A method and system for monitoring maritime navigation interference based on satellite receivers

Abstract

This invention relates to the technical field of satellite navigation interference detection, and particularly to a method and system for monitoring maritime navigation interference based on a satellite receiver. The method includes real-time acquisition of satellite navigation data from multiple satellites using a satellite navigation receiver. The satellite navigation data includes a carrier-to-noise ratio (C / NO) sequence and an automatic gain control (AGC) sequence. Based on the satellite navigation data, a signal interference analysis model is constructed. Based on the signal interference analysis model, a pairing correlation analysis is performed on the C / NO sequence and the AGC sequence to generate interference analysis results. Based on the interference analysis results, an improved Pearson correlation coefficient is calculated between different satellite signals, and a decision threshold is set according to the Neyman-Pearson criterion. Based on the improved Pearson correlation coefficient and the decision threshold, an interference detection result is output. This application effectively detects various types of interference signals and improves the reliability of ship satellite navigation in complex electromagnetic environments.

Description

Technical Field

[0001] This invention relates to the technical field of satellite navigation interference detection, and in particular to a method and system for monitoring maritime navigation interference based on a satellite receiver. Background Technology

[0002] Currently, for satellite navigation interference detection, some existing methods rely primarily on simple signal strength monitoring or changes in a single parameter to determine the presence of interference. For example, some methods infer interference by monitoring changes in the carrier-to-noise ratio (C / N0) of the satellite navigation receiver, but this approach lacks accuracy when faced with complex and varied interference types and low-power interference. Furthermore, some methods depend on the individual analysis of automatic gain control (AGC) parameters; however, AGC parameters are influenced by various factors, and their changes may not accurately reflect the presence of interference. Moreover, different types of interference cause inconsistent patterns in AGC changes, making reliable interference detection difficult to achieve solely based on AGC.

[0003] The shortcomings of existing technologies:

[0004] 1. Low detection accuracy: The detection capability for low-power interference and various complex interference types is limited, which can easily lead to misjudgment or missed judgment, and it is impossible to accurately identify the degree of impact of interference signals on satellite navigation.

[0005] 2. Lack of comprehensiveness: It relies solely on a single parameter (such as C / N0 or AGC) for judgment, without fully considering the correlation between different satellite signals and the advantages of joint analysis of multiple parameters, and cannot fully reflect the true state of interference to the satellite navigation system;

[0006] 3. Poor adaptability: It is difficult to adapt to the differences in hardware and software of different models of equipment. Different navigation devices have different chipsets, receiver characteristics, etc. Existing methods often cannot perform targeted interference detection based on these differences, resulting in poor performance in practical applications. Therefore, there is room for improvement. Summary of the Invention

[0007] In order to effectively detect various interference signals and improve the reliability of ship satellite navigation in complex electromagnetic environments, this application provides a method and system for monitoring maritime navigation interference based on a satellite receiver.

[0008] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution:

[0009] A method for monitoring maritime navigation interference based on a satellite receiver, the method comprising the following steps:

[0010] Satellite navigation data is collected in real time from multiple satellites using a satellite navigation receiver. The satellite navigation data includes a carrier-to-noise ratio (C / NO) sequence and an automatic gain control (AGC) sequence.

[0011] Based on the satellite navigation data, a signal interference analysis model is constructed, which includes a radio frequency front-end module, a digital baseband processor module, and a DSP module.

[0012] Based on the signal interference analysis model, a matching correlation analysis is performed on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC to generate interference analysis results.

[0013] Based on the interference analysis results, the improved Pearson correlation coefficient between different satellite signals is calculated, and a decision threshold is set according to the Neyman-Pearson criterion. Based on the improved Pearson correlation coefficient and the decision threshold, the interference detection results are output.

[0014] By adopting the above technical solution, and by acquiring the carrier-to-noise ratio (C / NO) sequence and automatic gain control (AGC) sequence of multiple satellites in real time, a comprehensive raw data foundation reflecting the dynamic changes of the electromagnetic environment is obtained, providing a multi-dimensional input source for subsequent accurate analysis. Secondly, by constructing a signal interference analysis model that includes an RF front-end module (to realize signal frequency conversion and anti-aliasing filtering), a digital baseband processor module (to strip carrier and pseudo-code interference), and a DSP module (to verify signal trackability), the signal transmission link in a real navigation environment is systematically simulated, solving the model distortion problem caused by neglecting hardware differences in traditional methods. Subsequently, based on this model, paired correlation analysis was performed on the C / NO and AGC sequences, overcoming the limitations of single-parameter detection. By quantifying the correlation characteristics between the sudden drop in C / NO and the sudden rise in AGC under continuous wave interference, complex signal patterns such as frequency sweep interference were accurately identified, reducing the false negative rate of low-power interference. An improved Pearson correlation coefficient was adopted, and a dynamic decision threshold was set by combining the Neyman-Pearson criterion. By comparing the weighted average Pearson coefficient with the threshold in real time, the positioning reliability of the ship navigation system in sea areas with strong electromagnetic interference was significantly enhanced, various interference signals were effectively detected, and the reliability of ship satellite navigation in complex electromagnetic environments was improved.

[0015] In a preferred embodiment, this application can be further configured as follows: Based on the original measurement data, a signal interference analysis model is constructed. The model includes a radio frequency front-end module, a digital baseband processor module, and a DSP module, specifically including:

[0016] Based on satellite navigation signals, the signal processing flow of the radio frequency front-end module is defined, including converting the satellite navigation signals to intermediate frequency and amplifying them, and outputting the amplified satellite navigation signals;

[0017] Configure the digital baseband processor module according to the amplified satellite navigation signal to remove residual carriers and pseudo-codes from the signal and generate a baseband signal;

[0018] Based on the baseband signal, the signal integrity and traceability are verified in the DSP module to ensure that the signal is suitable for subsequent analysis;

[0019] A signal interference analysis model is constructed based on common interference types and the configured RF front-end module, digital baseband processor module, and DSP module. The model parameters are set based on the measured interference power and frequency.

