A method and system for separating, identifying and monitoring radiation background noise

Abstract

The present application relates to a kind of separation identification and monitoring method and system of radiation background noise, in separation identification and monitoring method, background noise and equipment emission signal are synchronously collected by multiple fixed reference antennas and main antenna, and time synchronization is realized by central synchronous unit.First, based on spectrum comparison, background noise probability is calculated, and first grade is filtered out;Second, baseline model is established according to reference antenna data, abnormal signal is identified and its source is judged, and burst background noise and equipment real emission are distinguished;Second grade is filtered out by using pre-stored feature library for matching identification;Finally, the comparison result of real emission signal and background noise after filtering is displayed on test interface, and the data exceeding the standard is automatically marked, test margin is calculated and conclusion is generated.Compared with prior art, the present application can effectively solve the technical problem that background noise and equipment signal are difficult to separate in real time and accurately in complex electromagnetic environment, and the reliability and efficiency of radiation test are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic compatibility testing technology, and in particular to a method and system for separating, identifying and monitoring radiated background noise. Background Technology

[0002] Electromagnetic radiation monitoring is a key means of assessing the electromagnetic compatibility (EMC) and environmental impact of electronic equipment. However, in field testing, background noise interference severely affects the accuracy of monitoring data. Traditional background noise subtraction methods often struggle to cope with complex and variable electromagnetic environments, especially in scenarios with complex spatial distribution of noise sources and frequent sudden noise events, where existing technologies have significant limitations.

[0003] For example, the existing technology CN119936764A proposes a background noise subtraction method based on generative adversarial networks (GANs). Although this method can estimate noise through data-driven estimation, it relies on a large amount of historical data for training, resulting in poor model stability and an inability to respond in real time to dynamically changing noise (such as sudden interference). Furthermore, its single-antenna design makes it difficult to distinguish between spatial noise and device signals. Another existing technology, CN105842561A, uses a spectrum analyzer and adaptive filtering algorithm to collect noise data through maximum hold mode. However, limited by its single-antenna architecture, it cannot utilize spatial information to distinguish between co-located noise and actual transmissions. Moreover, the adaptive filtering is prone to misjudgment when noise spectra overlap, making it difficult to identify unknown or sudden noise types.

[0004] The aforementioned existing methods all share a common core problem: in complex spatial and dynamic noise environments, they cannot achieve accurate, real-time separation and identification of background noise and device transmitted signals. This leads to large deviations in the calculation of test result margins, affecting the reliability of EMC assessments. Summary of the Invention

[0005] The purpose of this invention is to overcome the defects of the prior art by providing a method and system for separating, identifying and monitoring radiated background noise. Through multi-antenna synchronous acquisition, probability modeling, baseline analysis and feature matching, this key technical problem is solved.

[0006] The objective of this invention can be achieved through the following technical solutions: This invention provides a method for separating, identifying, and monitoring radiated background noise, comprising the following steps: S1. Synchronously collect the background noise when the test equipment is not running and the transmission signal when it is running. The background noise is collected through multiple fixed reference antennas, the transmission signal is collected through the main antenna, and the central synchronization unit performs time synchronization on all data streams. S2. Based on the synchronized data, compare the spectrum of the main antenna signal and each reference antenna signal in the same frequency domain, calculate the background noise probability at frequency points with similar signal strength, and mark and filter out the high-probability background noise accordingly. S3. Establish a baseline model based on the background noise data collected by the reference antenna, identify signals in the subsequent collected data that exceed the normal fluctuation range of the baseline model as abnormal signals, and then determine the source of the abnormal signal: if it appears in both the main antenna and the reference antenna, it is determined to be sudden background noise and marked; if it only appears in the main antenna, it is determined to be the actual transmission of the test equipment. S4. Using a feature library containing known background noise sample frequencies, envelopes and modulation schemes, the burst background noise marked in step S3 and the real-time acquired signals are matched. The successfully matched signals are directly marked as background noise and filtered out in the second stage. S5. Based on the actual transmitted signal of the test equipment after the two-stage filtering, the comparison between the signal and the background noise is displayed on the test interface. The part that exceeds the preset limit is marked with color, the test data point closest to the limit is automatically located, the final test margin is calculated and the test conclusion is generated accordingly.

