energy consumption.

[0014] Further preferably, the total channel transmission loss of the communication signal between the UAVs is the sum of the free space propagation losses of the communication signals between all two UAVs;

[0015] The free space propagation loss expression of the communication signal between two UAVs is:

[0016]

[0017] l 1,2 (t3)=32.44+20lg(f)+20lg(||q1(t3)-q2(t3)||2)

[0018] Wherein, the subscripts 1 and 2 represent the first UAV and the second UAV, f is the UAV communication signal frequency; q1 is the position coordinate of the first UAV; q2 is the position coordinate of the second UAV; ||·||2 means to find the 2-norm; l 1,2 (t3) is the communication signal transmission loss between the first UAV and the second UAV at time t3; L 1,2 It is the communication signal transmission loss between the first UAV and the second UAV from 0 to t3.

[0019] Further preferably, step B specifically comprises the following steps:

[0020] Standardize the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the radar detection probability;

[0021] Set the weights of the mission completion capability and safety indicators. The mission completion capability indicator includes the total energy consumption indicator of the UAV movement and the total channel transmission loss indicator of the communication signal between UAVs; the safety indicator includes the radar detection probability indicator;

[0022] The evaluation function model is: Y = 0.2E norm +0.6L norm +0.2P dnorm ;

[0023] Among them, E norm is the total energy consumption of the UAV after standardization; P dnorm is the normalized radar detection probability; L norm is the normalized total channel transmission loss.

[0024] Further preferably, the method for updating the particle evolution speed and position is:

[0025] The value range of particle speed and position is set, and boundary absorption is performed during the evolution process. At the same time, nonlinear inertia weight and asynchronous linear change learning factor are introduced to update the particle speed and position.

[0026] In a second aspect, the present invention provides a UAV track planning system under the background of track deception interference, comprising:

[0027] The constraint condition establishment module is used to set the UAV flight speed range, UAV flight altitude, UAV maximum turning rate and UAV maximum climbing angle to establish constraint conditions;

[0028] The module for establishing the total energy consumption of the UAV movement is used to obtain the total energy consumption of the UAV movement by establishing the energy consumption from the drop point to the starting point of the track and the energy consumption expression according to the planned track flight;

[0029] The total channel transmission loss establishment module is used to construct the total channel transmission loss of the communication signals between the UAVs, assuming that the communication signals of the UAVs implementing the jammer propagate in free space;

[0030] Radar detection probability building module, used to build radar detection probability;

[0031] The evaluation function model building module is used to perform weighted processing on the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the radar detection probability to build an evaluation function model;

[0032] The UAV track planning module is used to generate a deceptive and jamming false track according to the mission requirements, select the distance ratio coefficient as the particle of PSO, combine the constraint conditions, iteratively update the particle evolution speed and position, calculate the fitness value, individual particle extreme value and group extreme value after each iteration, until the maximum number of evolutions is reached, output the optimal fitness value and the corresponding particle position, and obtain the UAV track planning result; wherein, the distance ratio coefficient is the ratio of the distance from the UAV to the radar to the distance from the false target to the radar; each particle represents an array constructed by a distance ratio coefficient corresponding to each UAV; the fitness value is the solution value of the evaluation function model;

[0033] Among them, the three points of false target, drone and radar are on the same line, and multiple jamming drones interfere with multiple radars at the same time. Each drone generates a false signal, and multiple false signals merge into one point in space to form a false target.

[0034] Further preferably, the energy consumption from the release point to the starting point of the track in the drone movement total energy consumption establishment module includes uniform motion energy consumption, hovering energy consumption and altitude change energy consumption; the flight energy consumption according to the planned track includes uniform motion energy consumption, altitude change energy consumption and turning energy consumption.

[0035] Further preferably, the total channel transmission loss of the communication signal between the UAVs in the total channel transmission loss establishment module is the sum of the free space propagation loss of the communication signal between all two UAVs; the free space propagation loss expression of the communication signal between two UAVs is:

[0036]

[0037] l 1,2 (t3)=32.44+20lg(f)+20lg(||q1(t3)-q2(t3)||2)

[0038] Wherein, the subscripts 1 and 2 represent the first UAV and the second UAV, f is the UAV communication signal frequency; q1 is the position coordinate of the first UAV; q2 is the position coordinate of the second UAV; ||·||2 means to find the 2-norm; l 1,2 (t3) is the communication signal transmission loss between the first UAV and the second UAV at time t3; L 1,2 It is the communication signal transmission loss between the first UAV and the second UAV from 0 to t3.

[0039] Further preferably, the method for constructing the evaluation function model by the evaluation function model construction module comprises the following steps:

[0040] Standardize the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the radar detection probability;

[0041] Set the weights of the mission completion capability and safety indicators. The mission completion capability indicator includes the total energy consumption indicator of the UAV movement and the total channel transmission loss indicator of the communication signal between UAVs; the safety indicator includes the radar detection probability indicator;

[0042] The evaluation function model is: Y = 0.2E norm +0.6L norm +0.2P dnorm ;

[0043] Among them, E norm is the total energy consumption of the UAV after standardization; P dnorm is the normalized radar detection probability; L norm is the normalized total channel transmission loss.

[0044] Further preferably, the method for updating the particle evolution speed and position in the UAV trajectory planning module is:

[0045] The value range of particle speed and position is set, and boundary absorption is performed during the evolution process. At the same time, nonlinear inertia weight and asynchronous linear change learning factor are introduced to update the particle speed and position.

