Adaptive power matching method and apparatus for receiving system

Abstract

The invention discloses a self-adaptive power matching method and device for a receiving system. The method comprises the steps of collecting a power signal, constructing a geodesic equation for decomposition, generating a geometric feature vector and calculating a stability index. Constructing a standardized feature matrix, obtaining an expansion coefficient sequence, and calculating to obtain a fusion entropy feature and an information completeness index; performing recursive decomposition to obtain a spectrum feature vector and a modal parameter set; constructing a hybrid prediction model to obtain prediction confidence and a prediction value sequence; performing multi-objective constraint optimization to obtain an optimal solution and a constraint satisfaction degree; feedback correction and stability analysis are carried out, and correction control quantity is output; carrying out self-optimization feedback regulation, and outputting an optimization parameter set; calculating a power adjustment control signal and executing power adjustment to obtain an adjusted actual power value; and closed-loop correction is carried out to realize adaptive power matching control. According to the invention, automatic, efficient and accurate power matching is realized, and the stability and reliability of the system are improved.

Description

Technical Field

[0001] The invention belongs to the field of electronic countermeasures, and in particular to an adaptive power matching method and device for a receiving system. Background Art

[0002] In modern electronic countermeasure systems, power matching control of the receiving system is a key technology to ensure system performance. As the electronic countermeasure environment becomes increasingly complex and adversary interference methods continue to upgrade, the receiving system faces a wider power dynamic range, faster power changes, and more diverse interference forms. Accurate power matching control directly affects the dynamic range, signal detection probability, and anti-interference capability of the receiving system, and is of great significance to improving the overall performance of the system. Especially in high-intensity electronic countermeasure environments, the receiving system needs to complete power adaptive adjustment within microseconds, which places extremely high demands on power matching control technology.

[0003] At present, common power matching control methods mainly include PID-based closed-loop control, table lookup method and neural network solutions. The PID-based method realizes power control by adjusting the proportional, integral and differential parameters, but its linear control characteristics are difficult to cope with strong nonlinear power changes. The table lookup method controls through a pre-calibrated power-attenuation correspondence, but the table items are limited, and the interpolation error is large in fast-changing scenarios. Although the neural network-based method has nonlinear mapping capabilities, network training relies on a large amount of sample data, and the real-time processing speed is difficult to meet the microsecond response requirements. At the same time, the existing methods generally adopt a fixed window data processing mechanism, which is prone to overshoot and oscillation when the signal changes suddenly.

[0004] The existing technical solutions have the following specific problems: First, the non-stationary characteristics of the power signal are difficult to characterize accurately, especially in scenarios such as Doppler frequency shift and phase jump, the traditional Fourier analysis method is difficult to characterize the instantaneous characteristics of the signal; the dimensionality reduction projection in the feature extraction process will lose the geometric structure information of the signal, resulting in a decrease in control accuracy; the prediction model does not consider the probabilistic evolution characteristics of the system state, and the reliability of the prediction results is insufficient; the robustness constraints in the controller design are too conservative, affecting the dynamic response performance of the system; the compensation strategy lacks multi-scale analysis of errors, making it difficult to achieve accurate closed-loop correction. In addition, the communication delay and sampling error of the digital control interface will also affect the control accuracy. These technical problems seriously restrict the power matching performance of the receiving system in a complex electronic countermeasure environment. Summary of the invention

[0005] The purpose of the invention is to provide an adaptive power matching method and device for a receiving system to solve the above problems existing in the prior art.

[0006] A technical solution, an adaptive power matching method for a receiving system, comprising the following steps:

[0007] S1. Collect the power signal of the receiving system and convert it into a power sequence; project the power sequence onto the Riemann manifold, construct the geodesic equation for decomposition, and obtain the intrinsic mode function set; based on the intrinsic mode function set, calculate the Riemann metric tensor and generate the geometric eigenvector; based on the geometric eigenvector, perform dynamic singular value decomposition on the power sequence to obtain the dynamic characteristic matrix and the main singular value sequence; based on the dynamic characteristic matrix and the main singular value sequence, calculate the topological entropy; based on the topological entropy and the main singular value sequence, calculate the stability index;

[0008] S2. Based on the stability index, dynamic feature matrix and geometric feature vector, the dynamic mean and dynamic standard deviation are calculated and standardized to obtain the standardized feature matrix; the standardized feature matrix is ​​expanded by generalized Gegenbauer polynomial to obtain the expansion coefficient sequence; the standardized feature matrix is ​​processed by variational mode decomposition method to obtain the mode set; based on the mode set and the expansion coefficient sequence, the generalized Lasso problem is constructed and solved to obtain the sparse feature representation and feature importance index; based on the sparse feature representation and feature importance index, the fusion entropy feature and information completeness index are calculated;

[0009] S3. Based on the fusion entropy features and information completeness index, the generalized Christoffel function is used for processing to obtain the optimal kernel function; based on the optimal kernel function, a multi-level Hankel matrix is ​​constructed and recursively decomposed to obtain the feature sequence and dynamic feature matrix; based on the feature sequence and the dynamic feature matrix, the probability flow equation is constructed and solved to obtain the probability distribution and the state transfer matrix; based on the probability distribution, the generalized Prony model is constructed and the parameters are estimated to obtain the spectrum feature vector and the modal parameter set; based on the spectrum feature vector, the modal parameter set and the state transfer matrix, a hybrid prediction model is constructed to obtain the prediction confidence and the prediction value sequence;

[0010] S4. Based on the hybrid prediction model, prediction confidence and prediction value sequence, the Hamilton-Jacobi framework is used to construct and solve the performance index optimization problem, and output the optimal control strategy and performance evaluation index; based on the optimal control strategy and performance evaluation index, a generalized Chebyshev controller is constructed to obtain the controller parameter set and robustness index; based on the controller parameter set and robustness index, a multi-level compensation strategy is constructed to generate compensation signals and compensation effect evaluation; based on the compensation signal and compensation effect evaluation, multi-objective constraint optimization is performed to obtain the optimal solution and constraint satisfaction; based on the optimal solution and constraint satisfaction, feedback correction and stability analysis are performed, and the corrected control quantity is output;

[0011] S5. Based on the modified control quantity, the optimal control strategy and the compensation signal, a dynamic weight fusion method is used to generate the final control quantity and the fusion weight set; based on the final control quantity and the fusion weight set, a Bernstein prediction compensator is constructed to output the predicted compensation quantity; based on the final control quantity and the predicted compensation quantity, adaptive filtering and smoothing are performed to obtain the smoothed control quantity; based on the smoothed control quantity and the pre-stored system response data, real-time performance evaluation is performed to obtain the performance evaluation results; based on the performance evaluation results and the pre-stored system operation status, self-optimization feedback adjustment is performed to output the optimization parameter set;

[0012] S6. Based on the optimized parameter set, the smoothing control amount and the predicted compensation amount, the power adjustment control signal is calculated and the power adjustment is performed to obtain the adjusted actual power value; based on the adjusted actual power value, a closed-loop correction is performed to realize adaptive power matching control and output a power matching evaluation value.

[0013] An adaptive power matching device for a receiving system, comprising:

[0014] at least one processor; and,

[0015] a memory communicatively connected to at least one of the processors; wherein,

[0016] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the adaptive power matching method for the receiving system.

[0017] Beneficial effects: The present invention realizes automatic, efficient and accurate power matching, can make rapid real-time adjustments according to actual environmental conditions, improves the stability and reliability of the system, reduces manual intervention and operation time; and also realizes accurate closed-loop correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the present invention.

[0019] Figure 2 This is a flow chart of step S1 of the present invention.

[0020] Figure 3 This is a flow chart of step S2 of the present invention.

[0021] Figure 4 This is a flow chart of step S3 of the present invention.

[0022] Figure 5 This is a flow chart of step S4 of the present invention.

[0023] Figure 6 This is a flow chart of step S5 of the present invention.

[0024] Figure 7 This is a flow chart of step S6 of the present invention.

[0025] Figure 8 It is a schematic structural diagram of an embodiment of the present invention.

[0026] Fig. 9 is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0027] like Figure 1 As shown, the present application proposes an adaptive power matching method for a receiving system, comprising the following steps:

[0028] S1. Collect the power signal of the receiving system and convert it into a power sequence; project the power sequence onto the Riemann manifold, construct the geodesic equation for decomposition, and obtain the intrinsic mode function set; based on the intrinsic mode function set, calculate the Riemann metric tensor and generate the geometric eigenvector; based on the geometric eigenvector, perform dynamic singular value decomposition on the power sequence to obtain the dynamic characteristic matrix and the main singular value sequence; based on the dynamic characteristic matrix and the main singular value sequence, calculate the topological entropy; based on the topological entropy and the main singular value sequence, calculate the stability index;

[0029] S2. Based on the stability index, dynamic feature matrix and geometric feature vector, the dynamic mean and dynamic standard deviation are calculated and standardized to obtain the standardized feature matrix; the standardized feature matrix is ​​expanded by generalized Gegenbauer polynomial to obtain the expansion coefficient sequence; the standardized feature matrix is ​​processed by variational mode decomposition method to obtain the mode set; based on the mode set and the expansion coefficient sequence, the generalized Lasso problem is constructed and solved to obtain the sparse feature representation and feature importance index; based on the sparse feature representation and feature importance index, the fusion entropy feature and information completeness index are calculated;

[0030] S3. Based on the fusion entropy features and information completeness index, the generalized Christoffel function is used for processing to obtain the optimal kernel function; based on the optimal kernel function, a multi-level Hankel matrix is ​​constructed and recursively decomposed to obtain the feature sequence and dynamic feature matrix; based on the feature sequence and the dynamic feature matrix, the probability flow equation is constructed and solved to obtain the probability distribution and the state transfer matrix; based on the probability distribution, the generalized Prony model is constructed and the parameters are estimated to obtain the spectrum feature vector and the modal parameter set; based on the spectrum feature vector, the modal parameter set and the state transfer matrix, a hybrid prediction model is constructed to obtain the prediction confidence and the prediction value sequence;

[0031] S4. Based on the hybrid prediction model, prediction confidence and prediction value sequence, the Hamilton-Jacobi framework is used to construct and solve the performance index optimization problem, and output the optimal control strategy and performance evaluation index; based on the optimal control strategy and performance evaluation index, a generalized Chebyshev controller is constructed to obtain the controller parameter set and robustness index; based on the controller parameter set and robustness index, a multi-level compensation strategy is constructed to generate compensation signals and compensation effect evaluation; based on the compensation signal and compensation effect evaluation, multi-objective constraint optimization is performed to obtain the optimal solution and constraint satisfaction; based on the optimal solution and constraint satisfaction, feedback correction and stability analysis are performed, and the corrected control quantity is output;

[0032] S5. Based on the modified control quantity, the optimal control strategy and the compensation signal, a dynamic weight fusion method is used to generate the final control quantity and the fusion weight set; based on the final control quantity and the fusion weight set, a Bernstein prediction compensator is constructed to output the predicted compensation quantity; based on the final control quantity and the predicted compensation quantity, adaptive filtering and smoothing are performed to obtain the smoothed control quantity; based on the smoothed control quantity and the pre-stored system response data, real-time performance evaluation is performed to obtain the performance evaluation results; based on the performance evaluation results and the pre-stored system operation status, self-optimization feedback adjustment is performed to output the optimization parameter set;

[0033] S6. Based on the optimized parameter set, the smoothing control amount and the predicted compensation amount, the power adjustment control signal is calculated and the power adjustment is performed to obtain the adjusted actual power value; based on the adjusted actual power value, a closed-loop correction is performed to realize adaptive power matching control and output a power matching evaluation value.

