Cyclic stationary signal detection method combining multi-harmonic characteristic decision smoothing and medium

Abstract

The invention relates to a cyclostationary signal detection method combining multi-harmonic characteristic decision smoothing and a medium, and relates to the technical field of signal processing. The method comprises the following steps: filtering based on a collected time sequence signal, and collecting a cyclic spectrum of each time point based on a preset collection frequency; calculating a weighted global probability score for each cyclic spectrum; for each cyclic spectrum, obtaining a judgment threshold value of the cyclic spectrum, and generating an instantaneous judgment result based on the weighted global probability score and the judgment threshold value; and for an instantaneous judgment result time sequence formed by the instantaneous judgment result, sliding window smoothing is adopted, the proportion of the instantaneous judgment result being true in a sliding window is calculated, and whether a cyclostationary signal is detected or not is judged. Compared with the prior art, the method has the advantages that isolated false alarms caused by instantaneous interference can be effectively filtered out, so that better balance can be obtained between the reliability of features and the stability of judgment, and the reliability of target detection is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of signal processing, in particular to a cyclic stationary signal detection method combining multi-harmonic feature decision smoothing and a medium. BACKGROUND

[0002] When analyzing a cyclic stationary signal (such as helicopter noise, communication signal, etc.), the cyclic spectrum (or spectral correlation density) of the signal is usually calculated to reveal its hidden periodicity. After obtaining the cyclic spectrum, a reliable detector is needed to determine whether the target feature exists.

[0003] To improve the reliability of detection, the existing technology usually adopts a multi-harmonic feature fusion strategy. This strategy first identifies feature peaks at the fundamental and its multiple harmonic frequencies, then fuses these discrete features into a single global probability score (P), and makes a decision based on this.

[0004] However, the above method cannot achieve a good balance between the reliability of the feature and the stability of the decision, resulting in limited performance and insufficient robustness in real and variable environments. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a cyclic stationary signal detection method combining multi-harmonic feature decision smoothing and a medium, which can achieve a better balance between the reliability of the feature and the stability of the decision.

[0006] In a first aspect, a cyclic stationary signal detection method combining multi-harmonic feature decision smoothing is provided in an embodiment, comprising: Filtering the collected time series signal, and collecting the cyclic spectrum of each time point based on the preset collection frequency; For each cyclic spectrum, calculating its weighted global probability score; For each cyclic spectrum, obtaining its decision threshold, and generating an instantaneous decision result based on the weighted global probability score and the decision threshold; For the instantaneous decision result time series formed by the instantaneous decision result, using a sliding window smoothing processing, calculating the proportion of the instantaneous decision result being true in the sliding window, and determining whether a cyclic stationary signal is detected.

[0007] In an embodiment, for any cyclic spectrum, the method for calculating its weighted global probability score comprises: Based on the adaptive threshold cyclic spectrum detection function, obtaining the spectral peak value of each expected cyclic spectrum cycle frequency band; Based on each spectral peak value, calculating the normalized local probability score of the spectral peak; Based on the reliability difference of each harmonic feature, a corresponding weight is assigned to each local probability score representing a different harmonic order, and a weighted global probability score of the current cyclic spectrum is calculated by weighted sum of all local probability scores based on the weights; wherein the assigned weight monotonically decreases with the increase of the harmonic order.

[0008] In an embodiment, the cyclic spectrum detection function based on the adaptive threshold obtains spectral peaks of a frequency band in which each of the plurality of expected cyclic spectrum cycle frequencies is located, including: wherein, represents a spectral peak value of a frequency band in which the i-th expected cyclic spectrum cycle frequency is located, 2≤i≤n, n represents the number of expected cyclic spectrum cycle frequencies; represents a cyclic spectrum detection function based on an adaptive threshold, used to obtain the amplitude of the integral of the cyclic spectrum correlation function along the frequency axis at each cycle frequency point; represents the k-th cycle frequency point in the frequency band in which the i-th expected cyclic spectrum cycle frequency is located, 1≤k≤K, K represents the number of cycle frequency points in the frequency band in which the i-th expected cyclic spectrum cycle frequency is located; B represents the spectral frequency range of the entire cyclic spectrum; represents the frequency band set for the i-th expected cyclic spectrum cycle frequency, f represents the distribution frequency of the energy of the collected time series signal in the frequency spectrum, represents the resolution of f, represents the cyclic spectrum correlation function.

