A dual-channel high-sensitivity signal spectrum monitoring method and system

Abstract

The present application relates to a dual-channel high-sensitivity signal spectrum monitoring method and system. The method includes: acquiring two signals synchronously collected by two data acquisition devices, performing cross-correlation and then calculating the cross-correlation modulus value; performing peak search based on the cross-correlation modulus value, performing oversampling analysis according to the peak search result to obtain the oversampling multiple, and calculating the bandwidth estimation value according to the oversampling multiple and the acquisition rate of the data acquisition device; performing time delay compensation on the two signals and then merging the signals to obtain a merged signal; performing time-frequency conversion on the merged signal, splicing the first effective window length of data in the time-frequency conversion result at the tail of the time-frequency conversion result to obtain reconstructed frequency domain data; performing sliding correlation processing on the reconstructed frequency domain data according to the frequency domain filtering coefficient, performing peak detection according to the sliding correlation result to obtain the starting position of the frequency point, and calculating the center frequency point according to the bandwidth estimation value and the starting position of the frequency point. Using this method can improve the reliability of weak signal spectrum monitoring.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technologies, and particularly to a dual-channel high-sensitivity signal spectrum monitoring method and system. Background Art

[0002] The global navigation satellite system (GNSS) has been increasingly widely used in the civilian field, and society's dependence on the positioning, navigation, and timing services provided by GNSS has gradually increased. Therefore, the security and stability of GNSS have also received great attention. After the navigation signal has propagated over an extremely long distance, its signal strength has been greatly attenuated, and it is extremely vulnerable to interference from other signals. A weak interference signal can also affect the performance of GNSS, and the complex and changing space electromagnetic environment can also pose a threat to the performance of GNSS.

[0003] In the face of the increasingly complex electromagnetic environment, spectrum monitoring has become increasingly indispensable. Traditional spectrum monitoring can no longer meet the requirements of high-sensitivity monitoring. Mechanism analysis and theoretical demonstration are carried out around the spectrum monitoring system to develop a high-sensitivity spectrum monitoring system to support its real-time monitoring and analysis of the complex and changing electromagnetic environment, so as to ensure the effective management, optimal utilization of spectrum resources, and the security of communication and the electromagnetic space.

[0004] However, traditional spectrum monitoring methods cannot detect weak signals submerged by noise. The missed detection and false alarm of weak signal spectrum monitoring lead to the inability to manage and optimize spectrum resources, restricting the application and development of wireless communication technologies. Summary of the Invention

[0005] Based on this, it is necessary to provide a dual-channel high-sensitivity signal spectrum monitoring method and system for the above technical problems.

[0006] A dual-channel high-sensitivity signal spectrum monitoring method, the method comprising:

[0007] Obtaining two signals synchronously collected by two data acquisition devices, performing cross-correlation on the two signals, and obtaining the cross-correlation modulus value of the cross-correlation result;

[0008] Performing peak search according to the cross-correlation modulus value, performing oversampling analysis according to the peak search result to obtain the oversampling multiple, and calculating the bandwidth estimation value according to the oversampling multiple and the acquisition rate of the data acquisition device;

[0009] Performing delay compensation on the two signals using the relative delay, and then performing signal merging to obtain a merged signal; the relative delay is calculated according to the peak position corresponding to the maximum peak in the peak search result and the acquisition rate of the data acquisition device;

[0010] Perform time-frequency conversion on the combined signal, splice the first effective window length of data in the time-frequency conversion result to the tail of the time-frequency conversion result to obtain the reconstructed frequency-domain data; the effective window length is calculated from the bandwidth estimation value and the spectrum detection resolution;

[0011] Perform sliding correlation processing on the reconstructed frequency-domain data according to the frequency-domain filtering coefficient, perform peak detection based on the sliding correlation result to obtain the starting position of the frequency point, and calculate the center frequency point based on the bandwidth estimation value and the starting position of the frequency point to achieve spectrum monitoring of weak signals; the frequency-domain filtering coefficient is constructed from the effective window length.

