Aircraft route planning method in complex scene, computer equipment and readable storage medium

Abstract

The invention provides an aircraft flight path planning method in a complex scene, computer equipment and a readable storage medium, and the method comprises the steps: constructing an aircraft flight path planning scene which comprises a first-class anti-nodeless scene, a second-class anti-nodeless scene, a main aircraft and an interference aircraft; the flight inclination angle cost, the anti-nodeless detection threat cost of the main aircraft, the first-class anti-nodeless threat cost and the second-class anti-nodeless threat cost are brought into calculation of the cost of the main aircraft, and a target function of the flight path of the main aircraft is formed; based on a grey wolf algorithm, calculating a flight path point of each main aircraft by using the target function of the flight paths of the main aircrafts; solving the flight path point of each interference aircraft according to the flight path point of each main aircraft; and obtaining a flight path planning result according to calculation results of the flight path points of the main aircraft and the flight path points of the interference aircrafts. According to the method, the flight path points of the main aircraft and the interference aircraft are solved respectively, the complexity of the algorithm is reduced, and the efficiency of the algorithm is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of trajectory planning, and in particular to a method, computer equipment and readable storage medium for aircraft trajectory planning in complex scenarios. Background Art

[0002] Aircraft trajectory planning technology has a wide range of applications in various fields. In the military, trajectory planning is used for drone reconnaissance and strike missions, ensuring that aircraft can complete their missions safely and efficiently. In the civilian sector, trajectory planning technology is used in tasks such as aerial photography, material transportation, and personnel search and rescue, effectively improving aircraft autonomy and mission efficiency.

[0003] Research on trajectory planning technology primarily focuses on traditional planning algorithms and intelligent optimization algorithms. Traditional algorithms, such as Dijkstra's algorithm and the A* algorithm, construct graph structures to find the optimal path. However, these algorithms suffer from low computational efficiency and difficulty ensuring path safety in complex environments. Intelligent optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms, and ant colony algorithms, are better able to handle path planning problems in complex environments and under multiple constraints.

[0004] The existing Chinese patent application with application number 202210151246.3 discloses a cluster collaborative interference planning method based on the optimal interference planning model. Its main content is: first, a cluster collaborative system including at least 2 jammers and 1 mission aircraft is constructed, and the planning model objective function is constructed according to the flight distance of the mission aircraft and the ratio of the power of the jammer to the target node to the distance between the jammer and the target node under the cluster system. Secondly, a multi-objective particle swarm algorithm is used to optimize the objective function of the interference planning model to generate an optimal solution set. Each set of parameters in the optimal solution set corresponds to its corresponding interference planning scheme, and a variety of interference planning schemes are obtained regarding the spatial position of each jammer, the power allocation of each jammer to the node, and the flight route of the mission aircraft. This invention improves the interference effect of multiple jammers and the utilization efficiency of interference resources. However, this method still has some shortcomings. When constructing the planning trajectory objective function, it only considers the mission flight distance and the ratio of the jammer's power to the target node to the jammer's distance from the target node, which will affect the trajectory safety. In addition, this method constructs the collaborative constraints between the mission aircraft and the jammer. It is not easy to solve the ideal track point under complex environmental conditions, and the number of iterations needs to be increased, resulting in low planning efficiency. Summary of the Invention

[0005] The present invention aims to solve at least one of the above-mentioned technical problems existing in the prior art.

[0006] To this end, a first aspect of the present invention provides a method for aircraft trajectory planning in complex scenarios.

[0007] A second aspect of the present invention provides a computer device.

[0008] A third aspect of the present invention provides a computer-readable storage medium.

[0009] The present invention provides a method for aircraft trajectory planning in complex scenarios, comprising:

[0010] Constructing an aircraft trajectory planning scenario, wherein the aircraft trajectory planning scenario includes a first type of anti-nodeless scenario, a second type of anti-nodeless scenario, K main aircraft, and M interfering aircraft;

[0011] The flight inclination cost, the detection threat cost of the anti-nodeless threat to the main aircraft, the first-class anti-nodeless threat cost and the second-class anti-nodeless threat cost are incorporated into the calculation of the main aircraft cost to form the objective function of the main aircraft trajectory;

[0012] Based on the Grey Wolf Algorithm, the track points of each main aircraft are calculated using the objective function of the main aircraft's track;

[0013] The track point of each jamming aircraft is solved according to the position of the anti-no node with the greatest threat level and the main lobe width of its antenna, the safe distance between each main aircraft and the jamming aircraft, and the track point of each main aircraft;

[0014] The trajectory planning result is obtained based on the calculation results of the main aircraft's track points and the interference aircraft's track points.

