Communication signal identification system and method based on complex similarity

Abstract

The invention discloses a communication signal identification system and method based on complex similarity, and belongs to the field of signal processing, and the method comprises the steps: obtaining the maximum value of the zero center normalization instantaneous amplitude spectrum density of a signal; classifying the signals into AM signals and other types of signals according to the maximum value of the zero center normalized instantaneous amplitude spectrum density; other signals are classified into CW signals, 2FSK signals, FM signals, BPSK signals, QPSK signals and 8PSK signals according to the over-threshold spectrum peak value proportion; distinguishing a CW signal and a 2FSK signal according to the signal spectrum peak value and the spectrum center difference value; distinguishing 2FSK signals through left and right spectrum over-threshold numbers; four kinds of signals including FM, BPSK, QPSK and 8PSK are distinguished through the phase similarity, and BPSK, QPSK and 8PSK can also be distinguished through the phase similarity. By adopting the system and the method disclosed by the invention, the problems of few signal types, low recognition accuracy and high complexity of the existing algorithm are solved, and the recognition effect of communication signal recognition under the conditions of noise influence and more modulation signals is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a communication signal recognition system and method based on complex similarity. Background Art

[0002] Modulation recognition technology is a key link in communication systems, aiming to identify the modulation method used by analyzing the characteristics of received signals. This process provides necessary information for subsequent signal demodulation to ensure accurate data recovery. Modulation recognition is widely used in complex communication environments such as battlefield communication, satellite measurement and control, and radio communication. In these scenarios, signals may be affected by various interferences such as Doppler effect and non-linear noise. Therefore, modulation recognition technology needs to have high robustness and anti-interference ability. The improvement of debugging algorithms can not only improve the accuracy of signal recognition, but also adapt to diverse communication needs, enhance the overall efficiency and reliability of the system, and is an indispensable part of modern communication technology.

[0003] In the prior art, the methods for distinguishing BPSK, QPSK, and 8PSK usually evaluate high-order cumulants. Among them, high-order cumulants mainly reflect the phase information in high-order moments. Therefore, it is necessary to down-convert the signal and perform high-order cumulant transformation on its baseband signal to correctly obtain the high-order moments of the signal. However, due to its low robustness to non-Gaussian noise, inaccurate down-conversion will cause serious distortion of the high-order cumulant value of the signal. Therefore, the method of evaluating high-order cumulants is relatively complex.

[0004] In the current complex electromagnetic environment, although traditional modulation recognition methods perform poorly, they have the following advantages: high stability, simple and easy-to-implement algorithms, and low requirements for computing resources. In contrast, deep learning methods have some disadvantages: they require a large amount of labeled data for training, have a long training time, consume a large amount of computing resources, and are prone to overfitting or a decrease in recognition accuracy in the case of insufficient data or severe noise. Therefore, traditional methods still play an important role in scenarios with limited resources and high real-time requirements. Summary of the Invention

[0005] The purpose of the present invention is to provide a communication signal recognition system and method based on complex similarity to solve the problems existing in the background art.

[0006] To achieve the above purpose, the present invention provides a communication signal recognition method based on complex similarity, including the following steps:

[0007] S1. Generate the baseband I and Q signals of each communication signal and obtain the modulation signal to be recognized;

[0008] S2. Obtain the maximum value γmax of the zero-centered normalized instantaneous amplitude spectral density of the signal through calculation;

[0009] S3. Set a suitable threshold value that can distinguish AM signals, so that the signals can be classified into AM signals and the remaining signals;

[0010] S4. Obtain the proportion of the spectral peak values above the threshold of the remaining signals;

[0011] S5. Classify the remaining major signals into wideband signals and narrowband signals according to the proportion of the spectral peak values above the threshold. Set a suitable threshold value to distinguish the two types. The narrowband signals are CW and 2FSK, and the wideband signals are FM, BPSK, QPSK, and 8PSK;

[0012] S6. Calculate the difference between the spectral peak value and the spectral center of the narrowband signal, and the number of spectral thresholds crossed on the left and right;

[0013] S7. Set the threshold value of the difference between the spectral peak value and the spectral center that can distinguish CW signals in the narrowband signals, and then determine the 2FSK modulation signal through the set number of spectral thresholds crossed on the left and right;

[0014] S8. Obtain the phase similarity of the wideband signal. According to the set phase similarity in the wideband signal, set the threshold value of the phase similarity to distinguish FM, BPSK, QPSK, and 8PSK.

