Archimedes optimization-based screening method for multi-source radio frequency signal reconstruction system

Abstract

The application relates to a screening method of a multi-source radio frequency signal reconstruction system based on Archimedes optimization, and belongs to the technical field of signal reconstruction. The method solves the problem of low efficiency in screening the multi-source radio frequency signal reconstruction system in the prior art. The method comprises the following steps: evaluating availability-related indexes and fidelity-related indexes of a plurality of multi-source radio frequency signal reconstruction systems; constructing a balanced relationship model between the availability and the fidelity of the multi-source radio frequency signal reconstruction system based on the availability-related index value and the fidelity-related index value of each multi-source radio frequency signal reconstruction system; constructing a target function and constraint conditions of the screening of the multi-source radio frequency signal reconstruction system based on the balanced relationship model; and determining the optimal system of the multi-source radio frequency signal reconstruction based on the Archimedes optimization algorithm based on the target function and the constraint conditions. The reconstruction system with the maximum comprehensive utility of the availability and the fidelity is quickly screened.

Description

Technical Field

[0001] This invention relates to the field of multi-source radio frequency signal reconstruction technology, and in particular to a screening method for multi-source radio frequency signal reconstruction systems based on Archimedes optimization. Background Technology

[0002] The multi-source radio frequency signal reconstruction system constructs the radio frequency signal simulation environment required for the detection equipment. It mainly includes a radio frequency target simulation subsystem (mainly including a scene model and a target simulator), a turntable, and a microwave anechoic chamber. The scene model includes a target model and an interference model, which are used to generate digital signals or signal characteristics of the target or interference to be simulated. The target simulator generates the echo signals required for target or interference simulation based on the generated digital signals or characteristics. The microwave anechoic chamber mainly creates an environment in the laboratory that approximates free space for electromagnetic waves to propagate. The detection equipment is installed on the turntable, which is used to simulate various attitudes of the aircraft during actual flight. The turntable and the radio frequency target simulation subsystem work together to simulate the relative positional relationship between the aircraft and the target.

[0003] The fidelity of a multi-source radio frequency signal reconstruction system refers to the degree to which the constructed environment accurately reproduces a certain aspect or the overall attributes, objects, or interactions of the battlefield electromagnetic environment. It reflects the relationship between the constructed environment and the real environment. If the constructed environment conforms to the real electromagnetic environment in terms of key elements or important characteristics, a certain degree of fidelity is achieved. In extreme cases, a fidelity of 1 indicates that the constructed environment completely reproduces the real environment, with no difference between the two; a fidelity of 0 indicates that there is no similarity between the constructed environment and the real environment. For the case requiring a fidelity of 1, it is usually difficult to achieve due to limited simulation resources; therefore, it can only be approached as close as possible.

[0004] The availability of a multi-source RF signal reconstruction system is evaluated from the user's perspective. It assesses whether such a system can be used to verify the functional performance of specific equipment in a specific usage scenario, the reliability of the verification, the user experience of the verification process (ease of use, reliability, efficiency, etc.), and the cost of building such a simulation verification system (economic efficiency). Therefore, availability is a more comprehensive and multi-dimensional indicator than realism. While there is a certain correlation between the two, they are not interchangeable.

[0005] Fidelity and availability are not entirely positively correlated; the system with the highest fidelity is not necessarily the most available solution, and vice versa. How to maximize the combined utility of availability and fidelity while meeting certain availability requirements, and how to quickly determine the optimal multi-source RF signal reconstruction system, is a problem that urgently needs to be solved. Summary of the Invention

[0006] Based on the above analysis, the present invention aims to provide a screening method for multi-source radio frequency signal reconstruction systems based on Archimedes optimization, in order to solve the problems of low efficiency and inability to balance fidelity and usability of existing screening multi-source radio frequency signal reconstruction systems.

[0007] On one hand, embodiments of the present invention provide a screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization, comprising the following steps:

[0008] The availability-related index and fidelity-related index of multiple multi-source radio frequency signal reconstruction systems are evaluated to obtain the availability-related index value and fidelity-related index value of each multi-source radio frequency signal reconstruction system;

[0009] Based on the availability-related index value and the fidelity-related index value of each multi-source radio frequency signal reconstruction system, a balance relationship model between the availability and fidelity of the multi-source radio frequency signal reconstruction system is constructed.

[0010] Based on the balance relationship model between availability and fidelity, construct the objective function and constraints for screening multi-source RF signal reconstruction systems;

[0011] Based on the objective function and constraints of the multi-source radio frequency signal reconstruction system selection, the optimal system for multi-source radio frequency signal reconstruction is determined by the Archimedes optimization algorithm.

[0012] Based on further improvements to the above methods, the availability-related indicators of multi-source radio frequency signal reconstruction systems include basic availability-related indicators, performance availability-related indicators, task achievement availability-related indicators, reliability-related indicators, economic-related indicators, and efficiency-related indicators.

[0013] The fidelity-related indicators of a multi-source radio frequency signal reconstruction system include: time domain consistency, frequency domain consistency, spatial domain consistency, energy domain consistency, polarization domain consistency, reconstructed signal resolution, and signal reconstruction capability.

[0014] Based on further improvements to the above method, and based on the objective function and constraints of the multi-source RF signal reconstruction system selection, the Archimedes optimization algorithm is used to determine the optimal system for multi-source RF signal reconstruction, including:

[0015] S41. Initialize the position, density, volume and acceleration of each individual in the population, where the position of an individual represents a fidelity-related index of the reconstructed RF signal from a single source.

[0016] S42. Determine the optimal individual in the current population based on the objective function and constraints of the multi-source radio frequency signal reconstruction system.

[0017] S43. Update the density, volume, acceleration, and position of each individual in the current population based on the best individual in the current population;

[0018] S44. Determine whether the maximum number of iterations has been reached. If not, return to step S42. Otherwise, determine the optimal individual in the current population based on the objective function and constraints selected by the multi-source radio frequency signal reconstruction system. The source radio frequency signal reconstruction system corresponding to the optimal individual in the current population is the optimal system.

[0019] Based on the above method, the position, density, volume, and acceleration of each individual in the population are initialized in the following way:

[0020] o i =lb+rand o ×(ub-lb)

[0021] acc i =lb+rand acc ×(ub-lb)

[0022] den i =rand den

[0023] vol i =rand vol

[0024] Among them, o i rand represents the position vector of the i-th individual. o Let represent the random vector of the initial position, ub represent the upper bound vector of the search space, lb represent the lower bound vector of the search space, and acc represent the lower bound vector of the search space. i rand represents the acceleration vector of the i-th individual. acc Den represents a random vector for initializing acceleration. i Let rand represent the density vector of the i-th individual. den vol represents a random vector with initial density. i rand represents the volume vector of the i-th individual. vol Represents a random vector for initializing the volume.

