A signal analysis device with multi-band on-screen display function

Abstract

This application provides a signal analysis device with multi-band simultaneous display capability, relating to the field of signal monitoring and analysis technology. The specific solution includes a radio frequency (RF) array module, a preprocessing module, and a signal processing module. The RF array module includes multiple parallel RF receiving units, each configured to receive at least one spatial diversity signal from an antenna array. The preprocessing module includes multiple preprocessing units, each signal-connected to one RF receiving unit, configured to receive the signal emitted by the RF receiving unit and convert it into a baseband data stream. The signal processing module is signal-connected to both the RF array module and the preprocessing module; the signal processing module includes a first scanning unit, a second scanning unit, and a control unit. This application's embodiment can adaptively allocate monitoring resources, improving wideband global situational awareness while performing refined analysis of the frequency bands where abnormal signals are located.

Description

Technical Field

[0001] This application relates to the field of signal monitoring and analysis technology, specifically to a signal analysis device with multi-band simultaneous display function. Background Technology

[0002] With the rapid development of wireless communication technology, the electromagnetic environment is becoming increasingly complex, and various radio signals are exhibiting highly dense, diverse, and dynamic characteristics in the frequency, time, and spatial domains. Radio monitoring, as a crucial means of ensuring the rational use of spectrum resources and maintaining electromagnetic space security, faces unprecedented technical challenges.

[0003] However, existing signal analysis devices, under limited hardware resources, struggle to achieve dynamic coordination between wideband coverage and high-precision measurement, and are unable to adaptively allocate monitoring resources based on anomaly detection results, thus hindering the improvement of global situational awareness across the wideband while simultaneously enabling refined analysis of the frequency bands where abnormal signals are located. Summary of the Invention

[0004] This application provides a signal analysis device with multi-band simultaneous display function, which can adaptively allocate monitoring resources, improve the global situational awareness of the wide frequency band, and perform refined analysis of the frequency band where abnormal signals are located.

[0005] The specific technical solution of this embodiment is as follows:

[0006] This application provides a signal analysis device with multi-band simultaneous display function, including a radio frequency array module, a preprocessing module and a signal processing module. The radio frequency array module includes multiple parallel radio frequency receiving units, each of which is configured to receive at least one spatial diversity signal from the antenna array.

[0007] The preprocessing module includes multiple preprocessing units, each of which is signal-connected to an RF receiving unit. The preprocessing unit is configured to receive signals from the RF receiving unit and convert those signals into a baseband data stream.

[0008] The signal processing module is connected to the RF array module and the preprocessing module respectively. The signal processing module includes a first scanning unit, a second scanning unit, and a control unit. The first scanning unit is configured to control the first group of RF receiving units in the RF array module to cover a first frequency band set with a first frequency resolution, and to control the preprocessing module to output a first baseband data stream. The second scanning unit is configured to control the second group of RF receiving units in the RF array module to cover a second frequency band set with a second frequency resolution, and to control the preprocessing module to output a second baseband data stream. The second frequency resolution is higher than the first frequency resolution, and the second frequency band set is a subset of the first frequency band set or partially overlaps with the first frequency band set. The control unit is connected to the first scanning unit and the second scanning unit respectively. The control unit is configured to dynamically adjust the number allocation and frequency coverage range of the first group of RF receiving units and the second group of RF receiving units according to the anomaly detection results.

[0009] In some embodiments, the signal analysis device with multi-band simultaneous display function further includes an anomaly judgment module, which is connected to the first scanning unit and the control unit respectively;

[0010] The anomaly detection module is configured to acquire the first baseband data stream and determine whether the first baseband data stream is abnormal. After determining that it is abnormal, it sends a control signal to the control unit so that the control unit sends a signal to perform fine resampling.

[0011] In some embodiments, the anomaly detection module includes a first feature extraction unit, a second feature extraction unit, and a feature fusion unit;

[0012] The first feature extraction unit is configured to acquire the baseband data stream output by the preprocessing module and extract a first feature set from the baseband data stream, the first feature set including time-domain transient features and frequency-domain sparse features.

