CN115859044A - Magnetic anomaly target detection method combining singular spectrum analysis and orthogonal base method - Google Patents
Magnetic anomaly target detection method combining singular spectrum analysis and orthogonal base method Download PDFInfo
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Abstract
The invention provides a magnetic anomaly target detection method, a device, equipment and a storage medium combining singular spectrum analysis and an orthogonal base method, wherein the method comprises the following steps: acquiring magnetic measurement data to be detected, and preprocessing the magnetic measurement data to be detected; based on a singular spectrum analysis method, carrying out noise reduction processing on the preprocessed magnetic measurement data to be detected to obtain noise reduction signal data; and carrying out abnormal signal detection on the noise reduction signal data based on an orthogonal basis detection method. The method comprises the steps of firstly converting one-dimensional measurement data into two-dimensional data by using a singular spectrum analysis method to construct a track matrix, then carrying out singular value decomposition on the data, reconstructing a high signal-to-noise ratio signal by selecting proper data components, and then judging whether the data to be detected contains a target abnormal signal by using an orthogonal basis detection algorithm; therefore, the magnetic anomaly signal detection capability under low signal-to-noise ratio is greatly improved.
Description
Technical Field
The invention relates to the technical field of aviation magnetic detection, in particular to a magnetic anomaly target detection method, a magnetic anomaly target detection device, magnetic anomaly target detection equipment and a magnetic anomaly target storage medium, wherein the magnetic anomaly target detection method is realized by combining singular spectrum analysis and an orthogonal basis method.
Background
The magnetic abnormal signal detection method is used for extracting weak target magnetic signals from complex background magnetic noise so as to find ferromagnetic targets, and is widely applied to the fields of resource exploration, sunken ship salvaging, detection of non-explosive substances and the like. In practical applications, magnetic anomaly signal detection faces two difficulties: firstly, the magnetic noise of the background environment is complex, and in the detection of a static platform, the magnetic sensor is mainly influenced by the magnetic interference of the surrounding environment such as the human magnetic interference and the magnetic interference of the environment such as the diurnal variation; in the aviation platform detection, the magnetic interference of the platform and the geological magnetic interference are also received. These magnetic disturbances are colored noises, which not only destroy the target magnetic anomaly signal in terms of waveform, but also have a larger amplitude than the target magnetic anomaly signal. Secondly, according to physical knowledge, the amplitude of the magnetic signal generated by the target is in a 3-power attenuation relation with the increase of the distance between the target and the measuring sensor, so that the target signal is rapidly reduced along with the increase of the distance and is submerged in noise. The orthogonal basis function method can lead out a magnetic signal generated by a target into a product sum form of three orthogonal basis functions and related coefficients thereof according to a magnetic dipole magnetic field formula, and a detector is formed by a coefficient square sum obtained by matching the orthogonal basis functions with actually measured magnetic data, so that the orthogonal basis functions and the actually measured magnetic data are commonly used for target signal detection. However, the detection capability is reduced due to the complicated magnetic interference noise, so that a noise suppression algorithm needs to be introduced to improve the detection capability.
Disclosure of Invention
The invention aims to provide a magnetic anomaly target detection method, a magnetic anomaly target detection device, magnetic anomaly target detection equipment and a magnetic anomaly target detection storage medium which are combined by a singular spectrum analysis method and an orthogonal base method, so that the technical problems are solved, and the magnetic anomaly signal detection capability under a complex magnetic interference noise environment can be effectively improved.
In order to solve the technical problem, the invention provides a magnetic anomaly target detection method combining singular spectrum analysis and an orthogonal basis method, which comprises the following steps:
acquiring magnetic measurement data to be detected, and preprocessing the magnetic measurement data to be detected;
based on a singular spectrum analysis method, carrying out noise reduction processing on the preprocessed magnetic measurement data to be detected to obtain noise reduction signal data;
and carrying out abnormal signal detection on the noise reduction signal data based on an orthogonal basis detection method.