[0025] Based on the above method, the density and volume of each individual in the population are updated using the following formula, based on the best individual in the current population:

[0026]

[0027]

[0028] Among them, den best vol represents the density of the optimal individual.best Represents the volume of the optimal individual. This represents the density of the i-th individual in the t-th generation. This represents the volume of the i-th individual in generation t. This represents the density of the i-th individual in the (t+1)-th generation. Let represent the volume of the i-th individual in the (t+1)-th generation, and rand represent a random vector.

[0029] Based on the above method, and with further improvements, the acceleration and position of each individual in the population are updated using the following method, based on the best individual in the current population:

[0030] Calculate the transition operator and determine the current stage based on the transition operator; if the current stage is exploration, calculate the acceleration and position of each individual using the following formula:

[0031]

[0032]

[0033]

[0034]

[0035] If we are currently in the development phase, the acceleration and position of each individual are calculated using the following formula:

[0036]

[0037]

[0038] in, This represents the density of the i-th individual in the (t+1)-th generation. Let represent the volume of the i-th individual in the (t+1)-th generation, and rand represent a random vector. Den represents the acceleration of the i-th individual in the (t+1)-th generation. mr vol represents the density of random individuals. mr acc represents the volume of a random individual. mr The variable represents the acceleration of a random individual, and rand represents a random vector. Let represent the acceleration of the i-th individual in the (t+1)-th generation, min(acc t+1 ) represents the minimum acceleration of the individual in the (t+1)th generation, max(acc) t+1 ) represents the maximum acceleration of the individual in the (t+1)th generation, u and l represent the mapping parameters, o mr O represents the position of a random individual. i t represents the position of the i-th individual in generation t. This represents the position of the i-th individual in generation t+1, where t represents the current iteration number. max Deny indicates the maximum number of iterations. best vol represents the density of the best individuals in the current population. best acc represents the volume of the current best individual in the population. best o represents the acceleration of the current best individual in the population. best The position of the current best individual in the population is represented by F, which indicates the change in parameters when the direction of movement is changed, T is a parameter proportional to the transition operator, and C1 and C2 are both constants.

[0039] Based on the further improvement of the above method, the transfer operator TF is calculated using the following formula:

[0040]

[0041] Where t represents the current iteration number, t max This indicates the maximum number of iterations.

[0042] Based on further improvements to the above method, a balance model between the availability and fidelity of a multi-source radio frequency signal reconstruction system is constructed based on the availability-related index and fidelity-related index of each system. This model includes:

[0043] Based on the availability-related index values ​​of each multi-source radio frequency signal reconstruction system, construct the relationship models between the basic availability, performance availability, task achievement availability, reliability, economy, efficiency, and fidelity of the multi-source radio frequency signal reconstruction system.

[0044] Based on the constructed relational models, a balance relationship model between availability and fidelity of the multi-source radio frequency signal reconstruction system is built.

[0045] Based on further improvements to the above method, an objective function for screening multi-source RF signal reconstruction systems is constructed based on a model balancing availability and fidelity:

[0046]

[0047] in,

[0048]

[0049]

[0050]

[0051] in, y represents the availability of the multi-source RF signal reconstruction system, and x represents the fidelity of the multi-source RF signal reconstruction system. fiThe i-th metric value representing the basic availability of a multi-source radio frequency signal reconfiguration system, ρ i The weight of the i-th metric representing basic availability, g i and h i The parameter x represents the model's relationship between the i-th metric of basic availability and fidelity. f Indicates the basic availability of a multi-source radio frequency signal reconfiguration system, x p Indicates the performance availability of a multi-source radio frequency signal reconfiguration system, x pi The i-th metric value representing performance availability, a i and b i The parameters of the model representing the relationship between the i-th performance availability metric and fidelity are: The weight of the i-th metric value representing performance availability, x t Indicates the availability of the multi-source radio frequency signal reconfiguration system in achieving its mission, x ti c represents the i-th metric value indicating the availability of task completion. i and d i θ represents the parameters of the model relating the i-th metric of task availability to realism. i The weight of the i-th metric value representing the availability of task completion is x. R The reliability of the multi-source RF signal reconstruction system is represented by R1, R2, and R3, which represent design reliability, process reliability, and assembly reliability, respectively. γ represents the position parameter, η represents the calibration parameter, and λ represents the relationship parameter in the model between reliability and fidelity. c This indicates the economics of a multi-source radio frequency signal reconfiguration system, where c0 represents the highest acceptable cost, and B... i A i ,δ i ,m i ,κ i (i = 1, 2, 3) represent the parameters of the model relating economy and realism, ζ1 and ζ2 represent the piecewise thresholds for realism, and x u Let μ1 and μ2 represent the efficiency of the multi-source RF signal reconstruction system, and μ1 and μ2 represent the parameters of the model relating the efficiency and fidelity of the multi-source RF signal reconstruction system. α and β represent the weights of availability and fidelity, respectively. i Let y7 represent the i-th index of realism, and π represent the i-th index of realism. i w represents the weight of the i-th metric of realism. i (i = 1, ..., 6) represents the weight of the i-th indicator of availability.

[0052] Based on further improvements to the above method, the constraints for screening multi-source RF signal reconstruction systems, constructed based on a model balancing availability and fidelity, include:

[0053] Availability of multi-source radio frequency signal reconfiguration systems Indicates the minimum acceptable value for availability;

[0054] The fidelity of a multi-source radio frequency signal reconstruction system is y≥y0, where y0 represents the minimum acceptable value of fidelity.

[0055] The total cost of a multi-source radio frequency signal reconfiguration system is c≤c0, where c0 represents the highest acceptable cost.

[0056]

[0057] y i >0(i=1,2,…,6);y7∈{0,1};

[0058] Among them, B i A i ,δ i ,m i ,κ i (i = 1, 2, 3) represent the parameters of the model relating economy and fidelity, ζ1 and ζ2 represent the segmented thresholds for fidelity, and y represents the fidelity of the multi-source RF signal reconstruction system. i y7 represents the i-th index of realism, and y7 represents the 7-th index of realism.

[0059] Compared with existing technologies, the screening method for multi-source radio frequency signal reconstruction systems based on Archimedes optimization provided in this embodiment evaluates the availability-related index value and the fidelity-related index value of each multi-source radio frequency signal reconstruction system, and then constructs a balance relationship model between the availability and fidelity of the multi-source radio frequency signal reconstruction system. Based on the constructed balance relationship model, an optimization objective is constructed. The optimal system that balances availability and fidelity can be selected through the Archimedes optimization algorithm, thereby maximizing the comprehensive utility of availability and fidelity while meeting certain availability requirements, and obtaining the optimal reconstruction system.

[0060] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0061] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0062] Figure 1This is a flowchart of a screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization, according to an embodiment of the present invention. Detailed Implementation

[0063] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0064] A specific embodiment of the present invention discloses a screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization, such as... Figure 1 As shown, it includes the following steps:

[0065] S1. Evaluate the availability-related index and fidelity-related index of multiple multi-source radio frequency signal reconstruction systems to obtain the availability-related index value and fidelity-related index value of each multi-source radio frequency signal reconstruction system.