[0007] Furthermore, S1 specifically includes: The multiple fixed reference antennas are deployed at multiple preset locations in the test site, and the main antenna is deployed at the center position; A unified timestamp signal is generated using the central synchronization unit and distributed to the data acquisition modules corresponding to each antenna, so that the acquired background noise data and the transmitted signal data are aligned in the time domain.

[0008] Furthermore, in S2, the process of calculating the background noise probability includes: At the same frequency, calculate the difference or ratio between the main antenna signal and the signal strength of at least one reference antenna, and combine it with a preset threshold function to map the difference or ratio into a background noise probability value. Signals with a probability value higher than a set threshold are marked as high-probability background noise and temporarily isolated from the analysis data stream of the main antenna.

[0009] Furthermore, in step S2, signals that were not filtered out by the first stage are retained and combined with the actual transmission signals of the test equipment identified in step S3 to form a set of intermediate signals to be further analyzed.

[0010] Furthermore, in S3, the process of establishing a baseline model includes: Based on the clean background noise data collected by the reference antenna within a preset time period, the mean and standard deviation of the signal strength at each frequency point are calculated by statistical analysis methods, and the normal fluctuation range is defined by the mean plus or minus a certain number of times the standard deviation. The subsequent data collected is: mixed signal data collected synchronously in real time by the main antenna and the reference antenna during the radiation test.

[0011] Furthermore, S3 also includes a process for judging the source of abnormal signals in real time: when it is determined to be sudden background noise, the system records the frequency, intensity and time information of the abnormal signal to a temporary interference log and excludes the signal from the effective transmission signal of the main antenna.

[0012] Furthermore, in S4, the feature library is constructed in advance by performing signal analysis on known background noise samples and extracting their inherent features; The matching process includes: calculating the similarity between the frequency, envelope shape, and modulation parameters of the real-time signal and the records in the feature library; when the similarity exceeds the matching threshold, it is determined to be background noise of a known type.

[0013] Furthermore, the feature library supports dynamic updates. When the sudden background noise marked in step S3 is recorded multiple times and confirmed as a new type of stable background noise, the user is prompted to extract its feature parameters and add them to the feature library for matching and filtering in subsequent tests.

[0014] A second aspect of the present invention provides a radiation background noise separation, identification and monitoring system for implementing the above-described method, comprising: a signal acquisition and synchronization module, a primary processing and filtering module, an anomaly detection and analysis module, an intelligent matching and filtering module, and a result presentation and judgment module; The signal acquisition and synchronization module is used to synchronously acquire background noise and the transmitted signal of the test equipment through multiple fixed reference antennas and the main antenna, and to ensure the time synchronization of all data streams through the central synchronization unit. The primary processing and filtering module is used to compare the spectra of the main antenna and the reference antenna based on the synchronized data, calculate the background noise probability, and perform the first-level filtering. The anomaly detection and analysis module is used to establish a background noise baseline model based on reference antenna data, identify and determine the source of abnormal signals, and distinguish between sudden background noise and actual equipment transmission. The intelligent matching and filtering module uses a pre-stored feature library of known noise features to match and identify the signal and perform a second-level filtering. The result presentation and judgment module is used to display the comparison results between the real transmitted signal and the background noise after two-stage filtering, perform out-of-range marking, margin calculation and generate test conclusions.

[0015] Furthermore, the central synchronization unit includes a high-precision clock source, a trigger signal distributor, and a timestamp marking module; The high-precision clock source is a GPS receiver or a temperature-controlled crystal oscillator, used to generate a unified time reference signal; The trigger signal distributor converts the time reference signal into a synchronous trigger pulse and distributes it to the data acquisition modules corresponding to each fixed reference antenna and the main antenna to control them to start data acquisition synchronously. The timestamp marking module is used to mark the data streams collected by each data acquisition module with a unified high-precision timestamp.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The radiated background noise separation, identification, and monitoring method provided by this invention has significant beneficial effects. This invention ensures data time consistency through multi-antenna synchronous acquisition and a central synchronization unit, and achieves first-level background noise filtering by combining spectrum comparison and probability calculation, effectively improving the real-time performance and accuracy of signal separation. By establishing a baseline model using reference antenna data, it can intelligently identify sudden background noise and distinguish between actual equipment transmissions, avoiding spatial dimension misjudgments. Further, a second-level matching filter is performed using a pre-stored feature library, enhancing adaptability to complex noise environments. Finally, through two-level filtering and visualization, the test margin is automatically calculated and conclusions are generated, significantly improving the reliability and efficiency of radiation testing and overcoming the limitations of traditional methods in dynamic noise processing. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the method for separating, identifying, and monitoring radiated background noise in this invention. Detailed Implementation

