CN113359192B - Weak magnetic anomaly target detection and positioning method - Google Patents

Weak magnetic anomaly target detection and positioning method Download PDF

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CN113359192B
CN113359192B CN202110624613.2A CN202110624613A CN113359192B CN 113359192 B CN113359192 B CN 113359192B CN 202110624613 A CN202110624613 A CN 202110624613A CN 113359192 B CN113359192 B CN 113359192B
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CN113359192A (en
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缪存孝
董琦
杜玉虎
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Beijing Zhongke Guidance And Control Technology Co ltd
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    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/08—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices
    • G01V3/081—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices the magnetic field is produced by the objects or geological structures
    • G—PHYSICS
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Abstract

The invention discloses a weak magnetic anomaly target detection and positioning method, which comprises the following steps: setting a search formation form of the unmanned aerial vehicle; denoising the acquired original signal by using a fast convergence wavelet neural network algorithm; obtaining characteristic time by using a signal-to-noise ratio matching method, and detecting a target by combining an orthogonal basis detection method; a target positioning model is established by basically detecting the state and the characteristic time of the unmanned aerial vehicle of a positioning unit, the target position and the target speed are solved by utilizing an optimization algorithm, and an optimal weighting coefficient is generated to fuse a plurality of groups of positioning results, so that the multi-unmanned aerial vehicle magnetic target positioning is realized. The method provided by the invention can detect the magnetic abnormal target under complex noise, and position the target on the basis of detection to obtain the speed and the position of the target.

Description

Weak magnetic anomaly target detection and positioning method
Technical Field
The invention relates to the field of electromagnetic detection, in particular to a weak magnetic anomaly target detection and positioning method which is widely applied to aeromagnetic detection, underwater target detection, metal resource detection, military nuclear submarine detection and the like.
Background
The ferromagnetic substance generates a magnetic abnormal signal under the action of the geomagnetic field, and the magnetic abnormal detection realizes the identification, positioning and tracking of the ferromagnetic substance by detecting the magnetic abnormal signal. The magnetic anomaly signals are weak, and the frequency is mainly distributed in 0.001-1 Hz, so that a detection algorithm capable of detecting the weak magnetic anomaly target needs to be designed and located.
The magnetic anomaly target detection method is mainly divided into two types, and the two types of methods are respectively based on target signal characteristics and background noise characteristics. The first method is mainly based on an orthogonal basis detection method, but the method mainly aims at Gaussian white noise, and the actual application of the method contains a lot of noises with different frequencies, so that the detection effect of the orthogonal basis detection method is poor. The second method mainly takes information entropy as a main part, and detects after statistical characteristics of background noise are obtained through a statistical method. However, this method requires a large amount of background noise to be collected for analysis, which increases the workload of the previous stage. Therefore, how to effectively detect the weak magnetic anomaly target in a complex noise environment is one of the difficulties in the field of magnetic detection.
After the target is detected, the target is further positioned, and different from the use of sensors, the positioning method can be divided into two types, one type is to detect and position by using a scalar magnetometer, and the other type is to detect and position by using a vector magnetometer. When the vector magnetometer is used for detecting and positioning, a positioning method of magnetic field gradient tensor is mostly adopted, and when the vector magnetometer is used for detecting, magnetic field information of a target in three directions of an X-Y-Z axis can be obtained, but the vector magnetometer is influenced by the posture of a carrier in detection, so that the precision is influenced. The scalar magnetometer is used for detecting and positioning, so that the influence of the attitude of a carrier can be avoided, but a single scalar magnetometer cannot position a target, and at present, a commonly used scalar magnetometer array positions the target, however, the calibration precision of a magnetic sensor array influences the positioning result.
