Interference analysis method, device and equipment based on GNSS baseband signal

Abstract

The application provides a GNSS baseband signal-based interference analysis method, device and equipment, and relates to the technical field of interference analysis. In the application, first, a target noise recognition model formed by training is used to perform interference recognition on a GNSS baseband signal collected by a GNSS receiver to obtain a target confidence degree corresponding to the GNSS baseband signal, wherein the target noise recognition model belongs to a neural network model, and the target confidence degree is used to reflect the probability that the GNSS baseband signal has interference; second, when the target confidence degree is greater than or equal to a preconfigured confidence threshold, a scanning received signal collected by a directional antenna array is acquired; and then, the scanning received signal is analyzed to obtain target interference analysis data, wherein the target interference analysis data is used to reflect at least one of the direction of arrival of an interference source and the type of interference. Based on the above, the problem of low efficiency and low reliability of interference analysis in the prior art can be improved.

Description

Technical Field

[0001] This application relates to the field of interference analysis technology, and more specifically, to an interference analysis method, apparatus, and device based on GNSS baseband signals. Background Technology

[0002] Global Navigation Satellite Systems (GNSS) are the core support for precise positioning and navigation, and the stability and reliability of their radio spectrum directly affect the operation of various scenarios that rely on navigation services. However, electromagnetic interference from the ground, such as suppression and deception interference, poses an increasingly serious threat to GNSS systems. Existing spectrum monitoring methods have significant shortcomings. For example, ordinary spectrum monitoring relies on signal power, making it difficult to detect weak, low-power suppression and deception interference from a distance. This often requires personnel to conduct close-range investigations, which is extremely inefficient, and the reliability of the investigation results is also difficult to guarantee. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide an interference analysis method, apparatus and equipment based on GNSS baseband signals, so as to improve the problems of low efficiency and low reliability of interference analysis in the prior art.

[0004] To achieve the above objectives, this application adopts the following technical solution: An interference analysis method based on GNSS baseband signals, comprising: Using a trained target noise identification model, interference is identified in GNSS baseband signals acquired by a GNSS receiver to obtain the target confidence level corresponding to the GNSS baseband signal. The target noise identification model is a neural network model, and the target confidence level is used to reflect the probability that interference exists in the GNSS baseband signal. When the target confidence level is greater than or equal to a pre-configured confidence level threshold, the scanned received signal acquired by the directional antenna array is obtained; The scanned received signal is analyzed to obtain target interference analysis data, wherein the target interference analysis data is used to reflect at least one of the incoming wave direction and interference type of the interference source.

[0005] In a preferred embodiment of this application, in the above-described interference analysis method based on GNSS baseband signals, the step of using a trained target noise identification model to perform interference identification on the GNSS baseband signals acquired by a GNSS receiver, and obtaining the target confidence level corresponding to the GNSS baseband signals, includes: Acquire GNSS baseband signals collected by a GNSS receiver; The carrier-to-noise ratio, pseudorange error, carrier phase deviation, and code tracking loop phase detection error are extracted from the GNSS baseband signal to form the corresponding carrier-to-noise ratio time series, pseudorange error time series, carrier phase deviation time series, and code tracking loop phase detection error time series. Using the trained target noise recognition model, interference is identified on the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series to obtain the target confidence level corresponding to the GNSS baseband signal.

[0006] In a preferred embodiment of this application, in the aforementioned interference analysis method based on GNSS baseband signals, the step of using a trained target noise identification model to perform interference identification on the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series to obtain the target confidence level corresponding to the GNSS baseband signal includes: Trend fitting is performed on the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series, respectively. Anomaly data is extracted from the trend fitting results to obtain the parameter abrupt change slope and anomaly duration. The correlation coefficients of the carrier-to-noise ratio time series and the code tracking loop phase discrimination error time series are calculated to obtain the first Pearson correlation coefficient, and the correlation coefficients of the pseudorange error time series and the carrier phase deviation time series are calculated to obtain the second Pearson correlation coefficient. Based on the parameter mutation slope, the duration of the abnormality, the first Pearson correlation coefficient, and the second Pearson correlation coefficient, a multi-dimensional feature vector is constructed. Using the trained target noise recognition model, interference recognition is performed on the multi-dimensional feature vector to obtain the target confidence level corresponding to the GNSS baseband signal.

[0007] In a preferred embodiment of this application, in the aforementioned interference analysis method based on GNSS baseband signals, the step of acquiring the scanned received signal collected by the directional antenna array when the target confidence level is greater than or equal to a pre-configured confidence level threshold includes: When the target confidence level is greater than or equal to a pre-configured confidence level threshold, the initial scanning received signal obtained by the directional antenna array according to the pre-configured initial operating parameters is acquired, wherein the directional antenna array includes multiple array elements, and the initial operating parameters include the number of operating array elements; Based on the initial scan reception signal, the target operating parameters are determined, and the scan reception signal obtained by the directional antenna array according to the target operating parameters is acquired.

[0008] In a preferred embodiment of this application, in the above-described interference analysis method based on GNSS baseband signals, the step of acquiring the initial scanned received signal obtained by the directional antenna array according to the pre-configured initial operating parameters when the target confidence level is greater than or equal to a pre-configured confidence level threshold includes: When the target confidence level is greater than or equal to a pre-configured confidence level threshold, the scanning step size of the directional antenna array is determined based on the target confidence level, wherein there is a negative correlation between the scanning step size and the target confidence level; The initial scan received signal is acquired by the directional antenna array according to the scan step size and the pre-configured initial operating parameters.

[0009] In a preferred embodiment of this application, in the aforementioned interference analysis method based on GNSS baseband signals, the steps of determining the target operating parameters based on the initial scan received signal and acquiring the scan received signal obtained by the directional antenna array according to the target operating parameters include: The signal-to-noise ratio (SNR) of the initial scan received signal is determined, and based on the SNR, the number of array elements operating in the directional antenna array is determined, and the number of array elements is determined as the target operating parameter, wherein there is a negative correlation between the SNR and the number of array elements; Acquire the scanning received signal obtained by the directional antenna array according to the target operating parameters.

[0010] In a preferred embodiment of this application, the step of analyzing the scanned received signal to obtain target interference analysis data in the above-described interference analysis method based on GNSS baseband signals includes: Determine at least one of the polarization characteristic parameters and spatial characteristic parameters corresponding to the scanned received signal, wherein the polarization characteristic parameters include at least one of linear polarization ratio and polarization angle, and the spatial characteristic parameters include at least one of inter-element phase difference and inter-element amplitude difference; Based on at least one of the polarization characteristic parameters and the spatial characteristic parameters, target interference analysis data is obtained.

[0011] In a preferred embodiment of this application, in the above-described interference analysis method based on GNSS baseband signals, the step of analyzing and obtaining target interference analysis data based on at least one of the polarization characteristic parameters and the spatial characteristic parameters includes: Based on the signal-to-noise ratio of the scanned received signal, the first weight parameter and the second weight parameter corresponding to the MUSIC algorithm and the ESPRIT algorithm in the MUSIC-ESPRIT weighted hybrid algorithm are determined respectively. Based on the MUSIC-ESPRIT weighted mixing algorithm, the first weight parameter, and the second weight parameter, signal analysis is performed on at least one of the polarization characteristic parameters and the spatial characteristic parameters to obtain target interference analysis data.

[0012] This application also provides an interference analysis device based on GNSS baseband signals, comprising: An interference identification module is used to identify interference in GNSS baseband signals acquired by a GNSS receiver using a trained target noise identification model, and to obtain the target confidence level corresponding to the GNSS baseband signal. The target noise identification model is a neural network model, and the target confidence level is used to reflect the probability that interference exists in the GNSS baseband signal. The signal acquisition module is used to acquire the scanned received signal collected by the directional antenna array when the target confidence level is greater than or equal to a pre-configured confidence level threshold. An interference analysis module is used to analyze the scanned received signal to obtain target interference analysis data, wherein the target interference analysis data reflects at least one of the incoming wave direction and interference type of the interference source.

[0013] Based on the above, this application also provides an electronic device, including: Memory, used to store computer programs; A processor connected to the memory is used to execute the computer program stored in the memory to implement the above-described interference analysis method based on GNSS baseband signals.

