Multi-point source target simulation method and device based on whale algorithm

Abstract

The invention discloses a multi-point source target simulation method and device based on a whale optimization algorithm in the technical field of radar navigation and intelligent optimization algorithms. The method and device are used for solving the problem of safe landing of monopulse radar under the influence of phase errors. The multi-point source target simulation method comprises the following steps: constructing a multi-point source target simulation device based on a golden whale algorithm; adopting Logistic chaotic mapping to generate an initial amplitude-phase parameter population; evaluating the parameter combination performance through the objective function; dynamically adjusting a convergence factor and a search step size; carrying out neighborhood spiral search in the optimal solution; guiding the population to gather towards an optimal solution; performing random exploration by adopting a golden sine strategy; and outputting the optimal amplitude-phase parameter. The device correspondingly realizes the modules. According to the multi-point source target simulation method and device, through a three-stage optimization strategy, a landing position is ensured to be in a safe range under a phase error, and the landing reliability in a complex environment is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar navigation and intelligent optimization algorithms, and in particular to a method and device for simulating targets with multiple point sources based on a golden whale algorithm. Background Art

[0002] Due to its high precision and reliability, monopulse radar is widely used in key scenarios such as search and rescue missions, weather monitoring, and drone navigation. For example, in natural disaster relief, drones equipped with monopulse radar can efficiently detect the location of trapped people, greatly improving search and rescue efficiency. However, due to the complex environment in disaster areas (such as mountains and ruins), drones often face the challenge of landing safely after completing their mission. Traditional landing methods rely on GPS or visual navigation, but their reliability can be significantly reduced under strong electromagnetic interference or severe weather conditions, resulting in equipment damage or mission failure. Currently, existing research has not yet provided an effective solution to the problem of safe landing of monopulse radar.

[0003] The direction-finding mechanism of monopulse radar makes it extremely sensitive to signals from multiple sources, and this characteristic can be used to accurately guide its landing. However, the existing technology has the following drawbacks:

[0004] 1. In real-world environments, factors such as hardware deviations, signal propagation delays, and environmental noise can introduce phase errors, leading to deviations in the directionality of radar received signals and, in turn, affecting landing accuracy.

[0005] 2. Multi-point source collaborative control lacks an anti-interference mechanism, and hardware deviations and signal propagation delays cause existing optimization algorithms to converge to local optimality.

[0006] Therefore, in the multi-point source cooperative control device, how to make the monopulse radar stably track the target signal under the influence of phase error has become a key problem that needs to be solved urgently. Summary of the Invention

[0007] In order to solve the above problems, the purpose of the present invention is to provide a multi-point source target simulation method and device based on the Golden Whale algorithm to solve the defects existing in the prior art. Through a three-stage iterative search, the optimal amplitude and phase parameters can be solved so that the corresponding landing position of the single-pulse radar is still within a safe range under the influence of the current error.

[0008] In order to achieve the above object, the technical solution of the present invention is as follows:

[0009] A method for simulating a target using multiple point sources based on a golden whale algorithm comprises the following steps:

[0010] S1. Construct a multi-point source simulation target device based on the Golden Whale algorithm, including an initialization module, a fitness evaluation module, a parameter dynamic adjustment module, a spiral local optimization module, a global encirclement module, a random exploration module, and an optimization output module;

[0011] S2. The initialization module of the multi-point source simulation target device based on the golden whale algorithm generates an initial population of amplitude and phase parameter combination selection schemes;

[0012] S3. The fitness evaluation module of the multi-point source simulation target device based on the Golden Whale algorithm evaluates the performance of each amplitude and phase parameter combination selection scheme;

[0013] S4. The parameter dynamic adjustment module of the multi-point source simulation target device based on the Golden Whale algorithm adaptively adjusts the key parameters in the algorithm to achieve a dynamic balance between exploration and development;

[0014] S5. The spiral local optimization module of the multi-point source simulation target device based on the Golden Whale algorithm performs a spiral search in the neighborhood of the current optimal solution;

[0015] S6. The global encirclement module of the multi-point source simulation target device based on the Golden Whale algorithm, guided by the current optimal solution, makes other solutions gradually approach the optimal solution;

[0016] S7. The random search module for multi-point source simulation target devices based on the Golden Whale algorithm can conduct extensive exploration in the solution space by adaptively selecting search directions and positions;

[0017] S8. The optimization output module of the multi-point source simulation target device based on the golden whale algorithm extracts the optimal amplitude and phase parameter combination from the final population and outputs the optimization result.