Along with the wide application of unmanned aerial vehicles, unmanned aerial vehicles carry magnetometers to carry out aeromagnetic detection, and the unmanned aerial vehicles are more and more common. Unmanned aerial vehicle aeromagnetic system has been developed to canadian kalton university to carry on the trial flight on unmanned aerial vehicle system, obtained fine effect. China aeromagnetic detection has also started, but is mostly single large-scale fixed wing unmanned aerial vehicles, and the hidden nature is relatively poor and exploration scope is little, and positioning accuracy is low, can't satisfy the practical application scene of higher requirement, and it will be the development trend that many small-size unmanned aerial vehicles form a formation to carry on aeromagnetic detection system in the future.
Disclosure of Invention
Aiming at the defects of insufficient capability and low positioning precision of weak magnetic abnormal targets in the prior art, the invention provides a method for detecting and positioning the weak magnetic abnormal targets, which comprises the steps of collecting magnetic abnormal signals by designing a formation of a plurality of unmanned aerial vehicles and carrying scalar magnetometers, processing original noise-containing signals by adopting a fast convergence wavelet neural network, and detecting the magnetic abnormal targets by adopting an orthogonal basis detection method; after detection, the detection information is used for establishing a simultaneous positioning equation, a global optimal solution is solved through an optimization algorithm, the quality of each sensor is fully considered, an optimal weighting coefficient is generated, a plurality of groups of positioning results are fused, and final positioning is achieved.
A weak magnetic anomaly target detection and positioning method comprises the following steps:
s1, setting a search formation form of unmanned aerial vehicles with N unmanned aerial vehicles by taking a triangular formation form formed by three adjacent unmanned aerial vehicles as a basic detection positioning unit, wherein N is greater than 3;
s2, each unmanned aerial vehicle respectively collects magnetic field signals X of the magnetic targets, denoising the collected original signals by using a fast convergence wavelet neural network algorithm, and the output sequence of the fast convergence wavelet neural network is Y;
s3, detecting the target by combining an orthogonal basis detection method to obtain the nearest time between the unmanned aerial vehicle and the target, and obtaining a characteristic time value by using a signal-to-noise ratio matching method, wherein the method comprises the following steps:
step 3.1, obtaining a characteristic time value by utilizing a signal-to-noise ratio matching method;
3.2, detecting the target by combining an orthogonal basis detection method to obtain the time of the unmanned aerial vehicle to the target, and obtaining the positioning of the unmanned aerial vehicle at the moment according to the time of the unmanned aerial vehicle to the target;
s4, establishing a target positioning model through the states and the characteristic time values of the unmanned aerial vehicles of the basic detection positioning units, and solving a global optimal solution by using an optimization algorithm to obtain the target positioning of one basic detection positioning unit;
and S5, generating optimal weighting coefficients to fuse multiple groups of positioning results, and realizing the positioning of the magnetic targets of the multiple unmanned aerial vehicles.
Preferably, the target positioning model in S4 is specifically:
the objective function is:
fobject＝f2+f4+f6+0.01×(f1+f3+f5) (14)
wherein f isobjectIs an objective function, f1、f2、f3、f4、f5、f6Respectively as follows:
Figure BDA0003101686950000031
wherein (x)1，y1，z1，v1x，v1y) (x) the location of the first unmanned UAV1 at the closest time to the target2，y2，z2，v2x，v2y) (x) the location of the second unmanned UAV2 at the closest time to the target3，y3，z3，v3x，v3y) Is the location of the third unmanned aerial vehicle UAV3 at the most recent time with the target; Δ t1Time difference, Δ t, between UAV2 and UAV1 and the target recent time2Time differences of UAV3 and UAV1 from the target recent time; tau is1、τ2And τ3Characteristic time values for UAV1, UAV2, and UAV3, respectively;
and solving the target location when the target function is minimum to obtain the target location of a basic detection location unit.
