Satellite navigation interference detection and suppression system and method

Abstract

The invention provides a satellite navigation interference detection and suppression system and method, and belongs to the technical field of signal analysis. The sensor module is used for determining a target area where a sensor is installed according to a target detection sky, and deploying and installing the sensor in the target area to obtain original signal data of a satellite; the signal processing module is used for standardizing the original signal data to obtain standard signal data and converting the standard signal data into a first frequency spectrum; the feature recognition module is used for analyzing features of the first frequency spectrum through fast Fourier transform and screening signal points with abnormal fluctuation; the interference analysis module is used for classifying the features of all the signal points and matching corresponding interference categories to obtain all interference categories of the original signal data; and the interference suppression module is used for adjusting parameters in real time to reduce interference by using an adaptive filter according to the dynamic characteristics of the interference category. The reliability of navigation signals is improved, and the anti-interference capability is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of signal analysis, and in particular to a satellite navigation interference detection and suppression system and method. Background Art

[0002] At present, with the continuous development of satellite technology, navigation satellites have become an important basic equipment for my country's positioning and navigation technology. However, when obtaining satellite signals from navigation satellites on the ground, there are interferences of different frequencies on the ground, which cause signal interference to the obtained satellite signals and lead to errors when analyzing navigation information. Therefore, the present invention proposes a satellite navigation interference detection and suppression system and method. Summary of the invention

[0003] The present invention provides a satellite navigation interference detection and suppression system and method, which is used to obtain satellite signals by installing sensors on the ground, obtain feature matching corresponding to interference categories after standardizing the satellite signals, and adjust the received signals to reduce interference in real time based on the interference categories using an adaptive filter.

[0004] In one aspect, the present invention provides a satellite navigation interference detection and suppression system, comprising: Sensor module: detects the sky according to the target, determines the target area for sensor installation, and deploys and installs sensors in the target area to obtain the original signal data of the satellite; Signal processing module: standardizes the original signal data to obtain standard signal data, and converts the standard signal data into a first spectrum; Feature recognition module: analyzes the features of the first spectrum through fast Fourier transform to screen signal points with abnormal fluctuations; Interference analysis module: classifies the features of all signal points and matches the corresponding interference categories to obtain all interference categories of the original signal data; Interference suppression module: According to the dynamic characteristics of the interference category, the adaptive filter is used to adjust parameters in real time to reduce interference.

[0005] On the other hand, the sensor module comprises: Environmental scanning unit: detects the ground corresponding to the sky according to a preset target, determines an initial ground area, scans the initial ground area using a high-resolution image sensor, and obtains a three-dimensional environmental model of the initial ground area; Target recognition unit: performs recognition processing on the three-dimensional environment model according to the convolutional neural network algorithm to identify multiple signal receiving positions in the initial ground area.

[0006] On the other hand, the sensor module comprises: Signal evaluation unit: measures satellite signal strength at any signal receiving location and constructs a relationship diagram between signal strength and geographic location; Sensor unit: According to the signal strength of all signal receiving positions in the relationship diagram, select the signal receiving positions that are higher than the preset threshold as the preferred signal receiving positions and configure a unique first number for each preferred signal receiving position, configure a unique second number for each sensor, and install and deploy all sensors according to the unique correspondence between the sensors and the preferred signal receiving positions.

[0007] On the other hand, the signal processing module comprises: Standardization unit: receiving raw signal data obtained by the sensor in real time, applying a filter to remove noise in the raw signal data, and obtaining first signal data; The first signal data is normalized to a preset signal range and standardized to obtain standard signal data: ;in, represents the standardized signal value of the i-th time node in the first signal data, represents the signal value of the i-th time node in the first signal data, represents the maximum value of the signal value in the first signal data, represents the minimum value of the signal value in the first signal data, represents the mean value of the signal value in the first signal data, represents a variance of a signal value in the first signal data; Spectrum conversion unit: applying a spectrum conversion tool to the standard signal data, converting the time domain signal into a frequency domain signal, and obtaining a first spectrum.

