Geological magnetic interference removal method and system based on principal component analysis

Abstract

The application provides a principal component analysis geological magnetic interference removal method and system, which comprises the following steps: collecting multi-source data; solving a magnetic interference compensation coefficient; obtaining a residual magnetic field; performing high-precision space-time matching on the residual magnetic field data of each detection aircraft to form a space-time aligned multi-channel magnetic survey data matrix; in the fifth step, using the multi-channel magnetic survey data matrix, combining principal component analysis (PCA), weighted average and sliding window cooperative filtering organically to generate a continuous and smooth regional geological magnetic interference field; subtracting the geological magnetic interference field point by point to realize independent interference removal processing on the original data of each aircraft, and finally generating clean magnetic anomaly data which removes the geological background and retains local anomalies. The technical scheme of the application solves the technical problem that the applicability of the first-order linear model constructed by using platform position information in the prior art is greatly reduced, the compensation result is not ideal, and the effective discrimination of the target signal is seriously affected.

Description

Technical Field

[0001] This invention relates to the field of airborne magnetic detection technology, and in particular to a method and system for removing geomagnetic interference using principal component analysis. Background Technology

[0002] Airborne magnetic surveying technology utilizes unmanned aerial vehicles (UAVs) equipped with high-sensitivity airborne magnetometers to measure the Earth's magnetic field in real time within complex magnetic environment. By analyzing the magnetically compensated signal, it detects and identifies ferromagnetic targets submerged within the magnetic field. During airborne magnetic surveying missions, the ferromagnetic targets typically generate weak, localized magnetic field anomalies. However, the metal structures, electronic equipment, propulsion systems, and flight attitude changes of the flight platform itself all contribute to significant airborne magnetic interference, far exceeding the target signal. While the maneuvering magnetic interference generated by the aircraft platform itself can be compensated for using a TL model, actual surveys are also affected by geomagnetic interference. Geomagnetic interference primarily originates from localized enrichment of underground ferromagnetic minerals, abrupt changes in magnetic susceptibility at lithological interfaces, and magnetic effects of fault zones. In actual surveys, geomagnetic interference typically has a large spatial distribution, covering several to hundreds of square kilometers, manifesting as regional background anomalies. Geomagnetic interference is closely related to geological structures, lithological distribution, and stratigraphic strike, and its intensity can reach several nanots to thousands of nanots, far exceeding the intensity of the target signal. Furthermore, geomagnetic interference is difficult to separate from slowly moving artificial target signals, exhibiting characteristics of locality, distortion, anomaly, and aliasing. It is not a simple approximate linear distribution. The applicability of the first-order linear model constructed using platform location information to geomagnetic interference is significantly reduced, resulting in unsatisfactory compensation results and severely impacting the effective discrimination of target signals. To overcome this problem, it is urgent to introduce a higher-performance airborne magnetic compensation algorithm to deeply model geomagnetic interference and extend the existing compensation model to a higher-precision nonlinear compensation model, thereby more accurately and comprehensively considering the impact of different types of magnetic interference on the detection results. Summary of the Invention

[0003] This invention provides a method and system for removing geomagnetic interference using principal component analysis, which can solve the technical problem that the applicability of the first-order linear model constructed using platform location information to geomagnetic interference is greatly reduced in the prior art, the compensation result is not ideal, and it seriously affects the effective discrimination of target signals.

[0004] According to one aspect of the present invention, a method for removing geomagnetic interference using principal component analysis is provided. The method includes: Step 1, each probe aircraft is equipped with a scalar magnetometer to measure the total magnetic field strength, and a vector magnetoresistive sensor to collect the northward, eastward, and vertical components of the geomagnetic field. Combined with an onboard high-precision GPS system, longitude, latitude, and altitude information during flight are simultaneously acquired to achieve spatiotemporal synchronous acquisition of multi-source data; Step 2, during high-altitude calibration flight missions, each aircraft performs multi-heading and multi-attitude flights in a relatively stable geomagnetic region with minimal interference, collecting magnetic field data under different attitudes. The magnetic interference compensation coefficients specific to each aircraft platform are then calculated based on a traditional magnetic interference compensation model; Step 3, during actual low-altitude probe flights, the magnetic interference compensation coefficients obtained in Step 2 are used, combined with a traditional magnetic interference compensation algorithm, to process the original magnetic field data collected by each aircraft to eliminate interference. The remaining magnetic field is obtained through the following steps: Step 4: High-precision spatiotemporal matching is performed on the remaining magnetic field data of each probe aircraft in the same time period and similar spatial area to form a spatiotemporally aligned multi-channel magnetic measurement data matrix, laying the foundation for common interference extraction; Step 5: Using the multi-channel magnetic measurement data matrix obtained by multiple aircraft in the same area and at the same time, which contains highly similar regional geomagnetic interference characteristics, the sliding window weighted principal component analysis (SWPCA) method is used to organically combine principal component analysis (PCA), weighted averaging and sliding window collaborative filtering to generate a continuous and smooth regional geomagnetic interference field; Step 6: The geomagnetic interference field is subtracted point by point to achieve independent interference removal processing of the original data of each aircraft, and finally clean magnetic anomaly data with geological background removed and local anomalies retained is generated, completing the geomagnetic interference removal.