[0012] In one embodiment, it further includes: calculating a peak threshold according to the peak amplitude corresponding to the maximum peak in the peak search result; obtaining the number of peak points greater than the peak threshold in the peak search result, and calculating the oversampling multiple according to the number of peak points.

[0013] In one embodiment, it further includes: performing delay compensation on one of the signals using the relative delay and then performing signal combination, and the combined signal is:

[0014] ;

[0015] wherein, is the combined signal, represents the sampling point, represents the number of sampling points for spectrum detection, is the first signal, is the second signal after delay compensation, is the relative delay.

[0016] In one embodiment, it further includes: performing time-frequency conversion on the combined signal, and the time-frequency conversion result is:

[0017] ;

[0018] wherein, is the time-frequency conversion result, is the combined signal, represents the sampling point, represents the number of sampling points for spectrum detection, is the imaginary unit symbol.

[0019] In one embodiment, it further includes: the effective window length is:

[0020] ;

[0021] wherein, is the effective window length, is the bandwidth estimation value, is the spectrum detection resolution, is the ceiling function.

[0022] In one embodiment, it further includes: The frequency-domain filtering coefficient is:

[0023] ;

[0024] wherein, is the frequency-domain filtering coefficient, represents the sampling point, represents the number of sampling points for spectrum detection, is the effective window length, represents the starting position of filtering.

[0025] In one embodiment, it further includes: performing sliding correlation processing on the reconstructed frequency-domain data according to the frequency-domain filtering coefficient, and obtaining the sliding correlation result as:

[0026] ;

[0027] wherein, is the sliding correlation result, is the filtering window function, is the signal sample at, represents the number of sampling points for spectrum detection, is the effective window length, represents the starting position of filtering.

[0028] A dual-channel high-sensitivity signal spectrum monitoring system, the system includes:

[0029] A cross-correlation module, configured to obtain two signals synchronously collected by two data acquisition devices, perform cross-correlation on the two signals, and obtain the cross-correlation modulus value of the cross-correlation result;

[0030] A bandwidth estimation module, configured to perform peak search according to the cross-correlation modulus value, perform oversampling analysis according to the peak search result to obtain the oversampling multiple, and calculate the bandwidth estimation value according to the oversampling multiple and the acquisition rate of the data acquisition device;

[0031] A signal merging module, configured to perform delay compensation on the two signals by using the relative delay, and then perform signal merging to obtain a merged signal; the relative delay is calculated through the peak position corresponding to the maximum peak in the peak search result and the acquisition rate of the data acquisition device;

[0032] A frequency-domain reconstruction module, configured to perform time-frequency conversion on the merged signal, splice the first effective window length of data in the time-frequency conversion result at the tail of the time-frequency conversion result to obtain the reconstructed frequency-domain data; the effective window length is calculated through the bandwidth estimation value and the spectrum detection resolution;

[0033] A spectrum monitoring module is configured to perform sliding correlation processing on the reconstructed frequency-domain data according to frequency-domain filtering coefficients, perform peak detection based on the sliding correlation results to obtain the starting position of a frequency point, and calculate the center frequency point based on the bandwidth estimation value and the starting position of the frequency point, so as to implement spectrum monitoring of weak signals; the frequency-domain filtering coefficients are constructed through an effective window length.

[0034] The above-mentioned dual-channel high-sensitivity signal spectrum monitoring method and system can highlight the commonalities of weak signals, weaken random noise, and initially improve the detection sensitivity by acquiring two-channel synchronous signals and performing cross-correlation. Through peak search and oversampling analysis, it helps to accurately determine the bandwidth, avoid misjudging weak signals as noise and missing detections. By using time-delay compensation to merge signals, it can concentrate the energy of weak signals and enhance their detectability. By performing time-frequency conversion on the merged signals, calculating the effective window length based on the bandwidth and resolution, and reconstructing the frequency-domain data, it helps to completely retain the frequency components of weak signals. Finally, constructing frequency-domain filtering coefficients and performing sliding correlation can highlight the characteristics of weak signals, accurately find the frequency points and bandwidth, and achieve accurate spectrum monitoring. The embodiments of the present invention can reduce the probability of missing detections and false alarms in weak signal spectrum monitoring, improve the ability of weak signal detection, co-frequency signal separation, and multi-mode positioning, and can achieve the maximum spectrum efficiency and minimize interference, thereby providing strong support for the reasonable planning and management of spectrum resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic flowchart of a dual-channel high-sensitivity signal spectrum monitoring method in an embodiment;

