CN109799535B - Filtering method for full-tensor magnetic gradient positioning detection data - Google Patents

Filtering method for full-tensor magnetic gradient positioning detection data Download PDF

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CN109799535B
CN109799535B CN201910193706.7A CN201910193706A CN109799535B CN 109799535 B CN109799535 B CN 109799535B CN 201910193706 A CN201910193706 A CN 201910193706A CN 109799535 B CN109799535 B CN 109799535B
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余景锋
陈庆作
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China Ship Marine Exploration Technology Research Institute Co ltd
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Abstract

The invention discloses a filtering method of full-tensor magnetic gradient positioning detection data, which relates to the field of magnetic measurement and comprises the following steps: acquiring magnetic field data at fixed points, determining magnetic field components of the magnetic field data in the x, y and z directions and first-order gradient components of each magnetic field component in the x, y and z directions respectively, and obtaining 12 component data; determining 5 independent components in the full tensor magnetic gradient data; for each component data, decomposing the component data into a sum of a predetermined number of eigenmode functions by an empirical mode decomposition algorithm; determining reconstruction parameters for 3 magnetic field components and 5 independent components by an energy threshold method for reconstruction; performing similar reconstruction on the 4 pieces of component data which do not participate in reconstruction by adopting the same reconstruction parameters according to the data corresponding relation in the full tensor; and integrating the reconstructed data of the 12 component data to be output as final filtered data. The data noise can be effectively eliminated, and the signal-to-noise ratio of the data is improved.

Description

Filtering method for full-tensor magnetic gradient positioning detection data
Technical Field
The invention relates to the field of magnetic measurement, in particular to a filtering method for full-tensor magnetic gradient positioning detection data.
Background
The full tensor magnetic field gradient is the derivation of the magnetic field in three-dimensional space along three directions, a total of nine gradient values are obtained, and the information of the position of the magnetic target, the matrix size and the like can be calculated according to the gradient value information and the size of the magnetic field.
By utilizing the method of magnetic field gradient tensor, magnetic anomaly detection and accurate positioning and tracking of a magnetic target under complex conditions can be realized by analyzing various magnetic anomaly characteristics, however, a large amount of interference and error information exist in the measurement process, and complex magnetic background information brings a large amount of difficulty for identification and positioning of the magnetic target, tensor data information needs to be filtered, and loss of useful signals is reduced as much as possible.
Disclosure of Invention
The invention provides a filtering method of full-tensor magnetic gradient positioning detection data, aiming at the problems and the technical requirements.
The technical scheme of the invention is as follows:
a method of filtering full tensor magnetic gradient localization detection data, the method comprising:
step 1, collecting magnetic field data at fixed points, determining magnetic field components of the magnetic field data in three directions of x, y and z and first-order gradient components of each magnetic field component in the directions of x, y and z according to a three-dimensional rectangular coordinate system, and obtaining 12 component data, wherein 9 first-order gradient components form full tensor magnetic gradient data;
step 2, determining 5 independent components in the full tensor magnetic gradient data;
step 3, decomposing the component data into the sum of a predetermined number of intrinsic mode functions by an empirical mode decomposition algorithm aiming at each component data;
step 4, determining reconstruction parameters for the 3 magnetic field components and the 5 independent components by an energy threshold method for reconstruction;
step 5, performing similar reconstruction on the 4 component data which do not participate in reconstruction by adopting the same reconstruction parameters according to the data corresponding relation in the full tensor;
and 6, integrating the reconstruction data of the 12 component data, and outputting the reconstruction data as final filtering data.