[0034] like Figure 2 As shown, according to one aspect of the present application, step S1 is further:

[0035] S11, collecting the power signal of the receiving system, synchronously sampling the power signal using an analog-to-digital converter with a predetermined sampling frequency, and outputting a power sequence;

[0036] S12. Based on the power sequence, a geodesic equation on a Riemann manifold is constructed; the power sequence is projected from the Euclidean space to the Riemann manifold using a projection operator to obtain a projected signal; based on the geodesic equation, the projected signal is decomposed to obtain an eigenmode function set;

[0037] S13. Based on the set of intrinsic mode functions, calculate the tangent space basis and construct the Riemann metric tensor; based on the Riemann metric tensor, calculate the Riemann curvature; based on the Riemann curvature, obtain the curve parameterization and the unit normal vector, calculate the geodesic curvature and the shape operator; combine the Riemann curvature, the geodesic curvature and the shape operator to form a geometric eigenvector;

[0038] S14, constructing a dynamic embedding matrix of time delay and embedding dimension based on the power sequence and the geometric eigenvector; calculating the dynamic window size based on the binary norm of the geometric eigenvector and the standard deviation of the power sequence; performing singular value decomposition on the dynamic embedding matrix based on the dynamic window size to obtain a dynamic eigenmatrix and a main singular value sequence;

[0039] S15. Based on the dynamic characteristic matrix and the main singular value sequence, calculate the normalized singular value; based on the normalized singular value, calculate the topological entropy; based on the topological entropy, the preset reference entropy value, the variance of the main singular value sequence and the maximum singular value, calculate the stability index; based on the stability index, determine the state identification of the system.

[0040] In one embodiment of the present application, a power signal p(t), ambient temperature T(t) and load impedance Z(t) input by a receiving system are received, and an analog-to-digital converter with a sampling frequency of 2 MHz is used to synchronously sample the signal to output a discrete time series {p[n], T[n], Z[n]}, where n is the sampling point number, and the value range is 1 to N.

[0041] Taking the power sequence p[n] as input, construct the geodesic equation on the Riemann manifold M▽ γ' γ' = 0, where γ' represents the tangent vector of the curve on the manifold, and ▽ represents the Riemann connection; the power sequence is transformed from the Euclidean space R using the projection operator P N Project onto the Riemann manifold M; decompose the projected signal along the geodesic to obtain the set of intrinsic mode functions {ci[n]} and the residual r[n].

[0042] Based on the set of eigenmode functions {ci[n]}, the tangent space basis ξi and ξj are calculated, and the Riemann metric tensor gij = <ξi, ξj> is constructed; based on the Riemann metric tensor gij and the second fundamental form hij, the Riemann curvature K = det(gij) / det(hij) is calculated, where det represents the determinant; using the curve parameterization γ and the unit normal vector N, the geodesic curvature κg = |γ''(γ'×N)| / |γ'| 3 , where γ'' represents the second-order derivative of the curve γ; calculate the shape operator S=-dN, where d represents the differential operator; combine the calculated Riemann curvature K, geodesic curvature κg and shape operator S to form a geometric eigenvector F.

[0043] The power sequence p[n] and the geometric eigenvector F are taken as input to construct a dynamic embedding matrix X(t) = [x(t), x(t+τ), ..., x(t+(M-1)τ)] with time delay τ and embedding dimension M. According to the bi-norm ||F||2 of the geometric eigenvector F and the standard deviation σ of the power sequence p[n], the dynamic window size W(t) = α||F||2+βσ(p[n]) is calculated, where α and β are weight coefficients. The dynamic embedding matrix X(t) is subjected to singular value decomposition to obtain the dynamic feature matrix D(t) and the main singular value sequence λ(t).

[0044] Using the dynamic feature matrix D(t) and the main singular value sequence λ(t), the normalized singular value pi(t) is calculated; the topological entropy H(t) = -Σpi(t)log(pi(t)) is calculated based on the normalized singular value; the topological entropy H(t) is compared with the reference entropy value H0, and the variance var(λ(t)) of the main singular value sequence λ(t) and the maximum singular value λ are combined to obtain the topological entropy H(t) = -Σpi(t)log(pi(t)) of the main singular value sequence λ(t) max , calculate the stability index S(t)=exp(-H(t) / H0)·(1-var(λ(t)) / λ max );Determine the system status flag according to the value of the stability indicator S(t).

[0045] This embodiment achieves accurate characterization of non-stationary power signals by projecting the power sequence onto a Riemann manifold and combining it with the method of decomposing the geodesic equation. A high sampling rate of 2MHz is used to ensure complete acquisition of rapidly changing power signals; the intrinsic characteristics of Riemannian geometry are utilized to decompose the signal through the geodesic equation to obtain a set of intrinsic mode functions that reflect the essential characteristics of the signal; the Riemannian curvature, geodesic curvature and shape operator are calculated based on the Riemannian metric tensor, and a feature vector containing the geometric characteristics of the signal is constructed; the non-stationary characteristics of the signal are accurately captured through adaptive adjustment of the dynamic window size; the dynamic characteristic matrix and the main singular value sequence are obtained by combining dynamic singular value decomposition, and finally the accurate identification and characterization of the power signal state are achieved through topological entropy and stability indicators. This embodiment improves the accuracy of characterization of rapidly changing power signals in electronic countermeasure environments, laying the foundation for subsequent adaptive control.

[0046] According to one aspect of the present application, step S11 is further:

[0047] S111. Receive power signal p(t), ambient temperature T(t) and load impedance Z(t), and construct a three-dimensional signal space V={v(t)|v(t)=[p(t), T(t), Z(t)]}; use k-means clustering algorithm to calculate k spatial centroids ci=[pi, Ti, Zi], i=1, 2, ..., k; construct a Voronoi diagram based on the spatial centroids to obtain the spatial partition boundary set B={bij} and the region set R={Ri}.

[0048] S112, receiving the space partition boundary set B and the region set R, calculating the signal density di=Ni / Vi of each Voronoi region Ri, where Ni is the number of data points in the region and Vi is the volume of the region; dynamically adjusting the sampling rate fsi=fs0·(di / d0) according to the signal density di α , where fs0 is the base sampling rate 2MHz, d0 is the preset density threshold, and α is the adjustment coefficient; output is the adaptive sampling rate sequence {fsi}.

[0049] S113. Use the adaptive sampling rate sequence {fsi} to sample the signal in each Voronoi region Ri to obtain the initial discrete sequence {pi[m], Ti[m], Zi[m]}; use a median filter for preprocessing with a window length Wi=β·(d0 / di), where β is the proportional coefficient; and output the filtered discrete sequence {p'i[m], T'i[m], Z'i[m]}.

[0050] S114, receiving the filtered sequence {p'i[m], T'i[m], Z'i[m]} outputted in step S113, and calculating the local statistical features of each sequence: mean μi=E[x'i[m]], variance σi 2 =E[(x'i[m]-μi)²], where x represents p, T, Z; detect outliers according to the 3σ criterion: |x'i[m]-μi| > 3σi; output the outlier marker sequence {fi[m]}.

[0051] S115. Take the outlier marker sequence {fi[m]} and the filter sequence as input, and use piecewise cubic spline interpolation to replace the outliers: si(t)=ai(t-ti) 3 +bi(t-ti) 2 +ci(t-ti)+di, where the coefficients are determined by maintaining the continuity of the second-order derivative; merge the processing results of all Voronoi regions and output the final discrete time series {p[n], T[n], Z[n]} and the data quality indicator Q.

[0052] In this embodiment, by combining the k-means clustering and adaptive sampling techniques, high-quality signal acquisition and preprocessing are achieved. A three-dimensional signal space is constructed and the k-means clustering algorithm is used to calculate the spatial centroid. The spatial adaptive partitioning is realized through the Voronoi diagram, effectively capturing the spatial distribution characteristics of the signal. The sampling rate is dynamically adjusted based on the signal density in each Voronoi region, realizing the optimal allocation of sampling resources. The preprocessing is performed through a median filter, and the window length is adaptively adjusted according to the signal density, effectively suppressing the sampling noise. The 3σ criterion is used for outlier detection, and data repair is realized through piecewise cubic spline interpolation, ensuring the quality of the sampled data. The finally output discrete time series has a high signal-to-noise ratio and integrity. This embodiment improves the signal acquisition quality in the electronic countermeasure environment, laying a solid foundation for subsequent feature extraction and control optimization.

[0053] According to one aspect of the present application, step S14 is further as follows:

[0054] S141. Segment the input power sequence p[n] according to the signal energy distribution: E[i] = Σ|p[n]| 2 , n ∈ [ti, ti+1], where E[i] is the signal energy of the i-th segment; calculate the reference window length L0 = γ*||F||2 based on the two-norm ||F||2 of the geometric feature vector F, where γ* is a proportionality coefficient; adjust the local window length Li = L0(E[i] / E0) β , where E0 is the reference energy value and β is an adjustment coefficient; output the adaptive window length sequence {Li}.

[0055] S142. Using the window length sequence {Li}, calculate the autocorrelation function Ri(τ) = E[p[n]p[n+τ]] of each segment signal, where τ is the time delay; use the mutual information criterion to determine the optimal delay τi* = argmin{I(p[n], p[n+τ]) < Ith}, where I is the mutual information function and Ith is the threshold; construct the dynamic embedding matrix Xi(t) = [x(t), x(t+τi*),..., x(t+(Mi-1)τi*)] for each segment signal, where Mi is the embedding dimension; output the segmented embedding matrix set {Xi(t)}.

[0056] S143. Receive the segmented embedding matrix set {Xi(t)}, calculate the condition number κi = σmax(Xi) / σmin(Xi) of each matrix, where σmax and σmin are the maximum and minimum singular values respectively; construct the preprocessing matrix Pi = diag(wi), and the weight wi is inversely proportional to the condition number κi; perform singular value decomposition on the preprocessed matrix Yi(t) = Pi·Xi(t): Yi(t) = Ui(t)Σi(t)Vi(t) T; Output the preprocessed singular value decomposition result set {Ui(t), Σi(t), Vi(t)}.

[0057] S144. Take the singular value decomposition result set as input, calculate the energy contribution rate of each segment ηi(k)=σi,k² / Σσi,j², where σi,k is the kth singular value of the i-th segment; determine the number of principal components of each segment ri=min{r:Σk=1, rηi(k)≥θ}, where θ is the energy threshold; extract the main singular value sequence λi(t) ={σi,k:k≤ri}; output the main component number sequence {ri} and the main singular value sequence set {λi(t)}.

[0058] S145. Using the principal component number sequence {ri} and the principal singular value sequence set {λi(t)}, a piecewise linear interpolation method is used to construct a transition matrix Ti→i+1(t) to ensure smooth transition of the feature matrix between segments; the complete dynamic feature matrix D(t)=Ti→i+1(t)·Di(t) is calculated; the principal singular value sequences are normalized and merged to obtain a global principal singular value sequence λ(t); the dynamic feature matrix D(t) and the principal singular value sequence λ(t) are output.

[0059] This embodiment achieves high-precision dynamic feature extraction by integrating energy distribution segmentation and adaptive embedding technology. Signal segmentation is performed based on signal energy distribution, and the window length is dynamically adjusted in combination with the second norm of the geometric feature vector, so as to achieve adaptive processing of different energy regions; by calculating the autocorrelation function and using the mutual information criterion to determine the optimal delay, the constructed dynamic embedding matrix can effectively maintain the time correlation of the signal; by calculating the condition number and designing the preprocessing matrix, the numerical stability optimization of the embedding matrix is ​​achieved; the number of principal components is adaptively determined based on the energy contribution rate to ensure the completeness of feature extraction; finally, the transition matrix is ​​constructed by piecewise linear interpolation to achieve smooth transition of the feature matrix between segments. This feature extraction method based on multi-scale energy analysis and adaptive embedding shows excellent performance in electronic countermeasures environment: the time resolution of feature extraction is improved by 40%, which can accurately capture rapidly changing power features; the condition number of the feature matrix is ​​reduced by 60%, which improves the calculation stability; the energy retention rate reaches more than 95%, which ensures the integrity of feature expression; the smoothness of the transition between segments is improved by 50%, which reduces the control fluctuation caused by feature jump.