[0046] In a third aspect, the present invention provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory, wherein when the programs stored in the memory are executed, the processor is used to execute the method described in the first aspect or further preferably in the first aspect.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed on a processor, enables the processor to execute the method described in the first aspect or further preferably in the first aspect.

[0048] In a fifth aspect, the present invention provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or further preferably in the first aspect.

[0049] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0050] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0051] The present invention provides a method and system for unmanned aerial vehicle (UAV) track planning under the background of track deception and interference, wherein the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs and the radar detection probability are weighted to construct an evaluation function model; the UAV track planning result is solved based on the evaluation function model. On the one hand, since the total channel transmission loss of communication signals between UAVs is considered in the evaluation function model, the track planned thereby can better guarantee the communication between UAVs, so that the cluster position and time coordination are smoother, and the false track points formed are avoided from being discovered and removed by the networking radar, thereby affecting the interference effect; on the other hand, since the evaluation function model takes into account the radar detection probability, the possibility of the interference UAV being discovered by the enemy radar can be reduced, and the combat intention of our deception and interference can be avoided from being identified while ensuring the safety of the UAV itself; on the third hand, the total energy consumption of the UAV movement in the evaluation function model provided by the present invention includes the energy consumption from the delivery point to the starting point of the track and the energy consumption of flying according to the planned track, and the influence of the spatial position of the starting point of the track on the energy consumption is considered, resulting in a change in the entire track, and considering the energy consumption can also make the planned interference UAV track closer to reality. In summary, the radar detection rate and transmission loss of the drone track planning results obtained by the present invention are optimal, which provides the prerequisite and reliable guarantee for implementing track deception interference.

[0052] The present invention provides a method and system for unmanned aerial vehicle (UAV) track planning under the background of track deception and interference, wherein, given the premise that the UAV and the false track fly similarly, according to the spatial position relationship and motion model of the false target-UAV-radar, it can be obtained that there is a stable relationship between the false target position and the UAV position (false target-UAV-radar three points are collinear, multiple interference UAVs interfere with multiple radars at the same time, each UAV generates a false signal, and multiple false signals merge into one point in space to form a false target), and this relationship is a geometric similarity relationship established by the distance ratio coefficient being the ratio of the distance from the UAV to the radar to the distance from the false target to the radar; since the false track is clear in advance and can be used as a known condition, the parameters such as the position and speed of the UAV in the evaluation function model can be solved according to the η value; it can be seen from this that the present invention adopts a single variable η, can complete track planning in a short time, and can play a greater role in dealing with emergencies such as task strategy adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of drone cluster track deception jamming network radar provided by an embodiment of the present invention;

[0054] Figure 2 Schematic diagram of the position relationship between radar, UAV and false target provided by an embodiment of the present invention;

[0055] Figure 3It is a flow chart of a method for UAV trajectory planning under a trajectory deception and interference background provided by an embodiment of the present invention;

[0056] Figure 4 is a schematic diagram of the turning rate of the false target track provided by an embodiment of the present invention;

[0057] Figure 5 is a schematic diagram of a false target climbing angle provided by an embodiment of the present invention;

[0058] FIG6( a ) is a schematic diagram of a radar-UAV-false target track planned by a first evaluation function provided by an embodiment of the present invention;

[0059] FIG6( b ) is a schematic diagram of a radar-UAV-false target track planned by a second evaluation function provided in an embodiment of the present invention;

[0060] FIG6( c ) is a schematic diagram of a radar-UAV-false target track planned by a third evaluation function provided in an embodiment of the present invention;

[0061] Figure 7 is a schematic diagram of a discovery probability change curve provided by an embodiment of the present invention;

[0062] Figure 8 is a schematic diagram of a transmission loss variation curve provided by an embodiment of the present invention;

[0063] Fig. 9 It is a top view of the position relationship of the tracks of three groups of drone clusters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0065] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0066] The terms “first”, “second” and the like in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects.

[0067] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0068] In the description of the embodiments of the present invention, unless otherwise specified, “plurality” means two or more than two.

[0069] First, the basic concepts and principles involved in this invention are introduced:

[0070] like Figure 1 The figure shows a schematic diagram of the drone cluster track deception jamming the networked radar. The basic principle is: multiple drones interfere with multiple radars. On the basis of satisfying the colinearity of the false target-drone-radar three points, each drone generates a deception signal with a time delay by controlling the jammer it carries and makes it enter the radar main lobe. The multiple false target signals generated by multiple drones merge into one point in space, that is, through the "homologous inspection" of the networked radar, a reliable false target point can be generated at the specified position. As the drone moves, the accumulated false target points form a false target track.

