Aviation shaft frequency magnetic field signal processing method and system based on multi-node correlation superposition

Abstract

The invention provides an aviation shaft frequency magnetic field signal processing method and system based on multi-node correlation superposition, and the method comprises the steps: 1, completing the observation of a whole survey line shaft frequency magnetic field based on an aviation platform, carrying out the preprocessing of survey line data, and separating large-scale trend term interference from high-frequency electromagnetic interference; 2, splitting the complete measuring line data after the large-scale trend term interference and the high-frequency electromagnetic interference are separated into a plurality of data segments at equal intervals, wherein each data segment is a group of node sequences; 3, performing cross-correlation processing on the shaft frequency magnetic field observation data segments of the adjacent nodes to obtain a cross-correlation sequence; and 4, performing Fourier transform on the cross-correlation sequence to obtain cross-power spectrums, superposing the cross-power spectrums of adjacent nodes to obtain a noise-reduced shaft frequency line spectrogram, and detecting a target line spectrum. By applying the technical scheme of the invention, the technical problem of difficulty in line spectrum identification caused by serious environmental noise interference in underwater target axis frequency magnetic field detection in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical fields of geophysics and underwater target detection, and particularly to a method and system for processing airborne axial-frequency magnetic field signals based on multi-node correlation superposition. Background Art

[0002] When an underwater target sails in seawater, it will generate an extremely low-frequency alternating magnetic field through the rotation modulation system of the main shaft such as a propeller, which is called the axial-frequency magnetic field. The axial-frequency magnetic field has rich time-domain and frequency-domain information and is a unique physical characteristic of underwater moving targets. The magnetic detection method using the underwater target axial-frequency magnetic field as a signal source has gradually become a research frontier at home and abroad. In particular, the airborne axial-frequency magnetic field detection technology with high-precision magnetic field sensors mounted on unmanned aerial vehicle platforms has prominent characteristics such as fast operation speed, high positioning accuracy, and all-weather application, and is a frontier direction vigorously developed at present at home and abroad. In actual work, the detection effect of the axial-frequency magnetic field is affected and restricted by environmental noise interference. The axial-frequency magnetic field attenuates with the square of the distance. Therefore, when detecting at a long distance, the target signal is very weak, and it is necessary to suppress the environmental noise to extract the target signal. Existing signal processing means include spectrum analysis, adaptive filtering, high-order spectrum analysis, etc., which are mainly used for axial-frequency magnetic field data obtained by static observation methods such as buoys and submersible buoys. The observation platform and the target are in the same seawater medium, the distance between the two is relatively close, and the data signal-to-noise ratio is relatively high. Therefore, the axial-frequency magnetic field signal can be effectively identified and extracted. For airborne axial-frequency magnetic field detection, the observation platform and the target are farther away, the signal is weaker, and the noise interference is more complex, and the adaptability of conventional signal processing means is limited. Therefore, it is of great significance to study a method for processing axial-frequency magnetic field signals suitable for airborne high-speed moving platforms. Summary of the Invention

[0003] The present invention provides a method and system for processing airborne axial-frequency magnetic field signals based on multi-node correlation superposition, which can solve the technical problem of difficult line spectrum recognition caused by serious environmental noise interference in the detection of underwater target axial-frequency magnetic fields in the prior art.

[0004] According to one aspect of the present invention, there is provided a method for processing airborne axial-frequency magnetic field signals based on multi-node correlation superposition. The method for processing airborne axial-frequency magnetic field signals based on multi-node correlation superposition includes: Step 1, based on an airborne platform, complete the axial-frequency magnetic field observation of the entire survey line, preprocess the survey line data, and separate large-scale trend item interference and high-frequency electromagnetic interference; Step 2, split the complete survey line data after separating the large-scale trend item interference and high-frequency electromagnetic interference into multiple equally spaced data segments, and each data segment is a group of node sequences; Step 3, perform cross-correlation processing on the axial-frequency magnetic field observation data segments of adjacent nodes respectively to obtain a cross-correlation sequence; Step 4, perform Fourier transform on the cross-correlation sequence to obtain a cross-power spectrum, superimpose the cross-power spectra of adjacent nodes to obtain a denoised axial-frequency line spectrum diagram, and detect the target line spectrum.