[0015] The method for aircraft trajectory planning in complex scenarios according to the above technical solution of the present invention may also have the following additional technical features:

[0016] In the above technical solution, the first type of anti-no-node is an anti-no-node that strikes a local area, and the horizontal cross-section of its threat area is fan-shaped or quasi-fan-shaped;

[0017] The second type of anti-no node is an anti-no node that strikes targets within a 360-degree range in the air, and the horizontal cross-section of its threat area is circular or quasi-circular.

[0018] In the above technical solution, the objective function of the main aircraft track is:

[0019]

[0020] Among them, F k The objective function representing the main aircraft trajectory; Represents the i-th category of cost included in the main aircraft cost calculation, including the flight angle cost Cost of detecting threats to the main aircraft from non-node-based aircraft A type of anti-nodeless threat cost and the second type of anti-nodeless threat cost k represents the main aircraft number; Represents the cost of category i The weight of .

[0021] In the above technical solution, the calculation method of the flight inclination cost includes:

[0022]

[0023] in, represents the kth main aircraft G k The angle between the straight line formed by the planned track point coordinates and its own position coordinates and the positive direction of the x-axis; represents the kth main aircraft G k The angle between the line formed by the current position coordinate and the end point position coordinate and the positive direction of the x-axis. || means taking the absolute value operation;

[0024] The calculation method of the detection threat cost of the anti-nodeless main aircraft includes:

[0025]

[0026] in, Represents the nth anti-nodeless R n Receive the kth master aircraft G k The echo power; N represents the total number of anti-no nodes;

[0027] One type of calculation method for anti-nodeless threat cost includes:

[0028]

[0029] in, represents the kth main aircraft G k In a class of anti-nodeless R n1 Threat cost value in the threat zone, d n1,Mmin Represents a class of anti-nodeless R n1 The closest distance to the threat zone; d n1,Mmax Represents a class of anti-nodeless R n1 The maximum distance to the threat zone; Represents a class of anti-nodeless R n1 With the kth master aircraft G k The distance between them; n1 represents the number of a class of anti-no nodes; N1 represents the total number of a class of anti-no nodes;

[0030] The calculation method for the second type of anti-nodeless threat cost includes:

[0031]

[0032] in, represents the kth main aircraft G k In the second type of anti-nodeless R n2 Threat cost value in the threat zone, d n2,Gmax is the second type of anti-nodeless R n2 Maximum distance to the threat zone; Represents the second type of anti-nodeless R n2 With the kth master aircraft G k The distance between them; n2 represents the number of the second type of anti-no nodes; N2 represents the total number of the second type of anti-no nodes.

[0033] In the above technical solution, the Grey Wolf Algorithm is used to calculate the track points of each master aircraft using the objective function of the master aircraft's track, including:

[0034] S31, in and P track points in different directions are selected between them, among which the position coordinates of the pth track point are The result calculated based on the objective function of the main aircraft track at the current track point position coordinate is defined as the fitness value of the track point. The three track points with the smallest fitness values among the P track points are selected and their position coordinates are X k,α 、X k,β and X k,δ , and their corresponding fitness values are F α 、F β and F δ ;

[0035] S32, determine the fitness value corresponding to the position coordinates of the p-th track point Is it less than F α , if so, update F α for Corresponding to X k,α Updated to If the corresponding track point position coordinates are found, execute step S34; otherwise, execute step S33;

[0036] S33, determine the fitness value corresponding to the position coordinates of the p-th track point Is it less than F β , if so, update F β for Corresponding to X k,β Updated to Corresponding track point position coordinates, execute step S34; otherwise, update F δ for Corresponding to X k,δ Updated to The corresponding track point position coordinates, execute step S34;

[0037] S34, according to the updated X k,α 、X k,β and X k,δ , update the position coordinates of each trajectory point corresponding to the next iteration:

[0038]

[0039] A=2a·r1-a

[0040]

[0041] C=2·r2

[0042] Among them, X k,p (u) represents the kth main aircraft G at the uth iteration k The position coordinates of the pth track point; X k,p (u+1) represents the kth main aircraft G at the u+1 iteration k The position coordinates of the pth track point; A represents the coefficient for adjusting the position of the track point coordinates; C represents the coefficient for adjusting the direction of the track point coordinates; r1 and r2 are random numbers between [0,1]; U represents the maximum number of iterations;

[0043] S35, determine whether the current number of iterations has reached the maximum number of iterations. If not, set u=u+1, and return to step S32 based on the position coordinates of each trajectory point in the next iteration determined in step S34; if so, set the current X k,α Track point coordinates As the kth master aircraft G k track points.