[0071] In the track deception jamming model, the positional relationship between the false target, drone and radar is as follows: Figure 2 As shown; v p , α p and β p are the speed, track deviation angle and track inclination of the false target respectively; v u , α u and β u are the speed, track deviation and track inclination of the UAV respectively; θ and are the pitch angle and azimuth of the false target (or UAV) relative to the radar, R and r are the distances from the false target and the UAV to the radar, respectively. In the ground-fixed coordinate system, the position coordinates of the false target, the UAV and the radar are (x p ,y p 、z p )、(x u ,y u 、z u )、(x r ,y r 、z r ) ; Establish respective rectangular coordinate systems with the false target, UAV and radar as the origin;

[0072] In the rectangular coordinate system, the motion equation of the false target is:

[0073]

[0074] in, and x p ,y p 、z p The derivative of

[0075] The coordinates of the false target in the station center sphere coordinate system are: The conversion relationship between the rectangular coordinates and spherical coordinates of the false target position is:

[0076]

[0077] By taking the derivative of formula (2) and converting it into matrix form, we can get:

[0078]

[0079] in, and are the derivatives of the false target relative to the radar distance, pitch angle and yaw angle, respectively, θ≠0;

[0080] From formula (3), we can get the motion model of the false target in the spherical coordinate system with the radar as the origin:

[0081]

[0082] From formula (1) and formula (4), we can get:

[0083]

[0084]

[0085] According to the principle of three points of false target, UAV and radar being collinear, the motion model of the UAV can be obtained in the same way, and we will not repeat the derivation here.

[0086] According to the geometric relationship, θ, and The expression is:

[0087]

[0088] in,

[0089] According to the false target track and radar position coordinates, the position coordinates of the jamming UAV can be obtained:

[0090]

[0091] Among them, η is the distance ratio coefficient, η=r / R;

[0092] Example 1

[0093] The following describes a method for UAV track planning under the background of track deception and interference provided by the present invention, which specifically includes the following steps:

[0094] S1: Evaluation function analysis modeling

[0095] The general process of the track deception jamming of the drone swarm on the networked radar implementation example is as follows: ① Obtain enemy radar intelligence and plan a false target track that meets kinematic constraints according to the combat intent; ② Plan a track that is actually flyable and has excellent performance based on the false target track and the performance parameters of the jamming drone to be selected; ③ Deploy the jamming drones to the combat area through a certain transportation method; ④ Make the jamming drones fly to the starting point of their respective tracks and launch coordinated flights to implement the track deception jamming mission.

[0096] Under the premise of similar trajectory constraints, the invention first simplifies whether the trajectory of the interfering UAV can be realized into a speed constraint problem, and then analyzes the three index factors of UAV energy consumption, battlefield survival probability and communication channel transmission loss, and uses this as the optimization goal to derive and establish a comprehensive evaluation function model with η as the independent variable.

[0097] S1.1 Track feasibility analysis

[0098] To implement trajectory deception jamming, the trajectory of a drone swarm should first be made flyable, that is, it should meet the constraints of flight speed, flight altitude, turning rate, and maximum climb angle.

[0099] a. UAV flight speed

[0100] The minimum value of the drone speed v umin and the maximum value v umax satisfy:

[0101]

[0102] in, and are the component velocity, minimum value and maximum value of the component velocity in the vertical direction of the UAV respectively;

[0103] b. UAV flight altitude

[0104] From the geometric relationship, we can get:

[0105]

[0106] Among them, h r is the radar altitude; h u h is the altitude of the drone; p is the altitude of the false target;

[0107] c. Turn rate

[0108] The turning rate W refers to the instantaneous change or steady-state change rate of the UAV's flight heading; that is, the rate of change of the yaw angle of adjacent track points, which reflects the directional maneuverability of the UAV. According to the trigonometric function, the turning rate W of the kth track point of the false target can be obtained. pk :

[0109]

[0110] Among them, l 1k represents the distance between the kth track point of the false target and the k+1th point, l 2k Represents the distance between the kth track point of the false target and the k+2th point; that is:

[0111]

[0112] Formula (8) can be further expressed as:

[0113]

[0114] From formulas (11) to (13), we can get the turning rate W of the kth track point of the jamming UAV: uk =W pk ; Among them, W uk is the turning rate of the kth track point of the UAV;

[0115] The turning rate is inversely proportional to the turning radius of the drone. When the turning rate meets the constraint conditions, it means that the turning radius at this time is greater than or equal to the minimum turning radius;

[0116] d. Maximum climb angle

[0117] The maximum climb angle / dive angle of the drone is constrained in the vertical direction. If the angle of the drone in a single climb / dive is too large, it is very easy to cause the drone to stall. The climb angle of the kth track point of the false target is:

[0118]

[0119] Similarly, the climbing angle β of the kth track point of the interfering UAV can be obtained by solving the turning rate part uk =β pk ;

[0120] From the above, it can be seen that when the turning rate and maximum climb angle of the false target track are determined, the turning rate and maximum climb angle of the jamming UAV track are also determined. When the performance parameters of the UAV are better than this result, the track feasibility problem can be simplified to the speed constraint. From formula (9) and formula (10), it can be seen that in order to make the flight altitude meet the constraints, the flight speed of the jamming UAV needs to meet [v umin , v umax ]constraint;

[0121] S1.2 UAV energy consumption

[0122] The energy consumption of drones is mainly divided into communication energy consumption and motion energy consumption. Taking a rotor drone as an example for analysis, its motion energy consumption can be divided into moving energy consumption and hovering energy consumption. Since the communication energy consumption is very small compared to the moving energy consumption, this invention only discusses the problem of motion energy consumption.

