Shaft frequency extraction method, shaft frequency extraction system and storage medium

Abstract

The invention relates to an axis frequency extraction method, an axis frequency extraction system and a storage medium, and the method comprises the steps: obtaining DEMON line spectrums and a line spectrum sequence of the DEMON line spectrums according to the to-be-recognized array element domain data, and obtaining a difference frequency array according to the frequency difference between every two line spectrums in the line spectrum sequence; widening a frequency range corresponding to each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum, and reducing a frequency interval between the difference frequencies in the difference frequency array according to the resolution of the DEMON line spectrum to obtain an axis frequency array; calculating harmonic waves, corresponding to different orders, of each axis frequency in the axis frequency array to obtain a plurality of harmonic cluster spectrums; matching the DEMON line spectrum with a pre-constructed identification template, and determining an axis frequency corresponding to the DEMON line spectrum and a harmonic sequence thereof according to a matching result; wherein the identification template is constructed based on the harmonic cluster spectrum. By adopting the method, the problem of low accuracy and robustness of shaft frequency feature extraction can be solved.

Description

Technical Field

[0001] The present application relates to the field of underwater target recognition, and particularly to an axle frequency extraction method, an axle frequency extraction system, and a storage medium. Background Art

[0002] Using the target radiated noise to classify the target is a very important research content in the underwater acoustic field. When the propeller rotates in the non-uniform wake, its cavitation noise will show a modulation phenomenon. The propeller cavitation noise consists of two parts. One part is generated by the collapse and rebound of a large number of transient cavitation bubbles in the area close to the propeller blade, and its spectrum is continuous; the other part is generated by the periodic forced vibration of a large number of stable cavitation bubbles in the area near the propeller, and its spectrum is a discrete line spectrum. The size, shape, rotation speed, number of blades, etc. of the propeller are closely related to the type of underwater target. Through the DEMON (Detection of Envelope Modulation On Noise) spectrum analysis of the target, physical characteristics such as the propeller rotation speed and the number of blades can be obtained, and these characteristics are often used as important bases for underwater target recognition.

[0003] When extracting the axle frequency characteristics of the target, the traditional greatest common divisor algorithm can only process simple data with a small number of targets and a high signal-to-noise ratio when identifying target characteristics. However, in the face of the complex and changeable underwater acoustic data in the actual ocean, affected by the insufficient resolution of the DEMON spectrum, there is a situation where the axle frequency characteristics cannot be extracted.

[0004] Aiming at the problem of low accuracy and robustness in axle frequency characteristic extraction in the related art, no effective solution has been proposed yet. Summary of the Invention

[0005] Based on this, in view of the above technical problems, it is necessary to provide an axle frequency extraction method, an axle frequency extraction system, and a storage medium that can solve the problems of low accuracy and robustness in axle frequency characteristic extraction.

[0006] In a first aspect, in the present embodiment, an axle frequency extraction method is provided, and the method includes:

[0007] Obtain the DEMON line spectrum and the line spectrum sequence of the DEMON line spectrum according to the array element domain data to be recognized, and obtain a difference frequency array according to the frequency difference between every two line spectra in the line spectrum sequence;

[0008] Widen the frequency range corresponding to each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum, and reduce the frequency interval between each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum to obtain an axle frequency array;

[0009] Calculate the harmonics corresponding to different orders of each shaft frequency in the shaft frequency array to obtain a plurality of harmonic cluster spectra;

[0010] Match the DEMON line spectrum with a pre-constructed recognition template, and determine the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result; wherein, the recognition template is constructed based on the harmonic cluster spectrum.

[0011] In some embodiments, before matching the DEMON line spectrum with the pre-constructed recognition template, the method further includes:

[0012] Interpolate the DEMON line spectrum;

[0013] Adjust the resolution of the harmonic cluster spectrum so that the resolution of the harmonic cluster spectrum is consistent with the resolution of the DEMON line spectrum obtained after interpolation processing.

[0014] In some embodiments, before adjusting the resolution of the harmonic cluster spectrum so that the resolution of the harmonic cluster spectrum is consistent with the resolution of the DEMON line spectrum obtained after interpolation processing, the method further includes:

[0015] Widen the upper and lower limits of the frequency range of the DEMON line spectrum by a specified multiple of the resolution.

[0016] In some embodiments, matching the DEMON line spectrum with the recognition template and determining the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result includes:

[0017] Calculate the inner products of the DEMON line spectrum and the sequences in a plurality of the recognition templates respectively to obtain a matching degree sequence corresponding to each recognition template;

[0018] Obtain a completeness sequence corresponding to each recognition template according to the number and frequency positions of the peak signals in the matching degree sequence;

[0019] Determine the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching degree sequence and the corresponding completeness sequence.

[0020] In some embodiments, obtaining a completeness sequence corresponding to each recognition template according to the number and frequency positions of the peak signals in the matching degree sequence includes:

[0021] When the number of the peak signals is not zero, obtain the frequency position;

[0022] When the frequency position and the resolution satisfy a multiple relationship, use the frequency corresponding to the peak as the harmonic of the shaft frequency corresponding to the recognition template.

[0023] When the multiple relationship is not satisfied between the frequency position and the resolution, obtain the distances between the peak value and multiple values of the resolution, and use the frequency corresponding to the peak value with the closest distance as the harmonic of the shaft frequency corresponding to the recognition template.

[0024] Obtain the integrity sequence according to the number of the harmonics obtained by matching.

[0025] In some embodiments, before matching the DEMON line spectrum with a pre-constructed recognition template, the method further includes:

[0026] Perform convolution processing on the harmonic cluster spectra respectively according to signals conforming to the Gaussian distribution to obtain a plurality of the recognition templates.

[0027] In some embodiments, broadening the frequency range corresponding to each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum includes:

[0028] Subtract the resolution of the DEMON line spectrum from the frequency value of each element in the difference frequency array to obtain the lower limit of the frequency value of each element;

[0029] Add the resolution of the DEMON line spectrum to the frequency value of each element in the difference frequency array to obtain the upper limit of the frequency value of each element.

[0030] In some embodiments, reducing the frequency interval between each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum includes:

[0031] Obtain a new frequency interval according to the resolution of the DEMON line spectrum;

[0032] Based on the frequency interval, insert elements between each difference frequency in the difference frequency array so that the frequency interval of the difference frequency array after inserting the elements is reduced.

[0033] In some embodiments, the receiving array includes a sonar array, a moored buoy, a surface buoy and a detection payload, and the sonar array includes one or more of the following: a towed line array, a shore-based array, a conformal array, a planar array, a ship's bow array. Obtaining the DEMON line spectrum and the line spectrum sequence of the DEMON line spectrum according to the array element domain data to be recognized includes:

[0034] Obtain the array element domain data collected by the receiving array;

[0035] Convert the array element domain data into synthetic wave data and demodulate the synthetic wave data;

[0036] Perform Fourier transform on the demodulated composite wave data to obtain the DEMON spectrum;

[0037] Extract the line spectra in the DEMON spectrum to obtain the DEMON line spectra and the line spectrum sequence.

[0038] In some embodiments, after matching the DEMON line spectra with pre-constructed recognition templates and determining the shaft frequencies and their harmonic sequences corresponding to the DEMON line spectra according to the matching results, the shaft frequency extraction method further includes:

[0039] Analyze based on the shaft frequencies and their harmonic sequences to obtain the propeller parameters corresponding to the element domain data.

