Data processing method for spiral aeromagnetic detection

Abstract

The invention relates to a data processing method for spiral aeromagnetic detection, and the method comprises the steps: obtaining data: collecting a detection signal through an optical pumping magnetometer when an unmanned plane flies in a spiral track; a data preprocessing step: performing fractional order Fourier transform on the detection signal, mapping the detection signal to each fractional order domain, and determining an optimal order by using a particle swarm search method; and a noise suppression step: in the fractional domain of the optimal order, detecting an impulse function peak value by using a moving median filtering method, marking an abnormal value based on a median, and performing zero setting to obtain a filtered signal. According to the invention, the specific dynamic noise of the spiral track is effectively suppressed, and the signal-to-noise ratio is improved.

Description

Technical Field

[0001] The present application relates to the field of magnetic detection technology, and in particular to a data processing method for spiral aeromagnetic detection. Background Art

[0002] Magnetic anomaly detection is a non-contact detection method that identifies potential targets by measuring and analyzing magnetic field variations in various media (such as air, soil, and water). This technology typically relies on highly sensitive magnetometers to collect magnetic field data and analyze magnetic anomaly signals to locate and identify ferromagnetic objects.

[0003] In recent years, with the rapid development of unmanned aerial vehicles (UAVs), the integration of magnetic anomaly detection technology onto drone platforms has led to the development of aeromagnetic surveying (abbreviated as "aeromagnetic surveying"), effectively increasing the spatial range and efficiency of data collection. Aeromagnetic technology has been widely used in a variety of fields, including the detection of buried unexploded ordnance (UXO), geological exploration, and archaeological research.

[0004] In aeromagnetic detection missions, flight trajectory design directly affects the quality of magnetic data acquisition and detection efficiency. Although traditional grid or linear trajectories are suitable for large-scale, systematic surveys, they have the problems of high time cost and slow response speed in specific scenarios. In order to improve search efficiency, spiral flight trajectories have gradually been adopted in some application scenarios due to their fast coverage speed and strong local focusing ability. Especially in practical applications, due to the complex dynamic motion characteristics of drones flying in a spiral mode, such as Figure 1 As shown, it is very easy to introduce strong low-frequency dynamic noise, which will be superimposed on the original magnetic field signal, causing distortion of the magnetic anomaly signal, covering up the true target characteristics, and seriously interfering with subsequent target identification and positioning work.

[0005] Although methods such as the TL model (Tolles-Lawson model) are currently used to attempt to compensate for low-frequency noise, this model has limited effectiveness in the face of nonlinear and multi-source disturbances and cannot effectively suppress the dynamic noise unique to spiral trajectories. Summary of the Invention

[0006] The embodiment of the present application provides a data processing method for spiral aeromagnetic detection, which is an efficient suppression technology for the low-frequency dynamic noise characteristics in spiral aeromagnetic detection, so as to improve the signal-to-noise ratio and obtain higher-quality magnetic anomaly data, thereby enhancing the detection capability and recognition accuracy of target objects.

[0007] To achieve the above objectives, the present application provides a data processing method for spiral aeromagnetic detection, comprising: Data acquisition steps: When the UAV flies in a spiral trajectory, the detection signal collected by the optically pumped magnetometer includes low-frequency dynamic noise; a data preprocessing step of performing a fractional Fourier transform on the detection signal, mapping the detection signal to various fractional order domains, and determining the optimal order using a particle swarm search method; In the noise suppression step, the peak of the impulse function is detected using the moving median filter method in the fractional domain of the optimal order, and the outliers are marked and set to zero based on the median to obtain the filtered signal.

[0008] In some embodiments, in the data acquisition step, the detection signal is modeled as the following calculation model: , in, is the imaginary part, is the initial frequency, indicating The instantaneous frequency of the detection signal, is the frequency change rate.

[0009] Perform a fractional Fourier transform on the detection signal. The fractional Fourier transform is expressed as the following calculation model: , ; in, is the angle of fractional transformation rotation, is the order of the fractional transformation rotation, , , .

[0010] In some embodiments, in the data preprocessing step, the signal characteristics obtained by fractional Fourier transform are expressed as: , when , When the detection signal reaches its maximum value in the fractional domain .