[0008] On the other hand, the feature recognition module includes: An analysis unit: analyzing all spectrum signals of the first spectrum by using a fast Fourier transform to obtain statistical characteristics of the first spectrum; Feature unit: construct a feature matrix according to each spectrum signal of the first spectrum, and perform covariance operation on the feature matrix to obtain frequency domain features of each spectrum signal; Abnormal fluctuation analysis: obtaining a signal abnormal fluctuation threshold based on the historical record library, and screening signal points with abnormal signal fluctuations by combining the statistical characteristics of the first spectrum and the frequency domain characteristics of each spectrum signal.

[0009] On the other hand, the interference suppression module includes: Interference classification unit: clustering is performed based on the clustering algorithm combined with the frequency domain features of each spectrum signal of the first spectrum. If the Manhattan distance of the frequency domain features of the spectrum signals of the same cluster is less than the preset maximum clustering distance, the spectrum signals of the same cluster are the same type of interference signals. After clustering is completed, isolated points are screened out; Classification matching unit: matches the interference categories corresponding to all clusters according to the interference category-frequency domain feature mapping table to obtain all interference categories of the original signal data.

[0010] On the other hand, the sensor module comprises: A filter parameter unit: initializing the order of the adaptive filter according to the interference category of the original signal data; setting the first parameter of the adaptive filter according to the dynamic characteristics of all interference categories; Testing unit: Use historical signal data to simulate the adaptive filter and obtain the filter test results. Perform deviation analysis based on the filter test results and standard satellite signals to obtain the monitoring signal-to-noise ratio. The adjustment unit adjusts the first parameter of the adaptive filter based on the monitored signal-to-noise ratio to obtain the final parameter, configures the adaptive filter according to the final parameter, receives the real-time satellite signal to obtain the filter performance index, and adjusts the filter parameter according to the filter performance index to suppress interference.

[0011] In another aspect, the present invention provides a satellite navigation interference detection and suppression method, comprising: Step 1: Detect the sky according to the target, determine the target area for sensor installation, and deploy and install sensors in the target area to obtain the original signal data of the satellite; Step 2: receiving original signal data in real time, standardizing the original signal data to obtain standard signal data, and converting the standard signal data into a first spectrum; Step 3: Analyze the abnormal fluctuation of the first spectrum by fast Fourier transform to obtain the characteristics of the interference signal; Step 4: classify the features of the interference signal according to the characteristics of the interference signal to obtain all interference categories of the original signal data; Step 5: Based on the dynamic characteristics of the interference category, use an adaptive filter to adjust the received signal in real time to reduce interference.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a satellite navigation interference detection and suppression system and method, which is used to obtain satellite signals by installing sensors on the ground, obtain feature matching corresponding to interference categories after standardizing the satellite signals, and adjust the received signals to reduce interference in real time based on the interference categories using an adaptive filter. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 It is a structural schematic diagram of a satellite navigation interference detection and suppression system provided by an embodiment of the present invention; Figure 2 It is a flow chart of a satellite navigation interference detection and suppression method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Embodiment 1: like Figure 1 As shown, a satellite navigation interference detection and suppression system provided by an embodiment of the present invention includes: Sensor module: detects the sky according to the target, determines the target area for sensor installation, and deploys and installs sensors in the target area to obtain the original signal data of the satellite; Signal processing module: standardizes the original signal data to obtain standard signal data, and converts the standard signal data into a first spectrum; Feature recognition module: analyzes the features of the first spectrum through fast Fourier transform to screen signal points with abnormal fluctuations; Interference analysis module: classifies the features of all signal points and matches the corresponding interference categories to obtain all interference categories of the original signal data; Interference suppression module: According to the dynamic characteristics of the interference category, the adaptive filter is used to adjust parameters in real time to reduce interference.