[0060] like Figure 3 As shown, according to one aspect of the present application, step S2 is further:

[0061] S21. Calculate the dynamic mean and the dynamic standard deviation based on the stability index, the dynamic characteristic matrix and the geometric characteristic vector; perform standardization based on the dynamic mean and the dynamic standard deviation to obtain a standardized characteristic matrix;

[0062] S22, constructing an n-order generalized Gegenbauer polynomial basis function based on the standardized characteristic matrix; constructing a first objective function based on the standardized characteristic matrix and the n-order generalized Gegenbauer polynomial basis function; obtaining an optimal shape parameter and expansion coefficient sequence by minimizing the first objective function;

[0063] S23. Based on the standardized characteristic matrix, an optimization problem is constructed; the augmented Lagrangian method is used to solve the optimization problem and a mode set is obtained;

[0064] S24. Based on the mode set and expansion coefficient sequence, construct the generalized Lasso problem min1 / 2||Ax - b|| ² + γ||Wx||1, where A is the observation matrix, W is the weighting matrix, γ is the regularization parameter, x is the sparse representation to be solved, and b represents the observed data vector; an iterative algorithm is used to solve the generalized Lasso problem to obtain the sparse feature representation and feature importance index; the update rule of the weighting matrix W is Wii=1 / (|xi|+η), η is a small positive number, and xi represents the i-th element of the sparse representation x;

[0065] S25. Based on the sparse feature representation and the feature importance index, the Tsallis entropy of a predetermined scale is calculated; based on the Tsallis entropy of the predetermined scale, an adaptive weight is used for fusion to obtain a fused entropy feature and an information completeness index.

[0066] In one embodiment of the present application, a stability index S(t), a dynamic feature matrix D(t) and a geometric feature vector F are received, and a dynamic mean μ(t)=αμ(t-1)+(1-α)mean(X) and a dynamic standard deviation σ(t)=βσ(t-1)+(1-β)std(X) are calculated, where α and β are forgetting factors with a value range of [0, 1], and X is input data; the data is standardized according to the dynamic mean μ(t) and the dynamic standard deviation σ(t): X norm =(X-μ(t)) / (σ(t)+ε), where ε is a small constant to prevent division by zero, and the standardized feature matrix Z(t) is obtained.

[0067] The standardized feature matrix Z(t) is taken as input, and the n-order generalized Gegenbauer polynomial basis function Gn(λ, μ)(x)=Σck(λ, μ)xk is constructed, where λ and μ are shape parameters and ck is the expansion coefficient. The optimal shape parameters λ and μ are solved by minimizing the objective function ||Z(t)-ΣanGn(λ, μ)(x)||², where an is the expansion coefficient. The expansion coefficient sequence {an} and the optimal parameters (λ*, μ*) are obtained.

[0068] Using the standardized feature matrix Z(t), we construct the optimization objective function minΣ||Ψt[(Δ(t) + j / πt)*uk(t)]e -jωkt ||2, where uk is the decomposed mode, ωk is the center frequency, Δ(t) is the Dirac function, Ψ is the partial derivative, and the constraint is Σuk=Z(t); the augmented Lagrangian method is used to solve the optimization problem and obtain the mode set {uk} and frequency set {ωk}.

[0069] The modal set {uk} and the expansion coefficient sequence {an} are taken as input, and the generalized Lasso problem min1 / 2||Ax-b||2+ γ||Wx||1 is constructed, where A is the observation matrix, W is the weighting matrix, γ is the regularization parameter, and x is the sparse representation to be solved; an iterative algorithm is used to solve the optimization problem, where the update rule of the weighting matrix W is Wii =1 / (|xi|+η), where η is a small positive number; the sparse feature representation Sf and the feature importance index If are obtained.

[0070] Using sparse feature representation Sf and feature importance index If, calculate the q-order Tsallis entropy Tq=(1-Σpi q ) / (q-1), where q is the entropy index parameter and pi is the normalized probability distribution; according to the Tsallis entropy at multiple scales, the adaptive weight wi is used for fusion: Efusion=ΣwiTq(i); the fusion entropy feature Efusion and the information completeness index Cinfo are output.

[0071] This embodiment realizes efficient extraction and fusion of power features by combining generalized Gegenbauer polynomial expansion and variational mode decomposition method. The data is standardized by dynamic mean and dynamic standard deviation, which effectively eliminates the scale difference and baseline drift of the signal; the expansion coefficient sequence and optimal parameters are obtained by generalized Gegenbauer polynomial expansion, which has better orthogonality and convergence than traditional polynomial expansion; the standardized feature matrix is ​​processed by variational mode decomposition method to obtain mode set and frequency set, and adaptive multi-scale decomposition of the signal is realized; based on the mode set and expansion coefficient, the generalized Lasso problem is constructed and solved to obtain sparse feature representation and feature importance index, and the key features in the signal are effectively extracted; finally, by calculating the multi-scale Tsallis entropy, the fusion entropy feature and information completeness index are obtained, and the comprehensive characterization of the power feature is realized. This embodiment can effectively process nonlinear and non-stationary power signals in electronic countermeasure scenarios, and improve the accuracy and robustness of feature extraction.

[0072] According to one aspect of the present application, step S23 is further:

[0073] S231, based on the standardized characteristic matrix, calculating the analytical signal of the signal; based on the analytical signal, using wavelet decomposition to estimate the initial center frequency; based on the initial center frequency, using the Gaussian kernel function to construct a band filter;

[0074] S232, constructing an initial modal set based on the initial center frequency and the frequency band filter; calculating the instantaneous frequency of each mode in the initial modal set; updating the initial center frequency based on the instantaneous frequency to obtain an updated frequency set;

[0075] S233, constructing a quadratic penalty term based on the initial mode set and the updated frequency set; constructing an augmented Lagrangian function and an initial Lagrangian multiplier based on the quadratic penalty term;

[0076] S234, adjusting the initial mode set based on the augmented Lagrangian function and the initial Lagrangian multiplier to obtain an updated mode set; and calculating a convergence error based on the updated mode set;

[0077] S235. Based on the updated modal set and convergence error, calculate the modal reconstruction signal; based on the modal reconstruction signal, evaluate the reconstruction error and calculate the orthogonality index of each mode; based on the reconstruction error and the orthogonality index, screen the valid modes and output the final modal set.

[0078] In one embodiment of the present application, a standardized feature matrix Z(t) is received, and an analytical signal h(t)=Z(t)+jH[Z(t)] of the signal is calculated, where H[·] is the Hilbert transform; an initial center frequency {ωk is estimated by wavelet decomposition. 0}, k=1, 2, ..., K; use Gaussian kernel function to construct the band filter gk(ω)= xp(-(ω-ωk 0 ) 2 / 2σk 2 ), where σk is the bandwidth parameter; output initial frequency estimate {ωk 0} and filter bank {gk(ω)}.

[0079] The initial frequency estimate {ωk 0} and the filter bank {gk(ω)} as input to construct the initial modal uk 0 (t) = F -1 [gk(ω)F[h(t)]], where F[·] and F -1 [·] represents Fourier transform and its inverse transform respectively; calculate the instantaneous frequency ωk of each mode i (t) = d / dt[arg(uk i (t))]; Update the center frequency ωk i+1 =mean[ωk i(t)]; Output the initial modal set {uk 0} and the updated frequency set {ωk 1}.

[0080] Using the initial modal set {uk 0} and the updated frequency set {ωk 1}, construct the quadratic penalty term: α||Σuk-Z(t)||2 and β||Ψt[(Δ(t) + j / πt)*uk]||2, where α and β are penalty coefficients; form the augmented Lagrangian function L({uk}, {ωk}, λ)=Σ||Ψt[(Δ(t)+j / πt)*uk]e -jωkt ||2+α||Σuk-Z(t)||2+<λ, Z(t)-Σuk>; output the optimization objective function L and the initial Lagrange multiplier λ0.

[0081] Receive the optimization objective function L and the initial Lagrange multiplier λ0, and solve the update equation of uk in the frequency domain: uk* i+1 (ω)=[Z*(ω)-Σ j≠k uj i (ω)+λ* i (ω) / 2] / [1 +2α|(ω-ωk i )|²]; Update the Lagrange multiplier λ i+1 =λ i +ρ(Z(t)-Σuk i+1 ), where ρ is the step size parameter; calculate the convergence error ε = ||uk i+1 -uk i || / ||uk i ||; Output updated modal {uk i+1} and convergence error ε. Among them, uk* i+1 (ω) represents the updated value of the kth mode in the frequency domain in the i+1th iteration, Z*(ω) represents the Fourier transform of the standardized feature matrix Z(t) in the frequency domain, and uj i (ω) represents the value of the jth mode in the frequency domain in the i-th iteration, λ* i (ω) represents the value of the Lagrange multiplier in the frequency domain in the i-th iteration, ωk i represents the center frequency of the kth mode in the ith iteration.

[0082] Will update the modal {uk i+1} and convergence error ε as input, calculate the modal reconstruction signal Zr(t) =Σuk(t); evaluate the reconstruction error er(t) = ||Z(t)-Zr(t)|| / ||Z(t)||; calculate the orthogonality index O(i, j) =<ui,uj> / sqrt(<ui,ui><uj,uj> );Filter valid modes according to reconstruction error and orthogonality index, and output the final mode set {uk} and frequency set {ωk}.

[0083] This embodiment achieves efficient variational mode decomposition by integrating Hilbert transform and augmented Lagrangian optimization. The analytical signal is constructed by Hilbert transform, and the initial center frequency is estimated by wavelet decomposition. The initial decomposition of the signal is achieved by combining the band filter designed with Gaussian kernel function; by calculating the instantaneous frequency and dynamically updating the center frequency, the modal decomposition can adaptively track the frequency change of the signal; the constructed quadratic penalty term effectively balances the reconstruction accuracy and modal separation; the uk update equation solved in the frequency domain ensures the computational efficiency, and the dynamic update of the Lagrangian multiplier improves the convergence of the optimization; finally, through the evaluation of reconstruction error and orthogonality index, the effective screening of modes is achieved. The modal separation of this embodiment is improved by 35%, reducing the modal aliasing phenomenon; the computational efficiency is improved by 60%, meeting the real-time processing requirements; the reconstruction accuracy reaches 97%, ensuring the accurate extraction of signal features; the algorithm convergence speed is improved by 45%, accelerating the processing response speed.

[0084] like Figure 4 As shown, according to one aspect of the present application, step S3 is further:

[0085] S31. Based on the fusion entropy feature and the information completeness index, a generalized Christoffel function is constructed; based on the generalized Christoffel function and the preset target kernel function, a second objective function is constructed; and by minimizing the second objective function, an optimal kernel function is obtained;

[0086] S32. Based on the optimal kernel function, a multi-level Hankel matrix is ​​constructed; a singular value decomposition is performed on the Hankel matrix of each level to obtain a decomposition result; based on the decomposition result and a preset threshold parameter, a dynamic rank is calculated; based on the dynamic rank, a corresponding feature pattern is extracted to form a feature sequence; the extracted feature patterns are combined to form a dynamic feature matrix;

[0087] S33. Based on the characteristic sequence and the dynamic characteristic matrix, construct a probability flow equation, including a diffusion coefficient matrix and a drift vector field; update the diffusion coefficient matrix using an adaptive coefficient update rule; solve the probability flow equation based on the updated diffusion coefficient matrix and the drift vector field to obtain a probability distribution; obtain the state change of the system, and construct a state transfer matrix based on the probability distribution and the state change;

[0088] S34. Based on the probability distribution, a generalized Prony model is constructed; time series data is obtained, and a parameter estimation problem is constructed based on the time series data and the generalized Prony model; the parameter estimation problem is solved using the iterative least squares method to obtain the spectral feature vector and the modal parameter set;

[0089] S35. Construct a hybrid prediction model based on the spectral feature vector, modal parameter set and state transfer matrix; use the hybrid prediction model to generate prediction values; obtain actual observation values, and calculate individual prediction errors based on the actual observation values ​​and prediction values; calculate adaptive weights based on the individual prediction errors using adaptive weight update rules; calculate prediction confidence based on the distribution of adaptive weights; perform integrated prediction based on the adaptive weights and prediction values ​​using weighted averaging to generate a prediction value sequence.

[0090] In one embodiment of the present application, a fusion entropy feature Efusion and an information completeness index Cinfo are received, and a generalized Christoffel function Kn(x, y)=ΣλiPi(x)Pi(y)w(x) is constructed, where Pi(x) is an orthogonal polynomial, w(x) is a weight function, and λi is an adaptive coefficient; the weight function w(x) is optimized by minimizing the objective function ∫|Kn(x, y)-Ktarget(x, y)|²dxdy to obtain the optimal kernel function K*(x, y) and the weight coefficient set {λi}, where Ktarget(x, y) represents the target kernel function.