Further, the acquiring of the data of magnetic measurement to be detected is to preprocess the data of magnetic measurement to be detected, and includes:
and acquiring magnetic measurement data to be detected, and carrying out high-pass filtering processing on the magnetic measurement data to be detected by utilizing a preset filter.
Further, the preset filter is a 4-order high-pass butterworth filter, and the cutoff frequency of the 4-order high-pass butterworth filter is set to be 0.025Hz.
Further, the denoising processing is performed on the preprocessed to-be-detected magnetic measurement data based on the singular spectrum analysis method to obtain denoising signal data, and the denoising signal data includes:
rearranging the to-be-detected magnetic measurement data in a sliding window mode based on the length of a pre-selected data window to construct a track matrix;
singular value decomposition is carried out on the track matrix to obtain singular values corresponding to the track matrix and corresponding eigenvectors;
constructing based on each decomposed singular value and the corresponding eigenvector to obtain a matrix component of the track matrix;
and classifying all matrix components of the track matrix by adopting a preset clustering algorithm based on the distribution condition of the singular values, selecting the matrix components of the target category according to a preset strategy, and reconstructing based on the matrix components of the target category to obtain the noise reduction signal data.
Further, the classifying all matrix components of the trajectory matrix by using a preset clustering algorithm based on the distribution of the singular values, selecting a matrix component of a target category according to a preset strategy, and reconstructing based on the matrix component of the target category to obtain the noise reduction signal data specifically includes:
based on the distribution condition of the singular values, adopting a preset clustering algorithm to divide all matrix components of the track matrix into three categories;
and selecting the matrix component of the target category from which the low-frequency component and the high-frequency noise are removed according to a preset strategy, and reconstructing based on the matrix component of the target category to obtain the noise reduction signal data.
Further, the performing abnormal signal detection on the noise reduction signal data based on the orthogonal basis detection method includes:
constructing an orthogonal basis detection function, and matching the noise reduction signal data by using the orthogonal basis function to obtain a matching coefficient;
determining an energy function based on the matching coefficients;
determining an energy threshold value based on energy corresponding to a preset historical time period before the current time;
and solving the energy of the noise reduction signal data by using the energy function, and determining whether the target abnormal signal exists in the magnetic measurement data to be detected based on the comparison result of the energy of the noise reduction signal data and the energy threshold.
Further, the determining whether the target abnormal signal exists in the to-be-detected magnetic measurement data based on the comparison result of the energy of the noise reduction signal data and the energy threshold specifically includes:
if the energy of the noise reduction signal data is larger than the energy threshold value and the energy of the noise reduction signal data reaches a peak point, determining that a target abnormal signal exists in the magnetic measurement data to be detected at the current moment;
and if the energy of the noise reduction signal data is not greater than the energy threshold value or the energy of the noise reduction signal data does not reach the peak value point, determining that no target abnormal signal exists in the magnetic measurement data to be detected at the current moment.
The invention also provides a magnetic anomaly target detection device combining the singular spectrum analysis and the orthogonal base method, which comprises the following steps:
the preprocessing module is used for acquiring the magnetic measurement data to be detected and preprocessing the magnetic measurement data to be detected;
the noise reduction module is used for carrying out noise reduction processing on the preprocessed magnetic measurement data to be detected based on a singular spectrum analysis method to obtain noise reduction signal data;
and the detection module is used for detecting abnormal signals of the noise reduction signal data based on an orthogonal basis detection method.
The invention also provides a terminal device, which comprises a processor and a memory for storing a computer program, wherein the processor realizes the magnetic anomaly target detection method combining the singular spectrum analysis and the orthogonal base method when executing the computer program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the magnetic anomaly target detection method combining the singular spectrum analysis and the orthogonal basis method of any one of the above.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a magnetic anomaly target detection method, a device, equipment and a storage medium combining singular spectrum analysis and an orthogonal base method, wherein the method comprises the following steps: acquiring magnetic measurement data to be detected, and preprocessing the magnetic measurement data to be detected; based on a singular spectrum analysis method, carrying out noise reduction processing on the preprocessed magnetic measurement data to be detected to obtain noise reduction signal data; and carrying out abnormal signal detection on the noise reduction signal data based on an orthogonal basis detection method. The method comprises the steps of firstly converting one-dimensional measurement data into two-dimensional data by using a singular spectrum analysis method to construct a track matrix, then carrying out singular value decomposition on the data, reconstructing a high signal-to-noise ratio signal by selecting proper data components, and then judging whether the data to be detected contains a target abnormal signal by using an orthogonal basis detection algorithm; therefore, the magnetic abnormal signal detection capability under low signal-to-noise ratio is greatly improved.