[0040] In a second aspect, in the present embodiment, a shaft frequency extraction system is provided, the system includes: a receiving array and a processing device; the receiving array includes a sonar array, a moored buoy, a surface buoy and a detection payload, and the sonar array includes one or more of the following: a towed line array, a shore-based array, a conformal array, a planar array, a ship's bow array; wherein,

[0041] The receiving array is used to obtain the element domain data of the acquisition signal source;

[0042] The processing device is used to implement the spectrum recognition method described in the first aspect above.

[0043] In a third aspect, in the present embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, it implements the shaft frequency extraction method described in the first aspect above.

[0044] The above shaft frequency extraction method, shaft frequency extraction system and storage medium adjust the frequency range and frequency interval of the difference frequency data to obtain a shaft frequency array, improving the resolution during shaft frequency matching; matching the DEMON line spectra with the recognition template constructed based on the shaft frequency array can obtain the shaft frequencies and their harmonic sequences of multiple targets, and achieve the effect of improving the accuracy and robustness of shaft frequency feature extraction. Description of the Drawings

[0045] Figure 1 It is an application environment diagram of the shaft frequency extraction method in an embodiment;

[0046] Figure 2 It is a flow diagram of the shaft frequency extraction method in an embodiment;

[0047] Figure 3 It is a flow diagram of the schematic diagram of the shaft frequency extraction and harmonic judgment method based on the DEMON spectrum in an embodiment;

[0048] Figure 4 It is a schematic diagram of the DEMON line spectrum in an embodiment;

[0049] Figure 5 It is a harmonic sequence template with an axis frequency of 4.47 Hz in an embodiment;

[0050] Figure 6 It is a schematic diagram of the axis frequency matching degree sequence in an embodiment;

[0051] Figure 7 It is a schematic diagram of the quality factor in an embodiment;

[0052] Figure 8 It is a structural block diagram of an axis frequency extraction system in an embodiment;

[0053] Figure 9 It is a structural block diagram of an axis frequency extraction device in an embodiment;

[0054] Figure 10 It is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0055] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0056] The method embodiments provided in this embodiment can be executed on a terminal, a computer or a similar computing device. For example, when running on a terminal, Figure 1 It is a hardware structural block diagram of a terminal for an axis frequency extraction method according to an embodiment of the present application. As Figure 1 shown, the terminal may include one or more ( Figure 1 only one is shown in Figure 1 processors 102 and a memory 104 for storing data. Among them, the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown in Figure 1 is only schematic and does not limit the structure of the above terminal. For example, the terminal may further include more or fewer components than

[0057] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the shaft frequency extraction method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0058] The transmission device 106 is used to receive or send data via a network. The above network includes a wireless network provided by a communication provider of the terminal. In one instance, the transmission device 106 includes a network adapter (abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0059] In this embodiment, a shaft frequency extraction method is provided. Figure 2 It is a flowchart of the shaft frequency extraction method of this embodiment, as Figure 2 shown. The process includes the following steps:

[0060] Step S202, obtaining the DEMON line spectrum and the line spectrum sequence of the DEMON line spectrum according to the data in the element domain to be recognized, and obtaining a difference frequency array according to the frequency difference between every two line spectra in the line spectrum sequence.

[0061] Among them, an element is a basic unit in an array, referring to devices such as antennas and sensors with signal receiving capabilities; the element domain data is the signal data received by the element. The DEMON line spectrum is the spectrum of a discrete signal extracted based on the DEMON technology; the line spectrum sequence is a signal sequence obtained based on the discrete frequency components in the DEMON line spectrum.

[0062] Optionally, perform frequency domain beamforming on the time domain data of each element channel to obtain composite wave data. In the composite wave data, the low-frequency modulation spectrum information is demodulated using the extraction technology of the DEMON spectrum in the medium and high frequency bands, and line spectrum extraction is performed to obtain the DEMON line spectrum and its line spectrum sequence.

[0063] Optionally, calculate the line spectrum sequence f mFor every two line spectra, calculate the difference frequency F between their corresponding frequency points i,j :

[0064] F i,j =f i -f j , where i, j = 1, 2, … M; i > j

[0065] Arrange the elements of the difference frequency array in ascending order to obtain the difference frequency array {F i,j}. By calculating the difference frequency, the shaft frequency characteristics of the line spectra in the line spectrum sequence can be obtained. Among them, M is the number of line spectra in the line spectrum sequence. Among them, if the difference frequencies obtained by taking the difference between the corresponding frequency points of every two line spectra are the same, then these difference frequency values can be merged.

[0066] Optionally, if it is necessary to further improve the accuracy of the difference frequency array, the line spectrum sequence can be merged first. The merging principle is as follows: For the DEMON line spectrum sequence f n containing N ordered line spectra, merge the line spectrum clusters in which the line spectrum frequencies are greater than f th1 hz and the interval is less than or equal to one spectral resolution into a single line spectrum, and retain the line spectrum with the largest amplitude in the line spectrum cluster. Obtain the line spectrum sequence f m , with a total of M ordered line spectra.

[0067] Step S204: Broaden the frequency range corresponding to each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum, and reduce the frequency interval between each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum to obtain the shaft frequency array.

[0068] Among them, the resolution of the DEMON line spectrum is the same as the resolution of the difference frequency. When performing DEMON spectrum analysis, due to insufficient resolution, it may lead to a situation where the harmonic sequence multiple relationship in the spectrum obtained based on the array element domain data is not exactly an integer multiple relationship, and further lead to inaccurate frequency ranges corresponding to each difference frequency in the difference frequency array.

[0069] Optionally, based on the size of the resolution of the DEMON line spectrum, increase the upper limit of the frequency range corresponding to the difference frequency, and / or reduce the lower limit of the frequency range corresponding to the difference frequency, reducing the possibility that inaccurate line spectrum sequences caused by insufficient resolution make the true line spectrum difference frequency unable to match the difference frequency array.

[0070] Optionally, obtain a new frequency interval according to the resolution of the DEMON line spectrum, generate denser data points within the frequency range of the difference frequency array based on the new frequency interval, and use the data points after modifying the frequency range and interval as all possible first harmonic frequencies of the original difference frequency data, thereby obtaining the shaft frequency data. Among them, by reducing the frequency interval of the data, the resolution of the calculated shaft frequency array can be improved; the value of the frequency interval can be set according to requirements.

[0071] Step S206 , calculating the harmonics of different orders corresponding to the frequency of each axis frequency in the axis frequency array, and obtaining a plurality of harmonic cluster spectra.

[0072] Among them, the harmonic cluster spectrum is a collection of the fundamental frequency and its harmonics. Optionally, for each axis frequency in the axis frequency array, the line spectrum frequencies of the first L orders are calculated to obtain the harmonic cluster spectrum of the frequency. According to the harmonic calculation results of multiple axis frequencies, multiple harmonic cluster spectra are obtained. Among them, the order when calculating harmonics can be set according to actual needs; the size of the order L of the line spectrum frequency can be preset or modified.

[0073] Step S208, matching the DEMON line spectrum with a pre-constructed recognition template, and determining the axial frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result; wherein the recognition template is constructed based on the harmonic cluster spectrum.