[0014] The interference analysis method, apparatus, and equipment based on GNSS baseband signals provided in this application firstly utilize a trained target noise identification model to identify interference in GNSS baseband signals acquired by a GNSS receiver, obtaining the target confidence level corresponding to the GNSS baseband signal. The target noise identification model is a neural network model, and the target confidence level reflects the probability of interference in the GNSS baseband signal. Secondly, when the target confidence level is greater than or equal to a pre-configured confidence level threshold, a scanning received signal acquired through a directional antenna array is obtained. Then, the scanning received signal is analyzed to obtain target interference analysis data, wherein the target interference analysis data reflects at least one of the incoming wave direction and interference type of the interference source. Based on the above, before conducting interference analysis, a target noise identification model is used to identify the GNSS baseband signal acquired by the GNSS receiver. This allows full utilization of the powerful learning and recognition capabilities of neural network models to effectively identify the presence of interference. Furthermore, if the probability of interference is high, the scanning received signal acquired through a directional antenna array is enhanced by the directional gain of the array. This overcomes the limitations of traditional spectrum monitoring, which relies on signal power, and enables long-range detection of low-power, weak suppression and deceptive interference. This significantly improves the distance and efficiency of interference detection without requiring personnel to approach the site for investigation. Moreover, the interference identification by the target noise identification model, as a pre-processing step, can improve the reliability of the analysis to a certain extent. Therefore, based on this scheme, the problems of low efficiency and low reliability in existing interference analysis technologies can be improved. Attached Figure Description

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings.

[0016] Figure 1 A structural block diagram of an electronic device provided in an embodiment of this application.

[0017] Figure 2 This is a flowchart illustrating the interference analysis method based on GNSS baseband signals provided in an embodiment of this application.

[0018] Figure 3 This is a schematic diagram of an interference monitoring and direction-of-arrival measurement method based on GNSS baseband signals and directional antenna arrays provided in an embodiment of this application.

[0019] Figure 4 This is a schematic diagram illustrating the processing procedure of the LSTM neural network provided in the embodiments of this application.

[0020] Figure 5 A flowchart of the MUSIC-ESPRIT algorithm provided in the embodiments of this application.

[0021] Figure 6 The diagram illustrates the principle of the unidirectional core transmission path and the bidirectional closed-loop feedback path in the GNSS interference monitoring process provided in this embodiment.

[0022] Figure 7 This is a block diagram of an interference analysis device based on GNSS baseband signals provided in an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0025] like Figure 1 As shown in the figure, this application provides an electronic device. The electronic device may include a memory, a processor, and an interference analysis device based on GNSS baseband signals.

[0026] In detail, the memory and the processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, the memory and the processor can be electrically connected via one or more communication buses or signal lines. The GNSS baseband signal-based interference analysis device includes at least one software functional module stored in the memory in the form of software or firmware. The processor is used to execute executable computer programs stored in the memory, such as the software functional modules and computer programs included in the GNSS baseband signal-based interference analysis device, to implement the GNSS baseband signal-based interference analysis method provided in this application embodiment.

[0027] Optionally, the memory may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0028] Furthermore, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0029] Understandable. Figure 1 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown may include, for example, a communication unit for exchanging information with other devices, such as a GNSS receiver.

[0030] Combination Figure 2 This application also provides an interference analysis method based on GNSS baseband signals applicable to the aforementioned electronic equipment. The method steps defined in the flowchart of the interference analysis method based on GNSS baseband signals can be implemented by the electronic equipment. The following will describe... Figure 2 The specific process shown will be explained in detail.

[0031] Step S110: Using the target noise recognition model formed by training, interference recognition is performed on the GNSS baseband signal collected by the GNSS receiver to obtain the target confidence level corresponding to the GNSS baseband signal.

[0032] In this embodiment, the electronic device can utilize a trained target noise recognition model to identify interference in GNSS baseband signals acquired by a GNSS receiver, thereby obtaining the target confidence level corresponding to the GNSS baseband signal. The target noise recognition model is a neural network model, and the target confidence level reflects the probability of interference in the GNSS baseband signal. In other words, the probability of interference in the GNSS baseband signal can be preliminarily determined using a neural network model, thus obtaining the target confidence level.

[0033] Step S120: When the target confidence level is greater than or equal to a pre-configured confidence level threshold, acquire the scanning received signal collected by the directional antenna array.

[0034] In this embodiment, after obtaining the target confidence level, the electronic device can first compare the target confidence level with the confidence threshold. Then, when the target confidence level is greater than or equal to the pre-configured confidence threshold, it can acquire the scanned received signal collected by the directional antenna array. That is, when the target confidence level is greater than or equal to the confidence threshold, it indicates a high probability of interference. Therefore, scanning can be performed using a directional antenna array, utilizing the directional gain of the directional antenna array to overcome the limitations of traditional spectrum monitoring that relies on signal power.

[0035] Step S130: Analyze the scanned received signal to obtain target interference analysis data.

[0036] In this embodiment, after receiving the scanned received signal, the electronic device can analyze the scanned received signal to obtain target interference analysis data. The target interference analysis data reflects at least one of the following: the direction of arrival of the interference source and the type of interference. That is, the scanned received signal can be analyzed to obtain the direction of arrival of the interference source, the type of interference, or both.

[0037] Based on the above, before conducting interference analysis, the GNSS baseband signal acquired by the GNSS receiver is first identified using a target noise identification model. This allows full utilization of the powerful learning and recognition capabilities of the neural network model, enabling effective identification of interference. Furthermore, if the probability of interference is high, the scanned received signal acquired through a directional antenna array is amplified by the directional gain of the array. This overcomes the limitations of traditional spectrum monitoring, which relies on signal power, and allows for long-range capture of low-power, weak suppression and deceptive interference. This eliminates the need for personnel to approach and investigate, significantly improving the distance and efficiency of interference detection, ensuring both efficiency and reliability. Moreover, the interference identification by the target noise identification model, as a pre-processing step, further enhances the reliability of the analysis. Therefore, this approach addresses the problems of low efficiency and unreliability in existing interference analysis technologies.

[0038] Firstly, regarding step S110, it should be noted that the specific method for identifying interference in GNSS baseband signals is not limited and can be selected according to actual needs.

[0039] For example, in an alternative implementation, in order to make the interference identification more reliable, the above step S110 may further include steps S111, S112 and S113, wherein the specific contents of each step are as follows.

[0040] Step S111: Acquire the GNSS baseband signal collected by the GNSS receiver.

[0041] In this embodiment, GNSS baseband signals acquired by a GNSS receiver can be obtained. That is, the GNSS receiver can acquire navigation baseband signals (i.e., GNSS baseband signals) in real time and send them to the electronic device, enabling the electronic device to acquire the navigation baseband signals.

[0042] Step S112: Extract the carrier-to-noise ratio, pseudorange error, carrier phase deviation, and code tracking loop phase detection error from the GNSS baseband signal to form corresponding time series of carrier-to-noise ratio, pseudorange error, carrier phase deviation, and code tracking loop phase detection error.

[0043] In this embodiment of the application, after obtaining the GNSS baseband signal, the carrier-to-noise ratio, pseudorange error, carrier phase deviation, and code tracking loop phase detection error can be extracted from the GNSS baseband signal to form corresponding carrier-to-noise ratio time series, pseudorange error time series, carrier phase deviation time series, and code tracking loop phase detection error time series.

[0044] Step S113: Using the trained target noise recognition model, interference recognition is performed on the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series to obtain the target confidence level corresponding to the GNSS baseband signal.

[0045] In this embodiment, after obtaining the carrier-to-noise ratio (CNR) time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase detection error time series, a trained target noise identification model can be used to perform interference identification on these time series, thereby obtaining the target confidence level corresponding to the GNSS baseband signal. It should be noted that because the CNR, pseudorange error, carrier phase deviation, and code tracking loop phase detection error have highly sensitive feedback characteristics to interference, using a target noise identification model for interference identification can achieve high-precision interference identification.

