CN110146839B - Correction method for magnetic gradient tensor system of mobile platform - Google Patents
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Abstract
The invention discloses a correction method of a magnetic gradient tensor system of a mobile platform, which comprises the following steps: s1, modeling the system error of the single sensor; s2, establishing a compensation model for the system error of the single sensor; s3, enabling the magnetic gradient tensor system to randomly change the posture in the three-dimensional space to acquire data, and measuring the total magnetic field in the experimental environment by using a proton magnetometer; s4, taking the total magnetic field in the experimental environment as the standard, inverting the data collected by the fluxgate sensor to obtain the system error parameter of the sensor, the correction method used by the invention gets rid of the dependence on the high-precision three-axis rotating table, only needs to collect data at any posture in the three-dimensional space, is convenient and easy to implement, can eliminate the influence of the three-axis rotating table error on the correction result, has higher inversion precision, and the simulation result shows that the system error parameter estimation error can reach the level of one thousandth, can greatly improve the correction precision of the magnetic gradient tensor system, and improve the performance of the magnetic gradient tensor system.
Description
Technical Field
The invention relates to the technical field of a magnetic gradient tensor system of a mobile platform, in particular to a correction method of the magnetic gradient tensor system of the mobile platform.
Background
Currently, a magnetic gradient tensor system based on a fluxgate sensor or a superconducting quantum interferometer is widely applied, and compared with the traditional magnetic vector measurement, the magnetic gradient tensor system has the advantages of being more sensitive to magnetic anomaly signals, high in spatial resolution, capable of providing more valuable information and the like, and is widely applied to various fields of aviation magnetic detection and navigation, search of unexploded bombs, reconnaissance and positioning of moving magnetic targets and the like.
Most of magnetic gradient tensor systems composed of fluxgate sensors adopt a difference approximation method principle to acquire data, and tensor measurement accuracy is influenced by single sensor system errors (zero offset, three-axis non-orthogonal errors and three-axis sensitivity inconsistency of the sensors) and multi-sensor non-alignment errors of the magnetic gradient tensor systems due to the limitation of a sensor manufacturing process and the installation accuracy of the magnetic gradient tensor systems. These errors, if not corrected, can amount to hundreds to thousands nT/m of tensor measurement errors, which can be more intense than the magnetic anomaly signal, and must be corrected.
Some researchers have studied the correction method of the magnetic gradient tensor system and achieved good results. A commonly used method for correcting a magnetic gradient tensor system can be divided into two steps: the first step is to correct the system error of a single sensor, and the correction of the system error of the single sensor can be divided into a vector correction method and a scalar correction method, wherein the vector correction method is to measure the geomagnetic field vector by adopting a high-precision vector magnetic field sensor and correct the fluxgate sensor by taking the geomagnetic field vector as a standard, and the method needs to measure the accurate geomagnetic field vector, so the cost is high. The scalar correction method adopts a high-precision proton magnetometer to measure the total geomagnetic field and corrects the fluxgate sensor by taking the total geomagnetic field as a standard, and the method is more convenient and has low cost; the second step is to correct the non-alignment error of the magnetic gradient tensor system, and correct the multiple sensors to the same coordinate system, wherein one method is to use one sensor in the magnetic gradient tensor system as a reference to correct the other sensors to the sensor coordinate system, and the other method is to use the magnetic gradient tensor system carrying platform coordinate system as a reference to correct the sensors in the magnetic gradient tensor system to the platform coordinate system, Yin gan et al estimate the error parameters by constructing a system error linear equation set, and the correction result is: the maximum estimation error of the sensitivity factor is 1.25%, the maximum estimation error of zero offset is 14.05%, the maximum estimation error of a triaxial non-orthogonal angle is 3.17%, the maximum estimation error of a non-alignment angle of a magnetic gradient tensor system is 6.77%, Pang Hongfeng et al use a vector method for correction, and adopt nonlinear fitting to estimate error parameters, and Yun Gang, Liqing bamboo et al realize integrated correction of the magnetic gradient tensor system based on a least square ellipsoid fitting method, and simulation results show that the root mean square error of the total magnetic field of the geomagnetic field can be reduced to be lower than 1nT, and the root mean square error of tensor components can be reduced to be lower than 2 nT/m.
