Spectral analysis method for multi-band on-screen display

Abstract

The invention discloses a multi-band same-screen display spectrum analysis method, and relates to the technical field of radio signal monitoring, and the method comprises the steps: collecting an electromagnetic signal in a to-be-monitored spectrum to generate original signal data; performing spectrum analysis on the original data, and extracting frequency and signal intensity features to form spectrum feature data; counting signal density based on the characteristic data, and generating signal density distribution data; dynamically dividing sub-frequency bands according to the density data and matching corresponding spectral analysis resolutions; analyzing each sub-frequency band to generate spectrum analysis data; and after normalization processing, performing same-screen rendering on the multi-sub-band data, and labeling corresponding spectrum parameters at the same time. According to the invention, through dynamic segmentation and resolution adaptation, monitoring precision and processing efficiency are considered, multi-band on-screen display visually presents full-spectrum features, the operation process is simplified, information reading convenience is improved, and the method is suitable for ultra-wide spectrum monitoring scenes.

Description

Technical Field

[0001] This invention relates to the field of radio signal monitoring technology, and in particular to a spectrum analysis method for multi-band simultaneous display. Background Technology

[0002] Currently, portable signal analyzers generally use a fixed-resolution spectrum analysis method when conducting spectrum analysis. The entire spectrum range to be monitored is divided into frequency bands according to a uniform segmentation pattern with equal bandwidth, and then spectrum analysis processing of the entire spectrum range is completed based on the uniform segmentation results. Some existing technologies can also perform simple processing on the spectrum analysis data of each segment to achieve multi-band simultaneous display, so as to meet the basic needs of on-site spectrum monitoring for the visualization of spectrum information.

[0003] In practical spectrum monitoring applications, the existing technical solutions mentioned above use a fixed resolution for spectrum analysis and divide the spectrum range to be monitored into uniform segments without considering the actual signal distribution. Neither of these methods is adapted to the actual signal distribution characteristics within the spectrum range to be monitored. As a result, the existing technologies cannot simultaneously achieve both spectrum monitoring accuracy and analysis processing efficiency during spectrum analysis.

[0004] When facing a spectrum range to be monitored with uneven signal distribution, dense signal areas are prone to insufficient resolution to accurately capture the spectral characteristics of the signal, resulting in a decrease in spectrum monitoring accuracy. On the other hand, sparse signal areas will generate unnecessary computation due to indiscriminate resolution, resulting in the ineffective consumption of resolution resources and a decrease in spectrum analysis and processing efficiency. Summary of the Invention

[0005] To address the technical problem of the difficulty in balancing monitoring accuracy and efficiency caused by fixed resolution and uniform segmentation mode in existing portable signal analyzers, this invention provides a spectrum analysis method with multi-band simultaneous display.

[0006] The technical solution adopted in this invention is:

[0007] A spectrum analysis method for multi-band simultaneous display includes the following steps:

[0008] Step 1: Collect electromagnetic signals within the frequency spectrum to be monitored and generate raw signal data;

[0009] Step 2: Perform spectral analysis on the original signal data to generate spectral feature data, which includes the frequency characteristics and signal strength characteristics of the signal;

[0010] Step 3: Based on frequency characteristics and signal strength characteristics, perform signal density statistics on the spectrum range to be monitored to generate signal density distribution data;

[0011] Step 4: Based on the signal density distribution data, the spectrum range to be monitored is dynamically segmented to generate several sub-band division data. The corresponding spectrum analysis resolution is matched for each sub-band to generate resolution adaptation data.

[0012] Step 5: Based on the sub-band division data, resolution adaptation data, and spectral feature data, perform spectral analysis processing on each sub-band to generate spectral analysis data for each sub-band.

[0013] Step 6: Normalize the spectral analysis data of all sub-bands to generate normalized spectral data for multiple sub-bands;

[0014] Step 7: Perform multi-band simultaneous rendering processing on the normalized spectrum data of multiple sub-bands, and label the corresponding spectrum parameters in the spectrum region of each sub-band.

[0015] The beneficial effects of the present invention are at least one of the following:

[0016] This invention addresses the technical problem of traditional portable signal analyzers, which struggle to balance monitoring accuracy and efficiency due to fixed resolution and uniform segmentation modes. By constructing a spectrum analysis process based on actual signal distribution characteristics, it replaces the indiscriminate fixed resolution and uniform segmentation processing method, achieving a synergistic balance between spectrum monitoring accuracy and processing efficiency.

[0017] This invention performs signal density statistics based on the frequency and signal strength characteristics of the spectrum range to be monitored, so that subsequent frequency band segmentation and resolution matching are based on the actual signal distribution. This avoids indiscriminate operation that is detached from the actual signal distribution in traditional processing methods, and can reduce resource consumption caused by over-analysis in sparse signal areas.

[0018] This invention dynamically segments the signal density distribution data and matches each sub-band with a corresponding spectrum analysis resolution. Then, it performs spectrum analysis processing on each sub-band separately, so that the spectrum analysis process fits the signal distribution characteristics of each region. This can effectively capture signal details in dense signal areas and improve the resolution of spectrum monitoring in those areas. At the same time, in sparse signal areas, the analysis processing is completed with an appropriate resolution, reducing unnecessary analysis calculations and thus improving the overall processing efficiency of spectrum analysis. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0020] Figure 2This is a schematic diagram of the panoramic scanning interface of the signal analyzer in an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of the interface for simultaneously displaying multiple sub-frequency bands in a signal analyzer in an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of the interface for resolution adaptation of the signal analyzer in an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of a multi-band simultaneous display interface for a signal analyzer in an embodiment of the present invention, which includes a centralized display of similar spectrum frequencies. Detailed Implementation

[0024] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] In practical spectrum monitoring applications of portable signal analyzers, the monitoring accuracy and processing efficiency of spectrum analysis are the core indicators for measuring equipment performance. On-site monitoring work requires both accurate capture of signal characteristics within the monitored spectrum range and high processing efficiency of the spectrum analysis process to meet the needs of real-time monitoring.

[0026] In the existing technology, the spectrum analysis scheme of portable signal analyzers generally adopts a fixed spectrum analysis resolution to analyze the spectrum to be monitored, and uses an equal bandwidth uniform segmentation mode to divide the spectrum range to be monitored. The design of this type of scheme does not take into account the actual signal distribution characteristics within the spectrum range to be monitored for adaptive adjustment.

[0027] When facing a spectrum of signals with uneven distribution, dense signal areas cannot accurately capture the core features of the signal, such as frequency domain and intensity, due to the limitation of fixed resolution. This results in a decrease in spectrum monitoring accuracy and makes it difficult to meet the needs of detailed analysis of dense signals. On the other hand, sparse signal areas will generate a lot of unnecessary computation due to the indiscriminate fixed resolution, resulting in the ineffective consumption of analytical resources. At the same time, the uniform segmentation mode will make the frequency band division of sparse areas too fine, further reducing the overall processing efficiency of spectrum analysis and failing to meet the efficiency requirements of real-time on-site monitoring.

[0028] Example 1

[0029] To address the core technical problem of the inability to simultaneously achieve both spectrum monitoring accuracy and processing efficiency in existing technologies, this embodiment provides a spectrum analysis method that displays multiple frequency bands on the same screen, such as... Figure 1 As shown, it includes the following steps:

[0030] Step 1: Collect electromagnetic signals within the frequency spectrum to be monitored to generate raw signal data.

[0031] It should be noted that, in this embodiment, the spectrum range to be monitored refers to the ultra-wide frequency range of 20MHz to 8GHz set according to actual monitoring needs; a local frequency range can also be selected from this range. Figure 2 As shown, electromagnetic signals refer to various radio frequency, communication, navigation, broadcasting and other electromagnetic radiation signals propagating within the spectrum range to be monitored; raw signal data refers to the raw digital data of electromagnetic signals that have been collected without any analysis or processing, and contains the raw time-domain characteristic information of the signal.

[0032] In the specific implementation process, the spectrum range to be monitored is first selected from the ultra-wide spectrum from 20MHz to 8GHz according to the actual monitoring needs. Electromagnetic signal acquisition in the corresponding frequency range is started through a portable signal analyzer. The received analog electromagnetic signals are converted into digital electrical signals through analog-to-digital conversion. The digital electrical signals are then sampled, quantized and encoded. The processed signal data is stored in the analyzer's local storage unit in time sequence to form standardized raw signal data.

[0033] Step 2: Perform spectral analysis on the original signal data to generate spectral feature data, which includes the frequency characteristics and signal strength characteristics of the signal.