[0005] Furthermore, step four specifically includes: synchronizing using the UTC time of each aircraft platform; using time interpolation to match magnetic field data sampled at different times to a unified time series; projecting the observation data onto a unified three-dimensional spatial grid based on the position parameters provided by the airborne GPS; and assembling the remaining magnetic field data of each platform into a multi-channel magnetic measurement data matrix after time and space alignment.

[0006] Furthermore, in step four, a time interpolation method is used to match the magnetic field data sampled at different times to a unified time series. Let the time observation sequence of the i-th aircraft at the k-th time series point be t. i,k Then the target t j The interpolation at time t is calculated as follows: Among them, t i,k+1 The time observation sequence of the (k+1)th time series point of the i-th aircraft, B r (ti,k ) represents the time observation sequence t i,k The residual magnetic field below, B r (t i,k+1 ) represents the time observation sequence t i,k+1 The residual magnetic field below, B r (t j ) for target t j Momentary.

[0007] Furthermore, step five specifically includes: (5.1) setting a spatiotemporal sliding window B on the unified grid multi-channel magnetic measurement data matrix B. w (5.2) For the observation data B in each window w Construct the covariance matrix C w (5.3) For the covariance matrix C w Eigenvalue decomposition is performed, and the first principal component v1 corresponds to the largest eigenvalue λ1, which represents the common magnetic interference variation characteristics within the window, thereby obtaining the estimated value of the geomagnetic interference; (5.4) By sliding the window through the entire observation area, steps (5.2) and (5.3) are repeated to obtain the continuous regional geomagnetic interference field B. geo (x,y,t).

[0008] Furthermore, in step five, the covariance matrix is... Where W = diag(w1, w2... w N B is the weight matrix. w The observation data within each window.

[0009] Furthermore, in step two, solving for the magnetic interference compensation coefficient specific to any aircraft platform specifically includes: acquiring all necessary data information during each aircraft's high-altitude calibration flight mission; constructing a conventional magnetic interference compensation model using the acquired data information; and solving for the aircraft platform-specific magnetic interference compensation coefficient 'a' using a linear regression method. i .

[0010] Furthermore, in step two, the high-altitude calibration flight performed by any aircraft platform specifically includes: the aircraft platform performing multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers sequentially from west to north; the aircraft platform performing multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers sequentially from north to east; the aircraft platform performing multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers sequentially from east to south; and the aircraft platform performing multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers sequentially from south to west.

[0011] Furthermore, in step two, the motion platform performs three maneuvers to obtain all the necessary data information. The maneuvers are roll, pitch, and yaw, with peak-to-peak angles of 10°, 5°, and 5°, respectively. Each maneuver is performed in three sets, with a cycle of 4 to 12 seconds.

[0012] Furthermore, in step three, platform-based maneuvering magnetic disturbance and geomagnetic gradient magnetic disturbance B are used. TLG Magnetic interference compensation coefficient a i The residual magnetic field, obtained from data acquired by a scalar magnetometer and a vector magnetoresistive analyzer, can be calculated as follows: B r =B t -B TLG Among them, B t This represents the total magnetic field strength measured by the scalar magnetometer.

[0013] According to another aspect of the present invention, a geomagnetic interference removal system based on principal component analysis is provided. This system uses the aforementioned principal component analysis method to remove magnetic interference. The system includes: a multi-source data acquisition module for synchronous spatiotemporal acquisition of multi-source data; a magnetic interference compensation coefficient calculation module for collecting magnetic field data under different attitudes during high-altitude calibration flight missions of each aircraft by conducting multi-heading and multi-attitude flights in relatively stable geomagnetic regions with minimal interference, and calculating the unique magnetic interference compensation coefficient for each aircraft platform based on a traditional magnetic interference compensation model; and a residual magnetic field acquisition module for processing the raw magnetic field data collected by each aircraft during actual low-altitude reconnaissance flights using the magnetic interference compensation coefficient, combined with a traditional magnetic interference compensation algorithm, to eliminate platform maneuvering magnetic interference caused by the aircraft's own metal structure and electronic equipment, as well as interference caused by geomagnetic space... The system employs several methods: First, it analyzes the geomagnetic gradient interference caused by changes in the residual magnetic field to obtain the residual magnetic field. Second, it uses a multi-channel magnetic data matrix acquisition module to perform high-precision spatiotemporal matching of residual magnetic field data from various probes within the same time period and similar spatial regions, forming a spatiotemporally aligned multi-channel magnetic data matrix to lay the foundation for common interference extraction. Third, it uses a geomagnetic interference field calculation module to utilize multi-channel magnetic data matrices obtained from multiple probes in the same area within the same time period. These matrices contain highly similar regional geomagnetic interference characteristics. The module uses sliding window weighted principal component analysis (SWPCA) to organically combine principal component analysis (PCA), weighted averaging, and sliding window collaborative filtering to generate a continuous and smooth regional geomagnetic interference field. Finally, it uses a geomagnetic interference removal module to subtract the geomagnetic interference field point by point, achieving independent interference removal processing for the original data of each probe. This ultimately generates clean magnetic anomaly data that removes geological background and retains local anomalies, completing the geomagnetic interference removal process.

