A method, program, device and storage medium for estimating radiation source signal parameters

Abstract

The present invention belongs to the technical field of radiation source signal processing, and particularly relates to a method, program, device and storage medium for estimating radiation source signal parameters. By integrating multiple multi-task learning models, setting different model weight parameters, and constructing a weighted average parameter estimation formula, the present invention realizes the complementarity of different models and improves the generalization of the method. At the same time, the present invention designs an electric eel bird algorithm to intelligently optimize the model weight parameters, initializes the population using an elite reverse strategy, introduces Cauchy mutation to enhance the ability of the algorithm to jump out of local optima, constructs a warning factor to manage the transition between the exploration stage and the exploitation stage, and improves the electric eel bird position update method, so that the combination of model weight parameters is continuously optimized with the number of iterations, thereby improving the accuracy of parameter estimation of the present invention.

Description

Technical Field

[0001] The present invention belongs to the technical field of radiation source signal processing, and particularly relates to a method, program, device and storage medium for estimating radiation source signal parameters. Background Art

[0002] Intra-pulse feature analysis is an important link in the field of electronic reconnaissance, aiming to identify the intra-pulse modulation mode of radar pulse signals and accurately estimate their intra-pulse modulation parameters. Only by obtaining accurate intra-pulse parameter information can support be provided for subsequent radar jamming, strategy formulation, etc. Therefore, how to accurately estimate intra-pulse parameters in a complex electromagnetic environment is crucial.

[0003] Currently, common parameter estimation methods include the maximum likelihood estimation method, cyclostationary processing method, time-frequency analysis method, etc. These methods usually rely on the setting of parameters in the algorithm. In the face of a complex electromagnetic environment, improper parameter setting will lead to a decline in their estimation performance, and their effectiveness is targeted at specific radar signals, and the generalization ability is generally not strong.

[0004] Through the retrieval of existing technical literature, it is found that Dong Jinpeng et al. in the invention "Method and System for Estimating Intra-pulse Parameters of Polyphase Coded Signals Based on FRFT" (patent number: CN202311794743.6) realizes the estimation of signal frequency modulation slope and intra-pulse parameters of polyphase coded signals by performing fractional Fourier transform on radar signals, but this method is only used for parameter estimation of polyphase code signals and has poor generalization ability. Zhang Jiawei in his paper "Recognition and Parameter Estimation of Digital Signal Modulation Modes Based on Convolutional Neural Network" constructs a regression-based network structure based on deep learning using CNN, BiLSTM and attention mechanism to realize the estimation of symbol rate and carrier frequency parameters of various digital signals, but the estimation accuracy needs to be further improved. If the model prediction values of multiple machine learning and deep learning models can be fused during the parameter estimation process, a weighted average parameter estimation formula can be constructed, and the intelligent algorithm can be further combined to optimize the weight parameters of each model, and finally the optimal parameter estimation value of the radiation source signal can be obtained, which will effectively improve the generalization and accuracy of the parameter estimation method. Summary of the Invention

[0005] The purpose of the present invention is to design a new method for estimating radiation source signal parameters in view of the problems of low accuracy and poor generalization of existing radiation source signal parameter estimation methods.

[0006] A method for estimating radiation source signal parameters includes the following steps:

[0007] Step 1: Obtain the modulation mode of the radiation source signal to be estimated for parameters, and generate a radiation source signal data set based on this modulation mode; the number of intra-pulse parameters of radiation source signals with the same modulation mode is the same;

[0008] Step 2: Select multiple radiation source signal parameter recognition models, input the radiation source signal data set into each radiation source signal parameter recognition model, and obtain the recognition results;

[0009] Step 3: Set weight parameters for each radiation source signal parameter recognition model, and construct a weighted average parameter estimation formula;

[0010] Step 4: Input the radiation source signal to be parameter - estimated into each radiation source signal parameter recognition model, substitute the recognition results output by each model into the weighted average parameter estimation formula, and obtain the parameter estimation result.

