A satellite navigation interference detection and suppression system and method

Abstract

The present invention provides a satellite navigation interference detection and suppression system and method, belonging to the technical field of signal analysis; the modules include: a sensor module: detecting the sky according to a target, determining a target area where the sensor is installed, and deploying and installing sensors in the target area to obtain the original signal data of the satellite; a signal processing module: normalizing the original signal data to obtain standard signal data, and converting the standard signal data into a first spectrum; a feature recognition module: analyzing the features of the first spectrum through fast Fourier transform to screen out signal points with abnormal fluctuations; an interference analysis module: classifying the features of all signal points and matching the corresponding interference categories to obtain all interference categories of the original signal data; an interference suppression module: according to the dynamic features of the interference categories, using an adaptive filter to adjust parameters in real time to reduce interference. The reliability of the navigation signal is improved and the anti-interference ability is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of signal analysis, and particularly 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 device for positioning and navigation technology in China. However, when obtaining satellite signals of navigation satellites on the ground, there are interferences of different frequencies on the ground, which cause signal interference to the obtained satellite signals, resulting in errors when analyzing navigation information.

[0003] Therefore, the present invention proposes a satellite navigation interference detection and suppression system and method. Summary of the Invention

[0004] The present invention provides a satellite navigation interference detection and suppression system and method, which are used to obtain satellite signals by installing sensors on the ground, standardize the satellite signals, obtain characteristic matching corresponding interference categories, and adjust the received signals in real time according to the interference categories by using an adaptive filter to reduce interference.

[0005] On the one hand, the present invention provides a satellite navigation interference detection and suppression system, including:

[0006] A sensor module: detecting the sky according to a target to determine a target area for installing sensors, and deploying and installing sensors in the target area to obtain original signal data of a satellite;

[0007] A signal processing module: standardizing the original signal data to obtain standard signal data, and converting the standard signal data into a first spectrum;

[0008] A feature recognition module: analyzing the features of the first spectrum through fast Fourier transform to screen out signal points with abnormal fluctuations;

[0009] An interference analysis module: classifying the features of all signal points and matching corresponding interference categories to obtain all interference categories of the original signal data;

[0010] An interference suppression module: adjusting parameters in real time according to the dynamic features of the interference categories by using an adaptive filter to reduce interference.

[0011] On the other hand, the sensor module includes:

[0012] An environment scanning unit: determining an initial ground area according to the corresponding ground for detecting the sky preset, scanning the initial ground area by using a high-resolution image sensor, and obtaining a three-dimensional environment model of the initial ground area;

[0013] Target recognition unit: performs recognition processing on the three-dimensional environment model according to the convolutional neural network algorithm, and recognizes multiple signal reception positions in the initial ground area.

[0014] On the other hand, the sensor module includes:

[0015] Signal evaluation unit: measures the satellite signal strength at any signal reception position, and constructs a relationship diagram between the signal strength and the geographical location;

[0016] Sensor unit: according to the signal strengths of all signal reception positions in the relationship diagram, selects the signal reception positions higher than the preset threshold as the preferred signal reception positions and assigns a unique first number to each preferred signal reception position, assigns a unique second number to each sensor, and installs and deploys all sensors according to the unique corresponding relationship between the sensors and the preferred signal reception positions.

[0017] On the other hand, the signal processing module includes:

[0018] Normalization unit: receives the original signal data obtained by the sensor in real time, applies a filter to remove the noise in the original signal data, and obtains the first signal data;

[0019] Normalizes the first signal data into a preset signal range and performs normalization to obtain the standard signal data:

[0020] ; where represents the normalized standard signal value at the i-th time node in the first signal data, represents the signal value at the i-th time node in the first signal data, represents the maximum value of the signal values in the first signal data, represents the minimum value of the signal values in the first signal data, represents the mean value of the signal values in the first signal data, represents the variance of the signal values in the first signal data;

[0021] Spectrum conversion unit: applies a spectrum conversion tool to the standard signal data to convert the time-domain signal into a frequency-domain signal, and obtains the first spectrum.

[0022] On the other hand, the feature recognition module includes:

[0023] Analysis unit: uses the fast Fourier transform to analyze all the spectrum signals of the first spectrum to obtain the statistical characteristics of the first spectrum;

[0024] Feature unit: constructs a feature matrix according to each spectrum signal of the first spectrum, and performs covariance operation on the feature matrix to obtain the frequency-domain features of each spectrum signal;

[0025] Abnormal fluctuation analysis: Obtain the abnormal fluctuation threshold of the signal based on the historical record library, and screen the signal points with abnormal signal fluctuations by combining the statistical characteristics of the first spectrum and the frequency domain characteristics of each spectrum signal.