[0074] Determine whether the DEMON line spectrum is similar to multiple recognition templates respectively, and obtain the matching degree between the DEMON line spectrum and multiple recognition templates. Sort multiple recognition templates according to the matching degree. Based on the order of matching degree from high to low, obtain the shaft frequency and its harmonic sequence corresponding to one or more recognition templates with the highest ranking, and use the shaft frequency and its harmonic sequence as the recognition result of the DEMON line spectrum. Among them, the identified shaft frequency and its harmonic sequence are the characteristics of the array element domain data to be identified, thereby realizing the recognition of the array element domain data.

[0075] Optionally, multiple harmonic cluster spectra can be directly used as multiple pre-constructed recognition templates. Alternatively, in order to improve the accuracy of recognition, the waveform distribution of the harmonic cluster spectrum can be changed to increase the template matching range, and the processed harmonic cluster spectrum can be used as multiple pre-constructed recognition templates. For example, the harmonic cluster spectrum is processed using a Gaussian function as a convolution kernel, or a window function or other custom convolution kernel is used to process the harmonic cluster spectrum.

[0076] In the above-mentioned shaft frequency extraction method, the frequency range and frequency interval of the difference frequency array are adjusted to improve the resolution during shaft frequency matching and the tolerance to phenomena such as Doppler frequency shift. The recognition template pre-constructed based on the shaft frequency data is compared with the DEMON line spectrum to accurately identify the shaft frequencies and harmonic sequences of multiple targets. This achieves the effect of improving the accuracy and robustness of shaft frequency feature extraction.

[0077] In one embodiment, before matching the DEMON line spectrum with a pre-constructed recognition template, the method further includes: performing convolution processing on the harmonic cluster spectrum respectively according to the signal conforming to the Gaussian distribution to obtain a plurality of recognition templates.

[0078] Among them, the signal frequency of the Gaussian distribution is consistent with the resolution of the harmonic cluster spectrum. The harmonic cluster spectrum is a line spectrum. Affected by the Doppler frequency shift phenomenon, there is a problem of frequency shift in the DEMON line spectrum obtained based on the array element domain data. In this embodiment, by convolving the harmonic cluster spectrum with a signal conforming to the Gaussian distribution respectively, the signal distribution of the discrete signals in the harmonic cluster spectrum can be changed, making its shape conform to the Gaussian distribution, increasing the template matching range, and improving the tolerance to spectrum frequency shift phenomena such as Doppler frequency shift.

[0079] In one embodiment, before matching the DEMON line spectrum with a pre-constructed recognition template, the method further includes: interpolating the DEMON line spectrum; adjusting the resolution of the harmonic cluster spectrum so that the resolution of the harmonic cluster spectrum is consistent with the resolution of the DEMON line spectrum obtained after the interpolation process.

[0080] Among them, by interpolating the frequency of the DEMON line spectrum, the resolution of the DEMON line spectrum is improved to prevent the DEMON line spectrum from being unable to match the recognition template due to Doppler translation. Optionally, the frequency resolution of the DEMON line spectrum after interpolation is less than or equal to the resolution of the shaft frequency array to avoid insufficient resolution of the shaft frequency array. Optionally, the resolution of the harmonic cluster spectrum and the DEMON line spectrum are unified by the method of rounding, and the harmonic cluster spectrum and the DEMON line spectrum are adjusted to the same scale for inner product, which is convenient for matching the DEMON line spectrum and the recognition template obtained based on the harmonic cluster spectrum.

[0081] Exemplarily, for each frequency f shaft in the shaft frequency array {F i}, calculate its first L (L = 3, 4, 5, 6, 7, 8, 9…, 16) order line spectrum frequencies to obtain the harmonic cluster spectrum {F i} of the shaft frequency sequence f i,h , h = 1, 2,…8. Interpolate the original DEMON line spectrum frequency by N2 times (N2 ≤ N1), and increase the spectral resolution from to . The resolution of the shaft frequency array is , and correspondingly, the resolution of the harmonic cluster spectrum is . The resolution of the harmonic cluster spectrum {F i,h} is unified with the DEMON line spectrum by the method of rounding, and the resolution of the harmonic cluster spectrum of the shaft frequency sequence is adjusted to .

[0082] Optionally, after adjusting the resolution of the harmonic cluster spectrum, the harmonic cluster spectrum can be convolved with a signal conforming to the Gaussian distribution respectively; at this time, the signal frequency of the Gaussian distribution can be set to , and the width of the Gaussian distribution signal can be set to twice the interpolation multiple, that is 。

[0083] In this embodiment, the resolution of the DEMON line spectrum is improved through interpolation; by unifying the resolution of the DEMON line spectrum after interpolation processing and the resolution of the harmonic cluster spectrum, the resolution is the same when matching the DEMON line spectrum and the recognition template obtained according to the harmonic cluster spectrum; it is beneficial to improve the accuracy of shaft frequency extraction.

[0084] Furthermore, in one embodiment, before adjusting the resolution of the harmonic cluster spectrum to make it the same as the resolution of the DEMON line spectrum obtained after interpolation processing, the method further includes: broadening the upper and lower limits of the frequency range of the DEMON line spectrum by a specified multiple of the resolution.

[0085] Optionally, broaden the upper and lower limits of the frequency range of the DEMON line spectrum , L = 1, 2, … 8. Wherein, L is the harmonic order corresponding to the broadened DEMON line spectrum. The higher the harmonic order, the greater the broadening width and the greater the tolerable error.

[0086] In this embodiment, the resolution of the DEMON line spectrum is improved by interpolating the DEMON line spectrum. By broadening the DEMON line spectrum sequence, the possibility that the frequency deviation caused by the Doppler shift leads to the inability to match the DEMON line spectrum and the recognition template is reduced, and the robustness of shaft frequency feature extraction is improved.

[0087] In one embodiment, matching the DEMON line spectrum with a pre-constructed recognition template, and determining the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result includes: respectively calculating the inner product of the DEMON line spectrum and the sequences in multiple recognition templates to obtain a matching degree sequence corresponding to each recognition template; obtaining a completeness sequence corresponding to each recognition template according to the number and frequency position of the peak signals in the matching degree sequence; determining the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching degree sequence and the corresponding completeness sequence.

[0088] Among them, the matching degree sequence is used to represent the similarity level between each signal in the DEMON line spectrum and each shaft frequency in the recognition template. The completeness sequence is used to represent the number of signals in the DEMON line spectrum that match each shaft frequency in the recognition template. The higher the matching degree and the higher the completeness, the greater the possibility that the shaft frequency corresponding to the recognition template belongs to the shaft frequency feature of the DEMON line spectrum.

[0089] After calculating the inner product of the DEMON line spectrum and the sequences in multiple recognition templates, if there is no peak in the spectrum corresponding to the matching degree sequence, the DEMON line spectrum does not match the recognition template; if the number of peaks in the spectrum generated after dot multiplication is greater than 0, the DEMON line spectrum matches the harmonics in the recognition template.

[0090] Optionally, according to the multiple relationship between the peak position and the resolution, determine the order of the harmonic matching the peak, select the first L harmonics according to the requirements, and count the number of the first L harmonics matched by each shaft frequency in the recognition template as a sequence for measuring the harmonic integrity, that is, obtain the integrity sequence.