[0020] By adopting the above technical solution, the satellite navigation signal is accurately converted to intermediate frequency and adaptively amplified through the radio frequency front-end module, effectively suppressing out-of-band noise and spectral aliasing, ensuring the improvement of the baseline signal-to-noise ratio of the subsequent processed signal. Relying on the digital baseband processor module for in-depth processing of the amplified signal, the residual carrier component and pseudo-code interference are completely removed, improving the signal-to-interference ratio of the baseband signal and providing a clean data source for correlation analysis. Through the real-time signal integrity verification of the DSP module (including carrier phase continuity detection and chip synchronization error analysis), distorted signals affected by multipath effects are dynamically eliminated, compressing the proportion of invalid data and significantly improving the reliability of subsequent interference analysis. Based on the measured interference parameters, a multi-dimensional interference model is constructed, and a full-link mapping relationship between the radio frequency front-end, baseband processing, and DSP verification is established in combination with the characteristics of the hardware modules, providing high-fidelity input for interference detection.

[0021] In a preferred embodiment, this application can be further configured such that: the matching correlation analysis of the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC based on the signal interference analysis model is performed to generate interference analysis results, specifically including:

[0022] The impact of interference on positioning accuracy is evaluated based on the aforementioned signal interference analysis model, and the change in positioning error under the interference power threshold is calculated.

[0023] Based on the results of the change in positioning error, the impact of interference on the carrier-to-noise ratio (C / NO) sequence is analyzed, including the magnitude and trend of the decrease in C / NO value under different types of interference.

[0024] Based on the analysis results of the carrier-to-noise ratio sequence C / NO, the impact of interference on the automatic gain control sequence AGC is evaluated, and the variation law of AGC under different types of interference is identified.

[0025] By combining the decrease and trend of C / NO values ​​under different interference types with the variation law of AGC, interference analysis results are generated.

[0026] By adopting the above technical solutions, the dynamic impact of interference on positioning accuracy is quantitatively evaluated through a signal interference analysis model (e.g., accurately capturing the nonlinear abrupt increase in positioning error from 1.2m to 8.5m when the continuous wave interference power reaches the -90dBm threshold), providing key critical value basis for anti-interference decision-making. Secondly, the interference response law of the carrier-to-noise ratio sequence C / NO is inverted based on the change in positioning error (e.g., the C / NO values ​​of multiple satellites decrease synchronously by 12-18dB-Hz under frequency sweep interference and exhibit periodic fluctuation characteristics). By establishing a mapping relationship between interference type and C / NO decrease magnitude, a preliminary classification of interference modes is achieved. Then, combined with the time-domain feature analysis of AGC sequence, a two-parameter joint discrimination matrix is ​​constructed to effectively distinguish between equipment gain adaptive adjustment and real interference scenarios. The C / NO trend chart and AGC change law are integrated to generate multi-dimensional interference analysis results.

[0027] In a preferred embodiment, this application can be further configured as follows: Based on the interference analysis results, the improved Pearson correlation coefficient between different satellite signals is calculated, and a decision threshold is set according to the Neyman-Pearson criterion. Based on the improved Pearson correlation coefficient and the decision threshold, the interference detection result is output, specifically including:

[0028] Based on the interference analysis results, the improved Pearson correlation coefficient between different satellite signals is calculated, and the carrier-to-noise ratio of each satellite signal is assigned a weight based on the improved Pearson correlation coefficient.

[0029] Based on the weight of the carrier-to-noise ratio of each satellite signal, an improved weighted average Pearson coefficient is calculated. According to the improved weighted average Pearson coefficient, a decision threshold is set by applying the Neyman-Pearson criterion. The improved weighted average Pearson coefficient is compared with the decision threshold, and the interference detection result is output.

[0030] By adopting the above technical solution, the improved Pearson correlation coefficient between different satellite signals is calculated to accurately quantify the correlation strength between signals, thereby improving the detection sensitivity of low-power interference. Based on this coefficient, the carrier-to-noise ratio (C / NO) of each satellite signal is dynamically weighted to effectively balance the differences in data quality and avoid overall misjudgment caused by the deviation of a single satellite. Then, the improved weighted average Pearson coefficient is calculated to integrate the scattered correlation indicators into a unified evaluation value in the frequency sweeping interference scenario, thereby realizing a global characterization of the interference state. Subsequently, the Neyman-Pearson criterion is applied to set the decision threshold. By comparing the improved weighted average Pearson coefficient with the decision threshold in real time, the interference detection result is output.

[0031] In a preferred embodiment, this application can be further configured such that the carrier-to-noise ratio weighted according to the improved Pearson correlation coefficient for each satellite signal specifically includes:

[0032] Based on the analysis results of the carrier-to-noise ratio sequence C / NO, the mean carrier-to-noise ratios E(A) and E(B) of each satellite signal are extracted;

[0033] Based on the average carrier-to-noise ratio of each satellite signal, the weight category is determined, weights are assigned according to the weight category, and the sum of weighted correlation coefficients is calculated.

[0034] By adopting the above technical solution, and accurately extracting the mean carrier-to-noise ratio (CNR) E(A) and E(B) of each satellite signal, an objective quantitative data basis is established, eliminating instantaneous noise fluctuation interference and improving the reliability of the weight allocation basis. A dynamic weighting strategy is implemented based on mean-based hierarchical classification, assigning weights when both E(A) and E(B) are >40 dB-Hz. Enhanced contribution from high signal-to-noise ratio satellites, all <30 dB-Hz Suppress the influence of low-quality signals; otherwise, take... By maintaining neutrality and effectively highlighting key signal characteristics, multi-dimensional data is integrated through the summation of weighted correlation coefficients. This enhances the contribution of strongly correlated satellite pairs in frequency sweeping interference scenarios, significantly improves the global identification of interference characteristics, and provides cross-platform stable anti-interference decision support for ship navigation.

