Aircraft detection method and system based on eddy current induced optical fiber micro-vibration

Abstract

The invention provides an aircraft detection method and system based on eddy current induced optical fiber micro-vibration, and relates to the technical field of aircraft detection.The aircraft detection method comprises the steps that alternating magnetic field data of aircraft eddy current is collected, and time and frequency dimension change features are analyzed and extracted to serve as a magnetic field feature benchmark; the magnetostrictive sensing rod of the magnetoelectric sensing unit is combined with the fiber bragg grating, and magnetic field change generated by the eddy current magnetic field effect of the aircraft is converted into a voltage signal. And decomposing the time-frequency characteristics of the voltage signals according to the magnetic field characteristic reference, generating electric signal components of different frequency band magnetic field energy evolved along with time, screening characteristic magnetic field change components, and matching the characteristic magnetic field change components with a standard magnetic field mode in a preset database to identify types. Motion characteristic parameters are selected according to identification types, motion states are analyzed and flight trajectories are predicted in combination with time sequence characteristics, graded early warning signals are generated according to the position relation between the trajectories and preset safety areas, and detection and safety early warning of the aircraft can be achieved.

Description

Technical Field

[0001] This application relates to the field of aircraft detection technology, and in particular to an aircraft detection method and system based on eddy current-induced fiber micro-vibration. Background Technology

[0002] In scenarios such as low-altitude security and airport airspace control, complex interferences often exist, including structural vibrations caused by wind and mechanical vibrations from ground vehicles. These interferences can mask the unique micro-vibration signals induced by aircraft eddies, making it difficult for traditional detection methods to accurately detect targets. In practical applications, a technical solution is needed that can accurately identify the micro-vibration patterns of aircraft eddies under complex mechanical vibration interference environments, thereby achieving efficient detection of low-altitude, low-speed, or stealth aircraft to ensure airspace safety and prevent accidents caused by aircraft intrusion.

[0003] Currently, the mainstream approach is aircraft detection technology based on acoustic sensors. This approach involves deploying an array of acoustic sensors to collect sound signals from the environment. The characteristic sound waves generated by the aircraft's engine operation and propeller rotation are then matched with a pre-defined aircraft acoustic database to achieve aircraft identification and location. Its core relies on acoustic signals generated by the aircraft's own moving parts, rather than eddy current-induced micro-vibration signals; the technical approach focuses on the extraction and analysis of acoustic features.

[0004] However, this existing solution has significant drawbacks. In complex mechanical vibration environments, airflow noise from wind and engine noise from ground vehicles can superimpose with the aircraft's acoustic signals, resulting in a significant reduction in the signal-to-noise ratio of the sensor's collected signals. This makes it difficult to accurately extract the aircraft's characteristic acoustic waves, leading to a significant decrease in recognition accuracy. For aircraft flying at low speeds or with low-noise designs, the acoustic signal strength they generate is weak and easily covered by environmental interference signals, resulting in limited detection range. This makes it impossible to meet the requirements for long-distance, high-precision low-altitude detection, and its anti-interference capability is far from suitable for use in complex vibration environments. Summary of the Invention

[0005] The purpose of this application is to provide an aircraft detection method and system based on eddy current induced fiber micro-vibration, so as to solve the problems of poor identification accuracy and weak anti-interference ability in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a method for detecting aircraft based on eddy current-induced fiber optic micro-vibrations, comprising: Data on alternating magnetic fields generated by eddy currents in aircraft are collected, and the alternating magnetic field data is analyzed to extract the variation characteristics in the time and frequency dimensions, which serve as the magnetic field characteristic benchmark. When a magnetoelectric sensing unit is installed, and the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod in the magnetoelectric sensing unit, the magnetostrictive sensing rod converts the magnetic field change into a voltage signal by combining with a fiber optic grating. Based on the magnetic field characteristic reference, the voltage signal is decomposed into time-frequency features to generate electrical signal components that characterize the evolution of magnetic field energy over time in different frequency bands. Characteristic magnetic field change components are selected from the electrical signal components. The electrical signal characteristics of the magnetic field change components are matched and compared with the standard magnetic field patterns of known aircraft in a preset feature database. The aircraft type is identified based on the matching results. Based on the type of aircraft, select the corresponding motion characteristic parameters, and combine them with the temporal characteristics of the magnetic field change components to analyze the change law of the aircraft's motion state in order to predict the flight trajectory. Based on the relative positional relationship between the flight trajectory and the preset safety area, generate graded early warning signals.