[0091] Optionally, perform an inner product on the interpolated and broadened DEMON line spectra and the recognition template {M i} respectively, and use the value obtained from the inner product as the matching degree q i of the shaft frequency f i corresponding to the recognition template {M i} of the harmonic sequence, and obtain the matching degree sequence {q n}. According to the harmonic corresponding to the peak of the spectrum generated after the dot product, determine that in the shaft frequency array {F shaft} corresponding to the recognition template, use the number of the first L harmonics matched by each shaft frequency f i as the integrity sequence {s n}. Multiply the matching degree sequence {q n} by the integrity sequence {s n} to obtain the quality factor , sort the shaft frequencies corresponding to the recognition template according to the quality factor m n to obtain the shaft frequency sequence. The larger m n is, the more likely this shaft frequency is the characteristic shaft frequency of the target. Delete the shaft frequency sequence with the quality factor less than the specified quality factor . Among them, based on the shaft frequency sequence, the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum can be obtained.

[0092] Optionally, the method for matching the DEMON line spectrum with the pre-constructed recognition template to obtain the matching degree sequence corresponding to each recognition template further includes: calculating the similarity between the DEMON line spectrum and the signal of the pre-constructed recognition template at different frequency shifts to generate the matching degree sequence; or, obtaining the matching degree sequence according to the statistical coincidence degree of the peak positions and the amplitude differences between the DEMON line spectrum and the template; or, obtaining the matching degree sequence based on the envelope similarity.

[0093] Furthermore, in order to improve the recognition accuracy, before obtaining the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum based on the shaft frequency sequence, the shaft frequencies corresponding to the same target and the shaft frequencies with a multiple frequency relationship can be merged, including:

[0094] Subtract each pair of elements in this shaft frequency sequence to obtain the difference sequence S i,j of the shaft frequencies:

[0095] ;

[0096] If , then determine whether they belong to the same target by judging the harmonic sequences of these two shaft frequencies. If more than half of the harmonic sequences of these two shaft frequencies are consistent, it is determined that the harmonic sequences of the two shaft frequencies correspond to the same target, and the {s n} larger shaft frequency sequence is retained. Among them, is the resolution of the original DEMON line spectrum.

[0097] Divide the shaft frequency sequences in pairs to obtain the remainder sequence D of the multiple frequencies i,j :

[0098] ;

[0099] K is the rounded value of. If , it is considered that there is a multiple frequency relationship between these two shaft frequencies, and then judge whether the harmonic sequences of the two shaft frequencies are consistent. If the harmonic sequence of Kf j is more than half consistent with the harmonic sequence of f i , it is determined that the harmonic sequences of the two shaft frequencies correspond to the same target, and only the s n larger shaft frequency is retained.

[0100] Finally, the retained shaft frequency and its harmonic sequence {F shaft} are the target feature recognition results based on the DEMON line spectrum.

[0101] In this embodiment, by performing inner product calculation and dot product calculation on the DEMON line spectrum and the sequences in multiple recognition templates, from two aspects of the matching similarity and integrity, evaluate the possibility that the sequences in the recognition templates correspond to the shaft frequency characteristics of the DEMON line spectrum, and improve the accuracy of shaft frequency characteristic extraction through multi-angle evaluation.

[0102] Further, in one embodiment, according to the number and frequency position of the peak signals in the matching degree sequence, obtain the integrity sequence corresponding to each recognition template, including: when the number of peak signals is not zero, obtain the frequency position; when the frequency position and the resolution satisfy a multiple relationship, use the frequency corresponding to the peak as the harmonic of the shaft frequency corresponding to the recognition template; when the frequency position and the resolution do not satisfy a multiple relationship, obtain the distance between the peak and multiple multiples of the resolution, and use the frequency corresponding to the peak with the closest distance as the harmonic of the shaft frequency corresponding to the recognition template; obtain the integrity sequence according to the number of harmonics obtained by matching.

[0103] Among them, if the frequency corresponding to the peak and the resolution satisfy a multiple relationship, or the distance between the frequency corresponding to the peak and a certain multiple value of the resolution is relatively close, then there is a possibility of matching between the DEMON line spectrum and the harmonics in the recognition template, and the multiple value corresponding to the resolution corresponds to the harmonic order.

[0104] Exemplarily, the DEMON line spectrum after interpolation broadening is dot-multiplied with the recognition template M i of the sequence of i to obtain a matching degree sequence and calculate the peak value in the matching degree sequence. Obtain the number of peak values and the frequency positions of the peak values. Among them, the number of peak values is used to determine whether a harmonic is matched: if there is no peak value, it is determined that no harmonic is matched, and the Kth harmonic is 0; if the number of peak values is greater than 0, it is determined that a harmonic is matched. The peak frequency position is used to determine the order of the matched harmonic: if the peak frequency position f m is an integer multiple of , that is m then f m is not an integer multiple of , then the frequency position of the DEMON line spectrum closest to

[0105] is taken as the Kth harmonic. Count the number of the first L-order harmonics matched by each shaft frequency f shaft in the shaft frequency array {F i} corresponding to the recognition template, and use it as the integrity sequence {s n} for measuring the integrity of the harmonic, that is, the frequency and harmonic sequence of the shaft frequency corresponding to the recognition template. To reduce the amount of calculation, the integrity sequence can be screened first: for the integrity sequence {s n}, delete the shaft frequency sequence where {s n} is less than the specified frequency . The specified frequency is a pre-set value, which is used to delete the shaft frequency with too small frequency and reduce the influence of noise and burrs on the recognition accuracy of the shaft frequency sequence.

[0106] In this embodiment, when the peak value is consistent with multiple times of the resolution, or the difference between the peak value and multiple times of the resolution is small, it is determined that there may be a match between the DEMON line spectrum and the harmonics in the recognition template, reducing the possibility that the multiple-frequency relationship of the harmonic sequence is not exactly an integer multiple relationship and other phenomena, which may cause the shaft frequency characteristics to not be extracted, and improving the robustness of shaft frequency extraction.

[0107] In one embodiment, according to the resolution of the DEMON line spectrum, the frequency range corresponding to each difference frequency in the difference frequency array is broadened, including: subtracting the resolution of the DEMON line spectrum from the frequency value of each element in the difference frequency array to obtain the lower limit of the frequency value of each element; adding the resolution of the DEMON line spectrum to the frequency value of each element in the difference frequency array to obtain the upper limit of the frequency value of each element.

[0108] Among them, the resolution of the DEMON line spectrum is , then the resolution of the difference frequency array is also , the difference frequency array is {F i,j}}. According to the adjustment, the frequency range of all difference frequency arrays is obtained, and the expanded difference frequency array is . In this embodiment, by adjusting the frequency range of all difference frequency arrays, the tolerance to phenomena such as Doppler frequency shift is improved.

[0109] In one embodiment, reducing the frequency interval between the difference frequencies in the difference frequency array according to the resolution of the DEMON line spectrum includes: obtaining a new frequency interval according to the resolution of the DEMON line spectrum; based on the frequency interval, elements are inserted between the difference frequencies of the difference frequency array so that the frequency interval of the difference frequency array after inserting the elements is reduced.

[0110] Among them, the step size is less than the resolution of the DEMON line spectrum. Optionally, the frequency interval of the array is adjusted to , and all possible fundamental harmonic frequencies in the difference frequency array are obtained, constituting the shaft frequency array {F shaft}}.

[0111] In this embodiment, by adjusting the frequency range of all difference frequency arrays, the number of elements in the difference frequency array is increased, and the resolution corresponding to the difference frequency array is improved. Thereby, the resolution of the recognition template can be improved, achieving the effect of improving the accuracy of shaft frequency feature extraction.