[0066] S2. Construct a balance relationship model between availability and fidelity of the multi-source radio frequency signal reconstruction system based on the availability-related index value and fidelity-related index value of each multi-source radio frequency signal reconstruction system;

[0067] S3. Construct the objective function and constraints for screening multi-source radio frequency signal reconstruction systems based on the balance relationship model between availability and fidelity.

[0068] S4. Based on the objective function and constraints of the multi-source radio frequency signal reconstruction system selection, the optimal system for multi-source radio frequency signal reconstruction is determined by the Archimedes optimization algorithm.

[0069] Compared with existing technologies, the screening method for multi-source radio frequency signal reconstruction systems based on Archimedes optimization provided in this embodiment evaluates the availability-related index value and the fidelity-related index value of each multi-source radio frequency signal reconstruction system, and then constructs a balance relationship model between the availability and fidelity of the multi-source radio frequency signal reconstruction system. Based on the constructed balance relationship model, an optimization objective is constructed. The optimal system that balances availability and fidelity can be selected through the Archimedes optimization algorithm, thereby maximizing the comprehensive utility of availability and fidelity while meeting certain availability requirements, and obtaining the optimal reconstruction system.

[0070] From the perspective of the availability index system for multi-source radio frequency signal reconstruction, not all indicators are related to fidelity. Among them, basic availability, performance availability, task achievement availability, reliability, economy, and efficiency are strongly correlated with fidelity and are controllable factors. Therefore, this invention constructs a balanced relationship between availability and fidelity from six aspects: basic availability, performance availability, task achievement availability, reliability, economy, and efficiency.

[0071] Therefore, the availability-related indicators of a multi-source radio frequency signal reconstruction system include basic availability-related indicators, performance availability-related indicators, task achievement availability-related indicators, reliability-related indicators, economic-related indicators, and efficiency-related indicators.

[0072] Since the fidelity of a multi-source radio frequency signal reconstruction system is composed of three dimensions—signal feature consistency, reconstructed signal resolution, and signal reconstruction capability—and signal feature consistency can be characterized from five dimensions—time domain, frequency domain, spatial domain, energy domain, and polarization domain—the fidelity-related indicators of a multi-source radio frequency signal reconstruction system include: time domain consistency, frequency domain consistency, spatial domain consistency, energy domain consistency, polarization domain consistency, reconstructed signal resolution, and signal reconstruction capability.

[0073] During implementation, step S1 first evaluates the availability-related index and fidelity-related index of multiple multi-source radio frequency signal reconstruction systems to obtain the availability-related index value and fidelity-related index value of each multi-source radio frequency signal reconstruction system.

[0074] During implementation, the consistency between the reconstructed signal and the original signal is compared in the time domain, frequency domain, spatial domain, energy domain, and polarization domain to obtain the consistency indexes for the time domain, frequency domain, spatial domain, energy domain, and polarization domain. Among these, the signal reconstruction capability indicates the ability of the multi-source RF signal reconstruction system to reconstruct the signal; this index has a value of 0 or 1. The reconstructed signal resolution index is obtained based on the resolution of the reconstructed signal.

[0075] From the perspective of systems engineering principles, the availability of multi-source radio frequency signal reconstruction requires that all functional components of the system (such as interference models, target models, simulators, turntables, anechoic chambers, etc.) and their interrelationships (signal interaction relationships, control and controlled relationships, etc.) be available.

[0076] Therefore, the basic availability of multi-source RF signal reconfiguration systems is x f Including target model availability x f1 The availability of the interference model x f2 Simulator basic availability x f3 Microwave anechoic chamber availability x f4 RF turntable availability x f5 .

[0077] During implementation, the availability of the target model can be obtained using existing methods such as the analytic hierarchy process (AHP) to assess the target distance simulation range and target angle simulation accuracy. The availability of the interference model can be obtained using existing methods such as the AHP to assess the interference bandwidth and aiming time. The basic availability of the simulator can be obtained using existing methods such as the AHP to assess the polarization availability and basic performance availability of the target simulator, as well as the interference mode availability and basic performance availability of the interference simulator. The availability of the microwave anechoic chamber can be assessed using existing methods such as the AHP to assess its length, width, and operating frequency. The availability of the RF turntable can be assessed using existing methods such as the AHP to assess its load-bearing capacity and positional accuracy.

[0078] The performance availability of multi-source RF signal reconfiguration systems is similar to that of fidelity, but their connotations differ. Fidelity reflects the consistency of the signal, while performance availability expresses whether the system is usable and to what extent it is usable once the consistency reaches a certain level. Furthermore, performance availability considers not only signal consistency but also the signal's fluctuation characteristics (fluctuation features).

[0079] Performance and availability of multi-source RF signal reconfiguration systems p Including time-domain feature availability x p1 Frequency domain feature availability x p2 Energy domain feature availability x p3 Spatial domain feature availability x p4 and polarization domain feature availability x p5 There are a total of 5 indicators.

[0080] In practice, the availability of time-domain features can be evaluated using existing methods such as the analytic hierarchy process (AHP) from the perspectives of pulse width and pulse repetition period; the availability of frequency-domain features can be evaluated using existing methods such as the AHP from the perspectives of center frequency, phase difference, and Doppler frequency; the availability of energy-domain features can be evaluated using existing methods such as the AHP from the perspectives of peak power and average power; the availability of spatial-domain features can be evaluated using existing methods such as the AHP from the perspectives of azimuth and elevation angles of the generated signal; and the availability of polarization-domain features can be evaluated from the perspective of polarization ratio.

[0081] Availability of Multi-Source RF Signal Reconfiguration System t Includes control process verification availability x t1 Anti-interference process verification availability x t2 Performance verification and availability x t3 and task completion rate availability x t4 There are a total of 4 indicators.

[0082] During implementation, the usability of control process verification can be evaluated using existing methods such as the Analytic Hierarchy Process (AHP) from the perspectives of time-domain interference capability achievement, spatial-domain interference capability achievement, and frequency-domain interference capability achievement. The usability of anti-interference process verification can be evaluated using existing methods such as the AHP from the perspectives of time-domain anti-interference capability achievement, spatial-domain anti-interference capability achievement, and frequency-domain anti-interference capability achievement. The usability of performance verification can be evaluated using existing methods such as the AHP from the perspectives of detection probability and positioning accuracy. Task completion rate usability can be calculated based on the proportion of tasks completed by the task station.

[0083] Higher fidelity tends to correlate with greater system complexity, and greater system complexity generally leads to lower reliability. Therefore, reliability and fidelity are negatively correlated, with system complexity being the primary factor. Consequently, system complexity is evaluated from both hardware and software perspectives.

[0084] The hardware component of the multi-source RF signal reconfiguration system mainly consists of a seeker, RF turntable, antenna array, and microwave feeding system. The hardware unit complexity scoring criteria can be found in Table 1. When evaluating hardware complexity according to Table 1, the assessment can be based on the quantity and scale of basic components and parts within the constituent units.