[0009] In an embodiment, the normalized local probability score of each spectral peak is calculated based on the spectral peak value, including: obtaining a dynamic threshold value of the current cyclic spectrum, and calculating the normalized local probability score of the spectral peak based on the dynamic threshold value and the spectral peak value; the dynamic threshold value is set based on a preset false alarm rate, and is the minimum value of the maximum amplitude number of the preset false alarm rate in the current cyclic spectrum.

[0010] In an embodiment, the dynamic threshold value of the current cyclic spectrum is obtained, and the normalized local probability score of the spectral peak is calculated based on the dynamic threshold value and the spectral peak value, including: wherein, represents the local probability score corresponding to the i-th expected cyclic spectrum cycle frequency, represents the dynamic threshold value of the q-th cyclic spectrum, 1≤q≤Q, Q represents the number of cyclic spectra, represents the false alarm rate.

[0011] In one embodiment, the reliability difference based on each harmonic feature is that a corresponding weight is assigned to each local probability score representing different harmonic orders, and a weighted global probability score of the current cyclic spectrum is calculated by weighted sum of all local probability scores based on the weights, including: wherein, represents the weighted global probability score of the qth cyclic spectrum; represents the weight of the local probability score, .

[0012] In one embodiment, for any cyclic spectrum, a decision threshold is obtained, and based on the weighted global probability score and the decision threshold, an instantaneous decision result is generated, including: calculating the average amplitude of the cyclic frequency points in the cyclic spectrum whose amplitudes reach a dynamic threshold value; calculating a normalized decision threshold based on the average amplitude and the corresponding dynamic threshold value; generating an instantaneous decision result based on the weighted global probability score and the corresponding decision threshold.

[0013] In one embodiment, the calculation of the average amplitude of the cyclic frequency points in the cyclic spectrum whose amplitudes reach a dynamic threshold value includes: wherein, represents the average amplitude of the qth cyclic spectrum, represents the amplitude of the uth cyclic frequency point in the qth cyclic spectrum, 1≤u≤U, U represents the number of cyclic frequency points in the qth cyclic spectrum; represents 1 if the amplitude of the uth cyclic frequency point is greater than the corresponding dynamic threshold value, otherwise 0; represents the number of cyclic frequency points in the qth cyclic spectrum whose amplitudes are greater than the corresponding dynamic threshold value; The calculation of the normalized decision threshold based on the average amplitude and the corresponding dynamic threshold value includes: wherein, represents the decision threshold of the qth cyclic spectrum; The generation of the instantaneous decision result based on the weighted global probability score and the corresponding decision threshold includes: wherein, represents the instantaneous decision result of the qth cyclic spectrum.

[0014] ​​In an embodiment, for a time sequence of instantaneous decision results formed by the instantaneous decision results, a sliding window smoothing processing is adopted to calculate the number or proportion of the instantaneous decision results that are true in the sliding window, and whether a cyclostationary signal is detected is judged, including: The number or proportion of the instantaneous decision results that are true in the sliding window is calculated, and if the number of the true results reaches a preset number threshold or the proportion reaches a preset proportion threshold, it is judged that the cyclostationary signal is detected, wherein the width W of the sliding window satisfies W >= 4, and the step S satisfies 1 <= S <= W.

[0015] In a second aspect, an embodiment provides a computer readable storage medium, wherein the medium stores a program, the program can be loaded and executed by a processor to perform the cyclostationary signal detection method in any of the above embodiments.