[0046] In an alternative implementation, the sampling frequency of the GNSS receiver can be set to 20Hz, the extraction time window for the four core parameters mentioned above is 50ms / frame, and each time series is first preprocessed using a moving average filter with a window size of 3 sampling points, and then trend fitting and anomaly extraction are performed to reduce the interference of instantaneous impulse noise on parameter features and ensure accuracy.

[0047] It is understood that in step S113 above, the specific method for interference identification of the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series is not limited. For example, in an alternative embodiment, in order to further improve the accuracy of interference identification, step S113 above may further include steps S113a, S113b, S113c, and S113d, wherein the specific contents of each step are as follows.

[0048] Step S113a: Perform trend fitting on the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series, respectively, and extract abnormal data from the trend fitting results to obtain the parameter abrupt change slope and the duration of the abnormality.

[0049] In this embodiment, trend fitting can be performed on the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series, respectively. Anomaly data extraction is then performed on the trend fitting results to obtain the parameter abrupt change slope and the duration of the anomaly (≥2 consecutive sampling points). For example, curve fitting can be performed on each time series separately. Then, abrupt change points are determined on the obtained fitted curves, and the slope of these abrupt change points is determined. For instance, if the pseudorange error suddenly changes drastically at a certain moment, it may indicate the presence of interference. Therefore, by extracting the parameter abrupt change slope, the presence of interference can be effectively identified. Exemplarily, anomalies can be determined based on the parameter abrupt change slope. For example, if the parameter abrupt change slope is greater than a set threshold, it is determined to be an anomaly; conversely, if the parameter abrupt change slope is not greater than the set threshold, it is determined not to be an anomaly.

[0050] Step S113b: Calculate the correlation coefficient between the carrier-to-noise ratio time series and the code tracking loop phase discrimination error time series to obtain the first Pearson correlation coefficient, and calculate the correlation coefficient between the pseudorange error time series and the carrier phase deviation time series to obtain the second Pearson correlation coefficient.

[0051] In this embodiment, the correlation coefficients between the carrier-to-noise ratio time series and the code tracking loop phase detection error time series can be calculated to obtain a first Pearson correlation coefficient, and the correlation coefficients between the pseudorange error time series and the carrier phase deviation time series can be calculated to obtain a second Pearson correlation coefficient. The specific calculation process can refer to relevant prior art. The Pearson correlation coefficients between the carrier-to-noise ratio time series and the code tracking loop phase detection error time series, and between the pseudorange error time series and the carrier phase deviation time series, can be used to eliminate misjudgments caused by single-parameter fluctuations due to natural noise.

[0052] Step S113c: Based on the parameter mutation slope, the duration of the anomaly, the first Pearson correlation coefficient, and the second Pearson correlation coefficient, a multi-dimensional feature vector is constructed.

[0053] In this embodiment of the application, after obtaining the parameter mutation slope, the duration of the anomaly, the first Pearson correlation coefficient, and the second Pearson correlation coefficient, a multi-dimensional feature vector can be constructed based on the parameter mutation slope, the duration of the anomaly, the first Pearson correlation coefficient, and the second Pearson correlation coefficient. That is, the multi-dimensional feature vector includes the parameter mutation slope, the duration of the anomaly, the first Pearson correlation coefficient, and the second Pearson correlation coefficient.

[0054] Step S113d: Using the trained target noise recognition model, interference recognition is performed on the multi-dimensional feature vector to obtain the target confidence level corresponding to the GNSS baseband signal.

[0055] In this embodiment, after obtaining the multi-dimensional feature vector, a trained target noise recognition model can be used to identify interference in the multi-dimensional feature vector, thereby obtaining the target confidence level corresponding to the GNSS baseband signal. It should be noted that the target noise recognition model can be an LSTM neural network, and can be trained based on historical interference signals and natural noise signals, enabling the LSTM neural network trained with sufficient interference and natural noise samples to accurately distinguish between interference and natural noise.

[0056] Secondly, it should be noted that the specific method for obtaining the scanning received signal collected by the directional antenna array is not limited and can be selected according to actual needs in step S120.

[0057] For example, in an alternative implementation, in order to ensure that the acquired scanning received signal has high reliability and that interference analysis can be carried out effectively, the above step S120 may further include steps S121 and S122, wherein the specific contents of each step are as follows.

[0058] Step S121: When the target confidence level is greater than or equal to a pre-configured confidence level threshold, acquire the initial scan reception signal obtained by the directional antenna array according to the pre-configured initial operating parameters.

[0059] In this embodiment, when the target confidence level is greater than or equal to a pre-configured confidence threshold, an initial scan received signal acquired by a directional antenna array according to pre-configured initial operating parameters can be obtained. The directional antenna array includes multiple array elements, and the initial operating parameters may include the number of operating array elements. That is, directional scanning is performed using the array elements corresponding to the number of operating array elements included in the initial operating parameters of the directional antenna array, thereby acquiring the initial scan received signal. For example, the confidence threshold can be 85%.

[0060] Step S122: Based on the initial scan reception signal, determine the target operating parameters, and acquire the scan reception signal obtained by the directional antenna array according to the target operating parameters.

[0061] In this embodiment, after obtaining the initial scan reception signal, the target operating parameters can be determined based on the initial scan reception signal, and the scan reception signal acquired by the directional antenna array according to the target operating parameters can be obtained. That is, the operating parameters can be re-determined based on some characteristics of the initial scan reception signal, i.e., the target operating parameters can be obtained. This allows the scan reception signal acquired based on the target operating parameters to better characterize the actual situation, ensuring the reliability of subsequent analysis.

[0062] It is understood that the specific method of obtaining the initial scan reception signal in step S121 above is not limited. For example, in an alternative implementation, in order to ensure the reliable operation of the scan and to make the obtained initial scan reception signal better reflect the actual situation, step S121 above may further include steps S121a and S121b, wherein the specific contents of each step are as follows.

[0063] Step S121a: When the target confidence level is greater than or equal to a pre-configured confidence level threshold, the scanning step size of the directional antenna array is determined based on the target confidence level.

[0064] In this embodiment, when the target confidence level is greater than or equal to a pre-configured confidence threshold, the scanning step size of the directional antenna array can be determined based on the target confidence level. There is a negative correlation between the scanning step size and the target confidence level. For example, initially, the scanning step size is 0.5°. When the target confidence level is greater than or equal to the pre-configured confidence threshold, the scanning step size can be reduced to 0.2° for refined directional scanning.

[0065] Step S121b: Obtain the initial scan received signal acquired by the directional antenna array according to the scan step size and the pre-configured initial operating parameters.

[0066] In this embodiment of the application, after obtaining the scanning step size, the initial scanning received signal can be acquired by the directional antenna array according to the scanning step size and the pre-configured initial operating parameters (such as 8 array elements).

[0067] It is understood that the specific method of acquiring the scan reception signal in step S122 above is not limited. For example, in an alternative implementation, in order to ensure that the acquired scan reception signal has high accuracy, step S122 above may further include steps S122a and S122b, wherein the specific contents of each step are as follows.

[0068] Step S122a: Determine the signal-to-noise ratio of the initial scan received signal, and based on the signal-to-noise ratio, determine the number of array elements operating in the directional antenna array, and set the number of array elements as the target operating parameter.

[0069] In this embodiment, the signal-to-noise ratio (SNR) of the initial scanned received signal can be determined, and based on the SNR, the number of array elements operating in the directional antenna array can be determined, and this number of array elements can be set as the target operating parameter. There is a negative correlation between the SNR and the number of array elements. For example, when the SNR is ≥10dB, 8 array elements can be selected, and when the SNR is <10dB, 10 array elements can be selected.

[0070] Step S122b: Obtain the scanning received signal acquired by the directional antenna array according to the target operating parameters.

[0071] In this embodiment, after determining the target operating parameters, the scanned received signal obtained by the directional antenna array according to the target operating parameters can be acquired. For example, the scanned received signal can be obtained by scanning 10 elements in the directional antenna array at a scan step size of 0.2°.

[0072] Thirdly, it should be noted that the specific method for analyzing the scanned received signal is not limited and can be selected according to actual needs.

[0073] For example, in an alternative implementation, in order to achieve reliable analysis of the scanned received signal and make the obtained target interference analysis data more reliable, the above step S130 may further include steps S131 and S132, wherein the specific contents of each step are as follows.

