An electromagnetic weak signal detection method, system, electronic device and medium

Abstract

The application discloses an electromagnetic weak signal detection method and system, electronic equipment and a medium, and relates to the field of electromagnetic signal detection. The method comprises the following steps: acquiring a target signal; constructing a stochastic resonance system by taking the maximum output signal-to-noise ratio as a first objective function, taking the shortest time required for a potential function to reach a maximum output amplitude as a second objective function, and taking the potential well value and the potential barrier value of the potential function as constraint conditions; determining the parameters of the stochastic resonance system by using a multi-strategy optimization algorithm to obtain an adaptive stochastic resonance system; and inputting the target signal into the adaptive stochastic resonance system and calculating an electromagnetic weak signal by using a fourth-order Runge-Kutta algorithm. The application can improve the accuracy of electromagnetic weak signal detection.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic signal detection, and in particular to a method, system, electronic device, and medium for detecting weak electromagnetic signals. Background Technology

[0002] With the rapid development of wireless communication technology, the number of mobile communication terminals is increasing, and the electromagnetic background environment is becoming increasingly complex, resulting in a variety of electromagnetic modulation signals. When transmitting weak electromagnetic modulation signals in a virtual anechoic chamber environment, due to the uncertainty of the airborne channel, the received signal may be mixed with interference signals and Gaussian noise. This can lead to situations where the signal is submerged by environmental noise and cannot be identified, or where the signal frequency band overlaps or cancels out with the noise, making it difficult to extract the effective signal characteristics. Although electromagnetic shielding chambers can reduce noise interference, they have disadvantages such as high cost, limited size of the tested equipment, fixed location, and high testing costs. To obtain the transmitted signal as independently as possible, signal detection needs to be performed promptly after the signal is received.

[0003] Currently, universally accepted methods for detecting weak electromagnetic signals fall into two categories: those based on traditional linear processing and those based on deep learning. Traditional linear processing methods primarily detect weak signals by suppressing noise, such as correlation detection, spectral analysis, wavelet transform, and empirical mode decomposition. However, these methods require prior information about the noise to map the signal to a separable transform domain for separation and denoising, resulting in poor generalization ability. Deep learning-based methods utilize neural networks to learn the inherent patterns and latent features of sample data to analyze signal information. They typically rely on sparse representations and nonlinear functions of the signal data, employing networks such as deep recurrent neural networks, long short-term memory, and wavelet networks to achieve "intelligent denoising." However, these deep models require a large number of training samples, and the performance of electromagnetic modulation signal denoising depends on the purity of the training signal samples. In reality, pure signals are difficult to obtain. Both types of methods share a common drawback: while suppressing noise, they lose some weak signal features, especially when the frequency characteristics of noise and signal are similar, making it difficult to extract accurate signal information for detection.

[0004] In recent years, emerging signal detection methods based on nonlinear theory have primarily achieved a degree of synergy between noise, signal, and stochastic resonance systems. This allows noise energy to be converted into low-frequency signal energy, further enhancing signal amplitude and improving the output signal-to-noise ratio (SNR), making it a signal detection system of significant research value. Therefore, stochastic resonance systems have been widely applied in fields such as communication, neural networks, and artificial intelligence. However, the potential well and barrier structures of stochastic resonance systems limit the range of Brownian particle transitions, causing the output performance of the stochastic resonance system to reach saturation prematurely. Furthermore, the parameter settings in the system rely on prior experience, lacking adaptability, thus weakening the system's ability to detect weak electromagnetic signals and hindering accurate analysis of electromagnetic signals. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, electronic device, and medium for detecting weak electromagnetic signals, which can improve the accuracy of detecting weak electromagnetic signals.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for detecting weak electromagnetic signals, the detection method comprising:

[0008] Acquire the target signal; the target signal includes noise and weak electromagnetic signals;

[0009] The first objective function is to maximize the output signal-to-noise ratio, the second objective function is to minimize the time required for the potential function to reach the maximum output amplitude, and the potential well value and potential barrier value of the potential function are used as constraints to construct a stochastic resonance system.

[0010] The parameters of the stochastic resonance system are determined by a multi-strategy optimization algorithm to obtain an adaptive stochastic resonance system.

