A multi-anti-fusion communication method, a storage medium and a communication terminal

Abstract

The application discloses a kind of multi-anti fusion communication methods, storage medium and communication terminal, it is related to signal anti-interference technical field, the method includes: obtaining preprocessed target signal;The preprocessed target signal is subjected to anti-suppression processing and / or anti-deception processing, obtains first anti-interference signal;Judge the signal type of first anti-interference signal;When the first anti-interference signal includes communication signal, the first anti-interference signal is subjected to anti-communication interference processing, obtains second anti-interference signal as anti-interference output result.The technical scheme provided in the present application can integrate a variety of interference signals, thereby simplifying signal anti-interference operation, meet the current requirement of equipment miniaturization, integration.

Description

Technical Field

[0001] This invention relates to the field of signal anti-interference technology, specifically to a multi-anti-interference fusion communication method, storage medium, and communication terminal. Background Technology

[0002] With the widespread application of Global Navigation Satellite Systems (GNSS) in mobile positioning, road transportation, aviation, maritime transport, timing, geodesy, and atmospheric sensing, the vulnerability of civilian GNSS signals to interference and deception has become increasingly prominent. Especially in electronic warfare environments, suppressive and deceptive jamming have become major threats.

[0003] Current technologies for countering suppression and deception interference in satellite communications primarily enhance anti-jamming capabilities by improving the robustness of the satellite system itself or modifying the GNSS open signaling system. However, these methods still struggle to comprehensively address multiple types of interference. Existing anti-spoofing detection methods lack universality, and single GNSS anti-spoofing algorithms have limited effectiveness in complex electromagnetic environments. Furthermore, the high occupancy rate of space radio resources in low-Earth orbit satellite communications leads to decreased communication stability, making it difficult to balance effective data bandwidth and reliability. Equipment power consumption and endurance also become bottlenecks in development.

[0004] In existing research, Reference 1 (CN111781615A) proposes using low-Earth orbit communication satellites to broadcast reliable ranging signals to achieve GNSS anti-spoofing, but it relies on external constellation support and has limited autonomous capabilities for a single terminal. Reference 2 (CN111965671A) uses TDOA / AOA fusion to locate interference sources, but it does not achieve multi-functional integration of a single terminal, and multi-station cooperation is complex. Other references (such as CN108241159A, CN119395726A, etc.) are mostly single-function or modular designs, lacking a fusion scheme for joint processing of spatial, temporal, and frequency domains, and cannot simultaneously solve the problems of suppression, spoofing, and communication interference. Moreover, their hardware structure is loose, which is not conducive to miniaturization, portability, and efficient data processing.

[0005] As can be seen from the above literature, since most existing communication terminals only perform anti-interference processing for a single interference signal, when facing multiple interference signals, cooperation between devices is usually required to complete the task. This results in extremely complex signal anti-interference operations and inconvenient device portability, failing to meet the current requirements for device miniaturization and integration. Summary of the Invention

[0006] To address the aforementioned technical problems, this application proposes a multi-anti-interference fusion communication method, storage medium, and communication terminal, which can integrate the processing of various interference signals, thereby simplifying signal anti-interference operations and meeting current requirements for equipment miniaturization and integration.

[0007] To achieve the above objectives, the technical solution of this application is implemented as follows: In a first aspect, this application provides a multi-resistance fusion communication method, comprising: Obtain the preprocessed target signal; The preprocessed target signal is subjected to anti-suppression processing and / or anti-spoofing processing to obtain the first anti-interference signal; Determine the signal type of the first anti-interference signal; When the first anti-interference signal includes a communication signal, the first anti-interference signal is processed to resist communication interference, and the second anti-interference signal is obtained as the anti-interference output result.

[0008] In some embodiments, the preprocessed target signal is subjected to anti-spoofing processing to obtain a first anti-interference signal, including: The DOA comparison method is used to perform anti-spoofing processing on the preprocessed target signal to obtain the first processed signal; The fingerprint matching method is used to perform anti-spoofing processing on the first processed signal to obtain the second processed signal; An image classification algorithm is used to perform anti-spoofing processing on the second processed signal to obtain the first anti-interference signal.

[0009] In some embodiments, the DOA comparison method is used to perform anti-spoofing processing on the preprocessed target signal to obtain a first processed signal, including: DOA estimation is performed on the preprocessed target signal to obtain the DOA estimate value of each signal in the preprocessed target signal; Obtain the theoretical DOA value of each signal in the preprocessed target signal; Calculate the difference between each DOA estimate and the corresponding theoretical DOA value; Signals with a difference greater than a first preset threshold are removed from the preprocessed target signal to obtain the first processed signal.

[0010] In some embodiments, fingerprint matching is used to perform anti-spoofing processing on the first processed signal to obtain a second processed signal, including: Obtain the fingerprint features of each signal in the first processed signal; Based on fingerprint features, a feature vector is generated; the feature vector includes multiple feature values. Calculate the similarity between each feature value and the corresponding predetermined feature value to obtain multiple similarity values; Signals with similarity values ​​less than a second preset threshold are removed from the first processed signal to obtain the second processed signal.

[0011] In some embodiments, an image classification algorithm is used to perform anti-spoofing processing on the second processed signal to obtain a first anti-interference signal, including: The second processed signal is converted into a multi-channel input image; Forward inference is performed on the multi-channel input image to obtain the probability value of each signal in the second processed signal being a deception signal; Signals with a probability value greater than a third preset threshold are removed from the second processed signal to obtain the first anti-interference signal.

