CN114518599A - Self-adaptive magnetic anomaly target imaging and detecting method - Google Patents
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Abstract
The invention discloses a self-adaptive magnetic anomaly target imaging and detecting method. The invention provides that the magnetic anomaly target signal is expressed by three self-adaptive basis functions, so that the moving path of the measuring platform can be arbitrary and is not limited to uniform linear motion, and the target signal can be detected at the corner of a measuring line; the invention provides a method adopting a self-adaptive detector, which integrally solves model coefficients and separates out a platform interference magnetic field, a geomagnetic gradient field, a diurnal variation magnetic field and an abnormal magnetic target signal; the invention provides a magnetic anomaly target imaging method, wherein the pixel value is the ratio of signal energy to data segment energy, and the probability of a target existing in the imaging position is reflected; finally, the invention provides a method for detecting whether the target exists along the direction of the measuring line, also provides a positioning method of the magnetic abnormal target, and eliminates the false target position by using the unchanged z component or 3 rd component characteristic of the magnetic abnormal target magnetic moment.
Description
Technical Field
The invention relates to a magnetic anomaly target detection technology, in particular to a self-adaptive magnetic anomaly target imaging and detection method.
Background
The earth's environment distributes a steady magnetic field and ferromagnetic objects cause a change in the distribution of the magnetic field around them, known as magnetic anomalies. The magnetic anomaly detection technique can find a magnetic anomaly target signal by detecting an anomalous magnetic field. The method has the advantages of being free from influence of natural factors such as hydrology, weather and the like, and therefore, the method is widely applied to detection of buried unexplosive objects, detection of concealed or hidden ferromagnetic objects, detection of submarine or underground buried pipelines, detection of sunken ships covered by submarine or river bottom silt and the like. In 2002, B.Ginzburg in Processing of man orthogonal scale gradiometer signals using orthogonal normalized functions, wherein signals are regarded as generated by magnetic dipoles and can be expressed in a linear combination form of three orthogonal basis functions, the three basis functions and measured data are subjected to matched filtering to obtain an energy function, and whether a target exists or not is detected by setting an energy threshold. In 2004, Ginzburg also proposed a method of matched filtering scalar magnetometer gradient data with four orthogonal basis functions to detect targets in An An effective method for processing a scalar magnetic gradiometer signals. In 2007, Sheinker et al, a.Procession of a scanner magnetic signal conditioned by 1/fαnoise "mentions a method of whitening data based on an autoregressive model, and then performs detection using an orthogonal basis function method. In 2008, the a. sheinker team proposed an entropy filter detection method in the article Magnetic and analog detection using entropy filter, which detects signals according to the variation characteristics of data entropy. In 2010, Dengpo et al, in the article "Application of adaptive filtering based on wave transmission in adaptive filtering measure" used the minimum mean square error criterion to design the adaptive filter, which can be used to reduce the interference of magnetic noise. In 2012, a. sheinker proposed a detection method based on high-order cross-over amount in Magnetic and analytical detection using high-order cross-over method. In 2013, a denoising method based on wavelet entropy is proposed in an article 'wavelet entropy based weak magnetic anomaly signal denoising processing' by surrounding people and the like. In 2018, Vancorubin et al, Magnetic and analog detection based on Magnetic resonance response, in Magnetic and analog detection based on Magnetic resonance response, pointed out a method for detecting a Magnetic anomaly target signal. In the same year, Guanyi Zhao et al proposes a wavelet-based Adaptive bandpass filtering detection method in the article Adaptive filtering method for magnetic analog detection. In 2019, kang Chong et al disclose a patent of magnetic target tracking method based on magnetic abnormal gradient. In the same year, Qinje et al disclose a patent of a magnetic anomaly detection vector magnetic target identification method. In 2020, Feng Yong et alA magnetic anomaly detection method based on a sensor array.
The most used detection mode at present is a detection method adopting a single mobile detection measurement platform and a single high-precision optical pump atomic magnetometer, the most widely used detection method of magnetic anomaly signals is an orthogonal basis function detection method, but when real measurement data are used for detection, because the total magnetic field measured by the optical pump magnetometer not only comprises magnetic anomaly target signals, but also comprises an interference magnetic field, geological magnetic gradient, daily variation magnetic noise and the like generated by the measurement platform, the detection probability of the target can be influenced; secondly, the current magnetic anomaly target signal model assumes that the detection measuring platform moves linearly at a constant speed, and the measuring line is a straight line, so that when the measuring platform turns, the signal model is incorrect, and if the target just appears in a turning section, the target is difficult to detect; in addition, imaging the magnetic anomaly target signal is also beneficial to subsequent detection, and further positioning the magnetic anomaly target signal after the magnetic anomaly target signal is detected is also a problem to be solved. Therefore, the method has important practical significance for reestablishing the magnetic anomaly target signal model expression, applying the self-adaptive detection algorithm and developing a new target imaging, detecting and positioning technology.
Disclosure of Invention
The invention provides a self-adaptive magnetic anomaly target imaging and detecting method, aiming at solving the problem that a measuring platform cannot move along any path to detect a target in the conventional method.