[0013] The second feature extraction unit is configured to acquire the baseband data stream output by the preprocessing module and extract a second feature set of the baseband data stream, the second feature set including modulation domain stability features, higher-order cumulants and radio frequency fingerprint features;

[0014] The feature fusion unit is connected to the first feature extraction unit and the second feature extraction unit respectively. The feature fusion unit is configured to perform time-aligned weighted fusion of the first feature set and the second feature set to generate a comprehensive feature vector, and obtain the anomaly judgment result based on the comprehensive feature vector.

[0015] In some embodiments, the feature fusion unit includes:

[0016] The timestamp alignment subunit is configured to obtain the first timestamp corresponding to the first feature set and the second timestamp corresponding to the second feature set; and align the first feature set and the second feature set to a unified time reference based on the time difference between the first timestamp and the second timestamp.

[0017] The feature weighting subunit is configured to dynamically allocate the first fusion weight and the second fusion weight according to the signal-to-noise ratio, detection confidence and feature extraction channel type corresponding to the first feature set and the second feature set, respectively.

[0018] The fusion calculation subunit is connected to the timestamp alignment subunit and the feature weighting subunit respectively. The fusion calculation subunit is configured to perform a weighted summation of the aligned first feature set and the second feature set according to the first fusion weight and the second fusion weight to obtain a comprehensive feature vector.

[0019] In some embodiments, the feature fusion unit further includes a judgment subunit configured to compare the integrated feature vector with the radio frequency fingerprint feature library and obtain an anomaly judgment result based on the comparison result.

[0020] In some embodiments, the signal analysis device with multi-band simultaneous display function further includes a perception enhancement module, which is connected to the signal processing module and the anomaly judgment module respectively. The perception enhancement module is configured to receive the first baseband data stream output by the signal processing module, extract weak candidate signals with a signal-to-noise ratio lower than a preset threshold from the noise floor of the first baseband data stream, and generate a weak signal detection flag.

[0021] The first feature extraction unit is configured to extract only a first feature set from the baseband data stream, including weak signal detection flags, when acquiring the baseband data stream output by the preprocessing module.

[0022] In some embodiments, the signal analysis device with multi-band simultaneous display function further includes a frequency division display module, which includes multiple display units, each display unit displaying a baseband data stream converted by a radio frequency receiving unit.

[0023] In some embodiments, the second resolution bandwidth is less than 1 / 10 to 1 / 5 of the first resolution bandwidth.

[0024] Compared with the prior art, the embodiments of this application have the following beneficial effects:

[0025] The signal analysis device with multi-band simultaneous display capability provided in this application embodiment can achieve dynamic coordination of wideband global monitoring and detailed analysis of specific frequency bands with limited hardware resources. The control unit flexibly allocates RF receiving unit resources according to the anomaly detection results. When the first scanning unit detects an abnormal signal, it can allocate more resources to the second scanning unit to perform a detailed scan of the second frequency band set where the anomaly is located with a higher second frequency resolution. This achieves both a situational awareness of a broad spectrum and accurate and in-depth analysis based on the abnormal signal, effectively solving the problem of balancing wideband coverage and high-precision measurement in the prior art. Detailed Implementation

[0026] The technical solutions in the embodiments of this application are described clearly and completely below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] This application provides a signal analysis device with multi-band simultaneous display function, including a radio frequency array module, a preprocessing module and a signal processing module. The radio frequency array module includes multiple parallel radio frequency receiving units, each of which is configured to receive at least one spatial diversity signal from the antenna array.

[0028] The preprocessing module includes multiple preprocessing units, each of which is signal-connected to an RF receiving unit. The preprocessing unit is configured to receive signals from the RF receiving unit and convert those signals into a baseband data stream.

