A multi-stage differential based interference detection method

Abstract

The application discloses a multi-stage difference-based interference detection method and belongs to the technical field of signal processing.The application converts a time-domain signal into a frequency-domain signal, performs small-window difference calculation on the frequency-domain sequence, and then completes first-stage interference detection by comparison with a high threshold; the frequency-domain sequence is subjected to large-window difference calculation, and second-stage interference detection is completed by comparison with a low threshold; after repeated interference signals and known emission signals are removed from the detection results, the calculation of the specified dB bandwidth and center frequency point of each interference signal is completed; finally, the average value of the noise power of the interference-free frequency band is compared with the noise power threshold, the detection of all types of interference is completed, the detection results are output, and the accurate detection of different types of interference in the full frequency band is realized.The application is suitable for the fields of signal processing and the like, the detection range is expanded, the interference detection rate is improved, and the calculation complexity is reduced through two-stage mean difference detection.

Description

Technical Field

[0001] This invention relates to an interference detection method based on multi-level differential, belonging to the field of signal processing technology. Background Technology

[0002] In satellite communications, the wide coverage, large number of satellite spot beams, and wide communication bandwidth enable stable performance and flexible handling of various service types. However, as an open system, satellite communication systems are less concealed and more susceptible to various intentional or unintentional interferences. To further improve the communication quality of satellite communications, it is necessary to detect these interference signals. Interference detection refers to the process of detecting the presence of interference in the receiver and obtaining information such as the frequency and bandwidth of the interference.

[0003] Currently, commonly used interference detection methods include energy detection algorithms, cyclostationary feature analysis methods, and higher-order cumulant detection methods. Among these, the energy detection algorithm is the optimal method when there is no prior information. It is simple to implement, requiring only a preset interference detection threshold to detect interference signals; however, its accuracy cannot be guaranteed in environments with low signal-to-noise ratios. Cyclostationary feature analysis methods primarily detect signals based on the different stationary characteristics and correlation properties of signals and noise. Higher-order cumulant detection methods mainly distinguish signals from interference through the higher-order characteristics of the signal.

[0004] The interference detection algorithms described above are mostly used for interference detection in the idle band, where the received signal contains no useful signal or the useful signal is covered by Gaussian white noise with low power. The received signal is then considered as idle band interference consisting only of interference and noise. When the detection system contains a superposition of useful signal and interference and noise, i.e., the interference signal is located within the bandwidth of the useful signal or in the transition band, parameters such as signal power, center frequency, and bandwidth will affect the detection performance. Summary of the Invention

[0005] To address the problems of existing interference detection methods, such as high implementation complexity, low detection rate, and inability to effectively detect different types of interference signals across the entire frequency band, the main objective of this invention is to propose a multi-level differential interference detection method. This method involves frequency domain transformation of the time-domain signal, followed by two-level mean differential detection of the frequency-domain signal. This enables the detection of different types of interference signals across the entire frequency band, expanding the detection range, improving the interference detection rate, and reducing computational complexity.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] This invention discloses a multi-level differential interference detection method. It converts a time-domain signal to a frequency-domain signal, performs small-window differential calculation on the frequency-domain sequence, and then completes first-level interference detection by comparing it with a high threshold. Next, it performs large-window differential calculation on the frequency-domain sequence and completes second-level interference detection by comparing it with a low threshold. After removing duplicate interference signals and known transmitted signals from the detection results, it calculates the specified dB bandwidth and center frequency of each interference signal. Finally, it compares the average noise floor power of the interference-free frequency band with the noise floor threshold to complete the detection of all types of interference and output the detection results. Ultimately, it achieves accurate detection of different types of interference across the entire frequency band, expands the detection range, improves the interference detection rate, and reduces computational complexity.