[0034] It should be noted that spectrum analysis processing refers to the signal processing operation of converting the original signal data in the time domain into frequency domain feature data. This invention uses Fast Fourier Transform to achieve this conversion. Spectrum feature data refers to the set of frequency domain features obtained after spectrum analysis processing of the original signal data, which is a digital representation of the frequency domain attributes of the electromagnetic signal. Frequency features refer to the characteristic information in the spectrum feature data that reflects the frequency position, frequency bandwidth, and frequency range of the electromagnetic signal. Signal strength features refer to the characteristic information in the spectrum feature data that reflects the power and field strength of the electromagnetic signal corresponding to each frequency position. The unit of signal strength is dBm.

[0035] Because the original signal data is time-domain data, it cannot directly reflect the frequency and intensity distribution characteristics of the signal. The core of spectrum analysis is to obtain the frequency domain characteristics of the signal. By converting the time-domain signal into a frequency-domain signal through the fast Fourier transform commonly used in existing technologies, the frequency and intensity characteristics of the signal can be efficiently extracted, providing analyzable frequency domain data for subsequent density statistics based on signal distribution characteristics.

[0036] In the specific implementation process, the signal processing module of the portable signal analyzer is invoked to perform a Fast Fourier Transform (FFT) on the raw signal data generated in step 1. The number of FFT points is set to 1024, converting the raw signal data in the time domain into signal data in the frequency domain. Information such as the frequency value and frequency bandwidth corresponding to each frequency point is extracted from the frequency domain signal data and integrated in ascending order of frequency to form frequency features. At the same time, information such as the signal power value and field strength value corresponding to each frequency point is extracted and mapped one-to-one with the frequency points to form signal strength features. The frequency features and signal strength features are correlated and matched according to the frequency points, integrated and stored to generate standardized spectral feature data.

[0037] Step 3: Based on frequency characteristics and signal strength characteristics, perform signal density statistics on the spectrum range to be monitored to generate signal density distribution data.

[0038] It should be noted that signal density statistics refers to the operation of statistically analyzing the density of signal distribution in each frequency interval within the monitored spectrum based on the frequency characteristics and signal strength characteristics of spectral feature data; signal density value refers to the numerical value that quantifies the density of signal within a single frequency statistical unit; signal density distribution data refers to the set of correlation data between the signal density values ​​of each frequency interval within the monitored spectrum and the corresponding frequency interval, reflecting the overall distribution of signal density in the monitored spectrum.

[0039] In the specific implementation process, the spectral feature data generated in step 2 is retrieved, the start and end points of the frequency range to be monitored are determined, and frequency units are divided. Taking the divided individual frequency units as statistical units, the signal density value in each frequency unit is calculated by combining frequency characteristics and signal strength characteristics. The formula for calculating the signal density value is: D=k1×N+k2×I, where D is the signal density value, N is the number of effective signal points in the frequency unit, I is the average signal strength in the frequency unit, and k1 and k2 are weighting coefficients, and k1+k2=1. The default values ​​of k1 and k2 are 0.6 and 0.4, respectively. The weighting coefficients can be adjusted according to actual monitoring needs. The signal density values ​​of all frequency units are associated with the corresponding frequency intervals, and the statistical results of all frequency units are integrated in ascending order of frequency to generate signal density distribution data.

[0040] This step combines frequency and signal strength characteristics for statistical analysis, improving the accuracy of signal density statistics and avoiding bias caused by single-feature statistics. The generated signal density distribution data can intuitively reflect the density distribution of signals within the 20MHz~8GHz ultra-wide spectrum, allowing subsequent frequency band division and resolution matching to conform to the actual signal distribution characteristics.

[0041] Step 4: Based on the signal density distribution data, the spectrum range to be monitored is dynamically segmented to generate several sub-band division data. The corresponding spectrum analysis resolution is matched for each sub-band to generate resolution adaptation data.

[0042] It should be noted that dynamic segmentation processing refers to dividing the monitored spectrum range of 20MHz to 8GHz into non-uniform bandwidth frequency bands based on the density characteristics of signal density distribution data, which is different from the uniform bandwidth segmentation of existing technologies; sub-band division data refers to the set of associated data of the frequency start boundary, frequency end boundary and sub-band number of each sub-band obtained after dynamic segmentation processing; spectrum analysis resolution refers to the frequency resolution accuracy when analyzing the spectrum, which is an indicator of the ability to capture details in spectrum analysis. The smaller the resolution value, the higher the resolution accuracy; resolution adaptation data refers to the set of associated data of each sub-band and the corresponding spectrum analysis resolution.

[0043] Dynamic segmentation based on signal density distribution data allows the sub-band width to match the signal density distribution characteristics. Dense signal areas are divided into narrow-band sub-bands for easier subsequent fine analysis, while sparse signal areas are divided into wide-band sub-bands to reduce subsequent analysis calculations. Simultaneously, the corresponding spectral analysis resolution is matched to the signal density characteristics of each sub-band, allowing dense, narrow-band sub-bands to match higher analytical resolutions and sparse, wide-band sub-bands to match suitable analytical resolutions. This achieves dynamic adjustment of spectral analysis from two dimensions: band division and analytical accuracy, solving the problem of poor adaptability of fixed segmentation and fixed resolution in the 20MHz~8GHz ultra-wide spectrum of existing technologies.

[0044] In the specific implementation process, the signal density distribution data generated in step 3 is retrieved first, and three levels of signal density threshold intervals are set: a signal density value greater than or equal to 30 is a high density interval, a signal density value less than or equal to 10 is a medium density interval, and a signal density value less than 10 is a low density interval.

[0045] Based on the density threshold range, the monitored spectrum from 20MHz to 8GHz is divided into dense signal regions, medium-density signal regions, and sparse signal regions. For high-density regions, a narrow bandwidth of 5MHz is used for sub-band division; for medium-density regions, a bandwidth of 50MHz is used; and for low-density regions, a wide bandwidth of 100MHz is used. The frequency start and end boundaries of each sub-band are recorded and integrated to generate several sub-band division data. Subsequently, based on the signal density range corresponding to each sub-band, a spectral analysis resolution of 1kHz is matched for the high-density region sub-bands, 10kHz for the medium-density region sub-bands, and 100kHz for the low-density region sub-bands. Each sub-band is then associated with its corresponding spectral analysis resolution to generate resolution adaptation data.

[0046] like Figure 4 As shown, the dynamic segmentation process in this step ensures that the sub-band division conforms to the signal density distribution characteristics of the 20MHz~8GHz ultra-wide spectrum, avoiding the problems of overly coarse division of dense areas and overly fine division of sparse areas caused by the uniform segmentation of existing technologies. The corresponding spectrum analysis resolution is matched for each sub-band, so that the resolution accuracy is adapted to the signal density characteristics of the sub-band. This effectively solves the shortcomings of existing technologies with fixed resolution that cannot take into account both high and low frequency monitoring, and improves the overall performance of ultra-wide spectrum analysis.

[0047] Step 5: Based on the sub-band division data, resolution adaptation data, and spectral feature data, perform spectral analysis processing on each sub-band to generate spectral analysis data for each sub-band.

[0048] It should be noted that spectrum analysis processing refers to the operation of performing targeted frequency domain analysis on the spectral characteristic data of each sub-frequency band based on the sub-frequency band division data and resolution adaptation data; analysis configuration data refers to the set of analysis parameters matched for each sub-frequency band and corresponding to the spectrum analysis resolution; spectrum analysis data refers to the set of signal data containing refined frequency domain characteristics obtained after spectrum analysis processing of each sub-frequency band, which is a refined representation of the signal characteristics of each sub-frequency band.

[0049] Based on the sub-band segmentation data, the analysis boundaries of each sub-band are determined. Based on the resolution adaptation data, the resolution accuracy of each sub-band is determined. Combined with the spectral feature data, targeted spectral analysis processing is performed. This ensures that the analysis process of each sub-band is tailored to its own signal characteristics and resolution accuracy requirements, avoiding insufficient accuracy or low efficiency caused by indiscriminate overall analysis. The generated spectral analysis data is the basis for subsequent normalization processing. Refined spectral analysis data can ensure the integrity of signal characteristics in subsequent simultaneous display, which is a key step in connecting dynamic segmented analysis and multi-band simultaneous display.

[0050] In the specific implementation process, the sub-band division data and resolution adaptation data generated in step 4, as well as the spectrum feature data generated in step 2, are retrieved; the spectrum feature data is split according to the frequency boundaries of each sub-band based on the sub-band division data to obtain the spectrum feature sub-data corresponding to each sub-band; and the corresponding spectrum analysis parameters are retrieved for each sub-band based on the resolution adaptation data to generate analysis configuration data.

[0051] For example, a 1kHz resolution corresponds to a Hanning window with a sampling rate of 200MS / s, while a 100kHz resolution corresponds to a rectangular window with a sampling rate of 100MS / s. The matched spectral analysis resolution is used to perform fine-grained frequency domain analysis on the spectral feature sub-data of each sub-band, extracting fine-grained features such as signal center frequency, bandwidth, peak intensity, and signal duration within each sub-band. The fine-grained frequency domain features of each sub-band are integrated and stored to generate the spectral analysis data corresponding to each sub-band.