[0044] In the above technical solution, the method of solving the track point of each interfering aircraft based on the position of the anti-no node with the greatest threat level and the main lobe width of its antenna, the safe distance between each main aircraft and the interfering aircraft, and the track point of each main aircraft includes:

[0045] Calculate each anti-nodeless R n Threat assessment value TA n :

[0046] TA n =z1u(RF n )+z2u(PW n )+z3u(PRI n )+z4u(D n )

[0047] Among them, u(RF n ) represents the anti-nodeless R n The membership value of the carrier frequency of the transmitted signal; u(PWn ) represents the anti-nodeless R n The membership value of the pulse width of the transmitted signal; u(PRI n ) represents the anti-nodeless R n The membership value of the pulse repetition period of the transmitted signal; u(D n ) represents the anti-nodeless R n The membership value of the duty cycle of the transmitted signal; z1, z2, z3 and z4 are u(RF n )、u(PW n ),u(PRI n ) and u(D n )’s weight;

[0048] Select the node R with the largest threat assessment value among N anti-no nodes max , according to R max Location coordinates The main lobe width of the antenna, the safe distance between each main aircraft and the interference aircraft, and the track point of each main aircraft are used to calculate the J of each interference aircraft. m The coordinates of the track point at time t are calculated using the following methods:

[0049] Multiple jamming aircraft are deployed on the side of the nearest main aircraft. The position coordinates of the nearest main aircraft are The anti-node R with the largest threat assessment value max The coordinates of the track point distance for:

[0050]

[0051] and The angle between the straight line and the x-axis for:

[0052]

[0053] The mth jammer aircraft J m Track point coordinates at time t for:

[0054]

[0055]

[0056] Where M represents the total number of jamming aircraft; Indicates the main lobe width of the anti-nodeless antenna with the maximum threat assessment value.

[0057] In the above technical solution, the anti-nodeless R nThe membership value of the carrier frequency of the transmitted signal is:

[0058]

[0059] Among them, RF n Represents anti-nodeless R n The carrier frequency of the transmitted signal;

[0060] The anti-nodeless R n The membership value of the pulse width of the transmitted signal is:

[0061]

[0062] Where k is a constant; PW n Represents anti-nodeless R n Pulse width of the transmitted signal;

[0063] The anti-nodeless R n The membership value of the pulse repetition period of the transmitted signal is:

[0064]

[0065] Where k' is a constant; PRI n Represents anti-nodeless R n The pulse repetition period of the transmitted signal; PRI1 represents the maximum value of the pulse repetition period among N anti-no nodes; PRI2 represents the minimum value of the pulse repetition period among N anti-no nodes;

[0066] The anti-nodeless R n The membership value of the duty cycle of the transmitted signal is:

[0067]

[0068] Among them, D n Represents anti-nodeless R n The duty cycle of the transmitted signal.

[0069] In the above technical solution, the method for calculating the weight of each membership value in the process of calculating the threat assessment value includes:

[0070] Define a1, a2, a3 and a4 as the importance factors of carrier frequency, pulse width, pulse repetition period and duty cycle, and construct the weight judgment matrix A as:

[0071]

[0072] Among them, a ef Indicates threat indicator a e Relatively f The importance ratio of ef and a feThey are reciprocals of each other, e,f∈[1,2,3,4];

[0073] According to the weight judgment matrix, the formula for solving the threat factor weight is established as follows:

[0074] AZ=λ max Z

[0075] Among them, λ max is the maximum eigenvalue of the weight judgment matrix A, and Z is the eigenvector of the weight judgment matrix A;

[0076] Normalizing Z can obtain the corresponding weight coefficients z1, z2, z3 and z4.

[0077] The present invention also provides a computer device comprising a processor and a memory, wherein a computer program is stored in the memory. When the computer program is loaded and executed by the processor, an aircraft trajectory planning method in a complex scenario as described in any one of the above technical solutions is implemented.

[0078] The present invention further provides a computer-readable storage medium storing a program, which, when loaded by a processor, implements the method for aircraft trajectory planning in complex scenarios as described in any one of the above technical solutions.