[0017] In this embodiment, target detection sky refers to identifying and locating an area that needs to be monitored in a specific environment.

[0018] In this embodiment, the sensor is a device that can detect physical or environmental changes and convert them into measurable signals, including: electromagnetic wave, temperature, pressure and other sensors.

[0019] In this embodiment, the target area refers to a spatial range selected for installing sensors according to specific standards.

[0020] In this embodiment, the raw signal data refers to unprocessed signal information directly obtained from the sensor.

[0021] In this embodiment, standardization is a processing method of converting data of different ranges or scales into a unified standard form.

[0022] In this embodiment, the standard signal data is signal data that has been subjected to standardization processing.

[0023] In this embodiment, the first spectrum refers to a frequency domain representation obtained by performing spectrum processing on the standard signal data.

[0024] In this embodiment, the fast Fourier transform is an efficient algorithm and a mathematical tool for calculating the discrete Fourier transform of a signal, which converts a time domain signal into a frequency domain signal.

[0025] In this embodiment, the feature refers to representative information extracted from the original data, such as the amplitude and phase of the frequency component.

[0026] In this embodiment, the signal point refers to a specific signal sample point extracted during the processing and analysis process.

[0027] In this embodiment, the interference category refers to the classification of different types of interference signals, such as electromagnetic interference, environmental noise, signal attenuation, etc.

[0028] In this embodiment, the dynamic feature refers to the temporal variation characteristics of the interference signal.

[0029] In this embodiment, the adaptive filter is a filter that can adjust its parameters in real time according to the characteristics of the input signal.

[0030] In this embodiment, the parameter refers to the coefficient of the filter, such as: step factor, weight, coefficient, etc.

[0031] The working principle and beneficial effects of the above technical solution are: by deploying sensors, real-time data standardization and spectrum analysis, interference signals are identified and classified, and the received signals are dynamically adjusted using adaptive filtering technology, which significantly improves the anti-interference capability and data processing efficiency of the satellite navigation system.

[0032] Embodiment 2: Based on the above embodiment 1, the sensor module includes: Environmental scanning unit: detects the ground corresponding to the sky according to a preset target, determines an initial ground area, scans the initial ground area using a high-resolution image sensor, and obtains a three-dimensional environmental model of the initial ground area; Target recognition unit: performs recognition processing on the three-dimensional environment model according to the convolutional neural network algorithm to identify multiple signal receiving positions in the initial ground area.

[0033] In this embodiment, the initial ground area refers to a specific ground area that is preset and needs to be focused on when performing environment scanning and target detection.

[0034] In this embodiment, the high-resolution image sensor is a device that can capture images with rich details and high precision.

[0035] In this embodiment, the three-dimensional environment model is a digital representation of the physical space, generated through measurement and data processing, and includes information such as the shape, position, size, and spatial relationship of objects in the scene.

[0036] In this embodiment, the convolutional neural network algorithm is a deep learning algorithm used to process image data and extract image features through a convolution layer. The convolution operation uses multiple filters (or convolution kernels) to slide on the input image to generate a feature map to capture local features.

[0037] In this embodiment, the signal receiving position refers to a specific point or area within a specific area where a signal (such as a radio signal, an optical signal, an audio signal, etc.) can be received.

[0038] The working principle and beneficial effects of the above technical solution are: generating a three-dimensional environmental model through a high-resolution image sensor, and using a convolutional neural network to identify the signal receiving position, thereby improving target detection accuracy and system decision-making efficiency, and having good adaptability and flexibility.

[0039] Embodiment 3: Based on the above embodiment 2, the sensor module includes: Signal evaluation unit: measures satellite signal strength at any signal receiving location and constructs a relationship diagram between signal strength and geographic location; Sensor unit: According to the signal strength of all signal receiving positions in the relationship diagram, select the signal receiving positions that are higher than the preset threshold as the preferred signal receiving positions and configure a unique first number for each preferred signal receiving position, configure a unique second number for each sensor, and install and deploy all sensors according to the unique correspondence between the sensors and the preferred signal receiving positions.