[0015] Preferably, the signal modulation methods identified by this method in S1 include: AM, FM, CW, 2FSK, BPSK, QPSK, and 8PSK.

[0016] Preferably, in S2, to obtain the maximum value γmax of the zero-centered normalized instantaneous amplitude spectral density of the signal, the specific steps are as follows:

[0017] S21. First, calculate the normalized instantaneous amplitude of the signal:

[0018]

[0019] where N is the length of the input signal, μ A represents the average amplitude of the signal, and n represents the sampling point index of the signal;

[0020] S22. Calculate the squared spectrum of the normalized instantaneous amplitude:

[0021]

[0022] S23. Calculate the normalized maximum value:

[0023]

[0024] Preferably, in S3, a suitable threshold value for distinguishing AM signals is set. The specific steps include:

[0025] The calculated γ max >γ AM_max , then the signal modulation method is an AM modulation signal;

[0026] If the calculated γ max <γ AM_max , then the signal modulation method is other modulation signals;

[0027] where γ AM_max is set to 2000.

[0028] Preferably, in S4, the proportion of the spectral peak above the threshold of other signals is obtained. The specific steps include:

[0029] S41. Perform FFT on the input signal and shift the spectrum of the result so that the spectrum center is in the middle;

[0030] S42. Calculate the logarithm of the spectral amplitude, convert the spectral amplitude to decibel dB units for subsequent threshold processing;

[0031] S43. Find the maximum value of the spectral amplitude and subtract 10 dB from it as the threshold for the peak level; this threshold is used to identify the peak points in the spectrum;

[0032] S44. Find the spectral points exceeding the peak level and calculate the proportion of these points:

[0033]

[0034] Preferably, in S5, the steps for distinguishing other major signals into wideband signals and narrowband signals according to the proportion of the spectral peak above the threshold are as follows:

[0035] S51. If spec_peak >= spec_level, the signal is a narrowband signal;

[0036] S52. If spec_peak < spec_level, the signal is a wideband signal;

[0037] where spec_level is set to 0.0002.

[0038] Preferably, the steps in S6 are as follows:

[0039] S61. Calculate the maximum value within the center spectrum range:

[0040] center_fre = max(abs_fft_sig[st:ed]);

[0041] S62. Calculate the difference threshold between the spectral peak and the spectral center:

[0042] sub_peak = ceter_fre - max(abs_fft_sig);

[0043] S63. Find the position of the spectral peak, and separately count the number of spectral peaks in the left half and the right half. The formula is:

[0044]

[0045] Preferably, in S7, the steps are as follows:

[0046] S71. Set the difference threshold between the spectral peak and the spectral center:

[0047] If sub_peak < sub_level, the narrowband signal is a CW signal; otherwise, determine whether it is a 2FSK signal. Here, sub_level is set to 10.

[0048] S72. Make a decision based on the counted number of spectral peaks in the left half and the right half:

[0049] If left num > 0 and right num > 0, then determine that the signal is a 2FSK signal.

[0050] Preferably, the specific steps of S8 are as follows:

[0051] S81. First, calculate the unwrapped phase angle and the phase difference of the signal;

[0052] S82. Set the cumulative sum of the phase difference within a window, accumulate the phase change rate within each window, represent the integral value of the phase change within the window, and take its absolute value;

[0053] S83. Set a window with a length twice as long. For each point, determine whether it is a peak within the sliding window:

[0054] If the value of the current point is less than any value in its neighborhood or the value of the current point is less than the threshold, it is not a peak; otherwise, it is a peak;

[0055] S84. Calculate the Euclidean distance between the detected peak and the expected peaks of different PSK modulations (BPSK, QPSK, 8PSK). Among them, the smaller the distance, the higher the similarity;

[0056] S85. Set the threshold of the phase similarity to distinguish FM, BPSK, QPSK, 8PSK. The specific steps are as follows:

[0057] S851. Determine the PSK modulation type according to the similarity;

[0058] S852. Further determine according to the similarity. Set a threshold value phdiff_level for further confirming the recognition result. For the similarity discrimination of the broadband signal, if the similarity phdiff_peak < phdiff_level, then distinguish and judge the PSK modulation type according to the above similarity. Otherwise, the broadband signal is an FM signal, where phdiff_level is set to 0.33.