[0012] In some embodiments, the image classification algorithm is used to perform anti-spoofing processing on the second processed signal to obtain a first anti-interference signal, which further includes: DOA estimation is performed on the preprocessed target signal to obtain the DOA estimate value of each signal in the preprocessed target signal; Obtain the theoretical DOA value of each signal in the preprocessed target signal; Calculate the difference between each DOA estimate and the corresponding theoretical DOA value; Obtain the fingerprint features of each signal in the first processed signal; Based on fingerprint features, a feature vector is generated; the feature vector includes multiple feature values. Calculate the similarity between each feature value and the corresponding predetermined feature value to obtain multiple similarity values; Based on the difference, similarity, and probability values, calculate the overall confidence that each signal in the second processed signal is a real signal; Signals with a comprehensive confidence level less than the fourth preset threshold are removed from the second processed signal to obtain the first anti-interference signal.

[0013] In some embodiments, the first anti-interference signal is subjected to anti-communication interference processing to obtain a second anti-interference signal as the anti-interference output result, including: A fast Fourier transform is performed on the first anti-interference signal to obtain the power spectral density sequence; The power spectral density sequence is sequentially processed by median filtering and exponential forgetting averaging to obtain the background noise estimate; Based on the background noise estimate, interference detection is performed on the power spectral density sequence to obtain the interference detection results. Based on the interference detection results, an anti-communication interference operation is performed to obtain a second anti-interference signal; wherein, the anti-communication interference operation includes: Dynamic frequency hopping is performed by selecting the channel with the lowest interference power from pre-generated candidate channels; and, Adjusting the MVDR beamforming weights enables array spatial diversity reception.

[0014] In a second aspect, this application provides a computer-readable storage medium storing a computer program adapted to be loaded and run by a processor to perform the multi-resistance fusion communication method of any of the technical solutions in the first aspect described above.

[0015] In a third aspect, this application provides a communication terminal, comprising: an antenna unit, a radio frequency front-end channel, at least one processor, and a memory communicatively connected to the at least one processor; the antenna unit, the radio frequency front-end channel, and the at least one processor are communicatively connected in sequence. The antenna unit is used to receive target signals; The radio frequency front-end channel is used to preprocess the target signal and input the preprocessed target signal to the processor; The memory stores a computer program, which, when executed by at least one processor, implements the multi-resistance fusion communication method of any one of the technical solutions in the first aspect described above.

[0016] In some embodiments, the antenna element includes multiple antenna array elements; the multiple antenna array elements are arranged in a ring array or a square array; the distance between adjacent antenna array elements is λ / 2, where λ is the dielectric wavelength of the antenna array element.

[0017] The multi-anti-interference fusion communication method, storage medium, and communication terminal provided in this application acquire a pre-processed target signal, perform anti-suppression processing and / or anti-spoofing processing on the pre-processed target signal to obtain a first anti-interference signal, and then determine the signal type of the first anti-interference signal. When the first anti-interference signal includes a communication signal, anti-communication interference processing is performed on the first anti-interference signal. This allows the present application to integrate the processing of multiple interference signals, effectively solving the technical problems of complex operation and inconvenient equipment carrying caused by the need for multiple devices to cooperate when facing multiple interference signals in the prior art. It can be seen that the technical solution provided by this application can integrate the processing of multiple interference signals, thereby simplifying the signal anti-interference operation and meeting the current requirements for equipment miniaturization and integration. Attached Figure Description

[0018] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0019] Figure 1 A flowchart illustrating the main steps of the multi-resistance fusion communication method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the structure of the multi-resistance fusion communication device in the communication terminal provided in the embodiments of this application; Figure 3A schematic diagram showing the antenna array elements arranged in a square array in the communication terminal provided in the embodiments of this application; Figure 4 A schematic diagram showing the antenna array elements arranged in a ring array in the communication terminal provided in the embodiments of this application; Figure 5 Schematic diagram of the communication terminal provided in the embodiments of this application Figure 1 ; Figure 6 Schematic diagram of the communication terminal provided in the embodiments of this application Figure 2 ; Figure 7 The original signal spatial energy distribution and the beam pattern after MVDR suppression are provided in the embodiments of this application; Figure 8 A bar chart comparing the signal-to-noise ratio before and after interference suppression, as provided in the embodiments of this application.

[0020] Reference numerals: 100-antenna array element; 200-multi-anti-fusion communication device; 201-GNSS anti-suppression module; 202-GNSS anti-spoofing module; 203-anti-communication interference module. Detailed Implementation

[0021] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0022] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0023] This application provides a multi-resistance fusion communication method, such as Figure 1As shown, the multi-antibody fusion communication method in this application embodiment mainly includes the following steps S101-S104: Step S101: Obtain the preprocessed target signal; In this embodiment, the target signal includes various satellite signals, such as navigation signals and communication signals. Preprocessing of the target signal includes amplification, filtering, and analog-to-digital conversion of the received target signal to ensure the smooth execution of subsequent signal anti-interference processes.

[0024] Step S102: Perform anti-suppression processing and / or anti-spoofing processing on the preprocessed target signal to obtain the first anti-interference signal; In this embodiment, depending on factors such as the signal type of the target signal, whether it contains corresponding interference signals, and actual application requirements, the preprocessed target signal can be selectively subjected to single anti-suppression processing, or the preprocessed target signal can be subjected to single anti-spoofing processing, or the preprocessed target signal can be subjected to both anti-suppression processing and anti-spoofing processing.

[0025] To more effectively resist spoofing of the preprocessed target signal and obtain a better anti-interference signal, this embodiment performs anti-spoofing processing on the preprocessed target signal to obtain a first anti-interference signal, including: performing anti-spoofing processing on the preprocessed target signal using the DOA comparison method to obtain a first processed signal; performing anti-spoofing processing on the first processed signal using the fingerprint matching method to obtain a second processed signal; and performing anti-spoofing processing on the second processed signal using an image classification algorithm to obtain the first anti-interference signal.

[0026] In this embodiment, three methods are used simultaneously: DOA (Direction of Arrival) comparison, fingerprint matching, and image classification algorithm to perform anti-spoofing processing on the preprocessed target signal.