[0052] This step involves splitting the spectral feature data by sub-band and performing targeted spectral analysis, making the analysis process more targeted and avoiding the indiscriminate analysis of the entire 20MHz~8GHz ultra-wide spectrum. Combining the analysis with the matched spectral analysis resolution ensures the analysis accuracy of signal-dense sub-bands, accurately capturing the detailed features of narrowband signals, while ensuring the analysis efficiency of signal-sparse sub-bands and reducing invalid calculations.

[0053] Step 6: Normalize the spectrum analysis data of all sub-bands to generate normalized spectrum data for multiple sub-bands.

[0054] It should be noted that normalization processing refers to the standardized processing operation of calibrating coordinates and unifying dimensions of spectrum analysis data of different sub-frequency bands, including frequency axis calibration, signal strength axis calibration and data format unification; normalized spectrum data of multiple sub-frequency bands refers to the standardized spectrum data set with a unified coordinate system and data dimensions obtained after normalization processing of spectrum analysis data of all sub-frequency bands.

[0055] The spectrum analysis data of each sub-band is obtained based on different spectrum analysis resolutions and frequency ranges. Their frequency axis coordinates, signal strength axis references, and data formats are different, making it impossible to directly render and display multiple frequency bands on the same screen. By normalizing the spectrum analysis data of each sub-band to calibrate the frequency axis and signal strength axis and unify the data dimensions, the spectrum data of different sub-bands can have a unified and comparable standard. This is a prerequisite for realizing multi-band simultaneous display and ensures that the spectrum characteristics displayed on the same screen are without deviation or misalignment.

[0056] In the specific implementation process, the spectrum analysis data of all sub-bands generated in step 5 are retrieved, and the frequency characteristic data of each sub-band are mapped to a unified frequency axis coordinate system from 20MHz to 8GHz, with the frequency axis scale interval set to 1MHz, completing the frequency axis calibration; the signal strength characteristic data of each sub-band are calibrated according to a unified strength benchmark in dBm, with the range set to -100dBm to 0dBm, completing the signal strength axis calibration; at the same time, the spectrum analysis data of each sub-band is unified in terms of data format and data dimensions, converting the analysis data of all sub-bands into a standardized format that can be recognized by the visualization module of the portable signal analyzer, including frequency points, signal strength, and signal type fields; the calibrated and unified spectrum data of each sub-band are associated with each sub-band number, and after integration, multiple sub-band normalized spectrum data are generated.

[0057] Step 7: Perform multi-band simultaneous rendering processing on the normalized spectrum data of multiple sub-bands to achieve simultaneous display of the spectrum of each sub-band on the same screen, and mark the corresponding spectrum parameters in the spectrum region of each sub-band.

[0058] It should be noted that multi-band simultaneous rendering processing refers to the operation of converting normalized spectrum data of multiple sub-bands into a visual spectrum image and performing fusion rendering on the same display interface; simultaneous spectrum display refers to the display method of presenting the visual spectrum images of all sub-bands on the same display interface of a portable signal analyzer; spectrum parameters refer to parameter information that can reflect the core characteristics of each sub-band signal, including quantitative parameters such as center frequency, bandwidth, and peak intensity, as well as characteristic information such as service band name and signal classification.

[0059] Multi-band simultaneous rendering of normalized spectrum data from multiple sub-bands can convert standardized digital spectrum data into visualized spectrum images, enabling simultaneous display of multiple sub-bands on the same screen. This allows monitoring personnel to intuitively obtain the overall signal distribution characteristics of the ultra-wide spectrum from 20MHz to 8GHz on a single interface, reducing the operational redundancy caused by viewing each sub-band individually. By labeling the corresponding spectrum parameters in the spectrum region of each sub-band, monitoring personnel can directly obtain the core quantitative characteristics and business information of the signal from the visualized image without additional data extraction and analysis, thus improving the efficiency of reading monitoring information.

[0060] In the specific implementation process, the normalized spectrum data of multiple sub-bands generated in step 6 is first retrieved. The digitized normalized spectrum data is converted into a visualized spectrum image through the visualization rendering module of the portable signal analyzer. The spectrum curve colors are distinguished according to the signal type: WiFi is blue, FM radio is red, digital intercom is green, and noise is gray. The spectrum images of all sub-bands are aligned with coordinates, with 20MHz as the starting point and 8GHz as the ending point. The horizontal axis is the frequency and the vertical axis is the signal strength. Then, the layers are fused to complete the multi-band simultaneous rendering processing.

[0061] like Figure 5 As shown, the analyzer's display interface displays the spectrum of each sub-band simultaneously, supporting centralized display of similar spectra. Then, it extracts the core spectral parameters of each sub-band from the normalized spectral data of multiple sub-bands, while simultaneously retrieving preset service frequency bands and frequency band division table data to obtain the service frequency band name, signal classification, and properties corresponding to each sub-band. Based on the position of each sub-band's spectrum image on the display interface, the spectral parameters, service frequency band name, and signal classification properties are marked at designated locations within the sub-band's spectral region. The core parameters are marked on the right side of the spectrum curve, and the service information is marked below the spectrum curve, completing the synchronous marking of the spectral parameters.

[0062] This step, which displays multiple frequency bands on the same screen, allows monitoring personnel to intuitively obtain the overall signal distribution characteristics of the 20MHz~8GHz ultra-wide spectrum on a single interface. It supports centralized display of similar spectrums, avoiding the operational redundancy of traditional single-band switching and viewing, and significantly improving the visualization efficiency and comparative analysis efficiency of spectrum monitoring.

[0063] Example 2

[0064] Considering that step 3 in Embodiment 1 uses a fixed frequency unit for signal density statistics, in the ultra-wide spectrum of 20MHz~8GHz, the signal distribution density varies greatly. A fixed frequency unit will result in overly coarse statistical units in dense signal areas, losing local signal density details, and overly fine statistical units in sparse signal areas, causing ineffective consumption of statistical resources. To solve this technical problem, in one possible implementation, step 3 includes the following sub-steps:

[0065] Sub-step 3.1: Extract the frequency features and signal strength features corresponding to each frequency point from the spectral feature data to generate single-frequency feature data.

[0066] For example, from the spectral feature data generated in step 2, frequency features such as frequency value and bandwidth, as well as signal strength features such as signal power value and field strength value, are extracted for each frequency point, and the two types of features for each frequency point are associated.

[0067] For example, a frequency value of 2437MHz corresponds to a signal power value of -48dBm and a bandwidth of 22MHz. After being integrated in ascending order of frequency from 20MHz to 8GHz, single-frequency feature data is generated.

[0068] Sub-step 3.2: Based on single-frequency feature data, predict the sparsity of signal distribution in the spectrum range to be monitored and generate spectrum sparsity partition data.

[0069] For example, a threshold for the effective signal point density is set. Within each 10MHz interval, a dense partition is defined as having 30 or more effective signal points, a medium-density partition is defined as having 10 or less effective signal points and less than 30 effective signal points, and a sparse partition is defined as having less than 10 effective signal points.

[0070] Based on single-frequency characteristic data, the number of effective signal points in each consecutive 10MHz interval within the range of 20MHz to 8GHz was calculated. The intervals such as FM broadcast band of 87 to 108MHz, WiFi band of 2400 to 2500MHz, and digital intercom band of 450 to 470MHz were identified as dense intervals. The intervals such as 200 to 450MHz and 600 to 700MHz were identified as medium-density intervals. Some planned but unused intervals of 6000 to 8000MHz were identified as sparse intervals.

[0071] The low-frequency band of 20 to 200 MHz is predicted by default according to the dense partitioning standard, and the high-frequency band of 700 MHz to 8 GHz is predicted by default according to the sparse partitioning standard. The start and end boundaries of the frequency of each partition are recorded.

[0072] For example, dense partitions of 87 to 108 MHz, medium-density partitions of 200 to 450 MHz, and sparse partitions of 6000 to 6500 MHz are used to generate spectral sparsity partition data.

[0073] Sub-step 3.3: Based on the spectral sparsity partitioning data, the sparse partitions are divided into wide-bandwidth statistical units, and the dense partitions are divided into narrow-bandwidth statistical units to generate adaptive spectral statistical unit data.

[0074] For example, based on the spectral sparsity partitioning data, dense partitions are divided into statistical units with a narrow bandwidth of 100kHz.

[0075] For example, the dense 87 to 108 MHz zone is divided into units such as 87.0 to 87.1 MHz and 87.1 to 87.2 MHz, and the medium-density zone is divided into statistical units with a bandwidth of 1 MHz.