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FIG. 1 is a schematic flow chart of a magnetic anomaly target detection method combining singular spectrum analysis and an orthogonal basis method provided by the present invention;
FIG. 2 is a second schematic flow chart of the magnetic anomaly target detection method combining the singular spectrum analysis and the orthogonal base method provided by the present invention;
fig. 3 is a schematic structural diagram of a magnetic anomaly target detection device combining singular spectrum analysis and an orthogonal basis method provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a magnetic anomaly target detection method combining singular spectrum analysis and an orthogonal basis method, which may include the steps of:
s1, acquiring magnetic measurement data to be detected, and preprocessing the magnetic measurement data to be detected;
s2, noise reduction processing is carried out on the preprocessed magnetic measurement data to be detected based on a singular spectrum analysis method, and noise reduction signal data are obtained;
and S3, carrying out abnormal signal detection on the noise reduction signal data based on an orthogonal basis detection method.
In the embodiment of the present invention, step S1 specifically includes:
and acquiring magnetic measurement data to be detected, and carrying out high-pass filtering processing on the magnetic measurement data to be detected by utilizing a preset filter.
In the embodiment of the present invention, further, the preset filter is a 4 th-order high-pass butterworth filter, and a cutoff frequency of the 4 th-order high-pass butterworth filter is set to 0.025Hz.
In the embodiment of the present invention, step S2 specifically includes:
rearranging the to-be-detected magnetic measurement data in a sliding window mode based on the length of a pre-selected data window to construct a track matrix;
singular value decomposition is carried out on the track matrix to obtain singular values corresponding to the track matrix and corresponding eigenvectors;
constructing to obtain a matrix component of the track matrix based on each decomposed singular value and the corresponding eigenvector;
and classifying all matrix components of the track matrix by adopting a preset clustering algorithm based on the distribution condition of the singular values, selecting the matrix components of the target category according to a preset strategy, and reconstructing based on the matrix components of the target category to obtain the noise reduction signal data.
In this embodiment of the present invention, further, the classifying all matrix components of the trajectory matrix by using a preset clustering algorithm based on the distribution of the singular values, selecting a matrix component of a target category according to a preset strategy, and reconstructing based on the matrix component of the target category to obtain the noise reduction signal data specifically includes:
based on the distribution condition of the singular values, adopting a preset clustering algorithm to divide all matrix components of the track matrix into three categories;
and selecting the matrix component of the target category from which the low-frequency component and the high-frequency noise are removed according to a preset strategy, and reconstructing based on the matrix component of the target category to obtain the noise reduction signal data.
In the embodiment of the present invention, step S3 specifically includes:
constructing an orthogonal basis detection function, and matching the noise reduction signal data by using the orthogonal basis function to obtain a matching coefficient;
determining an energy function based on the matching coefficients;
determining an energy threshold value based on energy corresponding to a preset historical time period before the current time;
and solving the energy of the noise reduction signal data by using the energy function, and determining whether the target abnormal signal exists in the magnetic measurement data to be detected based on the comparison result of the energy of the noise reduction signal data and the energy threshold.
In this embodiment of the present invention, further, the determining whether the target abnormal signal exists in the to-be-detected magnetic measurement data based on the comparison result between the energy of the noise reduction signal data and the energy threshold specifically includes:
if the energy of the noise reduction signal data is larger than the energy threshold value and the energy of the noise reduction signal data reaches a peak point, determining that a target abnormal signal exists in the magnetic measurement data to be detected at the current moment;
and if the energy of the noise reduction signal data is not larger than the energy threshold value or the energy of the noise reduction signal data does not reach a peak value point, determining that the target abnormal signal does not exist in the to-be-detected magnetic measurement data at the current moment.
