Transmitting power correction method, device and equipment of multi-array-element phased array system

Abstract

The invention discloses a transmitting power correction method, device and equipment of a multi-array-element phased array system. The method comprises the following steps: acquiring a transmitting signal parameter of the multi-array-element phased array system currently detected by a sensor system; performing signal feature extraction on the transmitting signal parameters to obtain transmitting signal features; carrying out feature fusion on the emission signal features to obtain fusion features; inputting the fusion features into a pre-trained transmitting power prediction model, and predicting to obtain a current transmitting power potential deviation of the multi-array-element phased array system; and calculating the corrected transmitting power of the multi-array-element phased array system according to the transmitting power and the transmitting power potential deviation. According to the invention, the transmitting power of the multi-array-element phased array system can be accurately corrected in real time.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a method, device and equipment for calibrating the transmission power of a multi-element phased array system. Background Art

[0002] In the field of phased array systems, especially in multi-element phased array radars or communication systems, accurate control of transmit power is one of the key factors to ensure system performance. In the past, the calibration of the transmit power of multi-element phased array systems was based on static preset values ​​or simple feedback loops. This approach often ignores the complexity and dynamic changes of the actual working environment, such as temperature fluctuations, hardware aging, and electromagnetic interference, which can cause deviations between the actual transmit power and the preset value. In addition, some traditional calibration methods usually include regular manual calibration, comparative measurement using a standard signal source, or adjustment through a simple built-in calibration circuit. For example, in some multi-element phased array systems, a fixed compensation coefficient is used to correct the known power loss, but this can only solve part of the problem and cannot adapt to various unknown or nonlinear power attenuation situations in real time.

[0003] Therefore, the main problem with existing correction schemes for the transmit power of multi-element phased array systems is that they lack the ability to independently correct the dynamic changes in transmit power, cannot accurately reflect the power deviation of the multi-element phased array system under actual working conditions, and cannot accurately correct the transmit power of the multi-element phased array system, thereby affecting the working reliability of the multi-element phased array system, especially in applications requiring high precision and stability. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and equipment for calibrating the transmit power of a multi-element phased array system, which can realize real-time and accurate calibration of the transmit power of the multi-element phased array system.

[0005] An embodiment of the present invention provides a method for calibrating transmit power of a multi-element phased array system, comprising:

[0006] Acquire the transmission signal parameters of the multi-element phased array system currently detected by the sensor system; the transmission signal parameters include transmission power, power spectrum density, energy concentration ratio and phase difference;

[0007] Extracting signal features from the transmission signal parameters to obtain transmission signal features; the transmission signal features include transmission power features, power spectrum density features, energy concentration ratio features, and phase difference features;

[0008] Performing feature fusion on the emission signal features to obtain fusion features;

[0009] Inputting the fusion feature into a pre-trained transmit power prediction model to predict the current potential deviation of the transmit power of the multi-element phased array system;

[0010] According to the transmit power and the potential deviation of the transmit power, the transmit power after correction of the multi-element phased array system is calculated.

[0011] As an improvement of the above scheme, the power spectrum density is: perform fast Fourier transform (FFT) on the signal to obtain the spectrum, and then calculate the square of its amplitude: S(f) is the power spectral density of signal s(t) at frequency f; is the Fourier transform of signal s(t), which represents the representation of signal s(t) in the frequency domain; It is the square of the modulus of the Fourier transform result, which represents the energy or power of the signal at frequency f;

[0012] The energy concentration ratio is: ECR is the energy concentration ratio, which reflects the degree to which the signal energy is concentrated within the main frequency bandwidth; l and f h They are the lower and upper frequency limits of the main frequency bandwidth respectively; is the energy or power of the signal within the main frequency bandwidth; is the total energy or power of the signal s(t) over the entire frequency range;

[0013] The phase difference is: Δφ ij =∠s i -∠s j ; Δφ ij is the phase difference between the ith array element and the jth array element; ∠s i and ∠s j are the phase angles of the signals of the i-th array element and the j-th array element respectively.

[0014] As an improvement of the above solution, the step of fusing the emission signal features to obtain fused features includes:

[0015] Let x p is the transmit power eigenvector, x psd is the power spectral density eigenvector, x ecr is the energy concentration ratio eigenvector, x pd is the phase difference eigenvector;

[0016] Scaling the feature vectors of each of the emission signal features using a maximum and minimum scaling method to ensure that all features are at the same magnitude;

[0017] The fusion weight of each of the emission signal features is dynamically adjusted by calculating the contribution of the feature vector of each of the emission signal features processed by scaling to the overall data variability; the calculation formula is: I i is the importance score of the ith feature in the transmitted signal features; the importance score is calculated by a feature importance scoring method;

[0018] Performing high-order statistical processing on the feature vectors of each of the transmission signal features after scaling processing to obtain statistics; the high-order statistical processing includes: calculating the mean value as the first-order moment, the variance as the second-order moment, the skewness as the third-order moment, and the kurtosis as the fourth-order moment of each feature vector to capture the central tendency, dispersion, skewness and peakedness of each of the transmission signal features;

[0019] All processed feature vectors and statistics are fused and calculated using the following feature fusion formula:

[0020] Represents the concatenation operation of vectors; x′ i is the feature vector x i The result after scaling, x i For x p 、x psd 、x ecr 、x pd ; i represents the subscript of p, psd, ecr, pd; I i is the importance score of the ith feature in the emission signal features; is the fusion weight of the ith feature in the emission signal features; μ i , γ 1，i , γ 2，i are respectively the mean value, variance, skewness and kurtosis of the i-th feature in the emission signal features; x fusion is the fused feature vector.