[0085] Table 1 Hardware Unit Complexity Scoring Rules

[0086]

[0087]

[0088] Extensive practical experience shows that the stability of software operation is inversely proportional to its complexity; the higher the complexity, the more shortcomings the software contains. In software engineering practice, the complexity of a software module is typically described using four types of data: basic software module information, complexity analysis, loop analysis, and data flow analysis. These four types of indicators each contain the following information:

[0089] ① Basic module information: number of executable lines, number of module entry points, and number of module exit points;

[0090] ② Complexity analysis: Cyclomatic complexity, basic cyclomatic complexity, and degree of structure;

[0091] ③ Loop analysis: number of loops, nesting depth, and maximum number of inner nests;

[0092] ④ Data flow analysis: number of global variables, fan-in number, and fan-out number.

[0093] For each software unit, the collection of four types of data information—basic information of the software module, complexity analysis, loop analysis, and data flow analysis—can be carried out according to the following methods:

[0094] ① For upgraded software units, evaluation software tools can be used to obtain the complexity indicators of the original software units, and then information on four types of indicators can be obtained according to a certain proportion based on the scale of the software upgrade.

[0095] ② For newly developed software units, experts estimate information on four types of indicators based on the software design scheme.

[0096] The sample tables of basic information, complexity analysis, loop analysis, and data flow analysis of each software unit are shown in Table 2 below.

[0097] Table 2 Sample data information for software module complexity analysis

[0098]

[0099] The 12 metrics from the above four categories are normalized as follows.

[0100] Suppose the system has n software unit modules, and the values ​​corresponding to a certain metric are υ1, υ2, ..., υ n Then the normalized result of each software unit of this indicator is

[0101]

[0102] If the normalized values ​​of the 12 metrics obtained by a certain software unit module are as follows: The complexity score of the software unit module can then be calculated using the following formula. (Measured on a scale of 0 to 1.0):

[0103]

[0104] The hardware complexity obtained by the above method is: The system complexity is obtained by weighted averaging of the two.

[0105] Reliability x R Besides system complexity It is related to design reliability R1, process reliability R2, assembly reliability R3, etc.

[0106] The economic performance of a multi-source radio frequency signal reconfiguration system is composed of the total system cost.

[0107] The efficiency metrics of a multi-source radio frequency signal reconfiguration system are divided into three sub-metrics: system configuration efficiency, system operation efficiency, and result analysis efficiency. During implementation, the system efficiency can be evaluated from the perspectives of system configuration efficiency, system operation efficiency, and result analysis efficiency to obtain the system efficiency metric values.

[0108] After obtaining the availability-related index value and the fidelity-related index value for each multi-source RF signal reconstruction system, a balance relationship model between the availability and fidelity of the multi-source RF signal reconstruction system is constructed based on the availability-related index value and the fidelity-related index value of each multi-source RF signal reconstruction system.

[0109] Specifically, step S2 involves constructing a balance model between the availability and fidelity of a multi-source radio frequency signal reconstruction system based on the availability-related index value and the fidelity-related index value of each system. This model includes:

[0110] S21. Based on the availability-related index values ​​of each multi-source radio frequency signal reconstruction system, construct a relationship model between the basic availability, performance availability, task achievement availability, reliability, economy, efficiency and fidelity of the multi-source radio frequency signal reconstruction system.

[0111] S22. Based on the constructed relational models, construct a balance relationship model between availability and fidelity of the multi-source radio frequency signal reconstruction system.

[0112] Due to the relationship between realism y and various metrics of basic usability x fi There is an increasing relationship between them. Therefore, we assume the relationship model between them is as follows:

[0113]

[0114] In the formula, g i and h i The parameter represents the model of the relationship between the i-th basic availability metric and the fidelity.

[0115] As can be seen from the above equation, the relationship between the basic availability and fidelity of a multi-source RF signal reconstruction system can be expressed as follows:

[0116]

[0117] Where, x fi The i-th metric value representing the basic availability of a multi-source radio frequency signal reconfiguration system, ρ i The weight of the i-th metric representing basic availability, g i and h i The parameter x represents the model's relationship between the i-th metric of basic availability and fidelity. f y represents the basic availability of the multi-source RF signal reconstruction system, and y represents the fidelity of the multi-source RF signal reconstruction system.

[0118] When the five performance and availability metrics of a multi-source RF signal reconstruction system are within the expected range, they are closely related to fidelity. Generally, fidelity y and x... pi There is a positive correlation between the two. Assuming they follow a linear positive correlation, then:

[0119] x pi =a i y+b i (5)

[0120] In the formula, a i and b i The parameters represent the relationship model between the i-th performance availability metric and the fidelity.

[0121] Therefore, the relationship between the performance availability and fidelity of a multi-source RF signal reconstruction system can be expressed as follows:

[0122]

[0123] Where, x p Indicates the performance availability of a multi-source radio frequency signal reconfiguration system, x pi The i-th metric value representing performance availability, a i and b i The parameters of the model representing the relationship between the i-th performance availability metric and fidelity are: The weight of the i-th metric value represents the performance availability, and y represents the fidelity of the multi-source RF signal reconstruction system.

[0124] The availability index of a multi-source radio frequency signal reconfiguration system is closely related to its fidelity. Generally speaking, the higher the fidelity y, the better the fidelity x. ti The larger the value, the stronger the correlation between the two. Assuming a linear positive correlation, then:

[0125] x ti =c i y+d i (7)

[0126] In the formula, c i and d i The parameters represent the relationship between the i-th metric of task availability and realism in the model.

[0127] Therefore, the relationship between the availability and fidelity of a multi-source radio frequency signal reconstruction system can be represented by the following model:

[0128]

[0129] Where, x tIndicates the availability of the multi-source radio frequency signal reconfiguration system in achieving its mission, x ti c represents the i-th metric value indicating the availability of task completion. i and d i θ represents the parameters of the model relating the i-th metric of task availability to realism. i The weight of the i-th metric value represents the availability of the task, and y represents the fidelity of the multi-source radio frequency signal reconstruction system.

[0130] Complexity of multi-source radio frequency signal reconstruction system Is it related to reliability x R Parameters with negative correlation and positive correlation between fidelity y and negative correlation.

[0131] Generally speaking, the higher the system complexity, the higher the realism. When a system increases in complexity from low to high, the realism tends to increase rapidly initially. However, once the system complexity reaches a certain level, the increase in realism will gradually slow down or even stop. This relationship between realism and system complexity can be expressed using a two-parameter exponential function.

[0132]

[0133] Here, γ and η represent the parameters of the model relating system complexity and realism.

[0134] Reliability x R Besides system complexity It is related to design reliability R1, process reliability R2, assembly reliability R3, etc. Therefore, a reliability x can be established. R With system complexity Relational model:

[0135]

[0136] From equation (9), we can obtain:

[0137] Based on equations (10) and (11), the relationship between the reliability and fidelity of a multi-source radio frequency signal reconstruction system can be expressed as follows:

[0138] x R =λ / (1+ηln(1-y)+γ)·R1·R2·R3 (12)

[0139] Where, x R R1, R2, and R3 represent the design reliability, process reliability, and assembly reliability, respectively. γ represents the position parameter, η represents the scale parameter, λ represents the relationship parameter of the model between reliability and fidelity, and y represents the fidelity of the multi-source RF signal reconstruction system.