[0011] The target signal is input into the adaptive stochastic resonance system, and the fourth-order Runge-Kutta algorithm is applied to calculate the weak electromagnetic signal.

[0012] Optionally, the potential function is:

[0013]

[0014] Where a and b are the positive system parameters of the potential function of the bistable stochastic resonance system, and the two stable points are... v is the system parameter of the Gaussian potential function, k is the uncertainty factor, U is the potential function, and x is the trajectory of the particle during its transition in the potential well.

[0015] Optionally, the potential barrier value of the potential function is:

[0016] ΔU=a 2 / 4b<0.3

[0017] The potential well value of the potential function is:

[0018]

[0019] Where a and b are the positive system parameters of the potential function of the bistable stochastic resonance system, ΔU is the potential barrier value of the potential function, and Δy is the potential well value of the potential function.

[0020] Optionally, a multi-strategy optimization algorithm is used to determine the parameters of the stochastic resonance system, specifically including:

[0021] The niche strategy is applied to solve the first objective function to obtain a local optimal parameter set;

[0022] The second objective function is solved using a multi-role particle swarm optimization algorithm to obtain the optimal solution set;

[0023] The parameters of the stochastic resonance system are determined by applying the crossover operator to the local optimal parameter set and the optimal solution set.

[0024] Optionally, the first objective function is:

[0025]

[0026] Where A is the amplitude of the input signal, D is the noise intensity of the weak electromagnetic signal, a and b are the positive system parameters of the potential function of the bistable stochastic resonance system, k is the uncertainty factor, and v is the system parameter of the Gaussian potential function.

[0027] An electromagnetic weak signal detection system, employing the aforementioned electromagnetic weak signal detection method, the detection system comprising:

[0028] The acquisition module is used to acquire the target signal; the target signal includes noise and weak electromagnetic signals.

[0029] The module is designed to construct a stochastic resonance system with the first objective function being the maximum output signal-to-noise ratio, the second objective function being the shortest time required for the potential function to reach its maximum output amplitude, and the potential well value and potential barrier value of the potential function as constraints.

[0030] The parameter determination module is used to determine the parameters of the stochastic resonance system using a multi-strategy optimization algorithm to obtain an adaptive stochastic resonance system.

[0031] The calculation module is used to input the target signal into the adaptive stochastic resonance system and apply the fourth-order Runge-Kutta algorithm to calculate the weak electromagnetic signal.

[0032] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to enable the electronic device to perform the above-described electromagnetic weak signal detection method.

[0033] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting weak electromagnetic signals.

[0034] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0035] This invention constructs a stochastic resonance system by using the maximum output signal-to-noise ratio as the first objective function, the shortest time required for the potential function to reach its maximum output amplitude as the second objective function, and the potential well and barrier values ​​of the potential function as constraints. A multi-strategy optimization algorithm is then employed to determine the parameters of the stochastic resonance system, resulting in an adaptive stochastic resonance system. The target signal is then input into the adaptive stochastic resonance system, and a fourth-order Runge-Kutta algorithm is applied to calculate the weak electromagnetic signal. This invention utilizes the stochastic resonance system to denoise the weak electromagnetic signal, performing time-frequency and numerical analysis to achieve accurate extraction of the weak electromagnetic signal. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of the electromagnetic weak signal detection method of the present invention;

[0038] Figure 2 The design flowchart of the adaptive stochastic resonance system provided by this invention;

[0039] Figure 3 The signal acquisition flowchart provided by this invention;

[0040] Figure 4 The multi-objective, multi-strategy flowchart provided by this invention;

[0041] Figure 5 The potential function curve of the stochastic resonance system provided by the present invention;

[0042] Figure 6 The output signal curve of the potential function provided by this invention;

[0043] Figure 7 The parameter optimization curve provided for this invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] The purpose of this invention is to provide a method, system, electronic device, and medium for detecting weak electromagnetic signals, which can improve the accuracy of detecting weak electromagnetic signals.