[0021] As an improvement of the above solution, the pre-trained transmit power prediction model includes:

[0022] The convolution layer extracts the fusion features of the input data through a set of learnable filter kernels. It is connected to the ReLU activation function to process the results of the convolution operation. The convolution operation is expressed as: Y(i, j) = ∑ m，nX(i+m, j+n)*K(m, n); X is the input tensor of the fused feature; K is the convolution kernel; * represents the multiplication operation, which refers to the point-by-point multiplication of the convolution kernel and the corresponding position elements of the input tensor; m and n are the indexes of the convolution kernel, and the convolution kernel will traverse the entire input tensor and perform convolution calculations; Y(i, j) is the value of the output tensor at position (i, j) after the convolution operation;

[0023] The LSTM recurrent layer takes as input the sequence data output by the convolutional layer, and outputs a hidden state vector that contains summary information of the time series data. It includes an input gate, a forget gate, an output gate, and a cell state.

[0024] The fully connected layer, whose input is the last hidden state of the LSTM recurrent layer, is expressed as: [l] =W [l] a [l-1] +b [l] ; z [l] is the linear combination output of the lth layer; W [l] is the weight matrix of the lth layer; a [l-1] is the activation output of the l-1th layer, which serves as the input of the lth layer; b [l] is the bias vector of the lth layer; the activation function of the fully connected layer is: [l] =g(z [l] );a [l] is the activation output of the lth layer, which outputs the predicted potential deviation of the current transmit power of the multi-element phased array system, that is, z after the activation function g [l] ; g is the ReLU, sigmoid or tanh activation function.

[0025] Another embodiment of the present invention provides a transmission power calibration device for a multi-element phased array system, including:

[0026] An acquisition module is used to acquire the transmission signal parameters of the multi-element phased array system currently detected by the sensor system; the transmission signal parameters include transmission power, power spectrum density, energy concentration ratio and phase difference;

[0027] A feature extraction module is used to extract signal features from the transmission signal parameters to obtain transmission signal features; the transmission signal features include transmission power features, power spectrum density features, energy concentration ratio features and phase difference features;

[0028] A feature fusion module, used for performing feature fusion on the emission signal features to obtain fusion features;

[0029] A power deviation prediction module, used to input the fusion feature into a pre-trained transmit power prediction model to predict the current potential deviation of the transmit power of the multi-element phased array system;

[0030] A power correction module is used to calculate the corrected transmit power of the multi-element phased array system according to the transmit power and the potential deviation of the transmit power.

[0031] As an improvement of the above scheme, the power spectrum density is: perform fast Fourier transform (FFT) on the signal to obtain the spectrum, and then calculate the square of its amplitude: S(f) is the power spectral density of signal s(t) at frequency f; is the Fourier transform of signal s(t), which represents the representation of signal s(t) in the frequency domain; It is the square of the modulus of the Fourier transform result, which represents the energy or power of the signal at frequency f;

[0032] The energy concentration ratio is: ECR is the energy concentration ratio, which reflects the degree to which the signal energy is concentrated within the main frequency bandwidth; l and f h They are the lower and upper frequency limits of the main frequency bandwidth respectively; is the energy or power of the signal within the main frequency bandwidth; is the total energy or power of the signal s(t) over the entire frequency range;

[0033] The phase difference is: Δφ ij =∠s i -∠s j ; Δφ ij is the phase difference between the ith array element and the jth array element; ∠s i and ∠s j are the phase angles of the signals of the i-th array element and the j-th array element respectively.

[0034] As an improvement of the above solution, the feature fusion module is specifically used for:

[0035] Let x p is the transmit power eigenvector, x psd is the power spectral density eigenvector, x ecr is the energy concentration ratio eigenvector, x pd is the phase difference eigenvector;

[0036] Scaling the feature vectors of each of the emission signal features using a maximum and minimum scaling method to ensure that all features are at the same magnitude;

[0037] The fusion weight of each of the emission signal features is dynamically adjusted by calculating the contribution of the feature vector of each of the emission signal features processed by scaling to the overall data variability; the calculation formula is: I iis the importance score of the ith feature in the transmitted signal features; the importance score is calculated by a feature importance scoring method;

[0038] Performing high-order statistical processing on the feature vectors of each of the transmission signal features after scaling processing to obtain statistics; the high-order statistical processing includes: calculating the mean value as the first-order moment, the variance as the second-order moment, the skewness as the third-order moment, and the kurtosis as the fourth-order moment of each feature vector to capture the central tendency, dispersion, skewness and peakedness of each of the transmission signal features;

[0039] All processed feature vectors and statistics are fused and calculated using the following feature fusion formula:

[0040]

[0041] Represents the concatenation operation of vectors; x′ i is the feature vector x i The result after scaling, x i For x p 、x psd 、x ecr 、x pd ; i represents the subscript of p, psd, ecr, pd; I i is the importance score of the ith feature in the emission signal features; is the fusion weight of the ith feature in the emission signal features; μ i , γ 1，i , γ 2，i are respectively the mean value, variance, skewness and kurtosis of the i-th feature in the emission signal features; x fusion is the fused feature vector.