[0027] Specifically, in order to effectively remove spoofing signals from the preprocessed target signal, this embodiment uses the DOA comparison method to perform anti-spoofing processing on the preprocessed target signal to obtain a first processed signal, including: performing DOA estimation on the preprocessed target signal to obtain the DOA estimated value of each signal in the preprocessed target signal; obtaining the theoretical DOA value of each signal in the preprocessed target signal; calculating the difference between each DOA estimated value and the corresponding theoretical DOA value; and removing signals with a difference greater than a first preset threshold from the preprocessed target signal to obtain the first processed signal.

[0028] Specifically, in order to effectively remove spoofing signals from the first processed signal, this embodiment uses a fingerprint matching method to perform anti-spoofing processing on the first processed signal to obtain a second processed signal, including: obtaining the fingerprint features of each signal in the first processed signal; generating a feature vector based on the fingerprint features; wherein the feature vector includes multiple feature values; calculating the similarity between each feature value and a predetermined feature value corresponding to that feature value to obtain multiple similarity values; removing signals with similarity values ​​less than a second preset threshold from the first processed signal to obtain the second processed signal.

[0029] Specifically, in order to effectively remove spoofing signals from the second processed signal, this embodiment uses an image classification algorithm to perform anti-spoofing processing on the second processed signal to obtain a first anti-interference signal, including: converting the second processed signal into a multi-channel input image; performing forward inference on the multi-channel input image to obtain the probability value of each signal in the second processed signal being a spoofing signal; and removing signals with probability values ​​greater than a third preset threshold from the second processed signal to obtain the first anti-interference signal.

[0030] In the process of anti-spoofing processing of the second processed signal using an image classification algorithm, in order to further effectively eliminate spoofing signals, this embodiment uses an image classification algorithm to perform anti-spoofing processing on the second processed signal to obtain a first anti-interference signal. This further includes: performing DOA estimation on the preprocessed target signal to obtain the DOA estimate value for each signal in the preprocessed target signal; obtaining the theoretical DOA value for each signal in the preprocessed target signal; calculating the difference between each DOA estimate value and the corresponding theoretical DOA value; obtaining the fingerprint feature of each signal in the first processed signal; generating a feature vector based on the fingerprint feature; wherein the feature vector includes multiple feature values; calculating the similarity between each feature value and a predetermined feature value corresponding to that feature value to obtain multiple similarity values; calculating the comprehensive confidence level that each signal in the second processed signal is a real signal based on the difference, similarity value, and probability value; and eliminating signals with a comprehensive confidence level less than a fourth preset threshold from the second processed signal to obtain the first anti-interference signal.

[0031] In this embodiment, the target signal after preprocessing is subjected to anti-suppression processing, which can be carried out using the GNSS zeroing anti-interference method described in Chinese Patent Application No. CN202610227215.X (titled: A GNSS zeroing anti-interference method and device). The specific process will not be described in detail here.

[0032] In this embodiment, when performing anti-suppression processing on the preprocessed target signal, the covariance matrix calculation module can use CORDIC-accelerated Q-less QR decomposition instead of direct Cholesky or matrix inversion; the ESPRIT DOA estimation module can use the Unitary ESPRIT algorithm, first performing a unitary transformation on the covariance matrix to convert complex numbers into real numbers for operations, reducing complexity by about 50%; in subspace decomposition, LU decomposition is used instead of traditional EVD decomposition, which can effectively reduce the amount of computation.

[0033] In practical applications, such as Figure 6 As shown, after the preprocessed target signal is subjected to anti-suppression processing in the FPGA chip, the suppressed signal is obtained and transmitted to the central processing unit (CPU). The CPU then performs anti-spoofing and / or anti-communication interference processing on the suppressed signal.

[0034] In practical applications, anti-spoofing and anti-communication interference processing can run simultaneously. Specifically, the task management module issues start commands for anti-spoofing and anti-communication interference processing simultaneously according to a preset update cycle T. Preferably, T = 10ms.

[0035] Specifically, the GNSS anti-spoofing module in the central processing unit employs a combination of "DOA comparison method + fingerprint matching method + image classification algorithm" to determine the nature of spoofing signals. The image classification algorithm can be the ResNet-18 classification algorithm. The anti-spoofing processing in this embodiment includes the following sub-steps: Step 1: The FPGA chip outputs the estimated DOA value of the preprocessed target signal, including the azimuth angle of the target signal. and pitch angle .

[0036] Step 2: Calculate the theoretical DOA (Digital Orbit) of all visible satellites at the current moment based on the local ephemeris, including the theoretical azimuth angle. and theoretical pitch angle .

[0037] Step 3: Compare the estimated DOA obtained in Step 1 with the theoretical DOA obtained in Step 2, and calculate the difference between the two: ; ; in, This is the azimuth difference. This represents the pitch angle difference.

[0038] Step 4, determine if the condition is met. ,in If the maximum value is found, the signal is marked as a deceptive signal and discarded; otherwise, proceed to step 5.

[0039] Step 5: Extract fingerprint features from the received signal after the above steps. Fingerprint features include carrier phase features, code structure features, and power spectrum features, and construct a feature vector L based on the fingerprint features.

[0040] Carrier phase characteristics include continuous phase deviation Δφ_c of the carrier phase; The structural features of the C / A code include the shape of the correlation peak, the number of correlation peaks, and the ratio of the height of the secondary peaks; Power spectrum characteristics include PSD peak value, linewidth, and cycle spectrum characteristics; Fingerprint features can also include signal-to-noise ratio, Doppler frequency shift rate, and other characteristics.

[0041] The feature vector L is: ; Where k is the number of fingerprint features.

