Method for realizing moving target magnetic anomaly extraction based on three-machine cooperative magnetic detection information

Abstract

The invention provides a moving target magnetic anomaly extraction method based on three-unmanned-aerial-vehicle cooperative magnetic detection information, and the method comprises the steps: enabling three unmanned aerial vehicles A, B and C to cooperatively fly according to a preset flight scheme, and forming a triangular flight formation, so as to achieve the complete coverage of a target region; magnetic detection data of the three unmanned aerial vehicles are transmitted to a ground control center in real time through a wireless communication system, the ground control center carries out magnetic anomaly extraction by adopting a function based on adjustable quality factor wavelet transform and a contig contraction algorithm, f is a function and is used for integrating the magnetic detection data and position information of the three unmanned aerial vehicles, and the magnetic anomaly is extracted by adopting the function. And extracting a magnetic anomaly signal. By applying the technical scheme of the invention, the technical problem that high-precision and high-efficiency magnetic anomaly extraction is difficult to realize due to the fact that a single unmanned aerial vehicle system in the prior art is often limited by magnetic field interference and detection range of the single unmanned aerial vehicle system is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of cooperative magnetic detection by unmanned aerial vehicles (UAVs), and in particular to a method for extracting magnetic anomalies of moving targets based on cooperative magnetic detection information of three drones. Background Art

[0002] In the field of magnetic detection, especially in the detection of magnetic anomalies of moving targets, traditional single-UAV systems are often limited by their own magnetic field interference and detection range, making it difficult to achieve high-precision and high-efficiency magnetic anomaly extraction. Summary of the invention

[0003] The present invention provides a method for extracting magnetic anomalies of moving targets based on the cooperative magnetic detection information of three machines, which can solve the technical problem that the existing single-UAV system is often limited by its own magnetic field interference and detection range, and it is difficult to achieve high-precision and high-efficiency magnetic anomaly extraction.

[0004] According to one aspect of the present invention, a method for extracting magnetic anomalies of moving targets based on the coordinated magnetic detection information of three drones is provided. The method for extracting magnetic anomalies of moving targets based on the coordinated magnetic detection information of three drones includes: three drones A, B, and C fly in coordination according to a predetermined flight plan to form a triangular flight formation to achieve comprehensive coverage of the target area; the magnetic detection data of the three drones are transmitted to a ground control center in real time through a wireless communication system, and the ground control center uses a function based on adjustable quality factor wavelet transform (TQWT) and overlapping group shrinkage algorithm (OGS) Magnetic anomaly extraction is performed, where f is a function used to integrate the magnetic detection data and position information of the three UAVs to extract magnetic anomaly signals. The function is based on the adjustable quality factor wavelet transform (TQWT) and the overlapping group shrinkage algorithm (OGS). is the magnetic detection data of UAV A, is the magnetic detection data of UAV B, is the magnetic detection data of UAV A, is the position of UAV A, is the position of drone B, is the position of UAV C, It is a magnetic anomaly signal.

[0005] Furthermore, the ground control center uses a function based on the tunable quality factor wavelet transform (TQWT) and the overlapping group shrinkage algorithm (OGS) The magnetic anomaly extraction specifically includes: performing TQWT transformation on the magnetic detection data of each UAV to obtain the frequency domain magnetic detection data of UAV A after TQWT transformation. Frequency domain magnetic detection data after TQWT transformation of UAV B And the frequency domain magnetic detection data after TQWT transformation of UAV C Apply the OGS algorithm to the frequency domain magnetic detection data of UAV A after TQWT transformation Frequency domain magnetic detection data after TQWT transformation of UAV B And the frequency domain magnetic detection data after TQWT transformation of UAV C Extract weak features separately to obtain OGS weak features of drone A OGS weak features of drone B And the OGS weak features of drone C The OGS weak feature of drone A OGS weak features of drone B And the OGS weak features of drone C Fusion to obtain magnetic anomaly signals

[0007] Furthermore, the magnetic anomaly signal According to Calculate and obtain, where α is the weight coefficient of drone A, β is the weight coefficient of drone B, and γ is the weight coefficient of drone C.