[0018] Furthermore, the S2 includes the following steps:

[0019] S21. Taking the amplitude and phase parameters E' and φ' of the multi-point source device as input, and determining the upper bound of the amplitude E' of the multi-point source device as 1 and the lower bound as 0 according to the actual situation, and the upper bound of the phase φ' of the multi-point source device as π and the lower bound as 0;

[0020] Among them, E' is the vector composed of the amplitude of antennas A, B, and C, E'=[E a +ΔE a ,E b +ΔE b ,E c +ΔE c ], φ' is the vector composed of the phases of antennas A, B, and C, φ'=[φ a +Δφ a ,φ b +Δφ b ,φc +Δφ c ], ΔE i and Δφ i are amplitude error and phase error, i = a, b, c;

[0021] S22. Randomly generate the initial population P0 based on the upper and lower bounds

[0022] P0={x1,x2,...x i ,...x N},i=1,...,N

[0023] x i =(x max -x min )*rand(·)+x min

[0024] Among them, x i is the individual in the population, N is the population size, x max and x min are the upper and lower bounds of the population, respectively. In the present invention, they are the upper and lower bounds of the amplitude and phase parameters E', φ' of the multi-point source device, and rand(·) is the (0,1) uniform distribution function.

[0025] Furthermore, the S2 includes the following steps:

[0026] S31. Construct a mathematical model for the multi-point source device array:

[0027] The coordinate system of the multi-point source device is established. The position formula of the simulated target of the multi-point source device is as follows:

[0028]

[0029] Among them, E a 、E b 、E c are the amplitudes of antennas A, B, and C, respectively, ab and δ ac are the phase differences between the interference signals emitted by antennas B and C of the multi-point source device and the interference signal emitted by antenna A, γ is the free space impedance, and θ max It represents the maximum pitch angle of the array element of the multi-point source device. represents the maximum azimuth;

[0030] The formula for converting rectangular coordinates into spherical coordinates is:

[0031]

[0032] S32. The simulated target position of the multi-point source device is obtained based on a theoretical formula. However, in actual situations, phase errors can cause deviations between the simulated target position of the multi-point source device and the theoretical position. The corresponding mathematical model formula in actual situations is:

[0033]

[0034]

[0035]

[0036] Among them, E' is the vector composed of antenna amplitude and target echo amplitude, E'=[E a +ΔE a ,E b +ΔE b ,E c +ΔE c ], φ' is the vector composed of antenna phase and target echo phase, φ'=[φ a +Δφ a ,φ b +Δφ b ,φ c +Δφ c ], ΔE i and Δφ i are the amplitude error and phase error, i = a, b, c; F(E', φ') means that E' and φ' are substituted into the position formula of the simulated target of the multi-point source device and the spherical coordinate position formula to calculate the simulated target of the multi-point source device and the center of the multi-point source device; R min and R max are the closest and farthest distances between the constraint range and the simulated target of the multi-point source device, R min =2d max , R max =6d max , d max is the maximum baseline length of the multi-point source device, m is the number of amplitude and phase combinations within the device error range, n is the total number of amplitude and phase combinations, E a 、E b 、E c are the amplitudes of antennas A, B, and C, respectively, a 、φ b 、φ c are the phases of antennas A, B, and C, ΔE min and ΔE max are the upper and lower bounds of the amplitude error, Δφ min and Δφ max are the upper and lower bounds of the phase error;

[0037] S33. Determine fitness: Use the corresponding mathematical model formula in the actual situation as the objective function of the algorithm, calculate the fitness of each multi-point source device baseline length selection scheme, and evaluate the performance according to size.

[0038] Furthermore, the S4 is specifically as follows:

[0039] A=2a·rand-a

[0040] C=2·rand

[0041] a=(2-2t / T max )

[0042] Among them, A and C are distance adjustment parameters, rand represents a random number uniformly distributed in [0, 1], and the convergence factor a decreases linearly from 2 to 0 as the number of iterations increases. max Indicates the maximum number of iterations.