Preferably, the step 3.2 of detecting the target by combining the orthogonal basis detection method to obtain the time between the unmanned aerial vehicle and the target is implemented by the following steps:
modeling a wavelet neural network output sequence Y by using a magnetic dipole model, and decomposing a modeling signal into a form S (v) of adding three orthogonal basis functions by adopting an orthogonal basis detection method, wherein the S (v) is as follows:
Figure BDA0003101686950000041
wherein r isestIs the closest distance of the target to the drone, αnIs the coefficient of the orthogonal basis,
Figure BDA0003101686950000047
in order to be the target magnetic moment,
Figure BDA0003101686950000042
τ is a characteristic time value, vrIs the relative speed of the target and the detection platform, t is the time node, fn(. nu.) is an orthogonal basis functionRepresented by the formula:
Figure BDA0003101686950000043
therefore, the orthogonal basis coefficient α can be derived from equation (9) after windowingnThe calculation formula is as follows:
Figure BDA0003101686950000044
wherein, an(m) orthogonal basis coefficient α for mth signal valuenK is the window width, m is the signal point being processed, S (v)m+i) For the m + i-th signal point,
Figure BDA0003101686950000045
the energy function is defined by:
Figure BDA0003101686950000046
and setting a threshold, wherein the target is detected when the energy function reaches the threshold, and when E (m) is the maximum, the sampling point time is the time when the unmanned aerial vehicle is closest to the target.
Preferably, the S5 generates an optimal weighting coefficient to fuse multiple sets of positioning results, and the implementation of the multi-drone magnetic target positioning specifically includes:
the optimal weighting coefficient is generated by:
Figure BDA0003101686950000051
wherein, muiThe optimal weighting coefficient corresponding to the positioning result of the unmanned aerial vehicle set of the ith group of basic detection positioning units; sigmai,nCarrying a measurement variance of a sensor for an nth unmanned aerial vehicle in an ith basic detection and positioning unit unmanned aerial vehicle set;
the final target location is expressed as:
Figure BDA0003101686950000052
wherein, XiAnd (3) carrying out fusion on N-2 groups of positioning results for the target positioning obtained by solving the unmanned aerial vehicle set of the ith group of basic detection and positioning units.
Preferably, the fast convergence wavelet neural network in S2 specifically includes:
the fast convergence wavelet neural network selects a Morlet wavelet function as an activation function of the wavelet neural network, and the Morlet wavelet function is expressed as follows:
Figure BDA0003101686950000053
wherein t is a time variable.
Training network weight by using a gradient descent method;
and on the premise of ensuring the stability of the system through the error quotient gradient, the learning rate is iteratively optimized by utilizing the error quotient.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention adopts unmanned aerial vehicle formation to detect and position the magnetic abnormal target, and the search range is enlarged;
2. the invention adopts the unmanned aerial vehicle formation to realize the multi-machine magnetic anomaly target detection, can realize the magnetic anomaly target detection under the low signal-to-noise ratio, and improves the detection performance;
3. the unmanned aerial vehicle formation adopted by the invention realizes multi-machine fusion positioning, and the positioning precision is higher than that of a single unmanned aerial vehicle.
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FIG. 1 is a flowchart of a method for detecting and locating a weak magnetic anomaly target according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a formation of multiple drones according to an embodiment of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the following detailed description of illustrative embodiments thereof and with reference to the accompanying drawings.
As shown in fig. 1, a method for detecting and positioning a weak magnetic anomaly target includes the following steps:
s1, a triangular formation formed by three adjacent unmanned aerial vehicles is used as a basic detection positioning unit, the unmanned aerial vehicle search formation formed by N unmanned aerial vehicles is set, N is more than 3, and the unmanned aerial vehicle formation formed by N unmanned aerial vehicles comprises N-2 basic detection positioning units, so that N-2 positioning results can be obtained. As shown in fig. 2, one "ten" in the drawing represents one drone, and N drones can form various images such as a rectangle, a triangle, a parallelogram, and the like, but no matter what total image the N drones form, an equilateral triangle formation formed by 3 neighboring drones is used as a basic detection and positioning unit, and each drone can be in a plurality of basic detection and positioning units.