[0011] Further, the weighted average parameter estimation formula in Step 3 is:

[0012]

[0013] Where, represents the final estimated value of the k - th parameter of the n - th radiation source signal; λ m represents the weight parameter of the m - th radiation source signal parameter recognition model, λ m ∈[0,1], and λ1 + λ2+…+λ N_model =1; N_model represents the number of radiation source signal parameter recognition models; represents the estimated value of the k - th parameter of the n - th radiation source signal in the m - th radiation source signal parameter recognition model.

[0014] Further, the weight parameters λ m of each radiation source signal parameter recognition model are optimized by using the electric eel bird algorithm, which specifically includes the following steps:

[0015] Step 3.1: Set the population size ps of the initial electric eel birds and the maximum number of iterations Maxit; initialize the current number of iterations iter = 1, and initialize the position W a ;

[0016] W a ={w a,1 ,w a,2 ,…,w a,b ,…,w a,N_model}

[0017] w a,b =r1×(ub b -lb b )+lb b

[0018] Where, w a,b represents the parameter of the a - th electric eel bird in the b - th dimension; r1 is a random number in the interval (0,1); ubb is the upper limit of the b-th dimensional parameter, lb b is the lower limit of the b-th dimensional parameter, a = 1, 2…ps, b = 1, 2,…, N_model;

[0019] Step 3.2: Calculate the fitness value Fit(W a ) of each electric eel bird in the initial electric eel bird population, and select the first Nps electric eel birds with the largest corresponding fitness value Fit(W a ) as the elite individual population p new ;

[0020]

[0021] Nps = ceil(0.5 × ps)

[0022] where Nfit is the total number of radiation source signals; N_para is the number of intra-pulse parameters of the radiation source signal; para_ac n ,k represents the accurate value of the k-th parameter of the n-th radiation source signal; ceil(·) represents rounding up;

[0023] Step 3.3: Calculate the reverse solution of the position W new of each electric eel bird in the elite individual population p a to form a reverse solution population

[0024]

[0025] w′ a,b = r2 × (lb b + ub b ) - w a,b

[0026]

[0027] where both r2 and r3 are random numbers in the interval (0, 1);

[0028] Step 3.4: Combine the elite individual population p new and its reverse solution population to form population o, calculate the fitness value of each electric eel bird in population o, and select the first ps electric eel birds with the largest corresponding fitness value to form population z; and take the position of the electric eel bird with the largest corresponding fitness value as the global best position z best ;

[0029] Step 3.5: Calculate the warning factor E. If E > 1, execute Step 3.6; otherwise, execute Step 3.7;

[0030]

[0031] Among them, r4 is a random number in the interval (0, 1);

[0032] Step 3.6: Calculate the potential position of each electric eel bird in population z using the exploration phase position update formula Then execute Step 3.8;

[0033]

[0034] Among them, z rand1 and z rand2 are the positions of two randomly selected electric eel birds in population z, rand1 and rand2 are random integers between [1, ps]; R1 is a random number uniformly distributed in the interval (0, 1); z a is the position of the a-th electric eel bird in population z, which is synonymous with W a , only different in terms of population, that is, z a ={w a,1 , w a,2 , …, w a,b , …, w a,N_model}; Brow is the Brown factor, Factor is the non-linear perturbation factor, and Cauchy is the Cauchy mutation factor;

[0035]

[0036]

[0037] Cauchy = tan(3.14 × rand(1, Dim) - 0.5)

[0038] Among them, R2 is a random number with a Gaussian distribution in the interval (0, 1); rand(1, Dim) represents a randomly generated 1×Dim array, where each element is a random number uniformly distributed in the interval (0, 1);

[0039] Step 3.7: Calculate the potential position of each electric eel bird in population z using the exploitation phase position update formula Then execute Step 3.8;

[0040]

[0041] Among them, both R3 and R4 are random numbers with a Gaussian distribution in the interval (0, 1), z rand3 is the position of a randomly selected electric eel bird in population z, rand3 is a random integer between [1, ps]; K randomly takes the integer 1 or 2; r5 is a random number uniformly distributed in the interval (0, 1);

[0042] Step 3.8: For each electric eel bird in population z, calculate the fitness value Fit(z a ) of the current position and the fitness value of the potential position If the fitness value of the potential position is greater, then update the current position of the electric eel bird;

[0043] Step 3.9: If there exists an electric eel bird in population z after updating the position, and the fitness value Fit(z a ) of its current position is greater than the fitness value Fit(z best ) of the global best position z best , then update the global best position z best ;

[0044] Step 3.10: If iter < Maxit, then set iter = iter + 1 and return to Step 3.5; otherwise, output the N_model parameters w best corresponding to the global best position z a,b , which are the weight parameters of the signal parameter identification model for each radiation source, i.e., λ b = w a,b .