The further technical scheme is as follows: the fixed point acquisition magnetic field data, according to three-dimensional rectangular coordinate system confirm magnetic field data in three directions x, y, z magnetic field component and every magnetic field component respectively in x, y, z direction first order gradient component, obtain 12 component data, include:
step 11, setting Bx,By,BzThree magnetic field components in space of a magnetic field vector B, BxA step in the x, y, z directionThe weight fractions are respectively
Figure BDA0001995153260000021
ByThe first order gradient components in the x, y, z directions are respectively
Figure BDA0001995153260000022
BzThe first order gradient components in the x, y, z directions are respectively
Figure BDA0001995153260000023
The full tensor magnetic gradient is denoted as G,
Figure BDA0001995153260000024
the 12 component data are respectively
Figure BDA0001995153260000025
And 12, simultaneously acquiring data aiming at 12 pieces of component data at a preset position, wherein the acquisition frequency is at least 2 times of the signal frequency, and continuously acquiring preset amount of data to wait for filtering processing.
The further technical scheme is as follows: the determining 5 independent components in the full tensor magnetic gradient data comprises:
step 21, according to the characteristic that the rotation and divergence of the passive field are zero, the full tensor magnetic gradient data meets the requirement
Figure BDA0001995153260000026
Then 5 independent components are
Figure BDA0001995153260000027
Figure BDA0001995153260000028
Signal combinations of any 5 components;
a set of signal combinations is selected among all signal combinations of the 5 components, step 22, depending on the signal quality.
The further technical scheme is as follows: for each of the component data, decomposing the component data into a sum of a predetermined number of eigenfunctions by an empirical mode decomposition algorithm, including:
step 31, identifying all maximum value points and minimum value points of the original signal aiming at each component data, and fitting an upper envelope line e of the signal by adopting cubic spline interpolationuppAnd a lower envelope elowAnd calculating the upper envelope euppAnd a lower envelope elowAverage value m of1；
Step 32, subtracting the average value m from the actual detection signal1To obtain a difference signal h1；
Step 33, verifying the difference signal h1Whether it is a basic eigenmode function;
step 34, if the difference signal h1Stopping decomposition to obtain difference signal h1As the first basic IMF component, otherwise the difference signal h1Repeating steps 31 to 34 as a new original signal until the basic component c is output1；
Step 35, subtracting the basic component c from the original signal1Obtaining the remaining part r1；
Step 36, the remaining part r1As a new original signal, repeating the steps 31 to 35, iterating n times until the stopping criterion is satisfied to obtain n basic components c1,c2,…,cnAnd remainder rn(ii) a The stopping criterion is rnIs a monotonic function;
step 37, repeating steps 31 to 36 for 12 component data to obtain IMF decomposition of each component data, and recording the IMF decomposition as IMFs,s＝1,…n。
The further technical scheme is as follows: the verification difference signal h1Whether it is a fundamental eigenmode function, including:
step 331, verifying the difference signal h1Whether the number of the local extreme points and the number of the zero-crossing points are equal or differ by one at most in the whole event range;
step 332, at any time point, whether the envelope of the local maximum and the envelope of the local minimum are equal to zero or not.
The further technical scheme is as follows: the reconstruction of the 3 magnetic field components and the 5 independent components by determining reconstruction parameters by an energy threshold method comprises the following steps:
step 41, for the magnetic field component BxBy IMFsK, … n, denoted Bx,k；
Step 42, calculate Bx,kAnd Bx,k+1Continuous mean square error of (A), denoted CMSE (B)x,k,Bx,k+1)；
Step 43, calculate
Figure BDA0001995153260000041
As energy index, JsThe component of the first significant change in CMSE, the minimum energy, then Bx,kIs a filtered signal;
step 44, repeating steps 41 to 44, calculating J for the other two magnetic field components and the selected 5 independent components respectivelysAnd performing reconstruction.
The further technical scheme is as follows: the similar reconstruction is performed on the 4 pieces of component data which do not participate in the reconstruction by adopting the same reconstruction parameters according to the data corresponding relation in the full tensor, and the similar reconstruction method comprises the following steps:
for 4 component data not participating in reconstruction, corresponding J is adoptedsAnd performing signal reconstruction.
The beneficial technical effects of the invention are as follows:
by filtering 12 component data of 3 magnetic field components and 9 full tensor magnetic gradient data and filtering the data by adopting an empirical mode decomposition algorithm and a signal reconstruction method based on an energy threshold value method, data noise can be effectively eliminated, the theoretical basis of the full tensor data is fully considered, the data correspondence is considered, the signal-to-noise ratio of the data is improved, and the magnetic target detection and positioning capacity of the gradiometer is improved.