[0042] As an improvement of the above solution, the pre-trained transmit power prediction model includes:

[0043] The convolution layer extracts the fusion features of the input data through a set of learnable filter kernels. It is connected to the ReLU activation function to process the result of the convolution operation. The convolution operation is expressed as: Y(i,j) = Σ m,n X(i+m,j+n)*K(m,n); X is the input tensor of the fused feature; K is the convolution kernel; * represents the multiplication operation, which refers to the point-by-point multiplication of the convolution kernel and the elements at the corresponding position of the input tensor; m and n are the indexes of the convolution kernel, and the convolution kernel will traverse the entire input tensor and perform convolution calculations; Y(i, j) is the value of the output tensor at position (i, j) after the convolution operation;

[0044] The LSTM recurrent layer takes as input the sequence data output by the convolutional layer, and outputs a hidden state vector that contains summary information of the time series data. It includes an input gate, a forget gate, an output gate, and a cell state.

[0045] The fully connected layer, whose input is the last hidden state of the LSTM recurrent layer, is expressed as: [l] =W [l] a [l-1] +b [l] ; z [l] is the linear combination output of the lth layer; W [l] is the weight matrix of the lth layer; a [l-1] is the activation output of the l-1th layer, which serves as the input of the lth layer; b [l] is the bias vector of the lth layer; the activation function of the fully connected layer is: [l] =g(z [l] );a [l] is the activation output of the lth layer, which outputs the predicted potential deviation of the current transmit power of the multi-element phased array system, that is, z after the activation function g [l] ; g is the ReLU, sigmoid or tanh activation function.

[0046] Another embodiment of the present invention provides a transmission power correction device for a multi-element phased array system, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the transmission power correction method for the multi-element phased array system described in the above-mentioned embodiment of the invention is implemented.

[0047] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0048] First, the system acquires the signal parameters of the multi-element phased array transmitter in real time, including key indicators such as transmit power, power spectrum density, energy concentration ratio and phase difference. Then, these parameters are feature extracted to extract features closely related to transmit power, such as power fluctuation mode, energy distribution and phase consistency between elements. Subsequently, through an innovative feature fusion algorithm, the extracted multi-dimensional features are integrated into a comprehensive fusion feature, thereby fully considering the correlation and weight between each feature to enhance the prediction ability of the model. The fusion feature is then input into a deep learning model, which has been trained with a large amount of historical data and can predict the potential deviation of the transmit power of the multi-element phased array system under current conditions. Finally, based on the predicted deviation value, the system automatically adjusts the transmit power to ensure that the actual output is consistent with the expected value, thereby achieving precise power control. Compared with the passive and static characteristics of traditional correction methods, the embodiments of the present invention can actively adapt to environmental changes through real-time analysis and intelligent decision-making, effectively solving the problems of low power correction accuracy and slow response speed in the prior art, and realizing accurate correction of the transmit power of the multi-element phased array system in a complex dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for calibrating transmit power of a multi-element phased array system provided by an embodiment of the present invention;

[0050] Figure 2 It is a structural schematic diagram of a transmission power calibration device for a multi-element phased array system provided by an embodiment of the present invention;

[0051] Figure 3 It is a structural schematic diagram of a transmission power calibration device for a multi-element phased array system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 , is a flow chart of a method for calibrating the transmit power of a multi-element phased array system provided by an embodiment of the present invention. The method for calibrating the transmit power of a multi-element phased array system comprises steps S10 to S14:

[0054] S10, obtaining transmission signal parameters of the multi-element phased array system currently detected by the sensor system; the transmission signal parameters include transmission power, power spectrum density, energy concentration ratio and phase difference;

[0055] S11, extracting signal features from the transmission signal parameters to obtain transmission signal features; the transmission signal features include transmission power features, power spectrum density features, energy concentration ratio features and phase difference features;

[0056] S12, performing feature fusion on the emission signal features to obtain fusion features;

[0057] S13, inputting the fusion feature into a pre-trained transmit power prediction model to predict the current potential deviation of the transmit power of the multi-element phased array system;

[0058] S14, calculating the corrected transmit power of the multi-element phased array system according to the transmit power and the potential deviation of the transmit power.

[0059] In an embodiment of the present invention, first, the system acquires the signal parameters of the multi-element phased array transmitter in real time, including key indicators such as transmit power, power spectrum density, energy concentration ratio and phase difference. Then, these parameters are feature extracted to extract features closely related to the transmit power, such as power fluctuation mode, energy distribution and phase consistency between elements. Subsequently, the extracted multi-dimensional features are integrated into a comprehensive fusion feature through an innovative feature fusion algorithm, thereby fully considering the correlation and weight between the features to enhance the prediction ability of the model. The fusion feature is then input into a deep learning model, which has been trained with a large amount of historical data and can predict the potential deviation of the transmit power of the multi-element phased array system under current conditions. Finally, based on the predicted deviation value, the system automatically adjusts the transmit power to ensure that the actual output is consistent with the expected value, thereby achieving precise power control. Compared with the passive and static characteristics of traditional correction methods, the embodiment of the present invention can actively adapt to environmental changes through real-time analysis and intelligent decision-making, effectively solving the problems of low power correction accuracy and slow response speed in the prior art, and realizing accurate correction of the transmit power of the multi-element phased array system in a complex dynamic environment.

[0060] Specifically, the corrected transmit power of the multi-element phased array system is calculated according to the sum of the transmit power and the transmit power potential deviation. In addition, based on the calculated correction amount, a specific power correction instruction is generated to achieve overall power balance and optimization. The correction instruction is sent to the control unit of the multi-element phased array system to perform power adjustment. If the transmit power potential deviation is a negative value, it means that the current power is too high and the transmit power needs to be reduced; conversely, if the transmit power potential deviation is a positive value, the transmit power needs to be increased.