The invention discloses a self-adaptive magnetic anomaly target imaging and detecting method, which comprises the following steps:
1) data acquisition:
an optical pump magnetometer, a three-component fluxgate magnetometer, a GPS and an attitude instrument are respectively arranged on the measuring platform; before detection, acquiring a section of daily magnetic noise data by an optical pump magnetometer; in actual detection, the optical pump magnetometer measures to obtain total magnetic field data, the three-component fluxgate magnetometer measures to obtain three magnetic field components, the GPS measures to obtain the longitude and latitude and the height of the measuring platform and the measured time, and the attitude gauge measures to obtain the yaw angle, the pitch angle and the roll angle of the measuring platform;
2) establishing a total magnetic field model:
the total magnetic field obtained by the measurement of the optical pump magnetometer comprises four magnetic fields, and the expression is as follows:
Bmea(r,t)＝Bpla(t)+Bgeo(r)+Bion(t)+S(r,r0) (1)
wherein, Bmea(r, t) is total magnetic field measured by optical pumping magnetometer, r and t are position and time of measuring platform, Bpla(t) interfering magnetic fields generated by the measuring platform as a function of time, Bgeo(r) is the geological magnetic gradient field, as a function of the observed position, Bion(t) is the magnetic noise of the diurnal variation, as a function of time, S (r, r)0) For the magnetic anomaly target signal, the measurement platform position r and the magnetic anomaly target position r0A function of (a); however, since the measurement platform is moving over time, the total magnetic field, the geomagnetic gradient field, and the magnetic anomaly target signal are all functions of time; the geographical coordinate system is established in such a way that the x-axis points to the north pole along the longitude line, the y-axis points to the east along the latitude line and the z-axis points vertically to the underground; in the detection process, the measured total magnetic field data is subjected to segmented detection;
3) the four magnetic fields are respectively modeled:
a) interference magnetic field model generated by the measuring platform:
using a classical T-L model, only retaining the inherent magnetic field and the induced magnetic field part to obtain an interference magnetic field B generated by the measuring platformpla(t)：
Wherein, i is 1iFor compensation factors of disturbing magnetic fields, hi(t) is a basis function for magnetic compensation, and the expression is as follows: h is1(t)＝u1(t)、h2(t)＝u2(t)、h3(t)＝u3(t)、
h5(t)＝u1(t)u2(t)、h6(t)＝u1(t)u3(t)、
h8(t)＝u2(t)u3(t)，u1(t)、u2(t) and u3(t) respectively represents the direction vectors of the local earth magnetic field relative to x, y and z axes of the measurement platform, and the expressions are respectively:
wherein, B1(t)、B2(t) and B3(t) three magnetic field components measured by a three-component fluxgate magnetometer respectively; b) geological magnetic gradient field model:
for the moving range of the measuring platform not to exceed five times of the distance between the measuring platform and the magnetic anomaly target, fitting the geological magnetic gradient field by adopting a linear function to obtain a geological magnetic gradient field Bgeo(t)：
Bgeo(t)＝g0+g1·[x(t)-x(t0)]+g2·[y(t)-y(t0)]+g3·[z(t)-z(t0)] (6)
Wherein, x (t) is the coordinate of the measuring platform along the x-axis direction along the time change, y (t) is the coordinate of the measuring platform along the y-axis direction along the time change, z (t) is the coordinate of the measuring platform along the z-axis direction along the time change, x (t), y (t) and z (t) are obtained by converting longitude, latitude and height obtained by GPS measurement arranged on the measuring platform, and x (t)0)、y(t0) And z (t)0) For the coordinate dataCenter position, g0Is a constant term, g1For magnetic gradients along the x-axis, g2For magnetic gradients in the y-axis direction, g3Is the magnetic gradient along the z-axis direction;
c) the model of the magnetic noise of the daily variation:
the daily variable magnetic noise mainly comes from ionosphere fluctuation, and a covariance matrix of the daily variable magnetic noise is estimated by measuring daily variable magnetic noise data for a period of time in advance and is continuously updated in the detection process; the optical pump magnetometer collects a section of daily variable magnetic noise data d (m) with the length of 2N +1 in advance, m belongs to < -2N,0 >, 0 is the starting time of actual detection, the daily variable magnetic noise data with the length of 2N +1 is written into (N +1) overlapped data sections with the length of (N +1), namely the daily variable magnetic noise data with the length of (N +1) are taken each time, the data are moved backwards one by one, and the data are taken for N +1 times, so that the nth section of daily variable magnetic noise data b (N):
b(n)＝[d(1-n)，d(-n)，...，d(1-n-N)]-d0(n)，n＝1，2...N+1 (7)
where d0(n) represents the mean of this segment of data, the covariance matrix R of the estimated diurnal magnetic noisennComprises the following steps:
Rnn＝[ri，j]，i，j＝1，2...N+1 (8)
wherein, the nth segment of the daily-variation magnetic noise data b (N) with the length of (N +1) is the data from the daily-variation magnetic noise data (d (m), m ∈ [ -2N,0]) D (1-N-N) represents the diurnal magnetic noise data points in the nth segment of the diurnal magnetic noise data, ri，jIs the value of the ith row and the jth column of the covariance matrix of the magnetic noise of the daily variation, b (i) and b (j) are respectively the data (d (m) from the magnetic noise of the daily variation, m ∈ [ -2N,0]) The segmented ith and jth sections of the daily variable magnetic noise data with the length of (N +1) are intercepted, d (1-i-k) is a daily variable magnetic noise value corresponding to a kth point in the ith section of the daily variable magnetic noise data b (i), d (1-j-k) is a daily variable magnetic noise value corresponding to a kth point in the jth section of the daily variable magnetic noise data b (j), and k is 0, 1.. N; size of sliding windowN +1 sampling points; if a new section of daily magnetic noise data with the length of (N +1) is obtained in subsequent measurement, the new section of daily magnetic noise data is assigned to b (1), the previous sections b (1) -b (N) are assigned to b (2) -b (N +1), the previous sections b (N +1) are abandoned, and the covariance matrix R is updated and calculatednn；
d) Magnetic anomaly target signal model:
regarding the magnetic abnormal target signal as the projection of the vector field generated by a magnetic dipole on the local earth magnetic field direction, the magnetic abnormal target signal S (r, r)0)：
Wherein, mu0Is the vacuum permeability, r is the observation point position, r0Is the position of the magnetic abnormal target, R is the position vector from the magnetic abnormal target to the measuring platform, R is the distance from the magnetic abnormal target to the measuring platform,
is a unit dyadic, M is a magnetic abnormal target moment, MiThree components of target magnetic moment for magnetic anomaly, i ═ 1,2,3, M ═ M1，M2，M3]TThe superscript T denotes transposition, Fi(r，r0) Is the basis function of the magnetic anomaly target signal, the expansion is expressed as:
wherein R is1＝x(t)-x0，R2＝y(t)-y0，R3＝z(t)-z0X (t) is the coordinate of the measuring platform along the x-axis direction, y (t) is the coordinate of the measuring platform along the y-axis direction, z (t) is the coordinate of the measuring platform along the z-axis direction, x0、y0And z0Is the coordinate position of the magnetic anomaly target, where x0＝x(t0)-Ysinα，y0＝y(t0)+Ycosα，z0＝z(t0)+const，x(t0)、y(t0) And z (t)0) For the center position of the piece of coordinate data, α is the yaw angle of the measurement platform, Y is the displacement of the target to the midpoint of the line of sight, and it is assumed that the magnetically anomalous target can only exist in one horizontal plane, so z0Considered as a constant;
wherein alpha, theta and beta are respectively a yaw angle, a pitch angle and a roll angle of the measuring platform and are measured by an attitude instrument;
for a magnetically abnormal target moment M, it is considered to be the sum of the intrinsic and induced moments, i.e.