[0043] Furthermore, the S5 is specifically as follows:

[0044] In the whale optimization algorithm, whales attack their prey by moving in an upward spiral and continuously shrinking the encirclement. Its mathematical model is expressed as follows:

[0045] x i (t+1)=x p (t)+D 1,i ×e bδ ×cos(2πl)

[0046] in, represents the position of the i-th iteration, D 1,i =|x p (t)-x i (t)| represents the distance between the whale and the current global optimal individual; b is generally set to 1; δ represents a random number in the range [-1, 1]; is the current optimal position.

[0047] Furthermore, the S6 is specifically as follows:

[0048] In the whale optimization algorithm, whales exchange information with each other and randomly walk to search for the location of prey. Each whale represents an individual, and the position of each individual in the search space represents a solution. Assuming that the current optimal position is the target prey, let All other individuals in the group move to the optimal position and update their positions using the following formula:

[0049] x i (t+1)=x p (t)-A×D 2,i

[0050] D 2,i =|C×x p (t)-x i (t)|

[0051] Furthermore, the S7 is specifically as follows:

[0052] When |A|≥1, it means that the whales are swimming outside the shrinking circle and performing random searches based on their positions, using the golden sine strategy. The mathematical model is as follows:

[0053] x i (t+1)=x i (t)·|sin(R1)|+R2·sin(R1)·|λ1·x p (t)-λ2·x mean (t)|

[0054] Among them, x p (t) is the optimal position at the tth iteration, x mean (t) is the average position at the t-th iteration, R1 is a random number in [0, 2π], which determines the individual's moving distance; R2 is a random number in [0, π], which determines the individual's moving direction, λ1 = -π + (1-τ) × 2π, λ2 = -π + τ × 2π is based on the golden ratio The coefficient obtained.

[0055] A multi-point source target simulation device based on the golden whale algorithm, comprising:

[0056] Initialization module, which uses logistic chaos mapping method to generate initial amplitude and phase parameter combinations; and

[0057] The fitness evaluation module is used to evaluate the performance of each amplitude and phase parameter combination selection scheme, and calculate the fitness value of each solution according to the preset objective function to quantify its quality; and

[0058] The parameter dynamic adjustment module uses an adaptive adjustment strategy based on the number of iterations to control the convergence factor and search step size through a nonlinear decreasing function, maintaining a large search range in the early stages of optimization and gradually narrowing the optimization neighborhood in the later stages; and

[0059] The Spiral Local Optimization module simulates the spiraling hunting behavior of whales and enables efficient and fine-grained search in parameter space; and

[0060] The global encirclement module simulates the behavior of a group of whales encircling their prey and is used in the global search phase. Under the guidance of the current optimal solution, other solutions gradually converge to the optimal solution, thus achieving a concentrated search for potential optimal areas; and

[0061] Random search module, which introduces randomness to prevent the algorithm from falling into local optimality. By randomly selecting search directions and positions, it can conduct extensive exploration in the solution space, thereby increasing the possibility of finding the global optimal solution; and

[0062] The optimization output module is responsible for extracting the optimal antenna array solution from the final population and outputting the relevant optimization results.

[0063] By adopting the above technical solution, the present invention has the following advantages:

[0064] The present invention provides a multi-point source target simulation method and device based on the Golden Whale algorithm, which solves the problem of safe landing of monopulse radar under the influence of phase error. Through a three-stage iterative search, it is possible to solve the amplitude and phase parameters that ensure that the corresponding landing position of the monopulse radar remains within a safe range under the influence of the current error. The Golden Whale optimization algorithm is used to solve the optimal amplitude parameters for anti-interference. It is suitable for the safe landing control of monopulse radar in a strong interference environment, ensures the safety range of the landing position under the phase error, and significantly improves the landing reliability in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a logical structure diagram of the multi-point source simulation target device based on the Golden Whale algorithm of the present invention;

[0066] Figure 2 Flowchart of the multi-point source target simulation method based on the Golden Whale algorithm of the present invention;

[0067] Figure 3 Schematic diagram of the coordinates of the multi-point source device in the present invention;

[0068] Figure 4 A complete plane distribution diagram of the target simulation for the multi-point source device of the present invention;

[0069] Figure 5 A mathematical model for simulating a target of a multi-point source device in a practical situation of the present invention;

[0070] Figure 6 It is the solution iteration curve of the Golden Whale optimization algorithm in the present invention;