[0007] Optionally, based on the magnetic field characteristic reference, the voltage signal is decomposed into time-frequency features to generate electrical signal components characterizing the evolution of magnetic field energy over time in different frequency bands, including: Based on the characteristic time scale and characteristic frequency range included in the magnetic field characteristic reference, the length of the time window corresponding to the characteristic time scale is set, and multiple analysis frequency bands are determined according to the characteristic frequency range; Based on the time window length, the voltage signal is segmented to obtain a series of signal segments, and the frequency components in each signal segment are separated by a bandpass filter bank. The signal amplitude within each analysis frequency band is obtained based on the amplitude of the frequency components, and the energy value of each analysis frequency band within a continuous time window is calculated based on the signal amplitude. The energy values ​​of each analysis frequency band are arranged in chronological order to construct an energy distribution structure with time as the horizontal axis and frequency band as the vertical axis. The energy distribution structure is used as an electrical signal component to characterize the evolution of magnetic field energy over time in different frequency bands.

[0008] Optionally, frequency components in each of the signal segments are separated using a bandpass filter bank, including: Construct a filter bank consisting of multiple parallel bandpass filters, wherein the passband boundary of each bandpass filter is consistent with the corresponding analysis frequency band boundary, and there is an overlap region of a preset width between adjacent analysis frequency bands; The signal segment is simultaneously input into the filter bank, and the instantaneous energy distribution of the output signal in the overlapping region is calculated based on the output signals of two adjacent bandpass filters in the overlapping region. The energy allocation weight is determined based on the ratio of the instantaneous energy distribution, and the energy in the overlapping area is redistributed based on the energy allocation weight. The signal attenuation characteristics at the boundary of each analysis frequency band are detected. When abnormal attenuation is detected at the boundary, the boundary position of the analysis frequency band is dynamically adjusted and the passband parameters of the corresponding bandpass filter are updated. The outputs of each channel after energy redistribution and boundary adjustment are determined as the separated frequency components.

[0009] Optionally, based on the aircraft type, corresponding motion characteristic parameters are selected, and combined with the temporal characteristics of the magnetic field change components, the change law of the aircraft's motion state is analyzed to predict the flight trajectory. Based on the relative positional relationship between the flight trajectory and the preset safety area, a graded early warning signal is generated, including: Based on the type of aircraft, select the corresponding typical motion characteristic parameters from the parameter library, and combine them with the temporal characteristics of the characteristic magnetic field change components to calculate the instantaneous velocity vector and the rate of change of motion direction of the aircraft. Based on the instantaneous velocity vector and the rate of change of motion direction, the position sequence of future time points is extrapolated using a kinematic model to form a predicted flight trajectory; Calculate the shortest distance between the predicted flight trajectory and the boundary of the preset safe area, and generate graded early warning signals of different levels based on the shortest distance and the distance threshold range.

[0010] Optionally, based on the instantaneous velocity vector and the rate of change of motion direction, a kinematic model is used to extrapolate the position sequence of future time points to form a predicted flight trajectory, including: Based on the instantaneous velocity vector and the rate of change of the motion direction, a spacecraft state vector is constructed, and a state recursion relationship of the spacecraft state vector is established. The state recursion relationship defines the transition rules from the current moment to the next moment. Based on the current aircraft state vector, the aircraft state vector at the next moment is calculated through the state recursion relationship, and the calculated aircraft state vector is updated to the current aircraft state vector. The calculation and update operations are repeated until a preset number of iterations are reached. The position components in the aircraft state vector obtained from each calculation are extracted, and the position components are stored and connected in chronological order to form a predicted flight trajectory.

[0011] Optionally, a magnetoelectric sensing unit is installed. When the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod in the magnetoelectric sensing unit, the magnetostrictive sensing rod converts the magnetic field change into a voltage signal by combining with a fiber optic grating, including: The magnetoelectric sensing unit is fixed in the monitoring area, and the long axis of the magnetostrictive sensing rod in the magnetoelectric sensing unit is aligned with the expected direction of magnetic field change. The fiber grating is then attached to the surface of the rod along the long axis of the magnetostrictive sensing rod. When the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod, the change in the magnetic field induces the rod to produce periodic deformation. The periodic deformation of the fiber optic grating caused by the sensing rod causes a change in the grating period, thereby obtaining the center wavelength shift of the reflected light signal. The reflected light signal from the fiber optic grating is received using a photoelectric conversion component, and the center wavelength offset is converted into a voltage signal.