[0035] In a preferred embodiment, this application can be further configured as follows: The step of setting a decision threshold based on the improved weighted average Pearson coefficient using the Neyman-Pearson criterion, comparing the improved weighted average Pearson coefficient with the decision threshold, and outputting the interference detection result specifically includes:

[0036] Based on the improved weighted average Pearson coefficient, the following assumptions are defined: H0 indicates no interference. H1 indicates the presence of interference.

[0037] Set the false alarm probability α to a fixed value, and calculate the false alarm probability P(H1|H0) based on the historical interference dataset;

[0038] Based on the false alarm probability α, optimize the decision threshold to maximize the correct decision probability P(H1|H1);

[0039] Compare the improved weighted average Pearson coefficient with the decision threshold. If the improved weighted average Pearson coefficient is less than the decision threshold, output an interference-free detection result; otherwise, output an interference-detected result.

[0040] By adopting the above technical solution, a statistical inference framework is constructed by defining binary assumptions (H0 for no interference and H1 for interference), providing a rigorous mathematical basis for interference detection. This allows the decision-making process to break free from dependence on empirical thresholds. The false alarm probability P(H1|H0) is calculated based on historical interference datasets, and the false alarm probability α is constrained to below 5%, significantly reducing the system's redundant alarm load. Then, the decision threshold is optimized with α as a constant value. By maximizing the correct decision probability P(H1|H1), the confidence of interference identification is significantly enhanced. The improved weighted average Pearson coefficient is compared with the decision threshold in real time. If the improved weighted average Pearson coefficient is less than the decision threshold, the result of no interference detection is output; otherwise, the result of interference detection is output.

[0041] In a preferred embodiment, this application can be further configured as follows: after calculating the improved Pearson correlation coefficient between different satellite signals based on the interference analysis results, setting a decision threshold according to the Neyman-Pearson criterion, and outputting the interference detection result based on the improved Pearson correlation coefficient and the decision threshold, the maritime navigation interference monitoring method based on the satellite receiver further includes:

[0042] ROC curves are generated based on the interference detection results, and the detection accuracy under different interference powers is analyzed based on the ROC curves.

[0043] Based on the analysis results of the detection accuracy, the decision threshold is adjusted to adapt to the characteristics of different equipment;

[0044] Based on the adjusted decision threshold, a final interference report is generated, including the interference type, detection probability, and positioning error correction suggestions.

[0045] By adopting the above technical solution, ROC curves are generated from the interference detection results, which accurately quantifies the detection accuracy under different interference powers. In particular, the false negative rate is reduced in low-power interference scenarios, and the weak signal recognition capability is improved. Based on the slope change analysis of the ROC curve, the decision threshold is dynamically adjusted to adapt to the characteristics of different devices, thereby reducing the generalization volatility of the detection system on heterogeneous hardware platforms. Finally, an interference report containing three-dimensional data is generated, which is convenient for relevant personnel to perform signal analysis.

[0046] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0047] A maritime navigation interference monitoring system based on a satellite receiver, the system comprising:

[0048] The satellite navigation data acquisition module is used to acquire satellite navigation data from multiple satellites in real time based on a satellite navigation receiver. The satellite navigation data includes a carrier-to-noise ratio sequence (C / NO) and an automatic gain control sequence (AGC).

[0049] An interference analysis model building module is used to build a signal interference analysis model based on the satellite navigation data. The model includes a radio frequency front-end module, a digital baseband processor module, and a DSP module.

[0050] The interference analysis module is used to perform matching correlation analysis on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC based on the signal interference analysis model, and generate interference analysis results.

[0051] The interference detection module is used to calculate the improved Pearson correlation coefficient between different satellite signals based on the interference analysis results, set a decision threshold according to the Neyman-Pearson criterion, and output the interference detection results based on the improved Pearson correlation coefficient and the decision threshold.

[0052] By adopting the above technical solution, and by acquiring the carrier-to-noise ratio (C / NO) sequence and automatic gain control (AGC) sequence of multiple satellites in real time, a comprehensive raw data foundation reflecting the dynamic changes of the electromagnetic environment is obtained, providing a multi-dimensional input source for subsequent accurate analysis. Secondly, by constructing a signal interference analysis model that includes an RF front-end module (to realize signal frequency conversion and anti-aliasing filtering), a digital baseband processor module (to strip carrier and pseudo-code interference), and a DSP module (to verify signal trackability), the signal transmission link in a real navigation environment is systematically simulated, solving the model distortion problem caused by neglecting hardware differences in traditional methods. Subsequently, based on this model, paired correlation analysis was performed on the C / NO and AGC sequences, overcoming the limitations of single-parameter detection. By quantifying the correlation characteristics between the sudden drop in C / NO and the sudden rise in AGC under continuous wave interference, complex signal patterns such as frequency sweep interference were accurately identified, reducing the false negative rate of low-power interference. An improved Pearson correlation coefficient was adopted, and a dynamic decision threshold was set by combining the Neyman-Pearson criterion. By comparing the weighted average Pearson coefficient with the threshold in real time, the positioning reliability of the ship navigation system in sea areas with strong electromagnetic interference was significantly enhanced, various interference signals were effectively detected, and the reliability of ship satellite navigation in complex electromagnetic environments was improved.

[0053] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0054] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for monitoring maritime navigation interference based on a satellite receiver.

[0055] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0056] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for monitoring maritime navigation interference based on a satellite receiver.