[0014] This invention provides a method for removing geomagnetic interference using principal component analysis. The method first uses a TL model to compensate for the constant and induced magnetic fields of the aircraft. Since geomagnetic interference exhibits similar characteristics in the same area, by comparing and analyzing magnetic data collected by multiple aircraft in the same area, a sliding window weighted principal component analysis (SWPCA) method is used to extract common magnetic interference components from the multi-aircraft observations. Geomagnetic interference is then identified and separated, and subtracted from each platform to retain the target signal, thus completing the geomagnetic interference removal using principal component analysis. Compared with existing technologies, the geomagnetic interference removal method using principal component analysis provided by this invention can generate clean magnetic anomaly data that removes geological background and retains local anomalies, ensuring that the data from each platform reflects the true target response and achieving effective discrimination of the target signal. Attached Figure Description

[0015] The accompanying drawings, which form part of this specification, are provided to further illustrate embodiments of the invention and, together with the textual description, explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0016] Figure 1 A flowchart of a geomagnetic interference removal method for principal component analysis according to a specific embodiment of the present invention is shown;

[0017] Figure 2 A schematic diagram of the motion maneuver of a calibration platform provided according to a specific embodiment of the present invention is shown;

[0018] Figure 3 A flowchart of the airborne geological magnetic interference compensation process according to a specific embodiment of the present invention is shown. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0021] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0022] like Figures 1 to 3 As shown, according to a specific embodiment of the present invention, a method for removing geomagnetic interference using principal component analysis is provided. This method includes: Step 1, each probe aircraft is equipped with a scalar magnetometer to measure the total magnetic field strength, and a vector magnetoresistive sensor to collect the northward, eastward, and vertical components of the geomagnetic field. Combined with an onboard high-precision GPS system, longitude, latitude, and altitude information during flight are simultaneously acquired to achieve spatiotemporal synchronous acquisition of multi-source data; Step 2, during high-altitude calibration flight missions, each aircraft performs multi-heading and multi-attitude flights in a relatively stable geomagnetic region with minimal interference, collecting magnetic field data under different attitudes. The magnetic interference compensation coefficients specific to each aircraft platform are solved based on a traditional magnetic interference compensation model; Step 3, during actual low-altitude probe flights, the magnetic interference compensation coefficients obtained in Step 2 are used, combined with a traditional magnetic interference compensation algorithm, to process the raw magnetic field data collected by each aircraft. The process involves six steps: First, eliminating platform maneuvering magnetic interference caused by the aircraft's own metal structure and electronic equipment, as well as geomagnetic gradient interference caused by spatial variations in the geomagnetic field, to obtain the residual magnetic field. Second, performing high-precision spatiotemporal matching on the residual magnetic field data of each probe aircraft within the same time period and similar spatial area to form a spatiotemporally aligned multi-channel magnetic measurement data matrix, laying the foundation for common interference extraction. Third, utilizing the multi-channel magnetic measurement data matrix obtained by multiple aircraft in the same area and at the same time, which contains highly similar regional geomagnetic interference characteristics, the sliding window weighted principal component analysis (SWPCA) method is used to organically combine principal component analysis (PCA), weighted averaging, and sliding window collaborative filtering to generate a continuous and smooth regional geomagnetic interference field. Fourth, subtracting the geomagnetic interference field point by point to achieve independent de-interference processing of the original data of each aircraft, ultimately generating clean magnetic anomaly data that removes geological background and retains local anomalies, thus completing the geomagnetic interference removal.

[0023] This configuration provides a principal component analysis-based method for removing geomagnetic interference. The method first uses a TL model to compensate for the aircraft's constant and induced magnetic fields. Since geomagnetic interference exhibits similar characteristics in the same area, by comparing and analyzing magnetic data collected by multiple aircraft in the same region, a sliding window weighted principal component analysis (SWPCA) method is used to extract common magnetic interference components from multi-aircraft observations. Geomagnetic interference is then identified and separated, and subtracted from each platform to retain the target signal, thus completing the geomagnetic interference removal through principal component analysis. Compared with existing technologies, the geomagnetic interference removal method provided by this invention can generate clean magnetic anomaly data that removes geological background and retains local anomalies, ensuring that data from each platform reflects the true target response and achieving effective discrimination of the target signal.