[0026] On the other hand, the interference suppression module includes:

[0027] Interference classification unit: Cluster based on the clustering algorithm combined with the frequency domain characteristics of each spectrum signal of the first spectrum. If the Manhattan distance of the frequency domain characteristics of the spectrum signals in the same cluster is less than the preset maximum clustering distance, the spectrum signals in the same cluster are the same type of interference signals. After clustering, the isolated points are screened out;

[0028] Classification matching unit: Match the interference categories corresponding to all clusters according to the interference category - frequency domain characteristic mapping table to obtain all the interference categories of the original signal data.

[0029] On the other hand, the sensor module includes:

[0030] Filter parameter unit: Initialize the order of the adaptive filter according to the types of interference categories of the original signal data; Set the first parameter of the adaptive filter according to the dynamic characteristics of all interference categories;

[0031] Testing unit: Simulate and test the adaptive filter using historical signal data to obtain the filtering test results, and perform deviation analysis based on the filtering test results and the standard satellite signal to obtain the monitored signal-to-noise ratio;

[0032] Adjustment unit: Adjust the first parameter of the adaptive filter based on the monitored signal-to-noise ratio to obtain the final parameter, configure the adaptive filter according to the final parameter, receive the real-time satellite signal to obtain the filter performance index, and adjust the filter parameters to suppress interference according to the filter performance index.

[0033] On the other hand, the present invention provides a satellite navigation interference detection and suppression method, including:

[0034] Step 1: Detect the sky according to the target to determine the target area where the sensor is installed, and deploy and install the sensor in the target area to obtain the original signal data of the satellite;

[0035] Step 2: Receive the original signal data in real time, standardize the original signal data to obtain the standard signal data, and convert the standard signal data into the first spectrum;

[0036] Step 3: Analyze the abnormal fluctuations of the first spectrum through fast Fourier transform to obtain the characteristics of the interference signal;

[0037] Step 4: Classify the characteristics of the interference signal according to the characteristics of the interference signal to obtain all interference categories of the original signal data;

[0038] Step 5: According to the dynamic characteristics of the interference category, use an adaptive filter to adjust the received signal in real time to reduce interference.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0040] 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, standardize the satellite signals, obtain characteristic matching corresponding interference categories, and adjust the received signal in real time according to the interference categories to reduce interference. Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 is a schematic structural diagram of a satellite navigation interference detection and suppression system provided by an embodiment of the present invention;

[0043] Figure 2 is a schematic flowchart of a satellite navigation interference detection and suppression method provided by an embodiment of the present invention. Detailed Embodiments

[0044] To make the purpose, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0045] Embodiment 1:

[0046] As Figure 1 shown, a satellite navigation interference detection and suppression system provided by an embodiment of the present invention includes:

[0047] Sensor module: Detect the sky according to the target, determine the target area where the sensor is installed, and deploy and install sensors in the target area to obtain the original signal data of the satellite;

[0048] Signal processing module: Standardize the original signal data to obtain standard signal data, and convert the standard signal data into the first spectrum;

[0049] Feature recognition module: Analyze the features of the first spectrum through fast Fourier transform to screen out signal points with abnormal fluctuations;

[0050] Interference analysis module: Classify the features of all signal points and match the corresponding interference categories to obtain all interference categories of the original signal data;

[0051] Interference suppression module: According to the dynamic characteristics of the interference categories, use an adaptive filter to adjust the parameters in real time to reduce interference.

[0052] In this embodiment, target detection of the sky means identifying and locating the area to be monitored in a specific environment.

[0053] In this embodiment, a 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.

[0054] In this embodiment, the target area refers to the spatial range selected for installing sensors according to specific criteria.

[0055] In this embodiment, the original signal data refers to the unprocessed signal information directly obtained from the sensor.

[0056] In this embodiment, standardization is a processing method that converts data in different ranges or scales into a unified standard form.

[0057] In this embodiment, the standard signal data is the signal data after standardization processing.

[0058] In this embodiment, the first spectrum refers to the frequency-domain representation obtained by performing spectrum processing on the standard signal data.

[0059] In this embodiment, the fast Fourier transform is an efficient algorithm for calculating the discrete Fourier transform of a signal and is a mathematical tool for converting a time-domain signal into a frequency-domain signal.

[0060] In this embodiment, features refer to the representative information extracted from the original data, such as the amplitude and phase of the frequency components.

[0061] In this embodiment, a signal point refers to a specific signal sample point extracted during the processing and analysis.

[0062] 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.

[0063] In this embodiment, the dynamic feature refers to the change characteristic of the interference signal over time.