Wherein M isperIs the inherent magnetic moment of the magnetic abnormal target, is invariant to the target coordinate system, C is a constant vector related to the magnetization characteristic of the magnetic abnormal target, BgeoIs the local magnetic field of the earth and,
is a transformation matrix from the geographical coordinate system to the target coordinate system,
for target orientation, "+" indicates a direct product; it should be noted that the z component or the 3 rd component of the intrinsic, induced and total magnetic moments is invariant regardless of the movement of the magnetic anomaly target in the horizontal plane, so this component is referred to as the invariant feature of the magnetic anomaly target, and the magnetic anomaly signal component generated by it is also referred to as the invariant feature of the magnetic anomaly target, meaning that it is independent of the target orientation;
4) solving the model coefficients:
i. establishing a coefficient equation by combining the models established in the step 2) and the step 3):
writing equation (1) to the form:
and then writing into a matrix form:
wherein B is a measured total magnetic field B with a length of N +1meaData or (N +1) × 1 column vectors, i.e. B ═ Bmea(t-N/2)，...，Bmea(t0)，...，Bmea(tN/2)]Where v represents an unknown diurnal variation magnetic noise of length N + 1; h is the basic function moment of the interference magnetic field generated by the measuring platformThe matrix is an (N +1) x 8 matrix,
the compensation coefficient vector of the interference magnetic field generated for the measuring platform is an 8 multiplied by 1 column vector; g is a basis function matrix of the geological magnetic gradient field, which is an (N +1) x 4 matrix,
is geomagnetic gradient coefficient column vector; f is a basis function matrix of the magnetic anomaly target signal, is an (N +1) multiplied by 3 matrix,
is a magnetic anomaly target magnetic moment vector;
according to the optimal signal detection theory, establishing an adaptive detector matrix as follows:
wherein the content of the first and second substances,
the inverse of the covariance matrix of the diurnal magnetic noise, H, obtained by solving for equation (8)TTransposing the basis function matrix H of the disturbing magnetic field generated for the measuring platform, GTAs a transpose of the basis function matrix G of the geological magnetic field, FTTranspose of basis function matrix F for magnetic anomaly target signal;
applying equation (19) to equation (18) to obtain the following equation:
c＝[c1 c2 … c8]T，g＝[g0 g1 g2 g3]T，M＝[M1 M2 M3]T (21)
Wherein the content of the first and second substances,
dcinvolving disturbing magnetic fields generated by the measuring platform, dgInvolving the geomagnetic gradient field, dmTo a magnetic anomaly target signal;
solving coefficients:
solving the formula (20), and simultaneously obtaining an interference magnetic field compensation coefficient, a geomagnetic gradient coefficient and a magnetic abnormal target magnetic moment generated by the measuring platform;
5) imaging a magnetic anomaly target:
the ratio sigma (r) of the energy of the magnetic anomaly target signal contained in the segment of total magnetic field data to the energy of the segment of total magnetic field0) The following calculations were made:
if the magnetic anomaly target signal in the segment of data is dominant, then σ (r)0) Close to 1, otherwise close to zero; taking the ratio as the value of a pixel point, and storing the value in r0Coordinate position, obtaining all pixels of a region to form an image; for a given total magnetic field data, only points on a straight line perpendicular to the direction of the survey line and passing through the center point are imaged, i.e. the target position r of the magnetic anomaly0＝(x0，y0，z0)：
Wherein, alpha is the yaw angle,y is the displacement to the midpoint of the line, X is the displacement along the line, and the magnetic anomaly target can only exist on one horizontal plane, so z0Is a constant, the formula (25) is substituted into the formula (24), and since the sliding window is continuously moved along the measuring line, a two-dimensional strip-shaped image σ (X, Y) is finally obtained:
wherein X is a coordinate along the measuring line direction, and Y is a coordinate perpendicular to the measuring line direction;
6) magnetic anomaly target detection and positioning:
defining the probability p (X) that a magnetic anomaly target exists along the inline direction coordinate X:
wherein, YmaxThe maximum displacement from the magnetic anomaly target to the midpoint of the measuring line; if the probability P (X) is larger than a set threshold value, judging that the magnetic abnormal target exists, otherwise, judging that the magnetic abnormal target does not exist; if the target exists, then it is determined to be located at (X, Y)opt) Or (X, -Y)opt)，(X，Yopt) A position coordinate in which the energy ratio σ (X, Y) is maximized; the magnetic anomaly target location now uses the invariant z-component or 3 rd component of the magnetic moment to exclude one of the false locations.
In the step 1), the measuring platform is an aerial manned or unmanned aerial vehicle, a ground manned or unmanned vehicle, a water surface or underwater autonomous vehicle which carries an optical pump magnetometer, a three-component fluxgate magnetometer, a GPS and an attitude instrument.
In the step 2), the length and the sampling rate of the total magnetic field data section are set according to the distance and the resolution of the magnetic anomaly target to be detected; the specific setting method is that the moving distance of the measuring platform is equal to 3-6 times of the distance from the target to the measuring line during the period of total magnetic field data acquisition, for example, the distance from the target to the measuring line to be detected is 10 meters, the measuring platform needs to move about 50 meters along the measuring line when the data is acquired, and if the resolution is 1 meter, the data is acquired every 1 meter of movement.
In step 3) d), the magnetic anomaly target signal is expressed by three adaptive basis functions, wherein the adaptive basis functions are automatically adjusted according to the moving path of the measuring platform, so that the measuring platform does not need to move linearly at a constant speed.
In step 4), the adaptive detector matrix is adopted to solve the interference magnetic field compensation coefficient, the geomagnetic gradient coefficient and the magnetic moment of the magnetic anomaly target generated by the measuring platform integrally, wherein the adaptive detector matrix represents that the detector matrix is continuously updated along with the basis function matrix of the interference magnetic field generated by the measuring platform, the basis function matrix of the geomagnetic gradient, the basis function matrix of the magnetic anomaly target signal and the covariance matrix of the magnetic noise in the day.
In step 4), if it is determined that the magnetic anomaly target signal does not exist in the section of total magnetic field data, deleting the submatrices of the third row and the third column of the matrix A, and only solving a compensation coefficient and a geomagnetic gradient coefficient of the interference magnetic field; if the interference magnetic field and the geomagnetic gradient generated by the measuring platform do not need to be compensated, only the FF subarray in the matrix A needs to be reserved, and the target magnetic moment is solved; for the solution of the system of linear equations (20), a ridge estimation method is used to prevent it from being ill-conditioned.
In step 5), the ratio of the energy of the magnetic anomaly target signal to the energy of the total magnetic field is used as the pixel value of the detected point, and the probability that the magnetic anomaly target exists in the detected point is reflected.