[0059] A communication signal recognition system based on complex similarity includes:

[0060] A wide / narrowband signal discrimination module that obtains the γ max value, discriminates between AM signals and other signals, calculates the proportion of the spectral peak above the threshold of other signals, and obtains the wide / narrowband signal;

[0061] A narrowband signal extraction module that calculates the maximum value within the central spectrum range of the narrowband signal, as well as the spectral peak and the spectral center difference threshold, and identifies the obtained narrowband signal according to the obtained results;

[0062] A broadband signal extraction module that sets a sliding window, calculates the phase similarity of the broadband signal, and sets a threshold value according to the obtained phase similarity to distinguish and identify the signal.

[0063] Therefore, the present invention adopts the above-mentioned communication signal recognition system and method based on complex similarity, and has the following beneficial effects:

[0064] (1) It can accurately identify various communication signals in a complex environment;

[0065] (2) It is applicable to signal processing and communication systems in complex electromagnetic environments. It can still maintain high recognition performance in the case of mixed signals and strong noise interference, providing important support for the development of communication technology;

[0066] (3) For BPSK, QPSK, and 8PSK modulation methods, a recognition method based on phase similarity is proposed, which improves the recognition accuracy of the above several communication signals to a certain extent.

[0067] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0068] Figure 1 It is a specific flowchart of a communication signal recognition method based on complex similarity implemented in an embodiment of the present invention;

[0069] Figure 2 Schematic diagram of the structure of a communication signal recognition system based on complex similarity according to an embodiment of the present invention;

[0070] Figure 3 Curve graph showing the calculated value of the maximum of the zero-centered normalized instantaneous amplitude spectral density of a communication signal according to an embodiment of the present invention varying with the signal-to-noise ratio;

[0071] Figure 4 Curve graph showing the spectral peak ratio for distinguishing narrowband signals and broadband signals according to an embodiment of the present invention varying with the signal-to-noise ratio;

[0072] Figure 5 Curve graph showing the phase similarity value for identifying several types of PSK varying with the signal-to-noise ratio according to an embodiment of the present invention. Detailed implementation manners

[0073] The following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0074] Please refer to Figure 1 , a communication signal recognition method based on complex similarity, comprising the following steps:

[0075] S1. Generate the baseband I and Q signals of each communication signal and obtain the modulation signal to be recognized. The signal modulation methods for recognition using the method of this embodiment include: AM, FM, CW, 2FSK, BPSK, QPSK, 8PSK.

[0076] S2. Obtain the maximum value γmax of the zero-centered normalized instantaneous amplitude spectral density of the signal by calculation.

[0077] To obtain the maximum value γmax of the zero-centered normalized instantaneous amplitude spectral density of the signal, the specific steps are as follows:

[0078] S21. First, calculate the normalized instantaneous amplitude of the signal:

[0079]

[0080] where N is the length of the input signal, μ A represents the average amplitude of the signal, and n represents the sampling point index of the signal;

[0081] S22. Calculate the squared spectrum of the normalized instantaneous amplitude:

[0082]

[0083] S23. Calculate the normalized maximum value:

[0084]

[0085] S3. Set a suitable threshold value that can distinguish AM signals, so that signals can be classified into AM signals and other signals.

[0086] The steps to set a suitable threshold value that can distinguish AM signals are as follows:

[0087] The calculated γ max >γ AM_max , then the signal modulation mode is an AM modulation signal;

[0088] If the calculated γ max <γ AM_max , then the signal modulation mode is other modulation signals;

[0089] where γ AM_max is set to 2000.

[0090] S4. Obtain the proportion of the spectral peak value above the threshold for other signals.

[0091] The steps to obtain the proportion of the spectral peak value above the threshold for other signals are as follows:

[0092] S41. Perform FFT on the input signal and shift the result in frequency spectrum so that the spectrum center is in the middle;

[0093] S42. Calculate the logarithm of the spectral amplitude, convert the spectral amplitude to decibel (dB) units for subsequent threshold processing;

[0094] S43. Find the maximum value of the spectral amplitude and subtract 10 dB from it as the threshold for the peak level; this threshold is used to identify the peak points in the spectrum;

[0095] S44. Find the spectral points exceeding the peak level and calculate the proportion of these points:

[0096]

[0097] S5. According to the proportion of the spectral peak value above the threshold, classify other major signals into wideband signals and narrowband signals, and set a suitable threshold value to distinguish the two types. The narrowband signals are CW and 2FSK, and the wideband signals are FM, BPSK, QPSK, and 8PSK.