[0064] In this embodiment, an adaptive filter is a filter that can adjust its parameters in real time according to the characteristics of the input signal.

[0065] In this embodiment, the parameters refer to the coefficients of the filter, such as: step size factor, weight, coefficient, etc.

[0066] The working principle and beneficial effects of the above technical solution are: by deploying sensors, real-time data standardization and spectrum analysis, identifying and classifying interference signals, and using adaptive filtering technology to dynamically adjust the received signal, the anti-interference ability and data processing efficiency of the satellite navigation system are significantly improved.

[0067] Embodiment 2:

[0068] Based on the above Embodiment 1, the sensor module includes:

[0069] Environmental scanning unit: Determine the initial ground area according to the corresponding ground of the preset target for sky detection, scan the initial ground area using a high-resolution image sensor, and obtain the three-dimensional environmental model of the initial ground area;

[0070] Target recognition unit: Perform recognition processing on the three-dimensional environmental model according to the convolutional neural network algorithm, and recognize multiple signal reception positions in the initial ground area.

[0071] In this embodiment, the initial ground area refers to a specific ground area that is preset and requires key attention during environmental scanning and target detection.

[0072] In this embodiment, a high-resolution image sensor is a device that can capture images with rich details and high precision.

[0073] In this embodiment, the three-dimensional environmental model is a digital representation of the physical space, generated through measurement and data processing, and contains information such as the shape, position, size, and spatial relationship of the objects in the scene.

[0074] In this embodiment, the convolutional neural network algorithm is a deep learning algorithm used to process image data and extract features of the image through convolutional layers. The convolution operation slides multiple filters (or convolutional kernels) on the input image to generate a feature map and capture local features.

[0075] In this embodiment, the signal reception position refers to a specific point or area within a certain specific area that can receive signals (such as radio signals, optical signals, audio signals, etc.).

[0076] The working principle and beneficial effects of the above technical solution are as follows: A three-dimensional environment model is generated by a high-resolution image sensor, and a convolutional neural network is used to identify the signal reception positions, improving the target detection accuracy and the system decision-making efficiency, and having good adaptability and flexibility.

[0077] Embodiment 3:

[0078] Based on the above Embodiment 2, the sensor module includes:

[0079] Signal evaluation unit: Measure the satellite signal strength at any signal reception position and construct a relationship diagram between the signal strength and the geographical location;

[0080] Sensor unit: According to the signal strengths of all signal reception positions in the relationship diagram, select the signal reception positions higher than the preset threshold as the preferred signal reception positions and assign a unique first number to each preferred signal reception position, assign a unique second number to each sensor, and install and deploy all sensors according to the unique corresponding relationship between the sensors and the preferred signal reception positions.

[0081] In this embodiment, the relationship diagram is a visualization tool used to represent and analyze the relationship between the signal strength and the geographical location.

[0082] In this embodiment, the signal strength refers to the intensity power of a wireless signal (such as a satellite signal, a radio signal, or other electromagnetic wave signals) received at a specific position.

[0083] In this embodiment, the geographical location refers to the specific coordinates or position of a point on the earth's surface, usually represented by longitude and latitude.

[0084] In this embodiment, the preferred signal reception position refers to the positions selected during the signal evaluation process according to the signal strength measurement results, where the signal quality is higher than the preset threshold.

[0085] In this embodiment, the first number refers to the unique identifier assigned to each preferred signal reception position.

[0086] In this embodiment, the second number is the unique identifier assigned to each sensor.

[0087] The working principle and beneficial effects of the above technical solution are as follows: By measuring the signal strength to construct a relationship diagram, selecting the preferred signal reception positions and assigning unique numbers, optimizing the signal reception and system performance, improving the management efficiency, and having good adaptability.

[0088] Embodiment 4:

[0089] Based on the above Embodiment 1, the signal processing module includes:

[0090] Normalization Unit: Receives the original signal data obtained by the sensor in real time, applies a filter to remove the noise in the original signal data, and obtains the first signal data;

[0091] Normalizes the first signal data into a preset signal range and performs standardization to obtain the standard signal data:

[0092] ; where, represents the standardized standard signal value at the i-th time node in the first signal data, represents the signal value at the i-th time node in the first signal data, represents the maximum value of the signal values in the first signal data, represents the minimum value of the signal values in the first signal data, represents the mean value of the signal values in the first signal data, represents the variance of the signal values in the first signal data;

[0093] Spectrum Transformation Unit: Applies a spectrum transformation tool to the standard signal data to transform the time-domain signal into a frequency-domain signal and obtains the first spectrum.

[0094] In this embodiment, noise refers to any unwanted signal data in the original signal during signal transmission or measurement.