In step 6), the threshold is set by using an adaptive threshold method or a fixed threshold, such as p (x) > 0.5.
In step 6), the target position of the magnetic anomaly is (X, Y)opt) Or (X, -Y)opt) The z component of the magnetic moment or the 3 rd component is an invariant feature, and the position where the z component of the magnetic moment is closer to the true value of a type of target is the position where the true target exists, and once the z component of the magnetic moment is obtained, the z component of the magnetic moment is invariant in the detection process.
The invention has the advantages that:
the invention provides that the magnetic anomaly target signal is expressed by three self-adaptive basis functions, so that the moving path of the measuring platform can be arbitrary and is not limited to uniform linear motion, and the target signal can be detected at the corner of a measuring line; the invention provides a method adopting a self-adaptive detector, which integrally solves model coefficients and separates an interference magnetic field, a geomagnetic gradient field, a daily variable magnetic field and a target signal generated by a measuring platform; the invention provides a magnetic anomaly target imaging method, wherein the pixel value is the ratio of signal energy to data segment energy, and the probability of a target existing in the imaging position is reflected; finally, the invention provides a method for detecting whether a target exists along the direction of a measuring line, also provides a positioning method of the magnetic abnormal target, and eliminates the position of the false magnetic abnormal target by using the unchanged z component or 3 rd component characteristic of the magnetic abnormal target magnetic moment.
Drawings
FIG. 1 is a schematic diagram of a sensor mounted on a measurement platform for detecting a magnetic anomaly target according to the adaptive magnetic anomaly target imaging and detecting method of the present invention;
FIG. 2 is a schematic diagram of magnetic anomaly target imaging for the adaptive magnetic anomaly target imaging and detection method of the present invention;
FIG. 3 is a flow chart of the adaptive magnetic anomaly target imaging and detection method of the present invention.
Detailed Description
The invention will be further elucidated by means of specific embodiments in the following with reference to the drawing.
The adaptive magnetic anomaly target imaging and detecting method of the embodiment, as shown in fig. 3, includes the following steps:
1) data acquisition:
an optical pump magnetometer, a three-component fluxgate magnetometer, a GPS and an attitude instrument are respectively arranged on the measuring platform; before detection, acquiring a section of daily magnetic noise data by an optical pump magnetometer; in actual detection, the optical pump magnetometer measures to obtain total magnetic field data, the three-component fluxgate magnetometer measures to obtain three magnetic field components, the GPS measures to obtain the longitude and latitude and the height of the measuring platform and the measured time, and the attitude gauge measures to obtain the yaw angle, the pitch angle and the roll angle of the measuring platform;
2) establishing a total magnetic field model:
the total magnetic field obtained by the measurement of the optical pump magnetometer comprises four magnetic fields, and the expression is as follows:
Bmea(r，t)＝Bpla(t)+Bgeo(r)+Bion(t)+S(r，r0) (1)
wherein, Bmea(r, t) is total magnetic field measured by optical pump magnetometer, r and t are position and time of measuring platform, Bpla(t) interfering magnetic fields generated by the measuring platform as a function of time, Bgeo(r) is the geological magnetic gradient field, as a function of the observed position, Bion(t) is the magnetic noise of the diurnal variation, as a function of time, S (r, r)0) For the magnetic anomaly target signal, the measurement platform position r and the magnetic anomaly target position r0A function of (a); however, since the measurement platform is moving over time, the total magnetic field, the geological magnetic gradient field, and the magnetic anomaly target signal are all functions of time; the geographical coordinate system is established in such a way that the x-axis points to the north pole along the longitude line, the y-axis points to the east along the latitude line, and the z-axis points vertically to the underground; in the actual detection, the sliding window is used for detecting the total magnetic field data segment by segment, so that the left side of the formula (1) represents a segment of the total magnetic field data; the length and the sampling rate of the data segment are set according to the distance and the resolution of the magnetic anomaly target to be detected; the specific setting method comprises the following steps: during the data acquisition, the moving distance of the measuring platform is equal to 3-6 times of the distance from the target to the measuring line, for example, the moving distance of the measuring platform is equal to 5 times of the distance from the target to the measuring line, the distance from the target to the measuring line to be detected is 10 meters, the measuring platform needs to move about 50 meters along the measuring line when the data is acquired, and if the resolution is 1 meter, the data is acquired every 1 meter;
3) the four magnetic fields are respectively modeled:
a) interference magnetic field model generated by the measuring platform:
using a classical T-L model (Tolles-Lawson), only the inherent magnetic field and the induced magnetic field part are reserved to obtain an interference magnetic field B generated by the measuring platformpla(t)：
Wherein, i is 1iFor compensation factors of disturbing magnetic fields, hi(t) is a basis function for magnetic compensation, and the expression is as follows: h is1(t)＝u1(t)、h2(t)＝u2(t)、h3(t)＝u3(t)、
h5(t)＝u1(t)u2(t)、h6(t)＝u1(t)u3(t)、
h8(t)＝u2(t)u3(t)，u1(t)、u2(t) and u3(t) respectively represents the direction vectors of the local earth magnetic field relative to x, y and z axes of the measurement platform, and the expressions are respectively:
wherein, B1(t)、B2(t) and B3(t) three magnetic field components measured by a three-component fluxgate magnetometer respectively;
b) geological magnetic gradient field model:
for the moving range of the measuring platform not to exceed five times of the distance between the measuring platform and the magnetic anomaly target, fitting the geological magnetic gradient field by adopting a linear function to obtain the geological magnetic gradientDegree field Bgeo(t)：
Bgeo(t)＝g0+g1·[x(t)-x(t0)]+g2·[y(t)-y(t0)]+g3·[z(t)-z(t0)] (6)