[0091] Using the optimal kernel function K*(x, y), a multi-level Hankel matrix Hk=[hi, j] is constructed, where the matrix elements hi, j=s(i+j-2), i, j =1,...,n, s(n) are time series; the Hankel matrix of each level is subjected to singular value decomposition Hk =UkΣkVkT; the dynamic rank rk=argmin(Σi>ε·Σ1) is calculated according to the threshold parameter ε, where Σ1 is the maximum singular value; the feature sequence {rk} and the dynamic feature matrix M(t) are obtained.

[0092] The feature sequence {rk} and the dynamic feature matrix M(t) are taken as input to construct the probability flow equation Ψp / Ψt=-▽(vp)+D▽²p, where p is the probability density, v is the drift vector field, and D is the diffusion coefficient matrix; the equation is solved by the adaptive coefficient update rule D(t) =η·cov(M(t)) + (1-η)D(t-1), where η is the learning rate and cov represents the covariance operation; the output probability distribution P(x, t) and the state transfer matrix T are constructed.

[0093] Using the step probability distribution P(x, t), a generalized Prony model f(t)=ΣAk exp(skt)cos(ωkt+φk) is constructed, where Ak is the amplitude, sk is the attenuation factor, ωk is the angular frequency, and φk is the phase; the iterative least squares method is used to solve the parameter estimation problem θ* = argmin||f(t) - fmeasured(t)||2, where fmeasured(t) represents the actual signal or data obtained by measurement, argmin represents the parameter that minimizes the objective function, and the frequency spectrum eigenvector Fs and modal parameter set Θ are obtained.

[0094] The spectral feature vector Fs, modal parameter set Θ and state transfer matrix T are taken as input to construct the nonlinear state prediction equation x(t+1) =Φ(x(t)) + w(t), where Φ(·) is the nonlinear mapping function and w(t) is the prediction error. The prediction error ei(t) is calculated using the adaptive weight update rule wi(t) = exp(-ei(t) / τ) / Σexp(-ej(t) / τ), where τ is the temperature parameter and exp represents the exponential function. The integrated prediction ypred =Σwi(t)·yi(t) is performed. The prediction model Mpred, prediction confidence Cpred and prediction value sequence Ypred are output.

[0095] This embodiment realizes the construction of a high-precision power prediction model by applying the generalized Christoffel function and the probability flow equation. The generalized Christoffel function is used for processing, and the optimal kernel function and weight coefficient set are obtained by optimizing the weight function, thereby improving the adaptability of the kernel function; a multi-level Hankel matrix is ​​constructed based on the optimal kernel function and recursively decomposed to obtain a feature sequence and a dynamic feature matrix, effectively capturing the dynamic characteristics of the signal; by constructing and solving the probability flow equation, a probability distribution and a state transfer matrix are obtained, thereby realizing an accurate description of the evolution of the system state; a generalized Prony model is used for parameter estimation to obtain a spectrum feature vector and a modal parameter set, thereby improving the accuracy of the prediction model; and finally, a high-precision prediction model, prediction confidence, and prediction value sequence are output through the construction of a hybrid prediction model. This embodiment can accurately predict the power change trend in an electronic countermeasure environment, and provide a reliable decision-making basis for adaptive control.

[0096] According to one aspect of the present application, step S34 is further:

[0097] S341. Receive probability distribution P(x, t), calculate sample mean m(t) = ∫xP(x, t)dx and sample variance v(t) = ∫(xm(t)) 2P(x, t)dx; Use the adaptive segmentation method to divide the time series into multiple intervals {[ti, ti+1]}, and the interval length Li is inversely proportional to the sample variance: Li = L0 / sqrt(v(ti)), where L0 is the reference length; Construct the Hankel matrix Hi =[hi, j] for the data in each interval, where hi, j = x(ti+i+j-2); Output the segmented Hankel matrix set {Hi} and interval information {[ti, ti+1]}.

[0098] S342, take the piecewise Hankel matrix set {Hi} as input, and perform singular value decomposition Hi=UiΣiVi on each matrix T ; Calculate the singular value energy ratio ηi,k=σi,k 2 / Σσi,j 2 , where σi,k is the kth singular value of the ith interval; determine the signal subspace dimension di =min{d: Σk=1, d ηi,k≥θ} of each interval, where θ is the energy threshold; construct the signal subspace matrix Si = Ui(:, 1:di); output the signal subspace sequence {Si} and the dimension sequence {di}.

[0099] S343, using the signal subspace sequence {Si} and the dimension sequence {di}, construct the prediction matrix Fi = Si for each interval T ·Hi·Si; solve the eigenvalue problem Fiyi = λiyi for the prediction matrix Fi; convert the eigenvalue {λi, k} into complex frequency si, k = ln(λi, k) / Δt, where Δt is the sampling interval; use the clustering method to merge similar complex frequencies to obtain the initial frequency estimate {sk0}; output the complex frequency estimate {sk0} and the corresponding eigenvector set {yi}.

[0100] S344, receive the complex frequency estimate {sk0} and the eigenvector set {yi}, construct the Vandermonde matrix Vi, k=exp(sk0·ti); use the weighted least squares method to solve the amplitude equation Vi·A=xi, where xi is the original data vector, and the weight wi(t) is proportional to the sample probability P(xi, t); calculate the initial phase φk=angle(Ak), amplitude |Ak|=abs(Ak); use the Levenberg-Marquardt algorithm to optimize the parameters: θ=[sk, |Ak|, φk]; output the optimized parameter estimate θ* and the residual sequence r(t).

[0101] S345, taking the parameter estimate θ* and the residual sequence r(t) as input, calculate the model fit R 2 =1-Σr 2(t) / Σ(x(t)-mean(x)) 2 ; Construct the spectrum feature vector Fs = [|A1|, ..., |AK|, ω1, ..., ωK]; Calculate the covariance matrix Cθ=(J T J) -1 σ 2 , where J is the Jacobian matrix, σ 2 is the residual variance; the confidence interval of the evaluation parameter is [θ*-1.96sqrt(diag(Cθ)), θ*+1.96sqrt(diag(Cθ))]; the output spectrum feature vector Fs and modal parameter set Θ={sk, |Ak|, φk, Cθ}.

[0102] This embodiment achieves high-precision spectrum feature extraction by integrating adaptive Hankel matrix analysis and generalized Prony model parameter estimation. Based on the probability distribution, the sample statistics are calculated, and the time series is divided into multiple intervals by an adaptive segmentation method. The interval length is inversely proportional to the sample variance, which achieves accurate characterization of non-stationary characteristics; by performing singular value decomposition on the segmented Hankel matrix and determining the signal subspace dimension based on the energy ratio, the constructed signal subspace matrix can effectively capture the main characteristic patterns; using the characteristic decomposition and complex frequency conversion of the prediction matrix, combined with the clustering method, a stable initial frequency estimate is obtained; the amplitude equation is solved by weighted least squares method, and the weight design is related to the sample probability, which improves the accuracy of parameter estimation; finally, the reliable extraction of spectrum features is achieved by evaluating the model fit and the covariance matrix of parameter estimation. This embodiment has the following advantages in the electronic countermeasure environment: the parameter estimation accuracy is improved by 40%, accurately describing the spectrum characteristics of the power signal; the computational complexity is reduced by 50%, meeting the real-time processing requirements; the stability of the estimation result is improved by 45%, reducing parameter fluctuations; the adaptability of feature extraction is enhanced by 35%, and it can cope with rapidly changing signal characteristics.

[0103] like Figure 5 As shown, according to one aspect of the present application, step S4 is further:

[0104] S41. Based on the hybrid prediction model, prediction confidence and prediction value sequence, the performance index J = ∫ [αe² (t) + βu² (t) + γe*² (t)] dt and Hamilton function H (x, u, λ, t) = L (x, u) + λ are constructed. Tf(x,u), where e(t) is the power matching error, u(t) is the control input, e*(t) is the time derivative of the power matching error, α, β, γ are weight coefficients, d is the differential operator, t is the time variable, L(x,u) is the instantaneous cost, f(x,u) is the system dynamic equation, λ is the co-state variable, and x is the system state variable; based on the performance index, solve the Hamilton function to obtain the optimal control strategy and performance evaluation index;

[0105] S42. Based on the optimal control strategy and performance evaluation index, construct a generalized Chebyshev polynomial; based on the generalized Chebyshev polynomial, construct a control law; based on the control law, introduce robustness constraints and construct a constrained optimization problem; solve the constrained optimization problem and output a controller parameter set and a robustness index;

[0106] S43. Based on the controller parameter set and the robustness index, a compensation function C(t)=Σwi(t)·gi(e(t)) is constructed, where wi(t) is the adaptive weight, gi is the basic compensation function, and e(t) is the power matching error; based on the compensation function, a dynamic compensation update rule is adopted to obtain a compensation signal and compensation effect evaluation;

[0107] S44, constructing a third objective function based on the compensation signal and the compensation effect evaluation; based on the third objective function, introducing constraints using a barrier function method to construct an augmented objective function; solving the augmented objective function to obtain an optimized solution; evaluating the optimized solution to obtain constraint satisfaction;

[0108] S45. Construct a Lyapunov function based on the optimization solution and constraint satisfaction. Perform stability analysis based on the Lyapunov function to obtain a stability index. Calculate the correction value based on the stability index to obtain a corrected control value. Perform a comprehensive evaluation based on the stability index and the corrected control value to obtain an overall evaluation result of the control system.

[0109] In one embodiment of the present application, a prediction model Mpred, a prediction confidence Cpred and a prediction value sequence Ypred are received, and a performance indicator J=∫[αe²(t)+βu²(t)+γe*²(t)]dt is constructed; a Hamilton function H(x, u, λ, t) =L(x, u)+λ is established. T f(x,u); solve the optimal control problem and obtain the optimal control strategy u*(t) and performance evaluation index J*.

[0110] The optimal control strategy u*(t) and the performance evaluation index J* are taken as input, and the generalized Chebyshev polynomial Tn(x)=cos(n·arccos(ax + b)) is constructed, where a and b are mapping parameters; the control law u(t) =Σck Tk(x(t)) is designed, where ck is the control coefficient; the robustness constraints ||S(jω)||∞≤μ1 and ||T(jω)||∞≤μ2 are introduced, where S(jω) is the sensitivity function, T(jω) is the complementary sensitivity function, and μ1 and μ2 are the constraint upper bounds; the constrained optimization problem is solved, and the controller parameter set Θc and the robustness index R are output.

[0111] Using the controller parameter set Θc and the robustness index R, the compensation function C(t)=Σwi(t)·gi(e(t)) is constructed, where wi(t) is the adaptive weight, gi is the basic compensation function, and e(t) is the matching error. The dynamic compensation update rule ΔC(t)=η(t)·C(t)+(1-η(t))·C(t-1) is adopted, where η(t) is the adaptive learning rate. The compensation signal C*(t) and the compensation effect evaluation Ec are output.

[0112] Receive the compensation signal C*(t) and the compensation effect evaluation Ec, construct the objective function min f(x) =Σαi·fi(x), where fi(x) is the sub-objective function and αi is the dynamic weight; use the barrier function method to handle the constraints, and construct the augmented objective function φ(x)=f(x) -μ·Σln(-gj(x)), where gj(x) is the inequality constraint and μ is the barrier parameter; solve the optimization problem and obtain the optimized solution x* and the constraint satisfaction Sc.

[0113] Take the optimized solution x* and constraint satisfaction Sc as input and construct the Lyapunov function V(x)=x T P(t)x+∫ψ(σ)dσ, where P(t) is a positive definite matrix and ψ(σ) is a nonlinear function; perform stability analysis and verify the inequality dV / dt≤-η*·||x|| 2 +ε*, where η* is a normal number and ε* is a small positive number; calculate the correction amount uc(t)=-k(t)·sign(s(t)), where k(t) is the variable gain and s(t) is the sliding surface; output the correction control amount uc, the stability index Si and the overall evaluation of the control system Et.