[0046] This invention constructs a symmetric piecewise potential function to improve the barrier height and well width, enabling the stochastic resonance system to generate particle transitions at relatively low energy. It determines a multi-objective optimization target, taking the maximum value of the output signal-to-noise ratio function as the first objective function and the shortest time for the potential function to reach its maximum value as the second objective function, setting the barrier height and well width as constraints. The optimal parameters are obtained through a multi-objective, multi-strategy particle swarm optimization algorithm, generating an adaptive stochastic resonance system. This stochastic resonance system is then used to denoise weak electromagnetic signals, achieving accurate detection of weak electromagnetic signals.

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Example 1

[0049] like Figure 1 As shown, the present invention provides a method for detecting weak electromagnetic signals, the detection method comprising:

[0050] Step S101: Acquire the target signal; the target signal includes noise and weak electromagnetic signals.

[0051] Step S102: With the maximum output signal-to-noise ratio as the first objective function, the shortest time required for the potential function to reach the maximum output amplitude as the second objective function, and the potential well value and potential barrier value of the potential function as constraints, construct a stochastic resonance system.

[0052] Specifically, such as Figure 4 As shown, a bistable stochastic resonance system has two stable points. When a periodic excitation signal s(t) is added, the equilibrium state of the stochastic resonance system is disrupted due to the modulation of the system's potential function by the periodic excitation signal. Under the influence of the periodic excitation signal, the potential well of the stochastic resonance system will tilt periodically, and the probability of transition of the moving point will also change accordingly. The potential barrier and potential well have a significant impact on the potential function. The potential function of this invention is:

[0053]

[0054] Where a and b are the positive system parameters of the potential function of the bistable stochastic resonance system, and the two stable points are... v is the system parameter of the Gaussian potential function, k is the uncertainty factor, U is the potential function, and x is the trajectory of the particle during its transition in the potential well.

[0055] The potential barrier value of the potential function is:

[0056] ΔU=a 2 / 4b<0.3.

[0057] The potential well value of the potential function is:

[0058]

[0059] Where a and b are the positive system parameters of the potential function of the bistable stochastic resonance system, ΔU is the potential barrier value of the potential function, and Δy is the potential well value of the potential function.

[0060] The first objective function is:

[0061]

[0062] Where A is the amplitude of the input signal, D is the noise intensity of the weak electromagnetic signal, a and b are the positive system parameters of the potential function of the bistable stochastic resonance system, k is the uncertainty factor, and v is the system parameter of the Gaussian potential function.

[0063] The second objective function is the shortest time required to reach the maximum output amplitude of the signal, obtained based on the output signal amplitude change information of the random system.

[0064] Specifically, the dynamic behavior of the system is first described using a threshold model with a stochastic term. During the simulation, Runge-Kutta is used to solve the differential equation, obtaining the system's evolution trajectory over time, and thus the shortest time required for the output amplitude to reach its maximum value. Here, the stochastic system is a threshold model with a stochastic term.

[0065] The threshold model is as follows:

[0066] dx / dt = F(x) + I(t)

[0067] Where x is the system state vector, F(x) is the nonlinear evolution function, representing the evolution law of the system in the absence of external excitation or noise, and I(t) represents the external excitation or noise term.

[0068] The expression for the external excitation I(t) is:

[0069] I(t)=A*ξ(t).

[0070] Where A is the noise intensity coefficient, and ξ(t) is Gaussian white noise with zero mean and unit variance.

[0071] Step S103: Use a multi-strategy optimization algorithm to determine the parameters of the stochastic resonance system and obtain an adaptive stochastic resonance system.

[0072] S103 specifically includes:

[0073] Step S1031: Apply the niche strategy to solve the first objective function to obtain the local optimal parameter set.

[0074] Specifically, it includes:

[0075] 1-1: Evaluate individuals in the population according to the objective function 1, and assign a fitness value to each individual.

[0076] 1-2: Select a mating pool based on the individuals and select the father. Use genetic operations to mutate the copies to obtain the corresponding offspring.

[0077] 1-3: Combine the offspring population with the parent population into one population, and use the niche non-dominated sorting method to select the next generation population.

[0078] 1-4: Determine if the termination condition is met. If not, repeat the selection of mating pools to generate offspring and the screening steps. If the termination condition has been met, terminate the loop and output the non-dominated solution set in the population, which is the optimal solution of the objective function, i.e., the local optimal parameter set.