[0076] For example, the medium-density partition of 200 to 450 MHz is divided into units such as 200 to 201 MHz and 201 to 202 MHz, and the sparse partition is divided into statistical units with a wide bandwidth of 10 MHz.

[0077] For example, the sparse partition of 6000 to 6500MHz is divided into units such as 6000 to 6010MHz and 6010 to 6020MHz; the frequency start and end boundaries and the partition to which all statistical units belong are recorded.

[0078] For example, 87.0 to 87.1 MHz is a dense partition, and 200 to 201 MHz is a medium-density partition, generating adaptive spectrum statistical unit data.

[0079] Sub-step 3.4: Based on single-frequency feature data, perform statistical calculations on the signal distribution state within each adaptive spectrum statistical unit to generate the signal density value of each statistical unit.

[0080] For example, taking each statistical unit in the adaptive spectrum statistical unit data as a unit, based on single-frequency point feature data, the signal density value in each statistical unit is calculated using the formula D=k1×N+k2×I, where D is the signal density value, N is the number of effective signal points, I is the average signal strength, and k1 and k2 are weighting coefficients.

[0081] Sub-step 3.5: Generate signal density distribution data for the spectrum range to be monitored based on the signal density values ​​of all statistical units and their corresponding frequency interval positions.

[0082] For example, the signal density values ​​of all statistical units are associated with the corresponding frequency range positions. For instance, 87.0 to 87.1 MHz corresponds to a signal density value of -9.4, 200 to 201 MHz corresponds to a signal density value of -18.4, and 6000 to 6010 MHz corresponds to a signal density value of -33.8. The statistical results of all statistical units are integrated in ascending order of frequency from 20 MHz to 8 GHz to generate signal density distribution data for the spectrum range to be monitored from 20 MHz to 8 GHz.

[0083] By predicting the sparsity of signal distribution, adaptive statistical unit partitioning is achieved, ensuring that the width of the statistical units closely matches the actual signal distribution characteristics and high- and low-frequency features of the 20MHz~8GHz ultra-wide spectrum. This approach not only preserves the statistical details of densely populated signal zones, especially the 20~200MHz low-frequency band, avoiding the loss of local density features and improving the accuracy of signal density statistics, but also reduces the number of statistical units in sparsely populated signal zones, lowering the computational load of the statistical process, reducing the consumption of statistical resources, and improving the efficiency of signal density statistics. The generated signal density distribution data more closely matches the actual signal distribution state of the ultra-wide spectrum, providing a more accurate basis for subsequent dynamic segmentation processing and resolution adaptation, further enhancing the performance of the entire solution in ultra-wide spectrum monitoring.

[0084] Example 3

[0085] Considering that in Embodiment 1, step 4 only performs dynamic segmentation based on the signal density threshold, in practical applications, the signal continuity at the sub-band boundaries is easily overlooked, resulting in the boundary signals being fragmented and divided into different sub-bands. Consequently, subsequent spectrum analysis cannot fully capture the boundary signal characteristics, and abnormal situations such as sub-bands being too narrow or too wide may occur, affecting the accuracy and efficiency of subsequent analysis. To solve this technical problem, in one possible implementation, the dynamic segmentation of the spectrum range to be monitored in step 4 includes the following:

[0086] The interval determination is performed on each signal density value in the signal density distribution data to generate the spectral density interval division result.

[0087] For example, three signal density threshold ranges are set: a signal density value greater than or equal to -10 is a high density range, -25 less than or equal to a signal density value less than -10 is a medium density range, and a signal density value less than -25 is a low density range. The signal density values ​​in the signal density distribution data generated in step 3 are classified according to the threshold ranges to determine the density level of each frequency range. The density level judgment criteria for the low frequency band of 20 to 200 MHz are appropriately relaxed, and a signal density value greater than or equal to -15 is judged as a high density range.

[0088] For example, the signal density value in the 87 to 108 MHz range is -9.4 to -12.5, which is determined to be a high-density range; the signal density value in the 200 to 450 MHz range is -18.4 to -22.1, which is determined to be a medium-density range; and the signal density value in the 6000 to 6500 MHz range is -33.8 to -30.2, which is determined to be a low-density range, thus generating the spectrum density range division results.

[0089] Based on the results of the spectral density interval division, the frequency start boundary and frequency end boundary of each sub-band are initially defined, and initial segmentation data is generated.

[0090] For example, based on the spectral density interval division results, consecutive frequency intervals of the same density level are merged, and the frequency start boundary and frequency end boundary of each sub-band are initially defined. The high-density interval of 87 to 108 MHz is merged and initially defined as the 87 to 108 MHz sub-band; the medium-density interval of 200 to 450 MHz is merged and initially defined as the 200 to 450 MHz sub-band; and the low-density interval of 6000 to 6500 MHz is merged and initially defined as the 6000 to 6500 MHz sub-band. The boundary information of each sub-band is recorded.

[0091] For example, the 87 to 108 MHz sub-band starts at 87 MHz and ends at 108 MHz, generating initial segment data.

[0092] Single-frequency feature data is retrieved, and the continuity of the signal at the boundary of each sub-frequency band in the initial segmented data is detected to generate the boundary signal continuity determination result.

[0093] For example, retrieve the single-frequency feature data generated in sub-step 3.1 of step 3, and analyze the correlation between signal strength and frequency of five adjacent frequency points at the boundary of each sub-frequency band in the initial segmented data, with an interval of 100kHz: if the signal strength fluctuation amplitude of adjacent points is less than or equal to 5dBm and the frequency is continuous without any breaks, it is determined that the boundary signal is continuous; if the signal strength fluctuation amplitude is greater than 5dBm or there are breaks in the frequency, it is determined that the boundary signal is fragmented; in particular, the detection of the low-frequency band boundary from 20 to 200MHz is strengthened.

[0094] For example, at the termination boundary of the 87-108MHz sub-band at 108MHz, the signal strengths of the five adjacent frequency points at 107.9MHz, 107.95MHz, 108MHz, 108.05MHz, and 108.1MHz are -48dBm, -49dBm, -50dBm, -20dBm, and -18dBm, respectively, with a fluctuation range of 32dBm, which is determined to be a boundary signal break. At the starting boundary of the 200-450MHz sub-band at 200MHz, the signal strength fluctuation range of the five adjacent frequency points is 3dBm, which is determined to be a boundary signal continuity. The boundary signal continuity determination result is generated.

[0095] Based on the boundary signal continuity determination results, the boundary positions of the initial segments are adjusted to ensure that the boundary signals completely belong to a single sub-frequency band, and the corrected segment data is generated.

[0096] For example, for boundaries that are discontinuous in the boundary signal continuity determination result, the boundary position is adjusted in the direction of higher or lower frequency until the continuity of the boundary signal is guaranteed.

[0097] For example, if there is a break in the 108MHz boundary of the 87 to 108MHz sub-band, the frequency is adjusted to 108.5MHz in the direction of increasing frequency. The signal strength fluctuation amplitude of the adjacent points from 108.45MHz to 108.55MHz is re-detected and found to be 2dBm, which is determined to be continuous, ensuring that the narrowband navigation signal at this location is completely attributed to the 87 to 108.5MHz sub-band. The adjustment amplitude of the boundary of the 20 to 200MHz low-frequency band is controlled within the range of 0.1 to 1MHz to avoid excessive adjustment that may cause abnormal sub-band range.

[0098] Record the adjusted sub-band boundary information, such as 87 to 108.5MHz, 200 to 450MHz, and 6000 to 6500MHz, and generate corrected segmented data.

[0099] The corrected segmented data is validated for rationality. Abnormal sub-bands that are too narrow or too wide are removed and re-divided to generate several sub-band division data.

[0100] For example, a reasonable threshold range for the frequency width of sub-bands is set: the reasonable width for the low-frequency band of 20 to 200 MHz is 10 to 50 MHz, the reasonable width for the mid-low band of 200 to 700 MHz is 30 to 100 MHz, and the reasonable width for the high-frequency band of 700 MHz to 8 GHz is 50 to 200 MHz.

[0101] Width verification was performed on each sub-band in the corrected segmented data. The width of the 87 to 108.5 MHz sub-band was 21.5 MHz, which met the 10 to 50 MHz threshold. The width of the 200 to 450 MHz sub-band was 250 MHz, which exceeded the 30 to 100 MHz threshold and was therefore deemed abnormal. The width of the 6000 to 6500 MHz sub-band was 500 MHz, which exceeded the 50 to 200 MHz threshold and was also deemed abnormal. The abnormal 200 to 450 MHz sub-band was split into three sub-bands: 200 to 280 MHz, 280 to 360 MHz, and 360 to 450 MHz, each with a width of 30 to 90 MHz, which met the threshold. The abnormal 6000 to 6500 MHz sub-band was split into three sub-bands: 6000 to 6150 MHz, 6150 to 6300 MHz, and 6300 to 6500 MHz, each with a width of 150 to 200 MHz, which also met the threshold.