[0011] In some embodiments, the data preprocessing step further includes: The optimal order search step uses the particle swarm search method to As a constraint, the peak value of the detection signal in the fractional domain is found with a step size of 1e-6 to determine the optimal order.

[0012] In some embodiments, the optimal order search step further comprises: Define the location of the initial particle swarm xi (0) Uniformly distributed between [0,2], i =1,2,…n, based on a position update formula and a velocity update formula, the position and velocity of the particle swarm are updated respectively; Randomly initialize the position of each particle x i (0) and speed , and initialize the individual best position and the global best position as: 、 ; Repeatedly calculate the fitness of each particle , update the individual best position ,like , then update ,like , then update ; Until the preset stopping condition is met, the global best position is the optimal order Each iteration gradually converges to the optimal solution through a balance between local search (individual optimality) and global guidance (group optimality), ensuring the unity of search accuracy and speed.

[0013] In some embodiments, the speed update formula is expressed as:

[0014] in, w is the inertia weight, which is used to control the inertia of particle velocity update. c 1, c 2 are individual learning factors and social learning factors respectively; r 1 and r 2 are independent random numbers used to increase the randomness of individual learning; The position update formula is expressed as: .

[0015] Compared with the related art, the data processing method for spiral aeromagnetic detection provided in the embodiment of the present application compares the spiral low-frequency dynamic noise to a swept-frequency interference signal, and uses a particle swarm search algorithm in the fractional domain to accurately locate the optimal order. In the fractional domain of the optimal order, the amplitude and energy of the noise signal reach a peak value, thereby achieving accurate identification and effective filtering of the noise signal, and being able to better suppress the spiral aeromagnetic low-frequency dynamic noise, obtain high-quality magnetic field data, and effectively improve the ability of aeromagnetic search targets.

[0016] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 This is a schematic diagram of aeromagnetic drone detection based on relevant technologies; Figure 2 is a flow chart of a data processing method according to an embodiment of the present application; Figure 3 is a schematic diagram of a detection trajectory according to a preferred embodiment of the present application; Figure 4 is a schematic diagram of a magnetic field curve of an original signal detected according to a preferred embodiment of the present application; Figure 5 is a time-frequency diagram of the original signal detected according to the preferred embodiment of the present application; Figure 6 is a schematic diagram of a particle swarm search result according to a preferred embodiment of the present application; Figure 7 2 is a schematic diagram of noise suppression results according to a preferred embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0019] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0020] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0021] Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by persons of ordinary skill in the art to which this application belongs. The terms "a," "an," "an," "the," and similar expressions used herein do not denote quantitative limitations and may refer to either the singular or the plural. The terms "comprise," "include," "have," and any variations thereof, used herein, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules (units) is not limited to the listed steps or units but may also include steps or units not listed, or may include other steps or units inherent to the process, method, product, or apparatus. The terms "connected," "connected," "coupled," and similar expressions used herein are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. As used herein, "plurality" means two or more. "And / or" describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" may mean: A exists alone; A and B exist simultaneously; or B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0022] This embodiment provides a data processing method for spiral aeromagnetic detection. Figure 2 is a flow chart of a data processing method according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps: In data acquisition step S1, when the UAV flies in a spiral trajectory, the detection signal collected by the optically pumped magnetometer includes low-frequency dynamic noise. Among them, a low-frequency dynamic noise similar to a swept-frequency interference signal is introduced into the detection signal. The frequency of this signal and the fluctuation frequency will decrease accordingly as the frequency of the spiral trajectory decreases. The frequency changes with time and has obvious time-frequency coupling characteristics.

[0023] In the data preprocessing step S2, the detection signal is subjected to a fractional Fourier transform, mapped to each fractional order domain, and the optimal order is determined using a particle swarm search method. In the fractional order domain of the optimal order, the low-frequency dynamic noise is transformed into a cluster of clear energy peaks, forming an impulse function. Specifically, the detection signal is modeled as the following calculation model: , in, is the imaginary part, is the initial frequency, indicating The instantaneous frequency of the detection signal, is the frequency change rate.

[0024] Perform a fractional Fourier transform on the detection signal. The fractional Fourier transform is expressed as the following calculation model: , ; in, is the angle of fractional transformation rotation, is the order of the fractional transformation rotation, , , .