[0036] Figure 2 It is a schematic diagram of the calculation results of correlation values in an embodiment;

[0037] Figure 3 It is a schematic diagram of the simulation of the correlation peak of 2-fold oversampling in an embodiment;

[0038] Figure 4 It is an enlarged schematic diagram of the simulation of the correlation peak of 2-fold oversampling in an embodiment;

[0039] Figure 5 It is a schematic diagram of the simulation of the correlation peak of 4-fold oversampling in an embodiment;

[0040] Figure 6 It is an enlarged schematic diagram of the 4-fold oversampling simulation in an embodiment;

[0041] Figure 7 It is a schematic flowchart of frequency point measurement in an embodiment;

[0042] Figure 8 It is a schematic diagram of data construction in an embodiment;

[0043] Figure 9 The frequency-domain schematic diagram of two signals in one embodiment;

[0044] Figure 10 The schematic diagram of the correlation result of two signals in one embodiment;

[0045] Figure 11 The schematic diagram of the simulation result of frequency point search in one embodiment;

[0046] Figure 12 The estimated signal frequency-domain schematic diagram in one embodiment;

[0047] Figure 13 The structural block diagram of a dual-channel high-sensitivity signal spectrum monitoring system in one embodiment. Specific implementation manners

[0048] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] In one embodiment, as Figure 1 shown, a dual-channel high-sensitivity signal spectrum monitoring method is provided, including the following steps:

[0050] Step 102, obtain two signals synchronously collected by two data acquisition devices, perform cross-correlation on the two signals, and obtain the cross-correlation modulus value of the cross-correlation result.

[0051] The present invention mainly performs calculations based on two synchronous signals. It should be noted that the synchronization of signal data in time is extremely important. The time synchronization of data acquisition devices can be completed by connecting to the same high-precision clock source. Each data acquisition device reports status data to the main control computer every second, including the time synchronization flag, and it can be known within one second after time synchronization.

[0052] After receiving the two-channel signal data of the two devices, select the data length for which the correlation operation needs to be performed. Theoretically speaking, the longer the data length, the higher the sensitivity of the algorithm, but the longer the data length, the longer the time spent on the correlation operation. Therefore, data with a time length of 1 ms or 2 ms is usually selected for the correlation operation.

[0053] It can be understood that in an environment with noise, the cross-correlation operation can highlight the common part between the two signals, that is, the characteristics of weak signals. By initially separating weak signals, it is beneficial to improve the sensitivity of detecting weak signals and reduce the missed detection of weak signals.

[0054] Step 104: Perform peak search based on the cross-correlation modulus value, conduct oversampling analysis according to the peak search result to obtain the oversampling multiple, and calculate the bandwidth estimation value based on the oversampling multiple and the acquisition rate of the data acquisition device.

[0055] Judge whether a weak signal submerged by noise is detected according to the cross-correlation operation result of the two signals. The spectrum monitoring software on the main control computer plots the result graph of the cross-correlation operation. If there is a weak signal, there will be several points in the result of the cross-correlation operation of the two signals whose values are much larger than those of other points, which will be manifested as an extremely high correlation peak in the result graph. By observing whether there is a correlation peak in the cross-correlation operation result graph plotted by the spectrum monitoring software, it can be judged whether a weak signal is detected.