[0040] In this embodiment, the relationship graph is a visualization tool used to represent and analyze the relationship between signal strength and geographical location.

[0041] In this embodiment, the signal strength refers to the strength power of a wireless signal (such as a satellite signal, a radio signal, or other electromagnetic wave signal) received at a specific location.

[0042] In this embodiment, the geographical location refers to the specific coordinates or position of a point on the surface of the earth, which is usually expressed by longitude and latitude.

[0043] In this embodiment, the preferred signal receiving positions refer to those positions whose signal quality is higher than a preset threshold value, which are selected according to the signal strength measurement results during the signal evaluation process.

[0044] In this embodiment, the first number refers to a unique identifier assigned to each preferred signal receiving position.

[0045] In this embodiment, the second number is a unique identifier assigned to each sensor.

[0046] The working principle and beneficial effects of the above technical solution are: constructing a relationship diagram by measuring signal strength, optimizing the signal reception position and assigning a unique number, optimizing signal reception and system performance, improving management efficiency, and having good adaptability.

[0047] Embodiment 4: Based on the above embodiment 1, the signal processing module includes: Standardization unit: receiving raw signal data obtained by the sensor in real time, applying a filter to remove noise in the raw signal data, and obtaining first signal data; The first signal data is normalized to a preset signal range and standardized to obtain standard signal data: ;in, represents the standardized signal value of the i-th time node in the first signal data, represents the signal value of the i-th time node in the first signal data, represents the maximum value of the signal value in the first signal data, represents the minimum value of the signal value in the first signal data, represents the mean value of the signal value in the first signal data, represents a variance of a signal value in the first signal data; Spectrum conversion unit: applying a spectrum conversion tool to the standard signal data, converting the time domain signal into a frequency domain signal, and obtaining a first spectrum.

[0048] In this embodiment, noise refers to any unwanted signal data of the original signal during the signal transmission or measurement process.

[0049] In this embodiment, the first signal data is original signal data obtained after being processed by a filter.

[0050] In this embodiment, normalization is the process of converting data to a standard range.

[0051] In this embodiment, the preset signal range refers to a target value range set for normalization or standardization during data processing.

[0052] In this embodiment, the spectrum conversion tool is an algorithm software tool for converting a time domain signal into a frequency domain signal.

[0053] In this embodiment, the time domain refers to the expression form of the signal changing over time. In the time domain, the amplitude of the signal changes over time.

[0054] In this embodiment, the frequency domain refers to the representation form of the signal in different frequency components. In the frequency domain, the signal is represented as the amplitude and phase information of each frequency component.

[0055] The working principle and beneficial effects of the above technical solution are: improving signal data quality through denoising and standardization to ensure consistency, and converting time domain signals into frequency domain to enhance frequency domain analysis capabilities, providing a reliable basis for subsequent processing and improving the overall efficiency of the system.

[0056] Embodiment 5: Based on the above embodiment 1, the feature recognition module includes: An analysis unit: analyzing all spectrum signals of the first spectrum by using a fast Fourier transform to obtain statistical characteristics of the first spectrum; Feature unit: construct a feature matrix according to each spectrum signal of the first spectrum, and perform covariance operation on the feature matrix to obtain frequency domain features of each spectrum signal; Abnormal fluctuation analysis: obtaining a signal abnormal fluctuation threshold based on the historical record library, and screening signal points with abnormal signal fluctuations by combining the statistical characteristics of the first spectrum and the frequency domain characteristics of each spectrum signal.

[0057] In this embodiment, the fast Fourier transform is an algorithm for efficiently calculating the discrete Fourier transform and its inverse transform.

[0058] In this embodiment, the spectrum signal refers to a representation of frequency components obtained by performing Fourier transform on a time domain signal.