S2, each unmanned aerial vehicle respectively collects magnetic field signals X of the magnetic targets, and the original signals collected by the scalar magnetometer are denoised by the fast convergence wavelet neural network algorithm.
The mentioned fast convergence wavelet neural network selects a Morlet wavelet function as an activation function of the wavelet neural network, and the Morlet wavelet function is expressed as follows:
Figure BDA0003101686950000061
wherein t is a time variable.
Sampling a magnetic field signal X, wherein the received ith original magnetic signal is sampled into X (i) and used as a network input, and an output sequence Y is a denoised signal, wherein Y (i) is the signal denoised from the ith original signal X (i) and is also an output signal of an ith network node. Training each network parameter by a gradient descent method, and defining a training error as follows:
Figure BDA0003101686950000062
where n is the number of input nodes, d (i) is the desired output, and y (i) is the ith network node output.
Training the network weights by using a gradient descent method, as shown in the following formula:
Figure BDA0003101686950000071
wherein, WjkFor training weights between the input and intermediate layers of the network, WkiIs the network weight between the intermediate layer and the output layer, ajAnd bjThe wavelet coefficient, T is training times, mu is learning rate, beta is momentum factor, j is input node number, i is output node number, and k is intermediate layer node number.
The wavelet neural network can be trained according to the activation function and the network weight, and in order to improve the network convergence speed and optimize the learning rate, the specific steps are as follows:
on the premise of ensuring the stability of the system through the error quotient gradient, the learning rate is optimized and the network convergence speed is improved by utilizing the error quotient iteration, wherein the error quotient and the error quotient gradient are defined as follows:
Figure BDA0003101686950000072
ΔkN＝kN-kN-1 (5)
wherein E isNError of Nth training, kNFor the Nth training error quotient, Δ kNThe nth training error quotient gradient.
When Δ kNK is more than 0 and 1 and more than or equal toNWhen the value is more than 0, the system is in a stable state, the learning rate can be increased, and the iteration speed is increased:
μN＝μN-1(1+kN) (6)
wherein, muNAnd muN-1Learning rates of Nth and N-1 th times, respectively.
When Δ kN< 0 or 1 < kNIn time, the system is in an unstable state, reducing the learning rate to improve the system stability:
μN＝μN-1(kN-1) (7)
and introducing the updated learning rate into a wavelet neural network, processing the input original magnetic signal, and finally realizing signal denoising.
And S3, obtaining a characteristic time value by using a signal-to-noise ratio matching method, and detecting the target by combining an orthogonal basis detection method.
And S3.1, obtaining a characteristic time value by utilizing a signal-to-noise ratio matching method.
The characteristic time value is realized by matching the signal-to-noise ratio, and the time when the signal-to-noise ratio reaches the maximum is the characteristic time value tau.
And S3.2, detecting the target by combining an orthogonal base detection method.
The wavelet neural network output sequence Y is a magnetic signal without noise, a magnetic dipole model is used for modeling the wavelet neural network output sequence Y, and the modeling signal is as follows:
Figure BDA0003101686950000081
wherein, mu0＝4π×10-7In order to achieve a magnetic permeability in a vacuum,
Figure BDA0003101686950000082
in order to be the target magnetic moment,
Figure BDA0003101686950000083
is the vector distance of the target and the drone.