[0042] Step 6: Calculate the i-th eigenvalue in the eigenvector L. With database template The corresponding eigenvalues similarity ; Similarity The calculation method is as follows: ; in, Represents an exponential function; The standard deviation parameter, expressed as the Gaussian kernel, is used to characterize the allowable fluctuation range of real satellite signal characteristics and is calibrated based on a large amount of measured data. In this embodiment, .

[0043] Step 7, based on the calculated similarity Threshold matching is performed, which measures the similarity of each feature value. If both conditions are met, the received signal is determined to be a real signal, and the suppressed I / Q data is transmitted to the baseband demodulation module; if the similarity is... Then proceed to step 8; if If the received signal is deemed a deceptive signal, it will be discarded.

[0044] Step 8: The central processing unit (CPU) extracts the multi-channel input image of each signal from the signals processed in the above steps.

[0045] Step 9: The central processing unit (CPU) normalizes the multi-channel input image of each signal, completes forward inference, and outputs a 4-dimensional probability that the signal is a deception signal. ,in, The probability of the true signal. For the probability of deceiving signals, To suppress the probability of interference, This represents the probability of multipath or ionospheric reflection.

[0046] Step 10: The central processing unit (CPU) calculates the overall confidence level of the received signal based on the 4-dimensional probability of the spoofing signal. The calculation method is as follows: ; ; Step 11, or If the signal is false, it is determined to be a deceptive signal and discarded; otherwise, it is determined to be a genuine signal and the suppressed I / Q data is transmitted to the baseband demodulation module.

[0047] Step S103: Determine the signal type of the first anti-interference signal; In this embodiment, anti-interference processing is only performed on communication signals; for other types of signals, only anti-suppression and / or anti-spoofing processing is performed.

[0048] Step S104: When the first anti-interference signal is a communication signal, the first anti-interference signal is subjected to anti-communication interference processing to obtain the second anti-interference signal as the anti-interference output result.

[0049] To effectively combat communication interference and obtain a cleaner, more accurate signal, this implementation performs anti-interference processing on a first anti-interference signal to obtain a second anti-interference signal as the anti-interference output. This includes: performing a Fast Fourier Transform on the first anti-interference signal to obtain a power spectral density sequence; sequentially applying median filtering and exponential forgetting averaging to the power spectral density sequence to obtain a background noise estimate; performing interference detection on the power spectral density sequence based on the background noise estimate to obtain an interference detection result; and performing anti-communication interference operations based on the interference detection result to obtain the second anti-interference signal. The anti-communication interference operations include: selecting the channel with the lowest interference power from pre-generated candidate channels for dynamic frequency hopping; and adjusting the MVDR beamforming weights to achieve array spatial diversity reception.

[0050] Specifically, the anti-communication interference processing in this embodiment includes the following sub-steps: Step 1: The central processing unit (CPU) performs a fast Fourier transform (FFT) on the received anti-suppression and / or anti-spoofing I / Q data, uses the Hanning window function to reduce spectral leakage, and processes the calculated power spectral density sequence through median filtering and exponential forgetting averaging to obtain a background noise estimate. An adaptive threshold Th is then used for interference detection. When performing FFT spectral analysis, directly truncating a finite-length time-domain signal can lead to spectral leakage, where signal energy leaks from the main lobe to the side lobes. This results in blurred interference peaks and increased noise floor, severely impacting the accuracy of interference detection. Therefore, employing the Hanning window function makes it easier to accurately detect weak interference signals in strong noise environments, improving the detection sensitivity of anti-interference modules.

[0051] In this embodiment, the Fast Fourier Transform has 4096 points, and the frequency resolution of the Hanning window function is... , Its resolution enables precise identification of common narrowband interference in low-Earth orbit satellite communication systems, such as single-frequency continuous wave interference and frequency sweeping interference, while effectively locating interference within the communication bandwidth (tens of kHz to several MHz).

[0052] Median filtering is applied to the power spectral density (PSD) sequence output by the FFT to remove isolated spike noise, smooth the spectral lines, and retain the true interference peaks. The advantage of median filtering is its robustness to impulse noise; unlike mean filtering, it is not significantly affected by a few strong noise points that can significantly inflate the background estimate. This embodiment uses a 3-point or 5-point median filtering window.

[0053] Furthermore, background noise is not constant and varies slowly with environmental factors, temperature, and satellite position. Therefore, an exponential forgetting recursive averaging algorithm is used to estimate the noise floor in real time. ; in, This is the background noise power estimate for the kth update. This is the power spectral density sequence after median filtering at the current moment. In this embodiment, the forgetting factor α = 0.95, corresponding to a memory length of approximately 20 updates, or approximately 200ms. This allows for rapid tracking of slow noise changes while avoiding prolonged contamination of background noise estimation by sudden strong interference.

[0054] An adaptive threshold is used for interference detection, whereby... , This represents the mean background noise after median filtering and exponential forgetting averaging. The standard deviation of the background noise after median filtering and exponential forgetting averaging is given.

[0055] Step 2: Based on the spectrum sensing results, the task management module generates a list of currently interference-free channels in real time, selects the channel with the lowest interference power from the 20 pre-generated candidate channels, and realizes dynamic frequency hopping; at the same time, it calls the covariance matrix calculated in the FPGA chip to adjust the MVDR beamforming weights and realize array spatial diversity reception to improve the signal-to-noise ratio.

[0056] Step 3: Optimize the transmit power (range -10dBm~20dBm) and coding rate by combining the ionospheric scintillation Kalman filter prediction model driven by historical data in the storage unit.

[0057] Step 4: The central processing unit (CPU) sends a local oscillator frequency switching command and an LNA gain adjustment command to the RF front-end channel; in this embodiment, the switching step is 1dB.

[0058] Step 5: The central processing unit (CPU) sends new MVDR steering vector and beam adjustment commands to the FPGA chip through the EMIO / GPIO interface to form a complete closed-loop adaptive control.