[0057] In summary, this application includes at least one of the following beneficial technical effects:

[0058] 1. By acquiring the carrier-to-noise ratio (C / NO) sequence and automatic gain control (AGC) sequence from multiple satellites in real time, a comprehensive raw data foundation reflecting the dynamic changes in the electromagnetic environment is obtained, providing a multi-dimensional input source for subsequent accurate analysis. Secondly, by constructing a signal interference analysis model including an RF front-end module (for signal frequency conversion and anti-aliasing filtering), a digital baseband processor module (for removing carrier and pseudo-code interference), and a DSP module (for verifying signal trackability), a systematic simulation of the signal transmission link under real navigation conditions is achieved, solving the model distortion problem caused by neglecting hardware differences in traditional methods. Subsequently, based on... This model performs paired correlation analysis on C / NO and AGC sequences, overcoming the limitations of single-parameter detection. By quantifying the correlation characteristics between the sudden drop in C / NO and the sudden rise in AGC under continuous wave interference, it accurately identifies complex signal patterns such as frequency sweep interference, reducing the false negative rate of low-power interference. It adopts an improved Pearson correlation coefficient and sets a dynamic decision threshold by combining the Neyman-Pearson criterion. By comparing the weighted average Pearson coefficient with the threshold in real time, it significantly enhances the positioning reliability of the ship navigation system in sea areas with strong electromagnetic interference, effectively detects various interference signals, and improves the reliability of ship satellite navigation in complex electromagnetic environments.

[0059] 2. The dynamic impact of interference on positioning accuracy is quantitatively evaluated through a signal interference analysis model (e.g., accurately capturing the nonlinear abrupt increase in positioning error from 1.2m to 8.5m when the continuous wave interference power reaches the -90dBm threshold), providing key critical value basis for anti-interference decision-making. Secondly, the interference response law of the carrier-to-noise ratio sequence C / NO is inverted based on the change in positioning error (e.g., the C / NO values ​​of multiple satellites decrease synchronously by 12-18dB-Hz under frequency sweep interference and exhibit periodic fluctuation characteristics). By establishing a mapping relationship between interference type and C / NO decrease magnitude, a preliminary classification of interference modes is achieved. Then, combined with the time domain feature analysis of AGC sequence, a dual-parameter joint discrimination matrix is ​​constructed to effectively distinguish between equipment gain adaptive adjustment and real interference scenarios. The C / NO trend chart and AGC change law are integrated to generate multi-dimensional interference analysis results.

[0060] 3. By calculating the improved Pearson correlation coefficient between different satellite signals, the correlation strength between signals is accurately quantified, thereby improving the detection sensitivity of low-power interference. Based on this coefficient, weights are dynamically allocated to the carrier-to-noise ratio (C / NO) of each satellite signal, effectively balancing data quality differences and avoiding overall misjudgment caused by the bias of a single satellite. Then, the improved weighted average Pearson coefficient is calculated, and in the case of frequency sweeping interference, the scattered correlation indicators are integrated into a unified evaluation value to achieve a global characterization of the interference state. Subsequently, the Neyman-Pearson criterion is applied to set a decision threshold, and the interference detection result is output by comparing the improved weighted average Pearson coefficient with the decision threshold in real time.

[0061] 4. By generating ROC curves from the interference detection results, the detection accuracy under different interference powers can be accurately quantified. In particular, the false negative rate can be reduced in low-power interference scenarios, improving the ability to identify weak signals. Based on the slope change analysis of the ROC curve, the decision threshold can be dynamically adjusted to adapt to the characteristics of different devices, thereby reducing the generalization volatility of the detection system on heterogeneous hardware platforms. Finally, an interference report containing three-dimensional data is generated to facilitate signal analysis by relevant personnel. Attached Figure Description

[0062] Figure 1 This is a flowchart of a maritime navigation interference monitoring method based on a satellite receiver, according to one embodiment of this application.

[0063] Figure 2 This is a flowchart illustrating the implementation of step S20 in a method for monitoring maritime navigation interference based on a satellite receiver, as described in one embodiment of this application.

[0064] Figure 3 This is a flowchart illustrating the implementation of step S30 in a method for monitoring maritime navigation interference based on a satellite receiver, as described in one embodiment of this application.

[0065] Figure 4 This is a flowchart illustrating the implementation of step S40 in a method for monitoring maritime navigation interference based on a satellite receiver, as described in one embodiment of this application.

[0066] Figure 5 This is a flowchart illustrating the implementation of step S41 in a method for monitoring maritime navigation interference based on a satellite receiver, according to an embodiment of this application.

[0067] Figure 6 This is another implementation flowchart of step S42 in a method for monitoring maritime navigation interference based on a satellite receiving mechanism in one embodiment of this application;

[0068] Figure 7 This is a flowchart illustrating the implementation of a maritime navigation interference monitoring method based on a satellite receiver in one embodiment of this application.

[0069] Figure 8 This is a schematic diagram of a maritime navigation interference monitoring system based on a satellite receiver, according to one embodiment of this application.

[0070] Figure 9 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0071] The present application will be further described in detail below with reference to the accompanying drawings.

[0072] In one embodiment, such as Figure 1 As shown, this application discloses a method for monitoring maritime navigation interference based on a satellite receiver, which specifically includes the following steps:

[0073] S10: Based on the real-time acquisition of satellite navigation data from multiple satellites by a satellite navigation receiver, the satellite navigation data includes the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC.

[0074] Specifically, the carrier-to-noise ratio (C / NO) sequence and automatic gain control (AGC) sequence of multiple satellites are acquired in real time to obtain a comprehensive raw data foundation that reflects the dynamic changes in the electromagnetic environment, providing a multi-dimensional input source for subsequent accurate analysis.

[0075] S20: Based on the satellite navigation data, construct a signal interference analysis model, which includes a radio frequency front-end module, a digital baseband processor module, and a DSP module.

[0076] Specifically, by constructing a signal interference analysis model that includes an RF front-end module (for signal frequency conversion and anti-aliasing filtering), a digital baseband processor module (for carrier and pseudo-code interference stripping), and a DSP module (for verifying signal trackability), the model systematically simulates the signal transmission link in a real navigation environment, thus solving the problem of model distortion caused by neglecting hardware differences in traditional methods.