[0024] To achieve the above objectives, the technical solution provided by the present invention includes the following steps:

[0025] Step 1: Each probe aircraft is equipped with a scalar magnetometer to measure the total magnetic field strength, and a vector magnetoresistive sensor to collect the northward, eastward and vertical components of the geomagnetic field. Combined with the onboard high-precision GPS system, it synchronously acquires longitude, latitude and altitude information during the flight, so as to realize the spatiotemporal synchronous acquisition of multi-source data.

[0026] Step two: When each aircraft performs a high-altitude calibration flight mission, it collects magnetic field data under different attitudes by flying in multiple headings and pitches in a high-altitude area where the geomagnetic field is relatively stable and interference is minimal. The magnetic interference compensation coefficients specific to each aircraft platform are then solved based on the traditional magnetic interference compensation model.

[0027] In step two, solving for the magnetic interference compensation coefficient specific to any aircraft platform specifically includes: acquiring all necessary data information during each aircraft's high-altitude calibration flight mission; constructing a conventional magnetic interference compensation model using the acquired data information; and solving for the aircraft platform-specific magnetic interference compensation coefficient 'a' using a linear regression method. i .

[0028] Specifically, the high-altitude calibration flight performed by any aircraft platform includes: from west to north, the aircraft platform sequentially performing multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers; from north to east, the aircraft platform sequentially performing multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers; from east to south, the aircraft platform sequentially performing multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers; and from south to west, the aircraft platform sequentially performing multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers.

[0029] As a specific embodiment of the present invention, the motion platform performs three maneuvers to obtain all the necessary data information. The maneuvers are roll, pitch, and yaw, with peak-to-peak angles of 10°, 5°, and 5°, respectively. Each maneuver is performed in 3 sets, with a cycle of 4 to 12 seconds.

[0030] Step 3: In actual low-altitude reconnaissance flights, the compensation coefficients obtained in the aforementioned calibration phase are used in conjunction with traditional magnetic interference compensation algorithms to process the original magnetic field data collected by each aircraft. This process eliminates platform maneuvering magnetic interference caused by the aircraft's own metal structure and electronic equipment, as well as geomagnetic gradient interference caused by spatial changes in the geomagnetic field. The remaining magnetic field obtained after compensation still contains geomagnetic interference components.

[0031] In step three, platform-based maneuvering magnetic interference and geomagnetic gradient magnetic interference B are used. TLG Magnetic interference compensation coefficient a i The residual magnetic field, obtained from data acquired by a scalar magnetometer and a vector magnetoresistive analyzer, can be calculated as follows: B r =B t -B TLG Among them, B t This represents the total magnetic field strength measured by the scalar magnetometer.

[0032] Step four involves high-precision spatiotemporal matching of magnetic measurement data from various probe aircraft within the same time period and similar spatial area. Based on the precise time (UTC) and three-dimensional position information provided by the airborne GPS, time interpolation and spatial gridding methods are used to unify the residual magnetic field data of each platform to the same time series or spatial grid nodes, forming a spatiotemporally aligned multi-channel magnetic measurement data matrix, laying the foundation for the extraction of common interferences.

[0033] In this invention, step four specifically includes: synchronizing using the UTC time of each aircraft platform; using time interpolation to match magnetic field data sampled at different times to a unified time series; projecting the observation data onto a unified three-dimensional spatial grid based on the position parameters provided by the airborne GPS; and assembling the remaining magnetic field data of each platform into a multi-channel magnetic measurement data matrix after time and space alignment.

[0034] In this method, time interpolation is used to match magnetic field data sampled at different times to a unified time series. Let the time observation sequence of the i-th aircraft at the k-th time series point be t. i,k Then the target t j The interpolation at time t is calculated as follows: Among them, t i,k+1 The time observation sequence of the (k+1)th time series point of the i-th aircraft, B r (t i,k ) represents the time observation sequence t i,k The residual magnetic field below, Br (t i,k+1 ) represents the time observation sequence t i,k+1 The residual magnetic field below, B r (t j ) for target t j Momentary.

[0035] Step 5: Extract common magnetic interference components from multi-aircraft observations. Utilize the residual magnetic fields measured by multiple aircraft flying synchronously in similar areas, which contain highly similar regional geomagnetic interference characteristics. Employ the sliding window weighted principal component analysis (SWPCA) method, organically combining principal component analysis (PCA), weighted averaging, and sliding window collaborative filtering to generate a continuous and smooth regional geomagnetic interference field.

[0036] In this invention, step five specifically includes: (5.1) setting a spatiotemporal sliding window B on the unified grid multi-channel magnetic measurement data matrix B. w (5.2) For the observation data B in each window w Construct the covariance matrix C w (5.3) For the covariance matrix C w Eigenvalue decomposition is performed, and the first principal component v1 corresponds to the largest eigenvalue λ1, which represents the common magnetic interference variation characteristics within the window, thereby obtaining the estimated value of the geomagnetic interference; (5.4) By sliding the window through the entire observation area, steps (5.2) and (5.3) are repeated to obtain the continuous regional geomagnetic interference field B. geo (x,y,t). In step five, the covariance matrix is... Where W = diag(w1, w2… w N B is the weight matrix. w The observation data within each window.