[0102] The final sub-band boundary information, such as 87 to 108.5MHz, 200 to 280MHz, 280 to 360MHz, 360 to 450MHz, 6000 to 6150MHz, 6150 to 6300MHz, and 6300 to 6500MHz, is integrated to generate several sub-band allocation data.

[0103] This implementation method effectively avoids the problem of boundary signal fragmentation by strengthening the detection and adjustment of boundary signal continuity, especially in the 20-200MHz low-frequency band, through boundary signal continuity detection and boundary position adjustment. This ensures the integrity of signals, especially narrowband signals, within each sub-band, allowing subsequent spectrum analysis to fully capture signal characteristics and improving analysis accuracy. By setting thresholds for high and low frequency regions for rationality verification, abnormal sub-bands are eliminated and re-divided, avoiding the impact of excessively narrow or wide sub-bands on subsequent analysis accuracy and efficiency, making the sub-band division more reasonable. The generated sub-band division data not only conforms to the signal density distribution characteristics of the 20MHz-8GHz ultra-wide spectrum but also ensures signal integrity and reasonable segmentation, providing a better frequency band foundation for subsequent resolution adaptation and spectrum analysis.

[0104] Considering that step 4 only matches the spectrum analysis resolution based on signal density characteristics, in the ultra-wide spectrum of 20MHz~8GHz, narrowband signals and wideband signals may coexist within the same sub-band of signal density. For example, narrowband navigation signals and wideband broadcast signals may coexist in the 20~200MHz low-frequency band, and narrowband Bluetooth signals and wideband WiFi signals may coexist in the 2.4GHz band. The two types of signals have different requirements for resolution. A single density feature cannot meet the resolution requirements of signals with different bandwidths. In order to make the resolution more closely match the actual signal characteristics within the sub-band, in one possible implementation, the spectrum analysis resolution matched for each sub-band in step 4 includes the following:

[0105] Retrieve signal density distribution data, sub-band division data, and spectral feature data. Extract the bandwidth features of the signal in each sub-band from the spectral feature data to generate fused feature data of density, frequency, and bandwidth for each sub-band.

[0106] For example, the signal density distribution data generated in step 3, the sub-band division data generated in step 4, and the spectrum feature data generated in step 2 are retrieved. The spectrum feature data is then split into feature data corresponding to each sub-band based on the sub-band division data. For example, the feature data corresponding to the 87 to 108.5 MHz sub-band includes FM broadcast signals with a bandwidth of 150 kHz and navigation signals with a bandwidth of 50 kHz; the feature data corresponding to the 2400 to 2405 MHz sub-band includes WiFi signals with a bandwidth of 22 MHz and Bluetooth signals with a bandwidth of 1 MHz.

[0107] The bandwidth characteristics of the signals are extracted from the feature data of each sub-band. For example, the 87 to 108.5 MHz sub-band contains narrowband signals less than 1 MHz and wideband signals from 100 kHz to 200 kHz. The bandwidth characteristics of the narrowband signals in the 20 to 200 MHz low-frequency band are extracted in a refined manner, such as the 50 kHz navigation signal. The density characteristics of each sub-band, such as the high density of 87 to 108.5 MHz, the frequency characteristics, such as the low frequency of 87 to 108.5 MHz, and the bandwidth characteristics, including narrowband and wideband signals, are integrated to generate fused feature data of density, frequency, and bandwidth of each sub-band.

[0108] Based on the fused feature data, resolution adaptation rules are established to generate resolution adaptation data for each sub-band by matching the spectral analysis resolution of sub-bands containing narrowband signals with narrowband signal features, and by matching the spectral analysis resolution of sub-bands containing only wideband signals with wideband signal features.

[0109] For example, resolution adaptation rules are established based on fused feature data: if the fused feature data of a sub-band contains narrowband signal features with a bandwidth of less than or equal to 1MHz, especially narrowband signals in the low-frequency band of 20 to 200MHz, then the sub-band is matched with the narrowband signal features using high-resolution spectral analysis, 100Hz to 1kHz; if the fused feature data of a sub-band only contains wideband signal features with a bandwidth greater than 1MHz, then the sub-band is matched with the wideband signal features using spectral analysis, 10kHz to 100kHz.

[0110] Each sub-band is associated with its corresponding spectrum analysis resolution: the 87 to 108.5 MHz sub-band contains 50 kHz narrowband navigation signals and is matched with a 500 Hz spectrum analysis resolution; the 2400 to 2405 MHz sub-band contains 1 MHz narrowband Bluetooth signals and is matched with a 1 kHz spectrum analysis resolution; the 200 to 280 MHz sub-band contains only 15 kHz wideband digital intercom signals and is matched with a 10 kHz spectrum analysis resolution; the 6000 to 6150 MHz sub-band contains only background noise and no effective signals and is matched with a 100 kHz spectrum analysis resolution; resolution adaptation data for each sub-band is generated.

[0111] This implementation method, by adding the extraction of signal bandwidth features and generating fused feature data, makes the basis for resolution adaptation more comprehensive. It considers both the signal density and frequency characteristics as well as the signal bandwidth characteristics, solving the problem of resolving signals with different bandwidths within the same sub-frequency band. It is particularly suitable for high-precision resolving of narrowband signals in the 20~200MHz low-frequency band. The resolution adaptation rules established based on the fused feature data ensure that the spectrum analysis resolution closely matches the actual signal characteristics within the sub-frequency band. Sub-frequency bands containing narrowband signals can achieve fine-grained resolving, while sub-frequency bands containing only broadband signals can achieve efficient resolving. This further improves the accuracy and efficiency of ultra-wide spectrum resolving from 20MHz to 8GHz, making the entire solution more suitable for the actual monitoring needs of complex electromagnetic environments.

[0112] Example 4

[0113] Considering that in the above embodiments, step 5 only performs frequency domain feature analysis on each sub-band, in ultra-wideband monitoring of 20MHz~8GHz, there are various transient noise signals in the field environment, such as industrial electromagnetic interference and environmental clutter. These signals are easily misjudged as valid signals in the frequency domain, resulting in the generated spectrum analysis data containing redundant noise information, affecting the accuracy of subsequent normalization processing and simultaneous display, and wasting computing resources. Especially in narrowband signal monitoring of the 20~200MHz low-frequency band, noise interference will seriously mask the characteristics of valid signals. In order to solve this technical problem, in one possible implementation, step 5 includes the following sub-steps:

[0114] Sub-step 5.1: Based on the sub-band division data and resolution adaptation data, retrieve the corresponding spectrum analysis parameters for each sub-band and generate the analysis configuration data for each sub-band.

[0115] For example, based on the sub-band segmentation data and resolution adaptation data generated in step 4, corresponding spectrum analysis parameters are matched for each sub-band. For the 87 to 108.5 MHz sub-band, the 500 Hz resolution is matched with a Hanning window, a sampling rate of 200 MS / s, and a resolution duration of 1 s; for the 2400 to 2405 MHz sub-band, the 1 kHz resolution is matched with a Hanning window, a sampling rate of 200 MS / s, and a resolution duration of 0.5 s; for the 200 to 280 MHz sub-band, the 10 kHz resolution is matched with a Hanning window, a sampling rate of 150 MS / s, and a resolution duration of 1 s; and for the 6000 to 6150 MHz sub-band, the 100 kHz resolution is matched with a rectangular window, a sampling rate of 100 MS / s, and a resolution duration of 1 s.

[0116] The system matches refined analytical window parameters for the 20 to 200 MHz low-frequency band, with a Hanning window attenuation coefficient of 60 dB. Each sub-band is associated with its corresponding analytical parameters to generate analytical configuration data for each sub-band.

[0117] Sub-step 5.2: Based on the parsed configuration data and spectral feature data, perform frequency domain feature analysis on each sub-band to generate frequency domain parsed data for each sub-band.

[0118] For example, based on the analytical configuration data of each sub-band, frequency domain feature analysis is performed on the spectral feature data corresponding to each sub-band generated in step 2. For the 87 to 108.5 MHz sub-band, a resolution of 500 Hz and a Hanning window are used to extract the center frequency of the FM broadcast signal (89.3 MHz), bandwidth (150 kHz), and peak intensity (-45 dBm), and the center frequency of the navigation signal (108 MHz), bandwidth (50 kHz), and peak intensity (-52 dBm). For the 2400 to 2405 MHz sub-band, a resolution of 1 kHz and a Hanning window are used to extract the center frequency of the WiFi signal (2437 MHz), bandwidth (22 MHz), and peak intensity (-48 dBm), and the center frequency of the Bluetooth signal (2440 MHz), bandwidth (1 MHz), and peak intensity (-55 dBm). The refined frequency domain features of each sub-band are extracted to generate the frequency domain analytical data of each sub-band.

