GPS interference phased array monitoring direction-finding antenna system for 1575 frequency band

Abstract

The invention discloses a GPS interference phased array monitoring direction-finding antenna system for a 1575 frequency band, and relates to the technical field of intelligent antennae, comprising a data integration module for integrating data of satellite ephemeris, carrier attitude and airspace coverage through a weighted average method and generating an environment parameter set, a prediction calibration module for predicting the data of the satellite ephemeris, the carrier attitude and the airspace coverage through a weighted average method and generating an environment parameter set, and a direction-finding antenna module for performing direction-finding on the environment parameter set. The phase drift prediction module is used for inputting an environment parameter set into a bidirectional LSTM network, obtaining a phase drift prediction value, carrying out nonlinear calibration on the phase drift prediction value by adopting a Kalman-particle hybrid filtering algorithm, and synchronously monitoring an IQ signal by utilizing a closed-loop feedback mechanism, and the beam generation module is used for carrying out orthogonal modulation and radio frequency up-conversion processing on the IQ signal, and outputting the processed IQ signal. According to the method, the bidirectional LSTM network is combined with the Kalman-particle hybrid filtering algorithm, so that phase drift caused by factors such as carrier vibration and the like can be accurately predicted and calibrated in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of smart antennas, in particular to a GPS interference phased array monitoring and direction finding antenna system for the 1575 MHz band. Background Art

[0002] In recent years, with the wide application of GPS in various fields such as civilian and military, the security and reliability of GPS receivers - phased array antennas have become the focus of research. In particular, phased array antennas for the 1575 MHz band, which are widely used in navigation and time synchronization services, have received increasing attention. In the civilian field, from personal navigation devices to intelligent transportation, to precision agriculture and disaster monitoring, GPS signals in the 1575 MHz band are ubiquitous, providing high-precision position information and time synchronization services for the civilian field. In military applications, GPS signals in the 1575 MHz band are even more crucial, used for precision-guided weapons, battlefield situation awareness, troop coordination, and communication synchronization. Therefore, researchers have begun to explore more advanced antenna array technologies and signal processing methods, such as the application of phased array antenna technology, which can form a beam pointing in a specific direction by adjusting the phase relationship of each array element, thereby enhancing the directional ability and anti-interference performance. At the same time, the development of digital signal processing technology has also provided more accurate methods for the modulation and demodulation of IQ signals, radio frequency upconversion, etc., further improving the quality and stability of signals. In addition, the introduction of machine learning algorithms, especially deep learning models, has brought new breakthroughs in fields such as satellite ephemeris prediction, phase drift calibration, and multi-source data fusion, making high-precision prediction based on time series analysis possible and promoting the progress of space-time filtering technology, laying a foundation for efficient interference suppression within the airspace coverage range.

[0003] However, there are still some deficiencies in the existing technologies. On the one hand, in a complex dynamic environment, traditional receivers cannot accurately predict in real time the phase drift caused by factors such as carrier vibration, making it difficult to calibrate in a timely manner, directly affecting the positioning accuracy and reliability. On the other hand, when existing weight allocation strategies perform multi-source data fusion, they usually adopt a single data processing algorithm and lack an adaptive adjustment mechanism for the characteristics of data from different sources. As a result, when facing a complex electromagnetic environment, they cannot effectively integrate data from different sensors or receivers, affecting the final positioning result and anti-interference performance. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a GPS interference phased array monitoring and direction finding antenna system for the 1575 MHz band to solve the problems of inaccurate phase drift prediction and insufficient multi-source data fusion.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: The present invention provides a GPS interference phased array monitoring and direction finding antenna system for the 1575 frequency band, which includes a data integration module for integrating data of satellite ephemeris, carrier attitude, and airspace coverage range by the weighted average method to generate an environmental parameter set; a prediction and calibration module for inputting the environmental parameter set into a bidirectional LSTM network to obtain a phase drift prediction value, and using a Kalman-particle hybrid filtering algorithm to perform nonlinear calibration on the phase drift prediction value, and simultaneously monitoring the IQ signal by using a closed-loop feedback mechanism; a beam generation module for performing quadrature modulation and radio frequency up-conversion processing on the IQ signal, and generating a Butler matrix beam by using a hybrid topology beam space transformation algorithm; an anti-interference positioning module for triggering the phased array antenna to scan within the airspace coverage range through the Butler matrix beam, and suppressing GPS interference by using space-time filtering during the scanning process to obtain preliminary positioning parameters; a report generation module for performing weighted aggregation and hierarchical optimization on the preliminary positioning parameters by using a federated learning mechanism to obtain optimized positioning parameters, and performing ephemeris consistency verification and pseudorange residual analysis on the optimized positioning parameters to generate a phased array antenna monitoring and direction finding report.

[0007] As a preferred solution of the GPS interference phased array monitoring and direction finding antenna system for the 1575 frequency band according to the present invention, wherein: the specific operation of generating the environmental parameter set is as follows Performing data cleaning and spatio-temporal alignment on satellite ephemeris data through multiple imputation; suppressing noise and removing outliers from carrier attitude data through Kalman filtering and Z-score standardization; performing discrete point interpolation on airspace coverage range data through Kriging interpolation; Assigning dynamic weights to the preprocessed satellite ephemeris, carrier attitude, and airspace coverage range data through the weighted average method, and performing fusion through Kalman filtering to generate an environmental parameter set.

[0008] As a preferred solution of the GPS interference phased array monitoring and direction finding antenna system for the 1575 frequency band according to the present invention, wherein: the specific operation of obtaining the phase drift prediction value is as follows In the PyTorch framework, constructing a bidirectional LSTM network by integrating the input layer, hidden layer, and fully connected layer through the bidirectional parameter method; Inputting the environmental parameter set into the bidirectional LSTM network through the time series recombination method, and the input layer performs local feature extraction and dimensionality increase to generate a three-dimensional time tensor; The forward LSTM of the hidden layer performs temporal dependence analysis on the three-dimensional time tensor to obtain the hidden state sequence; the backward LSTM performs reverse temporal analysis on the three-dimensional time tensor to generate the reverse hidden state sequence, and the hidden state sequence and the reverse hidden state sequence are concatenated to generate a bidirectional feature matrix; The fully connected layer performs feature dimensionality reduction on the bidirectional feature matrix through the ReLU activation function to obtain the phase drift prediction value.