[0018] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0019] Example 1 The method for separating, identifying, and monitoring radiated background noise in this embodiment is described in [reference needed]. Figure 1 This includes the following steps: S1. Synchronously collect the background noise when the test equipment is not running and the transmission signal when it is running. The background noise is collected through multiple fixed reference antennas, the transmission signal is collected through the main antenna, and the central synchronization unit performs time synchronization on all data streams. In specific implementation, S1 includes: The multiple fixed reference antennas are deployed at multiple preset locations in the test site, and the main antenna is deployed at the center position; A unified timestamp signal is generated using the central synchronization unit and distributed to the data acquisition modules corresponding to each antenna, so that the acquired background noise data and the transmitted signal data are aligned in the time domain.

[0020] S2. Based on the synchronized data, compare the spectrum of the main antenna signal and each reference antenna signal in the same frequency domain, calculate the background noise probability at frequency points with similar signal strength, and mark and filter out the high-probability background noise accordingly. In specific implementation, the process of calculating the background noise probability in S2 includes: At the same frequency, calculate the difference or ratio between the main antenna signal and the signal strength of at least one reference antenna, and combine it with a preset threshold function to map the difference or ratio into a background noise probability value. Signals with a probability value higher than a set threshold are marked as high-probability background noise and temporarily isolated from the analysis data stream of the main antenna.

[0021] In specific implementation, S2 also includes retaining the signals that were not filtered out by the first stage and summarizing them with the actual transmission signals of the test equipment identified in step S3 to form an intermediate signal set for further analysis.

[0022] S3. Establish a baseline model based on the background noise data collected by the reference antenna, identify signals in the subsequent collected data that exceed the normal fluctuation range of the baseline model as abnormal signals, and then determine the source of the abnormal signal: if it appears in both the main antenna and the reference antenna, it is determined to be sudden background noise and marked; if it only appears in the main antenna, it is determined to be the actual transmission of the test equipment. In practice, the process of establishing a baseline model in S3 includes: Based on the clean background noise data collected by the reference antenna within a preset time period, the mean and standard deviation of the signal strength at each frequency point are calculated by statistical analysis methods, and the normal fluctuation range is defined by the mean plus or minus a certain number of times the standard deviation. The subsequent data collected is: mixed signal data collected synchronously in real time by the main antenna and the reference antenna during the radiation test.

[0023] In specific implementation, S3 also includes a process of judging the source of abnormal signals in real time: when it is determined to be sudden background noise, the system records the frequency, intensity and time information of the abnormal signal to a temporary interference log and excludes the signal from the effective transmission signal of the main antenna.

[0024] S4. Using a feature library containing known background noise sample frequencies, envelopes and modulation schemes, the burst background noise marked in step S3 and the real-time acquired signals are matched. The successfully matched signals are directly marked as background noise and filtered out in the second stage. In specific implementation, in S4, the feature library is constructed in advance by extracting the inherent features of known background noise samples through signal analysis. The matching process includes: calculating the similarity between the frequency, envelope shape, and modulation parameters of the real-time signal and the records in the feature library; when the similarity exceeds the matching threshold, it is determined to be background noise of a known type.

[0025] In practice, the feature library supports dynamic updates. When the sudden background noise marked in step S3 is recorded multiple times and confirmed as a new type of stable background noise, the user is prompted to extract its feature parameters and add them to the feature library for matching and filtering in subsequent tests.

[0026] S5. Based on the actual transmitted signal of the test equipment after the two-stage filtering, the comparison between the signal and the background noise is displayed on the test interface. The part that exceeds the preset limit is marked with color, the test data point closest to the limit is automatically located, the final test margin is calculated and the test conclusion is generated accordingly.

[0027] In this embodiment, the method for separating, identifying, and monitoring radiated background noise begins with the data acquisition and synchronization process. After deploying fixed reference antennas at multiple preset locations on the test site and a main antenna at the center, the central synchronization unit generates a unified high-precision timestamp signal and distributes it to the corresponding data acquisition modules of each antenna, ensuring that all acquired background noise data and transmitted signal data are perfectly aligned in the time domain. This synchronization process relies on a GPS receiver or a temperature-controlled crystal oscillator as a high-precision clock source. A trigger signal distributor converts the time reference into synchronization trigger pulses, controlling each module to simultaneously start acquisition. When the test equipment is not running, multiple fixed reference antennas continuously acquire clean background noise data, while when the equipment is running, the main antenna acquires mixed data including the transmitted signal. All data streams are tagged with a unified timestamp for subsequent analysis.