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Fig. 1 is a flowchart of a method for filtering full tensor magnetic gradient localization detection data according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for filtering full tensor magnetic gradient localization detection data according to another embodiment of the present invention.
Figure 3 is a diagram of a full tensor magnetic gradient sensed data component correspondence provided by one embodiment of the present invention.
Figure 4 is a block diagram of a filtering process for full tensor magnetic gradient data, in accordance with one embodiment of the present invention.
Figure 5 is a graph illustrating the filtering effect of full tensor magnetic gradient inspection data provided by one embodiment of the present invention.
Detailed Description
The following further describes the embodiments of the present invention with reference to the drawings.
Fig. 1 is a flowchart of a method for filtering full tensor magnetic gradient localization detection data according to an embodiment of the present invention, as shown in fig. 1, the method may include the following steps:
step 1, collecting magnetic field data at fixed points, determining magnetic field components of the magnetic field data in three directions of x, y and z and first-order gradient components of each magnetic field component in the directions of x, y and z according to a three-dimensional rectangular coordinate system, and obtaining 12 component data, wherein 9 first-order gradient components form full tensor magnetic gradient data.
Each component data typically requires at least 1 ten thousand data or more when filtered.
Optionally, with reference to fig. 2, in practical applications, step 1 may include the following steps:
step 11, setting Bx,By,BzThree magnetic field components in space of a magnetic field vector B, BxThe first order gradient components in the x, y, z directions are respectively
Figure BDA0001995153260000051
ByThe first order gradient components in the x, y, z directions are respectively
Figure BDA0001995153260000052
BzIn the case of the x-ray diffraction pattern,the first order gradient components in the y and z directions are respectively
Figure BDA0001995153260000053
The full tensor magnetic gradient is denoted as G,
Figure BDA0001995153260000054
the 12 component data are respectively
Figure BDA0001995153260000055
And step 12, acquiring data simultaneously aiming at 12 component data at a preset position, wherein the acquisition frequency is at least 2 times of the signal frequency, and continuously acquiring and acquiring data of a preset amount to wait for filtering processing.
Step 2, 5 independent components in the full tensor magnetic gradient data are determined.
Optionally, referring to fig. 2 in combination, step 2 may include the following steps:
step 21, according to the characteristic that the rotation and divergence of the passive field are zero, the full tensor magnetic gradient data meets the requirement
Figure BDA0001995153260000056
Then 5 independent components are
Figure BDA0001995153260000057
Figure BDA0001995153260000058
Any 5 components of the signals are combined.
According to the characteristics of the passive field, the rotation and divergence of the passive field are zero, and theoretically, the magnetic gradient full tensor data satisfy the following relational expression:
Figure BDA0001995153260000059
Figure BDA0001995153260000061
Figure BDA0001995153260000062
Figure BDA0001995153260000063
according to the formulas (1) to (4), 5 of the 9 full tensor magnetic gradient data are independent and can be freely selected according to the data quality, and the optional 5 independent components can be
Figure BDA0001995153260000064
Or
Figure BDA0001995153260000065
Or
Figure BDA0001995153260000066
And so on. 3 magnetic field components are added, so that there are a total of 8 independent component data out of 12 component data.
A set of signal combinations is selected among all signal combinations of the 5 components, step 22, depending on the signal quality.
Illustratively, in the optional signal combining, the selection is based on signal quality
Figure BDA0001995153260000067
And 3, decomposing the component data into the sum of a predetermined number of intrinsic mode functions by an empirical mode decomposition algorithm aiming at each component data.
Empirical Mode Decomposition (EMD) is an adaptive signal Decomposition algorithm for nonlinear, non-stationary signals. And decomposing the measurement signal of each component data through an empirical mode decomposition algorithm to obtain different mode signals.