[0009] As a preferred scheme of the GPS interference phased array monitoring and direction finding antenna system for the 1575 MHz band described in the present invention, wherein: the Kalman-particle hybrid filtering algorithm is used to perform nonlinear calibration on the phase drift prediction value, and the specific operation is as follows. The particle filter processes the non-Gaussian noise in the phase drift prediction value through Monte Carlo sampling to obtain the particle state distribution; the covariance matrix is generated by performing weighted calculation on the particle state distribution through the second moment algorithm. The Gaussian noise in the covariance matrix is removed through Kalman filtering, and the filtering matrix is generated; the inverse function of the filtering matrix is derived through third-order polynomial fitting to generate the dynamic calibration curve, and based on the dynamic calibration curve, the phase drift prediction value is calibrated through the Bayesian weight assignment method.

[0010] As a preferred scheme of the GPS interference phased array monitoring and direction finding antenna system for the 1575 MHz band described in the present invention, wherein: the synchronization uses a closed-loop feedback mechanism to monitor the IQ signal, and the specific operation is as follows. Through the closed-loop feedback mechanism, dynamic error compensation is performed on the phase drift prediction value after nonlinear calibration, and the IQ signal is sampled using the digital down-conversion method.

[0011] As a preferred scheme of the GPS interference phased array monitoring and direction finding antenna system for the 1575 MHz band described in the present invention, wherein: the IQ signal is subjected to quadrature modulation and radio frequency up-conversion processing, and the Butler matrix beam is generated using the hybrid topology beam space transformation algorithm, and the specific operation is as follows. The image frequency of the IQ signal is suppressed through Bessel interpolation, and quadrature modulation is performed using the QPSK modulation method to obtain the suppressed radio frequency signal; the suppressed radio frequency signal is subjected to radio frequency up-conversion processing using the polyphase filtering method to generate the multi-beam radio frequency signal. The multi-beam radio frequency signal is subjected to spatial mapping using the hybrid topology beam space transformation algorithm to obtain the Butler matrix beam.

[0012] As a preferred scheme of the GPS interference phased array monitoring and direction finding antenna system for the 1575 MHz band described in the present invention, wherein: the phased array antenna is triggered by the Butler matrix beam to perform scanning within the spatial coverage range, and the specific operation is as follows. The Butler matrix beam is input into the feeding channel of the phased array antenna through a directional coupler. In the feeding channel, the beam pointing is optimized through the spatial domain phase gradient compensation algorithm, and the scanning trigger signal is obtained through a digital-to-analog converter. Based on the scanning trigger signal, the phased array antenna is driven to scan within the spatial coverage range through the time delay scanning mechanism to obtain the array signal.

[0013] As a preferred solution of the GPS interference phased array monitoring and direction finding antenna system for the 1575 frequency band described in the present invention, wherein: the space-time filtering is adopted to suppress the GPS interference and obtain the preliminary positioning parameters. The specific operations are as follows. The space-time filtering is used to perform space-time two-dimensional weighting on the array signal to obtain the time-domain signal; the GPS interference in the time-domain signal is suppressed through the space-time two-dimensional generalized sidelobe cancellation method, and the preliminary positioning parameters are generated by using the RAIM integrity monitoring algorithm.

[0014] As a preferred solution of the GPS interference phased array monitoring and direction finding antenna system for the 1575 frequency band described in the present invention, wherein: the optimized positioning parameters are obtained. The specific operations are as follows. The federated learning mechanism is adopted to suppress the noise of the preliminary positioning parameters, and the FedAvg algorithm is used for weighted aggregation to obtain the multi-source fusion parameters; the multi-source fusion parameters are hierarchically optimized by using the hierarchical layer-by-layer alignment method to obtain the optimized positioning parameters.

[0015] As a preferred solution of the GPS interference phased array monitoring and direction finding antenna system for the 1575 frequency band described in the present invention, wherein: the phased array antenna monitoring and direction finding report is generated. The specific operations are as follows. The Neville interpolation method is used to perform ephemeris consistency verification on the optimized positioning parameters to obtain the orbit monitoring data; the double-difference analysis method is used to perform pseudorange residual analysis on the optimized positioning parameters to obtain the direction monitoring data. The dynamic phase weighting algorithm is used to integrate the orbit monitoring data and the direction monitoring data to generate the phased array antenna monitoring and direction finding report.

[0016] The beneficial effects of the present invention are as follows: by adopting the bidirectional LSTM network combined with the Kalman-particle hybrid filtering algorithm, the phase drift caused by factors such as carrier vibration can be predicted and calibrated in real time and accurately. The federated learning mechanism is used for weighted aggregation and hierarchical optimization of the preliminary positioning parameters, realizing the adaptive adjustment of the data characteristics from different sources. Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is a schematic diagram of a GPS interference phased array monitoring and direction finding antenna system for the 1575 frequency band.

[0019] Figure 2 It is a flowchart for generating a phased array antenna monitoring and direction finding report based on preliminary positioning parameters.

[0020] Figure 3 It is a flowchart for the process of obtaining an environmental parameter set.

[0021] Figure 4 It is a flowchart for the process of obtaining the predicted value of phase drift. Specific Embodiments

[0022] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention with reference to the accompanying drawings of the specification.

[0023] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that mutually excludes other embodiments.

[0025] Referring to Figures 1 to 4 , which is an embodiment of the present invention. This embodiment provides a GPS interference phased array monitoring and direction finding antenna system for the 1575 frequency band, including the following steps: A data integration module, configured to integrate the data of satellite ephemeris, carrier attitude, and airspace coverage range through the weighted average method to generate an environmental parameter set.