In the step-by-step correction method, one method has larger estimation error, and the vector method is adopted for correction, so the cost is higher; in the integrated correction method, a high-precision three-axis nonmagnetic rotating table is required to provide standard azimuth information, the precision requirement on the rotating table is high, and the error of the three-axis rotating table can influence the correction result.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a method for correcting a magnetic gradient tensor system of a moving platform.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for correcting a magnetic gradient tensor system of a moving platform comprises the following steps:
s1, modeling the system error of the single sensor;
s2, establishing a compensation model for the system error of the single sensor;
s3, enabling the magnetic gradient tensor system to randomly change the posture in the three-dimensional space to acquire data, and measuring the total magnetic field in the experimental environment by using a proton magnetometer;
s4, taking the total magnetic field in the experimental environment as a standard, inverting the data collected by the fluxgate sensor to obtain a system error parameter of the sensor;
s5, modeling the misalignment error of the magnetic gradient tensor system;
s6, establishing a compensation model for the non-alignment error of the magnetic gradient tensor system;
s7, placing the magnetic gradient tensor system in a horizontal table to acquire data;
s8, inverting the data collected by the magnetic gradient tensor system to obtain a non-alignment error parameter of the magnetic gradient tensor system;
s9, carrying the magnetic gradient tensor system on the mobile platform, enabling the mobile platform to measure according to a pre-designed measuring line, and collecting magnetic field data;
and S10, correcting the acquired magnetic field data by using the sensor system error parameters and the magnetic gradient tensor system misalignment errors obtained in S4 and S8, and calculating tensor components.
Preferably, in S3, the magnetic gradient tensor system is enabled to arbitrarily change the posture of the acquired data in the three-dimensional space, and the high-precision three-axis rotation table is not required to provide the standard orientation information.
Preferably, in S4, the data collected by the fluxgate sensor is inverted by using a modified differential evolution algorithm.
Preferably, in S7, the method for acquiring data in the horizontal stage by the magnetic gradient tensor system is: firstly, enabling the X axis of a sensor to be vertical to a horizontal table, and acquiring a series of data by an arbitrary rotating magnetic gradient tensor system; then enabling the Y axis of the sensor to be vertical to the horizontal table, and acquiring a series of data by an arbitrary rotating magnetic gradient tensor system; and finally, enabling the Z axis of the sensor to be vertical to the horizontal table, and acquiring a series of data by an arbitrary rotating magnetic gradient tensor system.
Preferably, in S8, the data acquired by the magnetic gradient tensor system is inverted using a modified differential evolution algorithm.
The invention has the beneficial effects that:
1. the magnetic gradient tensor system is corrected by a common two-step method, a scalar correction method is adopted in the first step of correction, the cost is low, the realization is easy, and in the second step, all sensors in the magnetic gradient tensor system are corrected into a coordinate system of a mobile platform, so that the processing and the interpretation of data are facilitated;
2. the search strategy of the differential evolution algorithm is improved, the inversion precision is high, and a simulation result shows that the system error parameter estimation error can reach the level of one thousandth, so that the correction precision of the magnetic gradient tensor system can be improved to a great extent, and the performance of the magnetic gradient tensor system is improved;
in conclusion, the correction method used by the invention gets rid of the dependence on a high-precision three-axis rotating platform, only needs to acquire data at any posture in a three-dimensional space, is convenient and easy to implement, can eliminate the influence of errors of the three-axis rotating platform on a correction result, has a wide application range, and can be used for correcting various magnetic gradient tensor systems carried on mobile platforms (scientific investigation ships, underwater aircrafts such as AUV (autonomous underwater vehicle), Glider (global navigation system), Argo buoys, aircrafts such as helicopters and unmanned aerial vehicles, and ground vehicles).