[0037] Step six involves subtracting the geomagnetic interference field point by point to achieve independent de-interference processing of the raw data for each aircraft, ultimately generating clean magnetic anomaly data that removes the geological background and retains local anomalies, ensuring that the data from each platform reflects the true target response.

[0038] According to another aspect of the present invention, a geomagnetic interference removal system based on principal component analysis is provided. This system uses the geomagnetic interference removal method based on principal component analysis as described above to remove magnetic interference. The system includes: a multi-source data acquisition module, a magnetic interference compensation coefficient calculation module, a residual magnetic field acquisition module, a multi-channel magnetic measurement data matrix acquisition module, a geomagnetic interference field calculation module, and a geomagnetic interference removal module. The multi-source data acquisition module is used to achieve spatiotemporal synchronous acquisition of multi-source data. The magnetic interference compensation coefficient calculation module is used to collect magnetic field data under different attitudes during high-altitude calibration flight missions of each aircraft by conducting multi-heading and multi-attitude flights in high-altitude areas with relatively stable geomagnetism and less interference, and to solve for the magnetic interference compensation coefficient specific to each aircraft platform based on a traditional magnetic interference compensation model. The residual magnetic field acquisition module is used to process the original magnetic field data collected by each aircraft during actual low-altitude detection flights using the magnetic interference compensation coefficient in combination with a traditional magnetic interference compensation algorithm. The system employs several methods to eliminate platform maneuvering magnetic interference caused by the aircraft's own metal structure and electronic equipment, as well as geomagnetic gradient interference caused by spatial variations in the geomagnetic field, to obtain the residual magnetic field. A multi-channel magnetic measurement data matrix acquisition module performs high-precision spatiotemporal matching of residual magnetic field data from various aircraft within the same time period and similar spatial regions, forming a spatiotemporally aligned multi-channel magnetic measurement data matrix, laying the foundation for common interference extraction. A geomagnetic interference field calculation module utilizes multi-channel magnetic measurement data matrices obtained from multiple aircraft in the same area within the same time period, which contain highly similar regional geomagnetic interference characteristics. It employs a sliding window weighted principal component analysis (SWPCA) method, organically combining principal component analysis (PCA), weighted averaging, and sliding window collaborative filtering to generate a continuous and smooth regional geomagnetic interference field. A geomagnetic interference removal module subtracts the geomagnetic interference field point by point, achieving independent interference removal processing for the original data of each aircraft, ultimately generating clean magnetic anomaly data that removes geological background and retains local anomalies, thus completing the geomagnetic interference removal process.

[0039] This configuration provides a geomagnetic interference removal system based on principal component analysis. The system first uses a TL model to compensate for the constant and induced magnetic fields of the aircraft. Since geomagnetic interference exhibits similar characteristics in the same area, by comparing and analyzing magnetic data collected by multiple aircraft in the same region, the system employs sliding window weighted principal component analysis (SWPCA) to extract common magnetic interference components from multi-aircraft observations. Geomagnetic interference is then identified and separated, and subtracted from each platform to retain the target signal, thus completing the geomagnetic interference removal through principal component analysis. Compared with existing technologies, the geomagnetic interference removal system based on principal component analysis provided by this invention can generate clean magnetic anomaly data that removes geological background and retains local anomalies, ensuring that data from each platform reflects the true target response and achieving effective discrimination of the target signal.

[0040] To gain a further understanding of the present invention, the following description is provided in conjunction with... Figures 1 to 3 The geomagnetic interference removal method for principal component analysis provided by this invention will be described in detail.

[0041] Step 1: Each probe aircraft is equipped with a scalar magnetometer to measure the total magnetic field strength, and a vector magnetoresistive sensor to collect the northward, eastward and vertical components of the geomagnetic field. Combined with the onboard high-precision GPS system, it synchronously acquires longitude, latitude and altitude information during the flight, so as to realize the spatiotemporal synchronous acquisition of multi-source data.

[0042] Step two: When each aircraft performs a high-altitude calibration flight mission, it conducts multi-heading and multi-attitude flights in a high-altitude area with relatively stable geomagnetism and less interference, collects magnetic field data under different attitudes, and solves the magnetic interference compensation coefficients unique to each aircraft platform based on the traditional magnetic interference compensation model.