Orthogonal basis detection method for modeling signal
Figure BDA0003101686950000086
Decomposed into the form s (v) of the addition of three orthogonal basis functions, s (v) being:
Figure BDA0003101686950000084
wherein r isestIs the closest distance of the target to the drone, αnIs the coefficient of the orthogonal basis,
Figure BDA0003101686950000085
τ is a characteristic time value, vrIs the relative speed of the target and the detection platform, t is a time node, fnAnd (v) is an orthogonal basis function, which can be represented by the following equation:
Figure BDA0003101686950000091
therefore, the orthogonal basis coefficient α can be derived from equation (9) after windowingnThe calculation formula is as follows:
Figure BDA0003101686950000092
wherein, an(m) orthogonal basis coefficient α for mth signal valuenK is an integer window width, m is the signal point being processed, S (v)m+i) For the m + i-th signal point,
Figure BDA0003101686950000093
the energy function is defined by:
Figure BDA0003101686950000094
and setting a proper threshold, representing that the target is detected after the energy function reaches the threshold, and setting the sampling point time to be the nearest time between the unmanned aerial vehicle and the target when E (m) is the maximum. The unmanned aerial vehicle location at this moment can be obtained according to the unmanned aerial vehicle and the target recent time, and the unmanned aerial vehicle location comprises the unmanned aerial vehicle coordinate and the unmanned aerial vehicle speed when the unmanned aerial vehicle and the target recent time are available.
S4, establishing a target positioning model through unmanned aerial vehicle positioning and characteristic time values of the basic detection positioning unit, and solving a global optimal solution by using an optimization algorithm to obtain target positioning of the basic detection positioning unit.
The positioning equation set is composed of states of the unmanned aerial vehicles of the basic detection positioning units, the nearest time of the target and the characteristic time value, and a coordinate system is established by taking the unmanned aerial vehicle which detects the target in the first unmanned aerial vehicle in the N unmanned aerial vehicles in the formation of the unmanned aerial vehicles as an original point. Assuming no velocity in the z-axis direction of the target, the target location to be solved is (x)0，y0，z0，vx，vy) Wherein x is0、y0And z0Position coordinates v of x, y, z axes respectivelyxAnd vyThe speed in the x and y axis directions. The set of drone equations for one basic unit of detection and location is therefore as follows:
Figure BDA0003101686950000101
wherein (x)1，y1，z1，v1x，v1y) (x) location of the first unmanned UAV1 with the target at the most recent time2，y2，z2，v2x，v2y) (x) location of the second unmanned aerial vehicle UAV2 at the latest time of the target3，y3，z3，v3x，v3y) Is the location of the third drone UAV3 at the time of the last time of the target, where x, y, z with subscripts are the position coordinates of the x, y, z axes of the respective subscript drone, v with subscriptxAnd vyThe speeds of the unmanned aerial vehicles in the directions of the x axis and the y axis are respectively subscripted. Δ t1Time difference, Δ t, between UAV2 and UAV1 and the target recent time2Time difference between UAV3 and UAV1 and the target recent time. Tau is1、τ2And τ3Characteristic time values for UAV1, UAV2, and UAV3, respectively.
However, since the equation (13) cannot obtain an accurate analytical solution due to measurement errors and estimation differences, only the target location (x) can be obtained0，y0，z0，vx，vy) Of (2) an optimal solutionIn the invention, the optimization algorithm is an improved Powell method, so that the establishment of the target positioning model according to the formula (13) is as follows:
the objective function is as follows;
fobject＝f2+f4+f6+0.01×(f1+f3+f5) (14)
wherein f is1、f2、f3、f4、f5、f6As follows:
Figure BDA0003101686950000111
and solving the target location when the target function is minimum to obtain the target location of a basic detection location unit.
And S5, generating optimal weighting coefficients to fuse multiple groups of positioning results, and realizing the positioning of the magnetic targets of the multiple unmanned aerial vehicles.
Taking into full account the quality of the magnetic sensor, the mentioned optimal weighting coefficients are generated by:
Figure BDA0003101686950000112
wherein, muiThe optimal weighting coefficient corresponding to the positioning result of the unmanned aerial vehicle set of the ith group of basic detection positioning units; sigmai,nCarrying a measurement variance of a sensor for an nth unmanned aerial vehicle in an ith basic detection and positioning unit unmanned aerial vehicle set; the resulting final target location can be expressed as:
Figure BDA0003101686950000113
wherein, XiAnd (3) carrying out fusion on N-2 target positioning results for the target positioning obtained by solving the unmanned aerial vehicle set of the ith group of basic detection and positioning units.