[0098] The steps to classify other major signals into wideband signals and narrowband signals according to the proportion of the spectral peak value above the threshold are as follows:

[0099] S51. If spec_peak >= spec_level, the signal is a narrowband signal;

[0100] S52. If spec_peak < spec_level, the signal is a wideband signal;

[0101] Among them, spec_level is set to 0.0002.

[0102] The signal is classified into a wideband signal and a narrowband signal according to the ratio of the spectral peak above the threshold. The implementation of this method is based on the significant differences in spectral characteristics between the two types of signals. Since the spectral range of the narrowband signal is relatively narrow, it is usually concentrated in a specific frequency band, and has a fixed center frequency and a small spectral bandwidth. While the wideband signal has a wide spectral range, covering a wide range of frequencies and can carry multiple narrowband signals simultaneously.

[0103] Since the spectral energy of the narrowband signal is concentrated on a few frequency points, while the spectral energy of the wideband signal is distributed over a wide frequency range. Therefore, by reasonably setting the threshold value, the difference in the ratio of the spectral peak between the two types of signals can be accurately reflected.

[0104] S6. Calculate the difference between the spectral peak and the spectral center of the narrowband signal, and the number of spectral peaks above the threshold on the left and right.

[0105] S61. Calculate the maximum value within the central spectral range:

[0106] cebter_fre = max(abs_fft_sig[st:ed]);

[0107] S62. Calculate the threshold value of the difference between the spectral peak and the spectral center:

[0108] sub_peak = ceter_fre - max(abs_fft_sig);

[0109] S63. Find the position of the spectral peak, and respectively count the number of spectral peaks in the left half and the right half. The formula is:

[0110]

[0111] S7. Set the threshold value of the difference between the spectral peak and the spectral center that can distinguish the CW signal in the narrowband signal, and then determine the 2FSK modulation signal according to the set number of spectral peaks above the threshold on the left and right.

[0112] S71. Set the threshold value of the difference between the spectral peak and the spectral center:

[0113] If sub_peak < sub_level, the narrowband signal is a CW signal, otherwise, determine whether it is a 2FSK signal. Among them, sub_level is set to 10;

[0114] S72. Make a decision based on the number of spectral peak values in the statistically obtained left and right halves:

[0115] If left num > 0 and right num > 0, then determine that the signal is a 2FSK signal.

[0116] For a CW signal, since its spectrum is usually relatively concentrated, the spectral peak value will be relatively high, and the difference between spectral centers may be small. Therefore, a relatively small threshold for the difference between spectral centers can be set to identify the CW signal.

[0117] For a 2FSK signal, 2FSK signal modulation will generate two carrier frequencies (usually high frequency and low frequency), so there will be two obvious peaks in the spectrum.

[0118] S8. Obtain the phase similarity of the broadband signal. According to the phase similarity set in the broadband signal, set the threshold of the phase similarity to distinguish FM, BPSK, QPSK, and 8PSK. The specific steps are as follows:

[0119] S81. First, calculate the unwrapped phase angle and phase difference of the signal;

[0120] S82. Set the cumulative sum of the phase differences within a window, accumulate the phase change rate within each window, represent the integral value of the phase change within the window, and take its absolute value;

[0121] S83. Set a window with a length twice as long. For each point, determine whether it is a peak within the sliding window:

[0122] If the value of the current point is less than any value within its neighborhood or the value of the current point is less than the threshold, then it is not a peak; otherwise, it is a peak.

[0123] S84. Calculate the Euclidean distance between the detected peak and the expected peaks of different PSK modulations (BPSK, QPSK, 8PSK). Among them, the smaller the distance, the higher the similarity;

[0124] S85. Set the threshold of the phase similarity to distinguish FM, BPSK, QPSK, and 8PSK. The specific steps are as follows:

[0125] S851. Determine the PSK modulation type according to the similarity;

[0126] S852. Further judgment is made based on similarity. A threshold value phdiff_level is set to further confirm the recognition result. For the similarity discrimination of the broadband signal, if the similarity phdiff_peak < phdiff_level, the PSK modulation type is discriminated according to the above similarity discrimination, otherwise the broadband signal is an FM signal, where phdiff_level is set to 0.33.