[0005] Further, Step 1 specifically includes: performing empirical mode decomposition on the axial frequency magnetic field time series observed by the aerial platform along the entire survey line. Through empirical mode decomposition, the original survey line signal is decomposed into a finite number of intrinsic mode functions (IMFs) and a residual component (Res), representing signal components of different scales; the residual component (Res) and the last intrinsic mode function (IMF) represent the large-scale slow-varying components in the signal. Set them to zero, and use the remaining intrinsic mode functions (IMFs) to reconstruct the signal, that is, obtain the axial frequency magnetic field signal separated from the trend term interference; perform low-pass filtering on the axial frequency magnetic field signal separated from the trend term interference, retain the signal components below 50 Hz, and filter out the high-frequency electromagnetic interference above 50 Hz to obtain the axial frequency magnetic field signal separated from the trend term interference and high-frequency electromagnetic interference.

[0006] Further, Step 2 specifically includes: splitting the survey line data S(t) observed completely within time t into multiple equally spaced small data segments S 1 , S 2 , S 3 ,......, S N-1 , S N , and each small data segment is a node sequence S i = S(t i ~t i+1 ), i = 1~N. The axial frequency magnetic field signals in adjacent node sequences are similar. The meaning of S(t i ~t i+1 ) is to take the data within the corresponding time period (t i ~t i+1 ) in the data segment S.

[0007] Further, in Step 2, the time span Δt (i.e., t i+1 - t i ) of each data segment is determined by the minimum frequency resolution f 0 of the axial frequency line spectrum, that is, Δt = 1 / f 0 .

[0008] Further, in Step 3, perform cross-correlation processing on the observed data of adjacent nodes respectively, and the calculation formula is as follows: Among them, R i-1,i (j) is the cross-correlation sequence, the number of data points is 2m - 1, S i and S i+1Represent the observed data of two nodes respectively. The number of data points in each node sequence is m. k = 1, 2, 3,......, m is the serial number of the data points in the node sequence, i = 1, 2, 3,......, N is the overall serial number of the node sequence, and j = -m,......, -3, -2, -1, 0, 1, 2, 3,......, m is the serial number of the cross-correlation sequence data points.

[0009] Further, in step three, when the node sequence S i-1 is composed of the signal sequence X i-1 and the noise sequence N i-1 , and the node sequence S i is composed of the signal sequence X i and the noise sequence N i , its cross-correlation result is where is the cross-correlation operator.

[0010] Further, in step four, after obtaining the cross-correlation result R i,i+1 of the multi-node sequences, perform Fourier transform on it to obtain the power spectrum P i,i+1 of each section sequence: P i,i+1 = |fft(R i,i+1 )|, i = 1, 2, 3,......, N - 1, where fft() is the Fourier transform operator and || is the absolute value operator.

[0011] Further, in step four, perform superposition averaging on the power spectra of adjacent nodes. The calculation formula is as follows:

[0012] Further, in step four, perform superposition on the cross-power spectra of adjacent nodes to obtain the denoised shaft frequency line spectrum diagram. Extract the local maximum value in the line spectrum diagram and judge whether there is a multiple relationship between the corresponding frequency points, that is, whether the frequency points corresponding to the local maximum value satisfy f 0 , 2f 0 , 3f 0 ,......, nf 0 distribution law. If the above law is satisfied, it indicates that there is a shaft frequency line spectrum signal and the fundamental frequency is f 0 .

[0013] According to another aspect of the present invention, there is provided an aviation shaft frequency magnetic field signal processing system based on multi-node correlation superposition. The aviation shaft frequency magnetic field signal processing system based on multi-node correlation superposition uses the above-mentioned aviation shaft frequency magnetic field signal processing method based on multi-node correlation superposition to process the aviation shaft frequency magnetic field signal.

[0014] Applying the technical solution of the present invention, a method for processing an aviation shaft frequency magnetic field signal based on multi-node correlation superposition is provided. This method preprocesses the noisy time series, then draws the shaft frequency line spectrum diagram through time-frequency analysis, and then performs correlation superposition processing on the preprocessed shaft frequency line spectrum signal according to steps two to four. Finally, the shaft frequency line spectrum diagram is drawn. Through the correlation superposition processing, the environmental noise interference is suppressed, the shaft frequency line spectrum characteristics are more obvious, and the signal-to-noise ratio is improved. Therefore, compared with the prior art, the method for processing an aviation shaft frequency magnetic field signal based on multi-node correlation superposition provided by the present invention can improve the signal-to-noise ratio and effectively solve the technical problem that it is difficult to identify the line spectrum due to serious environmental noise interference in the existing underwater target shaft frequency magnetic field detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings included are used to provide a further understanding of the embodiments of the present invention, which form a part of the specification, are used to illustrate the embodiments of the present invention, and together with the text description are used to explain the principles of the present invention. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0016] Figure 1 FIG. shows a schematic structural diagram of a method for processing an aviation shaft frequency magnetic field signal based on multi-node correlation superposition provided according to a specific embodiment of the present invention;