[0012] Optionally, characteristic magnetic field variation components are selected from the electrical signal components, and the electrical signal characteristics of the magnetic field variation components are matched and compared with the standard magnetic field patterns of known aircraft in a preset feature database. The aircraft type is then identified based on the matching results, including: Candidate components with energy values ​​exceeding a preset energy threshold are selected from different frequency bands of the electrical signal components, and peak regions are determined based on the energy distribution structure of the electrical signal components. The candidate components located within the peak regions are identified as components with characteristic magnetic field changes. The oscillation period characteristics, bandwidth characteristics, and energy concentration characteristics of the characteristic magnetic field variation components are extracted to form a set of electrical signal characteristics to be matched. The electrical signal features to be matched are compared item by item with the standard magnetic field patterns of various known aircraft stored in the preset feature database, and the feature similarity score between the electrical signal features to be matched and each of the standard magnetic field patterns is calculated. The type of aircraft being detected is determined based on the aircraft model corresponding to the standard magnetic field pattern with the highest feature similarity score.

[0013] Secondly, this application provides an aircraft detection system based on eddy current-induced fiber optic micro-vibration, comprising: The extraction module is used to collect alternating magnetic field data generated by the eddy current of the aircraft, and analyze the alternating magnetic field data to extract the variation characteristics in the time and frequency dimensions as a magnetic field feature benchmark. The conversion module is used to install the magnetoelectric sensing unit. When the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod in the magnetoelectric sensing unit, the magnetostrictive sensing rod converts the magnetic field change into a voltage signal by combining with the fiber optic grating. The decomposition module is used to perform time-frequency feature decomposition on the voltage signal based on the magnetic field feature reference, so as to generate electrical signal components that characterize the evolution of magnetic field energy over time in different frequency bands. The matching module is used to filter out characteristic magnetic field change components from the electrical signal components, match and compare the electrical signal characteristics of the magnetic field change components with the standard magnetic field patterns of known aircraft in a preset feature database, and identify the aircraft type based on the matching results. The generation module is used to select corresponding motion characteristic parameters based on the aircraft type, and combine the temporal characteristics of the magnetic field change components to analyze the change law of the aircraft motion state in order to predict the flight trajectory. Based on the relative positional relationship between the flight trajectory and the preset safety area, a graded early warning signal is generated.

[0014] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the aircraft detection method based on eddy current induced fiber micro-vibration as described in the first aspect above.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the aircraft detection method based on eddy current-induced fiber micro-vibration as described in the first aspect above.

[0016] The aircraft detection method based on eddy current-induced fiber optic micro-vibration provided in this application collects alternating magnetic field data generated by eddy currents in the aircraft and analyzes and extracts the time and frequency dimension variation characteristics as magnetic field feature benchmarks, which can provide accurate reference for subsequent signal processing and aircraft identification. By installing a magnetoelectric sensing unit and using a combination of a magnetostrictive sensing rod and a fiber optic grating to convert magnetic field changes into voltage signals, it can transform magnetic field signals that are difficult to analyze directly into easily processed electrical signals. By performing time-frequency feature decomposition on the voltage signals based on the magnetic field feature benchmarks, electrical signal components characterizing the evolution of magnetic field energy over time in different frequency bands can be generated, enabling fine-grained decomposition of the magnetic field signals and clearly presenting the energy changes in different frequency bands. By screening characteristic magnetic field change components from the electrical signal components and matching and comparing them with the standard magnetic field patterns of known aircraft in a preset feature database, the aircraft type can be accurately identified. By selecting motion feature parameters based on the aircraft type, combining time-series feature analysis to predict the motion state, predict the flight trajectory, and generate graded early warning signals, the aircraft dynamics can be monitored in a timely manner, ensuring airspace safety.