[0095] In this embodiment, the first signal data is the original signal data obtained after being processed by the filter.

[0096] In this embodiment, normalization is a process of converting data into a standard range.

[0097] In this embodiment, the preset signal range refers to the target value range set for normalization or standardization during data processing.

[0098] In this embodiment, the spectrum transformation tool is an algorithm software tool for converting a time-domain signal into a frequency-domain signal.

[0099] In this embodiment, the time domain refers to the form of signal variation over time. In the time domain, the amplitude of the signal changes over time.

[0100] In this embodiment, the frequency domain refers to the form of signal representation in different frequency components. In the frequency domain, the signal is represented as the amplitude and phase information of each frequency component.

[0101] The working principle and beneficial effects of the above technical solution are: By denoising and standardizing, the quality of the signal data is improved, consistency is ensured, the time-domain signal is converted into the frequency domain, the frequency-domain analysis ability is enhanced, a reliable basis is provided for subsequent processing, and the overall efficiency of the system is improved.

[0102] Example 5:

[0103] Based on the above Example 1, the feature recognition module includes:

[0104] Analysis unit: Analyze all the spectral signals of the first spectrum using the fast Fourier transform to obtain the statistical characteristics of the first spectrum;

[0105] Feature unit: Construct a feature matrix according to each spectral signal of the first spectrum, and perform covariance operation on the feature matrix to obtain the frequency domain features of each spectral signal;

[0106] Abnormal fluctuation analysis: Obtain the signal abnormal fluctuation threshold based on the historical record library, and screen the signal points with signal abnormal fluctuations by combining the statistical characteristics of the first spectrum and the frequency domain features of each spectral signal.

[0107] In this embodiment, the fast Fourier transform is an algorithm for efficiently calculating the discrete Fourier transform and its inverse transform.

[0108] In this embodiment, the spectral signal refers to the representation of the frequency components obtained by performing Fourier transform on the time-domain signal.

[0109] In this embodiment, the statistical characteristics refer to the quantitative descriptions extracted by analyzing the data set, including: mean, variance, amplitude, phase, etc.

[0110] In this embodiment, the feature matrix represents the matrix of the feature information of the sample.

[0111] In this embodiment, the covariance operation is a tool in statistics for measuring the relationship between random variables.

[0112] In this embodiment, the frequency domain features refer to the features manifested in the frequency domain obtained by performing spectral analysis on the signal, such as: frequency, amplitude size, centroid position, etc.

[0113] In this embodiment, the historical record library refers to a database that stores past signal data and related features.

[0114] In this embodiment, the signal abnormal fluctuation threshold is a standard boundary for detecting signal abnormalities.

[0115] In this embodiment, abnormal fluctuation refers to that during the change process of the signal, the fluctuation amplitude, frequency or form of some points or intervals significantly deviate from the normal mode or expected range.

[0116] In this embodiment, combine the statistical characteristics and the frequency domain features to establish a scoring system. Each feature generates a weight coefficient, and then calculate a comprehensive score for each signal point to represent its degree of abnormality.

[0117] The working principle and beneficial effects of the above technical solution are as follows: By analyzing the spectral signal through fast Fourier transform, constructing a feature matrix and performing covariance operations, frequency domain features are extracted, and combined with historical data to identify abnormal fluctuations, thereby improving the signal analysis accuracy and system stability.

[0118] Example 6:

[0119] Based on the above Example 1, the interference suppression module includes:

[0120] Interference classification unit: Based on the clustering algorithm, clustering is performed on the frequency domain features of each spectral signal in the first spectrum. If the Manhattan distance of the frequency domain features of the spectral signals in the same cluster is less than the preset maximum clustering distance, then the spectral signals in the same cluster are the same type of interference signals. After clustering, isolated points are screened out;

[0121] Classification matching unit: According to the interference category - frequency domain feature mapping table, match all the interference categories corresponding to the clusters to obtain all the interference categories of the original signal data.

[0122] In this embodiment, the clustering algorithm is an unsupervised learning method used to group the samples in the dataset according to their features, so that the samples within the same group are similar to each other, while the samples between different groups have large differences.

[0123] In this embodiment, the Manhattan distance is a metric used to calculate the distance between two points.

[0124] In this embodiment, the preset maximum clustering distance refers to a threshold set by the user in the clustering algorithm to determine whether the frequency domain features of two spectral signals can be classified as the same type of interference signal.

[0125] In this embodiment, isolated points refer to those data points in the clustering analysis that cannot form an effective cluster with any other data points.

[0126] In this embodiment, the interference category - frequency domain feature mapping table is a table representing the mapping relationship between interference categories and frequency domain features.