[0140] Design reliability, process reliability, and assembly reliability can be assessed using existing methods for analyzing engineering reliability. During implementation, since the multi-source RF signal reconfiguration system is an assembly system, the process reliability is considered to be 1.

[0141] The total system cost *c* comprises the costs of the model, simulator, antenna array, and anechoic chamber. To improve the fidelity of the reconstructed signal, optimizing the model is the most cost-effective approach; followed by optimizing the simulator, and finally, selecting antenna arrays and anechoic chambers with higher performance. Therefore, the total system cost and fidelity are positively correlated, and the cost increases linearly after reaching a certain threshold as the fidelity requirement increases. Therefore, expressing the relationship between the total system cost and fidelity using a piecewise function is more reasonable.

[0142]

[0143] Where ζ1 and ζ2 represent the piecewise thresholds of fidelity, and g1(y), g2(y), and g3(y) represent the piecewise functions between the total system cost and fidelity.

[0144] Since the fidelity generally increases relatively slowly with the total system cost, a Weibull distribution with a shape parameter greater than 1 can effectively describe this situation. Let's assume...

[0145]

[0146] Among them, B i A i ,δ i ,m i ,κ i ,β i (i = 1, 2, 3) are all parameters of the piecewise function.

[0147] The higher the total system cost, the lower the availability. Let the highest acceptable cost be c0, then the availability determined by the total system cost is:

[0148] x c =c / c0 (15)

[0149] Based on equations (14) and (15), the relationship between the economy and fidelity of the multi-source radio frequency signal reconstruction system is expressed as follows:

[0150]

[0151] Where, x c The cost of the multi-source RF signal reconstruction system is represented by c0, where c0 represents the highest acceptable cost, y represents the fidelity of the multi-source RF signal reconstruction system, and B represents the economics of the system.i A i ,δ i ,m i ,κ i (i = 1, 2, 3) represents the parameters of the relational model.

[0152] The higher the fidelity of a multi-source radio frequency signal reconstruction system, the more complex the model and the higher the accuracy requirements. Consequently, the system's configuration efficiency, operating efficiency, and result analysis efficiency will all decrease to varying degrees.

[0153] Since efficiency and fidelity are negatively correlated, the relationship between efficiency and fidelity in a multi-source RF signal reconstruction system can be expressed as follows:

[0154] x u =μ1+μ2y 2 (17)

[0155] Where, x u Let μ1 and μ2 represent the efficiency of the multi-source radio frequency signal reconstruction system, μ1 and μ2 represent the model numbers of the relationship between the efficiency and fidelity of the multi-source radio frequency signal reconstruction system, and y represent the fidelity of the multi-source radio frequency signal reconstruction system.

[0156] After constructing the relationship models between the basic availability, performance availability, task achievement availability, reliability, economy, efficiency and fidelity of the multi-source radio frequency signal reconstruction system, a balanced relationship model between the availability and fidelity of the multi-source radio frequency signal reconstruction is constructed based on the constructed relationship models.

[0157] Since the fidelity of a multi-source RF signal reconstruction scheme is composed of three dimensions—signal characteristic consistency, reconstructed signal ratio, and signal reconstruction capability—and signal characteristic consistency can be characterized from five dimensions—time domain, frequency domain, spatial domain, energy domain, and polarization domain—the system fidelity y can be expressed as a relationship between these indicators:

[0158]

[0159] In the formula, y i (i = 1, 2, ... 5) represent the time-domain consistency, frequency-domain consistency, spatial-domain consistency, energy-domain consistency, and polarization-domain consistency indices, respectively; y6 represents the reconstructed signal resolution index; y7 represents the signal reconstruction capability index; π i Let y represent the weight of the i-th fidelity index. i (i = 1, 2, ..., 6) are performance indicators, 0 ≤ y i ≤1; y7 is a 0-1 type index, y7∈{0,1}.

[0160] Therefore, the equilibrium relationship between availability and fidelity can be represented by the following model:

[0161]

[0162]

[0163]

[0164] in, y represents the availability of the multi-source RF signal reconstruction scheme, and x represents the fidelity of the multi-source RF signal reconstruction scheme. fi The i-th metric value representing the basic availability of a multi-source radio frequency signal reconstruction scheme, ρ i The weight of the i-th metric representing basic availability, g i and h i The parameter x represents the model's relationship between the i-th metric of basic availability and fidelity. f Indicates the basic availability of multi-source RF signal reconstruction schemes, x p Indicates the performance availability of the multi-source RF signal reconstruction scheme, x pi The i-th metric value representing performance availability, a i and b i The parameters of the model representing the relationship between the i-th performance availability metric and fidelity are: The weight of the i-th metric value representing performance availability, x t Indicates the task completion availability of the multi-source RF signal reconfiguration scheme, x ti c represents the i-th metric value indicating the availability of task completion. i and d i θ represents the parameters of the model relating the i-th metric of task availability to realism. i The weight of the i-th metric value representing the availability of task completion is x. R The reliability of the multi-source RF signal reconstruction scheme is represented by R1, R2, and R3, which represent design reliability, process reliability, and assembly reliability, respectively. γ represents the position parameter, η represents the scale parameter, and λ represents the relationship parameter in the model between reliability and fidelity. c This indicates the economic viability of the multi-source RF signal reconstruction scheme, where c0 represents the highest acceptable cost, and B... i A i ,δ i ,m i ,κ i (i = 1, 2, 3) represent the parameters of the model relating economy and realism, ζ1 and ζ2 represent the piecewise thresholds for realism, and x u Let μ1 and μ2 represent the efficiency of the multi-source RF signal reconstruction scheme, and μ2 represent the parameters of the model relating the efficiency and fidelity of the multi-source RF signal reconstruction scheme. α and β represent the weights of availability and fidelity, respectively. iLet y7 represent the i-th index of realism, and π represent the i-th index of realism. i w represents the weight of the i-th metric of realism. i (i = 1, ..., 6) represents the weight of the i-th indicator of availability.

[0165] During implementation, it is necessary to first determine the parameters in the model that balance the relationship between availability and fidelity.

[0166] Equations (6) and (7) are linear models, and in equation (17) let y' = y 2 This can then be converted into a linear model. For a linear model, linear regression can be used to obtain the model parameters. Linear regression analysis represents the establishment of the model by fitting a straight line. In practice, for each relational model, such as equation (6)... Using fidelity as the independent variable and performance availability as the dependent variable, the regression coefficients are obtained through linear regression to obtain the parameter 'a' in the relational model. i b i .

[0167] Equations (4), (12), and (16) are nonlinear models. When implementing them, the model parameters can be obtained by using the least squares criterion.