[0026] Specifically, it includes the following steps. In the first step, collect satellite ephemeris, carrier attitude, and airspace coverage data. Among them, satellite ephemeris data is captured by high-precision GPS. GPS uses the multi-frequency tracking method to parse navigation messages, thereby collecting satellite ephemeris data in real time. At the same time, equip GPS with advanced anti-interference functions to ensure the accuracy and purity of satellite ephemeris data; For carrier attitude data, use an inertial measurement unit (IMU) to collect it. The inertial measurement unit can accurately sense the dynamic parameters of the carrier's acceleration and angular velocity, and through the built-in algorithm, perform integration and coordinate transformation on the dynamic parameters to obtain carrier attitude data, providing high-frequency and high-resolution data support for subsequent analysis; For airspace coverage data, it is collected through a distributed phased array antenna unit. The distributed phased array antenna unit can provide full coverage of the airspace according to the beam scanning control algorithm, capture signal strength and phase information from different directions, and integrate them into airspace coverage data through digital beamforming technology, providing a reliable data basis for precise monitoring and positioning within the airspace; In the second step, preprocess the collected satellite ephemeris, carrier attitude, and airspace coverage data. First, process the satellite ephemeris data: Based on the multiple imputation method, decompose the satellite ephemeris data through the Daubechies8 wavelet basis for multiple layers to obtain the ephemeris time series; and use the sliding window detection method to clean the data of the ephemeris time series, and at the same time use the dynamic time warping (DTW) algorithm for spatio-temporal alignment to ensure the time continuity and spatial consistency of the satellite ephemeris data; Secondly, process the carrier attitude data: Use Kalman filtering to update the measurement of the carrier attitude data to obtain attitude parameters; divide the attitude parameters into data segments of five sampling points through the sliding average window method, and perform weighted moving average on each data segment through the Hanning window function to suppress noise; perform normalization processing on the carrier attitude data through the Z-score normalization method, and use box plot analysis to remove outliers, ensuring the purity and reliability of the carrier attitude data; Finally, process the airspace coverage data: Use Kriging interpolation to perform non-uniform point sampling on the airspace coverage data to obtain continuous airspace data; use the local outlier factor (LOF) algorithm to perform density clustering analysis on the continuous airspace data to identify discrete points; perform interpolation processing on the discrete points through Kriging residual analysis to ensure the spatial continuity and signal strength consistency of the airspace coverage data; In the third step, the preprocessed satellite ephemeris, carrier attitude, and airspace coverage data are integrated to obtain an environmental parameter set. Specifically, in the operation, the entropy weights of the preprocessed satellite ephemeris, carrier attitude, and airspace coverage data are calculated through the weighted average method to obtain a fusion parameter set; the multi-modal correlation of the fusion parameter set is calculated through the principal component analysis method to generate a weighted parameter matrix; the dynamic weight distribution algorithm is used to optimize the real-time weights of the weighted parameter matrix to obtain dynamic weights; Based on the dynamic weights, the satellite ephemeris, carrier attitude, and airspace coverage data are further fused through Kalman filtering to generate an environmental parameter set. Specifically, in the operation, the attitude of the carrier attitude data is resolved using the quaternion Kalman filter (QKF), and the state vector is constructed through the vector splicing method; the orbital parameters of the satellite ephemeris data are extracted using the least squares fitting method, and the orbital parameters are differentially combined through the phase double difference method to construct an observation vector; According to the state vector and the observation vector, the Kalman filtering algorithm is used for prediction and update: in the prediction stage, the Newton-Euler equation is used to calculate the state transition of the state vector to obtain the observation noise matrix. In the update stage, the inverse matrix of the observation noise matrix is calculated using the weighted least squares method to obtain the weighted noise inverse matrix, and the dynamic weights are incorporated into the weighted noise inverse matrix through the matrix Hadamard product method to generate the Kalman gain matrix; the Kalman gain matrix is iterated through the Jacobi iterative method until the convergence threshold is reached, and the optimal environmental parameter set is output; the application of Kalman filtering can not only smooth the noise but also make the generated environmental parameter set closer to the actual situation; It should be noted that the convergence threshold is defined by the sliding window statistical method based on the 3σ criterion.

[0027] The prediction calibration module is used to input the environmental parameter set into the bidirectional LSTM network to obtain the phase drift prediction value, and the Kalman-particle hybrid filtering algorithm is used to non-linearly calibrate the phase drift prediction value, and the IQ signal is monitored using the closed-loop feedback mechanism.