[0127] Such as Figure 2 , a communication signal recognition system based on complex similarity, including:

[0128] A wide / narrowband signal discrimination module that obtains the γ max value of the signal, discriminates AM signals from other signals, and calculates the proportion of the spectral peak above the threshold of the other signals to obtain the wide / narrowband signal.

[0129] A narrowband signal extraction module that calculates the maximum value within the central frequency range of the narrowband signal and the threshold of the difference between the spectral peak and the spectral center, and identifies the obtained narrowband signal according to the obtained results.

[0130] A broadband signal extraction module that sets a sliding window, calculates the phase similarity of the broadband signal, and sets a threshold value according to the obtained phase similarity to discriminate and identify the signal.

[0131] The communication signals generated by the solution of this embodiment, the set of modulation methods of the test signals includes {CW, AM, FM, 2FSK, BPSK, QPSK, 8PSK}, where the AM modulation index is 0.3.

[0132] From Figure 3 it can be seen that for the seven modulation signals studied by the method proposed in this embodiment, under the influence of noise in the range of 0 - 20 dB, by obtaining the maximum value of the zero-centered normalized instantaneous amplitude spectral density of the signal, the AM signal can be clearly identified from the other communication signals.

[0133] At the same time, from Figure 4 it can be seen that the method proposed in this embodiment can identify the obtained broadband signal and narrowband signal under the same settings as above.

[0134] From Figure 5 it can be seen that the method proposed in this embodiment can still identify several PSK modulation signals with low noise ratio through the calculation of similarity eigenvalue even under poor conditions.

[0135] The method proposed in this embodiment has achieved good recognition rates for each of the seven modulation signals studied under all modulation methods.

[0136] Therefore, the present invention adopts the above-mentioned communication signal recognition system and method based on complex similarity. By integrating various feature extraction technologies, it can achieve accurate recognition of various communication signals in complex environments. This technology is applicable to signal processing and communication systems in complex electromagnetic environments. It can still maintain high recognition performance in the case of mixed signals and strong noise interference, providing important support for the development of communication technology.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A communication signal recognition method based on complex similarity, characterized in that: It includes the following steps: S1. Generate the baseband I and Q signals of each communication signal, and obtain the modulation signal to be recognized; S2. Obtain the maximum value γmax of the zero-centered normalized instantaneous amplitude spectral density of the signal through calculation; S3. Set a suitable threshold value that can distinguish AM signals, so that the signal can be classified into AM signals and the remaining signals; S4. Obtain the proportion of the spectral peaks exceeding the threshold of the remaining signals; S5. According to the proportion of the spectral peaks exceeding the threshold, classify the remaining major signals into wideband signals and narrowband signals, and set appropriate threshold values to distinguish the two types. The narrowband signals are CW and 2FSK, and the wideband signals are FM, BPSK, QPSK, and 8PSK; S6. Calculate the difference between the spectral peak and the spectral center of the narrowband signal, and the number of spectral peaks exceeding the threshold on the left and right; S7. Set the threshold value of the difference between the spectral peak and the spectral center that can distinguish the CW signal in the narrowband signal, and then determine the 2FSK modulation signal through the set number of spectral peaks exceeding the threshold on the left and right; S8. Obtain the phase similarity of the wideband signal, and according to the set phase similarity in the wideband signal, set the threshold value of the phase similarity to distinguish FM, BPSK, QPSK, and 8PSK.

2. A communication signal recognition method based on complex similarity according to claim 1, characterized in that: The signal modulation methods for recognition in S1 include: AM, FM, CW, 2FSK, BPSK, QPSK, and 8PSK.

3. A method for identifying communication signals based on complex similarity according to claim 2, characterized in that: In S2, to obtain the maximum value γmax of the zero-centered normalized instantaneous amplitude spectral density of the signal, the specific steps are as follows: S21. First, calculate the normalized instantaneous amplitude of the signal; Where N is the length of the input signal; μ A Represents the average amplitude of the signal; n represents the sampling point index of the signal; S22. Calculate the squared spectrum of the normalized instantaneous amplitude; S23. Calculate the normalized maximum value; 4. The method for identifying a communication signal based on complex similarity according to claim 3, characterized in that: In S3, to set a suitable threshold value that can distinguish AM signals, the specific steps include: The calculated γ max >γ AM_max , then the signal modulation mode is AM modulation signal; If the calculated γ max <γ AM_max , then the signal modulation mode is the other modulation signals; where γ AM_max Set to 2000.