[0079] Step S1032: Apply the multi-role particle swarm optimization algorithm to solve the second objective function and obtain the optimal solution set.

[0080] Specifically, it includes:

[0081] 2-1: Initialize the particle population for the objective function 2 and evaluate the particle fitness values.

[0082] 2-2: Perform particle swarm optimization on the population, and the three optimal solutions found are used as elite particles.

[0083] 2-3: Combining the elite particles and the standard deviation, the region is divided into three regions: the elite region, the normal region, and the deviation region, with the corresponding particle roles being: elite particles, normal particles, and deviation particles.

[0084] 2-4: Based on the particle roles described above, different state update rules are adopted to finally find the optimal solution of the objective function, i.e., the optimal solution set.

[0085] Steps 2-4 specifically include:

[0086] The aforementioned region division rules specifically include:

[0087] 2-4-1: When a particle is close to its corresponding elite particle, it is suitable for absorbing population experience, and the particle search range should be reduced; this area is called the elite zone. Within this zone, three elite particles are chosen as centers, ensuring that the distance difference between elite particles and random particles is within one standard deviation. When a particle is far from elite particles, it is more suitable for absorbing its own experience; therefore, the particle search range needs to be increased to enhance global search capabilities. This area is called the deviation zone, i.e., when the distance between a random particle and its corresponding elite particle in both the x-axis and y-axis directions is greater than the standard deviation, this area is considered the deviation zone. The areas where other particles are located are categorized as the normal zone.

[0088] 2-4-2: Based on the particle roles described above, different state update rules are adopted to finally find the optimal solution set of the objective function.

[0089] Step 2-4-2 specifically includes:

[0090] 2-4-2-1: When the particle is in the deviation region, the global optimization capability should be strengthened. Therefore, in this region, the inertial weight ω and learning factor c1 are relatively large, while the learning factor c2 is relatively small. In this paper, ω = 0.9, c1 = 3, and c2 = 1 are selected.

[0091] 2-4-2-2: When the particle is in the normal region, the local optimization ability should be strengthened. Therefore, the inertial weight ω and learning factor c1 are smaller in this region, while the learning factor c2 is larger. In this paper, ω = 0.5, c1 = 1.2, and c2 = 2.8 are selected.

[0092] 2-4-2-3: When a particle is in the elite region, the ability to dynamically balance global and local optimization should be considered. To improve the optimization speed in this region, the inertia weight is set to ω = 0.7, and the learning factors are set to c1 = 1.8 and c2 = 2.2.

[0093] Step S1033: Apply the crossover operator to the local optimal parameter set and the optimal solution set to determine the parameters of the stochastic resonance system.

[0094] Specifically, it includes:

[0095] 3-1: Perform multi-point cross-processing on the two optimal solution sets to obtain a new solution set.

[0096] 3-2: By iteratively finding the solution set with the maximum signal-to-noise ratio, the optimal solution set of the stochastic resonance system is obtained, which is the parameter of the stochastic resonance system.

[0097] Step S104: Input the target signal into the adaptive stochastic resonance system and apply the fourth-order Runge-Kutta algorithm to calculate the weak electromagnetic signal.

[0098] Specifically, the expression for the fourth-order Runge-Kutta algorithm is:

[0099]

[0100] Where f(t,u) is a given function obtained by differentiating the objective function with respect to t, and t n It is the x-coordinate of the current point, u n It is the solution at the current point, h is the iteration step size, and k is the solution at the current point. i (i = 1, 2, 3, 4) represents the slope of the intermediate point of the function at different stages.

[0101] The specific steps for solving the problem using the fourth-order Runge-Kutta algorithm include:

[0102] 4-1: Based on the objective function, the initial function is obtained by taking the derivative.

[0103] 4-2: Give the iteration step size h.

[0104] 4-3: Using the iteration step size h and the current function value f(t) n ,u n k1 is calculated.

[0105] 4-4: Using the iteration step size h and the current function value f(t) n ,u n k2 is obtained by calculating k1 and k2.

[0106] 4-5: Using the iteration step size h and the current function value f(t) n ,u n k3 is obtained by calculating k2 and k3.