[0028] Based on the synchronized data, the system enters the first-level processing stage, which involves comparing the spectral intensity of the main antenna signal with that of each reference antenna signal point-by-point within the same frequency domain. Specifically, the algorithm calculates the difference or ratio between the main antenna signal and at least one reference antenna signal at each frequency point, and then maps this value to a background noise probability value using a preset threshold function. If the probability value is higher than the set threshold, the frequency point is marked as high-probability background noise and temporarily isolated from the main antenna's analysis data stream. Unfiltered signals are retained and combined with the actual transmitted signals identified in subsequent steps to form an intermediate signal set. Simultaneously, the system establishes a baseline model based on the clean background noise data collected by the reference antennas, and calculates the mean and standard deviation of the signal intensity at each frequency point using statistical analysis methods. The normal fluctuation range is defined by adding or subtracting a certain multiple of the standard deviation from the mean. When the mixed signal data collected in real-time exceeds this range, the system identifies it as an abnormal signal and determines its source: if the abnormal signal appears simultaneously in both the main antenna and reference antennas, it is determined to be sudden background noise, and its frequency intensity and time information are recorded in a temporary interference log and excluded; if it only appears in the main antenna, it is determined to be a real transmission from the test equipment.

[0029] The second level of intelligent filtering is then executed, using a pre-built feature library to match the marked burst background noise with the real-time signal. The feature library is constructed by extracting inherent features such as the frequency envelope shape and modulation parameters from known background noise samples through signal analysis. During the matching process, the system calculates the similarity between the frequency envelope and modulation characteristics of the real-time signal and the records in the feature library. When the similarity exceeds the matching threshold, it is directly identified as known background noise and filtered out. The feature library supports a dynamic update mechanism. When burst background noise is recorded multiple times and confirmed as a new type of stable noise, the system prompts the user to extract its feature parameters and add them to the library to optimize subsequent matching accuracy. Finally, the actual transmitted signal after two levels of filtering is displayed on the test interface in comparison with the background noise. The interface automatically colors the parts exceeding the preset limit and locates the test data point closest to the limit. Based on these data, the final test margin is calculated and a test conclusion is generated. The entire process achieves accurate separation and monitoring of background noise in complex environments through multi-level collaborative processing.

[0030] Example 2 This embodiment provides a radiation background noise separation, identification and monitoring system for implementing the above-described method, including: a signal acquisition and synchronization module, a primary processing and filtering module, an anomaly detection and analysis module, an intelligent matching and filtering module, and a result presentation and judgment module; The signal acquisition and synchronization module is used to synchronously acquire background noise and the transmitted signal of the test equipment through multiple fixed reference antennas and the main antenna, and to ensure the time synchronization of all data streams through the central synchronization unit. The primary processing and filtering module is used to compare the spectra of the main antenna and the reference antenna based on the synchronized data, calculate the background noise probability, and perform the first-level filtering. The anomaly detection and analysis module is used to establish a background noise baseline model based on reference antenna data, identify and determine the source of abnormal signals, and distinguish between sudden background noise and actual equipment transmission. The intelligent matching and filtering module uses a pre-stored feature library of known noise features to match and identify the signal and perform a second-level filtering. The result presentation and judgment module is used to display the comparison results between the real transmitted signal and the background noise after two-stage filtering, perform out-of-range marking, margin calculation and generate test conclusions.

[0031] In practice, the central synchronization unit includes a high-precision clock source, a trigger signal distributor, and a timestamp marking module; The high-precision clock source is a GPS receiver or a temperature-controlled crystal oscillator, used to generate a unified time reference signal; The trigger signal distributor converts the time reference signal into a synchronous trigger pulse and distributes it to the data acquisition modules corresponding to each fixed reference antenna and the main antenna to control them to start data acquisition synchronously. The timestamp marking module is used to mark the data streams collected by each data acquisition module with a unified high-precision timestamp.