[0059] Each spectrum sensing result, interference parameters, frequency hopping record, and optimized communication parameters are stored in real time in the distributed signal feature database. If the same interference frequency band is detected three times consecutively, the task management module immediately triggers an alarm and reports the interference source location information via the low-orbit bidirectional communication antenna.

[0060] Through the synergy of the aforementioned 4096-point FFT spectrum sensing, dynamic frequency hopping, and array diversity, the anti-interference module can still maintain a communication bit error rate below 100% even under mixed interference conditions with J / S=65dB. The effective bandwidth is stable at 1.6Mbps~1.8Mbps, which significantly improves the reliability and anti-interference capability of low-orbit satellite communication links.

[0061] After the above anti-interference processing, the CPU outputs the anti-interference output result, i.e., the actual received signal, including actual navigation data and / or actual satellite communication data. Then, it verifies the output of the actual received signal and determines if any anomalies are detected. If an anomaly is detected, the CPU triggers an alarm in the task management module via GPIO and controls the FPGA or RF front-end to adjust parameters such as sampling and local oscillator, achieving iterative processing.

[0062] In practical applications, the task management module running on the CPU can automatically select between normal mode, RTK mode, and anti-interference mode based on a preset monitoring period (e.g., 1 second). In anti-interference mode, the system prioritizes MVDR+ESPRIT fusion, while in RTK mode, carrier phase differential is also enabled. In abnormal modes (e.g., SNR < 10dB or DOA deviation > 5°), an emergency procedure is initiated—switching to a backup frequency, enabling full array element diversity, and recording events to the database.

[0063] Based on the above steps S101-S104, this application can solve the technical problem that the prior art can only perform anti-interference processing for a single interference signal, resulting in complex anti-interference operation and inconvenient equipment carrying when facing multiple interference signals.

[0064] The technical solution provided in this application obtains a pre-processed target signal, performs anti-suppression processing and / or anti-spoofing processing on the pre-processed target signal to obtain a first anti-interference signal, and then determines the signal type of the first anti-interference signal. When the first anti-interference signal is a communication signal, it is subjected to anti-communication interference processing. This allows the present application to integrate the processing of multiple interference signals, effectively solving the technical problems of complex operation and inconvenient equipment carrying caused by the need for multiple devices to cooperate when facing multiple interference signals in the prior art. Therefore, the technical solution provided in this application can integrate the processing of multiple interference signals, thereby simplifying signal anti-interference operation and meeting the current requirements for equipment miniaturization and integration.

[0065] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of this application.

[0066] Furthermore, another aspect of this application provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program that performs the above-described multi-antibody fusion communication method, which can be loaded and run by a processor to implement the above-described multi-antibody fusion communication method. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be various electronic storage devices; optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0067] like Figure 5 As shown, this application also provides a communication terminal, including: an antenna unit, a radio frequency front-end channel, at least one processor, and a memory communicatively connected to the at least one processor; the antenna unit, the radio frequency front-end channel, and the at least one processor are communicatively connected in sequence; the antenna unit is used to receive a target signal; the radio frequency front-end channel is used to preprocess the target signal and input the preprocessed target signal to the processor; the memory stores a computer program, which, when executed by the at least one processor, implements the multi-anti-fusion communication method of any one of the above method embodiments.

[0068] In this embodiment, the target signals received by the antenna unit include various satellite signals, such as navigation signals and communication signals. The radio frequency front-end channel preprocesses the target signals, including amplification, filtering, and analog-to-digital conversion of the received target signals, to ensure that the subsequent signal anti-interference process can proceed smoothly.

[0069] In this embodiment, the antenna element includes multiple antenna array elements; the multiple antenna array elements are arranged in a ring array or a square array; the distance between adjacent antenna array elements is λ / 2, where λ is the dielectric wavelength of the antenna array element.

[0070] Specifically, such as Figure 6 As shown, this application provides a communication terminal, specifically a multi-resistance fusion satellite communication terminal, comprising: an antenna body, and a plurality of antenna array elements 100 and a signal processing unit disposed on the antenna body. The antenna array elements 100 are used to receive weak RF signals transmitted from the outside, including navigation GNSS signals, satellite communication signals, etc., referred to as target signals. The signal processing unit is used to receive the RF signals transmitted by the antenna array elements 100, and to perform anti-suppression processing and / or anti-spoofing processing on the RF signals. Then, when the target signal is a communication signal, it performs anti-communication interference processing on the data after anti-suppression and / or anti-spoofing.

[0071] The number of antenna elements 100 is at least one. When the number of antenna elements 100 is greater than one, all antenna elements 100 are arranged in a uniformly distributed ring array or square array, such as... Figure 3 and Figure 4 As shown. At this point, the distance between adjacent antenna elements 100 is λ / 2, where λ is the wavelength of the medium of the antenna element 100, to achieve optimal spatial diversity. The shape of the antenna element 100 is not limited; it can be circular, square, or any other feasible shape.

[0072] In this embodiment, the center frequency of each antenna element 100 can be 1.575 GHz (L1 band), with a gain of 5 dBi, a half-power beamwidth of 90°, and a polarization of left / right circular polarization. Alternatively, the center frequency of each antenna element 100 can be 2.49 GHz (S band), with a gain of 3 dBi, a 3 dB power beamwidth of 90°, and a polarization of left / right circular polarization.

[0073] like Figure 3 As shown, the number of antenna array elements 100 is 4, which are arranged in a uniformly distributed square array. The distance between adjacent antenna array elements 100 is λ / 2. With this layout, an antenna gain of 10dBi can be achieved for the antenna array.

[0074] like Figure 4As shown, the number of antenna array elements 100 is 8, which are arranged in a uniformly distributed ring array, and the distance between adjacent antenna array elements 100 is λ / 2.