[0107] 4-6: Using the iteration step size h and the current function value f(t) n ,u n k4 is obtained by calculating k3 and k4.

[0108] 4-7: Calculate u using k1, k2, k3, and k4. n+1 .

[0109] 4-8: If the iteration does not meet the termination condition, return to the calculation of the k1 value and iterate again; otherwise, obtain the denoised signal of the input signal.

[0110] As a specific implementation method, such as Figure 2 As shown, the specific process of the electromagnetic weak signal detection method provided by the present invention in practical application is as follows:

[0111] Step S1: Obtain a weak electromagnetic signal with noise.

[0112] Specifically, a communication system capable of actual transmission and reception was built using software-defined radio, and electromagnetic signals containing noise were acquired at the receiving end of the acquisition system.

[0113] like Figure 3 As shown, a communication system capable of actual transmission and reception mainly comprises three modules: a generation module, a transmission module, and a receiving module. The generation module primarily generates clean electromagnetic signals through a signal source host computer system controlled by a computer. The signal source and the transmitting antenna form the transmission module, responsible for signal transmission. After receiving the signal, the receiving antenna at the receiving end transmits it to the acquisition device, obtaining a signal containing noise.

[0114] Step S2: Improve the potential barrier and potential well of the potential function to obtain a stochastic resonance system.

[0115] Specifically, the stochastic resonance system is a nonlinear system, and the generalized Langevin gradient equation describing its motion state can be expressed as:

[0116] dx / dt=f(x(t),t)+ε(x(t),t).

[0117] Where x(t) is the trajectory of the particle during its transition in the potential well; f(x(t),t) is the nonlinear deterministic component; and ε(x(t),t) is the random component.

[0118] A nonlinear external force field is introduced into the study of the free motion of Brownian particles to describe the force situation acting on them, which is then expressed using differential equations. When Brownian particles move in a liquid, they collide randomly with liquid molecules, creating a force that hinders their free motion. The nonlinear stochastic resonance process is specifically described using the trajectory of an overdamped Brownian particle modulated by an external signal in a bistable potential well, which can be represented as:

[0119] dx / dt=-U'(x)+s(t)+w(t).

[0120] Where U(x) represents the potential function of the nonlinear external force field, s(t) is the external excitation signal, and w(t) is the Langevin force, whose statistical average value is given by the given value.<w(t)> =0, and are independent of each other at different times. The potential barrier and potential well of the system structure determine the structure of the potential function of the bistable stochastic resonance system, and affect the ease with which stochastic resonance is generated.

[0121] By improving the potential function, we obtain its expression.

[0122] Step S3: Determine the multi-objective optimization object: Select the maximum signal-to-noise ratio of the signal as the first objective function and the shortest time required for the maximum output amplitude as the second objective function, with potential well and potential barrier as constraints, and construct a set of multi-objective functions.

[0123] Specifically, the output performance of the stochastic resonance system is determined by the signal-to-noise ratio (SNR). The higher the SNR, the better the output performance. Taking the maximum SNR as the first objective function, the expression for the first objective function is obtained by applying the adiabatic approximation theory to the potential function.

[0124] The optimal selection of parameters a and b in the output signal-to-noise ratio determines both the best value of the signal-to-noise ratio and the fastest triggering of particle transitions in the stochastic resonance system formed by the potential function. In order to seek the best output effect of the system, the shortest time to generate stochastic resonance behavior is taken as the second objective function.

[0125] Step S4: Apply a multi-strategy optimization algorithm to the optimization object to find the optimal parameter combination.

[0126] Specifically, an adaptive optimization algorithm is used to find the optimal parameter combination for the above objective function set. The first objective function is subjected to a niche strategy and the second objective function is subjected to a multi-role particle swarm optimization algorithm. The two optimal solutions obtained are cross-processed and continuously updated iteratively to find the optimal solution for the multi-objective function set.

[0127] Step S5: Assemble the optimal parameters into an adaptive stochastic resonance system.

[0128] Specifically, the optimal solution obtained by the algorithm is fed into the system as the input parameter of the system to achieve adaptive updating of the system parameters and construct an adaptive stochastic resonance system.

[0129] Step S6: Based on the adaptive random resonance system, the signal is solved using the fourth-order Runge-Kutta algorithm to extract the weak electromagnetic signal.