[0071] Figure 7 Specific results of distances for simulating false targets for multi-point source devices. DETAILED DESCRIPTION

[0072] The technical solution of the present invention is described in detail below with reference to the accompanying drawings. It should be noted that, in this document, relational terms such as first and second, etc., are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include," "comprise," or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or apparatus that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0073] A multi-point source simulation target device based on the golden whale algorithm is as follows: Figure 1 As shown in Figure 1, the algorithm comprises an initialization module, a fitness evaluation module, a dynamic parameter adjustment module, a spiral local optimization module, a global encirclement module, a random exploration module, and an optimization output module. The initialization module is responsible for generating the initial amplitude and phase parameter combinations of the multi-point source device. Using the logistic chaos map initialization method, this module provides a set of feasible solutions as the optimization starting point, ensuring that the initial solutions are evenly distributed in the search space and fully exploring the diversity of the solution space. The fitness evaluation module quantitatively evaluates the performance of each amplitude and phase parameter combination based on a preset objective function, providing a core decision-making basis for subsequent optimization. The calculated fitness value directly influences the algorithm's search direction, ensuring that the optimization process consistently advances toward a more optimal solution. The dynamic parameter adjustment module adaptively adjusts key parameters (such as the convergence factor and search step size) to balance global exploration with local exploration capabilities. Initially, it focuses on a broad search, while later, it gradually narrows the optimization scope to improve solution accuracy and convergence speed. The spiral local optimization module employs a spiral search strategy during the local exploration phase, conducting refined exploration around the current optimal solution, enhancing the algorithm's local optimization capabilities and ensuring high-precision convergence. During the global search phase, the global encirclement module guides the population toward the optimal solution, improving the algorithm's convergence efficiency while avoiding the degradation of optimization efficiency caused by excessive random search. The random search module introduces controllable random perturbations, randomly selecting search directions and positions to expand the scope of the solution space and increase the likelihood of discovering the global optimal solution. After the algorithm iterations are complete, the optimization output module extracts the optimal amplitude and phase parameter combinations from the final population and outputs the relevant optimization results.

[0074] A multi-point source simulation target method based on the golden whale algorithm includes the following steps: Figure 2 As shown:

[0075] S1. Construct a multi-point source simulation target device based on the Golden Whale algorithm, including an initialization module, a fitness evaluation module, a parameter dynamic adjustment module, a spiral local optimization module, a global encirclement module, a random exploration module, and an optimization output module;

[0076] In this embodiment, the initialization module uses a logistic chaos mapping method to generate initial amplitude and phase parameter combinations. This method leverages chaotic properties to ensure a uniform distribution of initial solutions within the search space, providing diverse starting points for global optimization. The fitness evaluation module evaluates the performance of each amplitude and phase parameter combination. Based on a preset objective function, this module calculates the fitness value of each solution, thereby quantifying its performance. The dynamic parameter adjustment module uses an adaptive adjustment strategy based on the number of iterations, controlling the convergence factor and search step size through a nonlinear decreasing function. This maintains a large search range in the early stages of optimization and gradually narrows the optimization neighborhood in the later stages. The spiral local optimization module simulates the spiraling hunting behavior of whales, enabling efficient and detailed search in parameter space. The global encirclement module simulates the behavior of a group of whales encircling their prey and is used in the global search phase. Guided by the current optimal solution, other solutions gradually converge towards it, thereby achieving a focused search of potential optimal regions. The random search module introduces randomness to prevent the algorithm from falling into local optima. By randomly selecting search directions and locations, the algorithm enables extensive exploration of the solution space, increasing the likelihood of finding the global optimal solution. After the algorithm iteration is completed, the optimization output module is responsible for extracting the optimal antenna array solution from the final population and outputting the relevant optimization results.

[0077] S2. The initialization module of the multi-point source simulation target device based on the golden whale algorithm generates an initial population of amplitude and phase parameter combination selection schemes;

[0078] S2 includes the following steps:

[0079] S21. Take the amplitude and phase parameters E', φ' of the multi-point source device as input (E' is the vector composed of the amplitudes of antennas A, B, and C, E' = [E a +ΔE a ,E b +ΔE b ,E c +ΔE c ]; φ' is the vector composed of the phases of antennas A, B, and C, φ'=[φ a +Δφ a ,φ b +Δφ b ,φ c +Δφ c ]; ΔE i and Δφ i They are amplitude error and phase error, i=a,b,c;), and according to actual conditions, the upper limit of the amplitude E' of the multi-point source device is determined to be 1, and the lower limit is 0; the upper limit of the phase φ' of the multi-point source device is determined to be π, and the lower limit is 0.