[0074] Step S131: Determine at least one of the polarization characteristic parameters and spatial characteristic parameters corresponding to the scanned received signal.

[0075] In this embodiment, at least one of the polarization characteristic parameters and spatial characteristic parameters corresponding to the scanned received signal can be determined. The polarization characteristic parameters include at least one of linear polarization ratio and polarization angle, and the spatial characteristic parameters include at least one of inter-element phase difference and inter-element amplitude difference. It should be noted that the polarization ratio can be calculated by the amplitude ratio of the horizontal polarization component to the vertical polarization component of the array received signal, and the polarization angle can be measured based on the normal direction of the adaptive directional antenna array.

[0076] Step S132: Based on at least one of the polarization characteristic parameters and the spatial characteristic parameters, target interference analysis data is obtained.

[0077] In this embodiment, target interference analysis data can be obtained based on at least one of the polarization characteristic parameters and the spatial characteristic parameters. That is, after obtaining at least one of the polarization characteristic parameters and the spatial characteristic parameters, target interference analysis data is obtained based on at least one of the polarization characteristic parameters and the spatial characteristic parameters. For example, target interference analysis data can be obtained based on the polarization characteristic parameters, or based on the spatial characteristic parameters, or based on both the polarization characteristic parameters and the spatial characteristic parameters.

[0078] It is understood that the specific method of obtaining the target interference analysis data in step S132 above is not limited. For example, in an alternative implementation, in order to further improve the reliability of the interference analysis, step S132 above may further include steps S132a and S132b, wherein the specific contents of each step are as follows.

[0079] Step S132a: Based on the signal-to-noise ratio of the scanned received signal, determine the first weight parameter and the second weight parameter corresponding to the MUSIC algorithm and the ESPRIT algorithm, respectively, in the MUSIC-ESPRIT weighted hybrid algorithm.

[0080] In this embodiment, the first weight parameter and the second weight parameter corresponding to the MUSIC algorithm and the ESPRIT algorithm, respectively, in the MUSIC-ESPRIT weighted hybrid algorithm can be determined based on the signal-to-noise ratio (SNR) of the scanned received signal. For example, when the SNR is ≥10dB, the first weight parameter corresponding to the MUSIC algorithm can be 60%, and the second weight parameter corresponding to the ESPRIT algorithm can be 40%; when the SNR is <10dB, the first weight parameter corresponding to the ESPRIT algorithm can be 70%, and the second weight parameter corresponding to the MUSIC algorithm can be 30%. Based on this, the robustness of direction finding in low SNR scenarios can be improved to a certain extent.

[0081] Step S132b: Based on the MUSIC-ESPRIT weighted mixing algorithm, the first weight parameter, and the second weight parameter, perform signal analysis on at least one of the polarization feature parameters and the spatial feature parameters to obtain target interference analysis data.

[0082] In this embodiment of the application, after obtaining the first weight parameter and the second weight parameter, signal analysis can be performed on at least one of the polarization characteristic parameter and the spatial characteristic parameter based on the MUSIC-ESPRIT weighted mixing algorithm, the first weight parameter and the second weight parameter to obtain target interference analysis data, such as synchronously outputting the direction of arrival of the interference source (azimuth angle 0-360°, elevation angle -10°~90°) and the interference type (suppression type / deception type).

[0083] To facilitate understanding of the above-described interference analysis method based on GNSS baseband signals, this application embodiment also provides a specific application example, the details of which are as follows.

[0084] like Figure 3-6 As shown, a method for interference monitoring and direction-of-arrival measurement based on GNSS baseband signals and a directional antenna array includes the following steps (which can be described as follows): Figure 3 The system is implemented using three modules: interference monitoring module, adaptive beamforming module, and hybrid direction finding and type recognition module. The navigation baseband signal is acquired in real time by a GNSS receiver, and four core parameters are extracted: carrier-to-noise ratio, pseudorange error, carrier phase deviation, and code tracking loop phase discrimination error, forming a parameter time series. Trend fitting and anomaly extraction are performed on the time series of each parameter to construct a multi-dimensional feature vector; The system is trained on historical interference signals and natural noise signals using an LSTM neural network, and outputs a fusion confidence level of 0-100%. When the fusion confidence level is ≥85%, interference is determined to exist and a preliminary interference warning is output. By extracting four core parameters—carrier-to-noise ratio, pseudorange error, carrier phase deviation, and code tracking loop phase discrimination error—a multi-dimensional feature vector is constructed, including parameter mutation slope, anomaly duration, and cross-parameter correlation coefficient. Combined with an LSTM neural network trained with sufficient interference and natural noise samples, accurate distinction between interference and natural noise is achieved. The system integrates a warning threshold with a confidence level ≥85% and a model convergence prediction error ≤3%, providing a reliable basis for subsequent processing.

[0085] A multi-element adaptive directional antenna array is employed, such as a 12-element adaptive directional antenna array. The array element layout is optimized based on a genetic algorithm, and directional reception is achieved through beam pointing control. A dual-channel integrated control module dynamically adjusts the number of working array elements (8-10) according to the received signal-to-noise ratio (SNR). Eight elements are selected when the SNR is ≥10dB, and ten elements are selected when the SNR is <10dB. Simultaneously, the phase and amplitude attenuation of each element are adjusted. The phase adjustment range is 0-360° with an accuracy of 0.05° and a response time ≤1μs, while the attenuation range is 0-30dB with a step size of 0.1dB. This enables dynamic reconstruction of the directional beam and adaptive scanning of the monitoring area. Furthermore, the array sidelobe level is minimized based on a genetic algorithm (target value ≤-35dB) while simultaneously satisfying the monitoring area coverage (azimuth 360°, elevation -10°~90°). The element spacing is half the center wavelength of the GNSSL1 band. Through sidelobe suppression and pointing control of the directional beam, the directional reception gain of interference signals is improved.

[0086] For the signal received by beam scanning, two types of polarization feature parameters, linear polarization ratio and polarization angle, are extracted. The polarization features are then fused with the spatial features of the array received signal (phase difference and amplitude difference between array elements) through dimensional stitching and input into the MUSIC-ESPRIT weighted hybrid algorithm. The algorithm simultaneously outputs the direction of arrival of the interference source (azimuth 0-360°, elevation -10°~90°) and the interference type (suppression / spoofing). The total weight coefficient of the MUSIC-ESPRIT weighted hybrid algorithm is 1. The weight allocation is dynamically adjusted by the signal-to-noise ratio (SNR) of the array received signal: when the SNR is ≥10dB, the MUSIC algorithm accounts for 60% of the weight and the ESPRIT algorithm accounts for 40%; when the SNR is <10dB, the ESPRIT algorithm accounts for 70% of the weight and the MUSIC algorithm accounts for 30%, thus improving the robustness of direction finding in low SNR scenarios.

[0087] Based on this, the polarization characteristics and spatial characteristics of linear polarization ratio (measurement error ≤ ±0.02) and polarization angle (measurement error ≤ ±0.5°) are integrated and input into the MUSIC-ESPRIT weighted hybrid algorithm. Through a dynamic weight allocation strategy based on signal-to-noise ratio, the algorithm balances the direction finding accuracy in high signal-to-noise ratio scenarios with the robustness in low signal-to-noise ratio scenarios, thus adapting to complex electromagnetic environments.

[0088] Among them, the multi-dimensional feature vector includes parameter mutation slope, abnormal duration (≥2 consecutive sampling points), and cross-parameter correlation coefficient; the cross-parameter correlation coefficient is the Pearson correlation coefficient of two sets of parameters among the four core parameters: carrier-to-noise ratio-code tracking loop phase discrimination error and pseudorange error-carrier phase deviation, which is used to eliminate misjudgment of single parameter fluctuations caused by natural noise.

[0089] Verification of weight allocation for the MUSIC-ESPRIT weighted hybrid algorithm Experimental Design: Signal-to-Noise Ratio Gradient vs. Weights The MUSIC algorithm has high direction-finding resolution at high signal-to-noise ratios (suitable for strong interference), while the ESPRIT algorithm has low computational complexity and strong robustness at low signal-to-noise ratios (suitable for weak interference). To verify the impact of weight allocation on direction-finding performance under different signal-to-noise ratios, the experiment set up three signal-to-noise ratio intervals (low: 5dB, medium: 10dB, high: 15dB) and designed four weight ratios (5:5, 6:4 (original scheme at high signal-to-noise ratio), 7:3, 3:7 (original scheme at low signal-to-noise ratio)). The direction-finding error (azimuth / elevation angle) and direction-finding success rate were tested.