[0061] As an example, transmit power: refers to the actual transmit power level of each array element in the system. Power sensors measure the instantaneous power of each array element, and then summarize this data to understand the power distribution of the entire system. Power spectral density (PSD): Power spectral density provides the energy distribution of the signal in the frequency domain. Spectrum analyzers estimate PSD by performing a fast Fourier transform (FFT) on the signal, revealing the power levels of different frequency components. PSD helps identify the degree of power concentration at different frequencies. Energy concentration ratio (ECR): ECR is a measure of the proportion of signal energy concentrated in the main frequency bandwidth. It is calculated by comparing the total power in the main frequency bandwidth with the total power of the entire signal spectrum, which helps to evaluate the spectral purity and power efficiency of the signal. Phase difference: Phase difference is an indicator of the phase relationship of the signal between different array elements, reflecting phase consistency. Phase detectors can measure the phase difference between adjacent array elements or selected array element pairs, which is critical for maintaining phase synchronization and power synthesis in phased array systems.

[0062] Specifically, the power spectrum density is: perform fast Fourier transform (FFT) on the signal to obtain the spectrum, and then calculate the square of its amplitude: S(f) is the power spectral density of signal s(t) at frequency f; is the Fourier transform of signal s(t), which represents the representation of signal s(t) in the frequency domain; It is the square of the modulus of the Fourier transform result, which represents the energy or power of the signal at frequency f;

[0063] The energy concentration ratio is: ECR is the energy concentration ratio, which reflects the degree to which the signal energy is concentrated within the main frequency bandwidth; l and f h They are the lower and upper frequency limits of the main frequency bandwidth respectively; is the energy or power of the signal within the main frequency bandwidth; is the total energy or power of the signal s(t) over the entire frequency range;

[0064] The phase difference is: Δφ ij =∠s i -∠s j ; Δφ ij is the phase difference between the ith array element and the jth array element; ∠s i and ∠s j are the phase angles of the signals of the i-th array element and the j-th array element respectively.

[0065] After collecting the above-mentioned transmission signal parameters, the next step is to extract signal features from these parameters to convert them into more advanced feature representations for subsequent deep learning model processing. The goal of signal feature extraction is to extract the most representative features that best reflect the system status from the raw data. Specifically: Transmit power features: Extract features such as instantaneous power or power fluctuations from the transmit power data. These features reflect the overall level and stability of the power. Power spectral density features: Extract features such as main frequency, bandwidth, and power at peak frequency from PSD. These features provide in-depth insights into the signal spectrum characteristics. Energy concentration ratio features: Calculate the ECR value and possible ECR change rate to reflect the stability and consistency of the signal energy distribution. Phase difference features: Quantify the statistical characteristics of the phase difference, such as the average phase difference, the standard deviation of the phase difference, or the distribution of the phase difference. These features help diagnose phase synchronization problems between array elements.

[0066] The signal feature extraction process can use existing feature extraction methods, such as signal preprocessing (such as denoising, filtering), feature engineering (such as statistical analysis, spectrum analysis) and feature selection (identifying the most informative features). These features will then be fused to form a comprehensive feature vector as the input of the transmit power prediction model. In this way, the transmit power status of the multi-element phased array system can be accurately monitored and predicted even in complex and dynamic environments, providing a solid foundation for subsequent power correction.

[0067] As an example, the step of fusing the emission signal features to obtain fused features includes:

[0068] Let x p is the transmit power eigenvector, x psd is the power spectral density eigenvector, x ecr is the energy concentration ratio eigenvector, x pd is the phase difference eigenvector;

[0069] Scaling the feature vectors of each of the emission signal features using a maximum and minimum scaling method to ensure that all features are at the same magnitude;

[0070] The fusion weight of each of the emission signal features is dynamically adjusted by calculating the contribution of the feature vector of each of the emission signal features processed by scaling to the overall data variability; the calculation formula is: I i is the importance score of the ith feature in the transmitted signal features; the importance score is calculated by a feature importance scoring method;

[0071] Performing high-order statistical processing on the feature vectors of each of the transmission signal features after scaling processing to obtain statistics; the high-order statistical processing includes: calculating the mean value as the first-order moment, the variance as the second-order moment, the skewness as the third-order moment, and the kurtosis as the fourth-order moment of each feature vector to capture the central tendency, dispersion, skewness and peakedness of each of the transmission signal features;

[0072] All processed feature vectors and statistics are fused and calculated using the following feature fusion formula:

[0073] Represents the concatenation operation of vectors; x′ i is the feature vector x i The result after scaling, x i For x p 、x psd 、x ecr 、x pd ; i represents the subscript of p, psd, ecr, pd; I i is the importance score of the ith feature in the emission signal features; is the fusion weight of the ith feature in the emission signal features; μ i , γ 1，i , γ 2，i are respectively the mean value, variance, skewness and kurtosis of the i-th feature in the emission signal features; x fusion is the fused feature vector.

[0074] In this embodiment, by constructing a comprehensive feature fusion mechanism, the transmission signal parameters of the multi-element phased array system can be converted into fusion features that can be effectively processed by the deep learning model, so as to achieve accurate prediction of the potential deviation of the transmission power. Specifically, first, the extracted transmission power features, power spectrum density features, energy concentration ratio features and phase difference features are respectively expressed in vector form, and the magnitude of these features is unified through maximum and minimum scaling processing to ensure the balance of influence between different physical quantities. Then, a dynamic weight adjustment strategy is adopted to calculate the importance score of each feature based on its contribution to the overall data variability, and then adjust the fusion weight so that the features that contribute more to the model prediction obtain higher weights and enhance the decision-making basis of the model. Subsequently, the deep information of the feature vector is further mined through high-order statistical processing, and the mean, variance, skewness and kurtosis are calculated to fully reflect the statistical properties of the transmission signal. Finally, through the feature fusion formula, the scaled feature vector, the dynamically adjusted fusion weight and the high-order statistics are integrated into a fused feature vector as the input of the deep learning model for predicting the potential deviation of the transmission power. Through this embodiment, not only the problems of different feature magnitudes, unreasonable weight distribution and low information utilization in the traditional power correction method are solved, but also the model's recognition ability for complex signal patterns is effectively improved, ensuring the accuracy and real-time performance of the transmission power correction of the multi-element phased array system in a dynamic environment, and enhancing the stability and performance of the system in practical applications.