[0045] Furthermore, to reduce the computational complexity, in Step 2, select the N gj radiation source signals with the lowest parameter estimation accuracy for each radiation source signal parameter identification model, and set Nfit = N_model × N gj .

[0046] Furthermore, each radiation source signal parameter identification model in Step 2 is pre-trained before use, and some of the radiation source signals generated in Step 1 can be used as the training set.

[0047] A computer device / system, comprising a memory, a processor, and a computer program stored on the memory, where the processor executes the computer program to implement the steps of the above-mentioned radiation source signal parameter estimation method.

[0048] A computer-readable storage medium, on which a computer program / instruction is stored, and when the computer program / instruction is executed by a processor, the steps of the above-mentioned radiation source signal parameter estimation method are implemented.

[0049] A computer program product, comprising a computer program / instruction, and when the computer program / instruction is executed by a processor, the steps of the above-mentioned radiation source signal parameter estimation method are implemented.

[0050] The beneficial effects of the present invention are as follows:

[0051] The present invention realizes the complementarity of different models and improves the generalization of the method by integrating multiple multi-task learning models, setting different model weight parameters, and constructing a weighted average parameter estimation formula. At the same time, the present invention designs an electric eel bird algorithm to intelligently optimize the model weight parameters, initializes the population using an elite reverse strategy, introduces Cauchy mutation to enhance the algorithm's ability to jump out of local optima, constructs a warning factor to manage the transition between the exploration stage and the exploitation stage, and improves the electric eel bird position update method, so that the model weight parameter combination is continuously optimized with the number of iterations, thereby improving the accuracy of the parameter estimation of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a schematic diagram of the overall process of the present invention.

[0053] Figure 2 is a schematic diagram of the electric eel bird algorithm in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0054] The present invention will be further described below with reference to the accompanying drawings.

[0055] The object of the present invention is to design a new method for estimating radiation source signal parameters, mainly to solve the problems of low accuracy and poor generalization of existing parameter estimation methods. The present invention integrates multiple multi-task learning models, sets different model weight parameters, constructs a weighted average parameter estimation formula, designs an electric eel bird algorithm to intelligently optimize the model weight parameters, initializes the population using an elite reverse strategy, introduces Cauchy mutation to enhance the algorithm's ability to jump out of local optima, constructs a warning factor to manage the transition between the exploration stage and the exploitation stage, and the optimized algorithm can select appropriate model weight parameters, and finally realizes the accurate estimation of the in-pulse parameters of the radiation source signal.

[0056] As shown in the Figure 1 accompanying drawings, it is a schematic diagram of the radiation source signal parameter estimation process based on the electric eel bird algorithm of the present invention.

[0057] Step 1: Select a modulation method and generate a radiation source signal dataset X(t):

[0058] X(t) = {signal1(t), signal2(t), …, signal i (t), … signal N (t)}

[0059] where signal i (t) is the i-th signal in X(t), i = 1, 2, …, N, and N represents the number of signals in the set.

[0060] Step 2: Select N_model multi-task learning models, use the dataset generated in Step 1 as the input for model training, and save the trained models. Then, select N gj radiation source signals with the lowest in-pulse parameter estimation accuracy in each model, and construct a dataset using the model predicted values and accurate values of their in-pulse parameters. In this embodiment, N_model = 3, and the models are the MMOE model, the PLE model, and the K-nearest neighbor regression model, and N gj = 100.

[0061] Step 2.1: Use the dataset X(t) generated in Step 1 as the training input for the model, and save the trained model.