[0028] Specifically, it includes the following steps, In the first step, a bidirectional LSTM network is constructed. Specifically, in the operation, the LSTM is initialized in the PyTorch framework using the bidirectional parameter method, and the bidirectional structure is enabled through the bidirectional=True parameter to obtain a dual-path LSTM with forward and backward processing capabilities; based on the dual-path LSTM, the input layer, hidden layer, and fully connected layer are stacked through the Sequential container encapsulation method to construct a complete bidirectional LSTM network; the input layer enhances local features through one-dimensional convolution; multiple bidirectional processing nodes are set in the hidden layer, and the bidirectional feature matrix is output in a bidirectional splicing manner; the fully connected layer uses linear transformation and activation functions to achieve feature dimensionality reduction; Next, train the constructed bidirectional LSTM network. Specifically, in the operation, the environmental parameter set is divided into a training set, a validation set, and a test set by the stratified random sampling method; in the training set, forward propagation and backward propagation are performed through mini-batch gradient descent to obtain the error gradient; and the Adam optimizer is used to perform momentum adjustment on the error gradient, and at the same time, gradient clipping is used to prevent gradient explosion to generate updated LSTM network parameters; in the validation set, the early stopping mechanism is used to calculate the loss of the updated LSTM network parameters using the SmoothL1Loss function to obtain the smooth L1 loss value, and at the same time, overfitting is prevented through the Dropout layer and L2 regularization; the training process is accelerated by GPU parallel computing. In the validation set, when the smooth L1 loss value no longer decreases and reaches the DCT threshold, the training is terminated, and the trained bidirectional LSTM network is output synchronously. It should be noted that the DCT threshold is defined based on the change rate of the smooth L1 loss value. In the second step, the environmental parameter set is input into the bidirectional LSTM network through the time series recombination method. The input layer performs local feature extraction and dimensionality increase to generate a three-dimensional time tensor. Specifically, in the operation, the environmental parameter set is divided into multiple sliding windows by the time series recombination method, and the sliding windows are recombined using the sequence padding method to obtain the time series segment sequence; the time series segment sequence is input into the input layer of the bidirectional LSTM network through the DataLoader interface of PyTorch. In the input layer, a one-dimensional convolutional layer is used to perform local feature extraction on the time series segment sequence, and normalization is performed through layer normalization to obtain normalized features; a linear projection layer is used to perform spatial mapping on the normalized features, and a linear transformation is performed through the ReLU activation function to generate high-dimensional features; the high-dimensional features are restructured using the dimension reshaping method, and dimension transposition is performed through PyTorch to generate a three-dimensional time tensor. The forward LSTM in the hidden layer performs temporal dependence analysis on the three-dimensional time tensor to obtain the hidden state sequence. Specifically, in the operation, the LSTMCell (long short-term memory unit) of PyTorch is used to perform time step expansion calculation on the three-dimensional time tensor to obtain feature slices; the gating mechanism is used to perform linear transformation and bias superposition on the feature slices to obtain the gating activation value; the gating activation value is normalized through the sigmoid function, and temporal fusion is performed using the recursive update method to capture long-term dependence relationships; the hidden state in the long-term dependence relationship is extracted using the state output control method and integrated using the tensor concatenation method to output the hidden state sequence. The backward LSTM of the hidden layer performs backward temporal analysis on the three-dimensional temporal tensor to generate a sequence of backward hidden states. In the specific operation, the LSTMCell is used to unfold and calculate the three-dimensional temporal tensor in reverse time order to obtain reverse-order feature slices; the backward gating mechanism is used to perform a time-reversed linear transformation on the reverse-order feature slices to generate backward gating activation values; the sigmoid and tanh functions are used to normalize the backward gating activation values, and backward temporal analysis is performed through the backward state recursion algorithm to obtain backward hidden states, which are integrated using the tensor concatenation method to synchronously output a sequence of backward hidden states; The hidden state sequence and the backward hidden state sequence are feature concatenated through depthwise separable convolution and normalized through layer normalization to generate a bidirectional feature matrix; The fully connected layer performs feature dimensionality reduction on the bidirectional feature matrix through the ReLU activation function to obtain the phase drift prediction value. In the specific operation, the bidirectional feature matrix is flattened through the Flatten layer and converted into a two-dimensional matrix; two fully connected layers are used to reduce the dimensionality of the two-dimensional matrix: the first layer uses the ReLU activation function to reduce the dimensionality of the two-dimensional matrix to 64 dimensions, and the second layer further compresses the two-dimensional matrix to 1 dimension through a 1×1 convolution to obtain the original prediction value; the Sigmoid function is used to constrain the output of the original prediction value within the interval [0,1] to generate the final phase drift prediction value; In the third step, the Kalman-particle hybrid filtering algorithm is used to perform nonlinear calibration on the phase drift prediction value. In the specific operation, the particle filter is used to decompose the error of the phase drift prediction value to obtain multiple initial particles, and the initial particles are iteratively updated through Monte Carlo sampling: in the prediction stage, the state transition method is used to propagate the states of the initial particles to obtain a set of predicted particles. In the observation update stage, based on the 3σ criterion, the weighted likelihood estimation method is used to remove non-Gaussian noise from the set of predicted particles, and the importance resampling method is used to normalize the weights of the denoised set of predicted particles to generate the particle state distribution; Next, the weighted calculation of the particle state distribution is performed through the second-order moment algorithm to obtain the mean vector, and the mean vector is centered through the vector subtraction operation to generate the particle deviation vector; the mean normalization of the particle deviation vector is performed through the matrix subtraction operation to generate the deviation matrix; the transpose operation is performed on the deviation matrix to obtain the transpose of the deviation matrix; the deviation matrix is multiplied by the transpose of the deviation matrix, and the matrix accumulation is performed through the weighted outer product summation algorithm to obtain the covariance matrix; finally, the regularization term is added to the covariance matrix through the diagonal matrix superposition method to ensure the stability of the covariance matrix; Then, perform a time update on the covariance matrix through Kalman filtering to obtain an initial state estimate; calculate the Kalman gain for the initial state estimate through the innovation covariance method to obtain an optimal state estimate; based on the optimal state estimate, eliminate Gaussian noise in the covariance matrix through the Mahalanobis distance detection method, and perform covariance reset through the fixed ratio scaling method to generate a filtering matrix; Perform least squares regression on the filtering matrix through third-order polynomial fitting to obtain phase error parameters; perform inverse function derivation on the phase error parameters through matrix inversion algorithm to generate a phase compensation function; perform de-jitter processing on the phase compensation function through sliding window smoothing, and perform curve fitting using linear interpolation method to generate a dynamic calibration curve; Perform confidence weighting on the dynamic calibration curve through Bayesian weight assignment method to obtain a weighted calibration curve; perform discretized sampling on the weighted calibration curve using linear interpolation method to generate a phase lookup table; based on the phase lookup table, calculate the phase offset through real-time lookup table method to obtain an offset value; according to the offset value, perform phase rotation on the phase drift prediction value through phase compensation method to calibrate the phase drift prediction value; In the fourth step, after the nonlinear calibration is completed, synchronously monitor the IQ signal using a closed-loop feedback mechanism. Specifically, in the operation, calculate the error differential of the phase drift prediction value after the nonlinear calibration through the closed-loop feedback mechanism to obtain a phase error gradient; perform proportional-integral-derivative adjustment on the phase error gradient through a PID controller, and generate a closed-loop compensation amount using the error weighted accumulation algorithm; according to the closed-loop compensation amount, perform real-time phase modulation through a digital control oscillator to compensate for the error of the phase drift prediction value; after the error compensation is completed, use the FPGA timer to generate a timing trigger signal to drive the radio frequency front-end device to collect the initial IQ signal of the phased array antenna, and perform quadrature demodulation on the initial IQ signal using the digital down-conversion method to ensure the quadrature accuracy of the IQ signal to obtain the final IQ signal.

[0029] A beam generation module, which is used to perform quadrature modulation and radio frequency up-conversion processing on the IQ signal, and generate a Butler matrix beam using the hybrid topology beamspace transformation algorithm.