Based on the above scheme, in order to better understand the magnetic anomaly target detection method combining the singular spectrum analysis and the orthogonal basis method provided by the embodiment of the present invention, the following detailed description is made:
it should be noted that, in the embodiment of the present invention, first, an appropriate window length is selected for data to be measured, then, a one-dimensional measurement signal is converted into a two-dimensional trajectory matrix in a sliding window manner, then, the matrix is decomposed into singular values and eigenvector forms through singular value analysis, and appropriate singular values are selected to reconstruct a measurement signal with a high signal-to-noise ratio through analyzing a distribution rule of the singular values; and then, detecting the reconstructed signal by using an orthogonal basis method, and further judging whether the measurement data contains a target abnormal signal.
The embodiment of the invention can be realized by the following steps:
1) Loading magnetic measurement data to be detected, and preprocessing the data;
2) Acquiring data containing a target signal after denoising by using a singular spectrum analysis method;
3) And determining whether the de-noised data contains the target signal by using an orthogonal basis detection method.
As described in detail below.
Firstly, the magnetic measurement data to be detected is loaded, and for the real-time measurement data, a segmented detection method can be adopted, but it should be noted that the length of the segment should be larger than that of the signal to be detected. Then, the measurement data is subjected to filtering processing using a high-pass filter.
Next, the singular spectrum analysis method is used to process the measurement data, and the specific steps are as follows:
(1) Suppose B m For measuring data, let B m ＝[x 1 ,x 2 ,…,x N ]And N is the sequence length. Firstly, selecting proper window length L, and B m And rearranging to obtain a track matrix X.
Typically, L < N/2 is taken. Let K = N-L +1, then x can be expressed as:
(2) Singular Value Decomposition (SVD) is performed on X. First the covariance matrix of x is calculated:
S＝XX T (3)
then, the characteristic value of S is decomposed to obtain the characteristic value lambda 1 ,λ 2 …λ L And corresponding feature vector delta 1 ,δ 2 ,…,δ L Here, the eigenvalues and the corresponding eigenvectors are arranged in the order of the eigenvalues from large to small. Let δ = [ δ = 1 ,δ 2 ,…,δ L ],
At this time, it can beMth component of trajectory matrix x:
the formula is simplified to obtain:
wherein, the first and the second end of the pipe are connected with each other,
and representing a subspace formed by the feature vectors, and projecting x to the subspace to obtain the mth component of the trajectory matrix x, wherein the L components can be obtained by the steps.
(3) The above L components are grouped into c groups. Let O = [ g = 1 ,g 2 ,…,g u ]The method is to select u singular values obtained by decomposition and perform combined reconstruction. The trajectory matrix X of the O group O Can be expressed as:
the original trajectory matrix X can be represented as:
in the embodiment of the invention, the obtained singular values are classified by adopting a clustering method, the singular values correspond to the characteristic components thereof one by one, the distribution condition of the singular values represents the characteristics of the characteristic components to a certain extent, and all the singular values are classified according to the size and the dispersion condition of the singular values. By analysis, the measured data contains signals that can be classified into 3 categories, low frequency signals that characterize trends, high frequency portions that characterize near-gaussian noise, and intermediate portions that are interspersed with signals. The low-frequency component has a large singular value and the gaussian noise has a small singular value. Therefore, the obtained singular values are decomposed into 3 types by adopting a clustering method, and low-frequency and high-frequency components are removed.
(4) And converting the new track matrix obtained in the previous step into a new sequence with the length of N again. Let Y be an L × K matrix, where Y ij For the elements in the ith row and jth column in the matrix, let l min ＝min{L,K}，l max = max { L, K }, Y can be converted to
The one-dimensional time series of (1) is as follows: />
By this, a new noise reduction sequence Y (noise reduction signal data) in which most of the noise has been removed can be obtained.