[0075] It can be understood that feature importance scoring is a method to evaluate the contribution of each feature in a machine learning model to the prediction target. Feature importance scoring is used to dynamically adjust the fusion weights to ensure that the features that contribute the most to the overall data variability and prediction results occupy a more important position in the fusion process. As an example, the feature importance scoring can be calculated in the following way:

[0076] Gradient-based feature importance scoring: The importance of features is evaluated by calculating the gradient of the model output with respect to the input features. Specifically: Calculate the gradient: For each input feature, calculate the partial derivative of the model output (such as the predicted transmit power deviation) with respect to that feature, which can be done during the model training process using the backpropagation algorithm. The absolute value or square of the gradient: The absolute value or square of the gradient is usually used as a measure of feature importance, because a large gradient means that the model output is sensitive to small changes in the corresponding feature, that is, the feature has a large impact on the model output. Averaging or integrating: In order to obtain a stable feature importance score, the gradient metrics of all samples can be averaged or integrated.

[0077] Feature importance scores based on tree models: For example, they are calculated based on Random Forest or Gradient Boosting Trees. In tree models, feature importance can be calculated in two ways:

[0078] Based on the reduction of impurity (Gini Importance or Mean Decrease Impurity): Every time a feature is used to split a node, it reduces the impurity of the child node (such as Gini impurity or entropy). The feature importance is the average of the reduction of impurity in all trees.

[0079] Based on the reduction of prediction accuracy (Mean Decrease Accuracy or Permutation Importance): By randomly perturbing the value of a single feature, and then re-evaluating the prediction accuracy of the model. The feature importance is the degree of decrease in accuracy before and after perturbing the feature.

[0080] As an improvement of the above solution, the pre-trained transmit power prediction model includes:

[0081] The convolution layer extracts the fusion features of the input data through a set of learnable filter kernels. It is connected to the ReLU activation function to process the results of the convolution operation. The convolution operation is expressed as: Y(i, j) = ∑ m，n X(i+m, j+n)*K(m, n); X is the input tensor of the fused feature; K is the convolution kernel; * represents the multiplication operation, which refers to the point-by-point multiplication of the convolution kernel and the corresponding position elements of the input tensor; m and n are the indexes of the convolution kernel, and the convolution kernel will traverse the entire input tensor and perform convolution calculations; Y(i, j) is the value of the output tensor at position (i, j) after the convolution operation;

[0082] The LSTM recurrent layer takes as input the sequence data output by the convolutional layer, and outputs a hidden state vector that contains summary information of the time series data. It includes an input gate, a forget gate, an output gate, and a cell state.

[0083] The fully connected layer, whose input is the last hidden state of the LSTM recurrent layer, is expressed as: [l] =W [l] a [l-1] +b [l] ; z [l] is the linear combination output of the lth layer; W [l] is the weight matrix of the lth layer; a [l-1] is the activation output of the l-1th layer, which serves as the input of the lth layer; b [l]is the bias vector of the lth layer; the activation function of the fully connected layer is: [l] =g(z [l] );a [l] is the activation output of the lth layer, which outputs the predicted potential deviation of the current transmit power of the multi-element phased array system, that is, z after the activation function g [l] ; g is the ReLU, sigmoid or tanh activation function.

[0084] In this embodiment, by constructing a multi-stage deep learning model for predicting the potential deviation of the transmit power of a multi-element phased array system, the accuracy and adaptability of power control can be improved. Specifically, the model combines convolutional layers, LSTM recurrent layers, and fully connected layers. First, the convolutional layer extracts key patterns in the spatial or frequency domain from the fused features through a set of learnable filters, and uses the ReLU activation function to enhance the nonlinear expression ability of the model and effectively capture the complex structure of the features. Next, the LSTM recurrent layer receives the output of the convolutional layer, and uses its powerful timing processing capabilities to learn and retain long-term dependencies in the sequence data, and generates a hidden state vector containing time series information, providing rich context for subsequent predictions. Finally, the fully connected layer takes the hidden state of the LSTM recurrent layer as input, and maps the learned abstract features to the predicted value of the potential deviation of the transmit power through linear combinations and nonlinear activation functions, thereby achieving accurate prediction of the transmit power deviation. Therefore, the embodiment of the present invention can not only automatically learn and extract multi-level information in the characteristics of the transmitted signal through the multi-layer structure of the deep learning model, but also effectively handle the timing dependency, thereby achieving high-precision prediction of the potential deviation of the transmit power of the multi-element phased array system under dynamic and complex working conditions, and improving the real-time and accuracy of power correction.

[0085] It is understandable that the specific function structure of the LSTM loop layer can refer to the existing technology. The training method of the transmission power prediction model can refer to the existing model training technology.