[0112] In one embodiment, the receiving array includes a sonar array, a moored buoy, a surface buoy, and a detection payload. The sonar array includes one or more of the following: a towed array, a shore-based array, a conformal array, a planar array, a bow array. Obtaining the DEMON line spectrum and the line spectrum sequence of the DEMON line spectrum according to the array element domain data to be recognized includes: obtaining the array element domain data collected by the receiving array; converting the array element domain data into synthetic wave data and demodulating the synthetic wave data; performing a Fourier transform on the demodulated synthetic wave data to obtain the DEMON spectrum; extracting the line spectrum in the DEMON spectrum to obtain the DEMON line spectrum and the line spectrum sequence.

[0113] Among them, the towed array, the shore-based array, the conformal array, the planar array, and the bow array are different types of sonar arrays respectively. The moored buoy is a measurement system deployed underwater; the surface buoy is an observation platform floating on the sea surface. The detection payload is a technical equipment for performing detection tasks. The detection payload can be an AUV (Autonomous Underwater Vehicle), a UUV (Unmanned underwater vehicle), etc.

[0114] Optionally, collect the array element domain data emitted by the signal source through the receiving array; preprocess the array element domain data; perform beamforming on the array element domain data; demodulate the synthesized beam in the azimuth angle; perform Fourier transform on the demodulated synthesized beam data to obtain the DEMON spectrum, and then perform line spectrum extraction to obtain the DEMON line spectrum and its line spectrum sequence.

[0115] Among them, preprocessing the array element domain data includes: detecting bad channels for each array element channel data and removing the problematic channel data. The bad channels can be screened based on one or more of the following methods: quickly screening candidate bad channels based on statistical characteristics, screening bad channels through frequency domain analysis methods, and screening bad channels through correlation analysis methods.

[0116] Quickly screening candidate bad channels based on statistical characteristics includes: calculating the statistical characteristic parameters of the time domain data of each array element channel, such as mean, variance or energy value. Among them, the time domain data sequence of the i-th array element channel is x i (t), i = 1, 2, …, M, where M represents the number of array elements, and t = 1, 2, …, N, and N is the number of sampled data points. Then the data mean of the i-th array element channel is: , and the variance is , and the energy value is .

[0117] According to the calculated statistical characteristic parameters, set the dynamic threshold and use the 3σ principle to screen bad channels. For the mean, variance or energy value, calculate their overall mean , , and the overall standard deviation , , . If the statistical characteristic parameter of a certain array element channel exceeds the following range, it is regarded as a candidate bad channel: or ; or ; or .

[0118] Screening bad channels through frequency domain analysis methods includes: performing frequency domain analysis on each array element channel data, calculating the power spectral density (PSD) or performing spectral flatness measurement (SFM) to locate abnormal channels. The calculation of the power spectral density can use methods such as the periodogram method or the Welch method. Taking the Welch method as an example, segment the data sequence, perform window function processing on each segment of data and then perform fast Fourier transform (FFT), and then calculate the power spectral density of each segment and take the average to obtain the power spectral density estimate P i (f) of this channel.

[0119] The spectral flatness can be measured by calculating the ratio of the geometric mean to the arithmetic mean of the power spectral density. The specific formula is as follows:

[0120] ;

[0121] where N is the number of frequency points. If F i significantly deviates from the normal range, then this channel is regarded as a candidate bad track.

[0122] Screening for bad tracks through the correlation analysis method includes:

[0123] Calculating the correlation coefficient matrix of the data of each channel to further judge bad tracks. Let the correlation coefficient of the data of the i-th channel and the j-th channel be ρ ij , then the calculation formula is:

[0124] ;

[0125] where is the time-domain data sequence of the j-th element channel, is the data mean of the j-th element channel.

[0126] Calculating the average correlation coefficient of all channels:

[0127] ;

[0128] where ρ mj is the correlation coefficient of the data of the m-th channel and the j-th channel, and M is the number of ordered line spectra.

[0129] Setting the correlation coefficient threshold ρ th . If the average correlation coefficient of a certain channel with other channels is significantly lower than ρ th , then it is determined that this channel is a bad track.

[0130] After screening for bad tracks, preprocessing the data in the element domain can also include: performing signal consistency inspection and correction on the data of each element channel to ensure the consistency of each element channel in time, amplitude, and phase.

[0131] Optionally, calibrate the time delay of each element through the element position, and perform time delay correction based on the least mean square (LMS) algorithm. Let the reference channel be the k-th channel, and the time delay of other channels relative to the reference channel be :

[0132] ;

[0133] Where x is the position coordinate of the array element channel on the x-axis, y is the position coordinate of the array element channel on the y-axis, and z is the position coordinate of the array element channel on the z-axis; the subscripts k and i respectively refer to the k-th array element channel and the i-th array element channel; c is the speed of sound.

[0134] The algorithm iteratively adjusts the time delay estimation value :

[0135] ;

[0136] Where μ is the step size factor and e(t) is the error signal.

[0137] Calculate the data energy E of each channel i and the ratio with the data energy E of the reference channel k to detect the channels whose amplitude deviates from the threshold. Let the reference channel be the k-th channel, then the energy ratio of the i-th channel is:

[0138] ;

[0139] Set the amplitude ratio threshold R th . If it exceeds the range, it is considered that the amplitude of this channel is deviated. Channel equalization is performed based on algorithms such as the minimum mean square error (MMSE) and blind equalization. Taking the MMSE algorithm as an example, the equalization filter coefficient ω is solved by minimizing the objective function:

[0140] ;

[0141] Where d(t) is the desired signal and x(t) is the input signal vector.

[0142] The phase consistency is tested through phase difference statistics, and phase correction is performed based on the minimum mean square error (MMSE) algorithm. Calculate the phase difference between each channel and the reference channel:

[0143] ;

[0144] Where is the conjugate of the reference channel data. If the phase difference exceeds the set phase threshold , it is considered that this channel has a phase deviation. The MMSE algorithm corrects the phase by minimizing the mean square value of the phase error.

[0145] Detect and suppress abnormal strong transient signals in each channel. Methods such as wavelet transform and empirical mode decomposition (EMD) are proposed to decompose the signal and extract the transient signal components. Taking the wavelet transform as an example, select an appropriate wavelet basis to perform multi-scale decomposition on the signal to obtain wavelet coefficients at different scales. Detect abnormal peaks in the detail coefficients to capture strong transient signals.

[0146] Performing beamforming on the data in the array element domain specifically includes:

[0147] The time-domain signals of each array element are , where M represents the number of array elements, and these signals are transformed to the frequency domain through Fourier transform ;

[0148] ;

[0149] where f is the frequency point.

[0150] Performing phase compensation on the frequency-domain signals of each array element, and the compensation vector is:

[0151] ;

[0152] where d i is the distance from the i-th array element to the first array element; c is the speed of sound; θ is the scanning angle, 0 ≤ θ ≤ π;

[0153] After compensation, the array element domain frequency-domain signals at each azimuth angle become beam domain frequency-domain signals , where K represents the number of azimuth angles scanned.

[0154] Demodulating the synthesized beam at the azimuth angle specifically includes:

[0155] Performing band-pass filtering in the frequency domain on the signal at in the frequency band of

[0156] ;

[0157] where n is the order of the filter, and w c is the cut-off frequency.

[0158] Transforming the frequency-domain signal after band-pass filtering to the time domain through inverse Fourier transform .