[0077] S30: Based on the signal interference analysis model, perform matching correlation analysis on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC to generate interference analysis results.

[0078] Specifically, based on this model, paired correlation analysis is performed on C / NO and AGC sequences to overcome the limitations of single-parameter detection. By quantifying the correlation characteristics between the sudden drop in C / NO and the sudden rise in AGC under continuous wave interference (such as the C / NO mean value dropping by 15dB-Hz and the correlation coefficient with AGC reaching 0.87 under -90dBm interference), complex signal patterns such as frequency sweep interference are accurately identified, reducing the false negative rate of low-power interference.

[0079] S40: Based on the interference analysis results, calculate the improved Pearson correlation coefficient between different satellite signals, set a decision threshold according to the Neyman-Pearson criterion, and output the interference detection results based on the improved Pearson correlation coefficient and the decision threshold.

[0080] Specifically, an improved Pearson correlation coefficient (which enhances the contribution of high signal-to-noise ratio satellite data through a weighting mechanism) is used in conjunction with the Neyman-Pearson criterion to set a dynamic decision threshold. By comparing the weighted average Pearson coefficient with the threshold in real time, the positioning reliability of the ship navigation system in sea areas with strong electromagnetic interference is significantly enhanced, various interference signals are effectively detected, and the reliability of ship satellite navigation in complex electromagnetic environments is improved.

[0081] In this embodiment, the carrier-to-noise ratio (C / NO) sequence and automatic gain control (AGC) sequence of multiple satellites are acquired in real time to obtain a comprehensive raw data foundation reflecting the dynamic changes in the electromagnetic environment, providing a multi-dimensional input source for subsequent accurate analysis. Secondly, by constructing a signal interference analysis model including a radio frequency front-end module (for signal frequency conversion and anti-aliasing filtering), a digital baseband processor module (for removing carrier and pseudo-code interference), and a DSP module (for verifying signal trackability), a systematic simulation of the signal transmission link under a real navigation environment is achieved, solving the model distortion problem caused by neglecting hardware differences in traditional methods. Subsequently, the baseband... This model performs paired correlation analysis on C / NO and AGC sequences, overcoming the limitations of single-parameter detection. By quantifying the correlation characteristics between the sudden drop in C / NO and the sudden rise in AGC under continuous wave interference, it accurately identifies complex signal patterns such as frequency sweep interference, reducing the false negative rate of low-power interference. It adopts an improved Pearson correlation coefficient and sets a dynamic decision threshold in combination with the Neyman-Pearson criterion. By comparing the weighted average Pearson coefficient with the threshold in real time, it significantly enhances the positioning reliability of ship navigation systems in sea areas with strong electromagnetic interference, effectively detects various interference signals, and improves the reliability of ship satellite navigation in complex electromagnetic environments.

[0082] In one embodiment, such as Figure 2 As shown, in step S20, a signal interference analysis model is constructed based on the original measurement data. The model includes an RF front-end module, a digital baseband processor module, and a DSP module, specifically including:

[0083] S21: Based on satellite navigation signals, define the signal processing flow of the radio frequency front-end module, including converting the satellite navigation signals to intermediate frequency and amplifying them, and outputting the amplified satellite navigation signals.

[0084] Specifically, the satellite navigation signal is accurately converted to intermediate frequency and adaptively amplified (with a gain dynamic range of up to 60dB) through the radio frequency front-end module, effectively suppressing out-of-band noise and spectral aliasing, and ensuring that the baseline signal-to-noise ratio of the subsequent processed signal is improved by ≥8dB.

[0085] S22: Configure the digital baseband processor module according to the amplified satellite navigation signal to remove the residual carrier and pseudo-code in the signal and generate the baseband signal.

[0086] Specifically, by relying on the digital baseband processor module to perform in-depth processing of the amplified signal (using matched filtering and coherent integration techniques), the remaining carrier components and pseudo-code interference are completely removed, improving the signal-to-interference ratio of the baseband signal by more than 12dB, thus providing a clean data source for correlation analysis.

[0087] S23: Based on the baseband signal, verify the signal integrity and traceability in the DSP module to ensure that the signal is suitable for subsequent analysis.

[0088] Specifically, through real-time signal integrity verification by the DSP module (including carrier phase continuity detection and chip synchronization error analysis), distorted signals affected by multipath effects are dynamically eliminated, reducing the proportion of invalid data to below 5%, which significantly improves the reliability of subsequent interference analysis.

[0089] S24: Construct a signal interference analysis model based on common interference types and the configured RF front-end module, digital baseband processor module, and DSP module. The model parameters are based on the measured interference power and frequency settings.

[0090] Specifically, a multi-dimensional interference model is constructed based on measured interference parameters (such as the -90dBm power threshold of continuous wave interference and the 2MHz / s sweep rate characteristic of frequency sweep interference). A full-link mapping relationship between RF front-end, baseband processing and DSP verification is established in combination with the characteristics of hardware modules to provide high-fidelity input for interference detection, and ultimately improve the accuracy of interference identification of ship navigation systems in strong electromagnetic environments.

[0091] In one embodiment, such as Figure 3 As shown, in step S30, a matching correlation analysis is performed on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC based on the signal interference analysis model to generate interference analysis results, specifically including:

[0092] S31: Evaluate the impact of interference on positioning accuracy based on the signal interference analysis model, and calculate the change in positioning error under the interference power threshold.

[0093] Specifically, the dynamic impact of interference on positioning accuracy is quantitatively assessed through a signal interference analysis model. For example, when the continuous wave interference power reaches the -90dBm threshold, the nonlinear abrupt change in positioning error from 1.2m to 8.5m is accurately captured, providing key critical value basis for anti-interference decision-making.

[0094] S32: Based on the results of the change in positioning error, analyze the impact of interference on the carrier-to-noise ratio (C / NO) sequence, including the magnitude and trend of the decrease in C / NO value under different types of interference.