[0029] The signal processing module is connected to the RF array module and the preprocessing module respectively. The signal processing module includes a first scanning unit, a second scanning unit, and a control unit. The first scanning unit is configured to control the first group of RF receiving units in the RF array module to cover a first frequency band set with a first frequency resolution, and to control the preprocessing module to output a first baseband data stream. The second scanning unit is configured to control the second group of RF receiving units in the RF array module to cover a second frequency band set with a second frequency resolution, and to control the preprocessing module to output a second baseband data stream. The second frequency resolution is higher than the first frequency resolution, and the second frequency band set is a subset of the first frequency band set or partially overlaps with the first frequency band set. The control unit is connected to the first scanning unit and the second scanning unit respectively. The control unit is configured to dynamically adjust the number allocation and frequency coverage range of the first group of RF receiving units and the second group of RF receiving units according to the anomaly detection results.

[0030] Spatial diversity signaling refers to the acquisition of multiple independent fading copies of the same signal source by setting up multiple receiving antennas at different locations in space and taking advantage of the differences in the propagation paths of electromagnetic waves.

[0031] One radio frequency (RF) receiving unit in the RF array module can receive one spatial diversity signal from the antenna array, two spatial diversity signals, or more spatial diversity signals.

[0032] The number of preprocessing units in the preprocessing module is the same as the number of RF receiving units. The preprocessing module preprocesses the signal emitted by the RF receiving units and ultimately converts it into a baseband data stream. For example, the preprocessing unit can perform low-noise amplification on the signal emitted by the RF receiving unit, linearly amplifying the weak RF signal through a built-in low-noise amplifier to suppress noise interference, ensuring a sufficient signal-to-noise ratio for subsequent processing. The amplified signal can then undergo mixing to down-convert the RF signal to an intermediate frequency (IF) signal for subsequent filtering and sampling. The IF signal can pass through a bandpass filter to remove image frequencies and interference signals from other frequency bands generated during mixing, retaining the effective signal within the target frequency band. The filtered IF signal can then be sent to an analog-to-digital converter (ADC) to convert the analog signal into a digital signal. After preliminary digital filtering and gain adjustment, the digital signal forms a baseband data stream and is output to the signal processing module.

[0033] The first group of RF receiving units includes multiple RF receiving units. The phrase "the first group of RF receiving units covers the first frequency band set with a first frequency resolution" means that the first group of RF receiving units performs a rapid scan of a relatively wide first frequency band set (e.g., 20MHz ~ 8GHz) with a lower first frequency resolution (e.g., 1MHz), achieving global situational awareness of signals within the wide frequency band. Its main function is to quickly identify potential signal activity areas and the approximate location of abnormal signals. In this mode, the RF receiving units scan quickly, completing coverage of the entire first frequency band set in a short time, providing preliminary frequency band selection criteria for subsequent refined analysis.

[0034] The second group of RF receiving units covering the second frequency band set with a second frequency resolution means that the second group of RF receiving units scans the second frequency band set with a higher second frequency resolution (e.g., 100kHz, and satisfying that the second resolution bandwidth is less than 1 / 10 to 1 / 5 of the first resolution bandwidth). The second frequency band set is usually determined based on the preliminary detection results of the first scanning unit. It may be a sub-frequency band in the first frequency band set where there are suspected abnormal signals, or a specific frequency band of interest that partially overlaps with the first frequency band set. With a higher frequency resolution, the second scanning unit can acquire more refined baseband data, thereby enabling accurate analysis of signal characteristics within the target frequency band, such as signal spectral details, modulation methods, and signal strength variations.

[0035] As the core of the signal processing module, the control unit dynamically adjusts the allocation of the number of the first and second groups of RF receiving units and their respective frequency coverage ranges based on the anomaly detection results fed back by the anomaly detection module. For example, when the anomaly detection module detects an abnormal signal in a certain frequency band in the first baseband data stream, the control unit can reduce the number of the first group of RF receiving units and allocate some of them to the second group to enhance the fine-grained scanning capability of the abnormal frequency band. At the same time, it adjusts the coverage range of the second frequency band set to accurately include the frequency band where the abnormal signal is located, thereby enabling in-depth analysis of the abnormal signal while monitoring the entire frequency band.