[0119] Sub-step 5.3: retrieve the original signal data, perform time-domain stability analysis on the original signals corresponding to each sub-frequency band, and generate time-domain stability determination results for each sub-frequency band.

[0120] For example, the original signal data generated in step 1 is retrieved, and the original signals corresponding to each sub-band are separated according to the sub-band division data. The strength stability of the original signals of each sub-band in the time domain is analyzed, and a stability judgment threshold is set. The signal strength fluctuation amplitude within 1 second is less than or equal to 3dBm. In particular, the stability analysis of the low-frequency band signal from 20 to 200MHz is strengthened. The 89.3MHz FM broadcast signal in the 87 to 108.5MHz sub-band has a strength fluctuation amplitude of 2dBm within 1 second, which is judged as a valid signal. A 0.08MHz navigation signal with a strength fluctuation of 1dBm within 1 second is determined to be a valid signal. A signal at a certain frequency point at 100MHz, whose strength instantaneously rises from -60dBm to -30dBm and then drops to -80dBm within 0.1 seconds (a fluctuation of 50dBm), is determined to be transient noise. A 2437MHz WiFi signal in the 2400-2405MHz sub-band has a strength fluctuation of 2.5dBm within 1 second and is determined to be a valid signal. Time-domain stability determination results for each sub-band are generated.

[0121] Sub-step 5.4 combines the frequency domain analytical data and the time domain stability judgment results to remove the frequency domain feature data corresponding to noise and generate clean frequency domain analytical data for each sub-frequency band.

[0122] For example, the frequency domain analysis data of each sub-band is correlated with the time domain stability determination results, and the frequency domain feature data corresponding to the signal that is transient noise in the time domain stability determination results is removed: for the 87 to 108.5 MHz sub-band, the frequency domain feature data corresponding to 100 MHz transient noise is removed, with a center frequency of 100 MHz, bandwidth of 200 kHz, and peak intensity of -30 dBm, while the frequency domain feature data corresponding to FM broadcast and navigation signals are retained; for the 2400 to 2405 MHz sub-band, there is no transient noise, so all frequency domain feature data are retained; in particular, the noise feature data in the 20 to 200 MHz low-frequency band is removed to avoid masking the narrowband navigation signal; the frequency domain feature data corresponding to the effective signal is retained to generate clean frequency domain analysis data for each sub-band.

[0123] Sub-step 5.5 involves feature integration of the clean frequency domain analytical data of each sub-band to generate the spectral analytical data of each sub-band.

[0124] For example, various features in the clean frequency domain analysis data of each sub-band are integrated and stored according to the rules of signal type, center frequency, bandwidth, peak intensity, and stability to generate spectrum analysis data for each sub-band. For example, the 87 to 108.5MHz sub-band is integrated as FM radio, 89.3MHz, 150kHz, -45dBm, stable; navigation, 108MHz, 50kHz, -52dBm, stable; the 2400 to 2405MHz sub-band is integrated as WiFi, 2437MHz, 22MHz, -48dBm, stable; Bluetooth, 2440MHz, 1MHz, -55dBm, stable.

[0125] Example 5

[0126] Considering that step 6 only performs basic calibration of the frequency axis and signal strength axis, and given that there are significant differences in background noise levels among different sub-bands in the ultra-wide spectrum of 20MHz to 8GHz (e.g., low background noise in the 20-200MHz low-frequency band and high background noise in the 700MHz-8GHz high-frequency band), direct basic calibration would mask the effective signal characteristics of the low-noise sub-bands and result in insufficient signal contrast in the high-noise sub-bands, affecting the visualization effect and signal recognition efficiency of subsequent on-screen display. To improve the visualization effect of the normalized spectrum data, in one possible implementation, step 6 includes the following sub-steps:

[0127] Sub-step 6.1: Collect spectrum analysis data of all sub-bands to generate a multi-sub-band analysis data set.

[0128] For example, the spectrum analysis data of all sub-bands generated in step 5 are collected and integrated according to the sub-band number and frequency interval order to generate a multi-sub-band analysis data set.

[0129] For example, integrating the spectrum analysis data of sub-bands such as 87 to 108.5 MHz, 200 to 280 MHz, 2400 to 2405 MHz, and 6000 to 6150 MHz can form a complete dataset containing the signal characteristics of all sub-bands.

[0130] Sub-step 6.2 involves extracting background noise from the data of each sub-band in the multi-sub-band analytical dataset to generate noise level data for each sub-band.

[0131] For example, a sliding window noise extraction algorithm is used, with a window size of 10 consecutive frequency points, to extract background noise from the spectrum analysis data of each sub-band in the multi-sub-band analytical dataset.

[0132] For the low-frequency sub-band of 20 to 200 MHz, such as 87 to 108.5 MHz, continuous points with signal strength below -80 dBm are selected as noise samples, and the average intensity of all noise samples within this window is calculated as the noise level of this sub-band.

[0133] For the mid-to-low frequency bands of 200 to 700 MHz, such as 200 to 280 MHz, continuous points with signal strength below -75 dBm are selected as noise samples, and the average intensity of the noise samples within the window is calculated as the noise level of the sub-frequency band.

[0134] For sub-bands in the 700MHz to 8GHz high-frequency range, such as 2400 to 2405MHz and 6000 to 6150MHz, continuous points with signal strength below -70dBm are selected as noise samples, and the average intensity of the noise samples within the window is calculated as the noise level of the sub-band.

[0135] For example, the noise sample intensity mean of the 87 to 108.5 MHz sub-band is -90 dBm, and its noise level is recorded as -90 dBm; the noise sample intensity mean of the 200 to 280 MHz sub-band is -85 dBm, and its noise level is recorded as -85 dBm; the noise sample intensity mean of the 2400 to 2405 MHz sub-band is -80 dBm, and its noise level is recorded as -80 dBm; the noise sample intensity mean of the 6000 to 6150 MHz sub-band is -75 dBm, and its noise level is recorded as -75 dBm. After integration, the noise level data of each sub-band is generated.

[0136] Sub-step 6.3 involves performing signal strength axis calibration on the spectral analysis data of each sub-frequency band based on the noise level data, generating noise-calibrated analysis data.

[0137] For example, using the noise level data of each sub-band as a benchmark, the spectral analysis data of each sub-band is calibrated for signal strength axis. The calibration rule is to adjust the benchmark value of signal strength to the noise level plus 10dB, so as to ensure that the effective signal characteristics are clearly highlighted above the noise benchmark and to preserve the relative strength relationship between effective signals.

[0138] For example, the noise level in the 87 to 108.5 MHz sub-band is -90 dBm. The signal strength reference value is adjusted to -80 dBm. The original FM radio signal strength of -45 dBm in this sub-band remains unchanged, and the original background noise strength of -90 dBm is mapped to -80 dBm after calibration. Similarly, the noise level in the 2400 to 2405 MHz sub-band is -80 dBm. The signal strength reference value is adjusted to -70 dBm. The original WiFi signal strength of -48 dBm remains unchanged, and the original background noise strength of -80 dBm is mapped to -70 dBm after calibration. Finally, the noise level in the 6000 to 6150 MHz sub-band is -75 dBm. The signal strength reference value is adjusted to -65 dBm. There is no valid signal in this sub-band, and the original background noise strength of -75 dBm is mapped to -65 dBm after calibration.

[0139] Follow this rule to complete the signal strength axis calibration of all sub-bands and generate noise-calibrated parsed data.

[0140] Sub-step 6.4 involves performing frequency axis calibration and data dimension unification processing on the noise-calibrated parsed data to generate normalized spectrum data for multiple sub-bands.

[0141] For example, all noise-calibrated parsed data is mapped to a unified frequency axis coordinate system from 20MHz to 8GHz, with the frequency axis scale interval fixed at 1MHz and the horizontal axis linearly distributed from 20MHz to 8GHz, ensuring that the frequency coordinates of each sub-band are aligned and completing the frequency axis calibration; at the same time, the data format and data dimensions of each sub-band are unified and converted into a standardized format containing four core fields: frequency point, calibrated signal strength, signal type, and service label.

[0142] For example, the standardized data for the 87 to 108.5 MHz sub-band is as follows: frequency point 89.3 MHz, calibrated signal strength -45 dBm, signal type FM broadcast, service label civilian broadcast; the standardized data for the 2400 to 2405 MHz sub-band is as follows: frequency point 2437 MHz, calibrated signal strength -48 dBm, signal type WiFi, service label ISM civilian communication; the standardized data for the 6000 to 6150 MHz sub-band is as follows: frequency point 6050 MHz, calibrated signal strength -65 dBm, signal type background noise, service label not enabled. The calibrated and unified spectrum data of each sub-band are associated and integrated according to the sub-band number to generate multiple sub-band normalized spectrum data.