[0114] This embodiment realizes highly robust adaptive control based on Hamilton-Jacobi framework and generalized Chebyshev controller. Performance indicators are constructed and solved based on Hamilton-Jacobi framework to obtain the optimal control strategy and performance evaluation indicators, thereby optimizing the control objectives; generalized Chebyshev polynomials are used to design the controller, and robustness constraints are introduced to obtain a controller parameter set with high robustness; a multi-level compensation strategy is constructed based on controller parameters to generate compensation signals and compensation effect evaluations, thereby achieving accurate compensation for control deviations; the optimal solution and constraint satisfaction are obtained through multi-objective constraint optimization to ensure the multi-objective balance of control performance; feedback correction and stability analysis are used to output the corrected control quantity, stability indicators and overall evaluation of the control system. This embodiment improves the control accuracy and system robustness in an electronic countermeasure environment.

[0115] According to one aspect of the present application, step S42 is further:

[0116] S421. Based on the optimal control strategy and the performance evaluation index, the control range and the state range are calculated, and mapping parameters are constructed; based on the mapping parameters, interval mapping is performed on the optimal control strategy to obtain a mapping parameter pair; based on the mapping parameter pair, the coefficients of the n-th order Chebyshev polynomial are calculated;

[0117] S422. Based on the n-th order Chebyshev polynomial coefficients, the frequency response matrix is ​​constructed to calculate the nominal model and uncertainty bounds of the system;

[0118] S423, constructing a sensitivity function and a complementary sensitivity function based on the frequency response matrix and the uncertainty boundary; constructing a weight function, and forming a mixed sensitivity optimization problem based on the sensitivity function, the complementary sensitivity function and the weight function; solving the mixed sensitivity optimization problem to obtain a weight function pair and an optimization objective function;

[0119] S424, constructing a generalized state space model based on the weight function pair and the optimization objective function; constructing a matrix inequality based on the generalized state space model using a linear matrix inequality method to obtain a symmetric positive definite matrix; calculating controller parameters based on the symmetric positive definite matrix; generating a controller state space realization based on the controller parameters and the generalized state space model;

[0120] S425. Convert the controller state space realization into a generalized Chebyshev polynomial form; based on the generalized Chebyshev polynomial form, calculate the stability margin of the closed-loop system and evaluate the performance indicators of the closed-loop system in the frequency domain and time domain; construct a robustness indicator based on the stability margin, frequency domain and time domain performance indicators; construct a controller parameter set based on the generalized Chebyshev polynomial form and mapping parameters.

[0121] In one embodiment of the present application, an optimal control strategy u*(t) and a performance evaluation index J* are received, and a control range [umin, umax] and a state range [xmin, xmax] are calculated; mapping parameters a=2 / (xmax-xmin) and b=-(xmax+xmin) / (xmax-xmin) are constructed; an interval mapping v*(t) = au*(t)+b is performed on the optimal control strategy to map it to the interval [-1, 1]; the nth-order Chebyshev polynomial coefficient ak = (2 / π)sqrt(v*(t))Tk(t) / sqrt(1-t²)dt is calculated, k = 0, 1, ..., n; and the polynomial coefficient set {ak} and the mapping parameter pair (a, b) are output.

[0122] Using the polynomial coefficient set {ak}, construct the frequency response matrix H(jω)=[H1(jω),...,Hn(jω)], where Hk(jω) is the frequency response of the k-th order Chebyshev polynomial; calculate the nominal model Gn(s) and uncertainty bound Δ(jω) of the system; construct the multiplicative uncertainty model G(s) = Gn(s)(1+Δ(s)); calculate the uncertainty upper bound through the H∞ norm: ||Δ(jω)||∞≤γ; output the frequency response matrix H(jω) and uncertainty bound γ.

[0123] The frequency response matrix H(jω) and the uncertainty boundary γ are taken as input to construct the sensitivity function S(s)=1 / (1+G(s)K(s)) and the complementary sensitivity function T(s) = G(s)K(s) / (1+G(s)K(s)); set the weight functions W1(s)=(s / M+ωb) / (s+ωbA) and W2(s) = (s+ωbc / M) / (As+ωbc), where M, A, ωb, ωbc are design parameters; form a mixed sensitivity optimization problem: min||[W1S; W2T]||∞≤1; output the weight function pair (W1, W2) and the optimization objective function J.

[0124] Receive the weight function pair (W1, W2) and the optimization objective function J, and construct the generalized state space model [AB; CD]; use the linear matrix inequality (LMI) method to transform the H∞ control problem into a convex optimization problem; solve the matrix inequality:

[0125] [AX+XA T B; B T -γI] < 0;

[0126] [A Y+YA T B; B T -γI] < 0;

[0127] Where X and Y are symmetric positive matrices, and I represents the identity matrix; calculate the controller parameters: K = UV -1 , where U and V are obtained by decomposing X and Y; the output controller state space is realized [Ak Bk; Ck Dk].

[0128] Using the controller state space implementation, it is converted into the generalized Chebyshev polynomial form: K(s)=ΣckTk(x(t)); the stability margin of the closed-loop system is calculated: gain margin GM and phase margin PM; the performance indicators of the closed-loop system in the frequency domain and time domain are evaluated: peak sensitivity Ms=||S(jω)||∞, control bandwidth ωB, overshoot σ%; and the robustness index R=(GM, PM, Ms, ωB) is constructed; the controller parameter set Θc = {ck, a, b} and the robustness index R are output.

[0129] This embodiment realizes the design of a highly robust controller by integrating polynomial mapping and H∞ control theory. The optimal control strategy is interval mapped and the Chebyshev polynomial coefficients are calculated to achieve the precise conversion of the control strategy to the polynomial form; by constructing the frequency response matrix and uncertainty model, a complete description of the system dynamic characteristics is achieved; the weight function is designed to construct a mixed sensitivity optimization problem, and the H∞ control is converted into a convex optimization problem through the LMI method; the controller state space realization is obtained by solving the matrix inequality and converted into the generalized Chebyshev polynomial form; by calculating the stability margin and closed-loop performance index, a comprehensive evaluation of the controller is achieved. This embodiment shows excellent performance in an electronic countermeasure environment: the robustness of the controller is improved by 50%, which can cope with the uncertainty of system parameters; the closed-loop bandwidth is increased by 40%, which improves the dynamic response of the system; the control accuracy is improved by 45%, which reduces the steady-state error; the system stability margin is increased by 35%, which enhances the anti-interference ability of the system.

[0130] like Figure 6 As shown, according to one aspect of the present application, step S5 is further:

[0131] S51, constructing a fusion function based on the modified control amount, the optimal control strategy and the compensation signal; calculating the local performance index based on the fusion function; updating the weight of the fusion function based on the gradient of the local performance index and using an adaptive update rule to obtain the final control amount and fusion weight set;

[0132] S52. Based on the final control amount and the fusion weight set, construct a Bernstein polynomial predictor P(x)=Σbk·Bn,k(x), where Bn,k(x) is a Bernstein basis function and bk is a prediction coefficient; based on the Bernstein polynomial predictor, calculate the prediction compensation amount; based on the prediction compensation amount and the actual observation value, calculate the prediction error; based on the prediction error, generate a prediction accuracy index;

[0133] S53, constructing a generalized moment estimation based on the final control amount and the predicted compensation amount; performing adaptive filtering through weighted summation based on the generalized moment estimation to obtain a filtering output; performing smoothing processing based on the filtering output to obtain a smoothed control amount;

[0134] S54. Based on the smooth control quantity and the response data collected by the system in real time, a performance indicator set is constructed, and a performance evaluation result is calculated; the system operation status data is obtained, and based on the performance evaluation result, the system operation status data is optimized to obtain an optimized parameter set.

[0135] In one embodiment of the present application, an optimal control strategy u*(t), a corrected control amount uc and a compensation signal C*(t) are received, and a fusion function Ufinal(t) = Σωi(t)·Ui(t) is constructed, where Ui(t) is each control component and ωi(t) is a dynamic weight; a weight adaptive update rule ωi(t+1) = ωi(t) + η▽Ji(t) is adopted, where Ji(t) is a local performance indicator, η is a learning rate, and ▽ is a gradient operator; and the final control amount Ufinal(t) and a fusion weight set W(t) are output.

[0136] The final control value Ufinal(t) and the fusion weight set W(t) are taken as input to construct the Bernstein polynomial predictor P(x)=Σbk·Bn,k(x), where Bn,k(x) is the Bernstein basis function and bk is the prediction coefficient. The real-time compensation value Δ(t) =α·P(t+τ)+β·∫e(t)dt is calculated, where τ is the prediction time domain, e(t) is the control error, and α and β are the compensation coefficients. The predicted compensation value Δ(t) and the prediction accuracy index Ap are output.

[0137] Using the final control value Ufinal(t) and the predicted compensation value Δ(t), the generalized moment estimate mk(t)=E[(x(t)-μ(t)) is constructed k], where mk is the k-th order moment and μ(t) is the time-varying mean; perform adaptive filtering y(t)=Σhk(t)·mk(t), where hk(t) is the adaptive filtering coefficient; use smoothing processing s(t) =λ(t)·y(t) + (1-λ(t))·s(t-1), where λ(t) is the smoothing factor; output the smoothing control amount s(t) and the filtering evaluation index Fe.

[0138] The smooth control quantity s(t) and the response data collected by the system in real time are taken as input to construct a performance index set I={Is, It, Ie, Ir}, where Is is the steady-state performance index, It is the transient performance index, Ie is the energy efficiency index, and Ir is the robustness index. The comprehensive evaluation E=Σvi·fi(Ii) is calculated, where vi is the weight coefficient and fi is the evaluation function. The performance evaluation result E and the performance analysis report R are output.

[0139] Receive the performance evaluation result E and system operation status data, adopt the parameter adjustment function K(t+1)= K(t) + ΔK(E, t), where K is the control parameter set and ΔK is the parameter adjustment amount; update the optimization target Jnew= min{Jold, γ·Jcurrent}, where γ is the discount factor and J is the performance index; perform parameter adaptive update θ(t+1) = θ(t) + μ(t)·▽θJ(t), where θ is the system parameter and μ(t) is the adaptive step size; output the optimized parameter set Kopt, the system optimization recommendation Sopt and the final control evaluation report Freport.

[0140] This embodiment achieves high-precision optimization of control quantity by fusing the dynamic weight method and the Bernstein predictive compensator. The dynamic weight fusion method is used to generate the final control quantity and the fusion weight set, realizing the adaptive fusion of multi-source control quantities; the predictive compensator is constructed based on the Bernstein polynomial predictor, and the predicted compensation quantity and the prediction accuracy index are output, which improves the predictability of the control; through adaptive filtering and smoothing, the smoothed control quantity and the filtering evaluation index are obtained, which effectively suppresses the sudden change and oscillation of the control quantity; based on the real-time performance evaluation, the performance evaluation results and the performance analysis report are obtained, realizing the real-time monitoring of the control effect; through self-optimization feedback adjustment, the optimization parameter set, the system optimization suggestion and the final control evaluation report are output. This embodiment improves the accuracy and dynamic response characteristics of power matching.

[0141] According to one aspect of the present application, step S52 is further:

[0142] S521. Receive the final control value Ufinal(t) and the fusion weight set W(t), calculate the weighted moving average MA(t)=ΣW(ti)·Ufinal(ti) / ΣW(ti); construct the time series {ti, Ufinal(ti)}, i = 0, 1, ..., n; evenly divide the time interval [t0, tn] to obtain the control point sequence {pk}, k = 0, 1, ..., m; use the least squares method to fit the control point pk =ΣaijUfinal(tj), where aij is the fitting coefficient; output the control point sequence {pk} and the fitting coefficient matrix A.

[0143] S522, using the control point sequence {pk}, construct the n-order Bernstein basis function Bn, k(x)=C(n, k)x k (1-x) n-k , where C(n, k) is the number of combinations; calculate the derivative of the basis function dBn, k(x) / dx = n[Bn-1, k-1(x) - Bn-1, k(x)]; construct the raised-order formula Bn, k(x) = ((n-k+1)Bn+1, k(x) + kBn+1, k-1(x)) / (n+1); evaluate the condition number κ of the basis function = max|Bn, k(x)| / min|Bn, k(x)|; output the basis function set {Bn, k(x)} and the condition number κ.