[0036] Through the above-described embodiments, dynamic coordination between wideband global monitoring and refined analysis of specific frequency bands can be achieved with limited hardware resources. The control unit flexibly allocates RF receiving unit resources based on anomaly detection results. When the first scanning unit detects an abnormal signal, more resources can be allocated to the second scanning unit to perform a detailed scan of the second frequency band set where the anomaly occurs with higher second frequency resolution. This achieves both a comprehensive grasp of the situation across a broad spectrum and precise, in-depth analysis based on the abnormal signal, effectively solving the problem of balancing wideband coverage and high-precision measurement in existing technologies.

[0037] In some embodiments, the signal analysis device with multi-band simultaneous display function further includes an anomaly judgment module, which is connected to the first scanning unit and the control unit respectively. The anomaly judgment module is configured to acquire the first baseband data stream and determine whether the first baseband data stream is abnormal. After determining that it is abnormal, it sends a control signal to the control unit so that the control unit sends a signal to perform fine resampling.

[0038] The anomaly detection module, based on the first baseband data stream, determines whether the data stream is abnormal using the following steps: Feature extraction is performed on the first baseband data stream to construct a signal feature vector. This feature vector can include multiple dimensions of feature parameters, such as the signal's power spectral density, signal bandwidth, frequency hopping period, and signal duty cycle. Then, the extracted feature vector is compared with a preset normal signal feature model. The normal signal feature model is obtained based on a large number of baseband data stream samples under historical normal operating conditions, encompassing the feature distribution range of various common normal signals. When one or more feature parameters in the feature vector exceed the threshold range set by the normal signal feature model, it is preliminarily determined that the first baseband data stream may be abnormal. Furthermore, the spatiotemporal correlation of the signal can be used for auxiliary judgment. For example, if the signal strength of a certain frequency band experiences abnormal fluctuations within a specific time window, or does not match the signal activity patterns of other surrounding frequency bands, the credibility of the anomaly detection is further enhanced. Once the above judgment logic leads to an anomaly conclusion, the anomaly detection module immediately sends a control signal to the control unit, triggering the control unit to initiate a refined resampling process.

[0039] Through the above-described embodiments, the anomaly detection module can provide the control unit with more accurate anomaly triggering criteria, enabling the control unit to initiate resource allocation and fine-grained scanning only when there is a confirmed anomaly signal, reducing unnecessary resource waste and further optimizing the device's response efficiency and resource utilization efficiency in complex electromagnetic environments.

[0040] In some embodiments, the anomaly detection module includes a first feature extraction unit, a second feature extraction unit, and a feature fusion unit. The first feature extraction unit is configured to acquire the baseband data stream output by the preprocessing module and extract a first feature set from the baseband data stream, the first feature set including time-domain transient features and frequency-domain sparse features.

[0041] The second feature extraction unit is configured to acquire the baseband data stream output by the preprocessing module and extract a second feature set of the baseband data stream, the second feature set including modulation domain stability features, higher-order cumulants and radio frequency fingerprint features.

[0042] The feature fusion unit is connected to the first feature extraction unit and the second feature extraction unit respectively. The feature fusion unit is configured to perform time-aligned weighted fusion of the first feature set and the second feature set to generate a comprehensive feature vector, and obtain the anomaly judgment result based on the comprehensive feature vector.

[0043] In the above embodiments, the first feature extraction unit performs time-domain analysis on the baseband data stream to capture the transient change features of the signal in the time dimension, such as the rise time, fall time, pulse width, and duration of amplitude jumps. These features can effectively reflect the suddenness and instantaneous behavior of the signal. In the frequency domain, a sparse representation algorithm is used to extract sparse features of the signal, such as the distribution of non-zero components, peak frequency position, and energy proportion on the frequency axis, which can capture the key distribution characteristics of the signal in the spectrum.