[0008] According to another aspect of the present invention, a system for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information is provided. The system for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information uses the method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information as described above to extract magnetic anomalies of moving targets.

[0009] Furthermore, a system for extracting magnetic anomalies of moving targets based on the collaborative magnetic detection information of three drones includes UAV A, UAV B, UAV C and a ground control center. UAV A, UAV B and UAV C are all equipped with scalar magnetic sensors. The ground control center is used to receive the magnetic detection data of the three drones, and perform data processing and analysis to obtain the target magnetic anomaly signal.

[0010] The technical solution of the present invention provides a method for extracting magnetic anomalies of moving targets based on three-machine cooperative magnetic detection information. The method can effectively improve the extraction accuracy and real-time performance of magnetic anomaly signals through the three-machine cooperative flight plan and the cooperative processing of magnetic information, and has broad application prospects. Compared with a single drone system, the method can better suppress background noise and improve the accuracy and reliability of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The included drawings are used to provide a further understanding of the embodiments of the present invention, which constitute a part of the specification, are used to illustrate the embodiments of the present invention, and together with the text description, explain the principles of the present invention. Obviously, the drawings in the following description are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A flowchart of a method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information provided according to a specific embodiment of the present invention is shown. DETAILED DESCRIPTION

[0013] It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0015] Unless otherwise specifically stated, the relative arrangement of the parts and steps described in these embodiments, numerical expressions and numerical values ​​do not limit the scope of the present invention. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the accompanying drawings are not drawn according to the actual proportional relationship. The technology, method and equipment known to ordinary technicians in the relevant field may not be discussed in detail, but in appropriate cases, the technology, method and equipment should be regarded as a part of the authorization specification. In all examples shown and discussed here, any specific value should be interpreted as being merely exemplary, rather than as a limitation. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once a certain item is defined in an accompanying drawing, it does not need to be further discussed in subsequent drawings.

[0016] like Figure 1 As shown, according to a specific embodiment of the present invention, a method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information is provided. The method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information includes: three UAVs A, B, and C fly collaboratively according to a predetermined flight plan to form a triangular flight formation to achieve comprehensive coverage of the target area; the magnetic detection data of the three UAVs are transmitted to a ground control center in real time through a wireless communication system, and the ground control center uses a function based on adjustable quality factor wavelet transform (TQWT) and overlapping group shrinkage algorithm (OGS) Magnetic anomaly extraction is performed, where f is a function used to integrate the magnetic detection data and position information of the three UAVs to extract magnetic anomaly signals. The function is based on the adjustable quality factor wavelet transform (TQWT) and the overlapping group shrinkage algorithm (OGS). is the magnetic detection data of UAV A, is the magnetic detection data of UAV B, is the magnetic detection data of UAV A, is the position of UAV A, is the position of drone B, is the position of UAV C, It is a magnetic anomaly signal.

[0017] By using this configuration, a method for extracting magnetic anomalies of moving targets based on the coordinated magnetic detection information of three aircraft is provided. This method can effectively improve the extraction accuracy and real-time performance of magnetic anomaly signals through the coordinated flight plan of three aircraft and the coordinated processing of magnetic information, and has broad application prospects. Compared with a single UAV system, this method can better suppress background noise and improve the accuracy and reliability of detection.

[0018] Furthermore, in the present invention, the ground control center uses a function based on a tunable quality factor wavelet transform (TQWT) and an overlapping group shrinkage algorithm (OGS) The magnetic anomaly extraction specifically includes: performing TQWT transformation on the magnetic detection data of each UAV to obtain the frequency domain magnetic detection data of UAV A after TQWT transformation. Frequency domain magnetic detection data after TQWT transformation of UAV B And the frequency domain magnetic detection data after TQWT transformation of UAV C Apply the OGS algorithm to the frequency domain magnetic detection data of UAV A after TQWT transformation Frequency domain magnetic detection data after TQWT transformation of UAV B And the frequency domain magnetic detection data after TQWT transformation of UAV C Extract weak features separately to obtain OGS weak features of drone A OGS weak features of drone B And the OGS weak features of drone C The OGS weak feature of drone A OGS weak features of drone B And the OGS weak features of drone C Fusion to obtain magnetic anomaly signals

[0019] Among them, the magnetic anomaly signal According to Calculate and obtain, where α is the weight coefficient of drone A, β is the weight coefficient of drone B, and γ is the weight coefficient of drone C.