[0008] This invention discloses an interference detection method based on multi-level difference, comprising the following steps:

[0009] Step 1: Perform a Fast Fourier Transform on the received time-domain satellite communication signal to convert it into a frequency-domain signal;

[0010] Step 2: Perform two-level mean difference detection based on the frequency domain signal to detect all different types of interference signals, thereby expanding the detection range and improving the detection rate;

[0011] Two detection windows, one large and one small, with lengths l and s respectively, are set up to perform sequence detection on the frequency domain signal. The signal is segmented according to the window length, and the mean of the signal sequence within each segment is calculated. Then, the mean of the next segment is compared with the mean of the current segment to obtain a series of difference values. Two thresholds are preset for these difference values: a high threshold corresponds to a small detection window, and a low threshold corresponds to a large detection window. Each difference value is compared with the preset threshold. If the difference value is greater than the threshold, a rising edge of an interference signal is present in that segment; if the difference value is less than a negative threshold, a falling edge of an interference signal is present. First, mean difference detection is performed in the small window to detect interference signals with high energy concentration. Then, mean difference detection is performed in the large window to detect interference signals with low energy concentration. This two-stage mean difference detection process detects all interference signals within the frequency band.

[0012] Step 3: After removing duplicate interference signals and known transmitted signals from the detection results, calculate the specified dB bandwidth and center frequency of each interference signal;

[0013] Take the sampling point n1 with the smallest rising edge window of the interference signal obtained in step 2 as the lower limit and the sampling point n2 with the largest falling edge window as the upper limit to find the power spectral density peak within the interference range.

[0014] P max =max[P(n1:n2)]=P(i max )

[0015] Among them, P max Indicates the peak value of the power spectral density, i max For the corresponding sampling points;

[0016] Perform forward and backward searches centered on the peak value to find two frequency points that are xdB below the peak value, i.e., the xdB bandwidth corresponding to the peak value.

[0017]

[0018] In the formula, i L and i H Let x be the minimum and maximum index sampling points where the power spectral density value is xdB below the peak value, respectively. The bandwidth corresponding to the difference is the xdB bandwidth of the interference signal. The ratio of the xdB bandwidth of the interference signal to the total bandwidth of the power spectral density is expressed as:

[0019]

[0020] The bandwidth (Band) of the interference signal can be calculated from the known total bandwidth (Span) of the acquired power spectrum.

[0021] Band = Span × L

[0022] The center frequency of the interference signal is determined by the midpoint of the maximum and minimum subscript sampling points, and its position in the power spectrum is:

[0023]

[0024] From the known starting frequency StartFre of the signal acquisition power spectrum, the center frequency CenterFre can be obtained as:

[0025] CenterFre = Span × L c +StartFre

[0026] First, the bandwidth and frequency of the interference signals detected by the small window mean difference are calculated; then, the bandwidth and frequency of the interference signals detected by the large window mean difference are calculated. After removing duplicate interference signals and known transmitted signals from the detection results, the bandwidth and frequency information of all interference signals and the number of interference signals can be obtained.

[0027] Step 4: Compare the average noise floor power of the interference-free frequency band with the noise floor threshold, complete the detection of all types of interference and output the detection results, and achieve accurate detection of different types of interference across the entire frequency band.

[0028] The signal sequences within the known transmit signal bandwidth and the interference signal bandwidth obtained in step 3 are removed. The remaining signal sequences are sorted by power spectral density from smallest to largest, and the average of the power spectral density values ​​at the top of the sorted sequence is taken as the noise floor value. This value is compared with a preset noise floor power threshold. If the value is higher than the noise floor power threshold, noise floor interference exists; otherwise, there is no noise floor interference. This completes the interference detection across the entire frequency band.

[0029] Beneficial effects:

[0030] 1. The present invention discloses an interference detection method based on multi-level differential detection. The method uses multi-level differential interference detection and detects the interference by means of two detection windows of different sizes. At the same time, it compares the average noise power of the interference-free frequency band with the threshold, which can detect interference signals of all different types of wide and narrow band carriers in the whole frequency band and expand the detection range.