[0030] Specifically, it includes the following steps, In the first step, the IQ signal is subjected to image frequency suppression by the Bessel interpolation method and orthogonally modulated by the QPSK modulation method to obtain the suppressed radio frequency signal. Specifically, in the operation, a sixth-order Bessel interpolation filter is used to perform high-precision upsampling processing on the final IQ signal to obtain a high-times upsampled signal; the Bessel interpolation method is used to perform polyphase filtering calculation on the high-times upsampled signal to generate an image frequency suppression signal; the sixth-order Bessel interpolation filter realizes efficient operation through an optimized polyphase structure and ensures the operation accuracy by using a twelve-bit fixed-point algorithm; next, the window function weighting method is used to perform spectrum shaping on the image frequency suppression signal to suppress the image frequency component in the final IQ signal and generate a baseband signal without image frequency interference. After completing the image frequency suppression, it enters the QPSK modulation stage. Specifically, in the operation, based on the QPSK modulation method, the baseband signal stream without image frequency interference is mapped to the orthogonality points according to the Gray code rule, and amplitude quantization is performed by the linear quantization method to generate in-phase and quadrature components. Subsequently, the in-phase and quadrature components are bandwidth-limited by a cosine pulse shaping filter to effectively suppress out-of-band spectrum leakage, and the in-phase and quadrature components are complex mixed by a digital mixer to generate a modulated intermediate signal; finally, digital orthogonal upconversion technology is used to perform orthogonal modulation on the modulated intermediate signal, and at the same time, a carrier phase-locked loop is used for carrier synchronization to obtain the suppressed radio frequency signal. The application of the QPSK modulation method significantly improves the spectrum utilization rate on the premise of ensuring the quality of the baseband signal and is particularly suitable for phased array antennas with strict requirements for image frequency suppression. In the second step, the polyphase filtering method is used to perform radio frequency upconversion processing on the suppressed radio frequency signal to generate a multi-beam radio frequency signal. Specifically, in the operation, polyphase filtering is used to perform polyphase decomposition processing on the suppressed radio frequency signal, and each phase filtering uses the equiripple FIR design method to perform band-limiting processing on the suppressed radio frequency signal to generate a band-limited intermediate frequency signal; subsequently, the CORDIC algorithm is used to perform digital orthogonal upconversion on the band-limited intermediate frequency signal to generate a radio frequency modulation signal. Based on the radio frequency modulation signal, the complex mixer uses the direct digital frequency synthesis (DDS) method to perform radio frequency upconversion processing on the suppressed radio frequency signal, shifts the suppressed radio frequency signal to the target radio frequency, and uses the beamforming (MVDR) method to perform multi-channel amplitude addition weighting on the shifted suppressed radio frequency signal to generate a multi-beam radio frequency signal. It should be noted that the target radio frequency is defined by the standard operating frequency band of the 5G base station. The digital pre-equalization (DPE) method is used to perform nonlinear correction on the multi-beam radio frequency signal to ensure the amplitude consistency of each channel. At the same time, the pre-distortion compensation technology is used to correct the DAC nonlinearity of the multi-beam radio frequency signal to ensure accuracy and efficiency. In the third step, the hybrid topology beamspace transformation algorithm is adopted to perform spatial mapping on the multi-beam RF signal to obtain the Butler matrix beam. Specifically, in the digital baseband processor, the hybrid topology beamspace transformation algorithm is used to perform fast Fourier transform (FFT) processing on the multi-beam RF signal to convert the multi-beam RF signal into a beamspace signal; subsequently, the digital shift and add weighting method is adopted to weight the beamspace signal to obtain a phase calibration signal; the fixed phase gradient method is used to perform spatial mapping on the phase calibration signal to generate a primary beam signal; In the amplitude equalizer, the actual power of each feeding channel is collected by using the automatic gain control (AGC) method and compared with the reference power to obtain a power difference; the power difference is subjected to proportional-integral (PI) control operation to obtain the amplitude adjustment value of the feeding channel. The specific mathematical formula is as follows: ; where, represents the amplitude adjustment value of the th feeding channel at time ; represents the proportional coefficient; represents the th feeding channel at the current moment; represents the integral coefficient; represents the th feeding channel at time ; represents the sampling time interval; represents the feeding channel index; represents the time index; represents the accumulation variable; It should be noted that the reference power is defined based on the rollback point of the maximum radiation power, and the value range is ; the proportional coefficient is defined according to the actual response speed requirement, and the value range is 0.05~0.2; the integral coefficient is defined according to the actual steady-state error elimination requirement, and the value range is 0.001~0.01; the accumulation variable is defined based on the discrete-time integral operation, and the value range is ; ; According to the amplitude adjustment value of the feeding channel, the digital attenuator is used to perform feeding channel amplitude compensation on the primary beam signal to obtain an equalized beam signal; according to the equalized beam signal, the matrix operation method is used to perform beamspace transformation on the multi-beam RF signal to generate a Butler matrix beam; the Butler matrix beam not only reduces the complexity and cost of the phased array antenna, but also can maintain stable beam pointing performance.

[0031] An anti-interference positioning module is used to trigger a phased array antenna to scan within the airspace coverage range through a Butler matrix beam. During the scanning process, space-time filtering is adopted to suppress GPS interference and obtain preliminary positioning parameters.