[0168] While the least squares criterion can achieve satisfactory results in many practical nonlinear regression problems, it also has limitations. For example, when the collected data is limited and contains outliers, the results obtained using the least squares criterion are unacceptable. In such cases, applying the resulting regression equation or model for prediction or fitting results in very low accuracy, or even renders it unusable. In fact, when outliers are present, they exhibit significant bias, with relatively larger sums of squares. To reduce the sum of squares, these outliers must be "accepted," thus increasing the influence of data with large residuals on the regression line. Consequently, outliers pull the regression line closer to them, leading to significant distortion. Typically, outliers are removed based on intuition and experience, which has two drawbacks: firstly, the regression model obtained after removing outliers is inevitably affected (due to the reduced sample size, especially noticeable with limited data); secondly, outliers may indeed reflect specific information and should not be arbitrarily removed.

[0169] Therefore, when modeling and estimating parameters for data containing outliers, robust criteria should be selected to reduce the impact of singular data in order to obtain a model that conforms to reality. Furthermore, it has been theoretically proven that in problems where errors do not follow a normal distribution (for example, in econometrics, errors sometimes follow a distribution with a larger proportion of tails), the statistical performance of least squares estimation is superior to that of least squares estimation, giving it an irreplaceable advantage. In addition, the robustness of the least squares criterion is better than that of the least squares criterion, and it is less affected by outliers; therefore, the least squares criterion is also widely used in engineering practice.

[0170] Given the advantages of least squares mentioned above, this project will use the least squares criterion to solve nonlinear regression problems.

[0171] After obtaining the equilibrium relationship model between availability and fidelity, an optimization objective for screening multi-source RF signal reconstruction systems is constructed based on the equilibrium relationship model between availability and fidelity.

[0172] The optimization objective of a multi-source RF signal reconstruction system is to maximize the combined utility of system fidelity and usability. Therefore, the constructed objective function is:

[0173]

[0174] As can be seen from the above formula (20), availability The relationship between usability and fidelity y is strongly nonlinear, making it difficult to find a point where usability and fidelity are both optimal. The optimal point can only be found using a certain criterion.

[0175] In summary, optimizing a multi-source radio frequency signal reconstruction system is a multi-objective optimization problem, and the goal is to find a solution. y should be optimized simultaneously as much as possible, and there is no unique global optimal solution.

[0176] The purpose of optimizing a multi-source radio frequency signal reconfiguration system is, based on existing solutions and under the premise of achieving certain availability and fidelity requirements, and within a certain total cost (system total cost), to find the variable that maximizes the variable z in equation (22). Clearly, the variable in equation (20) is the fidelity y. i (i = 1, 2, ..., 7), determine the optimal y using an optimization algorithm. i This allows for the determination of availability indicators such as basic availability, performance availability, task achievement availability, reliability, economy, and efficiency, as well as fidelity indicators such as time domain consistency, frequency domain consistency, spatial domain consistency, energy domain consistency, polarization domain consistency, reconstructed signal resolution, and signal reconstruction capability.

[0177] When optimizing a multi-source radio frequency signal reconstruction system, several constraints often exist. In practice, the model balancing usability and fidelity is used to construct the constraints for screening multi-source radio frequency signal reconstruction systems.

[0178] Availability of multi-source radio frequency signal reconfiguration systems Indicates the minimum acceptable value for availability;

[0179] The fidelity of a multi-source radio frequency signal reconstruction system is y≥y0, where y0 represents the minimum acceptable value of fidelity.

[0180] The total system cost of a multi-source radio frequency signal reconfiguration system is c≤c0, where c0 represents the highest acceptable cost.

[0181] y i >0(i=1,2,…,6); y7∈{0,1}.

[0182] Among them, B i A i ,δ i ,m i ,κ i (i = 1, 2, 3) represent the parameters of the model relating economy and fidelity, ζ1 and ζ2 represent the segmented thresholds for fidelity, and y represents the fidelity of the multi-source RF signal reconstruction system. i y7 represents the i-th index of realism, and y7 represents the 7-th index of realism.

[0183] Based on the objective function and constraints, an intelligent optimization algorithm can be used to determine the optimal multi-source radio frequency signal reconstruction system.

[0184] During implementation, intelligent optimization algorithms such as genetic algorithms, particle swarm optimization, gray wolf algorithms, Harris Eagle algorithms, and L-SHADE (SHADE with Linear Population Reduction) can be used for optimization. In each optimization algorithm, an individual represents a solution, encoded by seven realism metrics. The optimal individual obtained through intelligent optimization is the optimal solution; that is, a system whose seven realism metrics match those of the optimal individual is the optimal system that balances availability and realism.

[0185] To improve screening efficiency, the Archimedes optimization algorithm can also be used during implementation. The Archimedes optimization algorithm (AOA) is a population-based algorithm. In this method, the individuals in the population are the immersed objects. Like other population-based metaheuristic algorithms, AOA begins its search process with an initial population of objects (candidate solutions) with random volumes, densities, and accelerations. At this stage, each object is also initialized with its random position in the fluid. After evaluating the fitness of the initial population, AOA works iteratively until a termination condition is met. In each iteration, AOA updates the density and volume of each object. The object's acceleration is updated based on its collision with any other neighboring object. The updated density, volume, and acceleration determine the object's new position.

[0186] The design of AOA effectively ensures simplicity, efficiency, adaptability, and flexibility. AOA is not only simple with few control parameters and strong robustness, but it also maintains the ability to adjust individual positions to avoid getting trapped in local optima. AOA is more efficient than algorithms such as genetic algorithms, particle swarm optimization, gray wolf algorithms, Harris Eagle algorithms, and L-SHADE (SHADE with Linear Population Reduction) in finding the global optimum.

[0187] In Archimedes' algorithm, each individual has four attributes:

[0188] Position: o=(o 1 ,o 2 ,…,o D )

[0189] Density: den = (den 1 ,den 2 ,…,den D )

[0190] Volume: vol = (vol 1 ,vol 2 ,…,vol D )

[0191] Acceleration: acc = (acc 1 ,acc 2 ,…,acc D )

[0192] Each iteration updates the density, volume, acceleration, and position of an individual in sequence, with acceleration and position being updated in stages according to the number of iterations.

[0193] Based on the objective function and constraints of the multi-source radio frequency signal reconstruction system selection, the optimal system for multi-source radio frequency signal reconstruction is determined using the Archimedes optimization algorithm, including:

[0194] S41. Initialize the position, density, volume and acceleration of each individual in the population, where the position of an individual represents a fidelity-related index of the reconstructed RF signal from a single source.

[0195] S42. Determine the optimal individual in the current population based on the objective function and constraints of the multi-source radio frequency signal reconstruction system.

[0196] S43. Update the density, volume, acceleration, and position of each individual in the current population based on the best individual in the current population;

[0197] S44. Determine whether the maximum number of iterations has been reached. If not, return to step S42. Otherwise, determine the optimal individual in the current population based on the objective function and constraints selected by the multi-source radio frequency signal reconstruction system. The source radio frequency signal reconstruction system corresponding to the optimal individual in the current population is the optimal system.