Drawings
FIG. 1 is a schematic diagram of a non-orthogonal model of a sensor;
FIG. 2 is a flow chart of a magnetic gradient tensor system correction;
FIG. 3 is a comparison graph of the total geomagnetic field measured by the sensor before and after correction;
FIG. 4 is a schematic diagram of a magnetic gradient tensor system coordinate system and a mobile platform coordinate system;
FIG. 5 is a comparison of tensor components Bxx measured by the magnetic gradient tensor system before and after correction;
FIG. 6 is a schematic diagram of a "cruciform" magnetic gradient tensor system;
FIG. 7 is a comparison graph of tensor components Bxx before and after correction;
fig. 8 is a comparison chart of tensor components Byy before and after correction.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1-8, a mobile platform magnetic gradient tensor system correction method includes the following steps:
s1, pairThe method comprises the steps of modeling a single sensor system error, wherein the single sensor system error comprises zero offset, triaxial non-orthogonal error and triaxial sensitivity inconsistent error, under an ideal condition, three axes of the triaxial fluxgate sensor are kept completely orthogonal, but due to the limitation of a manufacturing process, the sensor generally has the non-orthogonal error. The non-orthogonal model of the sensor is established as shown in fig. 1, wherein: O-XYZ as a standard reference coordinate system, O-X1Y1Z1For the actual coordinate system of the sensor, O-X2Y2Z2Is an ideal orthogonal coordinate system, O is the origin of coordinates, OZ1And OZ2Coaxial, Y1OZ1And Y2OZ2Coplanar. Let psi be the axis OY1And OY2The included angle between the two parts is included,
is axis OX1With surface X2OY2Theta is the axis OX2And axis OX1In the plane X2OY2On projection OX'1The included angle therebetween. Non-orthogonal angle
Once θ, ψ is determined, the ideal orthogonal system O-X of the sensor2Y2Z2And then uniquely determined.
Under the ideal condition, the output of the sensor should be zero in a nonmagnetic environment, but the triaxial output of the sensor is not zero due to the existence of residual magnetism in a magnetic core or zero offset in a circuit part, and the triaxial zero offset of the sensor can reach dozens of nT under the general condition; in addition, due to the limitation of the manufacturing process, the triaxial magneto-sensitive elements cannot be guaranteed to be completely consistent, so that the triaxial sensitivity is inconsistent.
Let us assume that the sensor triaxial null is I ═ Ix,iy,iz) The sensitivity scale factor is C ═ Cx,cy,cz) The actual output of the sensor is B1＝(B1x,B1y,B1z)TThe ideal output is B2＝(B2x,B2y,B2z)TThen, then
S2, establishing a compensation model for the system error of the single sensor, wherein the formula 1 is the ideal output B of the sensor2To the actual output B of the sensor1In the actual situation, we know the actual output B of the sensor1The system error of the sensor needs to be corrected to obtain the ideal output B of the sensor2The actual output B of the sensor can be obtained by inverse transformation of the formula 11To the ideal output B of the sensor2The conversion relationship of (1).
It is expressed in matrix form as
As shown in formula 3, if the compound is obtained
θ,ψ,cx,cy,cz,ix,iy,izNine parameters are used for completing the system error correction of the fluxgate sensor;
and S3, enabling the magnetic gradient tensor system to randomly change the posture in the three-dimensional space to acquire data, measuring the total magnetic field in the experimental environment by using the proton magnetometer, and searching an experimental site with a stable geomagnetic field to acquire data. After the configuration of the magnetic gradient tensor system is completed, the magnetic gradient tensor system is rotated to enable the magnetic gradient tensor system to acquire magnetic field data of any direction in a three-dimensional space, a proton magnetometer is used for measuring the total geomagnetic field in the same experimental area for a period of time, and the average value is taken as the local total geomagnetic field.
In simulation, assuming that the total field of the geomagnetic field is 52000nT, the declination angle is-7 degrees and the declination angle is 54 degrees, 5832 sensor postures are randomly generated, the magnetic field measured by the sensor in each posture is calculated, and random errors with the variance of 1nT and the mean value of 0nT are added in the simulation to serve as measurement noise.
In the simulation, the setting errors of the respective sensors are shown in table 1.
TABLE 1 setting errors of individual sensors
And S4, taking the total magnetic field in the experimental environment as a standard, and inverting the data acquired by the fluxgate sensor by using an improved differential evolution algorithm to obtain the system error parameter of the sensor. Ideally, the total geomagnetic field measured by each sensor in the magnetic gradient tensor system should be consistent with the total geomagnetic field measured by the proton magnetometer, but due to the existence of the systematic error of the fluxgate sensor, the total geomagnetic field measured by the fluxgate sensor has a certain deviation from the actual situation, a systematic error compensation model of a single sensor has been established in S2, in this step, the systematic error of each fluxgate sensor is estimated by using the total geomagnetic field measured by the proton magnetometer as a standard, i.e. an improved differential evolution algorithm is used to estimate the systematic error of each fluxgate sensor
θ,ψ,cx,cy,cz,ix,iy,izAnd carrying out inversion on the nine parameters until the error between the total geomagnetic field measured by the fluxgate sensor and the standard magnetic field measured by the proton magnetometer reaches an acceptable range.