The instantaneous frequency of any point of an Intrinsic Mode Function (IMF) is significant, one signal may be composed of a plurality of Intrinsic Mode functions, if the Intrinsic Mode functions are overlapped, a composite signal is formed, and the EMD decomposition aims to obtain the Intrinsic Mode functions.
Optionally, because of the complexity of the environment and interference that may be generated during the operation of the apparatus, the signal-to-noise ratio of the raw data is extremely low and cannot be directly used for target detection and positioning, the empirical mode decomposition method may decompose the signal into the set of eigenmode functions from the perspective of data experience, so as to analyze the data more clearly, with reference to fig. 2, step 3 may include the following steps:
step 31, identifying all maximum value points and minimum value points of the original signal aiming at each component data, and fitting the upper envelope line e of the signal by adopting cubic spline interpolationuppAnd a lower envelope elowAnd calculating the upper envelope euppAnd a lower envelope elowAverage value m of1。
Suppose for Bx(t) identifying all maximum points and minimum points of the original signal, and fitting the upper envelope e of the signal by cubic spline interpolationupp(t) and lower envelope elow(t) and calculating the average value of the upper envelope line and the lower envelope line, which is recorded as m1(t)：
Figure BDA0001995153260000071
Step 32, subtracting the average value m from the actual detection signal1To obtain a difference signal h1。
Subtracting m from the actual detection signal1(t) to obtain h1：h1(t)＝Bx(t)-m1(t)。
Step 33, verifying the difference signal h1Whether it is a fundamental eigenmode function.
Verification h1(t) is a fundamental eigenmode function.
Optionally, the difference signal h is verified1The method for judging whether the function is a basic intrinsic mode function is divided into two steps: first, the difference signal h is verified1Whether the number of the local extreme points and the number of the zero-crossing points are equal or differ by one at most in the whole event range; second, at any point in time, locallyWhether the envelope of the maxima and the envelope of the local minima average to zero. Determining the difference signal h when the above two conditions are satisfied simultaneously1Is a basic eigenmode function.
Step 34, if the difference signal h1Stopping decomposition to obtain difference signal h1As the first basic IMF component, otherwise the difference signal h1Repeating steps 31 to 34 as a new original signal until the basic component c is output1。
If h1(t) is the fundamental eigenmode function, the decomposition is stopped as the first fundamental IMF component, otherwise h is1(t) repeating steps 31 to 34 as a new original signal until a basic IMF component is output, denoted c1(t)。
Step 35, subtracting the basic component c from the original signal1Obtaining the remaining part r1。
Subtracting the fundamental component c from the original signal1(t) obtaining a remainder r1(t)：r1(t)＝Bx(t)-h1(t)。
Step 36, the remaining part r1As a new original signal, repeating the steps 31 to 35, iterating n times until the stopping criterion is satisfied to obtain n basic components c1,c2,…,cnAnd remainder rn。
Stopping criterion is rnIs a monotonic function.
Taking the rest part as a new original signal, repeatedly executing the steps 31 to 35, and iterating for n times until n basic components c are obtained by meeting the stop criterion1(t),c2(t),…,cn(t), and the remainder rn(t)：
Figure BDA0001995153260000081
Bx(t) can be finally expressed as
Figure BDA0001995153260000082
Step 37, repeating steps 31 to 36 for 12 component data to obtain IMF decomposition of each component data, and recording the IMF decomposition as IMFs,s＝1,…n。
And 4, determining reconstruction parameters for the 3 magnetic field components and the 5 independent components by an energy threshold method, and reconstructing.
And performing signal reconstruction of different component data by adopting an energy threshold method, and simultaneously considering the corresponding relation between signals.
Alternatively, exemplarily, for 3 magnetic field components Bx,By,BzAnd 5 selected independent components, assumed to be
Figure BDA0001995153260000083
I.e. the above-mentioned 8 independent components, calculating energy index according to EMD decomposition result, utilizing IMFsS 1, … n, with BxFor example, referring to fig. 2 in combination, step 4 may include the following steps:
step 41, for the magnetic field component BxBy IMFsK, … n, denoted Bx,k。
Figure BDA0001995153260000084
Step 42, calculate Bx,kAnd Bx,k+1Continuous mean square error of (A), denoted CMSE (B)x,k,Bx,k+1)。
Figure BDA0001995153260000085
Step 43, calculate
Figure BDA0001995153260000086
As energy index, JsThe component of the first significant change in CMSE, the minimum energy, then Bx,kIs a filtered signal.