[0159] ;

[0160] Then performing absolute value detection on the signal to obtain the time-domain signal, which is the preformed beam time-domain signal containing modulation information.

[0161] Performing Fourier transform on the demodulated synthesized beam data to obtain the DEMON spectrum, and then performing line spectrum extraction to obtain the DEMON line spectrum and its line spectrum sequence specifically includes:

[0162] The preformed beam time-domain signal containing modulation information By Fourier transforming to the frequency domain , a DEMON spectrum in the range of [0, f th Hz is obtained; the noise in the DEMON spectrum is suppressed to near the continuous spectrum while the signal is highlighted; the noise is removed according to the distribution law of the noise spectrum in the DEMON spectrum to achieve the effect of enhancing the target line spectrum; a dynamic threshold is set, and the 3σ principle is used to extract the DEMON line spectrum sequence.

[0163] In this embodiment, the DEMON line spectrum and the corresponding line spectrum sequence are obtained by processing the element domain data collected by the receiving array, which is applicable to the processing of underwater acoustic data in the underwater acoustic field.

[0164] In one embodiment, after matching the DEMON line spectrum with a pre-constructed recognition template and determining the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result, the shaft frequency extraction method further includes: analyzing the propeller parameters corresponding to the element domain data based on the shaft frequency and its harmonic sequence.

[0165] Among them, the propeller parameters include parameters such as the rotation frequency of the main propeller shaft, the number of blades, and the blade frequency. Optionally, the rotation frequency of the main propeller shaft is obtained according to the shaft frequency; the number of blades of the propeller is obtained according to the harmonic sequence. In this embodiment, the propeller parameters accurately analyzed based on the shaft frequency and its harmonic sequence are less affected by noise and have high accuracy.

[0166] In one embodiment, a shaft frequency extraction and harmonic judgment method based on the DEMON spectrum is provided. The method includes: performing frequency-domain beamforming on the time-domain data of each element channel, demodulating the low-frequency modulation spectrum information using the extraction technology of the DEMON spectrum in the medium and high frequency bands, and performing line spectrum extraction to obtain the DEMON line spectrum sequence. Generate all possible shaft frequency array harmonic sequence templates, match them with the widened and interpolated DEMON line spectrum, obtain the harmonic sequences of all shaft frequencies according to the matching degree, and finally merge the same target and the target with a multiple frequency relationship, and screen out the final target shaft frequency characteristics. Figure 3 The flow chart of the shaft frequency extraction and harmonic judgment method based on the DEMON spectrum in this embodiment is provided, as shown in Figure 3 shown, including:

[0167] Step S301, obtaining a line spectrum sequence.

[0168] Optionally, collect the original data of the elements of a fiber optic passive sonar towed array during a sea trial, with a sampling rate of 32 kHz and 192 elements in the channel number; perform preprocessing, beam synthesis and demodulation on the element domain data, and perform line spectrum extraction to obtain the DEMON line spectrum, including: performing beamforming on the underwater target radiated noise signal received by the receiving array, and picking out the interested beam X t (n), representing the time-domain signal of this beam at time t; the signal Xt (n) Perform FFT transformation to the frequency domain and perform band-pass filtering in the frequency domain within the frequency band of [f l , f h . Inverse-transform the frequency-domain signal after band-pass filtering to the time domain, and then perform absolute value detection on the signal to obtain the time-domain signal Y t (n); Perform FFT transformation of the time-domain signal Y t (n) to the frequency domain, and send it into a Butterworth low-pass filter with a cut-off frequency of f T to obtain the modulated frequency-domain signal Y t (ω); Use the DEMON spectrum analysis method to fit its continuous spectrum and extract the line spectrum to obtain the DEMON line spectrum sequence. Figure 4 Provide a schematic diagram of the DEMON line spectrum, as shown in Figure 4 , with a line spectrum resolution of 0.1 Hz.

[0169] Step S302, merge the line spectrum sequences. Perform preliminary merging on the obtained DEMON line spectrum sequence f n (N ordered line spectra) according to the following merging principle: Merge the line spectrum clusters with line spectrum frequencies greater than 2 hz and intervals less than or equal to one spectral resolution into a single line spectrum, and retain the line spectrum with the largest amplitude in the line spectrum cluster. Obtain the line spectrum sequence f m (M ordered line spectra). Taking the Figure 4 DEMON line spectrum as an example, there are 61 DEMON line spectra in total, and 51 line spectrum sequences are left after merging.

[0170] Step S303, calculate the difference frequency array. Among them, calculate the difference frequency F m between the M merged line spectrum sequences f i,j : F i,j = f i - f j , where i, j = 1, 2, … M; i > j. Arrange the elements of the difference frequency array in ascending order to obtain the difference frequency array {F i,j}.

[0171] Step S304, obtain the shaft frequency array {F shaft}: Arrange the difference frequency array and all the merged line spectrum sequences in ascending order together, and use it as all possible fundamental harmonic frequencies to obtain the shaft frequency array {F shaft}.

[0172] Continuing to refer to the Figure 4 DEMON line spectrum: If M is 51, then calculate the difference frequency between the 51 merged line spectrum sequences and arrange them in ascending order to obtain 438 groups of the difference frequency array {F i,j}. Obtain a shaft frequency array {F shaft} with a resolution of 0.01 Hz, a total of 9198 groups.

[0173] Step S305, interpolate and broaden the original DEMON line spectrum. Interpolate the frequency of the original DEMON line spectrum by 2 times, increase the spectral resolution from 0.1 Hz to 0.05 Hz, and then broaden the frequency of the DEMON line spectrum to the left and to the right , L = 1, 2, … 8. Among them, the resolution of the enhanced DEMON line spectrum is greater than that of the shaft frequency array. It can be understood that the resolution of the DEMON line spectrum frequency can also be increased to other values other than 0.05 Hz

[0174] Step S306, generate the harmonic sequence template {M i} of the shaft frequency array. Among them, the harmonic sequence template of the shaft frequency array is the recognition module in the above embodiment. For each frequency f shaft in the shaft frequency array {F i}, calculate the line spectrum frequencies of its first 16 orders to obtain the harmonic cluster spectrum {F i} of the shaft frequency sequence f i,h ; convolve the harmonic cluster spectrum {F i,h} with a waveform of Gaussian distribution as a filter to obtain the harmonic sequence template {M i} of the shaft frequency array. Optionally, use a waveform of Gaussian distribution with a width of as a filter to convolve the harmonic cluster spectrum {F } to obtain the harmonic sequence template {M i,h} of the shaft frequency array. The resolution of the harmonic sequence template is i . . Figure 5 This is a harmonic sequence template of a shaft frequency of 4.47 Hz in this embodiment

[0175] Step S307, calculate the matching degree sequence between the interpolated and broadened DEMON line spectrum and the sequence template {M i}. Make an inner product of the interpolated and broadened DEMON line spectrum and the template sequence {M i} respectively to obtain the shaft frequency matching degree sequence {q n}, Figure 6 which is a schematic diagram of the shaft frequency matching degree sequence

[0176] Step S308, calculate the number of peaks and peak positions of the shaft frequency matching degree sequence, determine the shaft frequencies matched by the shaft frequency array, and calculate and obtain the integrity sequence. The peaks, the number of peaks, and the peak positions of the shaft frequency matching degree sequence {q n} are 125 peaks in total. If the number of peaks is greater than 0, it means that it matches the harmonics, and then judge the frequencies of the first 16-order harmonics. Count the number of the first 16-order harmonics matched by each shaft frequency f shaft in the shaft frequency array {F i} as the sequence {sn}. Delete s n Axis frequency sequences less than 0.5 Hz.