[0062] Step 2.2: Re-input the dataset X(t) in Step 1 into the saved model, and select N gj radiation source signals with the lowest in-pulse parameter estimation accuracy in each model. Combine the model predicted values and accurate values of their in-pulse parameters to form a model predicted value dataset A and an accurate value dataset B. The specific design is as follows:

[0063] The specific expression of the model predicted value dataset A is:

[0064] A = {A1,…A m ,…A N_model}

[0065] where A m is the predicted value dataset obtained by the m-th model, m = 1, 2…N_model, and its expression is:

[0066]

[0067] Nfit = N_model × N gj

[0068] where represents the model predicted value of the in-pulse parameters of the n-th radiation source signal in A m , n = 1, 2…Nfit, and Nfit represents the total number of signals in the m-th model. The expression is:

[0069]

[0070] where represents the estimated value of the k-th parameter of the n-th radiation source signal in the m-th model, k = 1, 2,…N_para, and N_para represents the number of in-pulse parameters of the radiation source signal. In this embodiment, N_para = 3.

[0071] The specific expression of the accurate value dataset B is:

[0072] B = {para_ac 1 , para_ac 2 , … para_ac n … para_ac Nfit}

[0073] para_ac n = [para_ac n,1 , … para_ac n,k , … para_ac n,N_para

[0074] In the formula, para_ac n represents the accurate value of the in - pulse parameter of the nth radiation source signal, and para_ac n,k represents the accurate value of the kth parameter of the nth radiation source signal.

[0075] Step 3: Set the model weight parameters, construct the weighted average parameter estimation formula, further design the electric eel - bird algorithm, optimize the model weight parameters, and obtain the optimal model weight parameters.

[0076] Step 3.1: Construct the weighted average parameter estimation formula.

[0077]

[0078] In the formula, represents the final estimated value of the kth parameter of the nth radiation source signal, and λ m represents the weight parameter of the mth model, and its constraint condition is that λ m ∈[0, 1], and λ1 + λ2 + … + λ N_model = 1.

[0079] Step 3.2: Design the electric eel - bird algorithm to optimize each model weight parameter λ1, λ2, … λ N_model . The electric eel - bird algorithm is based on the secretary - bird optimization algorithm. It designs an elite reverse strategy to initialize the population, improves the quality of the initial population, uses Cauchy mutation to enhance the algorithm's ability to jump out of local optima, constructs a vigilance factor to effectively manage the transition between exploration and exploitation, thereby optimizing the position update mechanism of the electric eel - bird to obtain the optimal solution.

[0080] As shown in the appendix Figure 2 is the schematic diagram of the electric eel - bird algorithm of the embodiment of the present invention.

[0081] Step 3.2.1: Set the fitness function of the electric eel - bird algorithm. In this embodiment, the reciprocal of the mean square error between the final estimated value of the signal parameter and the accurate value is used as the evaluation index, and the fitness function formula is as follows:

[0082] ​

[0083] In the formula, Fit is the fitness value.

[0084] Step 3.2.2: Initialize the population size ps, population dimension Dim, and maximum number of iterations Maxit of the electric eel bird. Set the position of each electric eel bird to be composed of the model weight parameters λ1, λ2, …, λ N_model and use a random strategy to initialize the position of the a-th electric eel bird:

[0085] w a,b = r1 × (ub b - lb b ) + lb b

[0086] In the formula, w a,b represents the parameter of the a-th electric eel bird in the b-th dimension, r1 is a random number in the interval (0, 1), ub b is the upper limit of the b-th parameter, lb b is the lower limit of the b-th parameter, a = 1, 2, …, ps, b = 1, 2, …, Dim, Din = N_model. In this embodiment, the population size is set to 30, the population dimension is set to 3, and the maximum number of iterations is set to 500.

[0087] The population after initialization with the random strategy is:

[0088] w = {w1, w2, …, w a …, w ps}

[0089] w a = {w a,1 , w a,2 , …, w a,b …, w a,Dim}

[0090] In the formula, w a represents the position of the a-th electric eel bird.

[0091] Step 3.2.3: Adopt an elite reverse strategy to further process the population and improve the quality of the initial population to obtain a new population z.

[0092] Step 3.2.3.1: Calculate the individual fitness values using formulas (1) and (2), and sort the population in descending order according to the fitness values:

[0093] p = sort{w1, w2, …, w ps}

[0094] In the formula, sort{·} represents sorting all population individuals in descending order according to the fitness value.