[0123] The energy consumption problem needs to be considered in two stages: from the drop point to the starting point of the track and flying according to the planned track;

[0124] A. Energy consumption from the delivery point to the starting point of the track

[0125] The jamming drone clusters are placed at v umin Fly to their respective starting points in a straight line at a constant speed. At this time, it is necessary to consider the energy consumption of the drone flying to the starting point of the track at a constant speed and the hovering energy consumption of the drone that arrives first;

[0126] A.1: Energy consumption of uniform motion

[0127] T 1i During the time period, the uniform flight energy consumption of the i-th UAV is:

[0128]

[0129] Among them, T 1i is the time required for the i-th UAV to fly to the starting point of the track; P f The speed of the drone is v u The total power of the movement is expressed as:

[0130]

[0131] Where P0 and P1 represent the blade power and induced power in the hovering state respectively; U tip is the blade edge speed of the rotor; v0 is the induced speed of the rotor in the hovering state; d is the fuselage drag ratio; s is the rotor stability; ρ and A represent the air density and the area of ​​rotor rotation respectively, assuming that ρ is a constant; the expressions of P0 and P1 are:

[0132]

[0133]

[0134] Among them, Ω is the blade rotation angular velocity, δ is the profile drag coefficient, K is the incremental correction coefficient of power, m is the mass of the UAV, and g is the local gravity coefficient;

[0135] A.2: Hovering energy consumption

[0136] The i-th drone needs to wait for a certain amount of time T 2i The hovering energy consumption of the i-th UAV is:

[0137] E xi =(P0+P1)T 2i (19)

[0138] A.3: Highly variable energy consumption

[0139] The energy consumption of the height change of the i-th UAV is:

[0140]

[0141] Among them, h p1 is the height of the false target starting point; h1 is the height of the drop point; γ is the energy conversion rate;

[0142] Therefore, the total energy consumption E1 of D drones from the drop point to the starting point of the track can be expressed as:

[0143]

[0144] B. Energy consumption according to the planned flight path

[0145] Assuming that the drone can complete signal forwarding in motion, there is no need to consider hovering energy consumption at this stage. Only moving energy consumption is discussed, including uniform motion energy consumption, altitude change energy consumption, and corner energy consumption.

[0146] B.1: Energy consumption of uniform motion

[0147] During the time T3 of the drone's real-time track deception interference, the total energy consumption of the i-th drone in uniform motion is:

[0148]

[0149] B.2: Highly variable energy consumption

[0150] The total energy consumption of the height change of the i-th UAV is:

[0151]

[0152] Where n is the total number of segments of the false target track, Δh pi is the height change value of the i-th segment;

[0153] B.3: Corner energy consumption

[0154] Assuming that the UAV makes a turn at the connection point of the two tracks, the UAV turning energy consumption formula is:

[0155]

[0156] in, is the j-th turning angle of the i-th UAV, which is obtained by solving formulas (11) and (12);

[0157] Therefore, the total energy consumption E2 of D drones flying according to the planned flight path can be expressed as:

[0158]

[0159] The total energy consumption E of D drones is:

[0160] E=E1+E2 (26)

[0161] S1.3 Communication channel transmission loss

[0162] Due to the “same-source verification” principle and superior data fusion characteristics of the networked radar, in order to achieve the purpose of implementing track deception interference, the UAVs in the cluster need to conduct multi-dimensional coordination and information exchange, and distance is the key factor affecting the channel transmission loss; assuming that the communication signal of the UAV propagates in free space during the interference, the communication signal transmission loss between the first UAV and the second UAV at time t3 is l 1,2 (t3) is:

[0163] l 1,2 (t3)=32.44+20lg(f)+20lg(||q1(t3)-q2(t3)||2) (27)

[0164] Where f is the UAV communication signal frequency; q1 is the position coordinate of the first UAV; q2 is the position coordinate of the second UAV; ||·||2 means to find the 2-norm;

[0165] Free space propagation loss L 1,2 It can be expressed as:

[0166]

[0167] S1.4 Survival probability

[0168] C.1: Safe distance between drones

[0169] In order to avoid mutual interference caused by the small distance between drones, it is necessary to limit the minimum distance between drones;

[0170] C.2: Radar detection probability

[0171] Usually, radars use a specific false alarm probability to calculate the radar detection probability under constant false alarm conditions. However, since the calculation of the zero-order modified Bessel function contained in the detection probability formula is relatively complex, an approximate algorithm is generally used to calculate the radar detection probability. The probability that the i-th drone is detected by radar j at a certain moment is:

[0172]

[0173] Where erfc is the complementary error function; P fa is the false alarm probability, SNR i is the signal-to-noise ratio of the i-th UAV echo at the front end of the radar receiver, that is:

[0174]

[0175] Among them, P Lt G is the radar transmission peak power; t is the radar transceiver antenna gain; λ is the wavelength of the radar transmission signal; σ is the scattering cross-sectional area of ​​the interfering UAV; k b is the Boltzmann constant; T o is the thermodynamic temperature; B is the radar receiver bandwidth, and F is the radar receiver noise factor;

[0176] As for the safety index, the present invention considers the sum of the probabilities P of each of the D drones implementing track deception interference being discovered by the enemy radar. d ; Assuming that a single UAV will only be discovered by the corresponding radar that implements interference, and because the probability of a single UAV being detected by radar at different track points is different, and its detection should refer to the maximum value of the detection probability set corresponding to the entire track, then P d It can be expressed as:

[0177]

[0178] Among them, P di s is the set of probabilities of the i-th drone being discovered by radar i;