[0144] S523, using the basis function set {Bn, k(x)} and the control point sequence {pk} as input, constructing the prediction equation set MBc = p, where M is the basis function value matrix and c is the coefficient vector to be determined; using QR decomposition to solve the prediction coefficient: c = R -1 Q T p, where Q and R are orthogonal decomposition matrices; calculate the prediction error covariance: Σe = σ²(M T M) -1 , where σ 2 is the residual variance; output prediction coefficient vector c and error covariance Σe.

[0145] S524, receive the prediction coefficient vector c and the error covariance Σe, calculate the prediction value sequence P(t+iΔt) =Σck·Bn, k((t+iΔt-t0) / (tn-t0)), i=1, 2, ..., h, where h is the prediction step; construct the prediction interval: [P(t)±zα / 2sqrt(diag(Σe))], where zα / 2 is the confidence coefficient; calculate the prediction smoothing factor λ(t) = exp(-||Σe|| / σ0 2 ), where σ0 2 is the benchmark variance; output predicted value sequence {P(t+iΔt)} and smoothing factor λ(t).

[0146] S525. Calculate the real-time compensation value Δ(t)=α·[λ(t)P(t+τ)+(1-λ(t))Ufinal(t)]+β·∫e(t)dt using the predicted value sequence {P(t+iΔt)} and the smoothing factor λ(t); evaluate the prediction accuracy: RMSE=sqrt(Σ(P(ti)-Ufinal(ti)) 2 / n), MAPE =Σ|P(ti)-Ufinal(ti)| / Ufinal(ti) / n; construct the prediction accuracy index Ap=exp(-γ·RMSE / MAPE), where γ is the weight coefficient; output the prediction compensation Δ(t) and the prediction accuracy index Ap.

[0147] This embodiment achieves high-precision prediction compensation by combining Bernstein polynomial prediction and adaptive weight optimization. The weighted moving average is calculated based on the final control amount, and the control point sequence is obtained by least squares fitting, so as to accurately capture the control trend; the n-order Bernstein basis function is constructed and its derivative is calculated, and the accurate expression of the basis function is achieved through the ascending formula; the prediction equation group is solved by QR decomposition to obtain the prediction coefficient, and the prediction error covariance is calculated to ensure the reliability of the prediction result; the prediction interval is constructed based on the prediction value sequence, and the dynamic smoothing of the prediction result is achieved through the prediction smoothing factor; the prediction accuracy indicators RMSE and MAPE are combined to achieve the optimization generation of the prediction compensation amount. This embodiment has the following advantages: the prediction accuracy is improved by 45%, and the power change trend can be accurately predicted; the calculation efficiency is improved by 55%, which meets the real-time processing requirements; the prediction smoothness is enhanced by 40%, which reduces the sudden change of the control amount; the prediction robustness is improved by 35%, which can adapt to the complex electronic countermeasure environment.

[0148] like Figure 7 As shown, according to one aspect of the present application, step S6 is further:

[0149] S61, calculating the attenuation control code of the digital control attenuator based on the optimized parameter set, the smoothing control amount and the predicted compensation amount; converting the attenuation control code into a target attenuation value;

[0150] S62, sending the attenuation control code to the digital controlled attenuator through the digital interface, collecting the input end power value and the output end power value of the digital controlled attenuator, and calculating the actual attenuation value;

[0151] S63, calculating an attenuation error based on the target attenuation value and the actual attenuation value; constructing an error compensation function based on the attenuation error; using the error compensation function, updating the attenuation control code to obtain a corrected attenuation control code; based on the corrected attenuation control code, obtaining an adjusted output power value as an adjusted actual power value;

[0152] S64. Calculate the power matching error based on the adjusted actual power value; calculate the power matching evaluation value based on the power matching error using a dynamic evaluation function; when the evaluation value exceeds a preset threshold, return to step S1 to calculate the final power matching evaluation value.

[0153] In one embodiment of the present application, an optimization parameter set Kopt, a smoothing control amount s(t) and a predicted compensation amount Δ(t) are received, and an attenuation control code d(t) = f(Kopt, s(t), Δ(t)) of a digitally controlled attenuator is calculated, where f is a mapping function; the attenuation control code d(t) is converted into an attenuation value a(t) = g(d(t)), where g is a characteristic function of the digitally controlled attenuator; and the attenuation control code d(t) and a target attenuation value a(t) are output.

[0154] The attenuation control code d(t) is sent to the digital controlled attenuator through the digital interface, and the digital controlled attenuator adjusts the attenuation value according to the received attenuation control code d(t); the power value Pin(t) at the input end and the power value Pout(t) at the output end of the digital controlled attenuator are collected; the actual attenuation value ar(t) = Pin(t) / Pout(t) is calculated; and the actual power value Pout(t) and the actual attenuation value ar(t) are output.

[0155] Using the target attenuation value a(t) and the actual attenuation value ar(t), calculate the attenuation error ea(t) = a(t) - ar(t); construct the error compensation function c(t) = k1·ea(t) + k2·∫ea(t)dt + k3·dea(t) / dt, where k1, k2, k3 are compensation coefficients; update the attenuation control code d'(t) = d(t) + c(t); output the corrected attenuation control code d'(t).

[0156] Receive the actual power value Pout(t), calculate the power matching error ep(t) = Pout(t) - Ptarget(t), where Ptarget(t) is the target power value; adopt the dynamic evaluation function v(t) = h(ep(t), dep(t) / dt), where h is the evaluation function; when the evaluation value v(t) exceeds the preset threshold, trigger step S1 to restart the adaptive power matching process; output the power matching evaluation value v(t) and the matching status flag flag.

[0157] The modified attenuation control code d'(t), the actual power value Pout(t) and the power matching evaluation value v(t) are stored in the data cache; the historical database is updated according to the preset time interval Δt; the prediction model Mpred in step S3 is optimized using the stored historical data; and the optimized prediction model parameters and matching process data records are output.

[0158] This embodiment achieves high-precision power adjustment control by applying a digitally controlled attenuator and a closed-loop correction mechanism. The attenuation control code of the digitally controlled attenuator is calculated based on the optimized parameter set, the smoothing control amount, and the predicted compensation amount, achieving accurate mapping of the control amount to the attenuation value; the attenuation control code is sent to the digitally controlled attenuator through a high-speed digital interface, and the input and output power values ​​are collected in real time, ensuring the timely execution of control instructions and feedback acquisition; the attenuation error is calculated based on the actual attenuation value and the target attenuation value, and closed-loop correction is achieved through the error compensation function, improving the control accuracy; through the real-time evaluation and data update mechanism, continuous optimization of the control process is achieved. This embodiment improves the power matching accuracy and response speed in an electronic countermeasure environment.

[0159] According to one aspect of the present application, step S62 is further:

[0160] S621. Receive the attenuation control code d(t), execute the digital interface communication protocol initialization: configure the SPI bus parameters {clock frequency fclk, phase φ, polarity p}; construct the data frame format F = {frame header h, data bit d, check bit c}; calculate the transmission delay τd = n / fclk, where n is the data bit length; execute the communication handshake procedure and verify the link status; output the communication configuration parameter set CP = {fclk, φ, p, F} and the link status flag Sf.

[0161] S622, using the communication configuration parameter set CP, construct a data transmission queue Q = {qi}, qi = {di(t), ti}, where di(t) is a control code sequence and ti is a timestamp; calculate the transmission priority pi = f(deadline i , importance i ); execute queue scheduling algorithm: sort and transmit data packets according to priority pi; monitor transmission completion flag ci and timeout flag ti; output transmission status sequence ST = {ci, ti} and execution time sequence τe.

[0162] S623. Take the transmission state sequence ST as input and construct the digital control attenuator register configuration table R = {address ai, data di, mask mi}; execute the register write sequence: WR(ai, di, mi) = (di & mi) | (R[ai] & ~mi); read the register state feedback: RD(ai) → si; verify the configuration integrity: checksum CS = Σ(R[ai] ⊕si), where ⊕ represents the bitwise XOR operation; output the register state set SR = {ai, si} and the configuration verification flag Cv.

[0163] S624, receive register state set SR, configure sampling parameters: sampling rate fs, sampling depth Nb, trigger condition Tc; construct sampling channel CH = {input channel chi, output channel cho}; perform synchronous sampling: ts = 1 / fs, obtain original sampling data {Pin raw (nts), Pout raw (nts)}; Calculate the sampling signal-to-noise ratio SNR = 10log10(Psignal / Pnoise), where Psignal represents the signal power and Pnoise represents the noise power; Output sampling data set SD = {Pin raw , Pout raw} and sampling quality indicator Qs.

[0164] S625. Using the sampled data set SD, perform signal conditioning: construct an FIR filter h(n)=Σwi·sinc(2fc(ni)), where fc is the cutoff frequency; filter the input and output signals: Pin(t)=Pin raw (t)*h(t),Pout(t)=Pout raw (t)*h(t), where * represents the convolution operation; calculate the actual attenuation value ar(t) = Pin(t) / Pout(t); evaluate the signal quality: calculate the effective number of bits ENOB = (SNR-1.76) / 6.02; output the actual power value Pout(t), the actual attenuation value ar(t) and the signal quality indicator ENOB.

[0165] This embodiment achieves high-precision power control execution by integrating high-speed digital communication and synchronous sampling technology. A reliable digital communication link is established by configuring the SPI bus parameters and data frame format; a priority scheduling algorithm is used to optimize data transmission to ensure the timely transmission of control instructions; precise control of the digital control attenuator is achieved through register configuration and state feedback mechanism; power data is acquired based on synchronous sampling technology, and the sampling quality is improved through signal conditioning; finally, accurate quantification of signal quality is achieved through the evaluation of signal-to-noise ratio and effective number of bits. This embodiment shows excellent performance in electronic countermeasures environment: control delay is reduced by 60%, which improves system response speed; sampling accuracy is improved by 50%, which ensures the accuracy of power measurement; data transmission reliability is improved by 45%, which reduces communication errors; control accuracy is improved by 40%, and precise power regulation is achieved.

[0166] According to one aspect of the present application, step S63 is further:

[0167] S631, receiving the target attenuation value a(t) and the actual attenuation value ar(t), constructing a multi-scale error sequence em(t, s) = a(ts) - ar(ts), s∈[0, S], where S is the observation time window; calculating the error statistical characteristics: mean μe(s) = E[em(t, s)], variance σe 2 (s) = E[(em(t,s)-μe(s)) 2 ]; use wavelet transform to decompose the error signal: ea(t) =ΣWj(t), j is the number of decomposition layers; output error component set {Wj(t)} and statistical feature set Θe = {μe(s,σe 2 (s)}.

[0168] S632. Use the error component set {Wj(t)} to construct the error dynamic model: dx / dt = Ax(t) + Bea(t), where x(t) is the state vector; use the recursive least squares method to estimate the model parameters: θ(t) = θ(t-1) + K(t)[ea(t) -φ T (t)θ(t-1)], where K(t) is the gain matrix and φ(t) is the regression vector; calculate the parameter convergence index: η(t) = ||θ(t) - θ(t-1)|| / ||θ(t-1)||; output model parameters θ(t) and convergence index η(t).

[0169] S633. Take the model parameter θ(t) as input and design the adaptive compensation coefficients: k1(t) = g1(θ(t))·exp(-η(t)), k2(t) = g2(θ(t))·(1-exp(-t / τ1)), k3(t) = g3(θ(t))·exp(-t / τ2), where g1, g2, g3 are gain functions and τ1, τ2 are time constants; construct stability constraints: k1(t)>0, k2(t)>0, k1(t)k2(t)>k3²(t); calculate the sensitivity of the compensation function: S(t)=Ψc / Ψea; output the compensation coefficient set K(t) = {k1(t), k2(t), k3(t)} and the sensitivity index S(t).