Example 1
In the embodiment, the method is tested in a simulation mode, in order to simplify the description, the formation of the unmanned aerial vehicles only uses 4 unmanned aerial vehicles, the formation of the parallelograms is adopted, and a coordinate system is established by taking a target starting point as an origin. The skilled person in the art knows that the present invention can be implemented as long as the number of the unmanned aerial vehicles is larger than 3, the formation of the unmanned aerial vehicles can adopt any pattern, the origin of the coordinate system of the formation of the unmanned aerial vehicles can be set according to the needs, such as a control console, the departure point of a certain unmanned aerial vehicle in the formation of the unmanned aerial vehicles, and the like, and since the coordinate system is established by using the unmanned aerial vehicle which detects the target first in the N unmanned aerial vehicles in the formation of the unmanned aerial vehicles as the origin in the subsequent calculation, the initial origin of the coordinate system set before the target is detected is not important.
S1, establishing a coordinate system with the target starting point as an origin point to form a formation of unmanned aerial vehicles, and enabling the starting point coordinates of the UAV1 to be (-900, -100), the starting point coordinates of the UAV2 to be (-900, -200), the starting point coordinates of the UAV3 to be (-800, -200), the starting point coordinates of the UAV4 to be (-1000, -100), and the speeds of the unmanned aerial vehicles to be 30 m/S. Matlab simulates a magnetic anomaly signal, adds 30pt of noise (colored noise, white Gaussian noise and non-Gaussian noise), and has a target speed of 10 m/s.
S2, each unmanned aerial vehicle respectively collects magnetic field signals of the magnetic targets, and denoising the collected original signals by using a fast convergence wavelet neural network algorithm.
And S3, detecting the target by combining an orthogonal basis detection method, and obtaining a characteristic time value by utilizing a signal-to-noise ratio matching method.
Orthogonal basis detection setup window width-3<k<Setting a detection threshold value to be 0.8, and respectively obtaining the latest time t of 4 unmanned aerial vehicles and the target by combining an orthogonal basis detection method1(s)、t2(s)、t3(s) and t4(s). And respectively obtaining the states of 4 unmanned aerial vehicles according to the unmanned aerial vehicle and the target recent time.
Respectively obtaining the characteristic time tau of 4 unmanned aerial vehicles by using a signal-to-noise ratio matching method1、τ2、τ3And τ4。
The latest time and characteristic time of the unmanned aerial vehicle and the target are shown in table 1:
TABLE 1
Figure BDA0003101686950000121
And S4, solving a global optimal solution by using an optimization algorithm through a positioning equation set formed by the states and the characteristic time values of the unmanned aerial vehicles in the basic detection positioning units, so as to obtain the target positioning of one basic detection positioning unit.
And S5, generating optimal weighting coefficients to fuse multiple groups of positioning results, and realizing the detection of the target positioning by multiple unmanned aerial vehicles.
In this embodiment, the target location is obtained as (x) by using the method of the present invention through simulation0，y0，z0，vx，vy)＝(0，47.2，100，15.4，0)。
Comparing the positioning result obtained by the invention with the positioning result obtained by a single unmanned aerial vehicle positioning method, as shown in table 2:
TABLE 2
Figure BDA0003101686950000131
It can be seen that the positioning result obtained by the method is obviously higher in precision than that obtained by a single unmanned aerial vehicle positioning method.