[0025] In some embodiments, in the data preprocessing step, the signal characteristics obtained by fractional Fourier transform are expressed as: , when , When , the integral term becomes a Gaussian integral, resulting in energy The point forms an impulse function, and the detection signal can reach the maximum value in the fractional domain It can be seen that choosing the right and , the signal energy will be concentrated in the fractional domain at the appropriate angle to form an impulse function, which can provide a basis for subsequent extraction and recognition.

[0026] Based on the above processing, the detection signal is subjected to fractional Fourier transform, and the amplitude distribution model of the signal in different fractional-order domains is established. A nonlinear modulation term is introduced into the integral kernel to concentrate the signal energy in a specific fractional domain. The characteristic of energy concentration in the fractional domain improves the efficiency and accuracy of identifying time-varying noise, and significantly enhances the selective filtering capability of the entire data processing method.

[0027] In the noise suppression step S3, the peak of the impulse function is detected within the optimal-order fractional domain using a moving median filter. Outliers are marked based on the median and reset to zero, resulting in a filtered signal. Specifically, the median of each point is calculated, and points that deviate significantly from the median are marked as outliers, representing low-frequency dynamic noise. The outlier value |F(u)| corresponding to u is the energy accumulated by the low-frequency dynamic noise within the fractional domain. Setting this value to zero allows filtering out the energy, thereby suppressing this spiral-shaped low-frequency dynamic noise.

[0028] In the above embodiment, the window length of the median filter can be dynamically set according to the interference frequency distribution and the impulse function width.

[0029] Based on the above steps, in order to effectively identify and suppress this type of low-frequency dynamic noise interference, the embodiment of the present application uses fractional Fourier transform technology to convert the detection signal into the frequency domain and map its distribution into multiple fractional-order domains. This transformation can make the energy of the sweep-frequency interference signal modulated with the trajectory change appear as an obvious impulse feature in the fractional domain at a specific angle. By introducing the particle swarm optimization algorithm to search for the optimal order of each fractional-order domain, the domain where the noise signal energy is most concentrated can be determined, thereby achieving accurate extraction of this type of signal.

[0030] On this basis, the moving median filter method is further used to detect the peak of the impulse function within the optimal fractional domain, identify energy points that deviate far from the local median, and mark them as outliers. These outliers usually correspond to frequency clusters of noise signals. Finally, the frequency amplitudes corresponding to these marked points are set to zero, thereby filtering out the low-frequency dynamic interference caused by spiral flight and accurately extracting and retaining the true aeromagnetic signal.

[0031] The above method significantly improves the anti-interference capability of aeromagnetic signal processing under spiral flight conditions. By focusing and filtering out swept-frequency noise signals in the fractional domain, combined with parameter optimization using particle swarm optimization and outlier detection using median filtering, this method not only improves the accuracy of interference identification but also enhances the adaptability of filtering, making it applicable to a wide range of flight frequencies and scenarios with varying interference characteristics. This method requires no additional hardware and relies solely on software processing to separate complex signals, enhancing system integration and practical application convenience.

[0032] In some embodiments, the data preprocessing step S2 further includes: The optimal order search step S201 uses the particle swarm search method to As a constraint, the peak value of the detection signal in the fractional domain is found with a step size of 1e-6 to determine the optimal order.

[0033] The above steps use the peak amplitude of the signal energy in the transformed fractional domain as the objective function. The goal is to find the order parameter that maximizes this peak value, thereby achieving the optimal concentration of signal energy. During the search process, the position of each particle represents a candidate order value. The algorithm evaluates the maximum value of the output signal amplitude in the corresponding fractional Fourier transform result and uses this amplitude as the fitness function.

[0034] In some embodiments, the optimal order search step further comprises: Define the location of the initial particle swarm x i (0) Uniformly distributed between [0,2], i =1,2,…n, and update the position and velocity of the particle swarm based on a position update formula and a velocity update formula respectively.

[0035] The speed update formula is expressed as:

[0036] in, w is the inertia weight, which is used to control the inertia of particle velocity update. c 1, c 2 are individual learning factors and social learning factors respectively; r 1 and r 2 are independent random numbers used to increase the randomness of individual learning; The position update formula is expressed as: .