[0177] Step S309, calculate the quality factor. Multiply the matching degree sequence {q n} by the integrity sequence {s n} to obtain the quality factor . Sort the axis frequency sequences according to the quality factor m n . The larger m n , the more likely this axis frequency is the characteristic axis frequency of the target. Delete the axis frequency sequences with quality factors less than 3σ. At this time, the axis frequencies of three targets are obtained: 1.49 Hz, 2.24 Hz, and 4.47 Hz. Figure 7 Is the schematic diagram of the quality factor in this embodiment.

[0178] Step S310, merge the axis frequency sequences of the same target and the axis frequency sequences with a multiple relationship.

[0179] Take the difference between these axis frequency sequences pairwise to obtain the difference sequence of axis frequencies:

[0180] ;

[0181] If S i,j ≤0.1 Hz, then judge the harmonic sequences of these two axis frequencies. If more than half of the harmonic sequences are the same, it is judged as the same target, and only keep s n with a larger axis frequency.

[0182] Take the quotient of these axis frequency sequences pairwise to obtain the remainder sequence of multiple frequencies:

[0183] ;

[0184] K is the rounded value of. If D i,j ≤0.1 Hz, it is considered that there is a multiple frequency relationship between these two axis frequencies. Then judge their harmonic sequences. If the harmonic sequence of Kf j is more than half the same as the harmonic sequence of f i , only keep s n with a larger axis frequency. There is a multiple frequency relationship between 2.24 Hz and 4.47 Hz, and the quality factor of the 4.47 Hz axis frequency is higher. Therefore, keep the 4.47 Hz axis frequency.

[0185] The finally obtained axis frequencies and their harmonic sequences {F shaft} are the target feature recognition results based on the DEMON line spectrum. As shown in Table 1, there are the axis frequencies and their harmonic sequences of 2 targets in this DEMON line spectrum, which are 1.49 Hz and 4.47 Hz respectively.

[0186] Table 1: Final axis frequency characteristics

[0187]

[0188] In this embodiment, the resolution of shaft frequency matching is improved by expanding the shaft frequency array; the tolerance to phenomena such as Doppler frequency shift is enhanced by broadening the DEMON line spectrum sequence and performing Gaussian convolution; the accuracy and robustness of shaft frequency feature extraction are greatly improved by retaining the shaft frequencies and their harmonic sequences of multiple targets through matching degree sorting.

[0189] The method in this embodiment can be used for the identification of propellers. The continuous spectrum in propeller noise is modulated by the propeller shaft frequency and blade frequency and contains the structural feature information of the propeller; the physical meaning of DEMON spectrum extraction is clear, stable, and has good separability, and no prior information is required. Therefore, DEMON analysis (Detection of Envelope Modulation on Noise) can be used to demodulate the high-frequency continuous spectrum and extract the ultra-low frequency line spectrum including its shaft frequency and its harmonic components.

[0190] Based on the same inventive concept, an embodiment of the present application also provides a shaft frequency extraction system for implementing the shaft frequency extraction method involved above. The implementation solution provided by this system to solve problems is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the shaft frequency extraction system provided below can refer to the limitations on the shaft frequency extraction method in the above text and will not be elaborated here.

[0191] In one embodiment, as Figure 8 shown, a shaft frequency extraction system is provided, and the system includes: a receiving array and a processing device; the receiving array includes a sonar array, a moored buoy, a surface buoy, and a detection payload, and the sonar array includes one or more of the following: a towed array, a shore-based array, a conformal array, a planar array, a bow array; wherein, the receiving array is used to obtain the array element domain data of the acquisition signal source; the processing device is used to implement the steps in the above method embodiments.

[0192] Optionally, the processing device includes but is not limited to: a signal processing algorithm module, an AI acceleration calculation algorithm module, and a heterogeneous computing scheduling system. The steps in the above method embodiments are cooperatively executed by the signal processing algorithm module, the AI acceleration calculation algorithm module, and the heterogeneous computing scheduling system. Optionally, the processing device can be arranged on a signal processing service platform, and the heterogeneous computing server and the AI computing acceleration chip in the signal processing service platform provide computing power for the execution of the processing device.

[0193] Each module in the above shaft frequency extraction system can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in the processor of a computer device in hardware form or be independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0194] Based on the same inventive concept, an embodiment of the present application further provides a shaft frequency extraction device for implementing the shaft frequency extraction method involved above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the shaft frequency extraction device provided below can refer to the limitations on the shaft frequency extraction method in the above text, and will not be repeated here.

[0195] In one embodiment, Figure 9 A schematic diagram of a shaft frequency extraction device is provided, as Figure 9 shown. The shaft frequency extraction device includes: a difference frequency processing module, a shaft frequency processing module, a harmonic calculation module, and a matching module.

[0196] Among them, the difference frequency processing module is used to obtain the DEMON line spectrum and the line spectrum sequence of the DEMON line spectrum according to the array element domain data to be recognized, and obtain a difference frequency array according to the frequency difference between every two line spectra in the line spectrum sequence; the shaft frequency processing module is used to widen the frequency range corresponding to each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum, and reduce the frequency interval between each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum to obtain a shaft frequency array; the harmonic calculation module is used to calculate the harmonics corresponding to different orders of each shaft frequency in the shaft frequency array to obtain a plurality of harmonic cluster spectra; the matching module is used to match the DEMON line spectrum with a pre-constructed recognition template, and determine the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result; among them, the recognition template is constructed based on the harmonic cluster spectrum.

[0197] In one embodiment, the shaft frequency extraction device further includes a template processing module, which is used to perform convolution processing on the harmonic cluster spectra respectively according to signals conforming to the Gaussian distribution before matching the DEMON line spectrum with the pre-constructed recognition template to obtain a plurality of recognition templates.

[0198] In one embodiment, before the harmonic calculation module matches the DEMON line spectrum with a pre - constructed recognition template, the method further includes: interpolating the DEMON line spectrum; adjusting the resolution of the harmonic cluster spectrum so that the resolution of the harmonic cluster spectrum is consistent with the resolution of the DEMON line spectrum obtained after the interpolation process. Optionally, before the harmonic calculation module adjusts the resolution of the harmonic cluster spectrum so that the resolution of the harmonic cluster spectrum is consistent with the resolution of the DEMON line spectrum obtained after the interpolation process, the method further includes: widening the upper and lower limits of the frequency range of the DEMON line spectrum by a specified multiple of the resolution.

[0199] In one embodiment, the matching module matches the DEMON line spectrum with a pre - constructed recognition template, and determines the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result, including: calculating the inner product of the DEMON line spectrum and the sequences in multiple recognition templates respectively to obtain a matching degree sequence corresponding to each recognition template; obtaining an integrity sequence corresponding to each recognition template according to the number and frequency position of the peak signals in the matching degree sequence; determining the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching degree sequence and the corresponding integrity sequence.