In recent years, with the improvement of computer performance, evolutionary computation is fully developed, a differential evolutionary algorithm belongs to one of evolutionary algorithms, is a population-based parallel iterative optimization algorithm, has the performance mainly controlled by parameters such as variation factors, cross operators, population scale and the like, has the advantages of simple structure, rapid convergence, strong robustness and the like, and is successfully applied to specific problems such as data mining, mode recognition, multi-objective optimization and the like.
However, the standard differential evolution algorithm has the defects of premature convergence, low search efficiency and the like, and the optimization capability of the algorithm is limited.
In the differential evolution algorithm, other random model vectors different from the random vector are used for generating a differential vector to perform mutation operation on the random vector. The mutation strategy proved to be excellent is as follows:
strategy one DE/rand/1/bin
hij(t+1)＝xp1j(t)+F*(xp2j(t)-xp3j(t)) 4
Strategy two DE/best/2/bin
hij(t+1)＝xbj(t)+F*(xp1j(t)-xp2j(t)+xp3j(t)-xp4j(t)) 5
Strategy three DE/rand-best/2/bin
hij(t+1)＝xp1j(t)+F*(xbj(t)-xp1j(t)+xp2j(t)-xp3j(t)) 6
In formulas 4-6: the first term DE is an algorithm label; the second term is a designated variation vector, rand refers to variation of a random vector, best refers to variation of the optimal vector of the current generation, the third term is the number of vector differences used in the variation process, 1 is the difference between two vectors, and 2 is the difference between two four vectors; the fourth term is the crossover mode used in the crossover process, bin is the binomial crossover, and exp is the exponential crossover mode. h isij(t +1) refers to the jth parameter, x, in the ith individual in the t +1 generationij(t) is the j parameter of the ith individual in the t generation, p1, p2, p3, p4 are the random numbers generated, F is the mutation operator, xbj(t) means the best individual in the t generationThe jth parameter of (1).
In the invention, the search strategy is improved and can be specifically expressed as:
the improvement strategy is as follows: h isij(t+1)＝xij(t)+F*(xp1j(t)-xp2j(t)+xp3j(t)-xp4j(t)) 7
And (2) improving a strategy II: h isij(t+1)＝xij(t)+F*(xbj(t)-xij(t)+xp1j(t)-xp2j(t)) 8
The search strategy used by the method is an improved strategy I and an improved strategy II, and one strategy is randomly selected in each inversion iteration, so that the advantages of the two improved search strategies can be combined, the local optimal value is avoided, and the global search capability and the search efficiency of the evolutionary algorithm are improved.
After inversion iteration of the improved differential evolution algorithm, the method can obtain
θ,ψ,cx,cy,cz,ix,iy,izAnd the nine parameters are used for correcting the system error of the fluxgate sensor. The results of the nine parametric inversions are shown in table 2.
TABLE 2 sensor systematic error inversion results and inversion errors
Table 3 shows the comparison between the four sensors before and after correction of the mean value of the geomagnetic field measured at 5832 random poses. As can be seen from the results shown in Table 3, the total mean value of the geomagnetic field after the sensor is corrected converges to 52000nT, and the maximum error is 0.0109 nT.
TABLE 3 comparison of the total field of the geomagnetic field measured by the sensor before and after correction
| Total field of | Sensor | 1 | |
|
|
| Before correction (nT) | 57467.4391 | 57000.6378 | 53792.5866 | 53628.4093 | |
| After correction (nT) | 51999.9895 | 51999.9896 | 51999.9894 | 51999.9891 |
S5, modeling the misalignment error of the magnetic gradient tensor system, ideally, the three-axis directions of the four fluxgate sensors in the magnetic gradient tensor system should be consistent, but the three-axis directions of the four sensors are inconsistent due to insufficient installation precision, so that accurate tensor component values cannot be obtained.