[0095] Specifically, based on the interference response patterns of the carrier-to-noise ratio (C / NO) sequence retrieved from the changes in positioning error, such as the synchronous decrease of C / NO values ​​of multiple satellites under frequency sweep interference, which exhibits periodic fluctuation characteristics, a mapping relationship between interference type and the magnitude of C / NO decrease is established. For example, narrowband interference causes a sudden drop of 23 dB-Hz in C / NO of a single satellite, while broadband interference causes an average decrease of 9 dB-Hz in C / NO of multiple satellites, thus achieving a preliminary classification of interference modes.

[0096] S33: Based on the analysis results of the carrier-to-noise ratio sequence C / NO, evaluate the impact of interference on the automatic gain control sequence AGC and identify the AGC variation patterns under different types of interference.

[0097] Specifically, by combining the time-domain characteristics analysis of AGC sequences, such as the continuous wave interference triggering AGC to rise sharply by 4.2dB within 200ms, which shows a strong negative correlation of -0.91 with the decrease in C / NO, a two-parameter joint discrimination matrix is ​​constructed to effectively distinguish between device gain adaptive adjustment and real interference scenarios, thereby reducing the misjudgment rate.

[0098] S34: Combine the decrease and trend of C / NO values ​​under different interference types with the AGC change pattern to generate interference analysis results.

[0099] Specifically, by integrating the C / NO trend chart with the AGC change pattern to generate a multi-dimensional interference analysis report, under the same interference conditions, namely -90dBm continuous wave interference, the generated interference analysis results are effectively improved compared with traditional single-parameter detection, thereby increasing the confidence of interference identification in complex electromagnetic environments for ship navigation systems.

[0100] In one embodiment, such as Figure 4 As shown, in step S40, based on the interference analysis results, the improved Pearson correlation coefficient between different satellite signals is calculated, and a decision threshold is set according to the Neyman-Pearson criterion. Based on the improved Pearson correlation coefficient and the decision threshold, the interference detection result is output, specifically including:

[0101] S41: Based on the interference analysis results, calculate the improved Pearson correlation coefficient between different satellite signals, and assign weights to the carrier-to-noise ratio of each satellite signal based on the improved Pearson correlation coefficient.

[0102] Specifically, the improved Pearson correlation coefficient between different satellite signals is calculated to accurately quantify the correlation strength between signals, thereby improving the detection sensitivity of low-power interference. Based on this coefficient, weights are dynamically allocated to the carrier-to-noise ratio (C / NO) of each satellite signal to effectively balance data quality differences and avoid overall misjudgment caused by the bias of a single satellite.

[0103]

[0104] Here, a and b are the row and column indices, respectively. A and B represent the carrier-to-noise ratio data of any two satellite signals, and E(A) and E(B) are the mean values ​​of this pair of data. The weights for obtaining the measured data.

[0105] S42: Calculate the improved weighted average Pearson coefficient based on the weight of the carrier-to-noise ratio of each satellite signal. Based on the improved weighted average Pearson coefficient, set a decision threshold using the Neyman-Pearson criterion. Compare the improved weighted average Pearson coefficient with the decision threshold and output the interference detection result.

[0106] Specifically, an improved weighted average Pearson coefficient is calculated to fuse dispersed correlation indicators into a unified evaluation value in a frequency sweeping interference scenario, achieving a global characterization of the interference state. Then, the Neyman-Pearson criterion is applied to set a decision threshold. By comparing the improved weighted average Pearson coefficient with the decision threshold in real time, the interference detection result is output.

[0107] .

[0108] In one embodiment, such as Figure 5 As shown, in step S41, which assigns weights to the carrier-to-noise ratio of each satellite signal based on the improved Pearson correlation coefficient, the specific steps include:

[0109] S411: Based on the analysis results of the carrier-to-noise ratio sequence C / NO, extract the mean carrier-to-noise ratio E(A) and E(B) for each satellite signal.

[0110] Specifically, by accurately extracting the mean carrier-to-noise ratios E(A) and E(B) of each satellite signal, an objective quantitative data basis is established to eliminate instantaneous noise fluctuation interference, thereby improving the reliability of the weight allocation basis. A dynamic weighting strategy is implemented based on the mean-based hierarchical classification. When both E(A) and E(B) are >40 dB-Hz, a weight is assigned... Enhanced contribution from high signal-to-noise ratio satellites, all <30 dB-Hz Suppress the influence of low-quality signals; otherwise, take... Maintain neutrality and effectively highlight key signal characteristics.

[0111] S412: Based on the average carrier-to-noise ratio of each satellite signal, determine the weight category, assign weights according to the weight category, and calculate the sum of weighted correlation coefficients.

[0112] Specifically, by integrating multidimensional data through the summation of weighted correlation coefficients, the contribution of strongly correlated satellite pairs is increased in frequency sweeping interference scenarios, significantly enhancing the global identification of interference characteristics and providing cross-platform stable anti-interference decision support for ship navigation.

[0113] In one embodiment, such as Figure 6 As shown, in step S42, based on the improved weighted average Pearson coefficient, the Neyman-Pearson criterion is applied to set a decision threshold, the improved weighted average Pearson coefficient is compared with the decision threshold, and the interference detection result is output. Specifically, this includes:

[0114] S421: Based on the improved weighted average Pearson coefficient, the following assumptions are defined: H0 indicates no interference. H1 indicates the presence of interference.

[0115] S422: Set the false alarm probability α to a fixed value, and calculate the false alarm probability P(H1|H0) based on the historical interference dataset.

[0116] S423: Optimize the decision threshold based on the false alarm probability α to maximize the correct decision probability P(H1|H1).

[0117] S424: Compare the improved weighted average Pearson coefficient with the decision threshold. If the improved weighted average Pearson coefficient is less than the decision threshold, output the result of no interference detection; otherwise, output the result of interference detection.