[0032] Specifically, it includes the following steps: In the first step, trigger the phased array antenna to scan within the airspace coverage range through the Butler matrix beam. In specific operations, first, perform amplitude weighting on the Butler matrix beam through the least squares error method to effectively suppress the sidelobe level and improve the main lobe gain, and obtain the weighted Butler matrix beam; input the weighted Butler matrix beam into the directional coupler through the SPI digital control interface; the directional coupler distributes the weighted Butler matrix beam to each feed channel of the phased array antenna through an impedance coaxial cable. It should be noted that the sidelobe level refers to the maximum radiation level in the phased array antenna direction except for the main lobe; the main lobe gain refers to the power gain of the phased array antenna in the maximum radiation direction, which is a key index to measure the directivity of the phased array antenna. In the feed channel, optimize the beam pointing through the space domain phase gradient compensation algorithm: use the least squares error (LSE) algorithm to calculate the channel phase of the weighted Butler matrix beam to obtain the phase difference between the feed channels; and use the phase quantization mapping algorithm to matrixize the calculated phase difference between the feed channels to obtain the phase error distribution map; decompose the phase error distribution map through the gradient projection method to obtain the phase compensation gradient field; according to the phase compensation gradient field, perform optimal phase gradient calculation through the matrix iteration method; according to the optimal phase gradient, in the digital control phase shifter, use the gradient iteration compensation method to correct the phase gradient of each feed channel, and generate the corrected beam signal through the complex signal reconstruction algorithm; through the digital-to-analog converter, reconstruct the signal components of the corrected beam signal using analog quadrature modulation and perform time-domain waveform synthesis through the inverse discrete Fourier transform (IDFT) to generate the scan trigger signal. Secondly, based on the scan trigger signal, drive the phased array antenna to scan within the airspace coverage range through the time delay scanning mechanism to obtain the array signal. In specific operations, transmit the scan trigger signal to the phased array antenna through a low-jitter LVDS differential transmission line; the T / R (transmit / receive) unit in the phased array antenna uses the time delay scanning mechanism for time delay fine-tuning to generate the excitation signal. The least mean square (LMS) algorithm is used to perform time-delay iteration on the excitation signal to obtain the optimal time-delay amount; and the optimal time-delay amount is configured to the GaN RF front-end of each feeding channel through the SPI interface; the GaN RF front-end disassembles the optimal time-delay amount in the beam direction through the fast Fourier transform (FFT) algorithm to generate beam control parameters; according to the beam control parameters, the phased array antenna is driven by a digital controlled phase shifter array (such as HMC933LP6E) to scan within the spatial coverage range; during the scanning process, the array signal is synchronously collected by a high-speed ADC acquisition card; In the second step, during the scanning process, space-time filtering is used to suppress GPS interference to obtain preliminary positioning parameters. Specifically, first, space-time filtering performs multi-channel synchronous sampling on the array signal to obtain a discrete signal matrix; the sliding window method is used to divide the discrete signal matrix into multiple windows; within each window, statistical feature extraction is performed through the FFT acceleration algorithm; according to the extracted statistical features, the space-time two-dimensional covariance matrix is calculated through the Bartlett spectral algorithm; the QR decomposition algorithm is used to perform real-time inversion on the two-dimensional covariance matrix, and the weight value is solved through the Wiener filtering weighting algorithm to generate space-time weight values; multi-phase FIR filtering is used to perform time-domain synthesis on the space-time weight values to output a time-domain signal with strong interference suppression ability; Secondly, the space-time two-dimensional generalized sidelobe cancellation method is used to suppress GPS interference in the time-domain signal, and the RAIM integrity monitoring algorithm is used to generate preliminary positioning parameters. Specifically, the space-time two-dimensional generalized sidelobe cancellation method is used to perform spatial filtering on the time-domain signal to obtain a preliminary interference suppression signal, and the GPS interference in the time-domain signal is identified through the Doppler-code phase joint detection (DCPD) method; the GPS interference is removed from the preliminary interference suppression signal through the eigen-projection suppression (EPS) method to generate an optimized time-domain signal; The RAIM integrity monitoring algorithm is used to extract the pseudorange features of the optimized time-domain signal, and the pseudorange features are converted into satellite navigation data through the least squares residual projection method; the weighted least squares method is used to solve the positioning parameters of the satellite navigation data to generate preliminary positioning parameters.

[0033] The report generation module is used to perform weighted aggregation and hierarchical optimization on the preliminary positioning parameters by adopting a federated learning mechanism to obtain optimized positioning parameters, and perform ephemeris consistency verification and pseudorange residual analysis on the optimized positioning parameters to generate a phased array antenna monitoring and direction finding report.