[0056] According to the result of peak search measurement, conduct oversampling analysis to obtain the oversampling multiple. Through the sampling rate and the oversampling multiple, the bandwidth of the signal can be estimated. Through oversampling, more refined signal frequency information can be provided. Combining the acquisition rate to calculate the bandwidth estimation value can more accurately determine the frequency range of the weak signal.

[0057] Step 106: Perform delay compensation on the two signals using the relative delay, and then perform signal merging to obtain the merged signal.

[0058] Perform maximum value search on the result after correlation, and record the position where the peak is located, so as to calculate the relative delay between the two signals. The relative delay is calculated based on the peak position corresponding to the maximum peak in the peak search result and the acquisition rate of the data acquisition device.

[0059] Merge the two signals after delay compensation, and then search for the maximum power through sliding correlation to estimate the frequency point. After measuring the bandwidth and frequency point of the weak signal, the spectrum of the weak signal can be estimated.

[0060] Step 108: Perform time-frequency conversion on the merged signal, and splice the first effective window length data in the time-frequency conversion result to the tail of the time-frequency conversion result to obtain the reconstructed frequency domain data.

[0061] The effective window length is calculated based on the bandwidth estimation value and the spectrum detection resolution. Splicing the first effective window length data in the time-frequency conversion result to the tail of the result to obtain the reconstructed frequency domain data. This can ensure that the frequency domain data can completely contain the main frequency components of the weak signal and avoid missing detection of the weak signal due to data loss.

[0062] Step 110: Perform sliding correlation processing on the reconstructed frequency domain data according to the frequency domain filtering coefficient, perform peak detection according to the sliding correlation result to obtain the starting position of the frequency point, and calculate the center frequency point based on the bandwidth estimation value and the starting position of the frequency point to realize the spectrum monitoring of the weak signal.

[0063] The frequency-domain filtering coefficients are constructed based on the effective window length. By constructing the frequency-domain filtering coefficients according to the effective window length and performing sliding correlation processing on the reconstructed frequency-domain data, the frequency components related to weak signals can be highlighted, and noise and other interference signals can be suppressed.

[0064] In the above-mentioned dual-channel high-sensitivity signal spectrum monitoring method, by acquiring two synchronous signals and performing cross-correlation, the commonality of weak signals can be highlighted, random noise can be weakened, and the detection sensitivity can be initially improved. Through peak search and oversampling analysis, it helps to accurately determine the bandwidth and avoid missing weak signals misjudged as noise. By using time-delay compensation to merge signals, the energy of weak signals can be concentrated, enhancing their detectability. By performing time-frequency conversion on the merged signals, calculating the effective window length based on the bandwidth and resolution, and reconstructing the frequency-domain data, it helps to completely retain the frequency components of weak signals. Finally, constructing the frequency-domain filtering coefficients and performing sliding correlation can highlight the characteristics of weak signals, accurately find the frequency points and bandwidth, and achieve precise spectrum monitoring. The embodiments of the present invention can reduce the probability of missing detection and false alarm in weak signal spectrum monitoring, improve the capabilities of weak signal detection, co-frequency signal separation, and multi-mode positioning, and can achieve the maximum spectrum efficiency and minimize interference, thereby providing strong support for the reasonable planning and management of spectrum resources.

[0065] In one embodiment, using the relative time delay to perform time-delay compensation on the two signals and then merging the signals, the obtained merged signal includes: performing time-delay compensation on one of the signals using the relative time delay and then merging the signals, and the obtained merged signal is:

[0066] ;

[0067] wherein, is the merged signal, represents the sampling point, represents the number of sampling points for spectrum detection, is the first signal, is the second signal after time-delay compensation, is the relative time delay.