[0017] Figure 2 FIG. shows a schematic diagram of a shaft frequency magnetic field signal generated by simulation provided according to a specific embodiment of the present invention;

[0018] Figure 3 FIG. shows a line spectrum diagram obtained only by preprocessing a noisy shaft frequency magnetic field signal provided according to a specific embodiment of the present invention;

[0019] Figure 4 FIG. shows a line spectrum diagram obtained by preprocessing and performing correlation superposition processing on a noisy shaft frequency magnetic field signal provided according to a specific embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The description of at least one exemplary embodiment below is actually only illustrative and in no way limits the present invention and its application or use. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts belong to the scope of protection of the present invention.

[0021] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0022] Unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present invention. At the same time, it should be understood that, for the sake of convenience of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationships. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and devices should be regarded as part of the authorization specification. In all the examples shown and discussed herein, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0023] As Figure 1As shown in the figure, according to a specific embodiment of the present invention, a method for processing an aviation shaft frequency magnetic field signal based on multi-node correlation superposition is provided. The method for processing an aviation shaft frequency magnetic field signal based on multi-node correlation superposition includes: Step 1, based on an aviation platform, complete the shaft frequency magnetic field observation of the entire survey line, preprocess the survey line data, and separate the large-scale trend term interference and high-frequency electromagnetic interference; Step 2, split the complete survey line data after separating the large-scale trend term interference and high-frequency electromagnetic interference into multiple equally spaced data segments, and each data segment is a set of node sequences; Step 3, perform cross-correlation processing on the shaft frequency magnetic field observation data segments of adjacent nodes respectively to obtain a cross-correlation sequence; Step 4, perform Fourier transform on the cross-correlation sequence to obtain a cross-power spectrum, superimpose the cross-power spectra of adjacent nodes to obtain a denoised shaft frequency line spectrum diagram, and detect the target line spectrum.

[0024] Applying this configuration method, a method for processing an aviation shaft frequency magnetic field signal based on multi-node correlation superposition is provided. This method preprocesses the noisy time series, then draws the shaft frequency line spectrum diagram through time-frequency analysis, and then performs correlation superposition processing on the preprocessed shaft frequency line spectrum signal according to Steps 2 to 4. Finally, the shaft frequency line spectrum diagram is drawn. Through the correlation superposition processing, the environmental noise interference is suppressed, the shaft frequency line spectrum characteristics are more obvious, and the signal-to-noise ratio is improved. Therefore, compared with the prior art, the method for processing an aviation shaft frequency magnetic field signal based on multi-node correlation superposition provided by the present invention can improve the signal-to-noise ratio and effectively solve the technical problem that it is difficult to identify the line spectrum due to serious environmental noise interference in the existing underwater target shaft frequency magnetic field detection.

[0025] In the present invention, Step 1 specifically includes: performing empirical mode decomposition on the shaft frequency magnetic field time series observed by the aviation platform on the entire survey line. Through empirical mode decomposition, the original survey line signal is decomposed into a finite number of intrinsic mode functions (IMFs) and a residual component (Res), representing signal components of different scales; the residual component (Res) and the last intrinsic mode function (IMF) represent the large-scale slow-varying components in the signal. Set them to zero, and use the remaining intrinsic mode functions (IMFs) to reconstruct the signal, that is, obtain the shaft frequency magnetic field signal separated from the trend term interference; perform low-pass filtering on the shaft frequency magnetic field signal separated from the trend term interference, retain the signal components below 50 Hz, and filter out the high-frequency electromagnetic interference above 50 Hz to obtain the shaft frequency magnetic field signal separated from the trend term interference and high-frequency electromagnetic interference.

[0026] Further, Step 2 specifically includes: splitting the survey line data S(t) observed completely within time t into multiple equally spaced small data segments S 1 , S 2 , S 3 ,......, S N-1 , S N , and each small data segment is a node sequence Si =S(t i ~t i+1 ), i = 1 to N. The shaft frequency magnetic field signals in adjacent node sequences have similarity. The meaning of S(t i ~t i+1 ) is to take the data in the corresponding time period (t i ~t i+1 ) in the data segment S.