[0034] Specifically, it includes the following steps, In the first step, the federated learning mechanism is adopted to suppress the noise of the preliminary positioning parameters, and the FedAvg algorithm is used for weighted aggregation to obtain the multi-source fusion parameters. Specifically, in the operation, the local gradient of the preliminary positioning parameters is calculated by the federated learning mechanism to obtain the update amount of the positioning parameters; the differential privacy method is used to perform noise confusion processing on the update amount of the positioning parameters, and the gradient quantization compression method is used for compression to reduce the communication overhead and obtain the compressed transmission parameters; the FedAvg (Federated Average Algorithm) is used to perform weighted aggregation on the compressed transmission parameters, and the Mahalanobis distance metric method is used to calculate the credibility weight of the compressed transmission parameters to generate the weighted fusion parameters; the Kalman filtering method is used to perform spatio-temporal consistency processing on the weighted fusion parameters to generate the multi-source fusion parameters; In the second step, the hierarchical layer-by-layer alignment method is used to optimize the multi-source fusion parameters layer by layer to obtain the optimized positioning parameters. Specifically, in the operation, the multi-source fusion parameters are projected layer by layer by the hierarchical layer-by-layer alignment method, and the differential privacy algorithm is used for dynamic noise injection in each layer to ensure the privacy security and hierarchical consistency of the multi-source fusion parameters; at the same time, the weighted moving average method is used to filter and smooth the multi-source fusion parameters after noise injection to generate the optimized positioning parameters; In the third step, the Neville interpolation method is used to perform ephemeris consistency verification on the optimized positioning parameters to obtain the orbit monitoring data. Specifically, in the operation, the Neville interpolation method is used to perform interpolation fitting on the optimized positioning parameters to obtain the satellite trajectory data; the residual sum of squares (RSS) method is used to calculate the deviation of the satellite trajectory data to obtain the orbit deviation amount; based on the real-time collected satellite ephemeris data, the chi-square test method is used to perform ephemeris consistency verification on the satellite trajectory data. When the orbit deviation amount is lower than the deviation threshold, for example, the deviation threshold is set as: radial error ≤ 5 cm, tangential ≤ 10 cm, then when the measured orbit deviation amount is 3 cm radially and 8 cm tangentially, it is considered that the verification is passed; the sliding window mean filtering method is used to smooth the satellite trajectory data after passing the verification to generate the orbit monitoring data; It should be noted that the deviation threshold is defined based on the precise ephemeris accuracy standard published by the International GNSS Service (IGS); Next, the double-difference analysis method is used to perform pseudorange residual analysis on the optimized positioning parameters to obtain the direction monitoring data. Specifically, in the operation, the double-difference analysis method is used to perform differential processing on the optimized positioning parameters to obtain the double-difference pseudorange observations; the weighted least squares adjustment method is used to calculate and distribute the residuals of the double-difference pseudorange observations to generate the residual sequence; the moving window statistical test method is used to extract the deviation of the residual sequence to obtain the direction deviation vector; and the three-dimensional direction solution is performed on the direction deviation vector by the space vector decomposition method, and the calculated results are integrated by the weighted moving average method to generate the direction monitoring data; In the fourth step, the dynamic phase weighting algorithm is used to integrate the track monitoring data and the direction monitoring data to generate a phased array antenna monitoring and direction finding report. In specific operations, the phase difference compensation method is adopted to load the track monitoring data and the direction monitoring data into the same space-time for alignment; Next, the track monitoring data and the direction monitoring data are correlated through the dynamic phase weighting algorithm: the radial error component and the tangential velocity vector in the track monitoring data are extracted by the least squares fitting method; the direction monitoring data is disassembled by the quaternion decomposition method, and the sequences of azimuth and elevation angles are extracted by the sliding window method; the dynamic phase weighting algorithm is used to assign different fusion weights to the radial error component, the tangential velocity vector, and the sequences of azimuth and elevation angles; based on the fusion weights, integration is performed through a weighted fusion engine, and a phased array antenna monitoring and direction finding report is generated using statistical analysis tools (such as MATLAB); Based on the phased array antenna monitoring and direction finding report, the illegal signals interfering with the GPS environment are searched for and located through the Doppler-code phase joint analysis method, and the characteristic parameters of the interference signals are obtained. In specific operations, the phased array antenna monitoring and direction finding report is differentially processed using the pseudorange double difference, and noise is removed through the carrier phase smoothing pseudorange method to obtain a high-precision code phase observation sequence; The time-frequency characteristics of the code phase observation sequence are extracted using the Doppler-code phase joint analysis method, and the time-varying characteristics are extracted through the short-time Fourier transform (STFT). The time-frequency characteristics and the time-varying characteristics are dimensionally reduced through principal component analysis to obtain a time-frequency characteristic matrix; The cyclic frequency scanning of the time-frequency characteristic matrix is performed using the cyclic stationary feature detection method by invoking the spectral correlation function (SCF) to obtain the cyclic stationary feature parameters; the MUSIC algorithm is used to construct the spatial spectrum of the cyclic stationary feature parameters to generate the spatial spectrum distribution map of the interference signals; the particle swarm optimization (PSO) algorithm is used to search for the spectral peaks in the spatial spectrum distribution map of the interference signals to complete the search for and location of the illegal signals; The frequency domain characteristics of the located illegal signals are extracted using the fast Fourier transform (FFT) to generate the Doppler frequency shift; the carrier phase of the located illegal signals is tracked using the phase-locked loop (PLL) to obtain the carrier phase; the sliding window energy detection method is used to divide the located illegal signals into windows, and energy integration is performed using the Welch power spectrum estimation within each window to generate a signal intensity state vector; Different weights are assigned to the Doppler frequency shift, carrier phase, and signal strength state vectors through the entropy weight method. According to the assigned weights, multi-modal fusion is performed through the D-S evidence theory (Dempster-Shafer Theory) to generate a dynamic feature matrix of interference signals. The space-time adaptive processing method is used to perform space-time two-dimensional filtering on the dynamic feature matrix of interference signals to obtain the azimuth and elevation angles of the interference signals. The generalized likelihood ratio test (GLRT) is used to extract parameters from the azimuth and elevation angles of the interference signals to generate interference signal characteristic parameters such as the center frequency, bandwidth, azimuth angle, and signal type. Finally, the dynamic clustering algorithm is used to perform pattern classification on the interference signal characteristic parameters to obtain an optimized phased array antenna monitoring and direction finding report. Specifically, in the operation, the density clustering of the interference signal characteristic parameters is performed using the dynamic clustering algorithm to generate initial clustering clusters. The Mahalanobis distance metric is used to extract and iterate the center points of the initial clustering clusters to obtain optimized clustering center points. The spectral clustering method is used to divide the clustering center points according to the Gaussian kernel similarity to generate a Laplacian matrix. According to the Laplacian matrix, the clustering center points are divided into high-density core mode, transition edge mode, and isolated noise mode through the Lanczos iterative eigenvalue decomposition method. The FedAvg method is used to perform multi-node aggregation on the results of the pattern classification to generate an optimized phased array antenna monitoring and direction finding report.

[0035] In summary, the present invention can, through: the bidirectional LSTM network combined with the Kalman-particle hybrid filtering algorithm, accurately predict and calibrate the phase drift caused by factors such as carrier vibration in real time. The weighted aggregation and hierarchical optimization of the preliminary positioning parameters are realized by using the federated learning mechanism, achieving the adaptive adjustment of the data characteristics from different sources.

[0036] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limitations. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. GPS jammer phased array monitoring and direction finding antenna system for 1575 frequency band, characterized by: include, The data integration module is used to integrate the data of satellite ephemeris, carrier attitude and airspace coverage through weighted average method to generate an environmental parameter set; The prediction and calibration module is used to input the environmental parameter set into the bidirectional LSTM network, obtain the phase drift prediction value, and use the Kalman-particle hybrid filter algorithm to perform nonlinear calibration on the phase drift prediction value, and simultaneously use the closed-loop feedback mechanism to monitor the IQ signal; The beamforming module is used to perform orthogonal modulation and RF up-conversion processing on the IQ signal, and generate a Butler matrix beam using a hybrid topology beam space transformation algorithm; The anti-interference positioning module is used to scan within the airspace coverage range through the Butler matrix beam triggering phased array antenna. During the scanning process, space-time filtering is used to suppress GPS interference and obtain preliminary positioning parameters; The report generation module is used to perform weighted aggregation and hierarchical optimization on the preliminary positioning parameters using the federated learning mechanism, obtain the optimized positioning parameters, perform ephemeris consistency check and pseudorange residual analysis on the optimized positioning parameters, and generate a phased array antenna monitoring direction finding report.