[0095] Step 3.2.3.2: Select the first Nps individuals in population p as the elite individual population p new :

[0096] p new ={p1, p2, …, p Nps}

[0097] Nps = ceil(0.5 × ps)

[0098] In the formula, Nps represents the number of individuals in the elite population, and ceil(·) represents rounding up.

[0099] Step 3.2.3.3: The reverse solution of w a,b is solved by the following formula: The solution formula is as follows:

[0100] w′ a,b = r2 × (lb b + ub b ) - w a,b (3)

[0101]

[0102] In the formula, both r2 and r3 are random numbers in the interval (0, 1).

[0103] Then the reverse solution of w a is as follows: The following:

[0104]

[0105] Calculate the reverse solution of each individual in population p new according to formula (3) and formula (4), and form population

[0106]

[0107] Step 3.2.3.4: Combine the elite individual population p new and its reverse solution population to form a new population, and sort the population in descending order according to the individual fitness value:

[0108]

[0109] Select the first ps individuals from population o to form a new population z:

[0110] z = {z1, z2, …, z a , …, z ps}

[0111] z a ={z a,1, z a,2 , … z a,b … z a,Dim [[ID=6}}

[0112] In the formula, z a represents the position of the ath electric eel bird, and z a,b represents the parameter of the ath electric eel bird in the bth dimension.

[0113] Step 3.2.4: Use formulas (1) and (2) to calculate the fitness values of all individuals, and select the individual with the largest fitness value as the global best position of the electric eel bird.

[0114] Step 3.2.5: Calculate the warning factor E.

[0115]

[0116] In the formula, iter represents the current iteration number, and r4 is a random number in the interval (0, 1).

[0117] Step 3.2.6: Judge whether the warning factor E is greater than 1. If it is greater than 1, execute Step 3.2.7; if it is less than or equal to 1, execute Step 3.2.8.

[0118] Step 3.2.7: Use the position update formula in the exploration stage to update the positions of the electric eel bird individuals. The formula for calculating the potential position of the ath electric eel bird is as follows:

[0119]

[0120] In the formula, represents the potential position of the ath electric eel bird, z rand1 and z rand2 are the positions of the electric eel birds randomly selected from the current iteration population, where rand1 and rand2 are random integers between [1, ps], R1 is a random number uniformly distributed in the interval (0, 1), and z best is the global best individual position in the current iteration loop, Brow is the Brown factor, Factor is the non-linear perturbation factor, and Cauchy is the introduced Cauchy mutation factor.

[0121] The specific design of Brow is:

[0122]

[0123] In the formula, exp(·) represents the exponential function with base e, and R2 is a random number with a Gaussian distribution in the interval (0, 1).

[0124] The specific design of Factor is:

[0125]

[0126] Cauchy is specifically designed as follows:

[0127] Cauchy = tan(3.14 × rand(1, Dim) - 0.5)

[0128] Wherein, tan(·) represents the tangent function, and rand(1, Dim) represents a randomly generated array of dimension 1×Dim, where each element is a random number uniformly distributed in the interval (0, 1).

[0129] Step 3.2.8: Update the position of the electric eel bird individual using the position update formula in the development stage. The formula for calculating the potential position of the a-th electric eel bird is as follows:

[0130]

[0131] Wherein, both R3 and R4 are random numbers with a Gaussian distribution in the interval (0, 1), and z rand3 is the position of a randomly selected electric eel bird in the current iteration population, where rand3 is a random integer between [1, ps], K randomly takes the integer 1 or 2, and r5 is a random number uniformly distributed in the interval (0, 1).

[0132] Step 3.2.9: Calculate the fitness values of the position of the a-th electric eel bird individual and its potential position using formulas (1) and (2), and compare them to update the position of the electric eel bird individual. The formula is as follows:

[0133]

[0134] Wherein, Fit a represents the fitness value of the a-th electric eel bird, represents the fitness value of the potential position of the a-th electric eel bird.

[0135] Step 3.2.10: Calculate the fitness value of each electric eel bird individual using formulas (1) and (2), and update the global best position of the electric eel bird:

[0136]

[0137] Wherein, z max is the individual with the largest fitness value in the current iteration population, is the largest fitness value in the current population, is the fitness value of the global best position.