[0075] It should be noted that the number of antenna elements 100 is not limited to the above-mentioned 4 and 8. Any number of antenna elements that can achieve better spatial diversity effect is acceptable and are within the protection scope of this application.

[0076] In this embodiment, each antenna element includes a navigation antenna unit and / or a communication antenna unit, and the number of both navigation antenna units and communication antenna units is at least one. That is, each antenna element can be a single navigation antenna unit or a single communication antenna unit, or it can include both navigation antenna units and communication antenna units. When the number of antenna elements is one, the navigation antenna unit and the communication antenna unit are spatially stacked, and their positions are not limited; the navigation antenna unit can be on top, or the communication antenna unit can be on top. When the number of antenna elements is greater than one, each antenna element can include both stacked navigation antenna units and communication antenna units, or a single antenna element can include only a navigation antenna unit or a communication antenna unit.

[0077] When a spatially stacked configuration is used, a ceramic material serves as the base layer between the navigation antenna unit and the communication antenna unit. The upper antenna is fed through metallized vias on the ceramic base layer. Because there is a certain frequency band spacing between the L / B band used for navigation and the satellite communication band used for communication, there is no inter-frequency crosstalk between them.

[0078] In this embodiment, the antenna element 100 is made of high thermal conductivity copper and operates at a frequency of 1.6GHz~1.7GHz or 2~4GHz, supporting a bidirectional data rate of up to 120Mbps. An adjustable isolator is provided between the communication antenna element and the terminal antenna body. The isolation of the adjustable isolator is ≥60dB and can be adjusted from 0 to 20dB by the central processing unit (CPU), effectively preventing intermodulation interference between GNSS signals and satellite communication signals.

[0079] For the transmitting / receiving units of the communication antenna element, a spatial arrangement of them at intervals on the same layer is adopted, and the receiving unit and the transmitting unit are filled with an electromagnetic isolation medium.

[0080] like Figure 6 As shown, the signal processing unit includes: an RF front-end channel, a processor, and a memory, each connected to the navigation antenna unit and the communication antenna unit; wherein the processor includes: an FPGA chip and a central processing unit (CPU). The RF front-end channel includes: A low-noise amplifier (LNA) is connected to the navigation and communication antenna elements in the antenna array via a low-loss microstrip line. The weak RF signals received by the antenna elements are transmitted to the LNA, which amplifies the received RF signals. In this embodiment, the LNA is an ADL5523, with a noise figure of 0.8 dB and a gain of 20 dB. The bandpass filter (BPF) is connected to the low-noise amplifier (LNA) to receive the amplified RF signal and perform bandpass filtering on it. In addition, an ADC sampler is connected to a bandpass filter (BPF) to convert the RF signal into a digital sampled signal. In this embodiment, the ADC sampler is an ADS54J60 with a sampling rate of 40MHz, a dynamic range of 0dBm to 20dBm, a bit width of 14bit, and an SNR ≥ 72dB.

[0081] In this embodiment, the bandpass filtered RF signal can be sampled directly by the ADC sampler without undergoing down-conversion processing.

[0082] In other embodiments of this application, the RF front-end channel further includes: The downconverter mixer is connected to the bandpass filter (BPF) and is used to receive the bandpass-filtered RF signal and perform downconversion processing to output an intermediate frequency (IF) signal. The IF frequency is controlled at around 70MHz. The local oscillator (ROC) frequency is provided by the frequency synthesizer controlled by the FPGA. In this embodiment, the ROC phase noise is -120dBc / Hz@10kHz. Additionally, an IF filter is connected to both the downconverter mixer and the ADC sampler. The IF filter is used to filter the intermediate frequency signal and transmit it to the ADC sampler, which converts the intermediate frequency signal into an intermediate frequency digital sample signal. In this embodiment, the bandwidth of the IF filter is 40MHz.

[0083] Furthermore, such as Figure 2 As shown, the FPGA chip and central processing unit (CPU) in this embodiment include a multi-anti-interference fusion communication device 200. The multi-anti-interference fusion communication device 200 includes three anti-interference processing modules: a GNSS anti-suppression module 201, a GNSS anti-spoofing module 202, and an anti-communication interference module 203. Specifically, the GNSS anti-suppression module 201 uses beamforming and DOA estimation processing to suppress interference signals in navigation GNSS signals; the GNSS anti-spoofing module 202 uses a combination of DOA comparison, fingerprint matching, and image classification algorithms to determine the effectiveness of spoofing signals; and the anti-communication interference module 203 is used to implement anti-communication interference functionality for satellite communication signals.

[0084] The signal processing unit provided in this application is not limited to the above-mentioned GNSS anti-interference, GNSS anti-spoofing and anti-communication interference functions, but may also include common modules for implementing modulation and demodulation, encoding and decoding, forward error correction, channel estimation and equalization, peak-to-average power ratio suppression, etc.

[0085] In this embodiment, the FPGA chip is connected to the ADC sampler via an LVDS high-speed differential interface (preferably with a data rate of 1.28Gbps). The digital I / Q signal output by the ADC sampler (or intermediate frequency digital I / Q signal when a down-conversion mixer is used) is directly transmitted to the FPGA chip at the core of the signal processing unit via the LVDS high-speed differential interface. In this embodiment, the FPGA chip is a Xilinx Zynq-7000 series, model XC7Z020, with a main frequency of 1.2GHz and an integrated dual-core ARM Cortex-A9.

[0086] In this embodiment, the FPGA chip includes: a covariance matrix calculation module for calculating the covariance matrix; a beamforming module for matrix decomposition and solving for weight vectors; and a DOA estimation processing module for estimating the direction of arrival (DOA) of the spoofing signal based on the above calculation results, and performing anti-suppression processing on the input signal based on the DOA.