2. The GPS jamming phased array monitoring and direction finding antenna system for the 1575 frequency band as claimed in claim 1, characterized in that: The specific operations of generating the environment parameter set are as follows: The satellite ephemeris data is cleaned and aligned in time and space by multiple interpolation methods; the carrier attitude data is subjected to noise suppression and outlier removal by Kalman filtering and Z-score normalization; the spatial coverage data is interpolated at discrete points by Kriging interpolation; Dynamic weights are assigned to the preprocessed satellite ephemeris, carrier attitude and airspace coverage data through weighted averaging method, and fused through Kalman filtering to generate an environmental parameter set.

3. The GPS jamming phased array monitoring and direction finding antenna system for the 1575 frequency band as claimed in claim 2, characterized in that: The specific operation of obtaining the phase drift prediction value is as follows: In the PyTorch framework, the input layer, hidden layer, and fully connected layer are integrated through the bidirectional parameter method to build a bidirectional LSTM network; The environmental parameter set is input into the bidirectional LSTM network through the time series reorganization method, and the input layer performs local feature extraction and dimension upgrading to generate a time three-dimensional tensor; The forward LSTM of the hidden layer performs time-series dependency analysis on the time three-dimensional tensor to obtain the hidden state sequence; the backward LSTM performs reverse time-series analysis on the time three-dimensional tensor to generate a reverse hidden state sequence, and the hidden state sequence and the reverse hidden state sequence are concatenated to generate a bidirectional feature matrix; The fully connected layer uses the ReLU activation function to reduce the dimension of the bidirectional feature matrix and obtain the phase drift prediction value.

4. The GPS jamming phased array monitoring and direction finding antenna system for the 1575 frequency band as claimed in claim 1, characterized in that: The Kalman-particle hybrid filter algorithm is used to perform nonlinear calibration on the phase drift prediction value. The specific operation is as follows: The particle filter processes the non-Gaussian noise in the phase drift prediction value through Monte Carlo sampling to obtain the particle state distribution; the particle state distribution is weighted by the second-order moment operation to generate the covariance matrix; The Gaussian noise in the covariance matrix is ​​eliminated by Kalman filtering, and a filter matrix is ​​generated. The inverse function of the filter matrix is ​​derived by third-order polynomial fitting to generate a dynamic calibration curve. Based on the dynamic calibration curve, the phase drift prediction value is calibrated by the Bayesian weight allocation method.

5. The GPS jamming phased array monitoring and direction finding antenna system for the 1575 frequency band as claimed in claim 4, characterized in that: The synchronous use of a closed-loop feedback mechanism to monitor the IQ signal is specifically performed as follows: The phase drift prediction value after nonlinear calibration is dynamically compensated for errors through a closed-loop feedback mechanism, and the IQ signal is sampled using a digital down-conversion method.

6. The GPS jamming phased array monitoring and direction finding antenna system for the 1575 frequency band as claimed in claim 1, characterized in that: The IQ signal is subjected to orthogonal modulation and RF up-conversion processing, and a Butler matrix beam is generated using a hybrid topology beam space transformation algorithm. The specific operations are as follows: The IQ signal is image-suppressed by the Bessel interpolation method, and the QPSK modulation method is used for orthogonal modulation to obtain the suppressed RF signal; the suppressed RF signal is subjected to RF up-conversion processing by the multi-phase filtering method to generate a multi-beam RF signal; A hybrid topology beam space transformation algorithm is used to perform spatial mapping on multi-beam RF signals to obtain Butler matrix beams.

7. The GPS jamming phased array monitoring and direction finding antenna system for the 1575 frequency band as claimed in claim 1, characterized in that: The Butler matrix beam is used to trigger the phased array antenna to scan in the airspace coverage range. The specific operation is as follows: The Butler matrix beam is input into the feeding channel of the phased array antenna through a directional coupler. In the feeding channel, the beam pointing is optimized through a spatial phase gradient compensation algorithm, and a scanning trigger signal is obtained through a digital-to-analog converter. Based on the scanning trigger signal, the phased array antenna is driven to scan in the airspace coverage range through the time delay scanning mechanism to obtain the array signal.

8. The GPS jamming phased array monitoring and direction finding antenna system for the 1575 frequency band as claimed in claim 7, characterized in that: The space-time filtering is used to suppress GPS interference and obtain preliminary positioning parameters. The specific operations are as follows: Space-time filtering is used to perform space-time two-dimensional weighting on the array signal to obtain the time domain signal. The GPS interference in the time domain signal is suppressed by the space-time two-dimensional generalized sidelobe cancellation method, and the preliminary positioning parameters are generated using the RAIM integrity monitoring algorithm.

9. The GPS jamming phased array monitoring and direction finding antenna system for the 1575 frequency band as claimed in claim 1, characterized in that: The specific operations for obtaining the optimized positioning parameters are as follows: The federated learning mechanism is used to suppress the noise of the preliminary positioning parameters, and the FedAvg algorithm is used for weighted aggregation to obtain the multi-source fusion parameters. The hierarchical layer-by-layer alignment method is used to hierarchically optimize the multi-source fusion parameters to obtain the optimized positioning parameters.

10. The GPS jamming phased array monitoring and direction finding antenna system for the 1575 frequency band as claimed in claim 6, characterized in that: The specific operation of generating the phased array antenna monitoring direction finding report is as follows: The Neville interpolation method is used to perform ephemeris consistency check on the optimized positioning parameters to obtain orbit monitoring data; the double difference analysis method is used to perform pseudorange residual analysis on the optimized positioning parameters to obtain direction monitoring data; The track monitoring data and direction monitoring data are integrated using a dynamic phase weighting algorithm to generate a phased array antenna monitoring direction finding report.

Images