[0027] Among them, in step two, the time span Δt of each data segment (i.e., t i+1 -t i ) is determined by the minimum frequency resolution f 0 , that is, Δt = 1 / f 0 .

[0028] Furthermore, in step three, cross-correlation processing is performed on the observed data of adjacent nodes respectively, and the calculation formula is as follows: Among them, R i-1,i (j) is the cross-correlation sequence, the number of data points is 2m - 1, S i and S i+1 represent the observed data of two nodes respectively. The number of data points in each node sequence is m, k = 1, 2, 3,..., m is the serial number of the data points in the node sequence, i = 1, 2, 3,..., N is the serial number of the whole node sequence, and j = -m,..., -3, -2, -1, 0, 1, 2, 3,..., m is the serial number of the data points in the cross-correlation sequence.

[0029] Among them, in step three, when the node sequence S i-1 is composed of the signal sequence X i-1 and the noise sequence N i-1 , and the node sequence S i is composed of the signal sequence X i and the noise sequence N i , its cross-correlation result is Among them is the cross-correlation operator.

[0030] Furthermore, in step four, after obtaining the cross-correlation result R i,i+1 of the multi-node sequence, perform Fourier transform on it to obtain the power spectrum P i,i+1 of each section sequence: P i,i+1 = |fft(R i,i+1 )|, i = 1, 2, 3,..., N - 1, where fft() is the Fourier transform operator and || is the absolute value operator.

[0031] Among them, in step four, the power spectra of adjacent nodes are superimposed and averaged, and the calculation formula is as follows: In step four, the cross-power spectra of adjacent nodes are superimposed to obtain the denoised shaft frequency line spectrum diagram. The local maxima in the line spectrum diagram are extracted, and it is determined whether there is a multiple relationship between the corresponding frequency points, that is, whether the frequency points corresponding to the local maxima satisfy f 0 , 2f 0 , 3f 0 ,......, nf 0 's distribution rule. If the above rule is satisfied, it indicates that there is a shaft frequency line spectrum signal, and the fundamental frequency is f 0 .

[0032] According to another aspect of the present invention, there is provided an airborne shaft frequency magnetic field signal processing system based on multi-node correlation superposition. The airborne shaft frequency magnetic field signal processing system based on multi-node correlation superposition uses the airborne shaft frequency magnetic field signal processing method based on multi-node correlation superposition as described above to process the airborne shaft frequency magnetic field signal.

[0033] Applying this configuration method, there is provided an airborne shaft frequency magnetic field signal processing system based on multi-node correlation superposition. The system preprocesses the noisy time series, then draws the shaft frequency line spectrum diagram through time-frequency analysis, and then performs correlation superposition processing on the preprocessed shaft frequency line spectrum signal according to steps two to four. Finally, the shaft frequency line spectrum diagram is drawn. Through the correlation superposition processing, the environmental noise interference is suppressed, the shaft frequency line spectrum characteristics are more obvious, and the signal-to-noise ratio is improved. Therefore, compared with the prior art, the airborne shaft frequency magnetic field signal processing system based on multi-node correlation superposition provided by the present invention can improve the signal-to-noise ratio and effectively solve the technical problem that it is difficult to identify the line spectrum due to serious environmental noise interference in the existing underwater target shaft frequency magnetic field detection.

[0034] For a further understanding of the present invention, the following combines Figures 1 to 4 to elaborate in detail on the airborne shaft frequency magnetic field signal processing method based on multi-node correlation superposition provided by the present invention.

[0035] As Figures 1 to 4 shown, according to a specific embodiment of the present invention, there is provided an airborne shaft frequency magnetic field signal processing method based on multi-node correlation superposition, which can solve the technical problem that it is difficult to identify the line spectrum due to serious environmental noise interference in the existing underwater target shaft frequency magnetic field detection. The method specifically includes the following steps:

[0036] (1) Preprocessing of survey line data

[0037] The multi-node correlation superposition method proposed in this paper is mainly used to process the random noise interference in the shaft frequency band. In actual work, the original data also contains low-frequency large-scale trend term interference and higher-frequency alternating electromagnetic interference, which will cause distortion to the correlation superposition processing. Therefore, it is necessary to first preprocess the shaft frequency magnetic field time series observed by the airborne platform on the entire survey line, including empirical mode decomposition and low-pass filtering. The low-frequency trend term interference is mainly the interference of the Earth's main magnetic field, local geological anomaly magnetic field, etc. superimposed on the shaft frequency signal. This interference scale is much larger than the shaft frequency signal and changes slowly with the spatial position. The original survey line signal is decomposed into a finite number of intrinsic mode functions (IMFs) and a residual component (Res) through empirical mode decomposition, representing signal components of different scales. The residual component (Res) and the last intrinsic mode function (IMF) represent the large-scale slow-varying components in the signal. Set them to zero, and then reconstruct the signal using the remaining intrinsic mode functions (IMFs) to obtain the shaft frequency magnetic field signal separated from the trend term interference. Then, low-pass filtering is performed on the shaft frequency magnetic field signal. The fundamental frequency of the shaft frequency magnetic field signal is related to the target motion speed and is mainly distributed below 7 Hz. At the same time, to accurately identify the shaft frequency line spectrum, about 4-7 groups of harmonic signals need to be obtained simultaneously. Therefore, the effective frequency band of the shaft frequency magnetic field signal should be 0-50 Hz. Perform 50 Hz low-pass filtering on the original data, retain the signal components below 50 Hz, and filter out the high-frequency electromagnetic interference above 50 Hz for subsequent processing.

[0038] (2) Node sequence construction

[0039] After the preprocessing in step one, the large-scale trend term and high-frequency electromagnetic interference have been separated, and the remaining interference is the environmental background noise within the shaft frequency signal band. To carry out the correlation superposition processing, first split the observed data of the entire survey line to construct a multi-node time series. Since the flight speed of the airborne platform is much greater than the underwater target motion speed, a complete survey line can be detected quickly in a short time. At this time, the target can be approximately regarded as stationary, and the shaft frequency signal in the original survey line changes slowly with the distance from the target and does not show sudden changes. Therefore, split the survey line data S(t) observed completely within time t into multiple equally spaced small data segments S 1 , S 2 , S 3 ,......, S N-1 , S N , and each small data segment is a node sequence S i = S(t i ~t i+1 ), i = 1~N. The shaft frequency magnetic field signals in adjacent node sequences are similar. The meaning of S(t i ~t i+1 ) is to take the corresponding time period (t i ~ti+1 ) The data within. In the above splitting, the time span Δt of each data segment (i.e., t i+1 -t i ) is determined by the minimum frequency resolution f of the shaft frequency line spectrum, that is, Δt = 1 / f 0 . For example, when the required frequency resolution of the shaft frequency line spectrum is 0.05 Hz, the length of each data segment should be greater than 20 s. 0

[0040] (3) Perform cross-correlation processing on adjacent node sequences

[0041] Perform cross-correlation processing on the observed data of adjacent nodes respectively, and the calculation formula is as follows:

[0042]

[0043] where i = 1, 2, 3,..., N is the node sequence number, S i and S i+1 represent the observed data of two nodes respectively, and m is the number of data points included in each node sequence. Perform cross-correlation processing on all node sequences S 1 , S 2 , S 3 ,......, S N-1 , S N in turn to obtain the cross-correlation sequences R 1,2 , R 2,3 , R 3,4 ,......, R N-1,N . When the node sequence S i-1 is composed of the signal sequence X i-1 and the noise sequence N i-1 , and S i is composed of the signal sequence X i and the noise sequence N i , its cross-correlation result is as follows:

[0044]

[0045] where is the cross-correlation operator. Since the ocean ambient noise is random and irregular, and the shaft frequency magnetic field signals in different node sequences are homologous signals, there is a strong correlation between shaft frequency signals, so the amplitude of the term increases; at the same time, the correlation between noises and between noise and signal is weak, so the amplitudes of the three terms decrease. Therefore, after cross-correlation processing, the noise is suppressed and the signal is further enhanced.