Wherein, x (t) is the coordinate of the measuring platform along the x-axis direction along the time change, y (t) is the coordinate of the measuring platform along the y-axis direction along the time change, z (t) is the coordinate of the measuring platform along the z-axis direction along the time change, x (t), y (t) and z (t) are obtained by converting longitude, latitude and height obtained by GPS measurement arranged on the measuring platform, and x (t)0)、y(t0) And z (t)0) As the center position of the piece of coordinate data, g0Is a constant term, g1For magnetic gradients along the x-axis, g2For magnetic gradients in the y-axis direction, g3Is the magnetic gradient along the z-axis direction;
c) the model of the magnetic noise of the daily variation:
the daily variable magnetic noise mainly comes from ionosphere fluctuation, and a covariance matrix of the daily variable magnetic noise is estimated by measuring daily variable magnetic noise data for a period of time in advance and is continuously updated in the detection process; the optical pump magnetometer collects a section of daily variable magnetic noise data d (m) with the length of 2N +1 in advance, m belongs to < -2N,0 >, 0 is the starting time of actual detection, the daily variable magnetic noise data with the length of 2N +1 is written into (N +1) overlapped data sections with the length of (N +1), namely the daily variable magnetic noise data with the length of (N +1) are taken each time, the data are moved backwards one by one, and the data are taken for N +1 times, so that the nth section of daily variable magnetic noise data b (N):
b(n)＝[d(1-n)，d(-n)，...，d(1-n-N)]-d0(n)，n＝1，2...N+1 (7)
where d0(n) represents the mean of this segment of data, the covariance matrix R of the estimated diurnal magnetic noisennComprises the following steps:
Rnn＝[ri，j]，i，j＝1，2...N+1 (8)
wherein, the nth segment of the daily-variation magnetic noise data b (N) with the length of (N +1) is the data from the daily-variation magnetic noise data (d (m), m ∈ [ -2N,0]) D (1-N-N) represents the diurnal magnetic noise data points in the nth segment of the diurnal magnetic noise data, ri，jIs the value of the ith row and the jth column of the covariance matrix of the magnetic noise of the daily variation, b (i) and b (j) are respectively the data (d (m) from the magnetic noise of the daily variation, m ∈ [ -2N,0]) The segmented ith and jth sections of the daily variable magnetic noise data with the length of (N +1) are intercepted, d (1-i-k) is a daily variable magnetic noise value corresponding to a kth point in the ith section of the daily variable magnetic noise data b (i), d (1-j-k) is a daily variable magnetic noise value corresponding to a kth point in the jth section of the daily variable magnetic noise data b (j), and k is 0, 1.. N; the size of the sliding window is N +1 sampling points; if a new section of daily magnetic noise data with the length of (N +1) is obtained in subsequent measurement, the new section of daily magnetic noise data is assigned to b (1), the previous sections b (1) -b (N) are assigned to b (2) -b (N +1), the previous sections b (N +1) are abandoned, and the covariance matrix R is updated and calculatednn；
d) Magnetic anomaly target signal model:
regarding the magnetic abnormal target signal as a vector field generated by a magnetic dipole in the local earth magnetic field direction
The projection is shown in FIG. 1, the measuring platform moves along the X direction, and the angle between the measuring platform and the X axis is the heading angle or the yaw angle, the X axis is defined to point to the geographic north pole, and the magnetic anomaly target signal S (r, r)0)：
Wherein, mu0Is the vacuum permeability, r is the observation point position, r0Is the position of the magnetic abnormal target, R is the position vector from the magnetic abnormal target to the measuring platform, R is the distance from the magnetic abnormal target to the measuring platform,
is taken as a unit of a dyadic vector,m is the magnetic abnormal target moment, MiThree components of target magnetic moment for magnetic anomaly, i ═ 1,2,3, M ═ M1，M2，M3]TThe superscript T denotes transposition, Fi(r，r0) Is the basis function of the magnetic anomaly target signal, the expansion is expressed as:
wherein R is1＝x(t)-x0，R2＝y(t)-y0，R3＝z(t)-z0X (t) is the coordinate of the measuring platform along the x-axis direction, y (t) is the coordinate of the measuring platform along the y-axis direction, z (t) is the coordinate of the measuring platform along the z-axis direction, x0、y0And z0Is the coordinate position of the magnetic anomaly target, where x0＝x(t0)-Ysinα，y0＝y(t0)+Ycosα，z0＝z(t0)+const，x(t0)、y(t0) And z (t)0) For the center position of the piece of coordinate data, α is the yaw angle of the measurement platform, Y is the displacement of the target to the midpoint of the line of sight, and it is assumed that the magnetically anomalous target can only exist in one horizontal plane, so z0Considered as a constant;
wherein alpha, theta and beta are respectively a yaw angle, a pitch angle and a roll angle of the measuring platform and are measured by an attitude instrument; for a magnetically abnormal target moment M, it is considered to be the sum of the intrinsic and induced moments, i.e.
Wherein M isperIs the inherent magnetic moment of the magnetic abnormal target, is invariant to the target coordinate system, C is a constant vector related to the magnetization characteristic of the magnetic abnormal target, BgeoIs the local earth's magnetic field and,
is a transformation matrix from the geographical coordinate system to the target coordinate system,
for target orientation, "+" indicates a direct product; it should be noted that the z component or 3 rd component of the intrinsic, induced and total magnetic moments is invariant regardless of how the magnetic anomaly target moves in the horizontal plane, so this component is referred to as the invariant feature of the magnetic anomaly target, and the magnetic anomaly signal component generated by it is also referred to as the invariant feature of the magnetic anomaly target, meaning it is independent of target orientation;
4) solving the model coefficients:
i. establishing a coefficient equation by combining the models established in the step 2) and the step 3):
writing equation (1) to the form:
and then writing into a matrix form:
wherein B is a measured total magnetic field B with a length of N +1meaData or (N +1) × 1 column vectors, i.e. B ═ Bmea(t-N/2)，...，Bmea(t0)，...，Bmea(tN/2)]Where v represents an unknown diurnal variation magnetic noise of length N + 1; h is a basic function matrix of the interference magnetic field generated by the measuring platform, is an (N +1) multiplied by 8 matrix,
the compensation coefficient vector of the interference magnetic field generated for the measuring platform is an 8 multiplied by 1 column vector; g is a basis function matrix of the geological magnetic gradient field, which is an (N +1) x 4 matrix,
is geomagnetic gradient coefficient column vector; f is a basis function matrix of the magnetic anomaly target signal, is an (N +1) multiplied by 3 matrix,
is a magnetic anomaly target magnetic moment vector;
according to the optimal signal detection theory, establishing an adaptive detector matrix as follows:
wherein the subscript 15 × (N +1) indicates the size of this matrix,