5. A method for identifying communication signals based on complex similarity according to claim 4, characterized in that: In S4, to obtain the proportion of the spectral peaks exceeding the threshold of the remaining signals, the specific steps include: S41. Perform FFT on the input signal and shift the result in the frequency spectrum so that the spectral center is in the middle; S42. Calculate the logarithm of the spectral amplitude and convert the spectral amplitude to the unit of decibels dB; S43. Find the maximum value of the spectral amplitude and subtract 10 dB from it as the threshold of the peak level; S44. Find the spectral points exceeding the peak level and calculate the proportion of the spectral points exceeding the peak level; 6. The method for identifying a communication signal based on complex similarity according to claim 5, characterized in that: In S5, the steps to classify the remaining major signals into wideband signals and narrowband signals according to the proportion of the spectral peaks exceeding the threshold are as follows: S51. If spec_peak >= spec_level, the signal is a narrowband signal; S52. If spec_peak < spec_level, the signal is a wideband signal; Among them, spec_level is set to 0.0002.

7. The method for identifying a communication signal based on complex similarity according to claim 6, characterized in that: The steps in S6 are as follows: S61. Calculate the maximum value within the central spectral range; center_fre = max(abs_fft_sig[st:ed]); S62. Calculate the threshold value of the difference between the spectral peak and the spectral center; sub_peak = ceter_fre - max(abs_fft_sig); S63. Find the position of the spectral peak and count the number of spectral peaks in the left and right halves respectively. The formula is:

8. The method for identifying a communication signal based on complex similarity according to claim 7, characterized in that: In S7, the steps are as follows: S71. Set the threshold for the difference between the spectral peak and the spectral center: If sub_peak < sub_level, the narrowband signal is a CW signal; otherwise, determine whether it is a 2FSK signal. Here, sub_level is set to 10. S72. Make a decision based on the number of spectral peaks in the statistically obtained left and right halves of the spectrum: If left num >0 and right num >0, the signal is determined to be a 2FSK signal.

9. The method for identifying communication signals based on complex similarity according to claim 5, characterized in that: In S8, obtain the phase similarity of the broadband signal. The specific steps are as follows: S81. First, calculate the unwrapped phase angle and the phase difference of the signal. S82. Set the cumulative sum of the phase differences within a window, accumulate the phase change rate within each window, represent the integral value of the phase change within the window, and take its absolute value. S83. Set a window with a length twice as long. For each point, determine whether it is a peak within the sliding window: If the value of the current point is less than any value in its neighborhood or the value of the current point is less than the threshold, it is not a peak; otherwise, it is a peak. S84. Calculate the Euclidean distance between the detected peak and the expected peaks of different PSK modulations. Among them, the smaller the distance, the higher the similarity. S85. Set the threshold for the phase similarity to distinguish FM, BPSK, QPSK, and 8PSK. The specific steps are as follows: S851. Determine the PSK modulation type based on the similarity. S852. Make a further judgment based on the similarity. Set a threshold phdiff_level for further confirming the recognition result. For the similarity discrimination of the broadband signal, if the similarity phdiff_peak < phdiff_level, then determine the PSK modulation type according to the above similarity discrimination; otherwise, the broadband signal is an FM signal. Here, phdiff_level is set to 0.

33.

10. A system using a method for identifying a communication signal based on complex similarity as claimed in any one of claims 1 to 9, characterized in that: It includes: Wide / narrowband signal distinction module, obtains the signal's γ max value, distinguish the AM signal from the other signals, and calculate the peak ratio of the spectrum of the other signals that exceeds the threshold to obtain the wide / narrowband signal; A narrowband signal extraction module that calculates the maximum value within the central frequency range of the narrowband signal, as well as the threshold for the difference between the spectral peak and the spectral center, and identifies the obtained narrowband signal based on the calculated results. A broadband signal extraction module that sets a sliding window, calculates the phase similarity of the broadband signal, sets the threshold according to the obtained phase similarity, and distinguishes and identifies the signal.

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