[0017] Furthermore, the time window length is set based on the characteristic time scale of the magnetic field characteristic benchmark, and the analysis frequency band is determined in combination with the characteristic frequency range. The voltage signal is then segmented according to the time window length, and the frequency components of each signal segment are separated using a bandpass filter bank. Subsequently, the signal amplitude of each analysis frequency band is obtained based on the amplitude of the frequency components, and the energy value within the continuous time window is calculated. Finally, the energy values ​​of each analysis frequency band are arranged in chronological order to construct an energy distribution structure with time as the horizontal axis and frequency band as the vertical axis, serving as the target electrical signal component. By precisely setting the time window and analysis frequency band, targeted segmentation and frequency separation of the voltage signal are achieved. Through energy calculation and structured arrangement, the evolution of magnetic field energy over time in different frequency bands can be clearly and systematically presented. This provides more detailed and reliable data support for subsequent screening of characteristic magnetic field change components and accurate identification of aircraft, further improving the precision of signal processing and the accuracy of aircraft detection. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic flowchart of an aircraft detection method based on eddy current-induced fiber micro-vibration provided in this application embodiment; Figure 2 A flowchart illustrating a specific implementation of an aircraft detection method based on eddy current-induced fiber micro-vibration, provided in this application embodiment; Figure 3 A scene diagram illustrating an aircraft detection method based on eddy current-induced fiber micro-vibration provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an aircraft detection system based on eddy current-induced fiber micro-vibration, provided as an embodiment of this application. Detailed Implementation

[0020] In scenarios such as low-altitude security and airport airspace control, aircraft detection solutions based on acoustic sensors face two core challenges: First, noise generated by wind and complex mechanical vibrations from ground vehicles can superimpose on the aircraft's acoustic signals, significantly reducing the signal-to-noise ratio and making it difficult to accurately extract the aircraft's characteristic acoustic waves, resulting in a marked decrease in recognition accuracy. Second, for aircraft with low-speed flight or low-noise designs, their own acoustic signal strength is weak and easily masked by environmental interference, limiting the detection range and failing to meet the requirements for long-distance, high-precision detection. The root cause of these problems lies in the fact that this solution relies on the acoustic signals of the aircraft's moving parts, and these signals have insufficient anti-interference capabilities in complex interference environments, making it difficult to accurately capture targets.

[0021] To address the aforementioned issues, this application proposes an aircraft detection method based on eddy current-induced fiber optic micro-vibration. The core of this method is to capture the unique magnetic field signal generated by the aircraft's eddy currents, rather than relying on easily interfered acoustic signals. Specifically, the method first collects alternating magnetic field data of the aircraft's eddy currents and extracts features as a benchmark. Then, a magnetoelectric sensing unit converts the magnetic field changes into an analyzable voltage signal. Subsequently, the voltage signal is refined using the magnetic field feature benchmark to filter out the aircraft's unique magnetic field change components, which are then matched with a pre-set database to identify the aircraft type. Finally, the method can predict flight trajectories and generate tiered warnings. This method avoids easily interfered acoustic signals by selecting a signal source, instead utilizing the more stable characteristic signal of the aircraft's eddy current magnetic field. Furthermore, by establishing a magnetic field feature benchmark and refining the signal processing, it effectively filters out interference from complex mechanical vibrations. This not only improves the accuracy of aircraft identification but also overcomes the limitation of detection range for low-noise, low-speed aircraft, fundamentally solving the problem of insufficient adaptability of existing acoustic detection schemes in complex environments and better ensuring airspace safety.

[0022] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] The core of this application is to provide a method for detecting aircraft based on eddy current-induced fiber optic micro-vibrations, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes: S101. Collect alternating magnetic field data generated by the eddy current of the aircraft, and analyze the alternating magnetic field data to extract the variation characteristics in the time and frequency dimensions as a magnetic field characteristic benchmark. In this step, the alternating magnetic field data generated by the aircraft eddy currents refers to the magnetic field data that changes periodically over time as the aircraft flies, with metal components such as the fuselage and wings cutting through the Earth's magnetic field or generating eddy currents under the influence of their own alternating magnetic field sources. This data includes information on the fluctuation of magnetic field strength and direction over time. The time and frequency dimension variation characteristics refer to the fluctuation pattern of the magnetic field data on the time axis (such as the trend of signal amplitude increasing or decreasing over time) and the distribution characteristics in different frequency bands (such as the frequency range where signal energy is concentrated). The magnetic field feature benchmark is a reference standard constructed based on the time and frequency dimension features extracted from the collected eddy current alternating magnetic field data, which is used for subsequent signal processing and feature matching comparison.