[0200] Optionally, the matching module obtains an integrity sequence corresponding to each recognition template according to the number and frequency position of the peak signals in the matching degree sequence, including: when the number of peak signals is not zero, obtaining the frequency position; when the frequency position and the resolution satisfy a multiple relationship, taking the frequency corresponding to the peak as the harmonic of the shaft frequency corresponding to the recognition template; when the frequency position and the resolution do not satisfy a multiple relationship, obtaining the distance between the peak and multiple multiples of the resolution, and taking the frequency corresponding to the peak with the closest distance as the harmonic of the shaft frequency corresponding to the recognition template; obtaining the integrity sequence according to the number of the matched harmonics.

[0201] In one embodiment, the shaft frequency processing module widens the frequency range corresponding to each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum, including: subtracting the resolution of the DEMON line spectrum from the frequency value of each element in the difference frequency array to obtain the lower limit of the frequency value of each element; adding the resolution of the DEMON line spectrum to the frequency value of each element in the difference frequency array to obtain the upper limit of the frequency value of each element.

[0202] In one embodiment, the shaft frequency processing module reduces the frequency interval between the difference frequencies in the difference frequency array according to the resolution of the DEMON line spectrum, including: obtaining a step size according to the resolution of the DEMON line spectrum; inserting elements between the difference frequencies in the difference frequency array based on the step size, so that the frequency interval of the difference frequency array after inserting the elements is reduced. In one embodiment, the element domain data collected by the receiving array is obtained; the element domain data is converted into synthetic wave data, and the synthetic wave data is demodulated; the Fourier transform is performed on the demodulated synthetic wave data to obtain the DEMON spectrum; the line spectrum in the DEMON spectrum is extracted to obtain the DEMON line spectrum and the line spectrum sequence.

[0203] Each module in the above shaft frequency extraction device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor in the computer device in the form of hardware or be independent of it, or can be stored in the memory in the computer device in the form of software, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0204] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 10 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, a shaft frequency extraction method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, etc. The input device of the computer device can be a touch layer covering the display screen, or can be a button, a trackball, or a touchpad set on the computer device housing, or can also be an external keyboard, a touchpad, or a mouse, etc.

[0205] Those skilled in the art can understand, Figure 10The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component arrangement.

[0206] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0207] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0208] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0209] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0210] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0211] The above-described embodiments merely represent several implementation manners of this application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application should be subject to the appended claims.

Claims

1. A shaft frequency extraction method, characterized in that, The method includes: Obtaining a DEMON line spectrum and a line spectrum sequence of the DEMON line spectrum from the array element domain data to be recognized, and obtaining a difference frequency array according to the frequency difference between every two line spectra in the line spectrum sequence; Broadening the frequency range corresponding to each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum, and reducing the frequency interval between each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum to obtain an axial frequency array; Calculating harmonics corresponding to different orders of each axial frequency in the axial frequency array to obtain a plurality of harmonic cluster spectra; Matching the DEMON line spectrum with a pre-constructed recognition template, and determining the axial frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result; wherein, the recognition template is constructed based on the harmonic cluster spectra.

2. The shaft frequency extraction method according to claim 1, wherein Before matching the DEMON line spectrum with the pre-constructed recognition template, the method further includes: Interpolating the DEMON line spectrum; Adjusting the resolution of the harmonic cluster spectra so that the resolution of the harmonic cluster spectra is consistent with the resolution of the DEMON line spectrum obtained after the interpolation process.

3. The shaft frequency extraction method according to claim 2, characterized in that, Before adjusting the resolution of the harmonic cluster spectra so that the resolution of the harmonic cluster spectra is consistent with the resolution of the DEMON line spectrum obtained after the interpolation process, the method further includes: Broadening the upper and lower limits of the frequency range of the DEMON line spectrum by a specified multiple of the resolution respectively.

4. The shaft frequency extraction method according to claim 1, wherein Matching the DEMON line spectrum with the recognition template, and determining the axial frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result, including: Calculating the inner products of the DEMON line spectrum and the sequences in a plurality of the recognition templates respectively to obtain a matching degree sequence corresponding to each recognition template; Obtaining an integrity sequence corresponding to each recognition template according to the number and frequency positions of the peak signals in the matching degree sequence; Determining the axial frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching degree sequence and the corresponding integrity sequence.

5. The shaft frequency extraction method according to claim 4, wherein Obtaining an integrity sequence corresponding to each recognition template according to the number and frequency positions of the peak signals in the matching degree sequence, including: When the number of the peak signals is not zero, obtaining the frequency position; When the frequency position and the resolution satisfy a multiple relationship, taking the frequency corresponding to the peak as the harmonic of the axial frequency corresponding to the recognition template; When the frequency position and the resolution do not satisfy a multiple relationship, obtaining the distances between the peak and multiple multiples of the resolution, and taking the frequency corresponding to the peak with the closest distance as the harmonic of the axial frequency corresponding to the recognition template; Obtaining the integrity sequence according to the number of the matched harmonics.

6. The shaft frequency extraction method according to claim 1, wherein Before matching the DEMON line spectrum with the pre-constructed recognition template, the method further includes: Performing convolution processing on the harmonic cluster spectra respectively according to signals conforming to a Gaussian distribution to obtain a plurality of the recognition templates.

7. The shaft frequency extraction method according to claim 1, characterized in that Broadening the frequency range corresponding to each difference frequency in the difference frequency array according to the resolution of the DEMON line spectrum, including: Subtract the resolution of the DEMON line spectrum from the frequency values of the elements in the difference frequency array to obtain the lower limit of the frequency values of the elements. Add the resolution of the DEMON line spectrum to the frequency values of the elements in the difference frequency array to obtain the upper limit of the frequency values of the elements.

8. The shaft frequency extraction method according to claim 1, characterized in that Reducing the frequency interval between the difference frequencies in the difference frequency array according to the resolution of the DEMON line spectrum includes: Obtaining a new frequency interval according to the resolution of the DEMON line spectrum; Inserting elements between the difference frequencies in the difference frequency array based on the new frequency interval so that the frequency interval of the difference frequency array after the elements are inserted is reduced.

9. The shaft frequency extraction method according to claim 1, characterized in that, The receiving array includes a sonar array, a subsurface buoy, a surface buoy, and a detection payload. The sonar array includes one or more of the following: a towed line array, a shore-based array, a conformal array, a planar array, and a bow array. Obtaining the DEMON line spectrum and the line spectrum sequence of the DEMON line spectrum according to the array element domain data to be recognized includes: Obtaining the array element domain data collected by the receiving array; Converting the array element domain data into composite wave data and demodulating the composite wave data; Performing a Fourier transform on the demodulated composite wave data to obtain a DEMON spectrum; Extracting the line spectrum in the DEMON spectrum to obtain the DEMON line spectrum and the line spectrum sequence.

10. The shaft frequency extraction method according to claim 1, wherein After matching the DEMON line spectrum with a pre-constructed recognition template and determining the shaft frequency and its harmonic sequence corresponding to the DEMON line spectrum according to the matching result, the shaft frequency extraction method further includes: Analyzing the propeller parameters corresponding to the array element domain data based on the shaft frequency and its harmonic sequence.

11. An axle frequency extraction system, characterized in that, The system includes: a receiving array and a processing device; the receiving array includes a sonar array, a subsurface buoy, a surface buoy, and a detection payload. The sonar array includes one or more of the following: a towed line array, a shore-based array, a conformal array, a planar array, and a bow array; wherein, The receiving array is used to obtain the array element domain data of the acquisition signal source; The processing device is used to implement the method according to any one of claims 1 to 10.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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