the inverse of the covariance matrix of the diurnal magnetic noise, H, obtained by solving for equation (8)TTransposing the basis function matrix H of the disturbing magnetic field generated for the measuring platform, GTAs a transpose of the basis function matrix G of the geological magnetic field, FTTranspose of basis function matrix F for magnetic anomaly target signal;
applying equation (19) to equation (18) to obtain the following equation:
c＝[c1 c2 … c8]T，g＝[g0 g1 g2 g3]T，M＝[M1 M2 M3]T (21)
Wherein the content of the first and second substances,
dcinvolving disturbing magnetic fields generated by the measuring platform, dgInvolving the geomagnetic gradient field, dmTo a magnetic anomaly target signal;
solving coefficients:
solving the formula (20), and simultaneously obtaining an interference magnetic field compensation coefficient, a geomagnetic gradient coefficient and a magnetic abnormal target magnetic moment generated by the measuring platform; if it is determined that the magnetic anomaly target signal does not exist in the section of total magnetic field data, the submatrices of the third row and the third column of the matrix A are deleted, and only the compensation coefficient and the geomagnetic gradient coefficient of the interference magnetic field need to be solved; if the interference magnetic field and the geomagnetic gradient generated by the measuring platform do not need to be compensated, only the FF subarrays in the matrix A need to be reserved, and the target magnetic moment is solved; for the solution of the system of linear equations (20), a ridge estimation method is used to prevent it from being ill-conditioned;
5) imaging a magnetic anomaly target:
after solving the compensation coefficient, the gradient coefficient and the target magnetic moment, multiplying the compensation coefficient, the gradient coefficient and the target magnetic moment by respective basis functions to obtain respective components, namely decomposing the measured total magnetic field data into an interference magnetic field part, a geological magnetic gradient abnormal part, a target magnetic signal part and the remaining daily variable magnetic noise part which are generated by the measuring platform, wherein the daily variable magnetic noise data is used for updating the covariance matrix of the formula (8); the ratio sigma (r) of the energy of the magnetic anomaly target signal contained in the segment of total magnetic field data to the energy of the segment of total magnetic field0) The following calculations were made:
if the magnetic anomaly target signal in the segment of data is dominant, then σ (r)0) Close to 1, otherwise close to zero; taking the ratio as the value of a pixel point, and storing the value in r0Coordinate position, obtaining all pixels of a region to form an image; as shown in fig. 2, where the black curve is the moving track of the measuring platform, i.e. the line measuring direction, the square represents the sliding window, Δ X is the resolution along the line measuring direction, and Δ Y is the resolution perpendicular to the line measuring direction, for the total magnetic field data in a section of the sliding window, only the point on a straight line perpendicular to the line measuring direction and passing through the center point is imaged, i.e. the magnetic anomaly target position r0＝(x0，y0，z0)：
Where α is the yaw angle, Y is the displacement to the midpoint of the line of sight, X is the displacement along the line of sight, and the magnetic anomaly target can only exist on one horizontal plane, so z is0Is a constant, substituting the formula (25) intoIn equation (24), since the sliding window is continuously moved along the measuring line, a two-dimensional strip image σ (X, Y) is finally obtained:
wherein X is a coordinate along the measuring line direction, and Y is a coordinate perpendicular to the measuring line direction;
6) magnetic anomaly target detection and positioning:
defining the probability p (X) that a magnetic anomaly target exists along the line-direction coordinate X:
wherein, YmaxThe maximum displacement from the magnetic anomaly target to the midpoint of the measuring line; if the probability P (X) is larger than a set threshold value, judging that the magnetic abnormal target exists, otherwise, judging that the magnetic abnormal target does not exist; for setting the threshold, an adaptive threshold method can be used; since the adaptive detector implies a constant false alarm characteristic, a fixed threshold may also be used; if the target exists, then it is determined to be located at (X, Y)opt)，(X，Yopt) A position coordinate in which the energy ratio σ (X, Y) is maximized; in general, (X, Y)opt) And (X, -Y)opt) The magnetic anomaly target positions may be all magnetic anomaly target positions, and a false position can be excluded by using the invariant magnetic moment z component or the 3 rd component, because the magnetic moment z component or the 3 rd component is an invariant feature, the position where the magnetic moment z component is closer to the true value of a type of target is the position where the true target exists, and the magnetic moment z component is invariant in the detection process once obtained.
It is finally noted that the disclosed embodiments are intended to aid in the further understanding of the invention, but that those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of the invention and the appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.
Claims (9)
1. An adaptive magnetic anomaly target imaging and detecting method is characterized by comprising the following steps:
1) data acquisition:
an optical pump magnetometer, a three-component fluxgate magnetometer, a GPS and an attitude instrument are respectively arranged on the measuring platform; before detection, acquiring a section of daily magnetic noise data by an optical pump magnetometer; in actual detection, the optical pump magnetometer measures to obtain total magnetic field data, the three-component fluxgate magnetometer measures to obtain three magnetic field components, the GPS measures to obtain the longitude and latitude and the height of the measuring platform and the measured time, and the attitude gauge measures to obtain the yaw angle, the pitch angle and the roll angle of the measuring platform;
2) establishing a total magnetic field model:
the total magnetic field obtained by the measurement of the optical pump magnetometer comprises four magnetic fields, and the expression is as follows:
Bmea(r,t)＝Bpla(t)+Bgeo(r)+Bion(t)+S(r,r0) (1)