[0046] (4) Perform superposition averaging on the cross-correlation results

[0047] ​Obtain the multi-node sequence cross-correlation result After that, perform Fourier transform on it to obtain the power spectrum P of each section sequence i,i+1 :

[0048] P i,i+1 =|fft(R i,i+1 ), i = 1, 2, 3,......, N - 1

[0049] where fft() is the Fourier transform operator and || is the absolute value operator. Then perform superposition averaging on the power spectra of adjacent nodes, and the calculation formula is as follows:

[0050]

[0051] Since adjacent node series have strong correlation, there are the same shaft frequency line spectrum signals in adjacent power spectra, which are further enhanced after superposition; at the same time, due to the randomness of environmental noise interference, the line spectra in its power spectrum are scattered, and they are further weakened after superposition. Through the above processing, the signal-to-noise ratio of the shaft frequency line spectrum is further enhanced. Finally, extract the local maximum value in the line spectrum diagram and judge whether there is a multiple relationship between the corresponding frequency points, that is, whether the frequency points corresponding to the local maximum values satisfy f 0 , 2f 0 , 3f 0 ,......, nf 0 's distribution law. If the above law is satisfied, it indicates that there is a shaft frequency line spectrum signal, and the fundamental frequency is f 0 .

[0052] Illustrate the relevant superposition and noise reduction effect through simulation data. Simulate and generate a set of shaft frequency magnetic field time series, the flight speed of the aviation platform is 30m / s, the sampling rate is 200Hz, the shaft frequency magnetic field time series contains signal components with a fundamental frequency of 1Hz and multiple frequencies of 2Hz, 3Hz, 4Hz, 5Hz, 6Hz, 7Hz, 8Hz, 9Hz, 10Hz, and then add Gaussian random noise interference and low-frequency trend term interference to the shaft frequency magnetic field time series, as Figure 2 shown. First, preprocess the noisy time series according to Step 1 of this patent, and then draw the shaft frequency line spectrum diagram through time-frequency analysis, as Figure 3 shown. Then perform relevant superposition processing on the preprocessed shaft frequency line spectrum signal according to Step 2 to Step 4, and finally draw the shaft frequency line spectrum diagram, as Figure 4 shown. Through relevant superposition processing, the environmental noise interference is suppressed, the shaft frequency line spectrum characteristics are more obvious, and the signal-to-noise ratio is improved.

[0053] In summary, the present invention provides a method for processing the axial frequency magnetic field signal based on multi-node correlation superposition, including: completing the observation of the axial frequency magnetic field of the entire survey line based on an aviation platform, preprocessing the survey line data, and separating the large-scale trend term interference and high-frequency electromagnetic interference; splitting the complete survey line data into multiple equally spaced data segments, with each data segment being a set of node sequences; performing cross-correlation processing on the axial frequency magnetic field observation data of adjacent nodes to obtain a cross-correlation sequence; performing Fourier transform on the cross-correlation sequence to obtain a cross-power spectrum, superimposing the cross-power spectra of adjacent nodes to obtain a denoised axial frequency line spectrum diagram, and detecting the target line spectrum. The technical solution of the present invention is applied to solve the technical problem that it is difficult to identify line spectra due to serious environmental noise interference in the existing detection of the axial frequency magnetic field of underwater targets.

[0054] For ease of description, spatial relative terms such as "above", "over", "on the upper surface", "upper" etc. can be used here to describe the spatial position relationship between a device or feature shown in the figure and other devices or features. It should be understood that the spatial relative terms are intended to cover different orientations in use or operation in addition to the orientation described in the figure for the device. For example, if the device in the figure is inverted, the device described as "above" or "over" other devices or structures will then be positioned "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both the orientations of "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and corresponding interpretations should be made for the spatial relative descriptions used here.

[0055] In addition, it should be noted that the use of terms such as "first" and "second" to limit components is only for the convenience of distinguishing the corresponding components. Without further statement, the above terms have no special meaning, and thus should not be construed as limiting the protection scope of the present invention.

[0056] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for processing aviation shaft frequency magnetic field signals based on multi-node correlation superposition, characterized in that: The aviation axial frequency magnetic field signal processing method based on multi-node correlation superposition includes: Step 1: Complete the axial frequency magnetic field observation of the entire survey line based on the aviation platform, pre-process the survey line data, and separate the large-scale trend interference and high-frequency electromagnetic interference; Step 2: split the complete survey line data after separating the large-scale trend interference and high-frequency electromagnetic interference into multiple equally spaced data segments, each data segment being a set of node sequences; Step 3, cross-correlation processing is performed on the shaft frequency magnetic field observation data segments of adjacent nodes to obtain a cross-correlation sequence; Step 4: Perform Fourier transform on the cross-correlation sequence to obtain the cross-power spectrum, superimpose the cross-power spectra of adjacent nodes to obtain the denoised axial frequency line spectrum and detect the target line spectrum.