[0059] In this embodiment, the statistical characteristics refer to quantitative descriptions extracted by analyzing a data set, including mean, variance, amplitude, phase, etc.

[0060] In this embodiment, the feature matrix represents a matrix of feature information of samples.

[0061] In this embodiment, the covariance operation is a tool used in statistics to measure the relationship between random variables.

[0062] In this embodiment, the frequency domain feature refers to a feature expressed in the frequency domain obtained by performing spectrum analysis on the signal, such as frequency, amplitude, center of gravity position, etc.

[0063] In this embodiment, the historical record library refers to a database storing past signal data and related features.

[0064] In this embodiment, the signal abnormal fluctuation threshold is a standard limit for detecting signal abnormality.

[0065] In this embodiment, abnormal fluctuation refers to the situation in which the fluctuation amplitude, frequency or shape of certain points or intervals significantly deviates from the normal mode or expected range during the change of the signal.

[0066] In this embodiment, the statistical characteristics are combined with the frequency domain features to establish a scoring system, and each feature generates a weight coefficient. Then, a comprehensive score is calculated for each signal point to indicate its abnormality level.

[0067] The working principle and beneficial effects of the above technical solution are: analyze the spectrum signal through fast Fourier transform, construct the feature matrix and perform covariance operation, extract frequency domain features, combine historical data to identify abnormal fluctuations, and improve signal analysis accuracy and system stability.

[0068] Embodiment 6: On the basis of the above embodiment 1, the interference suppression module includes: Interference classification unit: clustering is performed based on the clustering algorithm combined with the frequency domain features of each spectrum signal of the first spectrum. If the Manhattan distance of the frequency domain features of the spectrum signals of the same cluster is less than the preset maximum clustering distance, the spectrum signals of the same cluster are the same type of interference signals. After clustering is completed, isolated points are screened out; Classification matching unit: matches the interference categories corresponding to all clusters according to the interference category-frequency domain feature mapping table to obtain all interference categories of the original signal data.

[0069] In this embodiment, the clustering algorithm is an unsupervised learning method for grouping samples in a data set according to their characteristics, so that samples in the same group are similar to each other, while samples in different groups have large differences.

[0070] In this embodiment, Manhattan distance is a metric used to calculate the distance between two points.

[0071] In this embodiment, the preset maximum clustering distance refers to a threshold value set by a user in a clustering algorithm, which is used to determine whether the frequency domain features of two spectrum signals can be classified as the same type of interference signals.

[0072] In this embodiment, isolated points refer to data points that cannot form effective clusters with any other data points in cluster analysis.

[0073] In this embodiment, the interference category-frequency domain feature mapping table is a table indicating the mapping relationship between interference categories and frequency domain features.

[0074] The working principle and beneficial effects of the above technical solution are: using a clustering algorithm to classify interference signals based on frequency domain features, using Manhattan distance to ensure clustering rationality and filter out isolated points, and finally obtaining all interference categories through mapping table matching, thereby improving the accuracy and efficiency of signal processing.

[0075] Embodiment 7: Based on the above embodiment 1, the sensor module includes: A filter parameter unit: initializing the order of the adaptive filter according to the interference category of the original signal data; setting the first parameter of the adaptive filter according to the dynamic characteristics of all interference categories; Testing unit: Use historical signal data to simulate the adaptive filter and obtain the filter test results. Perform deviation analysis based on the filter test results and standard satellite signals to obtain the monitoring signal-to-noise ratio. The adjustment unit adjusts the first parameter of the adaptive filter based on the monitored signal-to-noise ratio to obtain the final parameter, configures the adaptive filter according to the final parameter, receives the real-time satellite signal to obtain the filter performance index, and adjusts the filter parameter according to the filter performance index to suppress interference.

[0076] In this embodiment, the order refers to the complexity of the filter. The higher the order, the more complex the interference types that can be adapted, but the amount of calculation also increases.