[0020] According to another aspect of the present invention, a system for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information is provided. The system for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information uses the method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information as described above to extract magnetic anomalies of moving targets.

[0021] By using this configuration, a system for extracting magnetic anomalies of moving targets based on the coordinated magnetic detection information of three aircraft is provided. The system can effectively improve the extraction accuracy and real-time performance of magnetic anomaly signals through the coordinated flight plan of three aircraft and the coordinated processing of magnetic information, and has broad application prospects. Compared with the single-UAV system, this method can better suppress background noise and improve the accuracy and reliability of detection.

[0022] Furthermore, a system for extracting magnetic anomalies of moving targets based on the collaborative magnetic detection information of three drones includes UAV A, UAV B, UAV C and a ground control center. UAV A, UAV B and UAV C are all equipped with scalar magnetic sensors. The ground control center is used to receive the magnetic detection data of the three drones, and perform data processing and analysis to obtain the target magnetic anomaly signal.

[0023] In order to further understand the present invention, the following Figure 1 The method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information provided by the present invention is described in detail.

[0024] like Figure 1 As shown, according to a specific embodiment of the present invention, a method for extracting magnetic anomalies of moving targets based on collaborative magnetic detection information of three drones is provided. The method realizes efficient extraction of magnetic anomalies of moving targets through the collaborative work of three drones and collaborative processing of magnetic information, thereby improving the extraction accuracy and real-time performance of magnetic anomaly signals.

[0025] To achieve the above object, the present invention provides the following solutions:

[0026] 1. System composition

[0027] A three-machine collaborative magnetic detection system, including three UAVs, a ground control center, etc.

[0028] The three drones are labeled A, B, and C, and are all equipped with scalar magnetic sensors.

[0029] Ground control center: used to receive magnetic detection data from three drones and perform data processing and analysis.

[0030] 2. Three-aircraft coordinated flight plan

[0031] The three drones A, B, and C fly in coordination according to the predetermined flight plan, forming a triangular flight formation to achieve full coverage of the target area. The flight plan takes into account the relative positions and flight attitudes of the drones to minimize mutual magnetic field interference and maximize the detection range. The specific flight details are as follows:

[0032] Flight path planning: The flight path planning of UAVs A, B, and C must ensure the synchronization and coverage of the three drones over the target area to achieve synchronous data collection and processing.

[0033] Flight attitude control: The attitude control of the UAV needs to ensure the stable pointing of the magnetic sensor to reduce the measurement error caused by attitude changes.

[0034] 3. Co-processing of magnetic information

[0035] The magnetic detection data of the three drones are transmitted to the ground control center in real time through the wireless communication system. The ground control center uses the following algorithm to extract magnetic anomalies:

[0036] 3.1 Magnetic anomaly extraction algorithm

[0037] Assume that the magnetic detection data of drones A, B, and C are The positions of the drones are Then the magnetic anomaly extraction algorithm can be expressed as:

[0038]

[0039] Among them, f is a function used to integrate the magnetic detection data and position information of the three UAVs to extract magnetic anomaly signals. The function can be based on the adjustable quality factor wavelet transform (TQWT) and the overlapping group shrinkage algorithm (OGS) to improve the accuracy of feature extraction.

[0040] 3.2 Formula derivation

[0041] First, the magnetic detection data of each UAV is transformed by TQWT to obtain the frequency domain representation:

[0042]

[0043] in, is the frequency domain magnetic detection data of UAV A after TQWT transformation, is the frequency domain magnetic detection data after TQWT transformation of UAV B, It is the frequency domain magnetic detection data of UAV C after TQWT transformation.