[0068] Specifically, assuming that after receiving two-channel AD data, the two signals are respectively and , is the sampling point, performing cross-correlation calculation on the two signals, represents the number of sampling points for spectrum detection, represents the length of the traversal operation, and its value is mainly related to the time-delay difference between the two signals. The time-delay difference is related to two acquisition devices, the signal direction of arrival, and the antenna position, etc. Therefore, the time-delay difference is basically on the order of microseconds and generally remains within 5 microseconds. The baseband sampling rate is , then The value of Perform cross-correlation on the two signals:

[0069] ;

[0070] After cross-correlation, perform modulus operation on the result:

[0071]

[0072] Synchronously collect two signals through two acquisition devices. During acquisition, the sampling rate is 25 MHz, and perform cross-correlation modulus operation. After cross-correlation modulus operation, perform peak search and record the position of the peak. Let its peak position be , and the peak amplitude be . The relative time delay between the two channels can be calculated through the peak position .

[0073] Move the calculation result to the middle, and the result is as shown in Figure 2 the figure of the calculation result of the correlation value. It can be seen from the result that there is a data point at =1, with a time delay of 0.04 us, and the peak result is consistent with the theoretical value.

[0074] Since there are two-channel signal data, in order to maximize the utilization of the signal, the two-channel signal data are merged. If directly merged, due to the existence of time delay, it is equivalent to introducing multipath information, resulting in interference between signals and affecting the estimation of bandwidth, frequency point and power. Therefore, before performing frequency point measurement, it is necessary to perform time delay compensation on it.

[0075] In one embodiment, perform oversampling analysis according to the peak search result, and the obtained oversampling multiple includes: calculating the peak threshold according to the peak amplitude corresponding to the maximum peak in the peak search result; obtaining the number of peak points greater than the peak threshold in the peak search result, and calculating the oversampling multiple according to the number of peak points.

[0076] Specifically, after obtaining the peak measurement result, perform oversampling analysis to obtain the oversampling multiple. Through the sampling rate and the oversampling multiple, the signal bandwidth can be estimated. According to the measured peak amplitude , the threshold value required for bandwidth measurement can be set :

[0077]

[0078] Traverse all correlation values and count the number of peak points greater than the threshold . According to the number of peak points, calculate the oversampling multiple, and the calculation expression is as follows:

[0079] ;

[0080] represents the oversampling ratio, represents the number of peak points, represents rounding. According to the oversampling ratio and the sampling rate, the bandwidth value can be calculated. The bandwidth value has the following expression:

[0081]

[0082] Build a simulation platform through Matlab to verify the correctness of the oversampling ratio calculation. Generate two signals with random noise, set the sampling ratios of the two signals through the resampling function, and then calculate whether the oversampling ratio is consistent with the set oversampling ratio according to the above calculation process. Figure 3 is the correlation peak simulation diagram of the two signals set to 2 times the sampling rate, Figure 4 for Figure 3 the result diagram after magnification of the result; Figure 5 is the correlation peak simulation diagram of the two signals set to 4 times the sampling rate, Figure 6 for Figure 5 the result diagram after magnification of the result. It can be seen from the figure that when the oversampling ratio is set to 2 times, the number of points greater than the threshold value , then ; when the oversampling ratio is set to 4 times, the number of points greater than the threshold value , then , and the results are all consistent with the set sampling ratio.

[0083] In one embodiment, the time-frequency conversion of the combined signal includes: performing time-frequency conversion on the combined signal, and the time-frequency conversion result is:

[0084] ;

[0085] wherein, is the time-frequency conversion result, is the combined signal, represents the sampling point, represents the number of sampling points for spectrum detection, is the imaginary unit symbol.

[0086] In one embodiment, the effective window length is:

[0087] ;

[0088] wherein, is the effective window length, is the bandwidth estimation value, is the spectrum detection resolution, Is rounded up.

[0089] In one embodiment, the frequency domain filtering coefficient is:

[0090] ;

[0091] Where Is the frequency domain filtering coefficient, Represents the sampling point, Represents the number of sampling points for spectrum detection, Is the effective window length, Represents the starting position of filtering.