[0044] Modulation domain stability features include parameters reflecting the signal modulation pattern, such as modulation scheme, modulation index, and symbol rate. Higher-order cumulants can suppress the effects of Gaussian noise and highlight the non-Gaussian nature of the signal. RF fingerprint features, based on inherent differences in hardware circuits, extract subtle and unique characteristics of the signal in terms of amplitude, phase, and frequency, which can be used to distinguish different emission sources or identify abnormal signal sources.

[0045] The feature fusion unit first aligns the first and second feature sets with timestamps to ensure temporal synchronization of features across different dimensions. Then, it performs weighted fusion based on the contribution of each feature to anomaly detection, integrating the multi-dimensional features into a high-dimensional comprehensive feature vector. Finally, this comprehensive feature vector is input into a preset anomaly detection model. The model compares this vector with the comprehensive feature vector distribution of a normal signal, outputting an anomaly probability value. When the anomaly probability value exceeds a set threshold, it is determined to be an anomaly signal, and the anomaly judgment result is fed back to the control unit.

[0046] By setting up the above embodiments, the accuracy of identifying complex abnormal signals can be effectively improved, and the possibility of misjudgment or missed judgment that may occur due to single feature judgment can be reduced.

[0047] In some embodiments, the feature fusion unit includes a timestamp alignment subunit, a feature weighting subunit, and a fusion calculation subunit.

[0048] The timestamp alignment subunit is configured to obtain the first timestamp corresponding to the first feature set and the second timestamp corresponding to the second feature set; and align the first feature set and the second feature set to a unified time reference based on the time difference between the first timestamp and the second timestamp.

[0049] The feature weighting subunit is configured to dynamically allocate the first fusion weight and the second fusion weight according to the signal-to-noise ratio, detection confidence and feature extraction channel type corresponding to the first feature set and the second feature set, respectively.

[0050] The fusion calculation subunit is connected to the timestamp alignment subunit and the feature weighting subunit respectively. The fusion calculation subunit is configured to perform weighted summation of the aligned first feature set and the second feature set according to the first fusion weight and the second fusion weight to obtain the comprehensive feature vector.

[0051] In the above embodiments,

[0052] The timestamp alignment subunit can unify the time base of the first feature set and the second feature set through a difference synchronization algorithm. For example, the timestamp alignment subunit can extract the first timestamp corresponding to each feature parameter in the first feature set and the second timestamp corresponding to each feature parameter in the second feature set. By calculating the average time difference between the first and second timestamp sequences, and using this as a benchmark, the timestamps of one of the feature sets are corrected for overall offset.

[0053] For scenarios requiring higher accuracy, linear interpolation or nearest neighbor interpolation can be used to align the feature parameters in time for minor time deviations that still exist after correction. This allows the first and second features at the same time point to correspond more accurately, laying a time synchronization foundation for subsequent feature fusion.

[0054] The dynamic weight allocation mechanism for feature-weighted sub-units can be implemented as follows: Based on the first feature set, the weights can be determined according to the signal-to-noise ratio (SNR) of the time-domain transient features and the frequency-domain sparse features. When the SNR of a certain feature parameter is high, it indicates that the feature is less affected by noise interference and has high information reliability, so it can be assigned a higher weight; feature parameters with low SNR are assigned lower weights. The average detection confidence of the feature extraction channel in historical data can also be referenced. If the channel has a high accuracy rate in identifying abnormal signals, the overall weight of its corresponding feature set can be appropriately increased.

[0055] For the second feature set, the weight allocation can consider the signal-to-noise ratio of modulation domain stable features, higher-order cumulants, and RF fingerprint features, as well as the unique contribution of RF fingerprint features. RF fingerprint features will be given a certain priority in the weight allocation. Through this multi-factor dynamic weighting method, the fused comprehensive feature vector can better highlight effective information and suppress the negative impact of noise and interference.