[0016] The present application has the following beneficial effects: Based on the cyclostationary signal detection method and medium provided in the present application, the sliding window smoothing processing is adopted, the prior knowledge that the real target signal must have the time continuity is utilized, the time sequence formed by the generated instantaneous decision results is post-processed, the isolated false alarm caused by the instantaneous interference can be effectively filtered out, so that the final false alarm rate is reduced to a very low level without sacrificing the sensitivity, a better balance between the reliability of the feature and the stability of the decision can be achieved, and the reliability of the target detection is improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a cyclostationary signal detection method flowchart of the present application in an embodiment; Figure 2 is a method flowchart of calculating the weighted global probability score of any one cyclic spectrum in an embodiment of the present application; Figure 3 is a schematic diagram of setting a frequency band based on a preset cyclic frequency point in an embodiment of the present application; Figure 4 is a method flowchart of obtaining the decision threshold of any one cyclic spectrum, and generating the instantaneous decision result based on the weighted global probability score and the decision threshold in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The application will be described in further detail below with specific reference to the drawings. Like elements are marked with like numerals throughout the various figures. It will be understood that many of the details presented herein are by way of example and for purposes of illustration only. Those skilled in the art will readily recognize a variety of substitutions that can be implemented that are within the scope of the present application. Additionally, the description sometimes uses terms in accordance with the IEEE Standard Dictionary of Electrical and Electronics Terms, Sixth Edition, which can be obtained from IEEE, Piscataway, NJ, USA.

[0019] In addition, features, operations, or steps described in the detailed description can be performed in any order or sequence, unless otherwise specified or implied by context. Therefore, unless otherwise specifically stated or implied by context, the order or sequence of steps described in the detailed description is not limiting.

[0020] The use of terms such as "first", "second", and the like can be used in the description and in the claims to describe various elements, but such elements should not be limited by these terms. These terms can be used interchangeably when appropriate. Terms such as "including", "having", "containing", or "encompassing" can be used throughout the specification and claims to mean that various elements can be included or encompassed within the described embodiments.

[0021] For the purpose of illustrating the inventive subject matter, a brief description of a cyclostationary signal analysis technique is provided below.

[0022] Current cyclostationary signal analysis usually uses a multi-harmonic feature fusion strategy. This strategy first identifies feature peaks at the fundamental and multiple harmonic frequencies, then fuses these discrete features into a single global probability score (P), and makes a decision based on this.

[0023] However, the applicant found in the research that the above cyclostationary signal analysis has two deep technical defects, which leads to insufficient robustness in practical application, including: First, the feature fusion method is blind. The existing fusion method generally adopts simple arithmetic average of each harmonic feature to obtain a decision threshold. However, this method is physically unreasonable. It ignores the basic physical priori that the harmonic energy of the target signal generally decays with the increase of the harmonic order. As a result, it gives the same importance to the low-order harmonic with strong energy, high signal-to-noise ratio and high reliability and the high-order harmonic with weak energy, easy to be contaminated by noise and poor reliability. This "same treatment" fusion strategy leads to that the final global score P itself is not stable enough and is easily disturbed by random noise in the high frequency band, thereby reducing the robustness of the decision from the root.

[0024] Second, the decision result is easily affected by instantaneous interference and lacks a confirmation mechanism in the time dimension. The decision of the existing method completely depends on the global score calculated by a single data frame. This mechanism is very sensitive to isolated and short strong noise pulses. The signal characteristics of a real and persistent target will certainly appear stably in continuous time. The false characteristics caused by random noise are usually short and isolated. The existing method cannot effectively distinguish between the two cases. If a random noise happens to mimic the target characteristics at a certain time, the system will produce a false alarm. In order to suppress such false alarms, the decision threshold is often increased, but this will reduce the detection sensitivity of the real weak signal, resulting in a missed report.

[0025] Based on the cyclic stationary signal detection method and medium provided in the present application, the sliding window smoothing processing is adopted, the priori knowledge that the real target signal must have "time continuity" is used, the time sequence composed of the instantaneous decision results is post-processed, the isolated false alarm caused by instantaneous interference can be effectively filtered out, the final false alarm rate can be reduced to a very low level without sacrificing the sensitivity, a better balance between the reliability of the characteristics and the stability of the decision can be achieved, and the reliability of the target detection is improved.

[0026] The present application provides a cyclic stationary signal detection method combined with multi-harmonic feature decision smoothing. Please refer to Figure 1 , which comprises: Step S10, filtering based on the collected time sequence signal, and collecting the cyclic spectrum of each time point based on the preset acquisition frequency.