[0077] In this embodiment, the first parameter refers to a filter parameter used to adjust filter performance.

[0078] In this embodiment, the historical signal data refers to signal samples collected in the past and related to the current application scenario.

[0079] In this embodiment, the simulation test refers to a method of evaluating and verifying the adaptive filter using historical signal data in a control environment.

[0080] In this embodiment, the filtering test result refers to output data obtained by comparing the signal processed by the adaptive filter with the standard signal.

[0081] In this embodiment, deviation analysis refers to a step in signal processing to evaluate the performance and effectiveness of the filter by comparing the filter output signal with a standard reference signal.

[0082] In this embodiment, the monitoring signal-to-noise ratio refers to the ratio between the effective signal power and the noise power in the filtered signal.

[0083] In this embodiment, the final parameters are optimal parameter settings obtained by monitoring the signal-to-noise ratio during the adjustment process of the adaptive filter.

[0084] In this embodiment, the real-time satellite signal refers to instant signal data transmitted from the satellite to the receiving device.

[0085] In this embodiment, the filter performance index is a key parameter used to evaluate the effect of the adaptive filter in processing signals, such as signal-to-noise ratio, mean square error, filter bag width, gain stability, etc.

[0086] The working principle and beneficial effects of the above technical solution are: dynamically adjusting parameters through adaptive filters, optimizing according to interference categories and signal-to-noise ratios, improving signal processing accuracy and anti-interference capabilities, and ensuring the stability and reliability of real-time satellite signals.

[0087] Embodiment 8: like Figure 2 As shown, a satellite navigation interference detection and suppression method provided by an embodiment of the present invention includes: Step 1: Detect the sky according to the target, determine the target area for sensor installation, and deploy and install sensors in the target area to obtain the original signal data of the satellite; Step 2: receiving original signal data in real time, standardizing the original signal data to obtain standard signal data, and converting the standard signal data into a first spectrum; Step 3: Analyze the abnormal fluctuation of the first spectrum by fast Fourier transform to obtain the characteristics of the interference signal; Step 4: classify the features of the interference signal according to the characteristics of the interference signal to obtain all interference categories of the original signal data; Step 5: Based on the dynamic characteristics of the interference category, use an adaptive filter to adjust the received signal in real time to reduce interference.

[0088] The working principle and beneficial effects of the above technical solution are: by deploying sensors, real-time data standardization and spectrum analysis, interference signals are identified and classified, and the received signals are dynamically adjusted using adaptive filtering technology, which significantly improves the anti-interference capability and data processing efficiency of the satellite navigation system.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A satellite navigation interference detection and suppression system, characterized in that: include: Sensor module: detects the sky according to the target, determines the target area for sensor installation, and deploys and installs sensors in the target area to obtain the original signal data of the satellite; Signal processing module: standardizes the original signal data to obtain standard signal data, and converts the standard signal data into a first spectrum; Feature recognition module: analyzes the features of the first spectrum through fast Fourier transform to screen signal points with abnormal fluctuations; Interference analysis module: classifies the features of all signal points and matches the corresponding interference categories to obtain all interference categories of the original signal data; Interference suppression module: According to the dynamic characteristics of the interference category, the adaptive filter is used to adjust parameters in real time to reduce interference.

2. A satellite navigation interference detection and suppression system according to claim 1, characterized in that: The sensor module comprises: Environmental scanning unit: detects the ground corresponding to the sky according to a preset target, determines an initial ground area, scans the initial ground area using a high-resolution image sensor, and obtains a three-dimensional environmental model of the initial ground area; Target recognition unit: performs recognition processing on the three-dimensional environment model according to the convolutional neural network algorithm to identify multiple signal receiving positions in the initial ground area.