[0198] Specifically, the position, density, volume, and acceleration of each individual in the population are initialized in the following manner:

[0199]

[0200] Among them, o i rand represents the position vector of the i-th individual. o Let represent the random vector of the initial position, ub represent the upper bound vector of the search space, lb represent the lower bound vector of the search space, and acc represent the lower bound vector of the search space. i rand represents the acceleration vector of the i-th individual. acc Den represents a random vector for initializing acceleration. i Let rand represent the density vector of the i-th individual. den vol represents a random vector with initial density. i rand represents the volume vector of the i-th individual. vol Represents a random vector for initializing the volume.

[0201] In practice, the dimensions of position, density, volume, and acceleration of each individual are equal to the number of relevant indices for the fidelity of the multi-source radio frequency signal reconstruction system. In this invention, there are seven fidelity-related indices; therefore, the dimension D of position, density, volume, and acceleration of each individual is seven. Since 0 ≤ y in the fidelity indices... i Since ≤1 and y7∈{0,1}, the upper bound vector of the search space is a vector with all elements being 1, and the lower bound vector of the search space is a vector with all elements being 0.

[0202] A random vector is a vector whose elements are randomly generated and are values ​​between [0, 1]. Assume that the population has N individuals.

[0203] After generating the initial population, the optimal individual in the current population is determined based on the objective function and constraints selected by the multi-source radio frequency signal reconstruction system. During implementation, the individual's position vector is substituted into the objective function, and it is determined whether the constraints are satisfied. The individual that satisfies the constraints and maximizes the objective function z is identified as the optimal individual in the current population.

[0204] Then, based on the best individual in the current population, update the density, volume, acceleration, and position of each individual in the population.

[0205] During implementation, the density and volume of each individual in the population are first updated using the following formula based on the best individual in the current population:

[0206]

[0207]

[0208] Among them, den best vol represents the density of the optimal individual. best Represents the volume of the optimal individual. This represents the density of the i-th individual in the t-th generation. This represents the volume of the i-th individual in generation t. This represents the density of the i-th individual in the (t+1)-th generation. Let represent the volume of the i-th individual in the (t+1)-th generation, and rand represent a random vector.

[0209] The above formula allows the density and volume of individuals to approach the optimal level.

[0210] Then, the individual's acceleration and position are updated. In the Archimedes optimization algorithm, collisions occur initially, indicating an exploration phase. After a period of time, individuals attempt to reach an equilibrium state, transitioning to the development phase. Therefore, when updating an individual's acceleration and position, it is necessary to determine the current phase of the population.

[0211] Specifically, based on the best individual in the current population, the acceleration and position of each individual in the population are updated in the following way:

[0212] Calculate the transition operator and determine the current stage based on the transition operator; if the current stage is exploration, calculate the acceleration and position of each individual using the following formula:

[0213]

[0214]

[0215]

[0216]

[0217] If we are currently in the development phase, the acceleration and position of each individual are calculated using the following formula:

[0218]

[0219]

[0220] in, This represents the density of the i-th individual in the (t+1)-th generation. Let represent the volume of the i-th individual in the (t+1)-th generation, and rand represent a random vector. Den represents the acceleration of the i-th individual in the (t+1)-th generation. mr vol represents the density of random individuals. mr acc represents the volume of a random individual. mr The variable represents the acceleration of a random individual, and rand represents a random vector. Let represent the acceleration of the i-th individual in the (t+1)-th generation, min(acc t+1 ) represents the minimum acceleration of the individual in the (t+1)th generation, max(acc) t+1 ) represents the maximum acceleration of the individual in the (t+1)th generation, u and l represent the mapping parameters, o mr Indicates the position of a random individual. This represents the position of the i-th individual in generation t. This represents the position of the i-th individual in generation t+1, where t represents the current iteration number. max Deny indicates the maximum number of iterations. best vol represents the density of the best individuals in the current population. best acc represents the volume of the current best individual in the population. best o represents the acceleration of the current best individual in the population. best The position of the current best individual in the population is represented by F, which indicates the change in parameters when the direction of movement is changed, T is a parameter proportional to the transition operator, and C1 and C2 are both constants.

[0221] Specifically, the transfer operator TF is calculated using the following formula:

[0222]

[0223] Where t represents the current iteration number, t max This indicates the maximum number of iterations.

[0224] The transfer factor TF gradually increases over time until it reaches 1. By calculating the transfer operator TF, the current state is determined. When TF ≤ 0.5, it is the exploration stage; when TF > 0.5, it enters the development stage.

[0225] During the exploration phase, collisions occur between individuals; therefore, an individual (o) is randomly selected from the population. mr den mr vol mr acc mr The acceleration of the current individual is updated based on the random individual, indicating that the current individual is affected by the random individual.

[0226] After updating the acceleration of all individuals, the acceleration is normalized and linearly mapped to the [l, u] region, and then the position is updated. In practice, the value of l is 0.1 and the value of u is 0.9.

[0227] After obtaining the normalized acceleration, the position of each individual is updated using equation (28).

[0228] Equation (28) shows that the individual has moved a small step toward the random individual.

[0229] Once the development phase begins, there are no conflicts between individuals. Therefore, the acceleration and position of each individual can be updated based on the best individual in the current population. In practice, T increases over time and starts from a certain percentage of the global optimum. Starting T as a low percentage leads to a large difference between the global optimum and the current position, resulting in a high step size for random walks. As the search progresses, this percentage gradually increases to reduce the difference between the global optimum and the current position, achieving a proper balance between the exploration and development phases.

[0230] F is the indicator of a change in direction of motion, and the calculation formula is as follows:

[0231]

[0232] In the formula, P = 2 × rand - C4, where C4 is a constant, which can be taken as 0.5 in practice.

[0233] After updating each individual, check whether the maximum number of iterations has been reached. If not, return to step S42; otherwise, determine the optimal individual in the current population in the same way as the aforementioned method for determining the optimal individual. The source radio frequency signal reconstruction system corresponding to the optimal individual in the current population is the optimal system. That is, the system whose various fidelity indicators are the corresponding values ​​of the position vector of the optimal individual is the optimal system, thereby quickly obtaining the optimal multi-source radio frequency signal reconstruction system that balances availability and fidelity.