[0080] S22. Then randomly generate the initial population P0 based on the upper and lower bounds.

[0081]

[0082] Among them, x i is the individual in the population, N is the population size, x max and x min are the upper and lower bounds of the population, respectively. In the present invention, they are the upper and lower bounds of the amplitude and phase parameters E', φ' of the multi-point source device, and rand(·) is the (0,1) uniform distribution function.

[0083] S3. The fitness evaluation module of the multi-point source simulation target device based on the Golden Whale algorithm evaluates the performance of each amplitude and phase parameter combination selection scheme;

[0084] Among them, S3 includes the following specific steps:

[0085] S31. Construct a mathematical model for the multi-point source device array:

[0086] First, establish the coordinate system of the multi-point source device. The specific coordinate system of the multi-point source device is as follows: Figure 3 As shown, the position of the simulated target of the multi-point source device is shown in formula (2):

[0087]

[0088] Among them, E a 、E b 、E c are the amplitudes of antennas A, B, and C, respectively, ab and δ ac are the phase differences between the interference signals emitted by antennas B and C of the multi-point source device and the interference signal emitted by antenna A, γ is the free space impedance, and θ max It represents the maximum pitch angle of the array element of the multi-point source device. Represents the maximum azimuth.

[0089] The formula for converting rectangular coordinates into spherical coordinates is:

[0090]

[0091] According to equations (2) and (3), by controlling the values ​​of the six variables of the amplitude and phase of the multi-point source device, the complete plane distribution diagram of the simulated target of the multi-point source device under different array conditions can be simulated as follows: Figure 4 As shown. The amplitude of the multi-point source device is set to be free within the range of 0 to 1 with a step size of 0.1 (E a =0:0.1:1, E b =0:0.1:1, E c=0:0.1:1), the phase is free to take values ​​within 0~π with a step size of 0.1π(δ a =0:0.1π:π,δ b =0:0.1π:π,δ c =0:0.1π:π). Set the regular triangle array Isosceles triangle array θ max =d, in the isosceles right triangle array θ max =d.

[0092] S32. The simulated target position of the multi-point source device is obtained based on a theoretical formula. However, in actual situations, phase errors can cause deviations between the simulated target position of the multi-point source device and the theoretical position. Therefore, it is necessary to consider a certain phase error and find amplitude and phase parameters so that the amplitude and phase parameters corresponding to the simulated target position of the multi-point source device under the influence of the current error are still within a safe range. Figure 5 As shown in the figure, the corresponding mathematical model formula in actual situation is:

[0093]

[0094] Among them, E' is the vector composed of antenna amplitude and target echo amplitude, E'=[E a +ΔE a ,E b +ΔE b ,E c +ΔE c ]; φ' is the vector composed of antenna phase and target echo phase, φ'=[φ a +Δφ a ,φ b +Δφ b ,φ c +Δφ c ]; ΔE i and Δφ i are the amplitude error and phase error, i = a, b, c; F(E', φ') means that E' and φ' are substituted into formulas (2) and (3) to calculate the multi-point source device simulation target and the multi-point source device center; R min and R max are the closest distance and the farthest distance between the constraint range and the target simulated by the multi-point source device, respectively. In the present invention, R min =2d max , R max =6d max , d max is the maximum baseline length of the multi-point source device; m is the number of amplitude and phase combinations within the device error range; n is the total number of amplitude and phase combinations.a 、E b 、E c are the amplitudes of antennas A, B, and C, respectively, a 、φ b 、φ c are the phases of antennas A, B, and C respectively. ΔE min and ΔE max Are the upper and lower bounds of the amplitude error, both of which are 0 in the present invention. min and Δφ max are the upper and lower bounds of the phase error, which in the present invention are -3° and +3° respectively.

[0095] S33. Determine fitness: Use formula (4) as the objective function of the algorithm, calculate the fitness of each multi-point source device baseline length selection scheme, and evaluate the performance according to size.