[0044] Then, the OGS algorithm is applied to extract weak features:

[0045]

[0046] in, is the OGS weak feature of drone A, is the OGS weak feature of drone B, It is the OGS weak feature of drone C.

[0047] Finally, the OGS results of the three drones are fused to obtain the magnetic anomaly signal:

[0048]

[0049] Among them, α, β, and γ are weight coefficients, which are determined according to the position of the UAV and the signal-to-noise ratio of the detection data.

[0050] The three-drone cooperative magnetic detection method of the present invention can effectively improve the extraction accuracy and real-time performance of magnetic anomaly signals through the three-drone cooperative flight plan and the cooperative processing of magnetic information. Compared with the single-drone system, the present method can better suppress background noise and improve the accuracy and reliability of detection.

[0051] The present invention provides a method for extracting magnetic anomalies of moving targets based on the cooperative magnetic detection information of three aircraft. Through the cooperative flight plan of the three aircraft and the cooperative processing of magnetic information, the extraction accuracy and real-time performance of magnetic anomaly signals can be effectively improved, and the method has broad application prospects.

[0052] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used here to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0053] In addition, it should be noted that the use of terms such as "first" and "second" to limit components is only for the convenience of distinguishing the corresponding components. If not otherwise stated, the above terms have no special meaning and therefore cannot be understood as limiting the scope of protection of the present invention.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information, characterized in that: The method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information includes: The three drones A, B, and C fly in coordination according to the predetermined flight plan, forming a triangular flight formation to achieve full coverage of the target area; The magnetic detection data of the three UAVs are transmitted to the ground control center in real time through a wireless communication system. The ground control center uses a function based on the adjustable quality factor wavelet transform (TQWT) and the overlapping group shrinkage algorithm (OGS) Magnetic anomaly extraction is performed, where f is a function used to integrate the magnetic detection data and position information of the three UAVs to extract magnetic anomaly signals. The function is based on the adjustable quality factor wavelet transform (TQWT) and the overlapping group shrinkage algorithm (OGS). is the magnetic detection data of UAV A, is the magnetic detection data of UAV B, is the magnetic detection data of UAV A, is the position of UAV A, is the position of drone B, is the position of UAV C, It is a magnetic anomaly signal.

2. The method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information according to claim 1 is characterized in that: The ground control center uses a function based on the tunable quality factor wavelet transform (TQWT) and the overlapping group shrinkage algorithm (OGS) Magnetic anomaly extraction specifically includes: Perform TQWT transformation on the magnetic detection data of each UAV to obtain the frequency domain magnetic detection data of UAV A after TQWT transformation. Frequency domain magnetic detection data after TQWT transformation of UAV B And the frequency domain magnetic detection data after TQWT transformation of UAV C Apply the OGS algorithm to the frequency domain magnetic detection data of UAV A after TQWT transformation Frequency domain magnetic detection data after TQWT transformation of UAV B And the frequency domain magnetic detection data after TQWT transformation of UAV C Extract weak features separately to obtain OGS weak features of drone A OGS weak features of drone B And the OGS weak features of drone C The OGS weak feature of the drone A OGS weak features of drone B And the OGS weak features of drone C Fusion to obtain magnetic anomaly signals 3. The method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information according to claim 2 is characterized in that: The magnetic anomaly signal According to Calculate and obtain, where α is the weight coefficient of drone A, β is the weight coefficient of drone B, and γ is the weight coefficient of drone C.

4. A system for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information, characterized in that: The system for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information uses the method for extracting magnetic anomalies of moving targets based on three-machine collaborative magnetic detection information as described in claims 1 to 3 to extract magnetic anomalies of moving targets.

5. The system for extracting magnetic anomalies of moving targets based on three-machine cooperative magnetic detection information according to claim 4 is characterized in that: The system for extracting magnetic anomalies of moving targets based on the coordinated magnetic detection information of three drones includes UAV A, UAV B, UAV C and a ground control center. UAV A, UAV B and UAV C are all equipped with scalar magnetic sensors. The ground control center is used to receive the magnetic detection data of the three drones, and perform data processing and analysis to obtain target magnetic anomaly signals.

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