wherein, Bmea(r, t) is total magnetic field measured by optical pumping magnetometer, r and t are position and time of measuring platform, Bpla(t) interfering magnetic fields generated by the measuring platform as a function of time, Bgeo(r) is the geological magnetic gradient field, as a function of the observed position, Bion(t) is the magnetic noise of the diurnal variation, as a function of time, S (r, r)0) For the magnetic anomaly target signal, the measurement platform position r and the magnetic anomaly target position r0A function of (a); however, since the measurement platform is moving over time, the total magnetic field, the geomagnetic gradient field, and the magnetic anomaly target signal are all functions of time; the geographical coordinate system is established in such a way that the x-axis points to the north pole along the longitude line, the y-axis points to the east along the latitude line and the z-axis points vertically to the underground; in the detection process, the measured total magnetic field data is subjected to segmented detection;
3) the four magnetic fields are respectively modeled:
a) interference magnetic field model generated by the measuring platform:
using a classical T-L model, only retaining the inherent magnetic field and the induced magnetic field part to obtain an interference magnetic field B generated by the measuring platformpla(t)：
Wherein i is 1, …,8, ciFor compensation factors of disturbing magnetic fields, hi(t) is a basis function for magnetic compensation, and the expression is as follows: h is1(t)＝u1(t)、h2(t)＝u2(t)、h3(t)＝u3(t)、
h5(t)＝u1(t)u2(t)、h6(t)＝u1(t)u3(t)、
h8(t)＝u2(t)u3(t)，u1(t)、u2(t) and u3(t) respectively represents the direction vectors of the local earth magnetic field relative to x, y and z axes of the measurement platform, and the expressions are respectively:
wherein, B1(t)、B2(t) and B3(t) three-component fluxgate magnetic forces, respectivelyThree magnetic field components obtained by measurement of the instrument;
b) geological magnetic gradient field model:
for the moving range of the measuring platform not to exceed five times of the distance between the measuring platform and the magnetic anomaly target, fitting the geological magnetic gradient field by adopting a linear function to obtain a geological magnetic gradient field Bgeo(t)：
Bgeo(t)＝g0+g1·[x(t)-x(t0)]+g2·[y(t)-y(t0)]+g3·[z(t)-z(t0)] (6)
Wherein, x (t) is the coordinate of the measuring platform along the x-axis direction along the time change, y (t) is the coordinate of the measuring platform along the y-axis direction along the time change, z (t) is the coordinate of the measuring platform along the z-axis direction along the time change, x (t), y (t) and z (t) are obtained by converting longitude, latitude and height obtained by GPS measurement arranged on the measuring platform, and x (t)0)、y(t0) And z (t)0) As the center position of the piece of coordinate data, g0Is a constant term, g1For magnetic gradients along the x-axis, g2For magnetic gradients in the y-axis direction, g3Is the magnetic gradient along the z-axis direction;
c) the model of the magnetic noise of the daily variation:
the daily variable magnetic noise mainly comes from ionosphere fluctuation, and a covariance matrix of the daily variable magnetic noise is estimated by measuring daily variable magnetic noise data for a period of time in advance and is continuously updated in the detection process; the optical pump magnetometer collects a section of daily variable magnetic noise data d (m) with the length of 2N +1 in advance, m belongs to < -2N,0 >, 0 is the starting time of actual detection, the daily variable magnetic noise data with the length of 2N +1 is written into (N +1) overlapped data sections with the length of (N +1), namely the daily variable magnetic noise data with the length of (N +1) are taken each time, the data are moved backwards one by one, and the data are taken for N +1 times, so that the nth section of daily variable magnetic noise data b (N):
b(n)＝[d(1-n),d(-n),…,d(1-n-N)]-d0(n),n＝1,2…N+1 (7)
where d0(n) represents the mean of this segment of data, the covariance matrix R of the estimated diurnal magnetic noisennComprises the following steps:
Rnn＝[ri,j],i,j＝1,2…N+1 (8)
wherein, the nth segment of the daily-variation magnetic noise data b (N) with the length of (N +1) is the data from the daily-variation magnetic noise data (d (m), m ∈ [ -2N,0]) The nth section, d (1-N), d (-N) … d (1-N-N) of the truncation represents the diurnal magnetic noise data points in the nth section of the diurnal magnetic noise data, ri,jIs the value of the ith row and the jth column of the covariance matrix of the magnetic noise of the daily variation, b (i) and b (j) are respectively the data (d (m) from the magnetic noise of the daily variation, m ∈ [ -2N,0]) The segmented ith and jth sections of the daily variable magnetic noise data with the length of (N +1), d (1-i-k) is a daily variable magnetic noise value corresponding to a kth point in the ith section of the daily variable magnetic noise data b (i), d (1-j-k) is a daily variable magnetic noise value corresponding to a kth point in the jth section of the daily variable magnetic noise data b (j), and k is 0 and 1 … N; the size of the sliding window is N +1 sampling points; if a new section of daily magnetic noise data with the length of (N +1) is obtained in subsequent measurement, the new section of daily magnetic noise data is assigned to b (1), the previous sections b (1) -b (N) are assigned to b (2) -b (N +1), the previous sections b (N +1) are abandoned, and the covariance matrix R is updated and calculatednn；
d) Magnetic anomaly target signal model:
regarding the magnetic abnormal target signal as a vector field generated by a magnetic dipole in the local earth magnetic field direction
Projection of (d), magnetic anomaly target signal S (r, r)0)：
Wherein, mu0Is the vacuum permeability, r is the observation point position, r0Is the position of the magnetic abnormal target, R is the position vector from the magnetic abnormal target to the measuring platform, R is the distance from the magnetic abnormal target to the measuring platform,
is a unit dyadic, M is a magnetic abnormal target moment, MiThree components of target magnetic moment for magnetic anomaly, i ═ 1,2,3, M ═ M1,M2,M3]TThe superscript T denotes transposition, Fi(r,r0) Is the basis function of the magnetic anomaly target signal, and the expansion is expressed as:
wherein R is1＝x(t)-x0,R2＝y(t)-y0，R3＝z(t)-z0X (t) is the coordinate of the measuring platform along the x-axis direction, y (t) is the coordinate of the measuring platform along the y-axis direction, z (t) is the coordinate of the measuring platform along the z-axis direction, x0、y0And z0Is the coordinate position of the magnetic anomaly target, where x0＝x(t0)-Ysinα，y0＝y(t0)+Ycosα，z0＝z(t0)+const，x(t0)、y(t0) And z (t)0) For the center position of the piece of coordinate data, α is the yaw angle of the measurement platform, Y is the displacement of the target to the midpoint of the line of sight, and it is assumed that the magnetically anomalous target can only exist in one horizontal plane, so z0Considered as a constant;
and
respectively the direction of the earth's magnetic field
Along the x, y and z axes, the expression:
wherein alpha, theta and beta are respectively a yaw angle, a pitch angle and a roll angle of the measuring platform and are measured by an attitude instrument; for a magnetically abnormal target moment M, it is considered to be the sum of the intrinsic and induced moments, i.e.