Step 44, repeating steps 41 to 44, for the other two magnetic field components and the selected 5Independent component calculation JsAnd performing reconstruction.
And 5, performing similar reconstruction on the 4 pieces of component data which do not participate in reconstruction by adopting the same reconstruction parameters according to the data corresponding relation in the full tensor.
Corresponding relationship between signals referring to fig. 3, components indicated by both ends of a double-headed arrow are corresponding to each other, and J which can be calculated from one of the componentssAnd performing homogeneous reconstruction.
For the rest 4 component data which do not participate in reconstruction, corresponding J is adopted according to the corresponding relation between the signalssAnd performing signal reconstruction on the residual component data.
Giving the remaining 4 component data and corresponding J according to the 5 independent components selected in step 2 and the corresponding relationship between the signals in FIG. 3s(ii) a According to the selection of JsAnd performing signal reconstruction on the remaining 4 pieces of component data as data filtering output.
And 6, integrating the reconstruction data of the 12 component data, and outputting the reconstruction data as final filtering data.
The whole filtering flow is shown in fig. 4, and the filtering effect is shown in fig. 5.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiments. It is to be understood that other modifications and variations directly derivable or suggested by those skilled in the art without departing from the spirit and concept of the present invention are to be considered as included within the scope of the present invention.

Claims (7)

1. A method of filtering full-tensor magnetic gradient localization detection data, the method comprising:
step 1, collecting magnetic field data at fixed points, determining magnetic field components of the magnetic field data in three directions of x, y and z and first-order gradient components of each magnetic field component in the directions of x, y and z according to a three-dimensional rectangular coordinate system to obtain 12 component data, wherein 9 first-order gradient components form full tensor magnetic gradient data, and 12 component data are B component data respectivelyx,By,Bz,
Figure FDA0002625022080000011
The full tensor magnetic gradient data satisfies the condition that the rotation and divergence of the passive field are zero
Figure FDA0002625022080000012
Figure FDA0002625022080000013
Then 5 independent components are
Figure FDA0002625022080000014
Figure FDA0002625022080000015
Signal combinations of any 5 components;
step 2, determining 5 independent components in the full tensor magnetic gradient data;
step 3, decomposing the component data into the sum of a predetermined number of intrinsic mode functions by an empirical mode decomposition algorithm aiming at each component data;
step 4, determining reconstruction parameters for the 3 magnetic field components and the 5 independent components by an energy threshold method for reconstruction;
step 5, performing similar reconstruction on the 4 component data which do not participate in reconstruction by adopting the same reconstruction parameters according to the data corresponding relation in the full tensor;
step 6, integrating the reconstruction data of 12 component data to be output as final filtering data, and providing the remaining 4 component data and corresponding reconstruction parameters according to the selected 5 independent components and the data corresponding relation in the full tensor; and performing signal reconstruction on the remaining 4 pieces of component data according to the selected reconstruction parameters, and outputting the data as data filtering.
2. The method of claim 1, wherein the fixed-point acquiring of the magnetic field data, determining magnetic field components of the magnetic field data in three directions of x, y and z and first-order gradient components of each magnetic field component in the x, y and z directions according to a three-dimensional rectangular coordinate system, and obtaining 12 component data comprises:
step 11, setting Bx,By,BzThree magnetic field components in space of a magnetic field vector B, BxThe first order gradient components in the x, y, z directions are respectively
Figure FDA0002625022080000021
ByThe first order gradient components in the x, y, z directions are respectively
Figure FDA0002625022080000022
BzThe first order gradient components in the x, y, z directions are respectively
Figure FDA0002625022080000023
The full tensor magnetic gradient is denoted as G,
Figure FDA0002625022080000024
the 12 component data are respectively Bx,By,Bz,
Figure FDA0002625022080000025
And 12, simultaneously acquiring data aiming at 12 pieces of component data at a preset position, wherein the acquisition frequency is at least 2 times of the signal frequency, and continuously acquiring preset amount of data to wait for filtering processing.