Next, performing signal detection on the denoised quasi-target data by using an orthogonal basis detection method, specifically comprising the following steps:
(1) The orthogonal basis function detection method is a detection method comprising 3 orthogonal basis functions obtained by deducing a magnetic dipole theory formula, wherein the 3 basis functions are orthogonal and normalized, so that the orthogonal basis function detection method has high Gaussian white noise resistance. The magnetic signal formula derived from the magnetic dipole field formula can be written as:
wherein:
wherein, the first and the second end of the pipe are connected with each other,
according to the orthogonal nature of the basis functions, coefficient a j J =1,2,3 can be obtained by the following formula:
in actual detection, data collected by the magnetometer is discrete data, so that a continuous integral form of a formula should be transformed into a discrete summation form, and the expression is as follows:
where Δ w represents the length of the spatial sample. k represents the half window length, typically taken to be 3. In the detection process, a point-by-point detection method can be adopted, and m points represent the current detection point.
(2) An energy function is set for constructing the detector.
(3) And determining a threshold value for judging whether the target is contained, wherein the selection of the threshold value is usually a constant false alarm strategy in practical application and is set by utilizing a fixed multiple of the energy summation of the previous section of the current data.
In the above formula, m represents the current value, ξ represents the interval, and generally takes half the signal width; p is half the signal width and η represents a multiple greater than the noise energy, which determines the false alarm rate and the false alarm rate.
When E (m) > T res old (m) and E (m) is the peak point, it can be determined that the suspected target signal exists at this time.
The technical scheme of the embodiment of the invention can be realized by the following steps:
1. carrying out high-pass filtering processing on the measurement data to remove out-of-band low-frequency signals, wherein the adopted filter is a 4-order high-pass Butterworth filter, and the cut-off frequency is set to be 0.025Hz;
2. performing singular spectrum analysis on the measurement data, specifically comprising:
2A, firstly, selecting a proper data length, and constructing a track matrix X in a sliding window mode;
let B m For measuring data, let B m ＝[x 1 ,x 2 ,…,x N ]N is the sequence length and L is the selected window length.
2B, carrying out singular value decomposition on the track matrix X to obtain corresponding singular values and corresponding eigenvectors;
S＝XX T formula 2
[λ,δ]＝eig(XX T ) Formula 3
In the above equation, eig represents eigenvalue decomposition.
2C, constructing a new track matrix by using each decomposed singular value and the corresponding eigenvector, namely using the new track matrix as a component of the track matrix X;
λ＝[λ 1 ,λ 2 …λ L ]formula 4
δ＝[δ 1 ,δ 2 ,…,δ L ]Formula 5
Then it is possible to obtain:
wherein the content of the first and second substances,
and representing a subspace formed by the characteristic vectors, and projecting X to the subspace to obtain the mth component of the track matrix X, wherein L components can be obtained by the steps.
And 2D, classifying the L components by using a clustering algorithm according to the singular value distribution condition, selecting a proper class, and constructing a new track matrix.
Selecting proper category to reconstruct data, and enabling o = [ g = 1 ,g 2 ,…,g i ]And u singular values in the decomposed singular values are selected for combined reconstruction. O sets of trajectory matrix X O Can be expressed as:
and 2E, reconstructing the data Y after noise reduction by using the new track matrix.
Let Y be an L × K matrix, where Y ij For the elements in the ith row and jth column in the matrix, let l min ＝min{L,K}，l max = max { L, K }, Y can be converted to
The one-dimensional time series of (2) is obtained.
3. Detecting and identifying the denoised data by using an orthogonal basis detection algorithm, and specifically operating the following steps:
3A, constructing an orthogonal basis detection function;
selecting proper characteristic time tau value, constructing orthogonal base function with length set to 6 tau.f s Wherein f is s Is the sampling rate.