Wherein M isperIs the inherent magnetic moment of the magnetic abnormal target, is invariant to the target coordinate system, C is a constant vector related to the magnetization characteristic of the magnetic abnormal target, BgeoIs the local earth's magnetic field and,
is a transformation matrix from the geographical coordinate system to the target coordinate system,
for target orientation, "+" indicates a direct product; it should be noted that the z-component or 3 rd component of the intrinsic, induced and total magnetic moments is invariant regardless of the movement of the magnetic anomaly target in the horizontal plane, and therefore this component is referred to as the invariant feature of the magnetic anomaly target, the magnetic anomaly signal component produced by itAlso known as invariant features of the magnetic anomaly target, meaning that it is independent of target orientation;
4) solving the model coefficients:
i. establishing a coefficient equation by combining the models established in the step 2) and the step 3):
writing equation (1) as follows:
and then writing into a matrix form:
wherein B is a measured total magnetic field B with a length of N +1meaData or (N +1) × 1 column vectors, i.e. B ═ Bmea(t-N/2),…,Bmea(t0),…,Bmea(tN/2)]Where v represents an unknown diurnal variation magnetic noise of length N + 1; h is a basic function matrix of the interference magnetic field generated by the measuring platform, is an (N +1) multiplied by 8 matrix,
the compensation coefficient vector of the interference magnetic field generated for the measuring platform is an 8 multiplied by 1 column vector; g is a basis function matrix of the geological magnetic gradient field, which is an (N +1) x 4 matrix,
is geomagnetic gradient coefficient column vector; f is a basis function matrix of the magnetic anomaly target signal, is an (N +1) multiplied by 3 matrix,
is a magnetic anomaly target magnetic moment vector;
according to the optimal signal detection theory, establishing an adaptive detector matrix as follows:
wherein the content of the first and second substances,
the inverse of the daily variable magnetic noise covariance matrix, H, solved for equation (8)TTransposing the basis function matrix H of the disturbing magnetic field generated for the measuring platform, GTAs a transpose of the basis function matrix G of the geological magnetic field, FTTranspose of basis function matrix F for magnetic anomaly target signal;
applying equation (19) to equation (18) to obtain the following equation:
c＝[c1 c2 … c8]T，g＝[g0 g1 g2 g3]T，M＝[M1 M2 M3]T (21)
wherein the content of the first and second substances,
dcinvolving disturbing magnetic fields generated by the measuring platform, dgInvolving the geomagnetic gradient field, dmTo a magnetic anomaly target signal;
solving coefficients:
solving the formula (20), and simultaneously obtaining an interference magnetic field compensation coefficient, a geomagnetic gradient coefficient and a magnetic abnormal target magnetic moment generated by the measuring platform;
5) imaging a magnetic anomaly target:
the ratio sigma (r) of the energy of the magnetic anomaly target signal contained in the segment of total magnetic field data to the energy of the segment of total magnetic field0) The following calculations were made:
if the magnetic anomaly target signal in the segment of data is dominant, then σ (r)0) Close to 1, otherwise close to zero; taking the ratio as the value of a pixel point, and storing the value in r0Coordinate position, obtaining all pixels of a region to form an image; for a given section of total magnetic field data, only points on a straight line perpendicular to the direction of the survey line and passing through the center point are imaged, i.e. the target position r of the magnetic anomaly0＝(x0,y0,z0)：
Where α is the yaw angle, Y is the displacement to the midpoint of the line of sight, X is the displacement along the line of sight, and the magnetic anomaly target can only exist on one horizontal plane, so z is0Is a constant, the formula (25) is substituted into the formula (24), and since the sliding window is continuously moved along the measuring line, a two-dimensional strip-shaped image σ (X, Y) is finally obtained:
wherein X is a coordinate along the measuring line direction, and Y is a coordinate perpendicular to the measuring line direction;
6) magnetic anomaly target detection and positioning:
defining the probability p (X) that a magnetic anomaly target exists along the line-direction coordinate X:
wherein, YmaxThe maximum displacement from the magnetic anomaly target to the midpoint of the measuring line; if the probability P (X) is larger than a set threshold value, judging that the magnetic abnormal target exists, otherwise, judging that the magnetic abnormal target does not exist; if the target exists, then it is determined to be located at (X, Y)opt) Or (X, -Y)opt)，(X,Yopt) A position coordinate in which the energy ratio σ (X, Y) is maximized; the magnetic anomaly target location now uses the invariant z-component or 3 rd component of the magnetic moment to exclude one of the false locations.
2. The adaptive magnetic anomaly target imaging and detecting method according to claim 1, wherein in step 1), the measuring platform is an aerial manned or unmanned aerial vehicle, a ground manned or unmanned vehicle, a water surface or underwater autonomous vehicle carrying an optical pump magnetometer, a three-component fluxgate magnetometer, a GPS and an attitude indicator.
3. The adaptive magnetic anomaly target imaging and detection method as claimed in claim 1, wherein in step 2), the length and sampling rate of the segments of the total magnetic field data are set according to the distance and resolution of the magnetic anomaly target to be detected.
4. The adaptive magnetic anomaly target imaging and detecting method as claimed in claim 1, wherein in step 3) d), the magnetic anomaly target signal is expressed by three adaptive magnetic anomaly target signal basis functions, where the adaptive representation basis functions are automatically adjusted according to the moving path of the measuring platform, so that the platform does not need to move linearly at a constant speed.
5. The adaptive magnetic anomaly target imaging and detection method as claimed in claim 1, wherein in step 4) iii, the adaptive detector matrix is used to solve the interference magnetic field compensation coefficient, the geomagnetic gradient coefficient and the magnetic moment of the magnetic anomaly target generated by the measurement platform integrally, wherein the adaptive detector matrix represents that the detector matrix is continuously updated along with the basis function matrix of the interference magnetic field, the basis function matrix of the geomagnetic gradient, the basis function matrix of the magnetic anomaly target signal and the covariance matrix of the magnetic noise in day-to-day variation along with the advance of detection.
6. The adaptive magnetic anomaly target imaging and detecting method as claimed in claim 1, wherein in step 4) iii, if it is determined that no magnetic anomaly target signal exists in a section of total magnetic field data, the sub-arrays in the third row and the third column of the matrix a are deleted, and only the compensation coefficient and the geomagnetic gradient coefficient of the interference magnetic field need to be solved; if the interference magnetic field and the geomagnetic gradient generated by the measuring platform do not need to be compensated, only the FF subarray in the matrix A needs to be reserved, and the target magnetic moment is solved; for the solution of the system of linear equations (20), a ridge estimation method is used to prevent it from being ill-conditioned.
7. The adaptive magnetic anomaly target imaging and detection method as claimed in claim 1, wherein in step 5), the ratio of the energy of the magnetic anomaly target signal to the energy of the total magnetic field is used as the pixel value of the detected point, reflecting the probability of the magnetic anomaly target existing at the detected point.
8. The adaptive magnetic anomaly target imaging and detection method as claimed in claim 1, wherein in step 6), the set threshold uses an adaptive threshold method or a fixed threshold.
9. The adaptive magnetic anomaly target imaging and detecting method as claimed in claim 1, wherein in step 6), the magnetic anomaly target position is (X, Y)opt) Or (X, -Y)opt) Magnetic moment z minuteThe quantity or 3 rd component is an invariant feature, and the position where the z component of the magnetic moment is closer to the true value of a class of targets is the position where the true target exists, and once the z component of the magnetic moment is obtained, the z component of the magnetic moment is invariable in the detection process.
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