[0087] The central processing unit (CPU) is connected to the FPGA chip via the AXI4 bus. It includes: a first processing module for data processing, in which the ARM Cortex-A53 performs data deframe, data parsing, and data error correction; and a second processing module for application processing, in which the ARM Cortex-M4 performs anti-spoofing and anti-communication interference fusion algorithms.

[0088] The central processing unit (CPU) and the FPGA chip exchange control signals through the EMIO interface: the SPI bus is used for RF front-end gain adjustment (0~60dB in 1dB steps) and frequency switching; the I2C bus is used for ADC configuration and temperature sensor reading; and the GPIO is used for alarm signal output and mode switching.

[0089] storage unit ( Figure 6 (Not shown in the image) It uses eMMC flash memory and is interconnected with the CPU via an SD / MMC interface. The storage unit internally constructs a distributed signal feature database, which is divided into 8 independent nodes, each corresponding to 1 hour of historical data, using an SQLite distributed architecture.

[0090] The storage unit stores ephemeris data, real satellite signal fingerprints (carrier phase deviation, C / A code correlation peaks, power spectral density), and historical interference events (DOA angle, power, type). The CPU performs database mining once per second, using K-means clustering to optimize the MVDR steering vector and ESPRIT subspace parameters.

[0091] It should be noted that the communication terminal provided in this application, and the framework structure of each module in the communication terminal, are not limited to... Figure 2 , Figure 5 and Figure 6 The schematic diagram shown illustrates that the amplifiers or filters set between the various modules according to actual needs are easily conceived by those skilled in the art and are all within the protection scope of this application.

[0092] based on Figure 6 The complete signal processing method for the multi-resistance fusion satellite communication terminal shown in this embodiment is as follows: S1: The weak RF signal received by the antenna array elements (navigation antenna element and communication antenna element) is amplified and then filtered by a low-noise amplifier and a bandpass filter to obtain an RF signal that is then filtered by a bandpass filter; if the RF front-end channel is equipped with a down-conversion mixer, an intermediate frequency signal is obtained.

[0093] S2: The RF signal is processed by the ADC sampler to obtain a digital sampled signal.

[0094] S3: The digital sampling signal is transmitted to the FPGA chip. After processing within the FPGA chip, the digital sampling signal outputs suppressed I / Q data. The specific processing steps are as follows: the digital I / Q signal is processed by the covariance matrix calculation module, then processed by the beamforming module, then by the DOA estimation module, and finally outputs suppressed I / Q data.

[0095] S4: The suppressed I / Q data is transmitted to the central processing unit (CPU) for anti-spoofing and anti-communication interference processing.

[0096] S5: Outputs the actual received signal, i.e., obtains the anti-interference output result, which includes the actual navigation data or satellite communication data.

[0097] S6: Verify the above anti-interference output results and determine if there are any anomalies. If an anomaly is detected, the CPU triggers an alarm in the task management module via GPIO and controls the FPGA or RF front end to adjust parameters such as sampling and local oscillator to achieve iterative processing.

[0098] Figure 7 and Figure 8The simulation results of the signal interference method provided in this embodiment under a complex electromagnetic environment in the laboratory are as follows: 8-element ring array, sampling rate 40MHz, GNSS signal frequency band L1 band (1.575GHz), real GNSS signal signal-to-noise ratio SNR=20dB, suppression interference signal-to-interference ratio JSR=60dB, and the number of spoofed signals and the number of real GNSS satellites are both set to 2.

[0099] Figure 7 The graph shows the spatial energy distribution of the original signal and the beam pattern after MVDR suppression. In the graph, the blue "." indicates the angular position of the actual GNSS satellite (30°, 45°), and the "*" indicates the angular position of the GNSS spoofing satellite (32°, 47°). As can be seen from the curve comparison in the graph, the received signal after MVDR suppression forms a deep null at the angle of the GNSS spoofing satellite, with a maximum suppression depth approaching 60dB, achieving a good effect against GNSS spoofing signals.

[0100] Figure 8 The bar chart comparing the signal-to-noise ratio (SNR) before and after interference suppression shows that the SNR before suppression was -65.88 dB, and the SNR after suppression was 15.04 dB, with a suppression degree exceeding 80 dB. This indicates that the multi-anti-interference fusion communication method provided in this embodiment has outstanding interference suppression performance.

[0101] Furthermore, using the multi-resistance fusion communication method and communication terminal provided in this embodiment, the identification rate of spoofing interference (2 false signals) reaches 99.2%, and the positioning error is <1°; the bit error rate of the communication link under interference is also reduced. The effective bandwidth is increased to 1.8Mbps, the power consumption is reduced by 42% compared with traditional single-antenna terminals, and the battery life is more than 8 hours.

[0102] Existing satellite communication terminals have the following main shortcomings: (1) It is difficult to deal with suppression, deception and communication interference simultaneously in a single satellite communication terminal, and there is a lack of satellite communication solutions that integrate the three functions. (2) Low-orbit satellite communication has low data bandwidth and reliability, high power consumption of the terminal, and poor battery life; (3) Each functional module is designed independently and connected by radio frequency cables, resulting in redundant terminal hardware structure and low signal processing efficiency; at the same time, multiple devices are inconvenient for users to carry when going out and for technicians to maintain. (4) It has limited ability to identify, locate and suppress interference sources, and cannot provide users with highly reliable navigation and satellite communication services.

[0103] By providing the above-mentioned technical solution, this application can overcome the above-mentioned shortcomings of the prior art and has the following beneficial effects: (1) This application integrates three anti-interference functions into the same hardware platform to form a complete solution that effectively copes with complex interference environments; (2) This application uses a multi-element ring array and adaptive beamforming technology to improve the effective bandwidth and reliability of data, significantly reduce power consumption, and extend battery life; (3) The hardware structure of this application is highly integrated, the data flow and control signal path are clearly optimized, which makes it easy to carry and maintain, and improves the signal processing efficiency by more than 30%; (4) This application proposes a fusion algorithm of multi-element DOA estimation + signal fingerprint matching + spectrum sensing, which significantly improves the ability to identify, locate and eliminate interference.