[0032] The radiated background noise separation, identification, and monitoring system provided in this embodiment first achieves accurate data acquisition and time alignment through a signal acquisition and synchronization module. This module deploys fixed reference antennas at multiple preset locations within the test site and a main antenna at the center. A unified time reference signal is generated using a high-precision clock source from the central synchronization unit. This high-precision clock source can employ a GPS receiver or a temperature-controlled crystal oscillator to ensure signal stability. A trigger signal distributor converts the time reference signal into synchronization trigger pulses, distributing them to the data acquisition modules corresponding to each fixed reference antenna and the main antenna, controlling all modules to synchronously begin data acquisition. A timestamp marking module then adds a unified high-precision time tag to the data stream acquired by each data acquisition module, ensuring that the background noise data and the device's transmitted signal are completely aligned in the time domain. The synchronized data is then sent to a primary processing and filtering module. This module compares the spectral intensity of the main antenna signal with that of each reference antenna signal point-by-point within the same frequency domain. Specifically, the algorithm calculates the difference or ratio between the main antenna signal and at least one reference antenna signal intensity at the same frequency point, and then maps this value to a background noise probability value using a preset threshold function. If the probability value is higher than a set threshold, the frequency point is marked as high-probability background noise and temporarily isolated from the analysis data stream of the main antenna, completing the first stage of filtering. The unfiltered signals are retained and passed to subsequent modules.

[0033] The anomaly detection and analysis module performs fine signal differentiation. This module establishes a baseline model based on clean background noise data collected by the reference antenna within a preset time period. The modeling process uses statistical analysis to calculate the mean and standard deviation of signal strength at each frequency point, defining the normal fluctuation range as the mean plus or minus a certain multiple of the standard deviation. When the mixed signal data collected synchronously by the main antenna and the reference antenna in real time exceeds this range, the system identifies it as an abnormal signal and determines its source: if the abnormal signal appears simultaneously in both the main antenna and the reference antenna, it is determined to be sudden background noise. The system records the frequency intensity and time information of this signal in a temporary interference log and excludes it from the valid transmission signal of the main antenna; if the abnormal signal only appears in the main antenna, it is determined to be a real transmission from the test equipment. The intelligent matching and filtering module performs a second level of filtering using a pre-stored feature library of known noise features. The feature library is constructed by pre-analyzing known background noise samples to extract their inherent features such as frequency envelope shape and modulation parameters. The matching process calculates the similarity between the frequency envelope and modulation characteristics of the real-time signal and the feature library records. When the similarity exceeds the matching threshold, it is directly determined to be known type background noise and filtered out. The feature library supports dynamic updates. When sudden background noise is recorded multiple times and confirmed as a new type of stable noise, the system prompts the user to extract its feature parameters and add them to the library. The result presentation and judgment module ultimately displays the comparison results between the real transmitted signal and the background noise after two-stage filtering. It automatically marks the parts that exceed the preset limit with color and locates the test data point closest to the limit. Based on this, it calculates the final test margin and generates the test conclusion.

[0034] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A method for separating, identifying, and monitoring radiated background noise, characterized in that, Includes the following steps: S1. Synchronously collect the background noise when the test equipment is not running and the transmission signal when it is running. The background noise is collected through multiple fixed reference antennas, the transmission signal is collected through the main antenna, and the central synchronization unit performs time synchronization on all data streams. S2. Based on the synchronized data, compare the spectrum of the main antenna signal and each reference antenna signal in the same frequency domain, calculate the background noise probability at frequency points with similar signal strength, and mark and filter out the high-probability background noise accordingly. S3. Establish a baseline model based on the background noise data collected by the reference antenna, identify signals in the subsequent collected data that exceed the normal fluctuation range of the baseline model as abnormal signals, and then determine the source of the abnormal signal: if it appears in both the main antenna and the reference antenna, it is determined to be sudden background noise and marked; if it only appears in the main antenna, it is determined to be the actual transmission of the test equipment. S4. Using a feature library containing known background noise sample frequencies, envelopes and modulation schemes, the burst background noise marked in step S3 and the real-time acquired signals are matched. The successfully matched signals are directly marked as background noise and filtered out in the second stage. S5. Based on the actual transmitted signal of the test equipment after the two-stage filtering, the comparison between the signal and the background noise is displayed on the test interface. The part that exceeds the preset limit is marked with color, the test data point closest to the limit is automatically located, the final test margin is calculated and the test conclusion is generated accordingly.