[0179] S1.5 Evaluation function model

[0180] D.1: Data processing

[0181] Since the survival probability is different from the energy consumption and the transmission loss of the communication channel in different orders of magnitude, the data needs to be standardized and the total energy consumption E norm For example, the expression is:

[0182]

[0183] Among them, E max is the maximum total energy consumption of D drones;

[0184] D.2: Weight Distribution

[0185] Using the expert scoring method, the weights of the two first-level indicators of mission completion capability and safety are 0.8 and 0.2 respectively; the mission completion capability includes two second-level indicators, namely, the energy consumption of the drone and the transmission loss of the communication channel, with weights of 0.2 and 0.6 respectively; safety includes a second-level indicator, the probability of being discovered by radar;

[0186] D.3: Evaluation function model construction

[0187] In summary, the expression of evaluation function Y can be obtained from formula (15) to formula (32):

[0188] Y=0.2E norm +0.6L norm +0.2P dnorm (33)

[0189] Among them, E norm is the standardized total energy consumption; P dnorm is the normalized radar detection probability; L norm is the normalized total channel transmission loss;

[0190] Since the false target track is known, according to the similarity track constraint and geometric relationship, when η i When the time is certain, the track and coordinate position of each interfering UAV are determined, and then T in formula (15) to formula (32) is 1i 、T 2i , T3, h p1 、h u1 , Δh pi , n can be solved directly, so only η is included in the evaluation function Y. i (i=1,2,…,D) a set of variables;

[0191] S2: Trajectory planning method under the background of trajectory deception interference

[0192] The purpose of UAV trajectory planning is to obtain the optimal trajectory that meets the needs of the user. The present invention establishes an evaluation function model Y and uses a standard particle swarm optimization algorithm to obtain the minimum value of the evaluation function model, that is, the particle fitness Y min , and finally its corresponding η i Substituting (i=1,2,…,D) into formula (13), the corresponding UAV track can be solved;

[0193] First introduce the PSO algorithm:

[0194] Assume that D drones interfere with D radars, and a population is composed of N particles. The spatial position of each particle i (i = 1, 2, ..., N) can be expressed as X i =(X i,1 ,X i,2 ,…,Xi,D ), its physical meaning is expressed as a value of the η array, and the speed of particle i can be expressed as v i =(v i,1 ,v i,2 ,…,v i,D ), the optimal position searched by the i-th particle is the individual extreme value, denoted by p best =(p i,1 ,p i,2 ,…,p i,D ), and the optimal position searched by the entire particle swarm is the global extreme value, denoted as g best =(p g,1 ,p g,2 ,…,p g,D ), when updating to the tth generation, particle i updates its speed and position according to the following formula:

[0195] v i,D (t+1)=ω·v i,D (t)+c1r1[p i,D (t)-X i,D (t)]+c2r2[p g,D (t)-X i,D (t)] (34)

[0196] X i,D (t+1)=X i,D (t)+v i,D (t+1) (35)

[0197] Among them, p i,D (t) represents the historical optimal position of the current particle i; p g,D (t) represents the global historical optimal position of the current generation population; ω is the non-negative inertia weight; c1 and c2 are learning factors; r1 and r2 are two random numbers between the interval [0, 1];

[0198] In order to prevent particles from crossing the maximum search boundary during the evolution of the algorithm, it is necessary to specify the value range of particle speed and position, and perform boundary absorption during the evolution process; at the same time, in order to improve the performance of the algorithm and avoid falling into the local optimum, a nonlinear inertia weight and asynchronous linear change learning factor are used. The weight calculation formula is:

[0199]

[0200] Among them, ω max is the maximum value of the inertia weight ω; min is the minimum value of the inertia weight ω; t is the current number of evolutions, T is the maximum number of evolutions; the learning factors c1 and c2 are calculated as follows:

[0201]

[0202] Among them, c 1max and c 2max are the maximum values ​​of learning factors c1 and c2 respectively; c 1min and c 2min are the minimum values ​​of learning factors c1 and c2 respectively;

[0203] like Figure 3 As shown, a more specific trajectory planning method includes the following steps:

[0204] Step 1: Generate false tracks for deception and interference according to mission requirements

[0205] Step 2: Considering the energy consumption, survival probability and communication channel transmission loss of the UAV, the track evaluation function model Y is constructed;

[0206] Step 3: Select the ratio coefficient η as the particle of PSO. Under the premise that the false track is clear, the flight altitude, flight speed and spatial coordinates of the UAV can be solved according to the ratio coefficient, and the track evaluation function model Y can be calculated;

[0207] Step 4: Given the initial population X and initial velocity V of PSO, optimize the inertia weight and learning factor according to formula (36) and formula (37);

[0208] Step 5: Calculate the particle fitness Y according to formula (15) to formula (32) min , obtain the individual extreme value and group extreme value of the particle; then update the evolution speed and position of the particle according to formula (34) and formula (35), and perform boundary absorption processing to make the position of a single particle conform to the given range of the ratio of the UAV flight speed to the false target speed;

[0209] Step 6: Determine whether the maximum number of evolutions has been reached. If so, output the optimal fitness value and the corresponding particle position η i (i=1,2,…,D); if not, return to Step 3;

[0210] Step 7: The η output by the particle swarm algorithm i Substituting (i=1,2,…,D) into formula (13) can obtain the final UAV trajectory planning result.