[0096] S4. The parameter dynamic adjustment module of the multi-point source simulation target device based on the Golden Whale algorithm dynamically adjusts the key parameters in the algorithm to balance the algorithm's global exploration and local development capabilities. Specifically:

[0097]

[0098] Among them, A and C are distance adjustment parameters, rand represents a random number uniformly distributed in [0, 1], and the convergence factor a decreases linearly from 2 to 0 as the number of iterations increases. max Indicates the maximum number of iterations.

[0099] S5. The spiral local optimization module of the target device based on the Golden Whale algorithm simulates multiple point sources and performs a fine search through the spiral path. Specifically:

[0100] In the whale optimization algorithm, the whale attacks its prey by moving in an upward spiral and continuously shrinking the encirclement. Its mathematical model is shown in formula (6):

[0101] x i (t+1)=x p (t)+D 1,i ×e bδ ×cos(2πl) (6)

[0102] in, Indicates the position of the i-th iteration; D 1,i =|x p (t)-x i (t)| represents the distance between the whale and the current global optimal individual; b is generally set to 1; δ represents a random number in the range [-1, 1]; is the current optimal position.

[0103] S6. The global encirclement module of the multi-point source simulation target device based on the Golden Whale algorithm, under the guidance of the current optimal solution, makes other solutions gradually move closer to the optimal solution. Specifically:

[0104] In the whale optimization algorithm, whale groups exchange information with each other and randomly wander around to search for prey. Each whale represents an individual, and the position of each individual in the search space represents a solution. Assuming that the current optimal position is the target prey, let The other individuals in the group move to the optimal position and update their positions using formula (7):

[0105]

[0106] S7. The random search module of the multi-point source simulation target device based on the Golden Whale algorithm can conduct extensive exploration in the solution space by randomly selecting search directions and positions. Specifically:

[0107] When |A|≥1, it means that the whales are swimming outside the shrinking circle and performing random searches based on each other's positions. In order to increase randomness, the present invention adopts the golden sine strategy, and its mathematical model is expressed as shown in formula (8):

[0108] x i (t+1)=x i (t)·|sin(R1)|+R2·sin(R1)·|λ1·x p (t)-λ2·x mean (t)| (8)

[0109] Among them, x p (t) is the optimal position at the tth iteration; x mean (t) is the average position at the tth iteration; R1 is a random number in [0, 2π], which determines the individual's moving distance; R2 is a random number in [0, π], which determines the individual's moving direction. λ1=-π+(1-τ)×2π,λ2=-π+τ×2π is based on the golden ratio The obtained coefficients guide the individual to approach the optimal solution and accelerate the convergence of the algorithm.

[0110] S8. The optimization output module of the multi-point source simulation target device based on the golden whale algorithm extracts the optimal amplitude and phase parameter scheme from the final population and outputs the optimization results.

[0111] Finally, the multi-point source simulation target method based on the Golden Whale algorithm of the present invention is verified by simulation experiments. The solution iteration curve of the Golden Whale optimization algorithm is as follows: Figure 6 As shown in the figure, the golden whale optimization algorithm is used to solve the simulation target model of the multi-point source device, as shown in the figure. Figure 6As shown in the figure, the Golden Whale optimization algorithm has converged when the number of iterations is 14, and converged to the optimal value 1. At this time, the corresponding optimal solution is [0.66, 0.80, 1, 99, 254, 302]. It can make the distance of the multi-point source device simulating the false target corresponding to the amplitude and phase combination under the phase error of [-3°, 3°] within [2d max ,6d max ] range. The specific results of the distance of the multi-point source device simulating the false target corresponding to the amplitude and phase combination within the device error range are as follows Figure 7 As shown, from Figure 7 It can be seen that the distance of all synthetic false targets is within [2d max ,6d max ] range, ensuring the safety range of the landing position under phase error, significantly improving landing reliability in complex environments.

[0112] Finally, it should be pointed out that although the present invention has been described with reference to the current specific embodiments, ordinary technicians in this technical field should realize that the above embodiments are only used to illustrate the present invention and are not used to limit the present invention. Various equivalent changes or substitutions can be made without departing from the concept of the present invention. Therefore, as long as the changes and modifications to the above embodiments are within the scope of the essential spirit of the present invention, they will fall within the scope of the claims of the present invention.