[0092] In one embodiment, performing sliding correlation processing on the reconstructed frequency domain data according to the frequency domain filtering coefficient includes: performing sliding correlation processing on the reconstructed frequency domain data according to the frequency domain filtering coefficient, and obtaining the sliding correlation result as:

[0093] ;

[0094] Where Is the sliding correlation result, Is the filtering window function, Is The signal sample at, Represents the number of sampling points for spectrum detection, Is the effective window length, Represents the starting position of filtering.

[0095] In a specific embodiment, as Figure 7 Shown, a flow diagram of frequency point measurement is provided, and the specific process is as follows:

[0096] S1. Perform time delay compensation on the two signals. The time delay value Is obtained by correlation modulus operation, and directly compensate the second signal, and the result after compensation is .

[0097] S2. Merge the signals after time delay compensation. In the expression of the merged signal The value of depends on the resolution of the spectrum. According to the requirements of the spectrum detection resolution, perform Calculation. Assuming that the spectrum detection resolution requirement is , then The calculation expression of is as follows:

[0098] ;

[0099] Where Represents rounding up.

[0100] S3. Perform time-frequency conversion on the data to obtain the time-frequency conversion result.

[0101] S4. Frequency-domain data construction. Since the spectrum has positive and negative half-frequencies, in order to reduce the complexity of the calculation process, the data is reconstructed and analyzed. The bandwidth is , and the effective window length is calculated through the bandwidth . After obtaining the effective window length, reconstruct the data structure. Connect the data of the first points to the original data, as shown in the Figure 8 data construction diagram.

[0102] S5. Construct the filtering coefficients. According to the estimated bandwidth , construct a set of frequency-domain filtering coefficients.

[0103] S6. Sliding correlation processing. The sliding correlation calculation is achieved by modifying the starting position of the filter.

[0104] S7. Peak detection. According to the result of the sliding correlation, perform peak search. The expression is as follows:

[0105]

[0106] Find the maximum position which is the starting position of the frequency point. According to the bandwidth and the starting position of the frequency point, the center frequency point can be calculated as follows:

[0107] ;

[0108] From the above calculation process, the spectrum of the weak signal can be estimated.

[0109] In the case of a signal-to-noise ratio of 50 dB, a sampling rate of 25 MHz, an oversampling factor of 4, and 80,000 sampling points for the correlation points, simulations are performed on the signal. Figure 9 are the frequency-domain diagrams after performing FFT conversion on two signals respectively. Figure 10 is the result simulation diagram after performing correlation peak calculation on two signals. It can be seen that there are 7 points above the threshold value. Figure 11 is the simulation result diagram of frequency point search. Its peak is close to Figure 9 the starting position of the frequency domain of the frequency-domain diagram signal. Figure 12 is the estimated signal frequency-domain diagram, and it has a high similarity with Figure 9 the frequency-domain diagram. It can be seen that this method can estimate the spectrum of the signal to a certain extent.

[0110] It should be understood that although Figure 1The steps in the flowchart are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least a part of the steps in

[0111] In one embodiment, as Figure 13 shown, a dual-channel high-sensitivity signal spectrum monitoring system is provided, including:

[0112] A cross-correlation module 1302, configured to obtain two signals synchronously collected by two data acquisition devices, perform cross-correlation on the two signals, and obtain the cross-correlation modulus value of the cross-correlation result;

[0113] A bandwidth estimation module 1304, configured to perform peak search according to the cross-correlation modulus value, perform oversampling analysis according to the peak search result to obtain the oversampling multiple, and calculate the bandwidth estimation value according to the oversampling multiple and the acquisition rate of the data acquisition device;

[0114] A signal merging module 1306, configured to perform delay compensation on the two signals by using the relative delay, and then perform signal merging to obtain a merged signal; the relative delay is calculated according to the peak position corresponding to the maximum peak in the peak search result and the acquisition rate of the data acquisition device;

[0115] A frequency-domain reconstruction module 1308, configured to perform time-frequency conversion on the merged signal, splice the first effective window length of data in the time-frequency conversion result at the tail of the time-frequency conversion result to obtain the reconstructed frequency-domain data; the effective window length is calculated according to the bandwidth estimation value and the spectrum detection resolution;

[0116] A spectrum monitoring module 1310, configured to perform sliding correlation processing on the reconstructed frequency-domain data according to the frequency-domain filtering coefficient, perform peak detection according to the sliding correlation result to obtain the starting position of the frequency point, and calculate the center frequency point according to the bandwidth estimation value and the starting position of the frequency point to implement spectrum monitoring of weak signals; the frequency-domain filtering coefficient is constructed by the effective window length.