3. A satellite navigation interference detection and suppression system according to claim 2, characterized in that: The sensor module comprises: Signal evaluation unit: measures satellite signal strength at any signal receiving location and constructs a relationship diagram between signal strength and geographic location; Sensor unit: According to the signal strength of all signal receiving positions in the relationship diagram, select the signal receiving positions that are higher than the preset threshold as the preferred signal receiving positions and configure a unique first number for each preferred signal receiving position, configure a unique second number for each sensor, and install and deploy all sensors according to the unique correspondence between the sensors and the preferred signal receiving positions.

4. A satellite navigation interference detection and suppression system according to claim 1, characterized in that: The signal processing module comprises: Standardization unit: receiving raw signal data obtained by the sensor in real time, applying a filter to remove noise in the raw signal data, and obtaining first signal data; The first signal data is normalized to a preset signal range and standardized to obtain standard signal data: ;in, represents the standardized signal value of the i-th time node in the first signal data, represents the signal value of the i-th time node in the first signal data, represents the maximum value of the signal value in the first signal data, represents the minimum value of the signal value in the first signal data, represents the mean value of the signal value in the first signal data, represents a variance of a signal value in the first signal data; Spectrum conversion unit: applying a spectrum conversion tool to the standard signal data, converting the time domain signal into a frequency domain signal, and obtaining a first spectrum.

5. A satellite navigation interference detection and suppression system according to claim 1, characterized in that: The feature recognition module comprises: An analysis unit: analyzing all spectrum signals of the first spectrum by using a fast Fourier transform to obtain statistical characteristics of the first spectrum; Feature unit: construct a feature matrix according to each spectrum signal of the first spectrum, and perform covariance operation on the feature matrix to obtain frequency domain features of each spectrum signal; Abnormal fluctuation analysis: obtaining a signal abnormal fluctuation threshold based on the historical record library, and screening signal points with abnormal signal fluctuations by combining the statistical characteristics of the first spectrum and the frequency domain characteristics of each spectrum signal.

6. A satellite navigation interference detection and suppression system according to claim 1, characterized in that: The interference suppression module comprises: Interference classification unit: clustering is performed based on the clustering algorithm combined with the frequency domain features of each spectrum signal of the first spectrum. If the Manhattan distance of the frequency domain features of the spectrum signals of the same cluster is less than the preset maximum clustering distance, the spectrum signals of the same cluster are the same type of interference signals. After clustering is completed, isolated points are screened out; Classification matching unit: matches the interference categories corresponding to all clusters according to the interference category-frequency domain feature mapping table to obtain all interference categories of the original signal data.

7. A satellite navigation interference detection and suppression system according to claim 1, characterized in that: The sensor module comprises: A filter parameter unit: initializing the order of the adaptive filter according to the interference category of the original signal data; setting the first parameter of the adaptive filter according to the dynamic characteristics of all interference categories; Testing unit: Use historical signal data to simulate the adaptive filter and obtain the filter test results. Perform deviation analysis based on the filter test results and standard satellite signals to obtain the monitoring signal-to-noise ratio. The adjustment unit adjusts the first parameter of the adaptive filter based on the monitored signal-to-noise ratio to obtain the final parameter, configures the adaptive filter according to the final parameter, receives the real-time satellite signal to obtain the filter performance index, and adjusts the filter parameter according to the filter performance index to suppress interference.

8. A satellite navigation interference detection and suppression method, characterized in that: include Step 1: Detect the sky according to the target, determine the target area for sensor installation, and deploy and install sensors in the target area to obtain the original signal data of the satellite; Step 2: receiving original signal data in real time, standardizing the original signal data to obtain standard signal data, and converting the standard signal data into a first spectrum; Step 3: Analyze the abnormal fluctuation of the first spectrum by fast Fourier transform to obtain the characteristics of the interference signal; Step 4: classify the features of the interference signal according to the characteristics of the interference signal to obtain all interference categories of the original signal data; Step 5: Based on the dynamic characteristics of the interference category, use an adaptive filter to adjust the received signal in real time to reduce interference.

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