3B, utilizing the orthogonal basis function to perform denoising on the measurement data B s Matching to obtain corresponding matching coefficients;
3C, solving an energy function by using the matching coefficient;
determining a threshold value by using energy in a period of time before current data;
3E, judging whether a target signal is contained or not;
when E (m) > T res old (m) and E (m) is the peak point, it can be determined that the suspected target signal exists at this time.
Referring to fig. 2, the working process and principle of the present invention are illustrated by the following specific embodiments:
1. firstly, for the measured data B m High-pass filtering is performed, and the highpass filter is set to represent a high-pass filter which is a 4-order high-pass butterworth filter with a low cut-off frequency of 0.025Hz.
B filter ＝highpassfilter(B m )
2. Data B filter Is represented by N, B filter ＝[x 1 ，x 2 ，…，x N ]If the length L = N/2 of the sliding window is selected, a constructed track matrix is shown as the following formula;
3. singular value decomposition is carried out on the track matrix, firstly, a covariance matrix S of the track matrix is obtained, then, eigenvalue decomposition is carried out on the covariance matrix, and a plurality of eigenvalues (namely singular values) and eigenvectors are obtained;
s＝XX T
[λ，δ]＝eig(XX T )
4. dividing the singular values into 3 classes by using a clustering method, and selecting the 2 nd class to reconstruct the data to obtain a new track matrix; let o = [ g ] 1 ,g 2 ,…,g u ]And u singular values in the decomposed singular values are selected for combined reconstruction. The trajectory matrix X of the O group O Can be expressed as:
5. the section of denoised data is obtained by adding diagonals and using the new track matrix
/>
6. Constructing a basis function by using a formula, setting a proper characteristic time tau value, and setting the length of the basis function as 6. Tau. F s Wherein f is s Is the sampling rate.
7. Using the 3 orthogonal basis functions to denoise the measurement data B s Matching to obtain corresponding coefficients;
in the above formula, k =3,m represents the value of the current time, and all coefficients of the piece of data can be obtained by point-by-point sliding.
8. Constructing an energy function by using the coefficient obtained by matching;
9. determining a threshold value by using data in front of the current moment, wherein eta represents a threshold value multiple;
10. judging whether the signal is contained:
compared with the prior art, the embodiment of the invention has the following beneficial effects:
aiming at the problem that when magnetic abnormal signals are detected, measurement data usually contain human magnetic interference, daily magnetic noise and residual magnetic noise after compensation of a magnetic sensor carrying platform, and target signals cannot be effectively found by directly adopting the existing detection method. The embodiment of the invention provides a method for removing noise by using a singular spectrum analysis method aiming at nonlinear non-Gaussian signal decomposition, improving the detection signal-to-noise ratio, and then detecting the de-noised data by using an orthogonal basis detection method, thereby judging whether the measured data contains a target signal. The invention effectively improves the detection capability of the magnetic abnormal signal under low signal-to-noise ratio.
It should be noted that the above method or flow embodiment is described as a series of acts or combinations for simplicity, but those skilled in the art should understand that the present invention is not limited by the described acts or sequences, as some steps may be performed in other sequences or simultaneously according to the present invention. Further, those of skill in the art will appreciate that the embodiments described in the specification are exemplary embodiments and that no acts are necessarily required of the embodiments of the invention.
Referring to fig. 3, an embodiment of the present invention further provides a magnetic anomaly target detection apparatus combining singular spectrum analysis and an orthogonal basis method, including:
the preprocessing module 1 is used for acquiring the magnetic measurement data to be detected and preprocessing the magnetic measurement data to be detected;
the noise reduction module 2 is used for carrying out noise reduction processing on the preprocessed to-be-detected magnetic measurement data based on a singular spectrum analysis method to obtain noise reduction signal data;
and the detection module 3 is used for detecting abnormal signals of the noise reduction signal data based on an orthogonal basis detection method.
Further, the preprocessing module 1 is specifically configured to:
and acquiring magnetic measurement data to be detected, and carrying out high-pass filtering processing on the magnetic measurement data to be detected by utilizing a preset filter.