[0130] Furthermore, in order to verify the correctness and advancement of the method of the present invention, simulation experiments were conducted, and the effects are illustrated through the following examples.

[0131] The simulation environment is set as follows: input signal amplitude A = 1, sampling frequency f s =1000Hz, carrier frequency is f c =10Hz, number of sampling points N=10000, noise intensity D=0.8. By performing adaptive parameter optimization on the improved stochastic resonance system, the simulation signal is fed into the found optimal stochastic resonance system, and the detected weak electromagnetic signal is output.

[0132] Simulation 1:

[0133] Analyze the curve trends of different potential functions in a stochastic resonance system. The independent variable x gradually increases from -3 to 3. Figure 5 As can be seen, a bistable stochastic resonance system has both stable and unstable potential wells. The shallower the potential well, the closer the positive and negative steady-state points are, and the lower the corresponding potential barrier. In this case, Brownian particles are more likely to undergo inter-well transitions, and the input and output signals will continuously tend to synchronize, thus enhancing the output signal component. Experiments show that the algorithm of this invention has the widest potential well and the lowest potential barrier, which is more conducive to triggering the stochastic resonance phenomenon, resulting in enhanced output signal components and more accurate extraction of the input signal.

[0134] Simulation 2:

[0135] Analyze the performance changes of the signal-to-noise ratio (SNR) and noise intensity (D) of a random resonance system. The independent variable, noise intensity, gradually increases from 0 to 10. Figure 6 As can be seen, when the potential barrier and input signal amplitude are constant, the SNR curve shows a trend of first increasing and then decreasing with the increase of noise intensity D, producing a very obvious peak. The algorithm of this invention is significantly superior to other algorithms, with the largest peak value reaching 1.71412. Within the noise intensity D range of 0-1, the output signal-to-noise ratio is better than that of CBSR, UBSR, GPPBSR, and IUBSR algorithms. Experiments show that at the peak, the signal, noise, and nonlinear system achieve optimal synergy, indicating that there is a phenomenon of noise energy transfer to signal energy in this process, resulting in better system output performance.

[0136] Simulation 3:

[0137] Analyze the distribution of optimal values ​​during the optimization process of the adaptive algorithm. The number of rounds, the independent variable, increases sequentially from 0 to 100. Figure 7 As can be seen, the optimal value decreases in a step-like manner from -0.67798, reaching around -2 in the 26th round. This value remained stable for about 10 rounds before resuming its step-like decline, finally stabilizing at -5.87439 in the 83rd round. Experiments show that the algorithm of this invention avoids reaching local optima too quickly, achieving a better balance between local and global optima, and providing a more suitable parameter combination for stochastic resonance systems.

[0138] The adaptive stochastic resonance system provided by this invention constructs a symmetric piecewise potential function to improve the barrier height and well width, enabling particle transition motion to be triggered in the stochastic resonance system with relatively low energy. It determines a multi-objective optimization target, taking the maximum value of the output signal-to-noise ratio function as objective function 1 and the shortest time for the potential function to reach its maximum value as objective function 2, and setting the barrier height and well width as constraints. The optimal parameters are sought through a multi-objective, multi-strategy particle swarm optimization algorithm, resulting in an adaptive stochastic resonance system.

[0139] In summary, the adaptive stochastic resonance system disclosed in this invention, by changing the potential well width and barrier height of the potential function, uses a multi-objective, multi-strategy optimization algorithm to find the optimal parameter combination, thus constructing an adaptive stochastic resonance system that can effectively eliminate noise and extract more accurate signal information. Compared with traditional stochastic resonance systems, this invention's system has two significant advantages: First, by changing the structure of the potential function, the algorithm more easily triggers particle transitions, thereby shortening the system's response time and making it easier to satisfy the conditions for optimal resonance between noise, signal, and external excitation. Second, the multi-objective, multi-strategy optimization algorithm used in this invention achieves a balance between local and global optima, avoiding the situation of quickly falling into local optima and improving the output performance of the stochastic resonance system. Third, the adaptability of this invention reduces computational complexity and signal detection time while improving the system's robustness. These three points demonstrate that the method of this invention has high engineering application value.