[0024] Specifically, step S101 first involves deploying a sensor array composed of superconducting quantum interference devices (SQUIDs) to acquire data on the alternating magnetic field generated by the eddy currents of the aircraft. SQUIDs utilize the Josephson effect and magnetic flux quantization to convert weak magnetic field changes into measurable electrical signals. These signals are then converted from analog to digital and stored synchronously via a data acquisition module. For example, in a security scenario in airspace A, a hexagonal pyramidal superconducting full-tensor magnetic gradient probe is deployed, paired with a data acquisition controller from brand B, to synchronously acquire three-dimensional component data of the eddy current magnetic field during the flight of a certain type of aircraft. The sampling frequency is set to a reasonable value adapted to the magnetic field changes, ensuring the capture of complete magnetic field fluctuation information. Secondly, the acquired magnetic field data undergoes time-frequency feature extraction using a continuous wavelet transform algorithm. A complex Morlet wavelet basis function with specific parameters (e.g., wavelet bandwidth parameter 1.5, center frequency parameter 1.0) is selected, and a scale distribution covering the typical frequency bands of the target magnetic field is set. The time-domain data is converted into a time-frequency matrix, and then features such as the fluctuation period, amplitude change rate, and energy peak frequency band in the time dimension are extracted through sliding window analysis.

[0025] For example, the magnetic field data of the aircraft in the A airspace is processed using the continuous wavelet transform function in the continuous wavelet transform toolkit. After generating the time-frequency matrix, the average signal amplitude and energy concentration frequency of each time period are calculated through an 8-frequency sliding window. Finally, these features are integrated to form a magnetic field feature benchmark.

[0026] The above steps effectively captured the eddy current alternating magnetic field data of the aircraft through a high-precision sensor array, ensuring the integrity and accuracy of the original data. At the same time, with the help of professional time and frequency feature extraction algorithms, the core variation law of the magnetic field data in the time and frequency dimensions was accurately extracted. The constructed magnetic field feature benchmark can clearly characterize the unique properties of the aircraft's eddy current magnetic field, avoid the confusion of environmental interference signals, and improve the accuracy and reliability of the overall detection method.

[0027] S102. Install a magnetoelectric sensing unit. When the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod in the magnetoelectric sensing unit, the magnetostrictive sensing rod converts the magnetic field change into a voltage signal by combining with a fiber optic grating. Optionally, step S102 may specifically include the following steps: S1021. Fix the magnetoelectric sensing unit in the monitoring area, wherein the long axis of the magnetostrictive sensing rod in the magnetoelectric sensing unit is aligned with the expected direction of magnetic field change, and attach the fiber grating to the surface of the rod along the long axis of the magnetostrictive sensing rod. S1022. When the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod, the change in the magnetic field induces the rod to produce periodic deformation. S1023. The periodic deformation of the fiber optic grating caused by the sensing rod causes a change in the grating period of the fiber optic grating, so as to obtain the center wavelength offset of the reflected light signal. S1024. The reflected light signal of the fiber optic grating is received using a photoelectric conversion component, and the center wavelength offset is converted into a voltage signal.

[0028] In the above steps, the magnetoelectric sensing unit is a device integrating a magnetostrictive sensing rod, a fiber optic grating, and a photoelectric conversion component, used to convert changes in the magnetic field into analyzable electrical signals. The magnetostrictive sensing rod is a rod-shaped component made of magnetostrictive material, possessing the characteristic of deforming under the influence of a magnetic field. The expected direction of magnetic field change is the main fluctuation direction of the aircraft's eddy current magnetic field, predicted based on monitoring requirements. A fiber optic grating is an optical element with a periodic refractive index variation structure etched inside an optical fiber, capable of sensing by reflecting light signals of specific wavelengths. Periodic deformation is the time-varying expansion and contraction of the magnetostrictive sensing rod under the influence of an alternating magnetic field. The grating period is the interval between the periodic changes in refractive index within the fiber optic grating. The center wavelength offset of the reflected light signal is the magnitude by which the center wavelength of the reflected light deviates from its initial value due to the deformation of the fiber optic grating. The photoelectric conversion component is a device that converts optical signals into electrical signals, transforming the wavelength offset information into voltage changes.

[0029] In this embodiment, the magnetoelectric sensing unit is first fixed to the monitoring area in step S1021. During fixing, it is necessary to ensure that the long axis of the magnetostrictive sensing rod is aligned with the expected direction of magnetic field change. Simultaneously, the fiber optic grating is adhered to the surface of the rod along its long axis. This step requires first analyzing the main direction of the aircraft's eddy current magnetic field within the monitoring area using magnetic field simulation software to determine the expected direction of magnetic field change. Then, a mechanical bracket is used to securely install the magnetoelectric sensing unit. When adhering the fiber optic grating, UV-curing adhesive is used to ensure adhesion and stability. For example, in a security scenario in airspace A, if magnetic field simulation determines that the aircraft's eddy current magnetic field in this area mainly changes horizontally, the long axis of the magnetostrictive sensing rod is adjusted to be horizontal, and the fiber optic grating is smoothly adhered to the middle area of ​​the rod using UV-curing adhesive to ensure effective transfer of deformation energy.