[0079] In summary, due to the adoption of the above technical features, the beneficial effects of the present invention are:

[0080] The objective function of the main aircraft constructed by the present invention includes a type of anti-nodeless threat cost and a type of anti-nodeless threat cost, and based on the gray wolf algorithm, it can solve the track point position coordinates with a smaller objective function value, avoiding the influence of the existing technology on the track safety by only considering the mission flight distance and the ratio of the jammer's power to the target node and the distance between the jammer and the target node, thereby effectively improving the safety of the aircraft flight in the track.

[0081] The present invention avoids the defects of the prior art due to the collaborative constraints of the mission aircraft and the jammer by separately calculating the track point coordinates of each main aircraft and each jammer at the current moment, reduces the number of iterations, and can more easily solve the ideal track points under complex environmental conditions, effectively improving the efficiency of track planning.

[0082] Additional aspects and advantages of the invention will become apparent from the description which follows, or may be learned by practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0084] Figure 1 is a flow chart of a method for aircraft trajectory planning in a complex scenario according to an embodiment of the present invention;

[0085] Figure 2 Schematic diagram of a type of anti-nodeless threat zone in a method for aircraft trajectory planning in a complex scenario according to an embodiment of the present invention;

[0086] Figure 3 Schematic diagram of two types of anti-nodeless threat zones in a method for aircraft trajectory planning in a complex scenario according to an embodiment of the present invention;

[0087] Figure 4 This is a diagram showing the calculation principle of the flight inclination cost in a method for aircraft trajectory planning in a complex scenario according to an embodiment of the present invention. DETAILED DESCRIPTION

[0088] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0089] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0090] Refer to the following Figures 1 to 4 The following describes a method for aircraft trajectory planning in complex scenarios provided by some embodiments of the present invention.

[0091] Some embodiments of the present application provide a method for aircraft trajectory planning in complex scenarios.

[0092] like Figure 1 As shown, the first embodiment of the present invention proposes a method for aircraft trajectory planning in complex scenarios, including steps S1 to S5.

[0093] S1. Construct an aircraft trajectory planning scenario, wherein the aircraft trajectory planning scenario includes a first type of anti-nodeless scenario, a second type of anti-nodeless scenario, K main aircraft, and M interfering aircraft.

[0094] Among them, there are N anti-no nodes in the first category and N anti-no nodes in the second category. The number of anti-no nodes in the first category is N1, and the number of anti-no nodes in the second category is N2. Each main aircraft G k and each jammer J m The trajectory to be planned is composed of track points at T moments.

[0095] In some embodiments, it is required that N≥2, K≥1, M≥3, and T≥10. In the embodiment of the present disclosure, N=3, K=1, M=5, and T=11 are used as an example for description.

[0096] Specifically, the anti-no node type is an anti-no node that strikes a local area, and the horizontal cross-section of its threat area is fan-shaped or quasi-fan-shaped; Figure 2 As shown, KL is the far boundary of the killing zone, which is an arc with O as the center; MN is the near boundary of the killing zone, which is an arc with O as the center.

[0097] The second type of anti-no node is an anti-no node that strikes targets within a 360-degree range in the air, and the horizontal cross-section of its threat area is circular or quasi-circular. Specifically, Figure 3 As shown in the figure, the maximum attack area and effective attack area of the second type of anti-nodeless threat can be simplified into an ellipsoidal space with a circular horizontal section and an elliptical vertical section.

[0098] S2. Incorporate the flight inclination cost, the detection threat cost of the anti-nodeless threat to the main aircraft, the first-class anti-nodeless threat cost, and the second-class anti-nodeless threat cost into the calculation of the main aircraft cost to form the objective function of the main aircraft track.

[0099] In some embodiments, the objective function of the main aircraft trajectory is:

[0100]

[0101] Among them, F k The objective function representing the main aircraft trajectory; Represents the i-th category of cost included in the main aircraft cost calculation, including the flight angle cost Cost of detecting threats to the main aircraft from non-node-based aircraft A type of anti-nodeless threat cost and the second type of anti-nodeless threat cost k represents the main aircraft number; Represents the cost of category i The weight of .

[0102] In the above embodiment, by reasonably modeling the node detection, the first type of anti-nodeless threat and the second type of anti-nodeless threat costs, the rationality of the main aircraft's trajectory planning is improved and the safety of the trajectory planning is enhanced.

[0103] In a specific embodiment, Figure 4 As shown, the calculation method of the flight angle cost includes:

[0104]

[0105] in, represents the kth main aircraft G k The angle between the straight line formed by the planned track point coordinates and its own position coordinates and the positive direction of the x-axis; represents the kth main aircraft G k The angle between the line formed by the current position coordinates and the end point coordinates and the positive direction of the x-axis. || represents the absolute value operation.