[0143] This implementation extracts background noise based on high and low frequency differences and performs noise-adaptive signal strength axis calibration, ensuring that the signal strength benchmark of each sub-band matches its own noise level. This effectively avoids the problem of effective signals being masked in low-noise sub-bands, especially the 20-200MHz low-frequency band, and insufficient signal contrast in high-noise sub-bands, thus improving the contrast and recognizability of signals in each sub-band. The generated normalized spectrum data of multiple sub-bands has more prominent signal characteristics, providing a high-quality data source for subsequent multi-band simultaneous rendering processing, ensuring the visualization effect of multi-band simultaneous display in the 20MHz-8GHz ultra-wide spectrum, allowing monitoring personnel to more clearly identify the signal characteristics of each sub-band, and improving signal identification efficiency.

[0144] Example 6

[0145] Step 7 uses direct layer fusion for simultaneous rendering. In ultra-wide spectrum monitoring of 20MHz~8GHz, when there are many sub-bands or the spectrum includes similar services, problems arise such as the spectrum image of the core service band being obscured by non-core bands, and similar spectra being displayed in a scattered manner, making comparative analysis difficult. For example, in... Figure 3 , Figure 5 In this context, multiple spectrum types, such as WiFi, 5G, and aviation navigation, need to be displayed simultaneously. Direct fusion would lead to ambiguity in the core frequency band signal characteristics and would not be able to achieve centralized display of similar spectrum types, reducing the analysis efficiency of monitoring personnel. To improve the targeting and comparative analysis efficiency of the simultaneous display, in one possible implementation, step 7 of the multi-band simultaneous rendering processing of normalized spectrum data for multiple sub-bands includes the following:

[0146] Retrieve normalized spectrum data from multiple sub-bands to generate a data source for simultaneous on-screen rendering.

[0147] For example, retrieve the normalized spectrum data of all sub-bands within the ultra-wide spectrum range of 20MHz to 8GHz generated in step 6, and classify and integrate them according to service categories, including civil broadcasting, civil communication, industrial communication, navigation communication, etc.

[0148] For example, FM radio (87 to 108.5 MHz) and VHF navigation (108 to 118 MHz) are classified as broadcast navigation; WiFi (2400 to 2405 MHz and 5150 to 5350 MHz) is classified as ISM civilian communication; and digital intercom (450 to 470 MHz) is classified as industrial communication. After integration, a data source for simultaneous on-screen rendering is generated.

[0149] Provides a sub-band priority setting interface to receive user-input sub-band priority configuration information: The sub-band priority setting function entry is set in the operation interface of the portable signal analyzer, allowing users to select core service frequency bands and configure priority levels through button operation. The priority is divided into three levels: high, medium, and low. For example, users can set FM radio (87 to 108.5MHz) and WiFi (2400 to 2405MHz) as high-priority core sub-bands, digital intercom (200 to 280MHz) as medium-priority sub-bands, and planned but unused frequency bands (6000 to 6150MHz) as low-priority non-core sub-bands according to monitoring needs. The analyzer receives and stores this priority configuration information.

[0150] The data source for simultaneous on-screen rendering is divided into spectrum layers, generating independent spectrum rendering layer data for each sub-band. At the same time, a layer-on-top attribute is configured for the core sub-bands. The simultaneous on-screen rendering data source is split into independent spectrum data layers according to sub-bands and service categories. Each layer corresponds to the spectrum image of a sub-band, ensuring that the spectrum characteristics of each sub-band are presented independently. According to the priority configuration information input by the user, a layer-on-top attribute is configured for the spectrum rendering layer data of high-priority core sub-bands, clearly indicating that this type of layer is located on the top layer when displayed on the same screen, avoiding being obscured by layers of other levels.

[0151] All spectrum rendering layer data are aligned to coordinates, and the layers are merged in order of priority to generate multi-sub-band simultaneous rendering data, realizing simultaneous display of the spectrum of each sub-band.

[0152] For example, the spectrum rendering layer data of all sub-bands are uniformly aligned with coordinates, with 20MHz as the starting point and 8GHz as the ending point. The horizontal axis represents frequency, and the vertical axis represents the calibrated signal strength, ensuring that the coordinate system of each layer is completely consistent. Then, the layers are merged in order of priority from high to low, with the high-priority core sub-band layer displayed on top, the medium-priority sub-band layers superimposed in the middle layer, and the low-priority sub-band layers displayed at the bottom layer. At the same time, it supports the centralized display of spectrum for similar services. The spectrum images of broadcast and navigation sub-bands are displayed in the upper area of ​​the interface, the spectrum images of ISM civil communication sub-bands are displayed in the middle area of ​​the interface, and the spectrum images of industrial communication sub-bands are displayed in the lower area of ​​the interface. Finally, multi-band simultaneous display is achieved on the analyzer's display interface.

[0153] This implementation provides a sub-band priority setting interface, allowing users to customize core service frequency bands according to actual monitoring needs, thus improving the flexibility and targeting of the solution. It configures layer top-level attributes for core sub-bands and merges layers according to priority, effectively avoiding the problem of core sub-band spectrum images being obscured, ensuring that core signal characteristics are clearly visible. Simultaneously, it supports centralized display of similar spectrum types, allowing monitoring personnel to quickly compare and analyze signal characteristics of different frequency bands under the same service category, significantly improving the comparative analysis efficiency and visualization effect of 20MHz~8GHz ultra-wide spectrum monitoring, and better meeting the actual needs of on-site monitoring.

[0154] Considering that step 7 uses a static parameter labeling method without differentiation, in ultra-wide spectrum monitoring of 20MHz~8GHz, problems such as chaotic parameter labeling, lack of prominence of core parameters, and disconnect between service information and spectrum parameters are likely to occur. For example, in Figure 3 , Figure 5 In the current system, multiple service frequency bands are displayed simultaneously. Indiscriminate labeling can lead to the loss of key parameters and make it impossible to directly associate them with information such as service frequency band names and signal classifications. Monitoring personnel need to perform additional operations to obtain service information, which reduces the practicality and monitoring efficiency of parameter labeling. To improve the relevance, accuracy, and service linkage of spectrum parameter labeling, in one possible implementation, the spectrum parameters labeled in each sub-frequency band spectrum region in step 7 include the following:

[0155] Extract core spectrum parameters and service information to generate parameter priority data.

[0156] For example, core spectral parameters of each sub-band are extracted from normalized spectral data of multiple sub-bands, including center frequency, bandwidth, peak intensity, and signal duration. At the same time, preset service frequency band library and frequency band allocation table data are retrieved to obtain the service frequency band name, signal classification, and properties corresponding to each sub-band. According to the general rules of spectrum monitoring and actual application requirements, the parameters and service information are ranked by importance, with high priority for center frequency, peak intensity, and service frequency band name, medium priority for bandwidth and signal classification, and low priority for signal duration and signal properties.

[0157] Generate parameter priority data for each sub-band. For example, the high priority parameters for the 87 to 108.5MHz sub-band are 89.3MHz, -45dBm, and FM broadcast; the medium priority parameters are 150kHz and civilian broadcast; and the low priority parameters are continuous and legal services.

[0158] Matching annotation positions generates parameter annotation position data. For example, the parameter priority data of each sub-frequency band is matched with the corresponding spectrum rendering layer data. Annotation positions are assigned according to the layout area of ​​the sub-frequency band on the display interface to ensure that the annotation information does not obscure the core features of the spectrum curve: high-priority parameters are assigned to a prominent position on the right side of the sub-frequency band spectrum curve and annotated with bold black font to ensure visual prominence; medium-priority parameters are assigned to the bottom of the sub-frequency band spectrum curve and annotated with regular gray font to balance readability and simplicity; low-priority parameters are assigned to the corner positions of the sub-frequency band spectrum area and annotated with small light gray font to avoid occupying core visual space; the specific annotation coordinates of each parameter and business information are recorded to generate parameter annotation position data.

[0159] Overlay annotations onto the spectrum display area. For example, based on the parameter annotation location data, the core spectrum parameters and business information of each sub-band are overlaid and annotated in the form of digital text onto the corresponding sub-band spectrum area on the portable signal analyzer display interface.