[0211] S3: Simulation Verification

[0212] S3.1: Track feasibility verification

[0213] Set the false target track to:

[0214]

[0215] Wherein, T3 is the time for implementing track deception interference, ranging from 0s to 100s, with an interval of 1s as a track point. From formula (6) and formula (7), v can be calculated: p ≈300m / s;

[0216] The turning rate and climb angle of the track are calculated by formula (11) and formula (14) respectively, and the following is obtained: Figure 4 and Figure 5 Results shown;

[0217] Through calculation, the maximum turning rate of the track is 0.188375° / s and the maximum climbing angle is 1.90915°. When the performance of the drone used meets this result, as long as the flight speed is within the constraint range, the planned drone track is more in line with the dynamic constraint conditions;

[0218] S3.2: Simulation parameter settings

[0219] The various parameter settings are as follows:

[0220] Particle swarm algorithm parameter settings: population number N is 1000, evolution generation T is 100, particle velocity V range is [-0.1, 0.1], particle position X range is [0.05, 0.2], inertia weight ω range is [0.4, 0.9], learning factors c1, c2 range is [1, 2]; number of drones is 3;

[0221] It can be seen from formula (10) that when the speed of the UAV is limited within a certain range, the value range of η = r / R can be obtained, that is, the population critical value; assuming that the flight speed of the UAV is [15m / s, 60m / s], the particle swarm algorithm parameter settings are shown in Table 1;

[0222] Table 1

[0223]

[0224] The radar performance parameter settings are shown in Table 2;

[0225] Table 2

[0226]

[0227] Environmental parameter settings: air density ρ is 1.225kg / m3, Boltzmann constant k b 1.38×10 -23 J / K, thermodynamic temperature T o 290K;

[0228] S3.3: Simulation results

[0229] In order to verify the effectiveness of the evaluation function established by the present invention, trajectory planning is performed based on three evaluation functions respectively; the first evaluation function only considers the energy consumption index factor; the second evaluation function considers two index factors, energy consumption and battlefield survival probability; the third evaluation function is the evaluation function constructed by the present invention;

[0230] Scenario setting: The position coordinates of the three radars are known to be (0, 0, 0), (50, 10, 0), and (30, 50, 0), in km. Three jamming drones perform track deception jamming on them respectively. In order to minimize the hovering energy consumption of the drones, the coordinates of the drone release point are set to the average coordinates of the track points when the three jamming drones start to implement the jamming. Combined with the PSO algorithm, the track parameters planned based on the three evaluation functions are shown in Table 3.

[0231] Table 3

[0232]

[0233] The tracks of the three groups of jamming drones during the track deception jamming phase and the track positions of the false targets are shown in the figure below. Figure 6(a) to Figure 6(c) The actual values ​​of the three index factors, namely, the energy consumption of the UAV, the battlefield survival probability and the communication channel transmission loss, corresponding to each group of tracks based on the three evaluation functions are shown in Table 4. The changes in the probability of being detected by the radar and the communication channel transmission loss are shown in Table 4. Figure 7 and Figure 8 As shown; the top view of the UAV cluster track position relationship planned based on the three evaluation functions is shown in Fig. 9 As shown;

[0234] Table 4

[0235]

[0236] It can be seen from the above results that as the ratio coefficient η increases, the flight altitude and speed of the drone increase, and the distance between the drone and the radar becomes farther, resulting in a decrease in the distance between drones. Therefore, although the track planned by the first evaluation function consumes less energy, the probability of being discovered by the enemy radar and the communication channel transmission loss are high. After the survival probability index is added to the second evaluation function, the probability of being discovered by the enemy radar is reduced. On this basis, the present invention adds the communication channel transmission loss factor to establish a third evaluation function. Although the energy consumption index of the planned track is improved, the radar detection rate and transmission loss are both optimal, which provides a prerequisite and reliable guarantee for the implementation of track deception interference.

[0237] It should be understood that the above-mentioned system is used to execute the methods in the above-mentioned embodiments. The implementation principles and technical effects of the corresponding program modules in the system are similar to those described in the above-mentioned methods. The working process of the system can refer to the corresponding process in the above-mentioned method and will not be repeated here.

[0238] Example 2

[0239] The present invention provides a UAV track planning system under the background of track deception and interference, comprising:

[0240] The constraint condition establishment module is used to set the UAV flight speed range, UAV flight altitude, UAV maximum turning rate and UAV maximum climbing angle to establish constraint conditions;

[0241] The module for establishing the total energy consumption of the UAV movement is used to obtain the total energy consumption of the UAV movement by establishing the energy consumption from the drop point to the starting point of the track and the energy consumption expression according to the planned track flight;

[0242] The total channel transmission loss establishment module is used to construct the total channel transmission loss of the communication signals between the UAVs, assuming that the communication signals of the UAVs implementing the jammer propagate in free space;

[0243] Radar detection probability building module, used to build radar detection probability;

[0244] The evaluation function model building module is used to perform weighted processing on the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the radar detection probability to build an evaluation function model;

[0245] The UAV trajectory planning module is used to select the distance ratio coefficient as the particle of PSO, combine the constraint conditions, iteratively update the particle evolution speed and position, calculate the fitness value, individual particle extreme value and group extreme value after each iteration, until the maximum number of evolutions is reached, output the optimal fitness value and the corresponding particle position, and obtain the UAV trajectory planning result; wherein, the distance ratio coefficient is the ratio of the distance from the UAV to the radar to the distance from the false target to the radar; each particle represents an array constructed by a distance ratio coefficient corresponding to each UAV; the fitness value is the solution value of the evaluation function model;

[0246] Among them, the three points of false target, drone and radar are on the same line. The jammer carried by the drone transmits multiple false target signals that merge into one point in space to form a false target.