[0086] See also Figure 2 , is a schematic diagram of the structure of a transmission power correction device for a multi-element phased array system provided by an embodiment of the present invention. The transmission power correction device for a multi-element phased array system includes:

[0087] An acquisition module 10 is used to acquire the transmission signal parameters of the multi-element phased array system currently detected by the sensor system; the transmission signal parameters include transmission power, power spectrum density, energy concentration ratio and phase difference;

[0088] The feature extraction module 11 is used to extract signal features from the transmission signal parameters to obtain transmission signal features; the transmission signal features include transmission power features, power spectrum density features, energy concentration ratio features and phase difference features;

[0089] A feature fusion module 12 is used to perform feature fusion on the emission signal features to obtain fusion features;

[0090] A power deviation prediction module 13 is used to input the fusion feature into a pre-trained transmit power prediction model to predict the current potential deviation of the transmit power of the multi-element phased array system;

[0091] The power correction module 14 is used to calculate the corrected transmit power of the multi-element phased array system according to the transmit power and the potential deviation of the transmit power.

[0092] As an improvement of the above scheme, the power spectrum density is: perform fast Fourier transform (FFT) on the signal to obtain the spectrum, and then calculate the square of its amplitude: S(f) is the power spectral density of signal s(t) at frequency f; is the Fourier transform of signal s(t), which represents the representation of signal s(t) in the frequency domain; It is the square of the modulus of the Fourier transform result, which represents the energy or power of the signal at frequency f;

[0093] The energy concentration ratio is: ECR is the energy concentration ratio, which reflects the degree to which the signal energy is concentrated within the main frequency bandwidth; l and f h They are the lower and upper frequency limits of the main frequency bandwidth respectively; is the energy or power of the signal within the main frequency bandwidth; is the total energy or power of the signal s(t) over the entire frequency range;

[0094] The phase difference is: Δφ ij =∠s i -∠s j ; Δφ ij is the phase difference between the ith array element and the jth array element; ∠s i and ∠s j are the phase angles of the signals of the i-th array element and the j-th array element respectively.

[0095] As an improvement of the above solution, the feature fusion module is specifically used for:

[0096] Let x p is the transmit power eigenvector, x psd is the power spectral density eigenvector, x ecris the energy concentration ratio eigenvector, x pd is the phase difference eigenvector;

[0097] Scaling the feature vectors of each of the emission signal features using a maximum and minimum scaling method to ensure that all features are at the same magnitude;

[0098] The fusion weight of each of the emission signal features is dynamically adjusted by calculating the contribution of the feature vector of each of the emission signal features processed by scaling to the overall data variability; the calculation formula is: I i is the importance score of the ith feature in the transmitted signal features; the importance score is calculated by a feature importance scoring method;

[0099] Performing high-order statistical processing on the feature vectors of each of the transmission signal features after scaling processing to obtain statistics; the high-order statistical processing includes: calculating the mean value as the first-order moment, the variance as the second-order moment, the skewness as the third-order moment, and the kurtosis as the fourth-order moment of each feature vector to capture the central tendency, dispersion, skewness and peakedness of each of the transmission signal features;

[0100] All processed feature vectors and statistics are fused and calculated using the following feature fusion formula:

[0101] Represents the concatenation operation of vectors; x′ i is the feature vector x i The result after scaling, x i For x p 、x psd 、x ecr 、x pd ; i represents the subscript of p, psd, ecr, pd; I i is the importance score of the ith feature in the emission signal features; is the fusion weight of the ith feature in the emission signal features; μ i , γ 1，i , γ 2，i are respectively the mean value, variance, skewness and kurtosis of the i-th feature in the emission signal features; x fusion is the fused feature vector.

[0102] As an improvement of the above solution, the pre-trained transmit power prediction model includes:

[0103] The convolution layer extracts the fusion features of the input data through a set of learnable filter kernels. It is connected to the ReLU activation function to process the results of the convolution operation. The convolution operation is expressed as: Y(i, j) = Σ m，n X(i+m, j+n)*K(m, n); X is the input tensor of the fused feature; K is the convolution kernel; * represents the multiplication operation, which refers to the point-by-point multiplication of the convolution kernel and the corresponding position elements of the input tensor; m and n are the indexes of the convolution kernel, and the convolution kernel will traverse the entire input tensor and perform convolution calculations; Y(i, j) is the value of the output tensor at position (i, j) after the convolution operation;

[0104] The LSTM recurrent layer takes as input the sequence data output by the convolutional layer, and outputs a hidden state vector that contains summary information of the time series data. It includes an input gate, a forget gate, an output gate, and a cell state.

[0105] The fully connected layer, whose input is the last hidden state of the LSTM recurrent layer, is expressed as: [l] =W [l] a [l-1] +b [l] ; z [l] is the linear combination output of the lth layer; W [l] is the weight matrix of the lth layer; a [l-1] is the activation output of the l-1th layer, which serves as the input of the lth layer; b [l] is the bias vector of the lth layer; the activation function of the fully connected layer is: [l] =g(z [l] );a [l] is the activation output of the lth layer, which outputs the predicted potential deviation of the current transmit power of the multi-element phased array system, that is, z after the activation function g [l] ; g is the ReLU, sigmoid or tanh activation function.

[0106] See also Figure 3 , is a schematic diagram of a transmit power calibration device for a multi-element phased array system provided in an embodiment of the present invention. The transmit power calibration device for a multi-element phased array system of this embodiment includes: a processor 100, a memory 101, and a computer program stored in the memory 101 and executable on the processor 100, such as a transmit power calibration program for a multi-element phased array system. When the processor 100 executes the computer program, the steps in the above-mentioned transmit power calibration method embodiments for a multi-element phased array system are implemented. Alternatively, when the processor 100 executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0107] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the transmit power calibration device of the multi-element phased array system.