[0138] Step 3.2.11: Repeat steps 3.2.5 to 3.2.10 until the maximum number of iterations Maxit is reached to obtain the global optimal solution λ1, λ2, … λ N_model, use the optimal solution as the model weight parameter in the weighted average parameter estimation formula in step 3.1.

[0139] Step 4: Generate a single radiation source signal Y(t) with the same modulation method. First, use the model saved in step 2 to obtain the predicted value of the intra-pulse parameter model of the radiation source signal, and then substitute it and the optimal model weight parameter obtained in step 3 into the weighted average parameter estimation formula to obtain the optimal parameter estimation value, realizing the parameter estimation of the radiation source signal.

[0140] Step 4.1: Generate a radiation source signal Y(t) with the same modulation method.

[0141] Step 4.2: Use the model saved in step 2 to obtain the predicted value A of Y(t). new .

[0142] Step 4.3: Substitute the predicted value A in step 4.2 new and the optimal model weight parameter obtained in step 3.2 into the weighted average parameter estimation formula in step 3.1 to obtain the optimal parameter estimation value to complete the parameter estimation of the radiation source signal.

[0143] In view of the problems of low accuracy and poor generalization in the existing radiation source signal parameter estimation methods, the present invention designs a new radiation source signal parameter estimation method. The present invention integrates multiple multi-task learning models, sets different model weight parameters, constructs a weighted average parameter estimation formula, realizes the complementarity of different models, and improves the generalization of the method. At the same time, an electric eel bird algorithm is designed to intelligently optimize the model weight parameters. The population is initialized using the elite reverse strategy, the Cauchy mutation is introduced to enhance the ability of the algorithm to jump out of local optima, a warning factor is constructed to manage the transition between the exploration stage and the exploitation stage, and the electric eel bird position update method is improved, so that the combination of model weight parameters is continuously optimized with the number of iterations, thereby improving the accuracy of the parameter estimation of the method.

[0144] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for estimating radiation source signal parameters, characterized in that: The following steps are involved: Step 1: Obtain a modulation mode of a radiation source signal for parameter estimation, and generate a radiation source signal data set based on the modulation mode; radiation source signals of the same modulation mode have the same number of intra-pulse parameters; Step 2: Select multiple radiation source signal parameter recognition models, input the radiation source signal data set into each radiation source signal parameter recognition model, and obtain the recognition result; Step 3: Set weight parameters for each radiation source signal parameter identification model and construct a weighted average parameter estimation formula; in, represents the final estimated value of the kth parameter of the nth radiation source signal; m represents the weight parameter of the mth radiation source signal parameter identification model, λ m ∈[0,1], and λ1+λ2+…+λ N_model =1; N_model represents the number of radiation source signal parameter identification models; represents the estimated value of the kth parameter of the nth radiation source signal in the mth radiation source signal parameter identification model; The weight parameter λ of each radiation source signal parameter identification model m The electric eel bird algorithm is used for optimization, which includes the following steps: Step 3.1: Set the initial population size ps of the electric eel bird and the maximum number of iterations Maxit; initialize the current number of iterations iter = 1, and use a random strategy to initialize the position W of each electric eel bird a ; Step 3.2: Calculate the fitness value Fit (W) of each electric eel bird in the initial electric eel bird population a ), take the corresponding fitness value Fit(W a ) The largest pre-Nps electric eel bird, as an elite individual population p new ; Step 3.3: Calculate the elite individual population p new The position of each electric eel bird in W a The reverse solution of Forming a reverse solution population Step 3.4: Set the elite individual population p new and its reverse solution population Form a population o, calculate the fitness value of each electric eel bird in population o, take the first ps electric eels with the largest corresponding fitness value to form a population z; and use the position of the electric eel bird with the largest corresponding fitness value as the global optimal position z of the electric eel bird best ; Step 3.5: Calculate the warning factor E. If E>1, proceed to step 3.6; otherwise, proceed to step 3.7; Step 3.6: Use the exploration phase position update formula to calculate the potential position of each electric eel bird in population z Then proceed to step 3.8; Step 3.7: Use the development phase position update formula to calculate the potential position of each electric eel bird in population z Then proceed to step 3.8; Step 3.8: For each electric eel bird in population z, calculate the fitness value Fit(z a ) and the fitness value of the potential position If the fitness value of the potential position If it is larger, the current position of the electric eel bird is updated; Step 3.9: If the fitness value of the electric eel bird at the current position exists in the population z after the updated position is Fit(z a ) is greater than the global optimal position z best The fitness value Fit(z best ), then update the global optimal position z best ; Step 3.10: If iter < Maxit, set iter = iter + 1 and return to step 3.5; otherwise, output the global optimal position z best The corresponding N_model parameters w a,b , which is the weight parameter of the signal parameter identification model of each radiation source, that is, λ b =w a,b ; Step 4: Input the radiation source signal for which parameter estimation is to be performed into each radiation source signal parameter identification model, substitute the identification result output by each model into the weighted average parameter estimation formula, and obtain the parameter estimation result.