[0104] (5) This application realizes real-time accurate positioning and adaptive suppression of interference sources, providing users with high-precision and high-reliability navigation and communication services.

[0105] It should be noted that the program executing the multi-antibody fusion communication method of the above method embodiments can be divided into multiple subroutines. Each subroutine can be loaded and run by a processor to perform different steps of the multi-antibody fusion communication method of the above method embodiments. Specifically, each subroutine can be stored in different memories, and each processor can be configured to execute programs in one or more memories to jointly implement the multi-antibody fusion communication method of the above method embodiments.

[0106] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A multi-resistance fusion communication method, characterized in that, include: Obtain the preprocessed target signal; The preprocessed target signal is subjected to anti-suppression processing and / or anti-spoofing processing to obtain a first anti-interference signal; Determine the signal type of the first anti-interference signal; When the first anti-interference signal includes a communication signal, the first anti-interference signal is subjected to anti-communication interference processing to obtain a second anti-interference signal as the anti-interference output result.

2. The multi-resistance fusion communication method according to claim 1, characterized in that, The preprocessed target signal is subjected to anti-spoofing processing to obtain a first anti-interference signal, including: The preprocessed target signal is subjected to anti-spoofing processing using the DOA comparison method to obtain the first processed signal; The first processed signal is subjected to anti-spoofing processing using a fingerprint matching method to obtain the second processed signal; An image classification algorithm is used to perform anti-spoofing processing on the second processed signal to obtain the first anti-interference signal.

3. The multi-resistance fusion communication method according to claim 2, characterized in that, The preprocessed target signal is subjected to anti-spoofing processing using the DOA comparison method to obtain a first processed signal, including: DOA estimation is performed on the preprocessed target signal to obtain the DOA estimate value of each signal in the preprocessed target signal; Obtain the theoretical DOA value of each signal in the preprocessed target signal; Calculate the difference between each DOA estimate and the corresponding theoretical DOA value; Signals with a difference greater than a first preset threshold are removed from the preprocessed target signal to obtain the first processed signal.

4. The multi-resistance fusion communication method according to claim 2, characterized in that, The first processed signal is subjected to anti-spoofing processing using a fingerprint matching method to obtain a second processed signal, including: Obtain the fingerprint features of each signal in the first processed signal; Based on the fingerprint features, a feature vector is generated; wherein the feature vector includes multiple feature values; Calculate the similarity between each feature value and the corresponding predetermined feature value to obtain multiple similarity values; Signals with similarity values ​​less than a second preset threshold are removed from the first processed signal to obtain the second processed signal.

5. The multi-resistance fusion communication method according to claim 2, characterized in that, The second processed signal is subjected to anti-spoofing processing using an image classification algorithm to obtain the first anti-interference signal, including: The second processed signal is converted into a multi-channel input image; Forward reasoning is performed on the multi-channel input image to obtain the probability value of each signal in the second processed signal being a deception signal; Signals with a probability value greater than a third preset threshold are removed from the second processed signal to obtain the first anti-interference signal.

6. The multi-resistance fusion communication method according to claim 5, characterized in that, The image classification algorithm is used to perform anti-spoofing processing on the second processed signal to obtain the first anti-interference signal, which further includes: DOA estimation is performed on the preprocessed target signal to obtain the DOA estimate value of each signal in the preprocessed target signal; Obtain the theoretical DOA value of each signal in the preprocessed target signal; Calculate the difference between each DOA estimate and the corresponding theoretical DOA value; Obtain the fingerprint features of each signal in the first processed signal; Based on the fingerprint features, a feature vector is generated; wherein the feature vector includes multiple feature values; Calculate the similarity between each feature value and the corresponding predetermined feature value to obtain multiple similarity values; Based on the difference, similarity value, and probability value, calculate the overall confidence that each signal in the second processed signal is a real signal; Signals with a comprehensive confidence level less than a fourth preset threshold are removed from the second processed signal to obtain the first anti-interference signal.

7. The multi-resistance fusion communication method according to claim 1, characterized in that, The first anti-interference signal is subjected to anti-communication interference processing to obtain a second anti-interference signal as the anti-interference output result, including: Perform a fast Fourier transform on the first anti-interference signal to obtain a power spectral density sequence; The power spectral density sequence is sequentially processed by median filtering and exponential forgetting averaging to obtain the background noise estimate; Based on the background noise estimate, interference detection is performed on the power spectral density sequence to obtain the interference detection result; Based on the interference detection results, an anti-communication interference operation is performed to obtain the second anti-interference signal; wherein, the anti-communication interference operation includes: Dynamic frequency hopping is performed by selecting the channel with the lowest interference power from pre-generated candidate channels; and, Adjusting the MVDR beamforming weights enables array spatial diversity reception.

8. A computer-readable storage medium storing a computer program, characterized in that, The computer program is adapted to be loaded and run by a processor to perform the multi-resistance fusion communication method according to any one of claims 1 to 7.

9. A communication terminal, characterized in that, include: An antenna unit, a radio frequency front-end channel, at least one processor, and a memory communicatively connected to the at least one processor; The antenna unit, the radio frequency front-end channel, and the at least one processor are sequentially connected in communication. The antenna unit is used to receive target signals; The radio frequency front-end channel is used to preprocess the target signal and input the preprocessed target signal to the processor; The memory stores a computer program that, when executed by the at least one processor, implements the multi-antibody fusion communication method according to any one of claims 1 to 7.

10. The communication terminal according to claim 9, characterized in that, The antenna unit includes multiple antenna elements; the multiple antenna elements are arranged in a ring array or a square array; the distance between adjacent antenna elements is λ / 2, where λ is the dielectric wavelength of the antenna element.

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