[0108] The transmit power correction device of the multi-element phased array system may be a control device of the multi-element phased array system, or a computing device such as a desktop computer, a notebook, a PDA, and a cloud server that is communicatively connected to the multi-element phased array system. The transmit power correction device of the multi-element phased array system may include, but is not limited to, a processor and a memory. Those skilled in the art may understand that the schematic diagram is only an example of a transmit power correction device of a multi-element phased array system, and does not constitute a limitation on the transmit power correction device of a multi-element phased array system, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the transmit power correction device of the multi-element phased array system may also include input and output devices, network access devices, buses, etc.

[0109] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the transmission power calibration device of the multi-element phased array system, and uses various interfaces and lines to connect various parts of the transmission power calibration device of the entire multi-element phased array system.

[0110] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the transmission power correction device of the multi-element phased array system by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0111] Wherein, if the module / unit integrated in the transmit power correction device of the multi-element phased array system is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0112] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0113] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for calibrating the transmission power of a multi-element phased array system, characterized in that: include: Acquire the transmission signal parameters of the multi-element phased array system currently detected by the sensor system; the transmission signal parameters include transmission power, power spectrum density, energy concentration ratio and phase difference; Extracting signal features from the transmission signal parameters to obtain transmission signal features; the transmission signal features include transmission power features, power spectrum density features, energy concentration ratio features, and phase difference features; Performing feature fusion on the emission signal features to obtain fusion features; Inputting the fusion feature into a pre-trained transmit power prediction model to predict the current potential deviation of the transmit power of the multi-element phased array system; According to the transmit power and the potential deviation of the transmit power, the transmit power after correction of the multi-element phased array system is calculated.

2. The method for calibrating the transmission power of a multi-element phased array system according to claim 1, characterized in that: The power spectral density is: perform fast Fourier transform (FFT) on the signal to obtain the spectrum, and then calculate the square of its amplitude: S(f) is the power spectral density of signal s(t) at frequency f; is the Fourier transform of signal s(t), which represents the representation of signal s(t) in the frequency domain; It is the square of the modulus of the Fourier transform result, which represents the energy or power of the signal at frequency f; The energy concentration ratio is: ECR is the energy concentration ratio, which reflects the degree to which the signal energy is concentrated within the main frequency bandwidth; l and f h They are the lower and upper frequency limits of the main frequency bandwidth respectively; is the energy or power of the signal within the main frequency bandwidth; is the total energy or power of the signal s(t) over the entire frequency range; The phase difference is: Δφ ij =∠s i -∠s j ; Δφ ij is the phase difference between the i-th array element and the j-th array element; s i and ∠s j are the phase angles of the signals of the i-th array element and the j-th array element respectively.

3. The method for calibrating the transmission power of a multi-element phased array system according to claim 2, characterized in that: The step of fusing the emission signal features to obtain fused features includes: set up is the transmit power eigenvector, x psd is the power spectral density eigenvector, x ecr is the energy concentration ratio eigenvector, x pd is the phase difference eigenvector; Scaling the feature vectors of each of the emission signal features using a maximum and minimum scaling method to ensure that all features are at the same magnitude; The fusion weight of each of the emission signal features is dynamically adjusted by calculating the contribution of the feature vector of each of the emission signal features processed by scaling to the overall data variability; the calculation formula is: I i is the importance score of the ith feature in the transmitted signal features; the importance score is calculated by a feature importance scoring method; Performing high-order statistical processing on the feature vectors of each of the transmission signal features after scaling processing to obtain statistics; the high-order statistical processing includes: calculating the mean value as the first-order moment, the variance as the second-order moment, the skewness as the third-order moment, and the kurtosis as the fourth-order moment of each feature vector to capture the central tendency, dispersion, skewness and peakedness of each of the transmission signal features; All processed feature vectors and statistics are fused and calculated using the following feature fusion formula: Represents the concatenation operation of vectors; x′ i is the feature vector x i The result after scaling, x i For x p 、x psd 、x ecr 、x pd ; i represents the subscript of p, psd, ecr, pd; I i is the importance score of the ith feature in the emission signal features; is the fusion weight of the ith feature in the emission signal features; μ i , γ 1,i ,γ 2,i are respectively the mean value, variance, skewness and kurtosis of the i-th feature in the emission signal features; x fusion is the fused feature vector.

4. The method for calibrating the transmission power of a multi-element phased array system according to claim 3, characterized in that: The pre-trained transmit power prediction model includes: The convolution layer extracts the fusion features of the input data through a set of learnable filter kernels. It is connected to the ReLU activation function to process the results of the convolution operation. The convolution operation is expressed as: Y(i,j) = ∑ m,n X(i+m,j+n)*K(m,n); X is the input tensor of the fused feature; K is the convolution kernel; * represents the multiplication operation, which refers to the point-by-point multiplication of the convolution kernel and the corresponding position elements of the input tensor; m and n are the indexes of the convolution kernel, and the convolution kernel will traverse the entire input tensor and perform convolution calculations; Y(i,j) is the value of the output tensor at position (i,j) after the convolution operation; The LSTM recurrent layer takes as input the sequence data output by the convolutional layer, and outputs a hidden state vector that contains summary information of the time series data. It includes an input gate, a forget gate, an output gate, and a cell state. The fully connected layer, whose input is the last hidden state of the LSTM recurrent layer, is expressed as: [l] =W [l] a [l-1] +b [l] ;z [l] is the linear combination output of the lth layer; W [l] is the weight matrix of the lth layer; a [l-1] is the activation output of the l-1th layer, which serves as the input of the lth layer; b [l] is the bias vector of the lth layer; the activation function of the fully connected layer is: [l] =g(z [l] );a [l] is the activation output of the lth layer, which outputs the predicted potential deviation of the current transmit power of the multi-element phased array system, that is, z after the activation function g [l] ; g is the ReLU, sigmoid or tanh activation function.