[0027] The collected time sequence signal can be a helicopter noise, a communication signal, etc. The cyclic spectrum of each time point is collected based on the preset acquisition frequency after filtering the signal, so as to obtain Q cyclic spectrums.

[0028] Step S20, calculating the weighted global probability score of each cyclic spectrum.

[0029] In one embodiment, please refer to Figure 2 For any one cyclic spectrum, the method for calculating its weighted global probability score can include: In step S201, based on the cyclic spectrum detection function with adaptive threshold, the peak value of the spectrum in the frequency band where each of the plurality of expected cyclic spectrum cycle frequencies is located is obtained.

[0030] In the embodiments of the present application, the fixed threshold is abandoned, and the peak value of the spectrum in the frequency band where each of the plurality of expected cyclic spectrum cycle frequencies is located is obtained based on the cyclic spectrum detection function with adaptive threshold (taking the 90% quantile of each data point as the threshold), so that the threshold can automatically adapt to the fluctuation of the current background noise, and has strong adaptability.

[0031] Please refer to Figure 3 , the expected cycle frequencies are multiplied, in the diagram, the first expected cycle frequency is , and the frequency band where it is located is B1; the second expected cycle frequency =2 , and the frequency band where it is located is B2; the second expected cycle frequency =3 , and the frequency band where it is located is B3.

[0032] In one embodiment, step S201 can be represented as: Wherein, represents the peak value of the spectrum in the frequency band where the ith expected cyclic spectrum cycle frequency is located, 2≤i≤n, n represents the number of expected cyclic spectrum cycle frequencies; represents the cyclic spectrum detection function based on adaptive threshold, which is used to obtain the amplitude of the cyclic spectrum correlation function along the frequency axis for each cycle frequency point; represents the kth cycle frequency point in the frequency band where the ith expected cyclic spectrum cycle frequency is located, 1≤k≤K, K represents the number of cycle frequency points in the frequency band where the ith expected cyclic spectrum cycle frequency is located; B represents the frequency range of the entire cyclic spectrum; represents the frequency band set for the ith expected cyclic spectrum cycle frequency, f represents the distribution frequency of the energy of the collected time series signal on the frequency spectrum, represents the resolution of f, represents the cyclic spectrum correlation function.

[0033] In step S202, based on each peak value, the normalized local probability score of the spectrum peak is calculated.

[0034] In one embodiment, to reflect the significance of the peak value of the spectrum peak exceeding the noise floor, step S202 comprises: obtaining a dynamic threshold value of the current cyclic spectrum, and calculating the normalized local probability score of the spectrum peak based on the dynamic threshold value and the peak value of the spectrum peak. The dynamic threshold value is set based on a preset false alarm rate, and is the minimum value of the maximum number of amplitudes of the preset false alarm rate in the current cyclic spectrum. For example, if there are 100 cyclic spectrum cycle frequency points in the current cyclic spectrum, the amplitudes of the 100 cyclic spectrum cycle frequency points are obtained. If the preset false alarm rate is 10%, the maximum amplitude of the 100 cyclic spectrum cycle frequency points is obtained, and the minimum amplitude of the 10 maximum amplitudes is taken as the dynamic threshold value of the current cyclic spectrum.

[0035] Obtaining a dynamic threshold value of the current cyclic spectrum, and calculating the normalized local probability score of the spectrum peak based on the dynamic threshold value and the peak value of the spectrum peak comprises: wherein, represents the local probability score corresponding to the ith expected cyclic spectrum cycle frequency, represents the dynamic threshold value of the qth cyclic spectrum, 1≤q≤Q, Q represents the number of cyclic spectra, represents the false alarm rate.

[0036] The local probability score reflects the significance of the peak value of the spectrum peak exceeding the noise floor. The local probability score

[0037] Step S203 comprises: assigning a corresponding weight to each local probability score representing different harmonic orders based on the reliability difference of each harmonic feature, and performing weighted summation on all local probability scores based on the weight to calculate the weighted global probability score of the current cyclic spectrum. The assigned weight monotonically decreases with the increase of the harmonic order.