[0117] In one of the embodiments, it is further configured to calculate the peak threshold according to the peak amplitude corresponding to the maximum peak in the peak search result; obtain the number of peak points greater than the peak threshold in the peak search result, and calculate the oversampling multiple according to the number of peak points.

[0118] In one embodiment, it is also used to perform time-delay compensation on one of the signals using the relative time delay and then perform signal merging, and the merged signal is:

[0119] ;

[0120] wherein, is the merged signal, represents the sampling point, represents the number of sampling points for spectrum detection, is the first signal, is the second signal after time-delay compensation, is the relative time delay.

[0121] In one embodiment, it is also used to perform time-frequency conversion on the merged signal, and the time-frequency conversion result is:

[0122] ;

[0123] wherein, is the time-frequency conversion result, is the merged signal, represents the sampling point, represents the number of sampling points for spectrum detection, is the imaginary unit symbol.

[0124] In one embodiment, the effective window length is:

[0125] ;

[0126] wherein, is the effective window length, is the bandwidth estimation value, is the spectrum detection resolution, is rounding up.

[0127] In one embodiment, the frequency-domain filtering coefficient is:

[0128] ;

[0129] wherein, is the frequency-domain filtering coefficient, represents the sampling point, represents the number of sampling points for spectrum detection, is the effective window length, represents the starting position of filtering.

[0130] In one embodiment, it is also used to perform sliding correlation processing on the reconstructed frequency-domain data according to the frequency-domain filtering coefficient, and the sliding correlation result is:

[0131] ;

[0132] Wherein, is the result related to sliding, is the filtering window function, is the signal sample at, represents the number of sampling points for spectrum detection, is the effective window length, represents the starting position of filtering.

[0133] For the specific limitations of the dual-channel high-sensitivity signal spectrum monitoring system, reference can be made to the limitations of the dual-channel high-sensitivity signal spectrum monitoring method in the above text, which will not be elaborated here. Each module in the above dual-channel high-sensitivity signal spectrum monitoring system can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0134] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0135] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A dual-channel high-sensitivity signal spectrum monitoring method, characterized in that: The method comprises: Acquire two signals synchronously acquired by two data acquisition devices, perform cross-correlation on the two signals, and acquire a cross-correlation modulus value of the cross-correlation result; Perform peak search based on the cross-correlation modulus value, perform oversampling analysis based on the peak search result to obtain an oversampling multiple, and calculate a bandwidth estimation value based on the oversampling multiple and the acquisition rate of the data acquisition device; The two signals are delayed compensated by using the relative delay, and then the signals are merged to obtain a merged signal; the relative delay is calculated by the peak position corresponding to the maximum peak in the peak search result and the acquisition rate of the data acquisition device; Performing time-frequency conversion on the combined signal, splicing the first effective window length data in the time-frequency conversion result to the end of the time-frequency conversion result to obtain reconstructed frequency domain data; the effective window length is calculated by the bandwidth estimation value and the spectrum detection resolution; The reconstructed frequency domain data is subjected to sliding correlation processing according to the frequency domain filter coefficient, and peak detection is performed according to the sliding correlation result to obtain the starting position of the frequency point, and the center frequency point is calculated according to the bandwidth estimation value and the starting position of the frequency point to realize spectrum monitoring of weak signals; the frequency domain filter coefficient is obtained by constructing the effective window length.