[0056] After receiving the timestamp-aligned first and second feature sets, as well as the first and second fusion weights assigned by the feature weighting subunit, the fusion computing subunit performs a weighted summation operation. For example, each feature parameter in the first feature set can be multiplied by its corresponding first fusion weight, and each feature parameter in the second feature set can be multiplied by its corresponding second fusion weight. Then, all weighted feature parameters are arranged sequentially by dimension to form a comprehensive feature vector with a dimension equal to the sum of the dimensions of the first and second feature sets. For instance, if the first feature set contains 5 time-domain features and 3 frequency-domain sparse features, and the second feature set contains 4 modulation-domain features, 2 higher-order cumulant features, and 3 RF fingerprint features, then the comprehensive feature vector has a dimension of 5+3+4+2+3=17, where the value of each dimension is the product of the corresponding original feature parameter and its weight.

[0057] Through the settings of the above embodiments, the obtained comprehensive feature vector can fully reflect the comprehensive information of the signal in multiple dimensions such as time domain, frequency domain, modulation domain and transmitter characteristics, providing rich and reliable input for subsequent anomaly judgment.

[0058] In some embodiments, the feature fusion unit further includes a judgment subunit configured to compare the integrated feature vector with the radio frequency fingerprint feature library and obtain an anomaly judgment result based on the comparison result.

[0059] In the above embodiments, the radio frequency fingerprint feature library stores a large number of radio frequency fingerprint templates of known normal transmission sources. These templates are established by long-term collection and feature extraction of signals from various legitimate devices under standard operating conditions, and contain unique radio frequency fingerprint feature distributions of different device types, models, and even individual devices.

[0060] The judgment subunit compares the RF fingerprint features extracted from the comprehensive feature vector with the templates in the RF fingerprint feature library one by one. The comparison process can use measurement methods such as cosine similarity and Euclidean distance to calculate the similarity score between the RF fingerprint to be detected and each template in the library. When the highest similarity score is lower than the set matching threshold, it indicates that the source of the signal is not within the known normal equipment range and is judged as an abnormal signal. If the similarity score is higher than the threshold, but other feature parameters in the comprehensive feature vector exceed the normal model range, it will also be judged as abnormal. For example, when a legitimate device malfunctions or is maliciously tampered with, its RF fingerprint may remain basically unchanged, but other features of the signal will be abnormal.

[0061] The above-described embodiments enable the improvement of the accuracy and robustness of anomaly detection, effectively identifying abnormal signals disguised as normal signals or threat signals from unknown sources.

[0062] In some embodiments, the signal analysis device with multi-band simultaneous display function further includes a perception enhancement module, which is connected to the signal processing module and the anomaly judgment module respectively. The perception enhancement module is configured to receive the first baseband data stream output by the signal processing module, extract weak candidate signals with a signal-to-noise ratio lower than a preset threshold from the noise floor of the first baseband data stream, and generate a weak signal detection flag. The first feature extraction unit is configured to extract only the first feature set of the baseband data stream including the weak signal detection flag when acquiring the baseband data stream output by the preprocessing module.

[0063] In the above embodiments, the perception enhancement module can preprocess the first baseband data stream through an adaptive noise suppression algorithm. This step can adjust the suppression threshold according to the dynamic changes of the current noise floor. For example, a threshold denoising method based on wavelet transform can be used to effectively reduce broadband noise while preserving signal details.

[0064] The perception enhancement module can use a combination of energy detection and spectral correlation detection to screen weak candidate signals with a signal-to-noise ratio below a preset threshold from a noise substrate. For example, the energy value of each frequency point can be calculated first by sliding window energy detection. When the energy of a certain frequency point is continuously higher than three times the average noise energy in multiple consecutive windows, it is initially marked as a potential weak signal. Then, the cyclic spectral density of the potential signal is analyzed by spectral correlation detection. If it shows a significant peak at a specific cyclic frequency, it is further confirmed as a real weak signal, and a weak signal detection flag is generated. The flag contains key parameters such as the center frequency, bandwidth, and signal-to-noise ratio of the signal.