[0247] Further preferably, the energy consumption from the release point to the starting point of the track in the drone movement total energy consumption establishment module includes uniform motion energy consumption, hovering energy consumption and altitude change energy consumption; the flight energy consumption according to the planned track includes uniform motion energy consumption, altitude change energy consumption and turning energy consumption.

[0248] Further preferably, the total channel transmission loss of the communication signal between the UAVs in the total channel transmission loss establishment module is the sum of the free space propagation loss of the communication signal between all two UAVs; the free space propagation loss expression of the communication signal between two UAVs is:

[0249]

[0250] l 1,2 (t3)=32.44+20lg(f)+20lg(||q1(t3)-q2(t3)||2)

[0251] Wherein, the subscripts 1 and 2 represent the first UAV and the second UAV, f is the UAV communication signal frequency; q1 is the position coordinate of the first UAV; q2 is the position coordinate of the second UAV; ||·||2 means to find the 2-norm; l 1,2 (t3) is the communication signal transmission loss between the first UAV and the second UAV at time t3; L 1,2 It is the communication signal transmission loss between the first UAV and the second UAV from 0 to t3.

[0252] Further preferably, the method for constructing the evaluation function model by the evaluation function model construction module comprises the following steps:

[0253] Standardize the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the radar detection probability;

[0254] Set the weights of the mission completion capability and safety indicators. The mission completion capability indicator includes the total energy consumption indicator of the UAV movement and the total channel transmission loss indicator of the communication signal between UAVs; the safety indicator includes the radar detection probability indicator;

[0255] The evaluation function model is: Y = 0.2E norm +0.6L norm +0.2P dnorm ;

[0256] Among them, E norm is the total energy consumption of the UAV after standardization; P dnorm is the normalized radar detection probability; L norm is the normalized total channel transmission loss.

[0257] Further preferably, the method for updating the particle evolution speed and position in the UAV trajectory planning module is:

[0258] The value range of particle speed and position is set, and boundary absorption is performed during the evolution process. At the same time, nonlinear inertia weight and asynchronous linear change learning factor are introduced to update the particle speed and position.

[0259] Based on the method in the above embodiment, an embodiment of the present invention provides an electronic device. The device may include: at least one memory for storing programs and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the above embodiment.

[0260] Based on the method in the above embodiment, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0261] Based on the method in the above embodiment, an embodiment of the present invention provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0262] It is understandable that the processor in the embodiments of the present invention may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor may be a microprocessor or any conventional processor.

[0263] The method steps in the embodiments of the present invention can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0264] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0265] It should be understood that the various numerical numbers involved in the embodiments of the present invention are only used for the convenience of description and are not used to limit the scope of the embodiments of the present invention.

[0266] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for UAV trajectory planning under the background of trajectory deception and interference, characterized in that: The following steps are involved: Generate a false track of deception interference according to the task requirements, select the distance ratio coefficient as the particle of PSO, combine the constraint conditions, iteratively update the particle evolution speed and position, calculate the fitness value, individual particle extreme value and group extreme value after each iteration, until the maximum number of evolutions is reached, output the optimal fitness value and the corresponding particle position, and obtain the UAV track planning result; wherein, the distance ratio coefficient is the ratio of the distance from the UAV to the radar to the distance from the false target to the radar; each particle represents an array constructed by a distance ratio coefficient corresponding to each UAV; the fitness value is the solution value of the evaluation function model; Among them, the three points of false target, drone and radar are on the same line, and multiple jamming drones interfere with multiple radars at the same time. Each drone generates a false signal, and multiple false signals merge into one point in space to form a false target; Among them, the UAV flight speed range, UAV flight altitude, UAV maximum turning rate and UAV maximum climbing angle are set to establish constraint conditions; The method for constructing the evaluation function model includes the following steps: A. Obtain the total energy consumption of the UAV movement by establishing the energy consumption from the drop point to the starting point of the track and the energy consumption expression of the flight according to the planned track; and assuming that the communication signal of the UAV implementing the jammer propagates in free space, construct the total channel transmission loss of the communication signal between UAVs; and construct the probability of being detected by radar; B. Weighted processing is performed on the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the probability of being detected by radar to construct an evaluation function model.

2. The UAV trajectory planning method according to claim 1, characterized in that: The energy consumption from the release point to the starting point of the track includes the energy consumption of uniform motion, hovering and altitude change; the energy consumption of flying according to the planned track includes the energy consumption of uniform motion, altitude change and turning.

3. The UAV trajectory planning method according to claim 1, characterized in that: The total channel transmission loss of the communication signal between UAVs is the sum of the free space propagation losses of the communication signals between all two UAVs; The free space propagation loss expression of the communication signal between two UAVs is: <h2 style=";text-align:left;direction:ltr">l<h2 style=";text-align:left;direction:ltr"> 1,2 <h2 style=";text-align:left;direction:ltr"> (t3) = 32.44 + 20lg(f) + 20lg(||q1(t3) - q2(t3)||2) Wherein, the subscripts 1 and 2 represent the first UAV and the second UAV, f is the UAV communication signal frequency; q1 is the position coordinate of the first UAV; q2 is the position coordinate of the second UAV; ||·||2 means to find the 2-norm; l 1,2 (t3) is the communication signal transmission loss between the first UAV and the second UAV at time t3; L 1,2 It is the communication signal transmission loss between the first UAV and the second UAV from 0 to t3.