3. The method of claim 2, wherein said determining 5 independent components of said full tensor magnetic gradient data comprises:
step 21, according to the characteristic that the rotation and divergence of the passive field are zero, the full tensor magnetic gradient data meets the requirement
Figure FDA0002625022080000026
Then 5 independent components are
Figure FDA0002625022080000027
Figure FDA0002625022080000028
Signal combinations of any 5 components;
a set of signal combinations is selected among all signal combinations of the 5 components, step 22, depending on the signal quality.
4. The method of claim 2, wherein decomposing the component data into a sum of a predetermined number of eigenfunctions for each of the component data by an empirical mode decomposition algorithm comprises:
step 31, identifying all maximum value points and minimum value points of the original signal aiming at each component data, and fitting an upper envelope line e of the signal by adopting cubic spline interpolationuppAnd a lower envelope elowAnd calculating the upper envelope euppAnd a lower envelope elowAverage value m of1；
Step 32, subtracting the average value m from the actual detection signal1To obtain a difference signal h1；
Step 33, verifying the difference signal h1Whether it is a basic eigenmode function;
step 34, if the difference signal h1Stopping decomposition to obtain difference signal h1As the first basic IMF component, otherwise the difference signal h1Repeating steps 31 to 34 as a new original signal until the basic component c is output1；
Step 35, subtracting the basic component c from the original signal1Obtaining the remaining part r1；
Step 36, the remaining part r1As a new original signal, repeating the steps 31 to 35, iterating n times until the stopping criterion is satisfied to obtain n basic components c1,c2,…,cnAnd remainder rn(ii) a The stopping criterion is rnIs a monotonic function;
step 37, repeating steps 31 to 36 for 12 component data to obtain IMF decomposition of each component data, and recording the IMF decomposition as IMFs,s＝1,…n。
5. Method according to claim 4, characterized in that the verification difference signal h1Whether it is a fundamental eigenmode function, including:
verifying the difference signal h1Whether the number of the local extreme points and the number of the zero-crossing points are equal or differ by one at most in the whole event range;
at any point in time, whether the envelope of the local maxima and the envelope of the local minima are on average zero.
6. The method of claim 4, wherein said reconstructing 3 of said magnetic field components and 5 of said independent component-determined reconstruction parameters by energy thresholding comprises:
step 41, for the magnetic field component BxBy IMFsK, … n, denoted Bx,k；
Step 42, calculate Bx,kAnd Bx,k+1Continuous mean square error of (A), denoted CMSE (B)x,k,Bx,k+1)；
Step 43, calculate
Figure FDA0002625022080000031
As energy index, JsThe component of the first significant change in CMSE, the minimum energy, then Bx,kIs a filtered signal;
step 44, repeating steps 41 to 44, calculating J for the other two magnetic field components and the selected 5 independent components respectivelysAnd performing reconstruction.
7. The method according to claim 6, wherein the performing homogeneous reconstruction on the 4 component data not participating in the reconstruction by using the same reconstruction parameters according to the data correspondence relationship in the full tensor comprises:
for 4 component data not participating in reconstruction, corresponding J is adoptedsAnd performing signal reconstruction.