[0106] The purpose of trajectory planning is to minimize the risk of being detected by enemy nodes. Therefore, the total power of the main aircraft echo signal received by the node is used as an evaluation indicator of the detection threat cost. The node echo power is:

[0107]

[0108] in, Represents the nth anti-nodeless R n Receive the kth master aircraft G k The echo power; P t,n , G t,n , G r,n ,λ n and L n R n The transmission power, transmission antenna gain, receiving antenna gain, wavelength and loss factor, σ k Indicates G k The scattering cross-sectional area, Represents R n With G k The distance between them.

[0109] Then the calculation method of the detection threat cost of the anti-node to the main aircraft includes:

[0110]

[0111] Where N represents the total number of anti-no nodes.

[0112] Continue reading Figure 2 ,One type of calculation method for anti-nodeless threat cost includes:

[0113]

[0114] in, represents the kth main aircraft G k In a class of anti-nodeless R n1 Threat cost value in the threat zone, d n1,Mmin Represents a class of anti-nodeless R n1 The closest distance to the threat zone; d n1,Mmax Represents a class of anti-nodeless R n1 The maximum distance to the threat zone; Represents a class of anti-nodeless R n1 With the kth master aircraft G k The distance between them; n1 represents the number of a class of anti-no nodes; N1 represents the total number of a class of anti-no nodes;

[0115] Continue reading Figure 3 ,The calculation method of the second type of anti-nodeless threat cost includes:

[0116]

[0117] in, represents the kth main aircraft G k In the second type of anti-nodeless R n2 Threat cost value in the threat zone, d n2,Gmax is the second type of anti-nodeless R n2 Maximum distance to the threat zone; Represents the second type of anti-nodeless R n2 With the kth master aircraft G k The distance between them; n2 represents the number of the second type of anti-no nodes; N2 represents the total number of the second type of anti-no nodes.

[0118] S3. Based on the Grey Wolf Algorithm, the track point of each main aircraft is calculated using the objective function of the main aircraft track.

[0119] In a specific embodiment, step S3 includes the following steps S31-S35.

[0120] S31, initialize the number of iterations to u, the initial value of u is 1, set the maximum number of iterations to U, U ≥ 100, and Select P track points in different directions, P ≥ 60, where the position coordinates of the pth track point are The result calculated based on the objective function of the main aircraft track at the current track point position coordinate is defined as the fitness value of the track point. The three track points with the smallest fitness values among the P track points are selected and their position coordinates are X k,α 、X k,β and X k,δ , and their corresponding fitness values are F α 、F β and F δ ;

[0121] S32, determine the fitness value corresponding to the position coordinates of the p-th track point Is it less than F α , if so, update F α for Corresponding to X k,α Updated to If the corresponding track point position coordinates are found, execute step S34; otherwise, execute step S33;

[0122] S33, determine the fitness value corresponding to the position coordinates of the p-th track point Is it less than F β , if so, update F β for Corresponding to X k,β Updated to Corresponding track point position coordinates, execute step S34; otherwise, update F δ for Corresponding to X k,δ Updated to The corresponding track point position coordinates, execute step S34;

[0123] S34, according to the updated X k,α 、X k,β and X k,δ , update the position coordinates of each trajectory point corresponding to the next iteration:

[0124]

[0125] A=2a·r1-a

[0126]

[0127] C=2·r2

[0128] Among them, X k,p (u) represents the kth main aircraft G at the uth iteration k The position coordinates of the pth track point; X k,p (u+1) represents the kth main aircraft G at the u+1 iteration k The position coordinates of the pth track point; A represents the coefficient for adjusting the track point coordinate position; C represents the coefficient for adjusting the track point coordinate position direction; r1 and r2 are random numbers between [0, 1]; U represents the maximum number of iterations; it is understood that the above unexplained parameters are all intermediate process quantities, and the present disclosure has provided a specific calculation method, which will not be repeated here;

[0129] S35, determine whether the current number of iterations has reached the maximum number of iterations. If not, set u=u+1, and return to step S32 based on the position coordinates of each trajectory point in the next iteration determined in step S34; if so, set the current X k,α Track point coordinates As the kth master aircraft G k track points.

[0130] Because the Gray Wolf Algorithm has fewer parameters and is easy to implement and adjust, the algorithm contains a convergence factor and feedback mechanism that can be adaptively adjusted, which can achieve a balance between local optimization and global search. Compared with other optimization algorithms, the Gray Wolf Algorithm's optimization process is faster because it first obtains the answer and then compares and ranks different answers.