Finally, it should be noted that: the above examples are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (5)

1. A weak magnetic anomaly target detection and positioning method is characterized by comprising the following steps:
s1, setting a search formation form of unmanned planes with N unmanned planes by taking a triangular formation form formed by three adjacent unmanned planes as a basic detection and positioning unit, wherein N is greater than 3;
s2, each unmanned aerial vehicle respectively collects magnetic field signals X of the magnetic targets, denoising the collected original signals by using a fast convergence wavelet neural network algorithm, and the output sequence of the fast convergence wavelet neural network is Y;
s3, detecting the target by combining an orthogonal basis detection method to obtain the nearest time between the unmanned aerial vehicle and the target, and obtaining a characteristic time value by using a signal-to-noise ratio matching method, wherein the method comprises the following steps:
step 3.1, obtaining a characteristic time value by utilizing a signal-to-noise ratio matching method;
3.2, detecting the target by combining an orthogonal base detection method to obtain the time of the unmanned aerial vehicle to the target, and obtaining the positioning of the unmanned aerial vehicle at the moment according to the time of the unmanned aerial vehicle to the target;
s4, establishing a target positioning model through the states and the characteristic time values of the unmanned aerial vehicles of the basic detection positioning units, and solving a global optimal solution by using an optimization algorithm to obtain the target positioning of one basic detection positioning unit;
and S5, generating optimal weighting coefficients to fuse multiple groups of positioning results, and realizing the positioning of the magnetic targets of the multiple unmanned aerial vehicles.
2. The method for detecting and locating the object with weak magnetic anomaly according to claim 1, wherein the object location model in S4 specifically comprises:
the objective function is:
fobject＝f2+f4+f6+0.01×(f1+f3+f5) (14)
wherein f isobjectIs an objective function, f1、f2、f3、f4、f5、f6Respectively as follows:
Figure FDA0003600252040000021
wherein (x)0，y0，z0，vx，vy) For targeting, (x)1，y1，z1，v1x，v1y) (x) location of the first unmanned UAV1 with the target at the most recent time2，y2，z2，v2x，v2y) (x) the location of the second unmanned UAV2 at the closest time to the target3，y3，z3，v3x，v3y) Is the location of the third unmanned aerial vehicle UAV3 at the latest time of the target; Δ t1Time difference, Δ t, between UAV2 and UAV1 and the target recent time2Time differences of UAV3 and UAV1 from the target recent time; tau is1、τ2And τ3Characteristic time values for UAV1, UAV2, and UAV3, respectively;
and solving the target location when the target function is minimum to obtain the target location of a basic detection location unit.
3. The weak magnetic anomaly target detection and positioning method according to claim 1, wherein the step 3.2 is combined with an orthogonal basis detection method to detect the target, and the specific implementation step of obtaining the time of the unmanned aerial vehicle closest to the target is as follows:
a magnetic dipole model is used for modeling a wavelet neural network output sequence Y, an orthogonal basis detection method is adopted to decompose a modeling signal into a form S (upsilon) of adding three orthogonal basis functions, and the S (upsilon) is as follows:
Figure FDA0003600252040000022
wherein, mu0＝4π×10-7Is a vacuum permeability of restIs the closest distance of the target to the drone, αnIs the coefficient of the orthogonal basis,
Figure FDA0003600252040000023
in order to be the target magnetic moment,
Figure FDA0003600252040000024
τ is a characteristic time value, vrIs the relative speed of the target and the detection platform, t is a time node, fnAnd (v) is an orthogonal basis function, represented by the following equation:
Figure FDA0003600252040000031
therefore, the orthogonal basis coefficient α is derived from equation (9) after windowingnThe calculation formula is as follows:
Figure FDA0003600252040000032
wherein alpha isn(m) orthogonal basis coefficient α for mth signal valuenK is the window width, m is the signal point being processed, S (upsilon)m+i) For the m + i-th signal point,
Figure FDA0003600252040000033
the energy function is defined by:
Figure FDA0003600252040000034
and setting a threshold, wherein the target is detected when the energy function reaches the threshold, and when E (m) is the maximum, the sampling point time is the nearest time between the unmanned aerial vehicle and the target.