[0043] In this embodiment, a traditional magnetic compensation model for platform maneuvering magnetic interference and geomagnetic gradient magnetic interference is established, expressed as:

[0044]

[0045] Among them, a i Let μ be the magnetic compensation coefficient to be solved. i The model function related to magnetic compensation is expressed as:

[0046] μ1=cosα X μ2=cosα Y μ3=cosα Z

[0047] μ4=T g cosα X cosα X μ5=T g cosα X cosα Y μ6=T g cosα X cosα Z

[0048] μ7=T g cosα Y cosα Y μ8=T g cosα Y cosα Z

[0049] μ9=T g cosα X (cosα X)′,μ9=T g cosα X (cosα Y )′,μ 11 =T g cosα X (cosα Y )′

[0050] μ 12 =T g cosα Y (cosα X )′,μ 13 =T g cosα Y (cosα Y )′,μ 14 =T g cosα Y (cosα Z )′

[0051] μ 15 =cosα Z (cosα X )′,μ 16 =T g cosα Z (cosα Y )′

[0052] μ 17 =W,μ 18 =J,μ 19 =G

[0053] Among them, T g Represents the Earth's magnetic field, (cosα) X )′,(cosα Y )′,(cosα Z )′ represent cosα respectively X ,cosα Y ,cosα Z The differential of the geomagnetic field and the angles between the three axes of the coordinate system of the moving platform are denoted as α. X ,α Y ,α Y The cosine values ​​are expressed as:

[0054]

[0055] Among them, T X ,T Y ,T Z denoted by the three components of the vector magnetoresistive force; G represents the current height of the platform.

[0056] The platform performs calibration flights to obtain the magnetic compensation coefficient 'a'.i ; Figure 1 The diagram shows the process for calculating the magnetic compensation coefficient during calibration flight according to the present invention. The specific operations are as follows:

[0057] 2A. The motion platform performs three maneuvers to obtain all the necessary data information. The maneuvers are roll, pitch, and yaw, with peak-to-peak angles of 10°, 5°, and 5° respectively. Each maneuver is performed in 3 sets, with a cycle of 4–12 seconds. Figure 2 As shown;

[0058] 2B. Use the acquired data to build a new model;

[0059] 2C. Solve for the magnetic compensation coefficient α using linear regression. i .

[0060] Step 3: In actual low-altitude reconnaissance flights, the magnetic interference compensation coefficient obtained in Step 2 is used in conjunction with the traditional magnetic interference compensation algorithm to process the original magnetic field data collected by each aircraft in order to eliminate the platform maneuvering magnetic interference caused by the aircraft's own metal structure and electronic equipment, as well as the geomagnetic gradient interference caused by the spatial variation of the geomagnetic field, and obtain the residual magnetic field.

[0061] In actual detection, B is used TLG Compensation coefficient a i The residual magnetic field, obtained from data acquired by a scalar magnetometer and a vector magnetoresistive analyzer, can be calculated as follows:

[0062] B r =B t -B TLG

[0063] Among them B t This refers to the magnetic field value measured by the magnetometer.

[0064] Step four involves spatiotemporal alignment and unified gridding of the magnetic data, aiming to integrate the residual magnetic fields B collected by multiple aircraft at different times and locations. r Mapped onto a unified spatiotemporal reference, it provides a precisely matched data matrix for subsequent extraction of common interferences.

[0065] 4A. First, time alignment is performed using the UTC time of each platform for synchronization. For magnetic field data sampled at different times, time interpolation is used to match them to a unified time series. Let the time observation series of the i-th aircraft be t. i,k Then the target t j The interpolation at time t is calculated as follows:

[0066]

[0067] 4B. Based on the position parameters provided by the airborne GPS, project the observation data onto a unified three-dimensional spatial grid, and set the unified grid nodes as follows:

[0068] (x m ,y n ,z p ), m=1...M x n=1...M y p = 1...M z (M x M y (Represents the number of grid points in the xyz direction of three-dimensional space)

[0069] Reconstructing the inverse distance-weighted spatial magnetic field:

[0070]

[0071] Among them, R i (x m ,y n ,z p R represents the magnetic field that needs to be reconstructed at the current location. i (x j ,y j ,z j () represents a known magnetic field. Represents R i (x j ,y j ,z j ) Location to R i (x m ,y n ,z p The distance.

[0072] 4C. After time and space alignment, the residual magnetic field data from each platform are assembled into a multi-channel matrix:

[0073]

[0074] This matrix contains synchronous observations from multiple platforms at the same spatiotemporal point, providing a basis for common interference analysis.

[0075] Step 5: Using sliding window weighted principal component analysis (WPCA), common components, namely regional geomagnetic interference fields, are extracted from the residual magnetic fields measured by multiple aircraft in the same area at the same time.

[0076] 5A. Set a spatiotemporal sliding window on the unified grid data matrix B:

[0077] B w =B(x±Δ) x ,y±Δ y,t±Δ t )

[0078] The window size can be determined based on the flight speed and sampling rate, Δ x Δ y Δ z This represents the step size of the slide.

[0079] 5B. For the observation data within each window B w ∈B w×N Construct the covariance matrix:

[0080]

[0081] Where W = diag(w1, w2, ... w N ) is a weight matrix, and the weights are determined by the stability of the aircraft platform, the noise level of the instrument, or the measurement quality.