[0131] S4. Solve the track point of each jamming aircraft based on the position of the anti-no node with the greatest threat level and the main lobe width of its antenna, the safe distance between each main aircraft and the jamming aircraft, and the track point of each main aircraft.

[0132] In some embodiments, step S4 includes the following steps S41-S42.

[0133] S41. Calculate each anti-nodeless R n Threat assessment value TA n :

[0134] TA n =z1u(RF n )+z2u(PW n )+z3u(PRI n )+z4u(D n )

[0135] Among them, u(RF n ) represents the anti-nodeless R n The membership value of the carrier frequency of the transmitted signal; u(PW n ) represents the anti-nodeless R n The membership value of the pulse width of the transmitted signal; u(PRI n ) represents the anti-nodeless R n The membership value of the pulse repetition period of the transmitted signal; u(D n ) represents the anti-nodeless R n The membership value of the duty cycle of the transmitted signal; z1, z2, z3 and z4 are u(RF n )、u(PW n ),u(PRI n ) and u(D n )’s weight;

[0136] S42, select the node R with the largest threat assessment value among N anti-no nodes max , according to R max Location coordinates The main lobe width of the antenna, the safe distance between each main aircraft and the interference aircraft, and the track point of each main aircraft are used to calculate the J of each interference aircraft. m The coordinates of the track point at time t are calculated using the following methods:

[0137] Multiple jamming aircraft are deployed on the side of the nearest main aircraft. The position coordinates of the nearest main aircraft are The anti-node R with the largest threat assessment value max The coordinates of the track point distance for:

[0138]

[0139] and The angle between the straight line and the x-axis for:

[0140]

[0141] The mth jammer aircraft J m Track point coordinates at time t for:

[0142]

[0143] Where M represents the total number of jamming aircraft; Indicates the main lobe width of the anti-nodeless antenna with the maximum threat assessment value.

[0144] In a specific embodiment, the calculation method of each membership degree in the above step S41 is as follows:

[0145] The anti-nodeless R n The membership value of the carrier frequency of the transmitted signal is:

[0146]

[0147] Among them, RF n Represents anti-nodeless R n The carrier frequency of the transmitted signal;

[0148] The anti-nodeless R n The membership value of the pulse width of the transmitted signal is:

[0149]

[0150] Wherein, k is a constant, and in this embodiment, k=0.0005; PW n Represents anti-nodeless R n Pulse width of the transmitted signal;

[0151] The anti-nodeless R n The membership value of the pulse repetition period of the transmitted signal is:

[0152]

[0153] Wherein, k' is a constant, and in this embodiment, k'=0.97; PRI n Represents anti-nodeless R n The pulse repetition period of the transmitted signal; PRI1 represents the maximum value of the pulse repetition period among N anti-no nodes; PRI2 represents the minimum value of the pulse repetition period among N anti-no nodes;

[0154] The anti-nodeless R n The membership value of the duty cycle of the transmitted signal is:

[0155]

[0156] Among them, D n Represents anti-nodeless R n The duty cycle of the transmitted signal.

[0157] In addition, the weight of each membership value can be determined by the following method:

[0158] Define a1, a2, a3 and a4 as the importance factors of carrier frequency, pulse width, pulse repetition period and duty cycle, and construct the weight judgment matrix A as:

[0159]

[0160] Among them, a ef Indicates threat indicator a e Relatively f The importance ratio of ef and a fe They are reciprocals of each other, e,f∈[1,2,3,4];

[0161] According to the weight judgment matrix, the formula for solving the threat factor weight is established as follows:

[0162] AZ=λ max Z

[0163] Among them, λ max is the maximum eigenvalue of the weight judgment matrix A, and Z is the eigenvector of the weight judgment matrix A;

[0164] Normalizing Z can obtain the corresponding weight coefficients z1, z2, z3 and z4.

[0165] S5. Obtain a track planning result based on the calculation results of the track points of the main aircraft and the interference aircraft.

[0166] Specifically, for each master aircraft G at T time k The track points and each interfering aircraft J m Connect the track points to get G k and Jm planned trajectory.

[0167] Other embodiments of the present invention provide a computer device comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is loaded and executed by the processor, the method for aircraft trajectory planning in complex scenarios as described in any of the above embodiments is implemented.

[0168] Still other embodiments of the present invention provide a computer-readable storage medium storing a program, which, when loaded by a processor, implements the method for aircraft trajectory planning in complex scenarios as described in any of the above embodiments.

[0169] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples.