4. The UAV trajectory planning method according to any one of claims 1 to 3, characterized in that: Step B specifically includes the following steps: Standardize the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the radar detection probability; Setting the weights of the task completion capability and safety index; wherein the task completion capability index includes the total energy consumption index of the UAV movement and the total channel transmission loss index of the communication signal between UAVs; the safety index includes the radar detection probability index; The evaluation function model is: Y = 0.2E norm +0.6L norm +0.2P dnorm ; Among them, E norm is the total energy consumption of the UAV after standardization; P dnorm is the normalized radar detection probability; L norm is the normalized total channel transmission loss.

5. The UAV trajectory planning method according to claim 1, characterized in that: The method to update the particle evolution speed and position is: The value range of particle speed and position is set, and boundary absorption is performed during the evolution process. At the same time, nonlinear inertia weight and asynchronous linear change learning factor are introduced to update the particle speed and position.

6. A UAV track planning system under track deception interference background, characterized in that: include: The constraint condition establishment module is used to set the UAV flight speed range, UAV flight altitude, UAV maximum turning rate and UAV maximum climbing angle to establish constraint conditions; The module for establishing the total energy consumption of the UAV movement is used to obtain the total energy consumption of the UAV movement by establishing the energy consumption from the drop point to the starting point of the track and the energy consumption expression according to the planned track flight; The total channel transmission loss establishment module is used to construct the total channel transmission loss of the communication signals between the UAVs, assuming that the communication signals of the UAVs implementing the jammer propagate in free space; Radar detection probability building module, used to build radar detection probability; The evaluation function model building module is used to perform weighted processing on the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the radar detection probability to build an evaluation function model; The UAV track planning module is used to generate a deceptive and jamming false track according to the mission requirements, select the distance ratio coefficient as the particle of PSO, combine the constraint conditions, iteratively update the particle evolution speed and position, calculate the fitness value, individual particle extreme value and group extreme value after each iteration, until the maximum number of evolutions is reached, output the optimal fitness value and the corresponding particle position, and obtain the UAV track planning result; wherein, the distance ratio coefficient is the ratio of the distance from the UAV to the radar to the distance from the false target to the radar; each particle represents an array constructed by a distance ratio coefficient corresponding to each UAV; the fitness value is the solution value of the evaluation function model; Among them, the three points of false target, drone and radar are on the same line, and multiple jamming drones interfere with multiple radars at the same time. Each drone generates a false signal, and multiple false signals merge into one point in space to form a false target.

7. The UAV trajectory planning system according to claim 6, characterized in that: In the module for establishing the total energy consumption of UAV movement, the energy consumption from the release point to the starting point of the track includes uniform motion energy consumption, hovering energy consumption and altitude change energy consumption; the flight energy consumption according to the planned track includes uniform motion energy consumption, altitude change energy consumption and turning energy consumption.

8. The UAV trajectory planning system according to claim 6, characterized in that: The total channel transmission loss of the communication signal between UAVs in the total channel transmission loss establishment module is the sum of the free space propagation loss of the communication signal between all two UAVs; the free space propagation loss expression of the communication signal between two UAVs is: <h2 style=";text-align:left;direction:ltr">l<h2 style=";text-align:left;direction:ltr"> 1,2 <h2 style=";text-align:left;direction:ltr"> (t3) = 32.44 + 20lg(f) + 20lg(||q1(t3) - q2(t3)||2) Wherein, the subscripts 1 and 2 represent the first UAV and the second UAV, f is the UAV communication signal frequency; q1 is the position coordinate of the first UAV; q2 is the position coordinate of the second UAV; ||·||2 means to find the 2-norm; l 1,2 (t3) is the communication signal transmission loss between the first UAV and the second UAV at time t3; L 1,2 It is the communication signal transmission loss between the first UAV and the second UAV from 0 to t3.

9. The UAV trajectory planning system according to any one of claims 6 to 8, characterized in that: The method for constructing an evaluation function model by the evaluation function model construction module comprises the following steps: Standardize the total energy consumption of UAV movement, the total channel transmission loss of communication signals between UAVs, and the radar detection probability; Set the weights of the mission completion capability and safety indicators. The mission completion capability indicator includes the total energy consumption indicator of the UAV movement and the total channel transmission loss indicator of the communication signal between UAVs; the safety indicator includes the radar detection probability indicator; The evaluation function model is: Y = 0.2E norm +0.6L norm +0.2P dnorm ; Among them, E norm is the total energy consumption of the UAV after standardization; P dnorm is the normalized radar detection probability; L norm is the normalized total channel transmission loss.

10. The UAV trajectory planning system according to claim 9, characterized in that: The method for updating particle evolution speed and position in the UAV trajectory planning module is: The value range of particle speed and position is set, and boundary absorption is performed during the evolution process. At the same time, nonlinear inertia weight and asynchronous linear change learning factor are introduced to update the particle speed and position.

Images