[0038] In one embodiment, step S203 can be represented as: wherein, represents the weighted global probability score of the qth cyclic spectrum; represents the weight of the local probability score, .

[0039] As can be understood by those skilled in the art, from P1 to P n ​​​​, the harmonic order is increasing, and the energy is decreasing, therefore, based on the decreasing weight distribution, the fundamental and low order harmonics are given higher weights, which is more consistent with the physical priori that the harmonic energy of the helicopter target usually decays with the increase of the harmonic order, so that the detection result is more accurate. Thus, the weighted global probability score of the embodiment of the application integrates evidence from multiple harmonics, which is more reliable than a single feature. When a certain harmonic is weak, other stronger harmonics can still support the final decision, so that a more accurate detection result is obtained.

[0040] In an embodiment, an attenuation model is used to determine the weight of the local probability score.

[0041] In step S30, the decision threshold of each cyclic spectrum is obtained, and based on the weighted global probability score and the decision threshold, an instantaneous decision result is generated.

[0042] In an embodiment, please refer to Figure 4 For any cyclic spectrum, the decision threshold thereof is obtained, and based on the weighted global probability score and the decision threshold, an instantaneous decision result is generated, including: In step S301, the average amplitude of the cyclic frequency points with amplitudes reaching the dynamic threshold value in the cyclic spectrum is calculated.

[0043] In an embodiment, step S301 can be expressed as: wherein, represents the average amplitude of the qth cyclic spectrum, represents the amplitude of the u th cyclic frequency point in the q th cyclic spectrum, 1≤u≤U, U represents the number of cyclic frequency points in the q th cyclic spectrum; represents 1 when the amplitude of the u th cyclic frequency point is greater than the corresponding dynamic threshold value, otherwise 0; represents the number of cyclic frequency points in the q th cyclic spectrum whose amplitudes are greater than the corresponding dynamic threshold value.

[0044] In step S302, the normalized decision threshold is calculated based on the average amplitude and the corresponding dynamic threshold value.

[0045] In an embodiment, step S302 can be expressed as: wherein, represents the decision threshold of the q th cyclic spectrum.

[0046] The decision threshold reflects the overall strength level of the effective signal component in the current signal.

[0047] ​Step S303, based on the weighted global probability score and the corresponding decision threshold, a transient decision result is generated.

[0048] The weighted global probability score and the corresponding decision threshold are compared, thereby generating the transient decision result of the current data frame (cyclic spectrum). In one embodiment, it can be expressed as: wherein, represents the transient decision result of the qth cyclic spectrum.

[0049] In the above decision result, 1 indicates that true is detected, and 0 indicates that false is not detected.

[0050] Step S40, for the transient decision result time sequence formed by the transient decision result, a sliding window smoothing processing is adopted, and the proportion of the transient decision result being true in the sliding window is calculated, to determine whether the cyclostationary signal is detected.

[0051] In one embodiment, a decision-level post-processing filter is introduced, which does not operate on the signal itself, but on the transient decision result time sequence composed of a plurality of consecutive data frames (cyclic spectrum) is processed, specifically including: calculating the number or proportion of the transient decision result being true in the sliding window, if the number of true reaches a preset number threshold or the proportion reaches a preset proportion threshold, it is judged that the cyclostationary signal is detected, the width W of the sliding window satisfies W≥4, and the step S satisfies 1≤S≤W.

[0052] For example, in the case of the width W of the sliding window being 5, if the preset number threshold is 2, then the number of the transient decision result being true in the sliding window reaching 2 indicates that the cyclostationary signal is detected; if the preset number threshold is 3, then the number of the transient decision result being true in the sliding window reaching 3 indicates that the cyclostationary signal is detected; if the preset proportion threshold is 50%, then the proportion of the number of the transient decision result being true in the sliding window reaching 50% (i.e. 3 or more) indicates that the cyclostationary signal is detected.

[0053] Since a real and persistent target will inevitably produce a series of transient decision "1" in continuous time, while a random and short-term noise interference caused false alarm is difficult to form a persistent response in time. Therefore, this step utilizes the "time continuity" prior knowledge of the target, which can effectively filter out false alarms.