[0170] Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for aircraft trajectory planning in complex scenarios, characterized by: include: Constructing an aircraft trajectory planning scenario, wherein the aircraft trajectory planning scenario includes a first type of anti-nodeless scenario, a second type of anti-nodeless scenario, K main aircraft, and M interfering aircraft; The flight inclination cost, the detection threat cost of the anti-nodeless threat to the main aircraft, the first-class anti-nodeless threat cost and the second-class anti-nodeless threat cost are incorporated into the calculation of the main aircraft cost to form the objective function of the main aircraft trajectory; Based on the Grey Wolf Algorithm, the track points of each main aircraft are calculated using the objective function of the main aircraft's track; The track point of each jamming aircraft is solved according to the position of the anti-no node with the greatest threat level and the main lobe width of its antenna, the safe distance between each main aircraft and the jamming aircraft, and the track point of each main aircraft; The trajectory planning result is obtained based on the calculation results of the main aircraft's track points and the interference aircraft's track points.

2. The method for aircraft trajectory planning in complex scenarios according to claim 1, characterized in that: The first type of anti-no node is an anti-no node that attacks a local area, and the horizontal cross-section of the threat area is fan-shaped or quasi-fan-shaped; The second type of anti-no node is an anti-no node that strikes targets within a 360-degree range in the air, and the horizontal cross-section of its threat area is circular or quasi-circular.

3. The method for aircraft trajectory planning in complex scenarios according to claim 2, characterized in that: The objective function of the main aircraft trajectory is: Among them, F k The objective function representing the main aircraft trajectory; Represents the i-th category of cost included in the main aircraft cost calculation, including the flight angle cost Cost of detecting threats to the main aircraft from non-node-based aircraft A type of anti-nodeless threat cost and the second type of anti-nodeless threat cost k represents the main aircraft number; Represents the cost of category i The weight of .

4. The method for aircraft trajectory planning in complex scenarios according to claim 3, characterized in that: The calculation method of the flight angle cost includes: in, represents the kth main aircraft G k The angle between the straight line formed by the planned track point coordinates and its own position coordinates and the positive direction of the x-axis; represents the kth main aircraft G k The angle between the line formed by the current position coordinate and the end point position coordinate and the positive direction of the x-axis. || means taking the absolute value operation; The calculation method of the detection threat cost of the anti-nodeless main aircraft includes: in, Represents the nth anti-nodeless R n Receive the kth master aircraft G k The echo power; N represents the total number of anti-no nodes; One type of calculation method for anti-nodeless threat cost includes: in, represents the kth main aircraft G k In a class of anti-nodeless R n1 Threat cost value in the threat zone, d n1,Mmin Represents a class of anti-nodeless R n1 The closest distance to the threat zone; d n1,Mmax Represents a class of anti-nodeless R n1 The maximum distance to the threat zone; Represents a class of anti-nodeless R n1 With the kth master aircraft G k The distance between them; n1 represents the number of a class of anti-no nodes; N1 represents the total number of a class of anti-no nodes; The calculation method for the second type of anti-nodeless threat cost includes: in, represents the kth main aircraft G k In the second type of anti-nodeless R n2 Threat cost value in the threat zone, d n2,Gmax is the second type of anti-nodeless R n2 Maximum distance to the threat zone; Represents the second type of anti-nodeless R n2 With the kth master aircraft G k The distance between them; n2 represents the number of the second type of anti-no nodes; N2 represents the total number of the second type of anti-no nodes.

5. The method for aircraft trajectory planning in complex scenarios according to claim 4, characterized in that: The Grey Wolf Algorithm is based on which the objective function of the master aircraft's track is used to calculate the track points of each master aircraft, including: S31, in and P track points in different directions are selected between them, among which the position coordinates of the pth track point are The result calculated based on the objective function of the main aircraft track at the current track point position coordinate is defined as the fitness value of the track point. The three track points with the smallest fitness values among the P track points are selected and their position coordinates are X k,α 、X k,β and X k,δ , and their corresponding fitness values are F α 、F β and F δ ; S32, determine the fitness value corresponding to the position coordinates of the p-th track point Is it less than F α , if so, update F α for Corresponding to X k,α Updated to If the corresponding track point position coordinates are found, execute step S34; otherwise, execute step S33; S33, determine the fitness value corresponding to the position coordinates of the p-th track point Is it less than F β , if so, update F β for Corresponding to X k,β Updated to Corresponding track point position coordinates, execute step S34; otherwise, update F δ for Corresponding to X k,δ Updated to The corresponding track point position coordinates, execute step S34; S34, according to the updated X k,α 、X k,β and X k,δ , update the position coordinates of each trajectory point corresponding to the next iteration: A=2a·r1-a C＝2·r2 Among them, X k,p (u) represents the kth main aircraft G at the uth iteration k The position coordinates of the pth track point; X k,p (u+1) represents the kth main aircraft G at the u+1 iteration k The position coordinates of the pth track point; A represents the coefficient for adjusting the position of the track point coordinates; C represents the coefficient for adjusting the direction of the track point coordinates; r1 and r2 are random numbers between [0,1]; U represents the maximum number of iterations; S35, determine whether the current number of iterations has reached the maximum number of iterations. If not, set u=u+1 and return to step S32 based on the position coordinates of each trajectory point in the next iteration determined in step S34; if so, set the current X k,α Track point coordinates As the kth master aircraft G k track points.