Claims

1. A multi-point source simulation target method based on the golden whale algorithm, characterized in that: The following steps are involved: S1. Construct a multi-point source simulation target device based on the Golden Whale algorithm, including an initialization module, a fitness evaluation module, a parameter dynamic adjustment module, a spiral local optimization module, a global encirclement module, a random exploration module, and an optimization output module; S2. The initialization module of the multi-point source simulation target device based on the golden whale algorithm generates an initial population of amplitude and phase parameter combination selection schemes; S3. The fitness evaluation module of the multi-point source simulation target device based on the Golden Whale algorithm evaluates the performance of each amplitude and phase parameter combination selection scheme; S4. The parameter dynamic adjustment module of the multi-point source simulation target device based on the Golden Whale algorithm adaptively adjusts the key parameters in the algorithm to achieve a dynamic balance between exploration and development; S5. The spiral local optimization module of the multi-point source simulation target device based on the Golden Whale algorithm performs a spiral search in the neighborhood of the current optimal solution; S6. The global encirclement module of the multi-point source simulation target device based on the Golden Whale algorithm, guided by the current optimal solution, makes other solutions gradually approach the optimal solution; S7. The random search module for multi-point source simulation target devices based on the Golden Whale algorithm can conduct extensive exploration in the solution space by adaptively selecting search directions and positions; S8. The optimization output module of the multi-point source simulation target device based on the golden whale algorithm extracts the optimal amplitude and phase parameter combination from the final population and outputs the optimization result.

2. The method for simulating multiple point sources based on the golden whale algorithm according to claim 1, characterized in that: The S2 comprises the following steps: S21. Taking the amplitude and phase parameters E' and φ' of the multi-point source device as input, and determining the upper bound of the amplitude E' of the multi-point source device as 1 and the lower bound as 0 according to the actual situation, and the upper bound of the phase φ' of the multi-point source device as π and the lower bound as 0; Among them, E' is the vector composed of the amplitude of antennas A, B, and C, E'=[E a +ΔE a ,E b +ΔE b ,E c +ΔE c ], φ' is the vector composed of the phases of antennas A, B, and C, φ'=[φ a +Δφ a ,φ b +Δφ b ,φ c +Δφ c ], ΔE i and Δφ i are amplitude error and phase error, i = a, b, c; S22. Randomly generate the initial population P0 based on the upper and lower bounds P0={x1,x2,...x i ,...x N },i=1,...,N x i =(x max -x min )*rand(·)+x min Among them, x i is the individual in the population, N is the population size, x max and x min are the upper and lower bounds of the population, respectively. Here, they are the upper and lower bounds of the amplitude and phase parameters E', φ' of the multi-point source device, and rand(·) is the (0,1) uniform distribution function.

3. The method for simulating multiple point sources based on the golden whale algorithm according to claim 2, characterized in that: The S3 includes the following steps: S31. Construct a mathematical model for the multi-point source device array: The coordinate system of the multi-point source device is established. The position formula of the simulated target of the multi-point source device is as follows: Among them, E a 、E b 、E c are the amplitudes of antennas A, B, and C, respectively, ab and δ ac are the phase differences between the interference signals emitted by antennas B and C of the multi-point source device and the interference signal emitted by antenna A, γ is the free space impedance, and θ max It represents the maximum pitch angle of the array element of the multi-point source device. represents the maximum azimuth; The formula for converting rectangular coordinates into spherical coordinates is: S32. The simulated target position of the multi-point source device is obtained based on a theoretical formula. However, in actual situations, phase errors can cause deviations between the simulated target position of the multi-point source device and the theoretical position. The corresponding mathematical model formula in actual situations is: Among them, E' is the vector composed of antenna amplitude and target echo amplitude, E'=[E a +ΔE a ,E b +ΔE b ,E c +ΔE c ], φ' is the vector composed of antenna phase and target echo phase, φ'=[φ a +Δφ a ,φ b +Δφ b ,φ c +Δφ c ], ΔE i and Δφ i are the amplitude error and phase error, i = a, b, c; F(E', φ') means that E' and φ' are substituted into the position formula of the simulated target of the multi-point source device and the spherical coordinate position formula to calculate the simulated target of the multi-point source device and the center of the multi-point source device; R min and R max are the closest and farthest distances between the constraint range and the simulated target of the multi-point source device, R min =2d max , R max =6d max , d max is the maximum baseline length of the multi-point source device; m is the number of amplitude and phase combinations within the device error range; n is the total number of amplitude and phase combinations; E a 、E b 、E c are the amplitudes of antennas A, B, and C, respectively, a 、φ b 、φ c are the phases of antennas A, B, and C, ΔE min and ΔE max are the upper and lower bounds of the amplitude error, Δφ min and Δφ max are the upper and lower bounds of the phase error; S33. Determine fitness: Use the corresponding mathematical model formula in the actual situation as the objective function of the algorithm, calculate the fitness of each multi-point source device baseline length selection scheme, and evaluate the performance according to size.