Further, the preset filter is a 4-order high-pass butterworth filter, and the cutoff frequency of the 4-order high-pass butterworth filter is set to be 0.025Hz.
Further, the noise reduction module 2 is specifically configured to:
rearranging the to-be-detected magnetic measurement data in a sliding window mode based on the length of a pre-selected data window to construct a track matrix;
singular value decomposition is carried out on the track matrix to obtain singular values corresponding to the track matrix and corresponding eigenvectors;
constructing based on each decomposed singular value and the corresponding eigenvector to obtain a matrix component of the track matrix;
and classifying all matrix components of the track matrix by adopting a preset clustering algorithm based on the distribution condition of the singular values, selecting the matrix components of the target category according to a preset strategy, and reconstructing based on the matrix components of the target category to obtain the noise reduction signal data.
Further, the noise reduction module 2 is specifically further configured to:
based on the distribution condition of the singular values, adopting a preset clustering algorithm to divide all matrix components of the track matrix into three categories;
and selecting the matrix component of the target category from which the low-frequency component and the high-frequency noise are removed according to a preset strategy, and reconstructing based on the matrix component of the target category to obtain the noise reduction signal data.
Further, the detection module 3 is specifically configured to:
constructing an orthogonal basis detection function, and matching the noise reduction signal data by using the orthogonal basis function to obtain a matching coefficient;
determining an energy function based on the matching coefficients;
determining an energy threshold value based on energy corresponding to a preset historical time period before the current time;
and solving the energy of the noise reduction signal data by using the energy function, and determining whether the target abnormal signal exists in the magnetic measurement data to be detected based on the comparison result of the energy of the noise reduction signal data and the energy threshold.
Further, the detection module 3 is specifically further configured to:
if the energy of the noise reduction signal data is larger than the energy threshold value and the energy of the noise reduction signal data reaches a peak point, determining that a target abnormal signal exists in the magnetic measurement data to be detected at the current moment;
and if the energy of the noise reduction signal data is not greater than the energy threshold value or the energy of the noise reduction signal data does not reach the peak value point, determining that no target abnormal signal exists in the magnetic measurement data to be detected at the current moment.
It can be understood that the above-mentioned apparatus item embodiments correspond to the method item embodiments of the present invention, and the magnetic anomaly target detection apparatus combining the singular spectrum analysis and the orthogonal basis method provided in the embodiments of the present invention can implement the magnetic anomaly target detection method combining the singular spectrum analysis and the orthogonal basis method provided in any one of the method item embodiments of the present invention.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the magnetic anomaly target detection method combining the singular spectrum analysis and the orthogonal basis method of any one of the above.
It should be noted that the above-described embodiments of the apparatus are merely illustrative, where the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement without inventive effort.
It can be clearly understood by those skilled in the art that, for convenience and brevity, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
The terminal device may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor, a memory.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device and connects the various parts of the whole terminal device using various interfaces and lines.
The memory may be used to store the computer program, and the processor may implement various functions of the terminal device by executing or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The storage medium is a computer-readable storage medium, in which the computer program is stored, which computer program, when being executed by a processor, is adapted to carry out the steps of the above-mentioned respective method embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, and software distribution medium, etc. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A magnetic anomaly target detection method combining singular spectrum analysis and an orthogonal basis method is characterized by comprising the following steps:
acquiring magnetic measurement data to be detected, and preprocessing the magnetic measurement data to be detected;
based on a singular spectrum analysis method, carrying out noise reduction processing on the preprocessed magnetic measurement data to be detected to obtain noise reduction signal data;
and carrying out abnormal signal detection on the noise reduction signal data based on an orthogonal basis detection method.
2. The singular spectrum analysis and orthogonal basis method combined magnetic anomaly target detection method according to claim 1, wherein the acquiring of the magnetic measurement data to be detected and the preprocessing of the magnetic measurement data to be detected comprise:
and acquiring the magnetic measurement data to be detected, and performing high-pass filtering processing on the magnetic measurement data to be detected by using a preset filter.