2. The method according to claim 1, characterized in that Based on the peak search results, oversampling analysis is performed and the oversampling multiples obtained include: The peak threshold is calculated according to the peak amplitude corresponding to the maximum peak in the peak search result; The number of peak points greater than the peak threshold in the peak search result is obtained, and the oversampling multiple is calculated according to the number of peak points.

3. The method according to claim 1, characterized in that The relative delay is used to compensate the delay of the two signals, and then the signals are merged to obtain the merged signal including: The relative delay is used to compensate for the delay of one of the signals and then the signals are merged to obtain the merged signal: in, To merge the signals, represents the sampling point, Indicates the number of sampling points for spectrum detection, is the first signal, is the second signal after delay compensation, is the relative delay.

4. The method according to claim 1, characterized in that: The time-frequency conversion of the combined signal comprises: Perform time-frequency conversion on the combined signal, and the time-frequency conversion result is: in, is the time-frequency conversion result, To merge the signals, represents the sampling point, Indicates the number of sampling points for spectrum detection, is the symbol for the imaginary unit.

5. The method according to claim 1, characterized in that The effective window length is: in, is the effective window length, is the bandwidth estimate, is the spectrum detection resolution, To round up.

6. The method according to claim 1, characterized in that The frequency domain filter coefficient is: in, is the frequency domain filter coefficient, represents the sampling point, Indicates the number of sampling points for spectrum detection, is the effective window length, Indicates the starting position of filtering.

7. The method according to claim 1, characterized in that Performing sliding correlation processing on the reconstructed frequency domain data according to the frequency domain filter coefficient includes: The reconstructed frequency domain data is subjected to sliding correlation processing according to the frequency domain filter coefficient, and the sliding correlation result is obtained as follows: in, For sliding related results, is the filter window function, for The signal samples at Indicates the number of sampling points for spectrum detection, is the effective window length, Indicates the starting position of filtering.

8. A dual-channel high-sensitivity signal spectrum monitoring system, characterized in that: The system comprises: A cross-correlation module is used to obtain two signals synchronously collected by two data acquisition devices, perform cross-correlation on the two signals, and obtain a cross-correlation modulus value of the cross-correlation result; A bandwidth estimation module is used to perform peak search according to the cross-correlation modulus value, perform oversampling analysis according to the peak search result to obtain an oversampling multiple, and calculate a bandwidth estimation value according to the oversampling multiple and the acquisition rate of the data acquisition device; A signal merging module, used to compensate for the delay of two signals by using relative delay, and then merge the signals to obtain a merged signal; the relative delay is calculated by the peak position corresponding to the maximum peak in the peak search result and the acquisition rate of the data acquisition device; A frequency domain reconstruction module is used to perform time-frequency conversion on the combined signal, and splice the first effective window length data in the time-frequency conversion result to the end of the time-frequency conversion result to obtain reconstructed frequency domain data; the effective window length is calculated by the bandwidth estimation value and the spectrum detection resolution; The spectrum monitoring module is used to perform sliding correlation processing on the reconstructed frequency domain data according to the frequency domain filter coefficient, perform peak detection according to the sliding correlation result, obtain the starting position of the frequency point, and calculate the center frequency point according to the bandwidth estimation value and the starting position of the frequency point to realize spectrum monitoring of weak signals; the frequency domain filter coefficient is obtained by constructing the effective window length.

9. The system according to claim 8, characterized in that Based on the peak search results, oversampling analysis is performed and the oversampling multiples obtained include: The peak threshold is calculated according to the peak amplitude corresponding to the maximum peak in the peak search result; The number of peak points greater than the peak threshold in the peak search result is obtained, and the oversampling multiple is calculated according to the number of peak points.

10. The system according to claim 8, characterized in that The relative delay is used to compensate the delay of the two signals, and then the signals are merged to obtain the merged signal including: The relative delay is used to compensate for the delay of one of the signals and then the signals are merged to obtain the merged signal: in, To merge the signals, represents the sampling point, Indicates the number of sampling points for spectrum detection, is the first signal, is the second signal after delay compensation, is the relative delay.

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