[0127] The working principle and beneficial effects of the above technical solution are as follows: By classifying interference signals based on frequency domain features through the clustering algorithm, using the Manhattan distance to ensure the rationality of clustering and screening out isolated points, and finally matching through the mapping table to obtain all interference categories, which improves the accuracy and efficiency of signal processing.

[0128] Example 7:

[0129] Based on the above Example 1, the sensor module includes:

[0130] Filter parameter unit: Initialize the order of the adaptive filter according to the types of interference categories of the original signal data; Set the first parameter of the adaptive filter according to the dynamic characteristics of all interference categories.

[0131] Testing unit: Use historical signal data to conduct a simulation test on the adaptive filter to obtain a filtering test result, and perform deviation analysis based on the filtering test result and the standard satellite signal to obtain the monitored signal-to-noise ratio.

[0132] Adjustment unit: Adjust the first parameter of the adaptive filter based on the monitored signal-to-noise ratio to obtain the final parameter, configure the adaptive filter according to the final parameter, and receive real-time satellite signals to obtain the filter performance index, and adjust the filter parameters based on the filter performance index to suppress interference.

[0133] 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 to, but the computational amount also increases.

[0134] In this embodiment, the first parameter refers to the filter parameter used to adjust the filter performance.

[0135] In this embodiment, the historical signal data refers to the signal samples collected in the past and related to the current application scenario.

[0136] In this embodiment, the simulation test refers to a method of evaluating and validating the adaptive filter using historical signal data in a controlled environment.

[0137] In this embodiment, the filtering test result refers to the output data obtained by comparing the signal processed by the adaptive filter with the standard signal.

[0138] In this embodiment, the deviation analysis refers to a step in signal processing, which evaluates the performance and effectiveness of the filter by comparing the filter output signal with the standard reference signal.

[0139] In this embodiment, the monitored signal-to-noise ratio refers to the ratio between the effective signal power and the noise power in the filtered signal.

[0140] In this embodiment, the final parameter is the optimal parameter setting obtained by monitoring the signal-to-noise ratio during the adjustment process of the adaptive filter.

[0141] In this embodiment, the real-time satellite signal refers to the instant signal data transmitted from the satellite to the receiving device.

[0142] In this embodiment, the filter performance index is a key parameter used to evaluate the effect of the adaptive filter when processing signals, such as: signal-to-noise ratio, mean square error, filter bag width, gain stability, etc.

[0143] The working principle and beneficial effects of the above technical solution are as follows: By dynamically adjusting the parameters of the adaptive filter, optimizing according to the interference category and signal-to-noise ratio, the accuracy of signal processing and the anti-interference ability are improved, ensuring the stability and reliability of real-time satellite signals.

[0144] Example 8:

[0145] As Figure 2 shown, a satellite navigation interference detection and suppression method provided by an embodiment of the present invention includes:

[0146] Step 1: Detect the sky according to the target to determine the target area where the sensor is installed, and deploy and install the sensor in the target area to obtain the original signal data of the satellite;

[0147] Step 2: Receive the original signal data in real time, standardize the original signal data to obtain standard signal data, and convert the standard signal data into the first spectrum;

[0148] Step 3: Analyze the abnormal fluctuations of the first spectrum through fast Fourier transform to obtain the characteristics of the interference signal;

[0149] Step 4: Classify the characteristics of the interference signal according to the characteristics of the interference signal to obtain all interference categories of the original signal data;

[0150] Step 5: According to the dynamic characteristics of the interference category, use an adaptive filter to adjust the received signal in real time to reduce interference.

[0151] The working principle and beneficial effects of the above technical solution are as follows: By deploying sensors, real-time data standardization and spectrum analysis, interference signals are identified and classified, and an adaptive filtering technology is used to dynamically adjust the received signal, significantly improving the anti-interference ability and data processing efficiency of the satellite navigation system.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate 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: Based on the dynamic characteristics of the interference category, the adaptive filter is used to adjust parameters in real time to reduce interference; Wherein, the interference suppression 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.

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 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, an adaptive filter is used to adjust the received signal in real time to reduce interference; Wherein, step 5 includes: 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; Use historical signal data to simulate and test the adaptive filter to obtain the filter test results, and perform deviation analysis based on the filter test results and standard satellite signals to obtain the monitoring signal-to-noise ratio; Based on the monitored signal-to-noise ratio, a first parameter of the adaptive filter is adjusted to obtain a final parameter, the adaptive filter is configured according to the final parameter, and a real-time satellite signal is received to obtain a filter performance index, and the filter parameters are adjusted according to the filter performance index to suppress interference.

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