[0090] 1. Experimental variables and fixed conditions: Variable 1: Signal-to-noise ratio (SNR): 5dB (low), 10dB (medium), 15dB (high); Variable 2: MUSIC:ESPRIT weight ratio: 5:5, 6:4, 7:3, 3:7; Fixed conditions: The direction of the interference source is fixed (azimuth 120°, elevation 30°), 200 sets of data are collected at each signal-to-noise ratio, and the allowable range of direction finding error is ≤ ±1° (qualified).

[0091] 2. Experimental Results:

[0092] 3. Results Analysis (Rationality of Weight Allocation and Theoretical Logic) 31. Low signal-to-noise ratio (5dB) scenarios: Because the ESPRIT algorithm is based on subspace rotation invariance and does not require peak searching, it is more resistant to noise interference at low signal-to-noise ratios. The average direction finding error of the original scheme with a weight of "3:7" (ESPRIT accounting for 70%) is only ±0.85 / ±0.79°, with a direction finding success rate of 99.0%. However, if the proportion of MUSIC is too high (such as 7:3), the peak search is easily affected by noise interference, causing the direction finding error to rise sharply to ±1.85 / ±1.72°, with a success rate of only 45.2%. This proves that the weight of ESPRIT should be prioritized at low signal-to-noise ratios.

[0093] 32. Medium to high signal-to-noise ratio (10dB / 15dB) scenarios: The MUSIC algorithm can achieve higher direction finding resolution under high signal-to-noise ratio by fine-grained peak search. The average direction finding error of the original scheme with a weight of "6:4" (60% MUSIC) is as low as ±0.32 / ±0.29° at 15dB, with a success rate of 99.9%. If the weight is 5:5, the direction finding resolution is slightly lower (error of ±0.65 / ±0.61° at 15dB). If the MUSIC ratio is increased to 7:3, although the resolution is close to 6:4, the computational complexity increases by 20% (the number of peak search steps increases). Considering both efficiency and accuracy, 6:4 is the optimal balance.

[0094] 33. The theoretical logic of weight allocation: The total weight coefficients are 1 to ensure consistent algorithm output. The allocation is based on "signal-noise ratio-algorithm advantage matching": when the signal-noise ratio is high (≥10dB), the resolution advantage of MUSIC dominates, so it accounts for 60%; when the signal-noise ratio is low (<10dB), the robustness advantage of ESPRIT dominates, so it accounts for 70%. This logic has been verified by experiments: the original scheme has a 5:5 ratio of weights, a weight that is 6.7%-33.8% higher, and a direction finding error that is 28%-54% lower, significantly improving the direction finding robustness.

[0095] The interference type identification logic is as follows: The interference type identification logic is as follows: within a 1-second time window, if the carrier phase deviation fluctuation is ≤0.2 rad and the pseudorange error abruptly changes ≥1 m, it is determined to be spoofing interference; within a 1-second time window, if the carrier-to-noise ratio drops sharply ≥5 dB and the code tracking loop phase detection error for two consecutive sampling points is ≥0.1 rad, it is determined to be suppression interference. Based on the core parameter threshold logic within the 1-second time window, it can quickly distinguish between spoofing interference (carrier phase deviation fluctuation ≤0.2 rad and pseudorange error abruptly changes ≥1 m) and suppression interference (carrier-to-noise ratio drops sharply ≥5 dB and phase detection error for two consecutive sampling points is ≥0.1 rad), providing a basis for targeted anti-interference measures.

[0096] The GNSS receiver's sampling frequency is set to 20Hz, the extraction time window for the four core parameters is 50ms / frame, and the time series of each parameter are first preprocessed using a moving average filter with a window size of 3 sampling points, and then trend fitting and anomaly extraction are performed to reduce the interference of instantaneous impulse noise on parameter features and ensure the accuracy of feature extraction of the directional antenna array received signal.

[0097] For GNSS ground fixed monitoring station scenarios Background: This scenario has long faced the combined effects of suppressive interference, deceptive interference, and natural noise such as ionospheric scintillation and multipath effects in the urban electromagnetic environment. It is necessary to verify the optimality of the core parameter values ​​through multiple sets of parameter comparison experiments, and at the same time clarify the selection basis of key designs such as array element spacing.

[0098] Basic experimental setup: Hardware basics: It adopts a 12-element adaptive directional antenna array, a dual-channel integrated control module (phase control accuracy 0.05°, response time ≤1μs; amplitude attenuation adjustment step 0.1dB), the GNSS receiver supports adjustable sampling frequency, and the LSTM neural network training dataset is the same as the original scheme (100,000 sets of interference signal samples, 50,000 sets of natural noise samples, 500 training iterations, convergence prediction error ≤3%).

[0099] Software basics: The hybrid direction finding algorithm adopts the MUSIC-ESPRIT weighted hybrid algorithm. The interference type identification threshold follows the original scheme standard. The adaptive scanning logic has an initial step size of 0.5°, which switches to 0.2° when the fusion confidence is ≥90%.

[0100] Experimental environment: The GNSS monitoring site was selected in the suburbs of the city. There were communication base stations (2.4GHz band suppression interference sources) and simulated deception jammers in the vicinity. It can also capture natural noise such as ionospheric scintillation and multipath effects. The electromagnetic environment was stable during the experiment and there was no extreme weather interference.

[0101] Comparison of core parameters and analysis of results: Core parameter comparison experiment: This section designs multiple gradient experiments for three key parameters of the GNSS receiver: sampling frequency, parameter extraction window, and moving average filtering window. Each experiment is repeated 100 times, and the average value of the indicators is taken to compare the impact of different parameter values ​​on monitoring performance.

[0102] GNSS receiver sampling frequency comparison experiment: Experimental design: Set up 3 sets of sampling frequency gradients, namely 10Hz, 20Hz (original scheme value), and 30Hz, keep the parameter extraction window 50ms / frame and the moving average filtering window 3 sampling points unchanged, and test two indicators: interference warning delay and interference identification accuracy.

[0103] Experimental results:

[0104] Results Analysis: At a sampling frequency of 10Hz, the long data acquisition interval resulted in incomplete capture of interference signal features, leading to a significant increase in warning delay and an accuracy rate of less than 90%. When the sampling frequency was increased to 20Hz, the warning delay dropped to 150ms, the accuracy rate jumped to 99.3%, and the false judgment rate was only 0.7%, fully meeting the requirements for real-time monitoring. When the sampling frequency was further increased to 30Hz, although the warning delay and accuracy rate were slightly improved, the improvement was less than 3%, and it would lead to a 50% increase in data storage and a 28% increase in hardware power consumption. Considering the overall cost-effectiveness and practicality, 20Hz is the optimal sampling frequency.

[0105] 2. Parameter extraction window comparison experiment: Experimental design: Three sets of parameters were set to extract the gradient window, namely 30ms / frame, 50ms / frame (original scheme value), and 70ms / frame. The sampling frequency of 20Hz and the three sampling points of the moving average filter window were kept unchanged. The test indicators were the same as those in the above experiment.

[0106] Experimental results:

[0107] Results Analysis: The extraction window of 30ms / frame is too short, and the signal features contained in a single frame of data are insufficient, resulting in a low recognition accuracy and a feature extraction stability of only 88.3%, making it susceptible to transient noise interference. The extraction window of 50ms / frame can completely capture the core features of a set of interference signals, achieving a high accuracy of 99.3% and a stability of 99.1% under the premise of controllable warning delay. Although the stability of the 70ms / frame window is slightly improved, the warning delay increases by 20%, and the response sensitivity to sudden short pulse interference decreases. Therefore, 50ms / frame is the optimal choice.

[0108] 3. Comparison experiment of moving average filter window: Experimental design: Three sets of filter window gradients were set, with 2 sampling points, 3 sampling points (original values), and 4 sampling points respectively. The sampling frequency was kept constant at 20Hz and the parameter extraction window was kept constant at 50ms / frame. The focus was on testing the accuracy of interference recognition and the instantaneous noise suppression effect.