2. A radiation source signal parameter estimation method according to claim 1, characterized in that: In step 3.1, the position W of each electric eel bird is initialized using a random strategy. a for: IN a ={in a,1 ,In a,2 ,…,In a,b ,…,In a,N_model } w a,b =r1×(ub b -lb b )+lb b Among them, w a,b represents the parameter of the ath electric eel bird in the bth dimension; r1 is a random number in the interval (0,1); ubb is the upper limit of the bth dimension parameter, lb b is the lower limit of the b-th dimension parameter, a=1,2…ps, b=1,2,…,N_model; In step 3.2, the fitness value Fit (W) of each electric eel bird in the initial electric eel bird population is calculated. a )for: Nps=ceil(0.5×ps) Where Nfit is the total number of radiation source signals; N_para is the number of pulse parameters of the radiation source signal; para_ac n,k Indicates the exact value of the kth parameter of the nth radiation source signal; ceil(·) means rounding up; The elite individual population p is calculated in step 3.3 new The position of each electric eel bird in W a The reverse solution is: w′ a,b =r2×(lb b +ub b )-w a,b Among them, r2 and r3 are both random numbers in the interval (0,1).

3. The method for estimating radiation source signal parameters according to claim 1, wherein: The warning factor E calculated in step 3.5 is: Among them, r4 is a random number in the interval (0,1).

4. The method for estimating radiation source signal parameters according to claim 1, wherein: In step 3.6, the exploration phase position update formula is used to calculate the potential position of each electric eel bird in the population z. for: Among them, z rand1 and z rand2 are the positions of two electric eels in the randomly selected population z, rand1 and rand2 are random integers between [1, ps]; R1 is a random number uniformly distributed in the interval (0, 1); z a is the position of the ath electric eel bird in population z, and W a Synonymous, differing only in species, i.e. z a ={w a,1 ,w a,2 ,…,w a,b ,…,w a,N_model }; Brow is the Brownian factor, Factor is the nonlinear perturbation factor, Cauchy is the Cauchy variation factor; Cauchy=tan(3.14×rand(1,Dim)-0.5) R2 is a random number with Gaussian distribution in the interval (0,1); rand(1,Dim) represents a randomly generated array of dimension 1×Dim, in which each element is a random number uniformly distributed in the interval (0,1).

5. The method for estimating radiation source signal parameters according to claim 1, characterized in that: In step 3.7, the development phase position update formula is used to calculate the potential position of each electric eel bird in population z. for: Among them, R3 and R4 are random numbers with Gaussian distribution in the interval (0,1), z rand3 is the position of the electric eel bird in the randomly selected population z, rand3 is a random integer between [1,ps]; K is a random integer 1 or 2; r5 is a random number uniformly distributed in the interval (0,1).

6. A radiation source signal parameter estimation method according to claim 1, characterized in that: To reduce the amount of calculation, in step 2, the N with the lowest parameter estimation accuracy for each radiation source signal parameter identification model is selected. gj radiation source signal, let Nfit=N_model×N gj .

7. A radiation source signal parameter estimation method according to claim 1, characterized in that: The radiation source signal parameter recognition models in step 2 are pre-trained before use, and part of the radiation source signals generated in step 1 can be used as training sets.

8. A computer device / equipment / system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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