6. The method for aircraft trajectory planning in complex scenarios according to claim 1, characterized in that: The method of solving the track point of each interfering aircraft according to the position of the anti-no node with the greatest threat level and the main lobe width of its antenna, the safe distance between each main aircraft and the interfering aircraft, and the track point of each main aircraft includes: Calculate each anti-nodeless R n Threat assessment value TA n : TA n =z1u(RF n )+z2u(PW n )+z3u(PRI n )+z4u(D n ) Among them, u(RF n ) represents the anti-nodeless R n The membership value of the carrier frequency of the transmitted signal; u(PW n ) represents the anti-nodeless R n The membership value of the pulse width of the transmitted signal; u(PRI n ) represents the anti-nodeless R n The membership value of the pulse repetition period of the transmitted signal; u(D n ) represents the anti-nodeless R n The membership value of the duty cycle of the transmitted signal; z1, z2, z3 and z4 are u(RF n )、u(PW n ),u(PRI n ) and u(D n )’s weight; Select the node R with the largest threat assessment value among N anti-no nodes max , according to R max Location coordinates The main lobe width of the antenna, the safe distance between each main aircraft and the interference aircraft, and the track point of each main aircraft are used to calculate the J of each interference aircraft. m The coordinates of the track point at time t are calculated using the following methods: Multiple jamming aircraft are deployed on the side of the nearest main aircraft. The position coordinates of the nearest main aircraft are The anti-node R with the largest threat assessment value max The coordinates of the track point distance for: and The angle between the straight line and the x-axis for: The mth jammer aircraft J m Track point coordinates at time t for: Where M represents the total number of jamming aircraft; Indicates the main lobe width of the anti-nodeless antenna with the maximum threat assessment value.

7. The method for aircraft trajectory planning in complex scenarios according to claim 6, characterized in that: The anti-nodeless R n The membership value of the carrier frequency of the transmitted signal is: Among them, RF n Represents anti-nodeless R n The carrier frequency of the transmitted signal; The anti-nodeless R n The membership value of the pulse width of the transmitted signal is: Where k is a constant; PW n Represents anti-nodeless R n Pulse width of the transmitted signal; The anti-nodeless R n The membership value of the pulse repetition period of the transmitted signal is: Where k' is a constant; PRI n Represents anti-nodeless R n The pulse repetition period of the transmitted signal; PRI1 represents the maximum value of the pulse repetition period among N anti-no nodes; PRI2 represents the minimum value of the pulse repetition period among N anti-no nodes; The anti-nodeless R n The membership value of the duty cycle of the transmitted signal is: Among them, D n Represents anti-nodeless R n Duty cycle of the transmitted signal.

8. The method for aircraft trajectory planning in complex scenarios according to claim 7, characterized in that: The calculation method for the weight of each membership value in the threat assessment value calculation process includes: Define a1, a2, a3 and a4 as the importance factors of carrier frequency, pulse width, pulse repetition period and duty cycle, and construct the weight judgment matrix A as: Among them, a ef Indicates threat indicator a e Relatively f The importance ratio of ef and a fe They are reciprocals of each other, e,f∈[1,2,3,4]; According to the weight judgment matrix, the formula for solving the threat factor weight is established as follows: AZ=λ max Z Among them, λ max is the maximum eigenvalue of the weight judgment matrix A, and Z is the eigenvector of the weight judgment matrix A; Normalizing Z can obtain the corresponding weight coefficients z1, z2, z3 and z4.

9. A computer device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded and executed by the processor, the method for aircraft trajectory planning in complex scenarios as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that A program is stored, and when the program is loaded by a processor, the method for aircraft trajectory planning in complex scenarios according to any one of claims 1 to 8 is implemented.

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