2. The aviation axial frequency magnetic field signal processing method based on multi-node correlation superposition according to claim 1 is characterized in that: The step 1 specifically includes: The empirical mode decomposition is performed on the axial frequency magnetic field time series observed by the aerial platform along the entire survey line. The original survey line signal is decomposed into a limited number of intrinsic mode functions (IMFs) and a residual component (Res) through empirical mode decomposition, which represents signal components of different scales. The residual component (Res) and the last intrinsic mode function (IMF) represent the large-scale slowly varying components in the signal. They are set to zero and the signal is reconstructed using the remaining intrinsic mode function (IMF), that is, the axial frequency magnetic field signal with the trend term interference separated is obtained. The shaft-frequency magnetic field signal with the trend term interference separated is low-pass filtered to retain the signal components below 50 Hz and filter out the high-frequency electromagnetic interference above 50 Hz, so as to obtain the shaft-frequency magnetic field signal with the trend term interference and the high-frequency electromagnetic interference separated.

3. The aviation axial frequency magnetic field signal processing method based on multi-node correlation superposition according to claim 2 is characterized in that: The second step specifically includes: splitting the complete observed line data within time t into multiple equally spaced small data segments S1, S2, S3, ..., S N-1 ,S N , each small data segment is a node sequence S i =S(t i ~t i+1 ), i = 1 ~ N, the axial frequency magnetic field signals in the adjacent node sequence have similarity, S(t i ~t i+1 ) means to take the corresponding time period (t i ~t i+1 ) in the data.

4. The aviation axial frequency magnetic field signal processing method based on multi-node correlation superposition according to claim 3 is characterized in that: In step 2, the time span Δt (i.e., t i+1 -t i ) is determined by the minimum frequency resolution f0 of the axial frequency line spectrum, that is, Δt = 1 / f0.

5. The aviation axial frequency magnetic field signal processing method based on multi-node correlation superposition according to claim 3 is characterized in that: In step 3, cross-correlation processing is performed on the observation data of adjacent nodes respectively, and the calculation formula is as follows: Among them, R i-1,i (j) is the cross-correlation sequence, the number of data points is 2m-1, S i and S i+1 They represent the observation data of two nodes respectively. The number of data points in each node sequence is m. k=1,2,3,......,m is the sequence number of the node sequence data point. i=1,2,3,......,N is the sequence number of the entire node sequence. j=-m,......,-3,-2,-1,0,1,2,3,......,m is the sequence number of the cross-correlation sequence data point.

6. The aviation axial frequency magnetic field signal processing method based on multi-node correlation superposition according to claim 3 is characterized in that: In step 3, when the node sequence S i-1 By signal sequence X i-1 and the noise sequence N i-1 Composition, node sequence S i By signal sequence X i and the noise sequence N i When composed, the cross-correlation result is in is the cross-correlation operator.

7. The aviation axial frequency magnetic field signal processing method based on multi-node correlation superposition according to claim 6 is characterized in that: In step 4, the multi-node sequence cross-correlation result R is obtained. i,i+1 After that, Fourier transform is performed to obtain the power spectrum P of each section sequence i,i+1 :P i,i+1 =|fft(R i,i+1 )|,i=1,2,3,......,N-1, where fft() is the Fourier transform operator and | | is the absolute value operator.

8. The aviation axial frequency magnetic field signal processing method based on multi-node correlation superposition according to claim 7 is characterized in that: In step 4, the power spectra of adjacent nodes are superimposed and averaged, and the calculation formula is as follows: i=2,3,4,......,N-1.

9. The aviation axial frequency magnetic field signal processing method based on multi-node correlation superposition according to claim 8 is characterized in that: In the step four, the mutual power spectra of adjacent nodes are superimposed to obtain the axial frequency line spectrum after noise reduction, and the local maximum in the line spectrum is extracted to determine whether there is a multiple relationship between the corresponding frequency points, that is, whether the frequency points corresponding to the local maximum satisfy the distribution law of f0, 2f0, 3f0, ..., nf0. If the above law is satisfied, it means that there is an axial frequency line spectrum signal with a fundamental frequency of f0.

10. An aviation axial frequency magnetic field signal processing system based on multi-node correlation superposition, characterized in that: The aviation shaft-frequency magnetic field signal processing system based on multi-node correlation superposition uses the aviation shaft-frequency magnetic field signal processing method based on multi-node correlation superposition as described in any one of claims 1 to 9 to perform aviation shaft-frequency magnetic field signal processing.

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