[0054] In the embodiments of the present application, in the first aspect, since the sliding window smoothing processing is adopted, the prior knowledge that the real target signal must have time continuity is utilized, the time sequence composed of the generated instantaneous decision results is post-processed, isolated false alarms caused by instantaneous interference can be effectively filtered out, the final false alarm rate is reduced to a very low level without sacrificing the sensitivity, a better balance between the reliability of the feature and the stability of the decision can be achieved, and the reliability of the target detection is improved.

[0055] In the second aspect, since the introduced global weighted probability score calculation is based on the physical prior that the harmonic energy decays with the order, different weights are given to the local probability scores of different harmonics, which is essentially different from the simple arithmetic average. Therefore, the method can intelligently focus on more reliable low-order harmonics and suppress the interference of high-order harmonic noise, so that the instantaneous decision result is more robust. This directly improves the detection rate under low signal-to-noise ratio (such as far-field helicopter noise), and effectively avoids the false negatives caused by weak partial harmonic features.

[0056] In an embodiment of the present application, a computer readable storage medium is provided, and a program is stored on the storage medium. The stored program includes a method that can be loaded and processed by a processor.

[0057] Those skilled in the art can understand that all or part of the functions of the methods in the above embodiments can be realized by hardware or by a computer program. When all or part of the functions in the above embodiments are realized by a computer program, the program can be stored in a computer readable storage medium, which can include read-only memory, random access memory, magnetic disk, optical disk, hard disk, etc. The above functions are realized by executing the program by a computer. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, the above functions are realized. In addition, when all or part of the functions in the above embodiments are realized by a computer program, the program can also be stored in a server, another computer, a storage medium such as a disk, an optical disk, a flash disk or a mobile hard disk, and is downloaded or copied into the memory of the local device or the system of the local device is updated, and when the program in the memory is executed by the processor, the above functions are realized.

[0058] The above application of specific examples is used to illustrate the present application and is not intended to limit the present application. Those skilled in the art can make some simple deductions, deformations or substitutions according to the idea of the present application.

Claims

1. A method for cyclically stationary signal detection with multi-harmonic feature decision smoothing, characterized in that, The method comprises the following steps: filtering based on the collected time series signal, and collecting the cyclic spectrum of each time point based on the preset collection frequency; calculating the weighted global probability score of each cyclic spectrum; for each cyclic spectrum, obtaining a decision threshold value, and generating an instantaneous decision result based on the weighted global probability score and the decision threshold value; for the time series of instantaneous decision results formed by the instantaneous decision results, adopting sliding window smoothing processing, calculating the proportion of true instantaneous decision results in the sliding window, and determining whether a cyclically stationary signal is detected.

2. The method of cyclostationary signal detection of claim 1, wherein, The method for calculating the weighted global probability score of any cyclic spectrum comprises the following steps: an adaptive threshold-based cyclic spectrum detection function is used to obtain the spectral peak values of the frequency bands in which the plurality of expected cyclic spectrum cycle frequencies are located; based on each spectral peak value, the normalized local probability score of the spectral peak is calculated; based on the reliability differences of the harmonic characteristics, a corresponding weight is assigned to each local probability score representing different harmonic orders, and all local probability scores are weighted and summed based on the weights to calculate the weighted global probability score of the current cyclic spectrum; wherein the assigned weight monotonically decreases with the increase of the harmonic order.

3. The method of cyclic-spectrum signal detection of claim 2, wherein, The adaptive threshold-based cyclic spectrum detection function used to obtain the spectral peaks of the frequency bands in which the plurality of expected cyclic spectrum cycle frequencies are located comprises the following steps: wherein, represents the peak value of the spectrum in the frequency band where the ith expected cyclic spectral cycle frequency is located, 2≤i≤n, n represents the number of expected cyclic spectral cycle frequencies; represents the cyclic spectrum detection function based on the adaptive threshold, used to obtain the amplitude of the integral of the cyclic spectrum correlation function along the frequency axis at each cycle frequency point; represents the kth cycle frequency point in the frequency band where the ith expected cyclic spectral cycle frequency is located, 1≤k≤K, K represents the number of cycle frequency points in the frequency band where the ith expected cyclic spectral cycle frequency is located; B represents the spectral frequency range of the entire cyclic spectrum; represents the frequency band set for the ith expected cyclic spectral cycle frequency, f represents the distribution frequency of the energy of the collected time series signal in the frequency spectrum, represents the resolution of f, represents the cyclic spectrum correlation function.