[0065] Through the configuration of the above embodiments, the perception enhancement module can extract weak anomalous signals that are easily missed by traditional methods from strong noise backgrounds, providing a more comprehensive signal source for subsequent feature extraction. When the first feature extraction unit receives the baseband data stream output by the preprocessing module, it will first search whether the data stream carries a weak signal detection flag. If the flag exists, it will only extract time-domain transient features and frequency-domain sparse features for the weak signal frequency band pointed to by the flag, reducing redundant processing of strong signal frequency bands without anomalies. This significantly improves the efficiency and targeting of feature extraction while enhancing the comprehensiveness of anomalous signal detection.

[0066] In some embodiments, the signal analysis device with multi-band simultaneous display function further includes a frequency division display module, which includes multiple display units, each display unit displaying a baseband data stream converted by a radio frequency receiving unit.

[0067] Through the settings of the above embodiments, the frequency division display module can partition and synchronously display the baseband data streams processed by multiple RF receiving units on the same display interface. Each display unit corresponds to the signal data of a specific frequency band, and the display parameters can be independently configured, such as various signal characterization forms like time-domain waveform, spectrogram, constellation diagram, eye diagram, etc. The user can freely switch the display modes of each display unit according to the analysis requirements. For example, when the device simultaneously receives signals of three frequency bands, the display interface can be divided into three independent display units. The left unit displays the time-domain waveform and power spectral density of frequency band A in real time, the middle unit shows the constellation diagram of frequency band B to analyze the modulation quality, and the right unit presents the changing trend of the signal frequency of frequency band C over time in the form of a waterfall plot. Linkage operations are also supported between the display units. When the user selects a specific signal segment in a certain unit, other units will synchronously highlight the signal characteristics at the corresponding time nodes, facilitating the correlation analysis of cross-frequency band signals. In addition, the frequency division display module also has a signal marking function, which can color-label or symbol-mark abnormal signal events in the corresponding display unit, and supports exporting the marked multi-frequency band signal data as a report file, providing an intuitive and comprehensive visualization basis for subsequent signal analysis and fault location.

[0068] In some of these embodiments, the second resolution bandwidth is less than 1 / 10 - 1 / 5 of the first resolution bandwidth.

[0069] Through the settings of the above embodiments, the ability to resolve signal details can be significantly improved. When performing refined resampling, a smaller second resolution bandwidth can divide the signal spectrum more finely. Weak signal components, spectral sidelobe details, and adjacent signals with small frequency intervals that may have been masked or aliased under a wider first resolution bandwidth can all be clearly presented under the second resolution bandwidth. Exemplarily, if the first resolution bandwidth is 1 MHz and the second resolution bandwidth is set to 0.1 MHz, for two signals with a center frequency interval of only 0.5 MHz, they may be identified as a single signal due to spectral aliasing under the first resolution bandwidth, but can be accurately resolved as two independent signals under the second resolution bandwidth, thus providing a more accurate spectral data basis for the feature extraction and analysis of abnormal signals.

[0070] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, based on the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A signal analysis device with multi-band simultaneous display function, characterized in that, include: The radio frequency array module includes multiple parallel radio frequency receiving units, each of which is configured to receive at least one spatial diversity signal from the antenna array. The preprocessing module includes multiple preprocessing units, each of which is signal-connected to a radio frequency receiving unit. The preprocessing unit is configured to receive a signal emitted by the radio frequency receiving unit and convert the signal into a baseband data stream. A signal processing module is signal-connected to the radio frequency array module and the preprocessing module, respectively; the signal processing module includes a first scanning unit, a second scanning unit, and a control unit. The first scanning unit is configured to control the first group of radio frequency receiving units in the radio frequency array module to cover the first frequency band set with a first frequency resolution, and to control the preprocessing module to output the first baseband data stream; The second scanning unit is configured to control the second group of radio frequency receiving units in the radio frequency array module to cover the second frequency band set with a second frequency resolution, and to control the preprocessing module to output a second baseband data stream, wherein the second frequency resolution is higher than the first frequency resolution, and the second frequency band set is a subset of the first frequency band set or partially overlaps with the first frequency band set; The control unit is connected to the first scanning unit and the second scanning unit respectively. The control unit is configured to dynamically adjust the number allocation and frequency coverage of the first group of radio frequency receiving units and the second group of radio frequency receiving units according to the anomaly detection results.