3. The singular spectrum analysis and orthogonal basis method combined magnetic anomaly target detection method according to claim 2, wherein said preset filter is a 4 th order high-pass butterworth filter, and a cutoff frequency of said 4 th order high-pass butterworth filter is set to 0.025Hz.
4. The singular spectrum analysis and orthogonal basis method combined magnetic anomaly target detection method as claimed in claim 1, wherein the singular spectrum analysis method is used for performing noise reduction processing on preprocessed to-be-detected magnetic measurement data to obtain noise reduction signal data, and the method comprises the following steps:
rearranging the to-be-detected magnetic measurement data in a sliding window mode based on the length of a pre-selected data window to construct a track matrix;
singular value decomposition is carried out on the track matrix to obtain singular values corresponding to the track matrix and corresponding eigenvectors;
constructing based on each decomposed singular value and the corresponding eigenvector to obtain a matrix component of the track matrix;
and classifying all matrix components of the track matrix by adopting a preset clustering algorithm based on the distribution condition of the singular values, selecting the matrix components of the target category according to a preset strategy, and reconstructing based on the matrix components of the target category to obtain the noise reduction signal data.
5. The singular spectrum analysis and orthogonal basis method combined magnetic anomaly target detection method according to claim 4, wherein the method comprises, based on the distribution of the singular values, classifying all matrix components of the trajectory matrix by using a preset clustering algorithm, selecting matrix components of a target category according to a preset strategy, and reconstructing the matrix components of the target category to obtain the noise reduction signal data, specifically comprising:
based on the distribution condition of the singular values, adopting a preset clustering algorithm to divide all matrix components of the track matrix into three categories;
and selecting the matrix component of the target category from which the low-frequency component and the high-frequency noise are removed according to a preset strategy, and reconstructing based on the matrix component of the target category to obtain the noise reduction signal data.
6. The singular spectrum analysis and orthogonal basis method combined magnetic anomaly target detection method according to claim 1, wherein the performing anomaly signal detection on the noise reduction signal data based on the orthogonal basis detection method comprises:
constructing an orthogonal basis detection function, and matching the noise reduction signal data by using the orthogonal basis function to obtain a matching coefficient;
determining an energy function based on the matching coefficients;
determining an energy threshold value based on energy corresponding to a preset historical time period before the current time;
and solving the energy of the noise reduction signal data by using the energy function, and determining whether the target abnormal signal exists in the magnetic measurement data to be detected based on the comparison result of the energy of the noise reduction signal data and the energy threshold.
7. The singular spectrum analysis and orthogonal basis method combined magnetic anomaly target detection method as claimed in claim 6, wherein said determining whether the magnetic measurement data to be detected has a target anomaly signal based on the comparison result of the energy of the noise reduction signal data and the energy threshold specifically comprises:
if the energy of the noise reduction signal data is larger than the energy threshold value and the energy of the noise reduction signal data reaches a peak point, determining that a target abnormal signal exists in the magnetic measurement data to be detected at the current moment;
and if the energy of the noise reduction signal data is not greater than the energy threshold value or the energy of the noise reduction signal data does not reach the peak value point, determining that no target abnormal signal exists in the magnetic measurement data to be detected at the current moment.
8. A magnetic anomaly target detection device combining singular spectrum analysis and an orthogonal basis method is characterized by comprising the following steps:
the preprocessing module is used for acquiring the magnetic measurement data to be detected and preprocessing the magnetic measurement data to be detected;
the noise reduction module is used for carrying out noise reduction processing on the preprocessed magnetic measurement data to be detected based on a singular spectrum analysis method to obtain noise reduction signal data;
and the detection module is used for detecting abnormal signals of the noise reduction signal data based on an orthogonal basis detection method.
9. A terminal device comprising a processor and a memory storing a computer program, wherein the processor implements the magnetic anomaly target detection method combining singular spectrum analysis and orthogonal basis method of any one of claims 1 to 7 when executing the computer program.
10. A non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor implements the magnetic anomaly target detection method combining singular spectral analysis and an orthogonal basis method as claimed in any one of claims 1 to 7.
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