[0037] Then, the position of each particle is randomly initialized x i (0) and speed , and initialize the individual best position and the global best position as: 、 ; Repeatedly calculate the fitness of each particle , update the individual best position ,like , then update ,like , then update ; Until the preset stopping condition is met, the global best position is the optimal order Each iteration gradually converges to the optimal solution through a balance between local search (individual optimality) and global guidance (group optimality), ensuring the unity of search accuracy and speed.

[0038] Based on the above steps, the particle positions are initialized in the interval [0, 2] to cover all possible fractional order values, which can achieve high-precision order selection, ensure that the FrFT transform can concentrate the interference energy at the optimal angle, and enhance the pertinence and accuracy of subsequent interference filtering; at the same time, the particle swarm algorithm does not rely on function gradients, is suitable for complex objective function scenarios, and exhibits good stability and global search capabilities under dynamic interference conditions.

[0039] The embodiments of the present application are described and illustrated below through preferred embodiments.

[0040] Take a set of measured data using a spiral trajectory search as an example, Figure 3 This is a schematic diagram of the detection trajectory according to the preferred embodiment of the present application. Figure 3 As shown: starting from the start point, the flight area is 30m*30m, the measurement line spacing is 3m, and the flight altitude is 20m.

[0041] Figure 4 For the magnetic field curve of the detected original signal, refer to Figure 4 It can be seen that the signal frequency tends to decrease gradually. The time-frequency diagram of the signal is as follows: Figure 5 As shown, the time-frequency diagram of the signal in the figure is similar to the time-frequency diagram of the swept-frequency interference signal, showing obvious bands at specific frequencies.

[0042] Therefore, in step S2, the signal is subjected to fractional Fourier transform, and the optimal order is obtained by using the particle swarm search algorithm: 0.998713, as shown in Figure 6 shown.

[0043] Further, after filtering in step S3, we get Figure 7 The signal shown, compared Figure 4 and Figure 7 It can be seen that after the data processing method of the present application, the magnetic field strength tends to be stable, and the fluctuation amplitude caused by external interference is significantly reduced.

[0044] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0046] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for processing data of spiral aeromagnetic detection, characterized in that: include: Data acquisition steps: When the UAV flies in a spiral trajectory, the detection signal collected by the optically pumped magnetometer includes low-frequency dynamic noise; a data preprocessing step of performing a fractional Fourier transform on the detection signal, mapping the detection signal to various fractional order domains, and determining the optimal order using a particle swarm search method; In the noise suppression step, the peak of the impulse function is detected using the moving median filter method in the fractional domain of the optimal order, and the outliers are marked and set to zero based on the median to obtain the filtered signal.

2. The data processing method for spiral aeromagnetic detection according to claim 1, characterized in that: In the data acquisition step, the detection signal is modeled as the following calculation model: ， in, is the imaginary part, is the initial frequency, indicating The instantaneous frequency of the detection signal, is the frequency change rate; Perform a fractional Fourier transform on the detection signal. The fractional Fourier transform is expressed as the following calculation model: ， ； in, is the angle of fractional transformation rotation, is the order of the fractional transformation rotation, , , .

3. The data processing method for spiral aeromagnetic detection according to claim 2, characterized in that: In the data preprocessing step, the signal characteristics obtained by fractional Fourier transform are expressed as: ， when , When the detection signal reaches its maximum value in the fractional domain .

4. The data processing method for spiral aeromagnetic detection according to claim 3, characterized in that: The data preprocessing step further includes: The optimal order search step uses the particle swarm search method to As a constraint, the peak value of the detection signal in the fractional domain is found with a step size of 1e-6 to determine the optimal order.

5. The data processing method for spiral aeromagnetic detection according to claim 4, characterized in that: The optimal order search step further comprises: Define the location of the initial particle swarm x i (0) Uniformly distributed between [0,2], i =1,2,…n, based on a position update formula and a velocity update formula, the position and velocity of the particle swarm are updated respectively; Randomly initialize the position of each particle x i (0) and speed , and initialize the individual best position and the global best position as: 、 ; Repeatedly calculate the fitness of each particle , update the individual best position ,like , then update ,like , then update ; Until the preset stopping condition is met, the global best position is the optimal order .

6. The data processing method for spiral aeromagnetic detection according to claim 5, characterized in that: The speed update formula is expressed as: in, w is the inertia weight, c 1, c 2 are individual learning factors and social learning factors respectively; r 1 and r 2 are independent random numbers; The position update formula is expressed as: 。

Images