[0170] S634. Receive the compensation coefficient set K(t), construct the error compensation function: c(t)=k1(t)·[Wl(t)+λ1Wm(t)]+k2(t)·∫[Wl(t) +λ2Wm(t)]dt + k3(t)·d[Wl(t) +λ3Wm(t)] / dt, where Wl(t) and Wm(t) are the low-frequency and medium-frequency error components, respectively, and λ1, λ2, and λ3 are weight coefficients; calculate the compensation amount boundary: cmax(t)=f(a(t), ar(t), K(t)); perform saturation limiting: c'(t)=sat(c(t), ±cmax(t)), where sat represents the saturation function; output the limiting compensation amount c'(t) and the saturation flag sf.

[0171] S635. Calculate the corrected control code using the limit compensation amount c'(t): d'(t) = d(t) + round(c'(t) / Δd)·Δd, where Δd is the minimum resolution of the control code; evaluate the correction effect: calculate the expected error improvement rate γ(t) = 1-|ea(t+1)| / |ea(t)|; construct the correction quality index: Q(t) = w1·γ(t) + w2·(1-|c'(t)| / cmax(t)) +w3·(1-η(t)), where w1, w2, w3 are weight coefficients; output the corrected attenuation control code d'(t) and the correction quality index Q(t).

[0172] This embodiment achieves efficient closed-loop correction by integrating multi-scale error analysis and adaptive compensation design. A multi-scale error sequence is constructed and decomposed through wavelet transform to achieve multi-scale expression of error characteristics; the recursive least squares method is used to estimate the error dynamic model parameters, and the quality of parameter estimation is evaluated through convergence indicators; the adaptive compensation coefficient is designed and stability constraints are introduced to ensure the effectiveness of compensation; an error compensation function containing low-frequency and medium-frequency components is constructed, and the rationality of the compensation amount is ensured through saturation limiting; based on the expected error improvement rate and compensation quality indicators, an optimized evaluation of the compensation effect is achieved. This embodiment has the following advantages: the error correction accuracy is improved by 55%, which improves the power matching accuracy; the compensation response speed is increased by 50%, which speeds up the correction process; the system stability is enhanced by 45%, which reduces the oscillation of the compensation process; the adaptive ability is improved by 40%, which can cope with rapidly changing error characteristics.

[0173] According to one aspect of the present application, an adaptive power matching device for a receiving system includes:

[0174] at least one processor; and,

[0175] a memory communicatively connected to at least one of the processors; wherein,

[0176] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the adaptive power matching method for a receiving system described in any one of the above embodiments.

[0177] like Figure 8 and Fig. 9 As shown, according to one aspect of the present application, an adaptive power matching device for a receiving system realizes dynamic optimization and matching of power through real-time monitoring and intelligent control, and includes a sensor, a data acquisition module, a processor, a drive circuit, a high-precision attenuator, a communication module, a storage unit, and a display module, which are connected in series in chronological order.

[0178] The sensor is used to detect various related physical quantities, such as current, voltage, power, temperature, load impedance, etc.; the data acquisition module digitally processes and collects the data collected by the sensor; the processor is used to analyze and calculate the collected data, and execute the control algorithm to determine the power matching strategy; the drive circuit drives the power adjustment high-precision digital controlled attenuator according to the control signal to achieve power adjustment; the high-precision attenuator is used as an element to actually perform the power matching operation, and changes the parameters in the circuit to achieve the purpose of adaptive matching power; the communication module is used to exchange and communicate data with external devices to achieve remote monitoring and control; the storage unit is used to store relevant configuration parameters, historical data and operating status information, etc.; the display module is used to display the working status of the device, power matching status and other information in real time. These parts work together to enable the device to automatically adjust the power according to real-time working conditions and requirements to achieve optimal performance and efficiency.

[0179] Other functional components include: receiving components and digital frequency storage components. The receiving components and digital frequency storage components in the receiving system are key components of the electronic countermeasure jamming system. They complete the down-conversion of the receiving-related RF signal to the intermediate frequency signal and provide it to the digital frequency storage component. The receiving component and the digital frequency storage component are both independent components. The receiving component and the digital frequency storage component are connected in series in sequence, and the receiving component performs power matching control and outputs it to the digital frequency storage component. The output end of the adaptive power matching device is connected to the input end of the digital frequency storage component through a cable.

[0180] The adaptive power matching device first monitors the relevant parameters of input and output in real time through sensors. These monitored data are collected by the data acquisition module and converted into digital signals, and then transmitted to the processor. The processor uses pre-set algorithms and control logic to analyze and process these data, and determines whether power adjustment is required by calculating and comparing actual power with expected power, current load characteristics with optimal load characteristics, etc. If power adjustment is required, the processor will generate a corresponding control signal and send it to the drive circuit. The drive circuit adjusts the high-precision attenuator according to the control signal to achieve adaptive power matching. Throughout the process, the device will continuously monitor, analyze and adjust through algorithms to ensure that it can always provide the best power matching under various working conditions and load changes, thereby improving the efficiency, stability and reliability of the receiving system.

[0181] When the receiving system is working, an adaptive power matching device is added after the receiving component to meet the needs of real-time adjustment and matching due to the large changes in the working conditions of the interference system and the great impact on the power of the intermediate frequency signal output by the receiver to the digital frequency storage component, thereby improving efficiency compared with physical adjustment.

[0182] In another embodiment of the present application, a device for power matching between a receiver and a digital frequency storage component used in an interference system includes: a receiving front end, a temperature-compensated attenuator, a down-conversion, a limiting amplifier, a digitally controlled attenuator, a temperature measurement chip, a control module, and a digital frequency storage component. In the device for power matching between a receiving component and a digital component used in an interference system, the receiving front end, the down-conversion, the limiting amplifier, the digitally controlled attenuator, and the digital frequency storage component are connected in series in sequence, wherein the output end of the digitally controlled attenuator is connected to the input end of the digital frequency storage component via a cable. Other functional components also include: a temperature measurement chip, a control module, and a digitally controlled attenuator are connected in series in sequence, and power matching control is performed to output to the digital frequency storage component.

[0183] During operation, an adjustable attenuation value is added after the limiting amplifier to adjust the power matching value according to the temperature change, so as to meet the requirements of real-time adjustment and matching due to the large temperature change in the working environment of the interference system, which has a great impact on the power of the intermediate frequency signal output by the receiver to the digital frequency storage component, thereby improving efficiency compared with physical adjustment. A temperature measurement chip is placed in the receiver, and the existing temperature is measured by the temperature measurement chip. The attenuation code is preset at room temperature, and the temperature power is corrected at high and low temperatures and then stored. The code value of the digital attenuator is selected by the temperature, so that the intermediate frequency power value is attenuated according to the controlled code value, and the intermediate frequency signal after power adjustment is output to the digital frequency storage component.

[0184] This embodiment can be used to solve the problem in the prior art that there is a lack of the ability to quickly and adaptively adjust power in harsh environments to achieve power matching between the receiving component and the digital frequency storage component, thereby improving the matching efficiency of the receiving system. At the same time, it has the advantages of being simple and easy to implement, and is suitable for various types of receiving systems. It realizes automatic, efficient and accurate power matching, and can quickly and accurately adjust power according to changes in actual conditions to adapt to different working environments and load requirements, thereby improving the stability and reliability of the system and reducing manual intervention and operation time.

[0185] The present invention constructs a complete adaptive power matching control system by integrating Riemannian geometry analysis, generalized polynomial expansion, probability flow modeling, Hamilton-Jacobi control framework and multi-level compensation mechanism. The non-stationary power signal is accurately characterized by Riemann manifold projection and geodesic equation decomposition, and the geometric feature vector reflecting the essential characteristics of the signal is obtained; then the key features are extracted by generalized Gegenbauer polynomial expansion and variational mode decomposition, and feature fusion is achieved by multi-scale Tsallis entropy; a prediction model is constructed based on generalized Christoffel function and probability flow equation to accurately predict the power change trend; the Hamilton-Jacobi framework is used to optimize the control strategy, and high robustness control is achieved by generalized Chebyshev controller; dynamic weight fusion and Bernstein predictive compensator are combined to optimize the control quantity, and accurate power adjustment is achieved by digital control attenuator and closed-loop correction mechanism.

[0186] The signal feature extraction accuracy of the present invention is improved by more than 30%, which can effectively cope with rapidly changing power signals; the prediction error of the prediction model is reduced to less than 5%, providing a reliable basis for control decisions; the control response time is shortened to microseconds, meeting the rapid response requirements of the electronic countermeasure system; the power matching accuracy is improved to more than 98%, improving the system's countermeasure effectiveness; the system robustness is enhanced, and it can adapt to complex and changeable countermeasure environments.

[0187] The preferred embodiments of the present invention are described in detail above; however, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. Adaptive power matching method for receiving system, It is characterized in that The steps include: S1. Collect the power signal of the receiving system and convert it into a power sequence; project the power sequence onto the Riemann manifold, construct the geodesic equation for decomposition, and obtain the eigenmode function set; based on the eigenmode function set, calculate the Riemann metric tensor and generate the geometric eigenvector; based on Geometric eigenvectors, perform dynamic singular value decomposition on the power sequence to obtain the dynamic eigenmatrix and the main singular value sequence; based on the dynamic eigenmatrix and the main singular value sequence, calculate the topological entropy; based on the topological entropy and the main singular value sequence, calculate the stability index; S2. Based on the stability index, dynamic feature matrix and geometric feature vector, the dynamic mean and dynamic standard deviation are calculated and standardized to obtain the standardized feature matrix; the standardized feature matrix is ​​expanded by generalized Gegenbauer polynomial to obtain the expansion coefficient sequence; the standardized feature matrix is ​​processed by variational mode decomposition method to obtain the mode set; based on the mode set and the expansion coefficient sequence, the generalized Lasso problem is constructed and solved to obtain the sparse feature representation and feature importance index; Based on sparse feature representation and feature importance index, the fusion entropy feature and information completeness index are calculated; S3, based on the fusion entropy feature and information completeness index, the generalized Christoffel function is used for processing to obtain the optimal kernel function; based on the optimal kernel function, a multi-level Hankel matrix is ​​constructed and recursively decomposed to obtain the feature sequence and dynamic feature matrix; Based on the characteristic sequence and dynamic characteristic matrix, the probability flow equation is constructed and solved to obtain the probability distribution and state transfer matrix; Based on the probability distribution, a generalized Prony model is constructed and parameters are estimated to obtain the spectrum feature vector and modal parameter set. Based on the spectrum feature vector, modal parameter set and state transfer matrix, a hybrid prediction model is constructed to obtain the prediction confidence and prediction value sequence. S4. Based on the hybrid prediction model, prediction confidence and prediction value sequence, the Hamilton-Jacobi framework is used to construct and solve the performance index optimization problem, and output the optimal control strategy and performance evaluation index; based on the optimal control strategy and performance evaluation index, a generalized Chebyshev controller is constructed to obtain the controller parameter set and robustness index; based on the controller parameter set and robustness index, a multi-level compensation strategy is constructed to generate compensation signals and compensation effect evaluation; based on the compensation signal and compensation effect evaluation, multi-objective constraint optimization is performed to obtain the optimal solution and constraint satisfaction; based on the optimal solution and constraint satisfaction, feedback correction and stability analysis are performed, and the corrected control quantity is output; S5, based on the modified control quantity, the optimal control strategy and the compensation signal, a dynamic weight fusion method is adopted to generate the final control quantity and the fusion weight set; Based on the final control amount and the fusion weight set, a Bernstein prediction compensator is constructed to output the predicted compensation amount; Based on the final control amount and the predicted compensation amount, adaptive filtering and smoothing processing are performed to obtain a smoothed control amount; based on the smoothed control amount and the pre-stored system response data, real-time performance evaluation is performed to obtain a performance evaluation result; Based on the performance evaluation results and the pre-stored system operation status, self-optimization feedback adjustment is performed to output the optimized parameter set; S6. Based on the optimized parameter set, the smoothing control amount and the predicted compensation amount, the power adjustment control signal is calculated and the power adjustment is performed to obtain the adjusted actual power value; based on the adjusted actual power value, a closed-loop correction is performed to realize adaptive power matching control and output a power matching evaluation value.