[0109] Experimental results:

[0110] Results analysis: The filtering window with 2 sampling points is insufficient in suppressing transient impulse noise, with a suppression rate of only 82.4%, resulting in a low recognition accuracy; the window with 3 sampling points can effectively suppress noise (suppression rate of 95.7%) while controlling the signal distortion to 1.5%, avoiding excessive smoothing of interference features; although the window with 4 sampling points slightly improves the noise suppression rate, the signal distortion increases sharply to 3.8%, which may result in the loss of key abrupt changes in the interference signal. Therefore, the 3 sampling point window is the optimal filtering window.

[0111] Array element spacing comparison experiment: For the core design parameter of element spacing, the impact of different spacings on antenna array performance was compared. The center wavelength of the GNSS L1 band is 0.19m. Based on this, three sets of spacing gradients were set: 1 / 4 wavelength (0.0475m), 1 / 2 wavelength (0.095m, the original value), and 3 / 4 wavelength (0.1425m). The number of elements was kept at 12, and the optimization target of the genetic algorithm remained unchanged. Three indicators were tested: sidelobe level, directional receiving gain, and direction finding error of the incoming wave direction.

[0112] Experimental results:

[0113] Results Analysis: The sidelobe level of the antenna array with a 1 / 4 wavelength spacing is only -28.5dB, resulting in severe sidelobe interference, insufficient directional gain, and a direction finding error of ±1.8°, which cannot meet the requirements for accurate direction finding. After optimization by the genetic algorithm, the sidelobe level of the antenna array with a 1 / 2 wavelength spacing is reduced to -38.2dB, achieving the best sidelobe suppression effect. The directional receiving gain reaches 22.5dB, while the direction finding error is controlled within ±0.5°, balancing anti-interference capability and direction finding accuracy. Although the sidelobe level of the antenna array with a 3 / 4 wavelength spacing is better than that of the 1 / 4 wavelength spacing, it is lower than that of the 1 / 2 wavelength spacing, and the direction finding error is slightly larger. Therefore, the 1 / 2 wavelength spacing is the optimal element spacing.

[0114] Comprehensive verification experiment of optimal parameter combination: The optimal parameter combination of the original scheme (sampling frequency 20Hz, parameter extraction window 50ms / frame, filtering window 3 sampling points, array element spacing 1 / 2 wavelength) was comprehensively verified and tested under a complex interference environment. It was also compared with the non-optimal parameter combination (sampling frequency 10Hz, extraction window 30ms / frame, array element spacing 1 / 4 wavelength).

[0115] Experimental results:

[0116] Results analysis: The optimal parameter combination of the original scheme is 10.7 percentage points higher in overall interference identification accuracy than the non-optimal combination, the direction finding error is reduced by 73.7%, and the overall response time is shortened by 43.7%. It can quickly and accurately complete interference monitoring and incoming wave direction measurement, which fully proves the rationality of the core parameter values ​​and the superiority of the scheme.

[0117] The adaptive directional beam monitoring area scanning logic is as follows: the initial scanning step size is 0.5°, and when the fusion confidence level is ≥90%, the scanning step size is automatically reduced to 0.2° to focus on the suspected interference area for fine directional scanning; the scanning cycle is ≤2s, and it prioritizes covering the high-frequency interference area with an elevation angle of 0°~60° to improve the efficiency of directional direction finding.

[0118] Based on this, an adaptive scanning logic is adopted (initial step size 0.5°, reduced to 0.2° when the fusion confidence is ≥90%) to prioritize coverage of the high-frequency interference area with an elevation angle of 0°~60°. The scanning cycle is ≤2s. Combined with a 20Hz sampling frequency, a 50ms / frame parameter extraction window, and a 3-point moving average filter, the impact of instantaneous noise is reduced, ensuring monitoring response speed and accuracy.

[0119] The extraction accuracy of polarization feature parameters is as follows: linear polarization ratio measurement error ≤ ±0.02, polarization angle measurement error ≤ ±0.5°; the linear polarization ratio is calculated by the amplitude ratio of the horizontal polarization component to the vertical polarization component of the array received signal, and the polarization angle is measured with the adaptive directional antenna array normal direction as the reference to ensure the fusion accuracy of polarization features and directional spatial features.

[0120] The training dataset for the LSTM neural network includes interference signal samples and natural noise samples. The interference signal samples include deceptive interference samples and suppression interference samples, while the natural noise samples include ionospheric scintillation, multipath effects, and equipment thermal noise samples. There are 100,000 sets of interference signal samples and 50,000 sets of natural noise samples. There are 40,000 sets of deceptive interference samples and 60,000 sets of suppression interference samples. The training iterations are ≥500 rounds, and the prediction error after model convergence is ≤3%, ensuring the reliability of dynamic threshold self-learning and providing accurate interference judgment criteria for beam adjustment of adaptive directional antenna arrays.

[0121] GNSS interference monitoring and orientation finding for UAV precision mapping scenarios It is applied to precision mapping scenarios of UAVs at altitudes of 500-1000m and flight speeds of ≤60km / h. These scenarios require centimeter-level positioning accuracy and are susceptible to interference from surrounding communication base stations (2.4GHz band suppression interference), malicious deceptive jammers (simulating GNSS navigation signals), and natural noise from ionospheric scintillation. It is necessary to quickly achieve interference monitoring, accurate direction finding, and type identification to ensure the continuity of mapping data and the reliability of positioning.

[0122] Hardware configuration: A 12-element adaptive directional antenna array is employed, with the element layout optimized using a genetic algorithm. The element spacing is half of the GNSS1 band center wavelength (0.19m), i.e., 0.095m. The sidelobe level is optimized to -38dB, covering a monitoring area with an azimuth angle of 360° and an elevation angle of -10° to 90°. The phase control channel has an adjustment range of 0-360°, a control accuracy of 0.05°, and a response time of 0.8μs. The amplitude attenuation control channel has an attenuation range of 0-30dB and an adjustment step of 0.1dB. The GNSS receiver has a sampling frequency of 20Hz, a parameter extraction window of 50ms / frame, and is equipped with a 3-point moving average filter preprocessing module. The LSTM neural network training dataset includes 100,000 sets of interference signal samples (40,000 sets of deception-type and 60,000 sets of suppression-type) and 50,000 sets of natural noise samples (20,000 sets of ionospheric scintillation, 20,000 sets of multipath effects, and 10,000 sets of equipment thermal noise). The model is trained for 500 iterations, with a convergence prediction error of 2.8%.

[0123] Software algorithm configuration: Hybrid direction finding algorithm: MUSIC-ESPRIT weighted hybrid algorithm with dynamic allocation of weight coefficients; Interference type identification threshold: within a 1s time window, the fluctuation of carrier phase deviation is ≤0.2rad and the sudden change of pseudorange error is ≥1m (spoofing); the carrier-to-noise ratio drops sharply by ≥5dB and the phase detection error of two consecutive sampling points is ≥0.1rad (suppression); Adaptive scanning logic: the initial scanning step size is 0.5°, which switches to 0.2° when the fusion confidence is ≥90%, and prioritizes coverage of the elevation angle region of 0°~60°, with a scanning cycle of 1.8s.

[0124] During the drone's flight, the GNSS receiver acquires baseband signals in real time, extracting four types of parameters every 50ms: carrier-to-noise ratio (CNR), pseudorange error, carrier phase deviation, and code tracking loop phase detection error. After three-point moving average filtering, a multi-dimensional feature vector is constructed, including the slope of parameter mutations, the duration of abnormalities (≥2 sampling points), and cross-parameter correlation coefficients (CNR-phase detection error, pseudorange error-carrier phase deviation Pearson coefficient). The LSTM neural network identifies the feature vector and outputs a fusion confidence score. When the drone flies near a communication base station, the CNR drops sharply by 6dB, the phase detection error is 0.12rad for two consecutive sampling points, and the model outputs a fusion confidence score of 92% (≥85%), triggering an interference warning and identifying it as suspected suppression interference.