[0118] Specifically, a statistical inference framework is constructed by defining binary assumptions (H0 for no interference and H1 for interference), providing a rigorous mathematical foundation for interference detection. This allows the decision-making process to break free from dependence on empirical thresholds. The false alarm probability P(H1|H0) is calculated based on historical interference datasets, and the false alarm probability α is constrained to below 5% (α=4.7%), significantly reducing the system's redundant alarm load. Then, the decision threshold is optimized with α as a constant. By maximizing the correct decision probability P(H1|H1), the confidence in interference identification is significantly enhanced. The improved weighted average Pearson coefficient is compared with the decision threshold in real time. If the improved weighted average Pearson coefficient is less than the decision threshold, the result of no interference detection is output; otherwise, the result of interference detection is output.

[0119] In one embodiment, such as Figure 7As shown, after step S40, that is, after calculating the improved Pearson correlation coefficient between different satellite signals based on the interference analysis results, setting a decision threshold according to the Neyman-Pearson criterion, and outputting the interference detection result based on the improved Pearson correlation coefficient and the decision threshold, the maritime navigation interference monitoring method based on satellite receivers further includes:

[0120] S50: Generates ROC curves based on interference detection results, and analyzes the detection accuracy under different interference powers based on the ROC curves.

[0121] S60: Adjust the decision threshold based on the analysis results of the detection accuracy to adapt to the characteristics of different equipment.

[0122] S70: Based on the adjusted decision threshold, generate a final interference report, including interference type, detection probability, and positioning error correction suggestions.

[0123] Specifically, ROC curves are generated from the interference detection results to accurately quantify the detection accuracy under different interference powers, especially reducing the false negative rate in low-power interference scenarios and improving the weak signal identification capability. Based on the slope change analysis of the ROC curve, the decision threshold is dynamically adjusted to adapt to the characteristics of different devices, thereby reducing the generalization volatility of the detection system on heterogeneous hardware platforms. Finally, an interference report containing three-dimensional data is generated to facilitate signal analysis by relevant personnel.

[0124] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0125] In one embodiment, a maritime navigation interference monitoring system based on a satellite receiver is provided, which corresponds one-to-one with the maritime navigation interference monitoring method based on a satellite receiver described in the above embodiments. For example... Figure 8 As shown, this maritime navigation interference monitoring system based on a satellite receiver includes a satellite navigation data acquisition module, an interference analysis model construction module, an interference analysis module, and an interference detection module. Detailed descriptions of each functional module are as follows:

[0126] The satellite navigation data acquisition module is used to acquire satellite navigation data from multiple satellites in real time based on a satellite navigation receiver. The satellite navigation data includes a carrier-to-noise ratio sequence (C / NO) and an automatic gain control sequence (AGC).

[0127] An interference analysis model building module is used to build a signal interference analysis model based on the satellite navigation data. The model includes a radio frequency front-end module, a digital baseband processor module, and a DSP module.

[0128] The interference analysis module is used to perform matching correlation analysis on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC based on the signal interference analysis model, and generate interference analysis results.

[0129] The interference detection module is used to calculate the improved Pearson correlation coefficient between different satellite signals based on the interference analysis results, set a decision threshold according to the Neyman-Pearson criterion, and output the interference detection results based on the improved Pearson correlation coefficient and the decision threshold.

[0130] Specific limitations regarding the satellite receiver-based maritime navigation interference monitoring system can be found in the above description of the limitations on the satellite receiver-based maritime navigation interference monitoring method, and will not be repeated here. Each module in the aforementioned satellite receiver-based maritime navigation interference monitoring system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the electronic device, or stored in software in the memory of the electronic device, so that the processor can call and execute the corresponding operations of each module.

[0131] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the database. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for monitoring maritime navigation interference based on a satellite receiver.

[0132] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0133] Satellite navigation data is collected in real time from multiple satellites using a satellite navigation receiver. The satellite navigation data includes a carrier-to-noise ratio (C / NO) sequence and an automatic gain control (AGC) sequence.

[0134] Based on the satellite navigation data, a signal interference analysis model is constructed, which includes a radio frequency front-end module, a digital baseband processor module, and a DSP module.

[0135] Based on the signal interference analysis model, a matching correlation analysis is performed on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC to generate interference analysis results.

[0136] Based on the interference analysis results, the improved Pearson correlation coefficient between different satellite signals is calculated, and a decision threshold is set according to the Neyman-Pearson criterion. Based on the improved Pearson correlation coefficient and the decision threshold, the interference detection results are output.

[0137] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0138] Satellite navigation data is collected in real time from multiple satellites using a satellite navigation receiver. The satellite navigation data includes a carrier-to-noise ratio (C / NO) sequence and an automatic gain control (AGC) sequence.

[0139] Based on the satellite navigation data, a signal interference analysis model is constructed, which includes a radio frequency front-end module, a digital baseband processor module, and a DSP module.

[0140] Based on the signal interference analysis model, a matching correlation analysis is performed on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC to generate interference analysis results.

[0141] Based on the interference analysis results, the improved Pearson correlation coefficient between different satellite signals is calculated, and a decision threshold is set according to the Neyman-Pearson criterion. Based on the improved Pearson correlation coefficient and the decision threshold, the interference detection results are output.

[0142] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0143] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0144] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for monitoring maritime navigation interference based on a satellite receiver, characterized in that, The method for monitoring maritime navigation interference based on satellite receivers includes the following steps: Satellite navigation data is collected in real time from multiple satellites using a satellite navigation receiver. The satellite navigation data includes a carrier-to-noise ratio (C / NO) sequence and an automatic gain control (AGC) sequence. Based on the satellite navigation data, a signal interference analysis model is constructed, which includes a radio frequency front-end module, a digital baseband processor module, and a DSP module. Based on the signal interference analysis model, a matching correlation analysis is performed on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC to generate interference analysis results. Based on the interference analysis results, the improved Pearson correlation coefficient between different satellite signals is calculated, and a decision threshold is set according to the Neyman-Pearson criterion. Based on the improved Pearson correlation coefficient and the decision threshold, the interference detection results are output.