After the correction of S4, the system error of the sensor is compensated, and the total magnetic field measured by the sensor is converged to the accurate geomagnetic field, so that the sensor can be corrected to the coordinate system of the mobile platform through the rotation of the three axes. The sensor is defined to rotate around a Z axis as an azimuth angle gamma, rotate around a Y axis as a pitch angle beta and rotate around an X axis as a roll angle alpha;
s6, establishing a compensation model for the non-alignment error of the magnetic gradient tensor system, defining that the sensor rotates around the Z axis as an azimuth angle gamma, rotates around the Y axis as a pitch angle beta, rotates around the X axis as a roll angle alpha in S5, and setting the rotation sequence of the sensor as the Y axis, the X axis and the Z axis. The output of the sensor after the system error compensation is B according to the formula 32The ideal output in the platform coordinate system can then be expressed as
According to the formula 9, if three parameters of alpha, beta and gamma are obtained, the fluxgate sensor can be corrected to the coordinate system of the mobile platform;
s7, placing the magnetic gradient tensor system in the horizontal table to acquire data, and S7, wherein the method for acquiring the data in the horizontal table by the magnetic gradient tensor system comprises the following steps: firstly, enabling the X axis of a sensor to be vertical to a horizontal table, and acquiring a series of data by an arbitrary rotating magnetic gradient tensor system; then enabling the Y axis of the sensor to be vertical to the horizontal table, and acquiring a series of data by an arbitrary rotating magnetic gradient tensor system; and finally, enabling the Z axis of the sensor to be vertical to the horizontal table, and acquiring a series of data by an arbitrary rotating magnetic gradient tensor system. Assuming that a coordinate system where a horizontal table after leveling is located is a moving platform coordinate system, enabling X, Y and Z axes of sensors to be perpendicular to the horizontal table respectively, and enabling a rotary sensor to acquire a series of data;
s8, inverting the data collected by the magnetic gradient tensor system to obtain a non-alignment error parameter of the magnetic gradient tensor system, and inverting the data collected by the magnetic gradient tensor system by using a differential evolution algorithm, where we know that the data collected around a certain axis in the platform coordinate system of the sensor should be a constant value in the magnetic field value of the corresponding axis, that is, the following conditions are satisfied:
when the sensor is rotated about the X-axis, then B1x＝B2x＝B3x＝…＝B nx 10
When the sensor is rotated about the Y axis, then B1y＝B2y＝B3y＝…＝Bny 11
When the sensor is rotated about the Z-axis, then B1z＝B2z＝B3z＝…＝Bnz 12
In the formulae 10-12, Bx,By,BzThe components of the earth magnetic field are X, Y and Z respectively, and n is the number of acquired data.
Taking the formula 10-12 as a standard, inverting the data acquired by S7 by using a differential evolution algorithm to obtain three parameters of alpha, beta and gamma, and correcting the sensor to a platform coordinate system. The results of the three parameter corrections are shown in table 4. As can be seen from tables 2 and 4, the maximum error of the inversion result of each parameter is 0.0514%, which indicates that the correction method used in the present invention has high accuracy, and the inversion error can reach a level lower than one in thousandth.
TABLE 4 magnetic gradient tensor system non-alignment error parameter inversion result and inversion error
Table 5 shows the mean of tensor components measured by the magnetic gradient tensor system at 5832 random poses compared before and after correction. As can be seen from Table 5, the mean value of the tensor components can reach several hundred to several thousand nT/m before the magnetic gradient tensor system is not corrected, the maximum mean value of the tensor components after being corrected is-0.0044 nT/m, the difference between the mean value and 0nT in an ideal state is very small, and the correction method has high precision.
TABLE 5 contrast of tensor components measured before and after correction by the magnetic gradient tensor system
And S9, carrying the magnetic gradient tensor system on the mobile platform, and enabling the mobile platform to measure according to a pre-designed measuring line to acquire magnetic field data. When the detection distance is more than 2.5 times the length of the magnetic object, the magnetic target can be simplified into a magnetic dipole model. Magnetic moment vector m ═ m (m)x,my,mz) The magnetic dipole generated at a distance r ═ x, y, z can be expressed as:
in formula 13,. mu.0＝4π×10-7H/m is the vacuum permeability and R ═ R | is the distance between the magnetic dipole location and the measurement point.
The three components B of the magnetic vector field can be calculated from equation 13x,By,Bz。
The magnetic gradient tensor system uses a difference approximation method to obtain tensor components, and five independent tensor components can be expressed as follows:
in formula 14, Bnm(n is 1,2,3, 4; m is x, y, z), n is the sensor number, m is the direction of the magnetic vector, d is the baseline distance of the tensor system, which is assumed to be 0.16m in this embodiment.