4. The method of cyclic-spectrum signal detection of claim 3, wherein The method for calculating the normalized local probability score of each spectral peak based on each spectral peak value comprises the following steps:

5. The method of cyclic-spectrum signal detection of claim 4, wherein, a dynamic threshold value of the current cyclic spectrum is obtained, and the normalized local probability score of the spectral peak is calculated based on the dynamic threshold value and the spectral peak value; the dynamic threshold value is set based on a preset false alarm rate, and is the minimum value of the maximum amplitude in the preset false alarm rate of the current cyclic spectrum. wherein, represents a local probability score corresponding to the i-th expected cyclic spectral cycle frequency, represents a dynamic threshold value of the q-th cyclic spectrum, 1≤q≤Q, Q represents the number of cyclic spectra, represents a false alarm rate.

6. The method of cyclic-spectrum signal detection of claim 5, wherein, The method for obtaining the dynamic threshold value of the current cyclic spectrum and calculating the normalized local probability score of the spectral peak based on the dynamic threshold value and the spectral peak value comprises the following steps: wherein, represents the weighted global probability score of the qth cycle spectrum; represents the weight of the local probability score, .​​ 7. The method of cyclic-spectrum signal detection of claim 6, wherein, The method for calculating the weighted global probability score of the current cyclic spectrum based on the reliability differences of the harmonic characteristics, assigning a corresponding weight to each local probability score representing different harmonic orders, and weighting and summing all local probability scores comprises the following steps: The method for obtaining the decision threshold value of any cyclic spectrum and generating an instantaneous decision result based on the weighted global probability score and the decision threshold value comprises the following steps: the average amplitude of the cycle frequency points in the cyclic spectrum whose amplitudes reach the dynamic threshold value is calculated; the normalized decision threshold value is calculated based on the average amplitude and the corresponding dynamic threshold value; 8. The method of cyclic-spectrum signal detection of claim 7, wherein, the instantaneous decision result is generated based on the weighted global probability score and the corresponding decision threshold value. wherein, represents the average amplitude of the qth cyclic spectrum, represents the amplitude of the u-th cyclic frequency point in the qth cyclic spectrum, 1≤u≤U, U represents the number of cyclic frequency points in the qth cyclic spectrum; represents 1 when the amplitude of the u-th cyclic frequency point is greater than the corresponding dynamic threshold value, otherwise 0; represents the number of cyclic frequency points in the qth cyclic spectrum whose amplitudes are greater than the corresponding dynamic threshold value; The method for calculating the average amplitude of the cycle frequency points in the cyclic spectrum whose amplitudes reach the dynamic threshold value comprises the following steps: wherein, denotes the decision threshold for the qth cycle spectrum; The method for calculating the normalized decision threshold value based on the average amplitude and the corresponding dynamic threshold value comprises the following steps: wherein, represents the instantaneous decision result of the qth cycle spectrum.

9. The method of cyclic-spectrum signal detection of claim 7, wherein, The method for generating the instantaneous decision result based on the weighted global probability score and the corresponding decision threshold value comprises the following steps: For the time series of instantaneous decision results formed by the instantaneous decision results, adopting sliding window smoothing processing, calculating the number or proportion of true instantaneous decision results in the sliding window, and determining whether a cyclically stationary signal is detected. The number or proportion of the instantaneous decision results being true in the sliding window is calculated, and if the number of the results being true reaches a preset number threshold or the proportion of the results being true reaches a preset proportion threshold, it is determined that a cyclostationary signal is detected, wherein the width W of the sliding window satisfies W≥4, and the step S satisfies 1≤S≤W.

10. A computer-readable storage medium, characterized in that, The medium stores a program, and the program can be loaded and executed by the processor to perform the cyclostationary signal detection method in any one of claims 1 to 9.

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