[0031] 2. The present invention discloses an interference detection method based on multi-level difference, which improves the accuracy of interference detection by comparing and analyzing the mean difference detection threshold and the noise floor power threshold of two detection windows of different sizes.

[0032] 3. The interference detection method based on multi-level difference disclosed in this invention, compared with the traditional interference detection method, adopts the frequency domain sequence difference method, which can complete the detection of all interference signals without multiple spectral sequence sorting, thus reducing the computational complexity. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating an interference detection method based on multi-level difference disclosed in this invention.

[0034] Figure 2 This is a schematic diagram of the interference signal in this embodiment;

[0035] Figure 3 This is the difference map after small-window differencing in this embodiment;

[0036] Figure 4 This is the difference map after large window differencing in this embodiment. Detailed Implementation

[0037] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. The technical problems solved by the present invention and its beneficial effects are also described. It should be noted that the described embodiments are only intended to facilitate understanding of the present invention and do not constitute any limitation thereof.

[0038] This embodiment discloses an application of a multi-level differential interference detection method to QPSK signals, such as... Figure 1 As shown, it includes the following steps:

[0039] Step 1: Perform an N-point FFT transform on the received time-domain satellite communication signal to convert it into a frequency-domain signal and obtain its power spectrum;

[0040] Step 2: Perform two-stage mean difference detection based on the frequency domain signal to detect all interference signals;

[0041] First, mean-differential interference detection is performed within a small window. The frequency domain signal P(n) is divided into 1024 segments, each with a length s = N / 1024. The mean of each segment is then calculated.

[0042]

[0043] Calculate the difference between the mean of the next segment and the mean of the current segment:

[0044] D(p)=P'(m+1)-P'(m)p=0,1,...,1022

[0045] After obtaining the small window difference value, under the same interference-to-signal ratio, after performing small window mean difference, the interference signal located in the idle band and transition band can obtain a larger difference value compared with the large window difference, and the interference signal with high energy concentration located in the passband also has a larger difference value.

[0046] By comparing with a preset high threshold, if the difference value is higher than the threshold, the window is considered to be a rising edge window of an interference signal, and if it is lower than the threshold, the window is considered to be a falling edge window of an interference signal. Signals higher than the differential threshold can be effectively detected.

[0047] Interference signals with low energy concentration within the passband are difficult to detect;

[0048] Therefore, large-window mean-differential interference detection is required. The frequency domain signal P(n) is divided into 256 segments, each with a length l = N / 256, and the mean of each segment is calculated.

[0049]

[0050] Calculate the difference between the mean of the next segment and the mean of the current segment:

[0051] D'(p)=P”(m+1)-P”(m)p=0,1,...,254

[0052] The large window difference value is obtained. Under the same interference-to-signal ratio, the large window mean difference enables interference signals with low energy concentration in the passband to have obvious differential characteristics.

[0053] The signal can be effectively detected by comparing it with a preset low threshold. However, the differential characteristics of interference signals with high energy concentration in the passband are not easily detected due to the large window averaging.

[0054] By using the mean difference detection of two detection windows of different sizes, all interference signals with different energy concentrations within the entire frequency band can be detected.

[0055] The high and low thresholds can be set based on experience.

[0056] Step 3: Take the sampling point n1 with the smallest rising edge window of the obtained interference signal as the lower limit and the sampling point n2 with the largest falling edge window as the upper limit, and find the power spectral density peak within the interference range.

[0057] P max =max[P(n1:n2)]=P(i max )

[0058] Among them, P max Indicates the peak value of the power spectral density, i max For the corresponding sampling points;

[0059] Find the frequency range of two power spectral density values ​​that are 3dB below the peak value; this is the 3dB bandwidth of the signal. Let:

[0060]

[0061] In the formula, i L and i H Let these be the minimum and maximum index sampling points where the power spectral density value is 3dB below the peak value, respectively. The bandwidth corresponding to the difference between them is the 3dB bandwidth of the interference signal. The ratio of the 3dB bandwidth of the interference signal to the total bandwidth of the power spectral density is expressed as:

[0062]

[0063] The bandwidth (Band) of the interference signal can be calculated from the known total bandwidth (Span) of the acquired power spectrum.