4. The method for simulating multiple point sources based on the golden whale algorithm according to claim 3 is characterized in that: The S4 is specifically as follows: A=2a·rand-a C=2·rand a=(2-2t / T max ) Among them, A and C are distance adjustment parameters, rand represents a random number uniformly distributed in [0, 1], and the convergence factor a decreases linearly from 2 to 0 as the number of iterations increases. max Indicates the maximum number of iterations.

5. The method for simulating multiple point sources based on the golden whale algorithm according to claim 4 is characterized in that: The S5 is specifically as follows: In the whale optimization algorithm, whales attack their prey by moving in an upward spiral and continuously shrinking the encirclement. Its mathematical model is expressed as follows: x i (t+1)=x p (t)+D 1,i ×e bδ ×cos(2πl) in, represents the position of the i-th iteration, D 1,i =|x p (t)-x i (t)| represents the distance between the whale and the current global optimal individual; b is generally set to 1; δ represents a random number in the range [-1, 1]; is the current optimal position.

6. The method for simulating multiple point sources based on the golden whale algorithm according to claim 5, characterized in that: The S6 is specifically as follows: In the whale optimization algorithm, whales exchange information with each other and randomly walk to search for the location of prey. Each whale represents an individual, and the position of each individual in the search space represents a solution. Assuming that the current optimal position is the target prey, let All other individuals in the group move to the optimal position and update their positions using the following formula: x i (t+1)=x p (t)-A×D 2,i D 2,i =|C×x p (t)-x i (t)| 7. The method for simulating multiple point sources based on the golden whale algorithm according to claim 6, characterized in that: The S7 is specifically as follows: When |A|≥1, it means that the whales are swimming outside the shrinking circle and performing random searches based on their positions, using the golden sine strategy. The mathematical model is as follows: x i (t+1)=x i (t)·|sin(R1)|+R2·sin(R1)·|λ1·x p (t)-λ2·x mean (t)| Among them, x p (t) is the optimal position at the tth iteration, x mean (t) is the average position at the t-th iteration, R1 is a random number in [0, 2π], which determines the individual's moving distance; R2 is a random number in [0, π], which determines the individual's moving direction, λ1 = -π + (1-τ) × 2π, λ2 = -π + τ × 2π is based on the golden ratio The coefficient obtained.

8. A multi-point source simulation target device based on the Golden Whale algorithm, which implements the multi-point source simulation target method based on the Golden Whale algorithm as described in any one of claims 1 to 7, characterized in that: include: Initialization module, which uses logistic chaotic mapping method to generate initial amplitude and phase parameter combinations; and The fitness evaluation module is used to evaluate the performance of each amplitude and phase parameter combination selection scheme. According to the preset objective function, the fitness value of each solution is calculated to quantify its quality. and The parameter dynamic adjustment module uses an adaptive adjustment strategy based on the number of iterations to control the convergence factor and search step size through a nonlinear decreasing function, maintaining a large search range in the early stages of optimization and gradually narrowing the optimization neighborhood in the later stages; and The spiral local optimization module simulates the spiral hunting behavior of whales and achieves efficient and precise search in parameter space. and The global encirclement module simulates the behavior of a group of whales encircling their prey and is used in the global search phase. Guided by the current optimal solution, other solutions gradually converge towards the optimal solution, thus achieving a concentrated search for potential optimal areas. and The random search module introduces randomness to prevent the algorithm from falling into local optimality. By randomly selecting the search direction and position, it can conduct extensive exploration in the solution space, thereby increasing the possibility of finding the global optimal solution; and The optimization output module is responsible for extracting the optimal antenna array solution from the final population and outputting the relevant optimization results.

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