2. The signal analysis device with multi-band simultaneous display function as described in claim 1, characterized in that, The signal analysis device with multi-band simultaneous display function also includes an anomaly judgment module, which is connected to the first scanning unit and the control unit respectively. The anomaly detection module is configured to acquire the first baseband data stream and determine whether the first baseband data stream is abnormal. After determining that it is abnormal, it sends a control signal to the control unit so that the control unit sends a signal to perform fine resampling.

3. The signal analysis device with multi-band simultaneous display function as described in claim 2, characterized in that, The anomaly detection module includes a first feature extraction unit, a second feature extraction unit, and a feature fusion unit; The first feature extraction unit is configured to acquire the baseband data stream output by the preprocessing module and extract a first feature set of the baseband data stream, the first feature set including time-domain transient features and frequency-domain sparse features; The second feature extraction unit is configured to acquire the baseband data stream output by the preprocessing module and extract a second feature set of the baseband data stream, the second feature set including modulation domain stability features, higher-order cumulants and radio frequency fingerprint features; The feature fusion unit is signal-connected to the first feature extraction unit and the second feature extraction unit respectively. The feature fusion unit is configured to perform time-aligned weighted fusion of the first feature set and the second feature set to generate a comprehensive feature vector, and obtain an anomaly judgment result based on the comprehensive feature vector.

4. The signal analysis device with multi-band simultaneous display function as described in claim 3, characterized in that, The feature fusion unit includes: A timestamp alignment subunit is configured to obtain a first timestamp corresponding to the first feature set and a second timestamp corresponding to the second feature set; and align the first feature set and the second feature set to a unified time reference based on the time difference between the first timestamp and the second timestamp. A feature weighting subunit is configured to dynamically allocate a first fusion weight and a second fusion weight based on the signal-to-noise ratio, detection confidence, and type of feature extraction channel corresponding to the first feature set and the second feature set, respectively. The fusion calculation subunit is signal-connected to the timestamp alignment subunit and the feature weighting subunit, respectively. The fusion calculation subunit is configured to perform a weighted summation of the aligned first feature set and the second feature set according to the first fusion weight and the second fusion weight to obtain a comprehensive feature vector.

5. The signal analysis device with multi-band simultaneous display function as described in claim 3, characterized in that, The feature fusion unit further includes a judgment subunit, which is configured to compare the integrated feature vector with the radio frequency fingerprint feature library and obtain an anomaly judgment result based on the comparison result.

6. The signal analysis device with multi-band simultaneous display function as described in claim 3, characterized in that, The signal analysis device with multi-band simultaneous display function further includes a perception enhancement module. The perception enhancement module is connected to the signal processing module and the anomaly judgment module respectively. The perception enhancement module is configured to receive the first baseband data stream output by the signal processing module, extract weak candidate signals with a signal-to-noise ratio lower than a preset threshold from the noise floor of the first baseband data stream, and generate a weak signal detection flag. The first feature extraction unit is configured to extract only a first feature set of the baseband data stream, including weak signal detection flags, from the baseband data stream output by the preprocessing module.

7. The signal analysis device with multi-band simultaneous display function as described in claim 1, characterized in that, The signal analysis device with multi-band simultaneous display function also includes a frequency division display module, which includes multiple display units, each of which displays a baseband data stream converted by the radio frequency receiving unit.

8. The signal analysis device with multi-band simultaneous display function as described in claim 1, characterized in that, The second resolution bandwidth is less than 1 / 10 to 1 / 5 of the first resolution bandwidth.