[0082] 5C. Regarding the covariance matrix C w Perform eigenvalue decomposition:

[0083] C w v k =λ k v k

[0084] The first principal component v1 corresponds to the largest eigenvalue λ1, representing the common magnetic disturbance variation characteristics within the window, from which the estimated value of geomagnetic disturbance is obtained:

[0085]

[0086] Where α k These are time-smoothing weighting coefficients to ensure that the results are continuous in time and smooth in space.

[0087] By sliding the window across the entire observation area, a continuous regional geomagnetic interference field B can be obtained. geo (x,y,t).

[0088] Step six involves subtracting the geomagnetic interference field point by point to achieve independent de-interference processing of the original data for each aircraft, ultimately generating clean magnetic anomaly data that removes the geological background and retains local anomalies, thus completing the removal of geomagnetic interference.

[0089] In this embodiment, the final magnetic field is obtained by subtracting the geomagnetic interference field from the remaining magnetic field point by point. For each aircraft i, this is performed at the same spatiotemporal node:

[0090] B final,i =B i (x,y,t)-B geo (x,y,t)

[0091] This process uses calculations to remove geomagnetic interference.

[0092] In summary, this invention provides a geomagnetic interference removal system based on principal component analysis. This system first uses a TL model to compensate for the constant and induced magnetic fields of the aircraft. Since geomagnetic interference exhibits similar characteristics in the same area, by comparing and analyzing magnetic data collected by multiple aircraft in the same area, the system employs sliding window weighted principal component analysis (SWPCA) to extract common magnetic interference components from multi-aircraft observations, identify and separate geomagnetic interference, and subtract the geomagnetic interference from each platform to retain the target signal, thus completing the geomagnetic interference removal through principal component analysis. Compared with existing technologies, the geomagnetic interference removal system based on principal component analysis provided by this invention can generate clean magnetic anomaly data that removes geological background and retains local anomalies, ensuring that the data from each platform reflects the true target response and achieving effective discrimination of the target signal.

[0093] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0094] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for removing geomagnetic interference in principal component analysis, characterized in that, The geomagnetic interference removal method for principal component analysis includes: Step 1: Each probe aircraft is equipped with a scalar magnetometer to measure the total magnetic field strength, and a vector magnetoresistive sensor to collect the northward, eastward and vertical components of the geomagnetic field. Combined with the onboard high-precision GPS system, it synchronously acquires longitude, latitude and altitude information during the flight, so as to realize the spatiotemporal synchronous acquisition of multi-source data. Step 2: When each aircraft performs high-altitude calibration flight mission, it collects magnetic field data under different attitudes by flying in multiple headings and attitudes in a high-altitude area where the geomagnetic field is relatively stable and interference is minimal. The magnetic interference compensation coefficients unique to each aircraft platform are then solved based on the traditional magnetic interference compensation model. Step 3: In actual low-altitude reconnaissance flights, the magnetic interference compensation coefficient obtained in Step 2 is used in combination with the traditional magnetic interference compensation algorithm to process the original magnetic field data collected by each aircraft in order to eliminate the platform maneuvering magnetic interference caused by the aircraft's own metal structure and electronic equipment, as well as the geomagnetic gradient interference caused by the spatial variation of the geomagnetic field, and obtain the residual magnetic field. Step four involves performing high-precision spatiotemporal matching of the residual magnetic field data from each probe aircraft within the same time period and similar spatial region to form a spatiotemporally aligned multi-channel magnetic measurement data matrix, laying the foundation for common interference extraction. Step 5: Using the multi-channel magnetic data matrix obtained by multiple aircraft in the same area at the same time, which contains highly similar regional geomagnetic interference characteristics, the sliding window weighted principal component analysis (SWPCA) method is used to organically combine principal component analysis (PCA), weighted averaging and sliding window collaborative filtering to generate a continuous and smooth regional geomagnetic interference field. Step six involves subtracting the geomagnetic interference field point by point to achieve independent de-interference processing of the original data for each aircraft, ultimately generating clean magnetic anomaly data that removes the geological background and retains local anomalies, thus completing the removal of geomagnetic interference.

2. The method for removing geomagnetic interference in principal component analysis according to claim 1, characterized in that, Step four specifically includes: Synchronization is achieved using the UTC time of each aircraft platform, and magnetic field data sampled at different times are matched to a unified time series. Based on the position parameters provided by the airborne GPS, the observation data is projected onto a unified three-dimensional spatial grid; After time and space alignment, the remaining magnetic field data from each platform are assembled into a multi-channel magnetic measurement data matrix.

3. The method for removing geomagnetic interference in principal component analysis according to claim 2, characterized in that, In step four, the magnetic field data sampled at different times are matched to a unified time series. Let the time observation sequence of the i-th aircraft at the k-th time series point be t. i,k Then the target t j The interpolation at time t is calculated as follows: Among them, t i,k+1 The time observation sequence of the (k+1)th time series point of the i-th aircraft, B r (t i,k ) represents the time observation sequence t i,k The residual magnetic field below, B r (t i,k+1 ) represents the time observation sequence t i,k+1 The residual magnetic field below, B r (t j ) for target t j Momentary.