[0064] Band = Span × L

[0065] The center frequency of the interference signal is determined by the midpoint of the maximum and minimum subscript sampling points, and its position in the power spectrum is:

[0066]

[0067] From the known starting frequency StartFre of the signal acquisition power spectrum, the center frequency CenterFre can be obtained as:

[0068] CenterFre = Span × L c +StartFre

[0069] First, the bandwidth and frequency points of the interference signal detected by the small window mean difference are calculated.

[0070] Then, the bandwidth and frequency of the interference signals detected by the large window mean difference are calculated. After removing duplicate interference signals and known transmitted signals from the detection results, the bandwidth and frequency information of all interference signals and the number of interference signals can be obtained.

[0071] Step 4: Sort the frequency domain signals in ascending order of power spectral density value. Using the known transmit signal bandwidth and the calculated interference signal bandwidth, remove the sampling points of the transmit signal and interference signal, and take the average value of the first 50% of the power spectrum frequency band as its noise floor power.

[0072] The noise level is compared with a preset noise floor power threshold. If the noise level is higher than the threshold, noise floor interference is considered to exist, thus completing the interference detection across the entire frequency band.

[0073] Simulation verification

[0074] Simulation analysis was performed on the aforementioned multi-level differential interference detection method. This example uses a QPSK signal with a sampling rate of 100MHz and an interference-to-signal ratio of 5dB. Six interference signals with different energy concentrations (signal 1-signal 6) were introduced from left to right in the idle band, transition band, and passband. An FFT transformation with N=4096 points was performed to obtain their spectrum, as shown below. Figure 2 As shown;

[0075] It can be seen that interference signals with different energy concentrations exhibit different power spectra in the idle band, transition band, and passband.

[0076] First, a small-window mean difference is performed on the power spectrum, dividing it into 1024 segments, each with a length s = N / 1024 = 4. The mean of each segment is calculated. Then, the difference between the mean of the next segment and the mean of the current segment is calculated, resulting in the small-window difference plot as shown below. Figure 3 As shown;

[0077] It can be seen that signals 1, 2, 5 and 6, which are located in the idle band and transition band, have large difference values ​​after being subjected to mean difference through a small window. Signal 3, which is located in the passband and has high energy concentration, also has a large difference value.

[0078] By comparing with the preset high threshold value 15, the rising edge window and falling edge window of signal 1, signal 2, signal 3, signal 5 and signal 6 can be effectively detected;

[0079] The low-energy concentration of interference signals 4 within the passband is difficult to detect;

[0080] Therefore, a large-window mean difference is needed to divide the power spectrum into 256 segments, each with a length l = N / 256 = 16. After calculating the mean of each segment, the difference between the mean of the next segment and the mean of the current segment is calculated, resulting in the large-window difference plot as shown below. Figure 4 As shown;

[0081] It can be seen that signal 4 within the passband exhibits obvious differential characteristics;

[0082] Compared with the preset low threshold value 5, it can effectively detect the rising edge window and falling edge window of signals 1, 2, 4, 5 and 6;

[0083] Interference signals with high energy concentration within the passband are difficult to detect;

[0084] The power spectral density peak value within the range is obtained by taking the sampling point with the smallest rising edge window and the sampling point with the largest falling edge window of the interference signal obtained by the mean difference of the small window as the range.

[0085] Then, the minimum and maximum index sampling points with power spectral density values ​​greater than 3dB of the spectral peak value are obtained. The bandwidth corresponding to the difference is the 3dB bandwidth of the interference signal, and the frequency point corresponding to the midpoint is the center frequency point of the interference signal.