2. The maritime navigation interference monitoring method based on a satellite receiver according to claim 1, characterized in that, The signal interference analysis model is constructed based on the original measurement data. The model includes an RF front-end module, a digital baseband processor module, and a DSP module, specifically including: Based on satellite navigation signals, the signal processing flow of the radio frequency front-end module is defined, including converting the satellite navigation signals to intermediate frequency and amplifying them, and outputting the amplified satellite navigation signals; Configure the digital baseband processor module according to the amplified satellite navigation signal to remove residual carriers and pseudo-codes from the signal and generate a baseband signal; Based on the baseband signal, the signal integrity and traceability are verified in the DSP module to ensure that the signal is suitable for subsequent analysis; A signal interference analysis model is constructed based on common interference types and the configured RF front-end module, digital baseband processor module, and DSP module. The model parameters are set based on the measured interference power and frequency.

3. The maritime navigation interference monitoring method based on a satellite receiver according to claim 1, characterized in that, The process involves performing a matching correlation analysis on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC based on the signal interference analysis model, generating interference analysis results, specifically including: The impact of interference on positioning accuracy is evaluated based on the aforementioned signal interference analysis model, and the change in positioning error under the interference power threshold is calculated. Based on the results of the change in positioning error, the impact of interference on the carrier-to-noise ratio (C / NO) sequence is analyzed, including the magnitude and trend of the decrease in C / NO value under different types of interference. Based on the analysis results of the carrier-to-noise ratio sequence C / NO, the impact of interference on the automatic gain control sequence AGC is evaluated, and the variation law of AGC under different types of interference is identified. By combining the decrease and trend of C / NO values ​​under different interference types with the variation law of AGC, interference analysis results are generated.

4. The maritime navigation interference monitoring method based on a satellite receiver according to claim 1, characterized in that, Based on the interference analysis results, the improved Pearson correlation coefficient between different satellite signals is calculated, and a decision threshold is set according to the Neyman-Pearson criterion. Based on the improved Pearson correlation coefficient and the decision threshold, the interference detection result is output, specifically including: Based on the interference analysis results, the improved Pearson correlation coefficient between different satellite signals is calculated, and the carrier-to-noise ratio of each satellite signal is assigned a weight based on the improved Pearson correlation coefficient. Based on the weight of the carrier-to-noise ratio of each satellite signal, an improved weighted average Pearson coefficient is calculated. According to the improved weighted average Pearson coefficient, a decision threshold is set by applying the Neyman-Pearson criterion. The improved weighted average Pearson coefficient is compared with the decision threshold, and the interference detection result is output.

5. A method for monitoring maritime navigation interference based on a satellite receiver according to claim 4, characterized in that, The carrier-to-noise ratio (CNR) weighted according to the improved Pearson correlation coefficient for each satellite signal specifically includes: Based on the analysis results of the carrier-to-noise ratio sequence C / NO, the mean carrier-to-noise ratios E(A) and E(B) of each satellite signal are extracted; Based on the average carrier-to-noise ratio of each satellite signal, the weight category is determined, weights are assigned according to the weight category, and the sum of weighted correlation coefficients is calculated.

6. The method for monitoring maritime navigation interference based on a satellite receiver according to claim 4, characterized in that, The process of setting a decision threshold based on the improved weighted average Pearson coefficient using the Neyman-Pearson criterion, comparing the improved weighted average Pearson coefficient with the decision threshold, and outputting the interference detection result specifically includes: Based on the improved weighted average Pearson coefficient, the following assumptions are defined: H0 indicates no interference. H1 indicates the presence of interference. Set the false alarm probability α to a fixed value, and calculate the false alarm probability P(H1|H0) based on the historical interference dataset; Based on the false alarm probability α, optimize the decision threshold to maximize the correct decision probability P(H1|H1); Compare the improved weighted average Pearson coefficient with the decision threshold. If the improved weighted average Pearson coefficient is less than the decision threshold, output an interference-free detection result; otherwise, output an interference-detected result.

7. A method for monitoring maritime navigation interference based on a satellite receiver according to claim 1, characterized in that, After calculating the improved Pearson correlation coefficient between different satellite signals based on the interference analysis results, setting a decision threshold according to the Neyman-Pearson criterion, and outputting the interference detection result based on the improved Pearson correlation coefficient and the decision threshold, the maritime navigation interference monitoring method based on the satellite receiver further includes: ROC curves are generated based on the interference detection results, and the detection accuracy under different interference powers is analyzed based on the ROC curves. Based on the analysis results of the detection accuracy, the decision threshold is adjusted to adapt to the characteristics of different equipment; Based on the adjusted decision threshold, a final interference report is generated, including the interference type, detection probability, and positioning error correction suggestions.

8. A maritime navigation interference monitoring system based on a satellite receiver, characterized in that, The satellite-based maritime navigation interference monitoring system includes: The satellite navigation data acquisition module is used to acquire satellite navigation data from multiple satellites in real time based on a satellite navigation receiver. The satellite navigation data includes a carrier-to-noise ratio sequence (C / NO) and an automatic gain control sequence (AGC). An interference analysis model building module is used to build a signal interference analysis model based on the satellite navigation data. The model includes a radio frequency front-end module, a digital baseband processor module, and a DSP module. The interference analysis module is used to perform matching correlation analysis on the carrier-to-noise ratio sequence C / NO and the automatic gain control sequence AGC based on the signal interference analysis model, and generate interference analysis results. The interference detection module is used to calculate the improved Pearson correlation coefficient between different satellite signals based on the interference analysis results, set a decision threshold according to the Neyman-Pearson criterion, and output the interference detection results based on the improved Pearson correlation coefficient and the decision threshold.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the maritime navigation interference monitoring method based on a satellite receiver as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the maritime navigation interference monitoring method based on a satellite receiver as described in any one of claims 1 to 7.

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