[0140] Example 2

[0141] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, an electromagnetic weak signal detection system is provided below. The detection system includes:

[0142] An acquisition module is used to acquire a target signal; the target signal includes noise and weak electromagnetic signals.

[0143] The module is designed to construct a stochastic resonance system with the following objectives: maximizing the output signal-to-noise ratio as the first objective function, minimizing the time required for the potential function to reach its maximum output amplitude as the second objective function, and using the potential well and barrier values ​​of the potential function as constraints.

[0144] The parameter determination module is used to determine the parameters of the stochastic resonance system using a multi-strategy optimization algorithm to obtain an adaptive stochastic resonance system.

[0145] The calculation module is used to input the target signal into the adaptive stochastic resonance system and apply the fourth-order Runge-Kutta algorithm to calculate the weak electromagnetic signal.

[0146] Example 3

[0147] This invention provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor runs the computer program to enable the electronic device to perform the electromagnetic weak signal detection method of Embodiment 1.

[0148] Alternatively, the aforementioned electronic device may be a server.

[0149] In addition, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the electromagnetic weak signal detection method of Embodiment 1.

[0150] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0151] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for detecting weak electromagnetic signals, characterized in that, The detection method includes: Acquire the target signal; the target signal includes noise and weak electromagnetic signals; The first objective function is to maximize the output signal-to-noise ratio, the second objective function is to minimize the time required for the potential function to reach the maximum output amplitude, and the potential well value and potential barrier value of the potential function are used as constraints to construct a stochastic resonance system. The first objective function is: in, The amplitude of the input signal. The noise intensity of weak electromagnetic signals. The potential function of the bistable stochastic resonance system is the positive system parameter. For uncertain factors, The system parameters are the Gaussian potential function; The parameters of the stochastic resonance system are determined by a multi-strategy optimization algorithm to obtain an adaptive stochastic resonance system. The target signal is input into the adaptive stochastic resonance system, and the fourth-order Runge-Kutta algorithm is applied to calculate the weak electromagnetic signal.

2. The electromagnetic weak signal detection method according to claim 1, characterized in that, The potential function is: in, Let be the potential function of a bistable stochastic resonance system with positive system parameters. The two stable points are... , The system parameters are the Gaussian potential function. Let U be the uncertainty factor, U be the potential function, and x be the trajectory of the particle during its transition in the potential well.

3. The electromagnetic weak signal detection method according to claim 1, characterized in that, The potential barrier value of the potential function is: The potential well value of the potential function is: in, The potential function of the bistable stochastic resonance system is the positive system parameter. Let be the potential barrier value of the potential function. Let be the potential well value of the potential function.

4. The electromagnetic weak signal detection method according to claim 1, characterized in that, The parameters of the stochastic resonance system are determined using a multi-strategy optimization algorithm, specifically including: The niche strategy is applied to solve the first objective function to obtain a local optimal parameter set; The second objective function is solved using a multi-role particle swarm optimization algorithm to obtain the optimal solution set; The parameters of the stochastic resonance system are determined by applying the crossover operator to the local optimal parameter set and the optimal solution set.

5. A weak electromagnetic signal detection system, characterized in that, The detection system includes: The acquisition module is used to acquire the target signal; the target signal includes noise and weak electromagnetic signals. The module is designed to construct a stochastic resonance system with the first objective function being the maximum output signal-to-noise ratio, the second objective function being the shortest time required for the potential function to reach its maximum output amplitude, and the potential well value and potential barrier value of the potential function as constraints. The first objective function is: in, The amplitude of the input signal. The noise intensity of weak electromagnetic signals. The potential function of the bistable stochastic resonance system is the positive system parameter. For uncertain factors, The system parameters are the Gaussian potential function; The parameter determination module is used to determine the parameters of the stochastic resonance system using a multi-strategy optimization algorithm to obtain an adaptive stochastic resonance system. The calculation module is used to input the target signal into the adaptive stochastic resonance system and apply the fourth-order Runge-Kutta algorithm to calculate the weak electromagnetic signal.

6. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the electromagnetic weak signal detection method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the electromagnetic weak signal detection method as described in any one of claims 1 to 4.

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