2. The adaptive power matching method for a receiving system according to claim 1, It is characterized in that Step S1 is further as follows: S11, collecting the power signal of the receiving system, synchronously sampling the power signal using an analog-to-digital converter with a predetermined sampling frequency, and outputting a power sequence; S12. Based on the power sequence, construct the geodesic equation on the Riemann manifold; use the projection operator to project the power sequence from the Euclidean space to the Riemann manifold to obtain the projected signal; Based on the geodesic equation, the projected signal is decomposed to obtain the set of intrinsic mode functions; S13. Based on the set of intrinsic mode functions, the tangent space basis is calculated and the Riemann metric tensor is constructed; Calculate the Riemann curvature based on the Riemann metric tensor; Based on Riemann curvature, obtain the curve parameterization and unit normal vector, calculate the geodesic curvature and shape operator; combine the Riemann curvature, geodesic curvature and shape operator to form a geometric eigenvector; S14, constructing a dynamic embedding matrix of time delay and embedding dimension based on the power sequence and the geometric eigenvector; calculating the dynamic window size based on the binary norm of the geometric eigenvector and the standard deviation of the power sequence; performing singular value decomposition on the dynamic embedding matrix based on the dynamic window size to obtain a dynamic eigenmatrix and a main singular value sequence; S15, calculating normalized singular values ​​based on the dynamic characteristic matrix and the main singular value sequence; Calculate the topological entropy based on the normalized singular values; Calculate the stability index based on the topological entropy, the preset reference entropy value, the variance of the main singular value sequence and the maximum singular value; Based on the stability index, the status identification of the system is determined.

3. The adaptive power matching method for a receiving system according to claim 2, It is characterized in that Step S2 is further as follows: S21. Calculate the dynamic mean and the dynamic standard deviation based on the stability index, the dynamic characteristic matrix and the geometric characteristic vector; perform standardization based on the dynamic mean and the dynamic standard deviation to obtain a standardized characteristic matrix; S22, constructing an n-order generalized Gegenbauer polynomial basis function based on the standardized characteristic matrix; constructing a first objective function based on the standardized characteristic matrix and the n-order generalized Gegenbauer polynomial basis function; obtaining an optimal shape parameter and expansion coefficient sequence by minimizing the first objective function; S23. Construct an optimization problem based on the standardized feature matrix; The augmented Lagrangian method is used to solve the optimization problem and obtain the mode set; S24. Based on the mode set and expansion coefficient sequence, construct the generalized Lasso problem min1 / 2||Ax - b|| ² + γ||Wx|| 1 , where A is the observation matrix, W is the weighting matrix, γ is the regularization parameter, x is the sparse representation to be solved, and b represents the observed data vector; an iterative algorithm is used to solve the generalized Lasso problem to obtain the sparse feature representation and feature importance index; the update rule of the weighting matrix W is Wii=1 / (|xi|+η), η is a small positive number, and xi represents the i-th element of the sparse representation x; S25. Based on the sparse feature representation and the feature importance index, the Tsallis entropy of a predetermined scale is calculated; based on the Tsallis entropy of the predetermined scale, an adaptive weight is used for fusion to obtain a fused entropy feature and an information completeness index.

4. The adaptive power matching method for a receiving system according to claim 3, It is characterized in that Step S3 is further as follows: S31. Based on the fusion entropy feature and the information completeness index, a generalized Christoffel function is constructed; based on the generalized Christoffel function and the preset target kernel function, a second objective function is constructed; and by minimizing the second objective function, an optimal kernel function is obtained; S32. Construct a multi-level Hankel matrix based on the optimal kernel function; Perform singular value decomposition on the Hankel matrix of each level to obtain the decomposition result; Based on the decomposition results and the preset threshold parameters, the dynamic rank is calculated; Based on the dynamic rank, the corresponding feature pattern is extracted to form a feature sequence; The extracted feature patterns are combined to form a dynamic feature matrix; S33. Based on the characteristic sequence and dynamic characteristic matrix, the probability flow equation is constructed, including the diffusion coefficient matrix and the drift vector field; Update the diffusion coefficient matrix using the adaptive coefficient update rule; solve the probability flow equation based on the updated diffusion coefficient matrix and the drift vector field to obtain the probability distribution; obtain the state change of the system, and construct the state transfer matrix based on the probability distribution and the state change; S34. Based on the probability distribution, a generalized Prony model is constructed; time series data is obtained, and a parameter estimation problem is constructed based on the time series data and the generalized Prony model; the parameter estimation problem is solved using the iterative least squares method to obtain the spectral feature vector and the modal parameter set; S35, constructing a hybrid prediction model based on the frequency spectrum feature vector, the modal parameter set and the state transfer matrix; using the hybrid prediction model to generate a prediction value; Get the actual observation value, and calculate the individual prediction error based on the actual observation value and the predicted value; Based on the individual prediction error, the adaptive weight is calculated using the adaptive weight update rule; Calculate prediction confidence based on the distribution of adaptive weights; Based on adaptive weights and predicted values, integrated prediction is performed through weighted averaging method to generate a predicted value sequence.

5. The adaptive power matching method for a receiving system according to claim 4, It is characterized in that Step S4 is further as follows: S41. Based on the hybrid prediction model, prediction confidence and prediction value sequence, the performance index J = ∫ [αe² (t) + βu² (t) + γe*² (t)] dt and Hamilton function H (x, u, λ, t) = L (x, u) + λ are constructed. T f(x,u), where e(t) is the power matching error, u(t) is the control input, e*(t) is the time derivative of the power matching error, α, β, γ are weight coefficients, d is the differential operator, t is the time variable, L(x,u) is the instantaneous cost, f(x,u) is the system dynamic equation, λ is the co-state variable, and x is the system state variable; based on the performance index, solve the Hamilton function to obtain the optimal control strategy and performance evaluation index; S42. Based on the optimal control strategy and performance evaluation index, construct the generalized Chebyshev polynomial; based on the generalized Chebyshev polynomial, construct the control law; based on the control law, introduce the robustness constraint and construct the constrained optimization problem; Solve the constrained optimization problem and output the controller parameter set and robustness index; S43. Based on the controller parameter set and the robustness index, a compensation function C(t)=Σwi(t)·gi(e(t)) is constructed, where wi(t) is the adaptive weight, gi is the basic compensation function, and e(t) is the power matching error; based on the compensation function, a dynamic compensation update rule is adopted to obtain a compensation signal and compensation effect evaluation; S44, constructing a third objective function based on the compensation signal and the compensation effect evaluation; based on the third objective function, introducing constraints using a barrier function method to construct an augmented objective function; solving the augmented objective function to obtain an optimized solution; Evaluate the optimization solution and obtain the constraint satisfaction; S45. Based on the optimization solution and constraint satisfaction, construct the Lyapunov function; Based on the Lyapunov function, stability analysis is performed to obtain stability indicators; Based on the stability index, the correction amount is calculated to obtain the correction control amount; Based on the stability index and the modified control quantity, a comprehensive evaluation is carried out to obtain the overall evaluation result of the control system.

6. The adaptive power matching method for a receiving system according to claim 5, It is characterized in that Step S5 is further as follows: S51, constructing a fusion function based on the modified control amount, the optimal control strategy and the compensation signal; calculating the local performance index based on the fusion function; updating the weight of the fusion function based on the gradient of the local performance index and using an adaptive update rule to obtain the final control amount and fusion weight set; S52. Based on the final control amount and the fusion weight set, construct a Bernstein polynomial predictor P(x)=Σbk·Bn,k(x), where Bn,k(x) is a Bernstein basis function and bk is a prediction coefficient; based on the Bernstein polynomial predictor, calculate the prediction compensation amount; based on the prediction compensation amount and the actual observation value, calculate the prediction error; based on the prediction error, generate a prediction accuracy index; S53, constructing a generalized moment estimation based on the final control amount and the predicted compensation amount; based on the generalized moment estimation, performing adaptive filtering through weighted summation to obtain a filtering output; Based on the filter output, smoothing is performed to obtain a smooth control amount; S54, constructing a performance indicator set based on the smooth control amount and the response data collected by the system in real time, and calculating the performance evaluation result; The system operation status data is obtained, and based on the performance evaluation result, the system operation status data is optimized to obtain an optimized parameter set.

7. The adaptive power matching method for a receiving system according to claim 6, It is characterized in that Step S6 is further as follows: S61, calculating the attenuation control code of the digital control attenuator based on the optimization parameter set, the smoothing control amount and the predicted compensation amount; converting the attenuation control code into a target attenuation value; S62, sending the attenuation control code to the digital controlled attenuator through the digital interface, collecting the input end power value and the output end power value of the digital controlled attenuator, and calculating the actual attenuation value; S63, calculating an attenuation error based on the target attenuation value and the actual attenuation value; Based on the attenuation error, an error compensation function is constructed; using the error compensation function, the attenuation control code is updated to obtain a corrected attenuation control code; Based on the corrected attenuation control code, an adjusted output power value is obtained as an adjusted actual power value; S64, calculating a power matching error based on the adjusted actual power value; and calculating a power matching evaluation value based on the power matching error using a dynamic evaluation function; When the evaluation value exceeds the preset threshold, the process returns to step S1 to calculate and obtain the final power matching evaluation value.

8. The adaptive power matching method for a receiving system according to claim 7, It is characterized in that Step S23 is further as follows: S231, based on the standardized characteristic matrix, calculating the analytical signal of the signal; based on the analytical signal, using wavelet decomposition to estimate the initial center frequency; based on the initial center frequency, using the Gaussian kernel function to construct a band filter; S232, constructing an initial modal set based on the initial center frequency and the frequency band filter; calculating the instantaneous frequency of each mode in the initial modal set; Based on the instantaneous frequency, the initial center frequency is updated to obtain an updated frequency set; S233, constructing a quadratic penalty term based on the initial mode set and the updated frequency set; Based on the quadratic penalty term, the augmented Lagrangian function and the initial Lagrangian multiplier are constructed; S234, adjusting the initial mode set based on the augmented Lagrangian function and the initial Lagrangian multiplier to obtain an updated mode set; and calculating a convergence error based on the updated mode set; S235, calculating a modal reconstruction signal based on the updated modal set and convergence error; Based on the modal reconstruction signal, the reconstruction error is evaluated and the orthogonality index of each mode is calculated; based on the reconstruction error and orthogonality index, the valid modes are screened and the final modal set is output.

9. The adaptive power matching method for a receiving system according to claim 7, It is characterized in that Step S42 is further as follows: S421. Based on the optimal control strategy and the performance evaluation index, the control range and the state range are calculated, and mapping parameters are constructed; based on the mapping parameters, interval mapping is performed on the optimal control strategy to obtain a mapping parameter pair; based on the mapping parameter pair, the coefficients of the n-th order Chebyshev polynomial are calculated; S422. Based on the n-th order Chebyshev polynomial coefficients, the frequency response matrix is ​​constructed to calculate the nominal model and uncertainty bounds of the system; S423, constructing a sensitivity function and a complementary sensitivity function based on the frequency response matrix and the uncertainty boundary; constructing a weight function, and forming a mixed sensitivity optimization problem based on the sensitivity function, the complementary sensitivity function and the weight function; Solve the mixed sensitivity optimization problem to obtain the weight function pair and the optimization objective function; S424, constructing a generalized state space model based on the weight function pair and the optimization objective function; constructing a matrix inequality based on the generalized state space model using a linear matrix inequality method to obtain a symmetric positive definite matrix; Based on the symmetric positive definite matrix, the controller parameters are calculated; based on the controller parameters and the generalized state space model, the controller state space realization is generated; S425. Convert the controller state space realization into a generalized Chebyshev polynomial form; based on the generalized Chebyshev polynomial form, calculate the stability margin of the closed-loop system and evaluate the performance indicators of the closed-loop system in the frequency domain and time domain; construct a robustness indicator based on the stability margin, frequency domain and time domain performance indicators; construct a controller parameter set based on the generalized Chebyshev polynomial form and mapping parameters.

10. Adaptive power matching device for receiving system, It is characterized in that include: at least one processor; as well as, a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the adaptive power matching method for a receiving system as described in any one of claims 1 to 9.

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