5. A transmission power calibration device for a multi-element phased array system, characterized in that: include: An acquisition module is used to acquire the transmission signal parameters of the multi-element phased array system currently detected by the sensor system; the transmission signal parameters include transmission power, power spectrum density, energy concentration ratio and phase difference; A feature extraction module is used to extract signal features from the transmission signal parameters to obtain transmission signal features; the transmission signal features include transmission power features, power spectrum density features, energy concentration ratio features and phase difference features; A feature fusion module, used for performing feature fusion on the emission signal features to obtain fusion features; A power deviation prediction module, used to input the fusion feature into a pre-trained transmit power prediction model to predict the current potential deviation of the transmit power of the multi-element phased array system; A power correction module is used to calculate the corrected transmit power of the multi-element phased array system according to the transmit power and the potential deviation of the transmit power.

6. The transmission power calibration device for a multi-element phased array system according to claim 5, characterized in that: The power spectral density is: perform fast Fourier transform (FFT) on the signal to obtain the spectrum, and then calculate the square of its amplitude: S(f) is the power spectral density of signal s(t) at frequency f; is the Fourier transform of signal s(t), which represents the representation of signal s(t) in the frequency domain; It is the square of the modulus of the Fourier transform result, which represents the energy or power of the signal at frequency f; The energy concentration ratio is: ECR is the energy concentration ratio, which reflects the degree to which the signal energy is concentrated within the main frequency bandwidth; l and f h They are the lower and upper frequency limits of the main frequency bandwidth respectively; is the energy or power of the signal within the main frequency bandwidth; is the total energy or power of the signal s(t) over the entire frequency range; The phase difference is: Δφ ij =∠s i -∠s j ; Δφ ij is the phase difference between the ith array element and the jth array element; ∠s i and ∠s j are the phase angles of the signals of the i-th array element and the j-th array element respectively.

7. The transmission power calibration device of a multi-element phased array system according to claim 6, characterized in that: The feature fusion module is specifically used for: Let x p is the transmit power eigenvector, x psd is the power spectral density eigenvector, x ecr is the energy concentration ratio eigenvector, x pd is the phase difference eigenvector; Scaling the feature vectors of each of the emission signal features using a maximum and minimum scaling method to ensure that all features are at the same magnitude; Dynamically adjusting the fusion weight of each of the emission signal features by calculating the contribution of the feature vector of each of the emission signal features processed by scaling to the overall data variability; The calculation formula is: I i is the importance score of the ith feature in the transmitted signal features; the importance score is calculated by a feature importance scoring method; Performing high-order statistical processing on the feature vectors of each of the transmission signal features after scaling processing to obtain statistics; the high-order statistical processing includes: calculating the mean value as the first-order moment, the variance as the second-order moment, the skewness as the third-order moment, and the kurtosis as the fourth-order moment of each feature vector to capture the central tendency, dispersion, skewness and peakedness of each of the transmission signal features; All processed feature vectors and statistics are fused and calculated using the following feature fusion formula: Represents the concatenation operation of vectors; x′ i is the feature vector x i The result after scaling, x i For x p 、x psd 、x ecr 、x pd ; i represents the subscript of p, psd, ecr, pd; I i is the importance score of the ith feature in the emission signal features; is the fusion weight of the ith feature in the emission signal features; μ i , γ 1，i , γ 2，i are respectively the mean value, variance, skewness and kurtosis of the i-th feature in the emission signal features; x fusion is the fused feature vector.

8. The transmission power calibration device for a multi-element phased array system according to claim 7, characterized in that: The pre-trained transmit power prediction model includes: The convolution layer extracts the fusion features of the input data through a set of learnable filter kernels. It is connected to the ReLU activation function to process the results of the convolution operation. The convolution operation is expressed as: Y(i, j) = Σ m，n X(i+m, j+n)*K(m, n); X is the input tensor of the fused feature; K is the convolution kernel; * represents the multiplication operation, which refers to the point-by-point multiplication of the convolution kernel and the corresponding position elements of the input tensor; m and n are the indexes of the convolution kernel, and the convolution kernel will traverse the entire input tensor and perform convolution calculations; Y(i, j) is the value of the output tensor at position (i, j) after the convolution operation; The LSTM recurrent layer takes as input the sequence data output by the convolutional layer, and outputs a hidden state vector that contains summary information of the time series data. It includes an input gate, a forget gate, an output gate, and a cell state. The fully connected layer, whose input is the last hidden state of the LSTM recurrent layer, is expressed as: [l] =W [l] a [l-1] +b [l] ;z [l] is the linear combination output of the lth layer; W [l] is the weight matrix of the lth layer; a [l-1] is the activation output of the l-1th layer, which serves as the input of the lth layer; b [l] is the bias vector of the lth layer; the activation function of the fully connected layer is: [l] =g(z [l] );a [l] is the activation output of the lth layer, which outputs the predicted potential deviation of the current transmit power of the multi-element phased array system, that is, z after the activation function g [l] ; g is the ReLU, sigmoid or tanh activation function.

9. A transmission power calibration device for a multi-element phased array system, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for correcting the transmission power of a multi-element phased array system according to any one of claims 1 to 4 is implemented.

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