We assume that there is a magnetic target 5 meters below the seafloor, with coordinates (50,0, -5), and a magnetic moment of 10000 A.m2The magnetic declination is 40 degrees, the magnetic dip angle is 54 degrees, a moving platform carrying a magnetic gradient tensor system passes 2 meters above the magnetic target at a constant speed along the y direction, the coordinates of the starting point of a measuring line are (0,0,2), the coordinates of the terminal point of the measuring line are (100,0,2), and 4 sensors in the magnetic gradient tensor system uniformly acquire 1001 data on the measuring line;
s10, correcting the acquired magnetic field data by using the sensor system error parameters and the magnetic gradient tensor system non-alignment errors obtained in S4 and S8, and calculating the tensor components after correction, wherein if the sensor system errors and the magnetic gradient tensor system non-alignment errors are not corrected, the measured tensor and the theoretical value have large difference, so that the data processing and data interpretation work can be seriously influenced, the tensor components after correction are in accordance with the theoretical value, and the correction effect is good.
When the method is used, firstly, a mathematical model and a compensation model are established for the system error of a single sensor in a magnetic gradient tensor system, and the system error is inverted by utilizing an improved differential evolution algorithm based on a scalar correction method; then, establishing a mathematical model and a compensation model for the non-alignment error of the magnetic gradient tensor system, and inverting the non-alignment error by using an improved differential evolution algorithm; and finally, correcting the line measurement data and solving a tensor component.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (3)
1. A method for correcting a magnetic gradient tensor system of a moving platform is characterized by comprising the following steps:
s1, modeling the system error of the single sensor;
s2, establishing a compensation model for the system error of the single sensor;
s3, enabling the magnetic gradient tensor system to randomly change the posture in the three-dimensional space to acquire data, and measuring the total magnetic field in the experimental environment by using a proton magnetometer;
s4, taking the total magnetic field in the experimental environment as a standard, and inverting the data acquired by the fluxgate sensor by using an improved differential evolution algorithm to obtain a system error parameter of the sensor;
s5, modeling the misalignment error of the magnetic gradient tensor system;
s6, establishing a compensation model for the non-alignment error of the magnetic gradient tensor system;
s7, placing the magnetic gradient tensor system in a horizontal table to acquire data;
s8, inverting the data acquired by the magnetic gradient tensor system by using an improved differential evolution algorithm to obtain a non-alignment error parameter of the magnetic gradient tensor system;
s9, carrying the magnetic gradient tensor system on the mobile platform, enabling the mobile platform to measure according to a pre-designed measuring line, and collecting magnetic field data;
s10, correcting the acquired magnetic field data by using the sensor system error parameters and the magnetic gradient tensor system non-alignment errors obtained in S4 and S8, and calculating tensor components;
the improved differential evolution algorithm comprises:
the improvement strategy is as follows: h isij(t+1)＝xij(t)+F*(xp1j(t)-xp2j(t)+xp3j(t)-xp4j(t))；
And (2) improving a strategy II: h isij(t+1)＝xij(t)+F*(xbj(t)-xij(t)+xp1j(t)-xp2j(t))；
Wherein h isij(t +1) refers to the jth parameter, x, in the ith individual in the t +1 generationij(t) is the jth parameter in the ith individual in the t generation, F is the mutation operator, p1, p2, p3, p4 are the generated random numbers, xbj(t) refers to the jth parameter in the best individual in the t generation;
in each inversion iteration, one strategy is randomly selected from the first improvement strategy and the second improvement strategy.
2. The method for calibrating a magnetic gradient tensor system of a mobile platform as recited in claim 1, wherein in S3, the magnetic gradient tensor system is enabled to arbitrarily change the posture of the acquired data in the three-dimensional space without providing the standard orientation information by a high-precision three-axis rotation table.
3. The method for correcting the magnetic gradient tensor system of the mobile platform as recited in claim 1, wherein in the step S7, the method for acquiring data in the horizontal table by the magnetic gradient tensor system is as follows: firstly, enabling the X axis of a sensor to be vertical to a horizontal table, and acquiring a series of data by an arbitrary rotating magnetic gradient tensor system; then enabling the Y axis of the sensor to be vertical to the horizontal table, and acquiring a series of data by an arbitrary rotating magnetic gradient tensor system; and finally, enabling the Z axis of the sensor to be vertical to the horizontal table, and acquiring a series of data by an arbitrary rotating magnetic gradient tensor system.
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