[0160] For example, the spectrum curve for the 87 to 108.5 MHz sub-band is labeled with a center frequency of 89.3 MHz, peak strength of -45 dBm, and FM broadcast service on the right; below it is labeled with a bandwidth of 150 kHz and a signal classification of civilian broadcasting; the corner indicates continuous signal duration and legal signal nature. The spectrum curve for the 2400 to 2405 MHz sub-band is labeled with a center frequency of 2437 MHz, peak strength of -48 dBm, and WiFi service on the right; below it is labeled with a bandwidth of 22 MHz and a signal classification of ISM civilian communication; the corner indicates continuous signal duration and legal signal nature. The spectrum curve for the 6000 to 6150 MHz sub-band is labeled with no effective signal, average signal strength of -65 dBm, and service band planning not enabled on the right; below it is labeled with a signal classification of background noise; the corner indicates continuous signal duration and no service occupation.

[0161] Real-time monitoring and dynamic updates are implemented. For example, real-time monitoring of normalized spectrum data for multiple sub-bands is enabled, and thresholds for judging signal characteristic changes are set. When the signal strength fluctuation of a sub-band exceeds 5dBm or the center frequency shift exceeds 1MHz, it is determined that the signal characteristics have changed. The core spectrum parameters of the sub-band are automatically re-extracted and the parameter priority data is updated. The annotation position data is adjusted synchronously to ensure that the annotation information is consistent with the changes in signal characteristics. At the same time, the annotation content of service information is dynamically adjusted according to the update of the service frequency band library. For example, when the service attributes of a frequency band change, the corresponding signal classification and property annotations are automatically updated to ensure the real-time performance and accuracy of the annotation information.

[0162] This implementation prioritizes and assigns differentiated labeling positions to spectral parameters, highlighting core parameters and business information to avoid labeling confusion and improve the efficiency of monitoring personnel in reading core information. By monitoring signal characteristic changes in real time and updating labeling parameters and positions synchronously, the accuracy and timeliness of labeling information are guaranteed, avoiding information lag caused by static labeling. At the same time, it realizes the linkage labeling of business information and spectral parameters, allowing monitoring personnel to directly obtain the core characteristics and business nature of the signal from the display interface without additional manual review. This significantly improves the information reading efficiency and accuracy of 20MHz~8GHz ultra-wide spectrum monitoring, further enhancing the practicality and field adaptability of the entire solution.

[0163] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A spectrum analysis method for multi-band simultaneous display, characterized in that, Includes the following steps: Step 1: Collect electromagnetic signals within the frequency spectrum to be monitored and generate raw signal data; Step 2: Perform spectral analysis on the original signal data to generate spectral feature data, which includes the frequency characteristics and signal strength characteristics of the signal; Step 3: Based on frequency characteristics and signal strength characteristics, perform signal density statistics on the spectrum range to be monitored to generate signal density distribution data; Step 4: Based on the signal density distribution data, the spectrum range to be monitored is dynamically segmented to generate several sub-band division data. The corresponding spectrum analysis resolution is matched for each sub-band to generate resolution adaptation data. Step 5: Based on the sub-band division data, resolution adaptation data, and spectral feature data, perform spectral analysis processing on each sub-band to generate spectral analysis data for each sub-band. Step 6: Normalize the spectral analysis data of all sub-bands to generate normalized spectral data for multiple sub-bands; Step 7: Perform multi-band simultaneous rendering processing on the normalized spectrum data of multiple sub-bands, and label the corresponding spectrum parameters in the spectrum region of each sub-band.

2. The spectrum analysis method for multi-band simultaneous display according to claim 1, characterized in that, Step 3 includes the following sub-steps: Sub-step 3.1: Extract the frequency features and signal strength features corresponding to each frequency point from the spectral feature data to generate single-frequency feature data; Sub-step 3.2: Based on single-frequency feature data, predict the sparsity of signal distribution in the spectrum range to be monitored and generate spectrum sparsity partition data. Sub-step 3.3: Based on the spectral sparsity partitioning data, the sparse partitions are divided using wide-bandwidth statistical units, and the dense partitions are divided using narrow-bandwidth statistical units, to generate adaptive spectral statistical unit data; Sub-step 3.4: Based on single-frequency feature data, perform statistical calculations on the signal distribution state within each adaptive spectrum statistical unit to generate the signal density value of each statistical unit; Sub-step 3.5: Generate signal density distribution data for the spectrum range to be monitored based on the signal density values ​​of all statistical units and their corresponding frequency interval positions.

3. The spectrum analysis method for multi-band simultaneous display according to claim 1, characterized in that, Step 4, which involves dynamically segmenting the spectrum range to be monitored, includes the following: The intervals of each signal density value in the signal density distribution data are determined to generate the spectral density interval division results. Based on the results of the spectral density interval division, the frequency start boundary and frequency end boundary of each sub-band are initially defined, and initial segmentation data is generated; Single-frequency feature data is retrieved, and the continuity of the signal at the boundary of each sub-frequency band in the initial segmented data is detected to generate the boundary signal continuity determination result. Based on the boundary signal continuity determination results, the boundary positions of the initial segments are adjusted to ensure that the boundary signals completely belong to a single sub-frequency band, and the corrected segment data is generated. The corrected segmented data is validated for rationality. Abnormal sub-bands that are too narrow or too wide are removed and re-divided to generate several sub-band division data.

4. The spectrum analysis method for multi-band simultaneous display according to claim 1, characterized in that, The spectrum analysis resolution for matching each sub-band in step 4 includes the following: Retrieve signal density distribution data, sub-band division data, and spectral feature data; extract bandwidth features of signals within each sub-band from the spectral feature data; and generate fused feature data of density, frequency, and bandwidth for each sub-band. Based on the fused feature data, resolution adaptation rules are established to generate resolution adaptation data for each sub-band by matching the spectral analysis resolution of sub-bands containing narrowband signals with narrowband signal features, and by matching the spectral analysis resolution of sub-bands containing only wideband signals with wideband signal features.

5. The spectrum analysis method for multi-band simultaneous display according to claim 1, characterized in that, Step 5 includes the following sub-steps: Sub-step 5.1: Based on the sub-band division data and resolution adaptation data, retrieve the corresponding spectrum analysis parameters for each sub-band and generate the analysis configuration data for each sub-band; Sub-step 5.2: Based on the parsed configuration data and spectrum feature data, perform frequency domain feature analysis on each sub-band to generate frequency domain parsed data for each sub-band; Sub-step 5.3: retrieve the original signal data, perform time-domain stability analysis on the original signals corresponding to each sub-frequency band, and generate time-domain stability determination results for each sub-frequency band; Sub-step 5.4: Combining the frequency domain analytical data and the time domain stability judgment results, remove the frequency domain feature data corresponding to noise and generate clean frequency domain analytical data for each sub-frequency band; Sub-step 5.5 involves feature integration of the clean frequency domain analytical data of each sub-band to generate the spectral analytical data of each sub-band.

6. The spectrum analysis method for multi-band simultaneous display according to claim 1, characterized in that, Step 6 includes the following sub-steps: Sub-step 6.1: Collect spectrum analysis data of all sub-bands and generate a multi-sub-band analysis data set; Sub-step 6.2 involves extracting background noise from the data of each sub-band in the multi-sub-band analytical dataset to generate noise level data for each sub-band. Sub-step 6.3: Based on the noise level data, perform signal strength axis calibration on the spectrum analysis data of each sub-frequency band to generate noise-calibrated analysis data; Sub-step 6.4 involves performing frequency axis calibration and data dimension unification processing on the noise-calibrated parsed data to generate normalized spectrum data for multiple sub-bands.

7. The spectrum analysis method for multi-band simultaneous display according to claim 1, characterized in that, Step 7, which involves multi-band simultaneous rendering of normalized spectrum data from multiple sub-bands, includes the following: Retrieve normalized spectrum data from multiple sub-bands to generate a data source for simultaneous on-screen rendering; Provides a sub-band priority setting interface to receive sub-band priority configuration information input by the user; The spectrum layer of the data source for simultaneous rendering is divided, and independent spectrum rendering layer data is generated for each sub-band. At the same time, the layer is set to top for the core sub-band. All spectrum rendering layer data are aligned to coordinates, and the layers are merged in order of priority to generate multi-sub-band simultaneous rendering data, realizing simultaneous display of the spectrum of each sub-band.

8. The spectrum analysis method for multi-band simultaneous display according to claim 1, characterized in that, Step 7, which involves labeling the corresponding spectral parameters in each sub-band spectral region, includes the following: Extract the core spectral parameter information of each sub-band from the normalized spectral data of multiple sub-bands, and generate parameter priority data for each sub-band by sorting the parameters according to their importance; The parameter priority data of each sub-frequency band is matched with the corresponding spectrum rendering layer data to generate parameter annotation position data by assigning prominent annotation positions to high-priority parameters and auxiliary annotation positions to low-priority parameters. Based on the parameter labeling location data, the spectral parameters are superimposed and labeled onto the spectral display area of ​​each sub-band; The system monitors changes in normalized spectrum data across multiple sub-bands in real time, and updates the corresponding spectrum parameters and labeling positions synchronously when signal characteristics change.

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