4. The method for removing geomagnetic interference in principal component analysis according to claim 3, characterized in that, Step five specifically includes: (5.1) Set a spatiotemporal sliding window B on the unified grid multi-channel magnetic measurement data matrix B. w ; (5.2) For the observation data B in each window w Construct the covariance matrix C w ; (5.3) For the covariance matrix C w Eigenvalue decomposition is performed, and the first principal component v1 corresponds to the largest eigenvalue λ1, which represents the common magnetic disturbance variation characteristics within the window, thereby obtaining the estimated value of the geomagnetic disturbance. (5.4) By sliding the window across the entire observation area, repeat steps (5.2) and (5.3) to obtain the continuous regional geomagnetic interference field B. geo (x,y,t).

5. The method for removing geomagnetic interference in principal component analysis according to claim 4, characterized in that, In step five, the covariance matrix is Where W = diag(w1, w2... w N B is the weight matrix. w The observation data within each window.

6. The method for removing geomagnetic interference in principal component analysis according to claim 1, characterized in that, In step two, solving for the magnetic interference compensation coefficient specific to any aircraft platform specifically includes: All necessary data and information are acquired when each aircraft performs a high-altitude calibration flight mission; A traditional magnetic interference compensation model is constructed using the obtained data. The magnetic interference compensation coefficient 'a' specific to this aircraft platform was determined using linear regression. i .

7. The method for removing geomagnetic interference in principal component analysis according to claim 6, characterized in that, In step two, the high-altitude calibration flight performed by any aircraft platform specifically includes: The aircraft platform performed multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers in sequence from west to north. The aircraft platform performed multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers sequentially from north to east. The aircraft platform performed multiple sets of roll maneuvers, multiple sets of pitch maneuvers, and multiple sets of yaw maneuvers in sequence from east to south. The aircraft platform performed multiple sets of roll maneuvers, pitch maneuvers, and yaw maneuvers sequentially from south to west.

8. The method for removing geomagnetic interference in principal component analysis according to claim 7, characterized in that, In step two, the motion platform performs three maneuvers to obtain all the necessary data information. The maneuvers are roll, pitch, and yaw, with peak-to-peak angles of 10°, 5°, and 5°, respectively. Each maneuver is performed in three sets, with a cycle of 4 to 12 seconds.

9. The method for removing geomagnetic interference in principal component analysis according to any one of claims 8, characterized in that, In step three, platform-based maneuvering magnetic interference and geomagnetic gradient magnetic interference B are used. TLG Magnetic interference compensation coefficient a i The residual magnetic field, obtained from data acquired by a scalar magnetometer and a vector magnetoresistive analyzer, can be calculated as follows: B r =B t -B TLG Among them, B t This represents the total magnetic field strength measured by the scalar magnetometer.

10. A geomagnetic interference removal system for principal component analysis, characterized in that, The geomagnetic interference removal system for principal component analysis uses the geomagnetic interference removal method for principal component analysis as described in any one of claims 1 to 9 to remove magnetic interference, and the geomagnetic interference removal system for principal component analysis comprises: A multi-source data acquisition module, which is used to realize the spatiotemporal synchronous acquisition of multi-source data; The magnetic interference compensation coefficient calculation module is used to collect magnetic field data under different attitudes during high-altitude calibration flight missions of each aircraft by flying in multiple headings and attitudes in a high-altitude area where the geomagnetic field is relatively stable and interference is small. The module is then used to solve the magnetic interference compensation coefficient unique to each aircraft platform based on the traditional magnetic interference compensation model. The residual magnetic field acquisition module is used to process the original magnetic field data collected by each aircraft during actual low-altitude detection flights by using the magnetic interference compensation coefficient and combining it with the traditional magnetic interference compensation algorithm. This process aims to eliminate platform maneuvering magnetic interference caused by the aircraft's own metal structure and electronic equipment, as well as geomagnetic gradient interference caused by spatial changes in the geomagnetic field, in order to obtain the residual magnetic field. A multi-channel magnetic measurement data matrix acquisition module is used to perform high-precision spatiotemporal matching of the residual magnetic field data of each probe aircraft in the same time period and similar spatial area to form a spatiotemporally aligned multi-channel magnetic measurement data matrix, laying the foundation for common interference extraction. The geomagnetic interference field calculation module is used to utilize the multi-channel magnetic measurement data matrix obtained by multiple aircraft in the same area at the same time, which contains highly similar regional geomagnetic interference characteristics. The module adopts the sliding window weighted principal component analysis (SWPCA) method, which organically combines principal component analysis (PCA), weighted averaging and sliding window collaborative filtering to generate a continuous and smooth regional geomagnetic interference field. The geomagnetic interference removal module is used to subtract the geomagnetic interference field point by point, realize independent interference removal processing of each aircraft's original data, and finally generate clean magnetic anomaly data that removes geological background and retains local anomalies, thus completing the geomagnetic interference removal.

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