[0234] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0235] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization, characterized in that, Includes the following steps: The availability-related index and fidelity-related index of multiple multi-source radio frequency signal reconstruction systems are evaluated to obtain the availability-related index value and fidelity-related index value of each multi-source radio frequency signal reconstruction system; Based on the availability-related index value and the fidelity-related index value of each multi-source radio frequency signal reconstruction system, a balance relationship model between the availability and fidelity of the multi-source radio frequency signal reconstruction system is constructed. Based on the balance relationship model between availability and fidelity, construct the objective function and constraints for screening multi-source RF signal reconstruction systems; Based on the objective function and constraints of the multi-source radio frequency signal reconstruction system selection, the optimal system for multi-source radio frequency signal reconstruction is determined by the Archimedes optimization algorithm. The availability-related indicators of a multi-source radio frequency signal reconstruction system include basic availability-related indicators, performance availability-related indicators, task achievement availability-related indicators, reliability-related indicators, economic-related indicators, and efficiency-related indicators. The fidelity-related indicators of a multi-source radio frequency signal reconstruction system include: time domain consistency, frequency domain consistency, spatial domain consistency, energy domain consistency, polarization domain consistency, reconstructed signal resolution, and signal reconstruction capability. An objective function for screening multi-source RF signal reconstruction systems is constructed based on a model that balances availability and fidelity: in, ， in, y represents the availability of the multi-source RF signal reconstruction system, and y represents the fidelity of the multi-source RF signal reconstruction system. The i-th metric value represents the basic availability of the multi-source radio frequency signal reconfiguration system. The weight of the i-th metric representing basic availability. and The parameters of the model representing the relationship between the i-th metric of basic availability and fidelity are... This indicates the basic availability of the multi-source radio frequency signal reconfiguration system. This indicates the performance availability of the multi-source radio frequency signal reconstruction system. The i-th metric value represents the performance availability. and The parameters of the model representing the relationship between the i-th performance availability metric and fidelity are: The weight of the i-th metric value represents the performance availability. This indicates the availability of the multi-source radio frequency signal reconfiguration system to achieve its mission objectives. The i-th metric value represents the availability of task completion. and The parameters of the model representing the relationship between the i-th metric of task availability and realism are: The weight of the i-th metric value represents the availability of task completion. This indicates the reliability of the multi-source radio frequency signal reconstruction system. , and These represent design reliability, process reliability, and assembly reliability, respectively, with γ representing the position parameter. Indicates the scale parameter. The parameters representing the relationship between reliability and fidelity in the model. This indicates the economic efficiency of a multi-source radio frequency signal reconfiguration system. This indicates the highest acceptable value for cost. The parameters of the model representing the relationship between economy and realism. and The segmentation threshold representing the fidelity. This indicates the efficiency of a multi-source radio frequency signal reconfiguration system. and The parameters represent the model relating the efficiency and fidelity of a multi-source radio frequency signal reconstruction system. and These represent the weights for usability and realism, respectively. The i-th index representing realism The i-th index representing realism The weight of the i-th metric representing realism. The weight of the i-th metric representing availability; The constraints for screening multi-source RF signal reconstruction systems based on the balance between availability and fidelity model include: Availability of multi-source radio frequency signal reconfiguration systems , Indicates the minimum acceptable value for availability; The fidelity of multi-source radio frequency signal reconstruction systems , This represents the minimum acceptable level of realism. Total cost of multi-source radio frequency signal reconfiguration system , Indicates the highest acceptable cost; ; ； ； in, The parameters of the model representing the relationship between economy and realism. and The segmentation threshold represents the fidelity, and y represents the fidelity of the multi-source RF signal reconstruction system. The i-th index representing realism The 7th indicator of realism.

2. The screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization according to claim 1, characterized in that, Based on the objective function and constraints of the multi-source radio frequency signal reconstruction system selection, the optimal system for multi-source radio frequency signal reconstruction is determined using the Archimedes optimization algorithm, including: S41. Initialize the position, density, volume and acceleration of each individual in the population, where the position of an individual represents a fidelity-related index of the multi-source radio frequency signal reconstruction system. S42. Determine the optimal individual in the current population based on the objective function and constraints of the multi-source radio frequency signal reconstruction system. S43. Update the density, volume, acceleration, and position of each individual in the current population based on the best individual in the current population; S44. Determine whether the maximum number of iterations has been reached. If not, return to step S42. Otherwise, determine the optimal individual in the current population based on the objective function and constraints selected by the multi-source radio frequency signal reconstruction system. The source radio frequency signal reconstruction system corresponding to the optimal individual in the current population is the optimal system.

3. The screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization according to claim 1, characterized in that, The position, density, volume, and acceleration of each individual in the population are initialized in the following manner: in, Let represent the position vector of the i-th individual. A random vector representing the initial position. The vector representing the upper bound of the search space. This represents the lower bound vector of the search space. This represents the acceleration vector of the i-th individual. This represents a random vector that initializes acceleration. This represents the density vector of the i-th individual. Represents a random vector with initial density. This represents the volume vector of the i-th individual. Represents a random vector for initializing the volume.

4. The screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization according to claim 1, characterized in that, The density and volume of each individual in the current population are updated using the following formula based on the best individual in the current population: in, This represents the density of the optimal individual. Represents the volume of the optimal individual. This represents the density of the i-th individual in the t-th generation. This represents the volume of the i-th individual in generation t. This represents the density of the i-th individual in the (t+1)-th generation. This represents the volume of the i-th individual in the (t+1)-th generation. This represents a random vector.

5. The screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization according to claim 1, characterized in that, Based on the best individual in the current population, update the acceleration and position of each individual in the population in the following way: Calculate the transition operator and determine the current stage based on the transition operator; if the current stage is exploration, calculate the acceleration and position of each individual using the following formula: If we are currently in the development phase, the acceleration and position of each individual are calculated using the following formula: in, This represents the density of the i-th individual in the (t+1)-th generation. This represents the volume of the i-th individual in the (t+1)-th generation. Represents a random vector. This represents the acceleration of the i-th individual in the (t+1)-th generation. Represents the density of random individuals. Represents the volume of a random individual. Represents the acceleration of a random individual. Represents a random vector. This represents the acceleration of the i-th individual in the (t+1)-th generation. This represents the minimum acceleration of an individual in the (t+1)th generation. This represents the maximum acceleration of the individual in the (t+1)th generation, where u and l represent the mapping parameters. Indicates the position of a random individual. This represents the position of the i-th individual in generation t. This represents the position of the i-th individual in generation t+1, where t represents the current iteration number. Indicates the maximum number of iterations. This represents the density of the best individuals in the current population. This represents the volume of the best individual in the current population. This represents the acceleration of the current best individual in the population. This represents the position of the current best individual in the population, F represents the change in parameters due to changing the direction of movement, and T is a parameter proportional to the transition operator. and All are constants.

6. The screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization according to claim 1, characterized in that, The transfer operator TF is calculated using the following formula: Where t represents the current iteration number, This indicates the maximum number of iterations.

7. The screening method for a multi-source radio frequency signal reconstruction system based on Archimedes optimization according to claim 1, characterized in that, Based on the availability-related index and fidelity-related index of each multi-source RF signal reconstruction system, a balance relationship model between availability and fidelity of the multi-source RF signal reconstruction system is constructed, including: Based on the availability-related index values ​​of each multi-source radio frequency signal reconstruction system, construct the relationship models between the basic availability, performance availability, task achievement availability, reliability, economy, efficiency, and fidelity of the multi-source radio frequency signal reconstruction system. Based on the constructed relational models, a balance relationship model between availability and fidelity of the multi-source radio frequency signal reconstruction system is built.

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