[0125] After the interference warning is triggered, the signal-to-noise ratio of the received signal is detected in real time to be 8dB (<10dB), and the number of working array elements is automatically adjusted to 10. Based on the beam pointing control logic optimized by the genetic algorithm, the phase control channel adjusts the phase of each array element to the target direction (accuracy 0.05°), and the amplitude attenuation control channel dynamically allocates an attenuation amount of 0-15dB according to the signal strength (in 0.1dB steps) to achieve directional beam reconstruction. Because the fusion confidence is 92%≥90%, the scanning step size is reduced from 0.5° to 0.2°, and the high-frequency interference area with a focusing elevation angle of 0°~60° is scanned in a fine manner. The beam sidelobe level is stabilized at -38dB, and the directional receiving gain is improved by 12dB.

[0126] After receiving interference signals, the directional beamform extracts two types of polarization features: linear polarization ratio (measured value 1.23, error ±0.01) and polarization angle (measured value 35.2°, error ±0.3°). These features are then combined with spatial features such as phase difference between array elements (average 0.8 rad) and amplitude difference (average 3 dB). Since the signal-to-noise ratio is 8 dB < 10 dB, the weight allocation of the MUSIC-ESPRIT algorithm is 70% for ESPRIT and 30% for MUSIC. After inputting the fused features, the algorithm synchronously outputs: direction of arrival (azimuth 135.6°, elevation 28.3°) and interference type (suppression type, matching the sudden drop in carrier-to-noise ratio and phase detection error threshold conditions).

[0127] Implementation results: Closed-loop coordination enables rapid monitoring and orientation of suppressive interference within 1.8 seconds, with direction finding accuracy (azimuth error ±0.5°, elevation error ±0.8°) and interference type identification accuracy of 99.2%. After the directional beam receiving gain is increased by 12dB, the UAV's GNSS positioning error fluctuates from 0.8cm (no interference) to 1.5cm, meeting the centimeter-level accuracy requirements of precision mapping, without data interruption or positioning failure.

[0128] GNSS interference monitoring and direction finding for vehicle navigation scenarios For vehicle navigation scenarios applied to urban roads and highways, where vehicles travel at speeds of 0-120 km / h, they are susceptible to electromagnetic noise from surrounding traffic monitoring equipment, malicious deceptive jammers (forging GNSS signals to tamper with positioning), and natural noise from multipath effects. It is necessary to achieve real-time monitoring, rapid direction finding, and type identification of interference in mobile scenarios to ensure the accuracy of navigation path planning and driving safety.

[0129] Hardware configuration: A 12-element adaptive directional antenna array was used, with an element spacing of 0.095m (half the center wavelength of the GNSS1 band). After optimization by a genetic algorithm, the sidelobe level was -36dB, and the monitoring coverage was 360° azimuth and -10° to 90° elevation. The phase control response time was 0.9μs, and the amplitude attenuation adjustment step was 0.1dB. The GNSS receiver sampling frequency was 20Hz, the parameter extraction window was 50ms / frame, and a 3-point moving average filter was used. The LSTM neural network training dataset was as described above, with a convergence prediction error of 2.5%.

[0130] Software algorithm configuration: The threshold for interference type identification is as described above; adaptive scanning logic: initial step size 0.5°, switching to 0.2° when the fusion confidence is ≥90%, prioritizing coverage of the pitch angle region from 0° to 60°, with a scanning cycle of 1.9s.

[0131] When a vehicle is traveling on a highway, the GNSS receiver acquires baseband signals in real time. After filtering and preprocessing, four types of core parameters are extracted to construct a multi-dimensional feature vector. When encountering malicious deceptive interference, the pseudorange error suddenly changes by 1.2m, and the carrier phase deviation fluctuates by 0.15rad (≤0.2rad). The cross-parameter correlation coefficient shows a strong correlation between the pseudorange error and the carrier phase deviation (Pearson coefficient 0.89). The LSTM neural network outputs a fusion confidence level of 94% (≥85%), triggering an interference warning and initially identifying it as suspected deceptive interference.

[0132] The received signal-to-noise ratio is detected to be 11dB (≥10dB), and the number of working array elements is automatically adjusted to 8. The phase control channel precisely adjusts the phase of each array element (accuracy 0.05°), and the amplitude attenuation control channel allocates an attenuation amount of 0-8dB (step 0.1dB) to achieve directional beam reconstruction. Due to the fusion confidence level of 94% ≥90%, the scanning step size is reduced to 0.2°, prioritizing coverage of the high-frequency interference area with an elevation angle of 0°~60°. The scanning period is 1.9s, the beam sidelobe level is stabilized at -36dB, the directional receiving gain is improved by 10dB, and the ability to resist multipath noise interference is enhanced.

[0133] After receiving the interference signal, the directional beam extracts the linear polarization ratio (measured value 0.98, error ±0.02) and polarization angle (measured value 42.7°, error ±0.4°), and fuses them with the spatial features of inter-element phase difference (average 0.5 rad) and amplitude difference (average 2 dB). Since the signal-to-noise ratio is 11 dB ≥ 10 dB, the weight allocation of the MUSIC-ESPRIT algorithm is 60% for MUSIC and 40% for ESPRIT. After inputting the fused features, the synchronous output is: direction of arrival (azimuth 270.3°, elevation 15.6°) and interference type (spoofing, matching pseudorange error abrupt change and carrier phase deviation fluctuation threshold conditions).

[0134] Implementation results: In a high-speed driving scenario of 120km / h, the system achieves rapid detection and orientation of deceptive interference within 1.9s, with direction finding accuracy (azimuth error ±0.6°, pitch error ±0.9°) and interference type identification accuracy of 99.0%. After a 10dB increase in directional beam receiving gain, the vehicle-mounted GNSS navigation positioning error was controlled from 1.0m (no interference) to 1.8m. The navigation system automatically switches to anti-spoofing mode based on the interference type identification results, without any path deviation or positioning tampering, ensuring driving safety and navigation accuracy.

[0135] Based on this, leveraging the high sensitivity of GNSS baseband signals to interference feedback characteristics such as carrier-to-noise ratio and pseudorange error, combined with the multi-dimensional feature recognition capabilities of LSTM neural networks, and further enhanced by the directional gain of directional antenna arrays, this approach overcomes the limitations of traditional spectrum monitoring that relies on signal power. It can capture low-power, weak suppression and deceptive interference at long distances, eliminating the need for personnel to approach and investigate, significantly improving the distance and efficiency of interference detection. Simultaneously, the beam directionality of the directional antenna array enables directional signal sensing and dynamic scanning. Combined with the interference-sensitive characteristics of GNSS baseband signals, it outputs the direction of arrival of the interference source. For high-power interference sources emitted into the sky, it effectively solves the technical difficulties of traditional ground equipment in sensing and positioning deviations of tens of kilometers, significantly improving the accuracy of interference source positioning and accelerating the interference investigation process.

[0136] Combination Figure 7 This application also provides an interference analysis device based on GNSS baseband signals that can be applied to the aforementioned electronic equipment. The interference analysis device based on GNSS baseband signals may include an interference identification module, a signal acquisition module, and an interference analysis module.

[0137] The interference identification module is used to identify interference in GNSS baseband signals acquired by a GNSS receiver using a trained target noise identification model, and to obtain the target confidence score corresponding to the GNSS baseband signal. The target noise identification model is a neural network model, and the target confidence score reflects the probability of interference in the GNSS baseband signal. In this embodiment, the interference identification module can be used to perform... Figure 2 The relevant content regarding the interference identification module in step S110 shown can be found in the previous description of step S110.

[0138] The signal acquisition module is used to acquire the scanned received signal collected by the directional antenna array when the target confidence level is greater than or equal to a pre-configured confidence level threshold. In this embodiment of the application, the signal acquisition module can be used to perform... Figure 2The relevant content regarding the signal acquisition module in step S120 shown can be found in the previous description of step S120.

[0139] The interference analysis module is used to analyze the scanned received signal to obtain target interference analysis data, wherein the target interference analysis data reflects at least one of the direction of arrival and the type of interference. In this embodiment, the interference analysis module can be used to perform... Figure 2 The relevant content regarding the interference analysis module in step S130 shown can be found in the previous description of step S130.