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Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110989005B (en) * 2019-12-23 2021-12-28 西北工业大学青岛研究院 Weak magnetic anomaly self-adaptive real-time detection method based on scalar magnetometer array
CN111274692B (en) * 2020-01-16 2022-04-05 西安交通大学 Modeling method for nonlinear control system of aircraft engine
CN113420423A (en) * 2021-06-01 2021-09-21 郑州步始智能科技有限公司 Method for forward modeling full tensor from magnetic field vertical component based on regularization method in frequency domain
CN113703059B (en) * 2021-09-02 2023-11-17 中船海洋探测技术研究院有限公司 Remote magnetic detection method for water ferromagnetic target clusters

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2171523B (en) * 1985-02-23 1988-03-09 Stc Plc Magnetic gradient detection
CN104545875A (en) * 2013-10-18 2015-04-29 中国科学院上海微系统与信息技术研究所 Full-tensor space gradient compensation method and system used for biomagnetic detection
CN206573701U (en) * 2017-03-06 2017-10-20 中国船舶工业系统工程研究院 It is a kind of to move magnetic survey quantity sensor under water
CN107516065A (en) * 2017-07-13 2017-12-26 天津大学 The sophisticated signal denoising method of empirical mode decomposition combination dictionary learning
CN107748734A (en) * 2017-10-31 2018-03-02 电子科技大学 One kind parsing empirical mode decomposition method
CN108182415A (en) * 2017-12-29 2018-06-19 哈尔滨工业大学 Based on the method for obtaining time-frequency distributions from filtering frequency conversion empirical mode decomposition
CN108182296A (en) * 2016-12-08 2018-06-19 中南大学 A kind of rail based on empirical mode decomposition examines tradition to data processing method
CN108227024A (en) * 2017-12-04 2018-06-29 中国科学院地质与地球物理研究所 A kind of method and system using full tensor magnetic gradient data inversion underground magnetic susceptibility
CN108710153A (en) * 2017-07-31 2018-10-26 中国地质大学（北京） A kind of wave-number domain method of the full tensor gradient inverting subsurface three-dimensional magnetism distribution of magnetic
CN109100009A (en) * 2018-06-01 2018-12-28 国网江苏省电力有限公司南京供电分公司 Tap switch vibration signal noise-reduction method based on empirical mode decomposition EMD

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7065449B2 (en) * 2004-03-05 2006-06-20 Bell Geospace, Inc. Method and system for evaluating geophysical survey data
US9798038B2 (en) * 2012-08-15 2017-10-24 Bell Geospace Inc. Directional filter for processing full tensor gradiometer data

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2171523B (en) * 1985-02-23 1988-03-09 Stc Plc Magnetic gradient detection
CN104545875A (en) * 2013-10-18 2015-04-29 中国科学院上海微系统与信息技术研究所 Full-tensor space gradient compensation method and system used for biomagnetic detection
CN108182296A (en) * 2016-12-08 2018-06-19 中南大学 A kind of rail based on empirical mode decomposition examines tradition to data processing method
CN206573701U (en) * 2017-03-06 2017-10-20 中国船舶工业系统工程研究院 It is a kind of to move magnetic survey quantity sensor under water
CN107516065A (en) * 2017-07-13 2017-12-26 天津大学 The sophisticated signal denoising method of empirical mode decomposition combination dictionary learning
CN108710153A (en) * 2017-07-31 2018-10-26 中国地质大学（北京） A kind of wave-number domain method of the full tensor gradient inverting subsurface three-dimensional magnetism distribution of magnetic
CN107748734A (en) * 2017-10-31 2018-03-02 电子科技大学 One kind parsing empirical mode decomposition method
CN108227024A (en) * 2017-12-04 2018-06-29 中国科学院地质与地球物理研究所 A kind of method and system using full tensor magnetic gradient data inversion underground magnetic susceptibility
CN108182415A (en) * 2017-12-29 2018-06-19 哈尔滨工业大学 Based on the method for obtaining time-frequency distributions from filtering frequency conversion empirical mode decomposition
CN109100009A (en) * 2018-06-01 2018-12-28 国网江苏省电力有限公司南京供电分公司 Tap switch vibration signal noise-reduction method based on empirical mode decomposition EMD

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
漏磁信号处理中的经验模态分解和能量法阈值;曾小红等;《无损检测》;20111231;第33卷(第3期);第27-30页 *
磁记忆梯度张量测量信号预处理方法;陈海龙等;《系统工程与电子技术》;20170331;第39卷(第3期);第488-493页 *

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