2. The method for separating, identifying, and monitoring radiated background noise according to claim 1, characterized in that, S1 specifically includes: The multiple fixed reference antennas are deployed at multiple preset locations in the test site, and the main antenna is deployed at the center position; A unified timestamp signal is generated using the central synchronization unit and distributed to the data acquisition modules corresponding to each antenna, so that the acquired background noise data and the transmitted signal data are aligned in the time domain.

3. The method for separating, identifying, and monitoring radiated background noise according to claim 1, characterized in that, In S2, the process of calculating the background noise probability includes: At the same frequency, calculate the difference or ratio between the main antenna signal and the signal strength of at least one reference antenna, and combine it with a preset threshold function to map the difference or ratio into a background noise probability value. Signals with a probability value higher than a set threshold are marked as high-probability background noise and temporarily isolated from the analysis data stream of the main antenna.

4. The method for separating, identifying, and monitoring radiated background noise according to claim 1, characterized in that, S2 also includes retaining the signals that were not filtered out in the first stage and summarizing them with the actual transmission signals of the test equipment identified in step S3 to form an intermediate signal set for further analysis.

5. The method for separating, identifying, and monitoring radiated background noise according to claim 1, characterized in that, In S3, the process of establishing a baseline model includes: Based on the clean background noise data collected by the reference antenna within a preset time period, the mean and standard deviation of the signal strength at each frequency point are calculated by statistical analysis methods, and the normal fluctuation range is defined by the mean plus or minus a certain number of times the standard deviation. The subsequent data collected is: mixed signal data collected synchronously in real time by the main antenna and the reference antenna during the radiation test.

6. The method for separating, identifying, and monitoring radiated background noise according to claim 5, characterized in that, S3 also includes a process for judging the source of abnormal signals in real time: when it is determined to be sudden background noise, the system records the frequency, intensity and time information of the abnormal signal to a temporary interference log and excludes the signal from the effective transmission signal of the main antenna.

7. The method for separating, identifying, and monitoring radiated background noise according to claim 1, characterized in that, In S4, the feature library is constructed in advance by performing signal analysis on known background noise samples and extracting their inherent features; The matching process includes: calculating the similarity between the frequency, envelope shape, and modulation parameters of the real-time signal and the records in the feature library; when the similarity exceeds the matching threshold, it is determined to be background noise of a known type.

8. The method for separating, identifying, and monitoring radiated background noise according to claim 7, characterized in that, When the sudden background noise marked in step S3 is recorded multiple times and confirmed as a new type of stable background noise, the user is prompted to extract its feature parameters and add them to the feature library for matching and filtering in subsequent tests.

9. A system for separating, identifying, and monitoring radiated background noise to implement the method of claim 1, characterized in that, include: It includes a signal acquisition and synchronization module, a primary processing and filtering module, an anomaly detection and analysis module, an intelligent matching and filtering module, and a result presentation and judgment module; The signal acquisition and synchronization module is used to synchronously acquire background noise and the transmitted signal of the test equipment through multiple fixed reference antennas and the main antenna, and to ensure the time synchronization of all data streams through the central synchronization unit. The primary processing and filtering module is used to compare the spectra of the main antenna and the reference antenna based on the synchronized data, calculate the background noise probability, and perform the first-level filtering. The anomaly detection and analysis module is used to establish a background noise baseline model based on reference antenna data, identify and determine the source of abnormal signals, and distinguish between sudden background noise and actual equipment transmission. The intelligent matching and filtering module uses a pre-stored feature library of known noise features to match and identify the signal and perform a second-level filtering. The result presentation and judgment module is used to display the comparison results between the real transmitted signal and the background noise after two-stage filtering, perform out-of-range marking, margin calculation and generate test conclusions.

10. The radiated background noise separation, identification, and monitoring system according to claim 9, characterized in that, The central synchronization unit includes a high-precision clock source, a trigger signal distributor, and a timestamp marking module; The high-precision clock source is a GPS receiver or a temperature-controlled crystal oscillator, used to generate a unified time reference signal; The trigger signal distributor converts the time reference signal into a synchronous trigger pulse and distributes it to the data acquisition modules corresponding to each fixed reference antenna and the main antenna to control them to start data acquisition synchronously. The timestamp marking module is used to mark the data streams collected by each data acquisition module with a unified high-precision timestamp.

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