4. The weak magnetic anomaly target detection and positioning method according to claim 1, wherein the step S5 generates optimal weighting coefficients to fuse multiple sets of positioning results, and the implementation of multi-drone magnetic target positioning specifically comprises:
the optimal weighting coefficient is generated by:
Figure FDA0003600252040000035
wherein, muiThe optimal weighting coefficient corresponding to the positioning result of the unmanned aerial vehicle set of the ith group of basic detection positioning units; sigmai,nCarrying a measurement variance of a sensor for an nth unmanned aerial vehicle in an ith basic detection and positioning unit unmanned aerial vehicle set;
the final target location is expressed as:
Figure FDA0003600252040000041
wherein, XiAnd (3) carrying out fusion on N-2 groups of positioning results for the target positioning obtained by solving the unmanned aerial vehicle set of the ith group of basic detection and positioning units.
5. The method for detecting and locating the weak magnetic anomaly target according to claim 1, wherein the fast convergence wavelet neural network in S2 specifically comprises:
the fast convergence wavelet neural network selects a Morlet wavelet function as an activation function of the wavelet neural network, and the Morlet wavelet function is expressed as follows:
Figure FDA0003600252040000042
wherein t is a time variable;
training network weight by using a gradient descent method;
and on the premise of ensuring the stability of the system through the error quotient gradient, the learning rate is iteratively optimized by utilizing the error quotient.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113866834A (en) * 2021-09-15 2021-12-31 吉林大学 Entropy filtering-based field source center position inversion method
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8639396B1 (en) * 2008-10-08 2014-01-28 Raytheon Company Cooperative control of unmanned aerial vehicles for tracking targets
CN108072906A (en) * 2016-11-18 2018-05-25 北京自动化控制设备研究所 A kind of distribution magnetic detection magnetic target identification method
CN109739263A (en) * 2019-01-25 2019-05-10 清华大学 A kind of latent machine air navigation aid of spy that submarine detection is carried out based on magnetic signal continuation algorithm
CN111220932A (en) * 2019-11-21 2020-06-02 北京自动化控制设备研究所 Unmanned aerial vehicle magnetic interference calibration method and distributed magnetic anomaly detection system
CN111399066A (en) * 2020-04-03 2020-07-10 西北工业大学青岛研究院 Method for processing scalar magnetic anomaly gradient signal based on orthogonal basis function
CN112415613A (en) * 2020-11-18 2021-02-26 北京自动化控制设备研究所 Multi-machine cluster magnetic target positioning method and aerial cluster heterogeneous platform using same
CN112633147A (en) * 2020-12-22 2021-04-09 电子科技大学 Magnetic anomaly detection method based on multi-feature fusion and intelligent iterative optimization algorithm

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8639396B1 (en) * 2008-10-08 2014-01-28 Raytheon Company Cooperative control of unmanned aerial vehicles for tracking targets
CN108072906A (en) * 2016-11-18 2018-05-25 北京自动化控制设备研究所 A kind of distribution magnetic detection magnetic target identification method
CN109739263A (en) * 2019-01-25 2019-05-10 清华大学 A kind of latent machine air navigation aid of spy that submarine detection is carried out based on magnetic signal continuation algorithm
CN111220932A (en) * 2019-11-21 2020-06-02 北京自动化控制设备研究所 Unmanned aerial vehicle magnetic interference calibration method and distributed magnetic anomaly detection system
CN111399066A (en) * 2020-04-03 2020-07-10 西北工业大学青岛研究院 Method for processing scalar magnetic anomaly gradient signal based on orthogonal basis function
CN112415613A (en) * 2020-11-18 2021-02-26 北京自动化控制设备研究所 Multi-machine cluster magnetic target positioning method and aerial cluster heterogeneous platform using same
CN112633147A (en) * 2020-12-22 2021-04-09 电子科技大学 Magnetic anomaly detection method based on multi-feature fusion and intelligent iterative optimization algorithm

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
磁目标探测联合仿真技术研究;孙晓永;《中国优秀硕士学位论文全文数据库 信息科技辑》;20190115(第01期);第4章 *
航空磁异常探潜技术发展综述;成建波 等;《声学与电子工程》;20180915(第03期);第46-49页 *

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