[0086] The same operation is performed on the interference signal obtained by the large window mean difference to find the 3dB bandwidth and center frequency of the interference signal. Only one detection result with the same large and small window mean difference is retained. By comparing it with the known bandwidth and frequency of the transmitted signal, the final bandwidth and frequency information of the interference signal and the number of interference signals can be obtained.

[0087] This invention reduces the computational complexity of existing algorithms while ensuring detection performance, and can perform interference detection across the entire frequency band, thus expanding the detection range. Moreover, the implementation method is simple.

[0088] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An interference detection method based on multi-level difference, characterized in that: Includes the following steps, Step 1: Perform a Fast Fourier Transform on the received time-domain satellite communication signal to convert it into a frequency-domain signal; Step 2: Perform two-level mean difference detection based on the frequency domain signal to detect all different types of interference signals, thereby expanding the detection range and improving the detection rate; The implementation method for step 2 is as follows: Set two detection windows of different sizes, with window lengths of [missing information]. and The process involves sequence detection of the frequency domain signal. The signal is segmented by window length, and the mean of the signal sequence within each window segment is calculated. Then, the mean of the next segment is compared with the mean of the current segment to obtain a series of difference values. Two thresholds are preset for these difference values: a high threshold corresponds to a small detection window, and a low threshold corresponds to a large detection window. Each difference value is compared with the preset threshold. If the difference value is greater than the threshold, a rising edge of an interference signal is present in that segment; if the difference value is less than a negative threshold, a falling edge of an interference signal is present. First, mean difference detection is performed within the small window to detect interference signals with high energy concentration. Then, mean difference detection is performed within the large window to detect interference signals with low energy concentration. This two-stage mean difference detection process detects all interference signals within the frequency band. Step 3: After removing duplicate interference signals and known transmitted signals from the detection results, calculate the specified dB bandwidth and center frequency of each interference signal; Step 4: Compare the average noise floor power of the interference-free frequency band with the noise floor threshold, complete the detection of all types of interference and output the detection results, and achieve accurate detection of different types of interference across the entire frequency band.

2. The interference detection method based on multi-level difference as described in claim 1, characterized in that: The implementation method for step 3 is as follows: Take the sampling point with the smallest rising edge window from the interference signal obtained in step 2. As the lower bound, the sampling point with the largest falling edge of the window. As an upper limit, search for the peak power spectral density within the interference range; in, Indicates the peak value of the power spectral density. For the corresponding sampling points; Perform forward and backward searches centered on the peak value to find two frequency points that are xdB below the peak value, i.e., the xdB bandwidth corresponding to the peak value. In the formula, and Let x be the minimum and maximum index sampling points where the power spectral density value is xdB below the peak value, respectively. The bandwidth corresponding to the difference is the xdB bandwidth of the interference signal. The ratio of the xdB bandwidth of the interference signal to the total bandwidth of the power spectral density is expressed as: The total bandwidth of the known collected power spectrum The bandwidth of the interference signal can then be calculated. : The center frequency of the interference signal is determined by the midpoint of the maximum and minimum subscript sampling points, and its position in the power spectrum is: The starting frequency of the power spectrum is obtained from the known signal. The center frequency can be obtained. for: First, the bandwidth and frequency of the interference signals detected by the small window mean difference are calculated; then, the bandwidth and frequency of the interference signals detected by the large window mean difference are calculated. After removing duplicate interference signals and known transmitted signals from the detection results, the bandwidth and frequency information of all interference signals and the number of interference signals can be obtained.

3. The interference detection method based on multi-level difference as described in claim 2, characterized in that: The implementation method for step 4 is as follows: The signal sequences within the known transmit signal bandwidth and the interference signal bandwidth obtained in step 3 are removed. The remaining signal sequences are sorted by power spectral density from smallest to largest. The average of the power spectral density values ​​of the first segment of the sort is taken as the noise floor value. It is compared with the preset noise floor power threshold. If it is higher than the noise floor power threshold, there is noise floor interference; otherwise, there is no noise floor interference. Thus, the interference detection in the entire frequency band is completed.

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