[0140] In this embodiment of the application, corresponding to the above-described interference analysis method based on GNSS baseband signals applied to the electronic device, a computer-readable storage medium is also provided, which stores a computer program that executes the various steps of the interference analysis method based on GNSS baseband signals when the computer program is run.

[0141] The steps executed by the aforementioned computer program during runtime will not be described in detail here, but can be found in the explanation of the interference analysis method based on GNSS baseband signals described above.

[0142] In summary, the interference analysis method, apparatus, and device based on GNSS baseband signals provided in this application firstly utilize a trained target noise identification model to identify interference in GNSS baseband signals acquired by a GNSS receiver, obtaining the target confidence level corresponding to the GNSS baseband signal. The target noise identification model is a neural network model, and the target confidence level reflects the probability of interference in the GNSS baseband signal. Secondly, when the target confidence level is greater than or equal to a pre-configured confidence level threshold, a scanning received signal acquired through a directional antenna array is obtained. Then, the scanning received signal is analyzed to obtain target interference analysis data, wherein the target interference analysis data reflects at least one of the direction of arrival and interference type of the interference source. Based on the above, before conducting interference analysis, a target noise identification model is used to identify the GNSS baseband signal acquired by the GNSS receiver. This allows full utilization of the powerful learning and recognition capabilities of neural network models to effectively identify the presence of interference. Furthermore, if the probability of interference is high, the scanning received signal acquired through a directional antenna array is enhanced by the directional gain of the array. This overcomes the limitations of traditional spectrum monitoring, which relies on signal power, and enables long-range detection of low-power, weak suppression and deceptive interference. This significantly improves the distance and efficiency of interference detection without requiring personnel to approach the site for investigation. Moreover, the interference identification by the target noise identification model, as a pre-processing step, can improve the reliability of the analysis to a certain extent. Therefore, based on this scheme, the problems of low efficiency and low reliability in existing interference analysis technologies can be improved.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0144] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0145] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0146] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An interference analysis method based on GNSS baseband signals, characterized in that, include: Using a trained target noise identification model, interference is identified in GNSS baseband signals acquired by a GNSS receiver to obtain the target confidence level corresponding to the GNSS baseband signal. The target noise identification model is a neural network model, and the target confidence level is used to reflect the probability that interference exists in the GNSS baseband signal. When the target confidence level is greater than or equal to a pre-configured confidence level threshold, the scanned received signal acquired by the directional antenna array is obtained; The scanned received signal is analyzed to obtain target interference analysis data, wherein the target interference analysis data is used to reflect at least one of the incoming wave direction and interference type of the interference source.

2. The interference analysis method based on GNSS baseband signals according to claim 1, characterized in that, The step of using a trained target noise recognition model to perform interference recognition on GNSS baseband signals acquired by a GNSS receiver and obtaining the target confidence level corresponding to the GNSS baseband signals includes: Acquire GNSS baseband signals collected by a GNSS receiver; The carrier-to-noise ratio, pseudorange error, carrier phase deviation, and code tracking loop phase detection error are extracted from the GNSS baseband signal to form the corresponding carrier-to-noise ratio time series, pseudorange error time series, carrier phase deviation time series, and code tracking loop phase detection error time series. Using the trained target noise recognition model, interference is identified on the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series to obtain the target confidence level corresponding to the GNSS baseband signal.

3. The interference analysis method based on GNSS baseband signals according to claim 2, characterized in that, The step of using a trained target noise recognition model to perform interference recognition on the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series to obtain the target confidence level corresponding to the GNSS baseband signal includes: Trend fitting is performed on the carrier-to-noise ratio time series, the pseudorange error time series, the carrier phase deviation time series, and the code tracking loop phase discrimination error time series, respectively. Anomaly data is extracted from the trend fitting results to obtain the parameter abrupt change slope and anomaly duration. The correlation coefficients of the carrier-to-noise ratio time series and the code tracking loop phase discrimination error time series are calculated to obtain the first Pearson correlation coefficient, and the correlation coefficients of the pseudorange error time series and the carrier phase deviation time series are calculated to obtain the second Pearson correlation coefficient. Based on the parameter mutation slope, the duration of the abnormality, the first Pearson correlation coefficient, and the second Pearson correlation coefficient, a multi-dimensional feature vector is constructed. Using the trained target noise recognition model, interference recognition is performed on the multi-dimensional feature vector to obtain the target confidence level corresponding to the GNSS baseband signal.

4. The interference analysis method based on GNSS baseband signals according to claim 1, characterized in that, The step of acquiring the scanned received signal through the directional antenna array when the target confidence level is greater than or equal to a pre-configured confidence level threshold includes: When the target confidence level is greater than or equal to a pre-configured confidence level threshold, the initial scanning received signal obtained by the directional antenna array according to the pre-configured initial operating parameters is acquired, wherein the directional antenna array includes multiple array elements, and the initial operating parameters include the number of operating array elements; Based on the initial scan reception signal, the target operating parameters are determined, and the scan reception signal obtained by the directional antenna array according to the target operating parameters is acquired.

5. The interference analysis method based on GNSS baseband signals according to claim 4, characterized in that, The step of acquiring the initial scanned received signal obtained by the directional antenna array according to the pre-configured initial operating parameters when the target confidence level is greater than or equal to a pre-configured confidence level threshold includes: When the target confidence level is greater than or equal to a pre-configured confidence level threshold, the scanning step size of the directional antenna array is determined based on the target confidence level, wherein there is a negative correlation between the scanning step size and the target confidence level; The initial scan received signal is acquired by the directional antenna array according to the scan step size and the pre-configured initial operating parameters.

6. The interference analysis method based on GNSS baseband signals according to claim 4, characterized in that, The steps of determining the target operating parameters based on the initial scan received signal and acquiring the scan received signal obtained by the directional antenna array according to the target operating parameters include: The signal-to-noise ratio (SNR) of the initial scan received signal is determined, and based on the SNR, the number of array elements operating in the directional antenna array is determined, and the number of array elements is determined as the target operating parameter, wherein there is a negative correlation between the SNR and the number of array elements; Acquire the scanning received signal obtained by the directional antenna array according to the target operating parameters.

7. The interference analysis method based on GNSS baseband signals according to any one of claims 1-6, characterized in that, The step of analyzing the scanned received signal to obtain target interference analysis data includes: Determine at least one of the polarization characteristic parameters and spatial characteristic parameters corresponding to the scanned received signal, wherein the polarization characteristic parameters include at least one of linear polarization ratio and polarization angle, and the spatial characteristic parameters include at least one of inter-element phase difference and inter-element amplitude difference; Based on at least one of the polarization characteristic parameters and the spatial characteristic parameters, target interference analysis data is obtained.

8. The interference analysis method based on GNSS baseband signals according to claim 7, characterized in that, The step of analyzing and obtaining target interference analysis data based on at least one of the polarization characteristic parameters and the spatial characteristic parameters includes: Based on the signal-to-noise ratio of the scanned received signal, the first weight parameter and the second weight parameter corresponding to the MUSIC algorithm and the ESPRIT algorithm in the MUSIC-ESPRIT weighted hybrid algorithm are determined respectively. Based on the MUSIC-ESPRIT weighted mixing algorithm, the first weight parameter, and the second weight parameter, signal analysis is performed on at least one of the polarization characteristic parameters and the spatial characteristic parameters to obtain target interference analysis data.

9. An interference analysis device based on GNSS baseband signals, characterized in that, include: An interference identification module is used to identify interference in GNSS baseband signals acquired by a GNSS receiver using a trained target noise identification model, and to obtain the target confidence level corresponding to the GNSS baseband signal. The target noise identification model is a neural network model, and the target confidence level is used to reflect the probability that interference exists in the GNSS baseband signal. The signal acquisition module is used to acquire the scanned received signal collected by the directional antenna array when the target confidence level is greater than or equal to a pre-configured confidence level threshold. An interference analysis module is used to analyze the scanned received signal to obtain target interference analysis data, wherein the target interference analysis data reflects at least one of the incoming wave direction and interference type of the interference source.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor connected to the memory is used to execute the computer program stored in the memory to implement the interference analysis method based on GNSS baseband signals as described in any one of claims 1-7.

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