[0030] Secondly, step S1022 transforms the magnetic field change into rod deformation. When the alternating magnetic field generated by the aircraft's eddy currents acts on the magnetostrictive sensing rod, the magnetostrictive effect induces periodic deformation in the rod. Magnetostriction is an inherent characteristic of magnetostrictive materials. When the applied magnetic field changes, the arrangement of magnetic domains within the material alters, causing macroscopic expansion and contraction, and the deformation amplitude is related to the change in magnetic field strength. For example, in a security scenario in airspace A, when an aircraft enters the monitoring area, the alternating magnetic field generated by its eddy currents acts on the magnetostrictive sensing rod. The rod elongates and shortens accordingly with the periodic strengthening and weakening of the magnetic field, forming periodic deformation consistent with the frequency of the magnetic field change.

[0031] Next, step S1023 completes the conversion from deformation to optical signal change. After the periodic deformation of the fiber optic grating sensing rod, its grating period changes accordingly, resulting in a shift in the center wavelength of the reflected light signal. The core characteristic of a fiber optic grating is that it only reflects light with a specific center wavelength, and the grating period directly determines this center wavelength. When the rod deforms and causes the fiber optic grating to stretch or contract, the change in the grating period causes a corresponding shift in the center wavelength of the reflected light, and the shift is positively correlated with the deformation. For example, in a security scenario in airspace A, the periodic stretching and contraction of the magnetostrictive sensing rod causes the fiber optic grating attached to its surface to deform synchronously. The grating period increases as the rod stretches and decreases as the rod shortens, causing the center wavelength of the light reflected by the fiber optic grating to shift periodically, forming a wavelength change signal that can be captured.

[0032] Finally, step S1024 converts the optical signal into an electrical signal. A photoelectric conversion component receives the reflected optical signal from the fiber Bragg grating, calculates and converts it into a voltage signal based on the center wavelength offset of the optical signal. The photoelectric conversion component has a built-in photodetector and signal processing module. It first receives the reflected optical signal and converts it into an initial electrical signal, then extracts the center wavelength offset using wavelength demodulation technology, and finally converts the offset into a voltage signal according to a preset wavelength-voltage conversion relationship. For example, in a security scenario in airspace A, a C-type photoelectric conversion component integrating a photodiode is used. After receiving the reflected optical signal from the fiber Bragg grating, the built-in wavelength demodulation circuit extracts the center wavelength offset, and then converts the offset into a corresponding voltage signal according to the calibrated conversion relationship, completing the conversion from magnetic field change to voltage signal.

[0033] In practical applications, in a low-altitude security monitoring scenario in area A, staff first determined through magnetic field simulation that the main direction of change in the eddy current magnetic field of aircraft in the area is horizontal. Then, the magnetoelectric sensing unit was fixed to the support at the monitoring point using a mechanical bracket. The long axis of the magnetostrictive sensing rod was adjusted to be horizontal, and a fiber optic grating was adhered to the rod surface along its long axis using UV-curable adhesive. When an aircraft enters the area, the alternating magnetic field generated by its eddy currents acts on the magnetostrictive sensing rod, causing periodic deformation of the rod synchronized with the magnetic field changes. The fiber optic grating, sensing this deformation, changes its period, resulting in a shift in the center wavelength of the reflected light. The photoelectric conversion component receives this reflected light signal and converts the center wavelength shift into a voltage signal through an internal demodulation and conversion circuit.

[0034] In the overall scheme of step S102 above, by precisely fixing the magnetoelectric sensing unit and the fiber optic grating, it is ensured that changes in the magnetic field can be efficiently transmitted to the core sensing component, laying the structural foundation for signal conversion. Utilizing the magnetostrictive effect and the optical sensing characteristics of the fiber optic grating, the precise conversion of magnetic field changes into optical signal changes is achieved, effectively capturing weak magnetic field fluctuations. Through signal processing by the photoelectric conversion component, the optical signal, which is not easily analyzed directly, is converted into a voltage signal that is easy to process subsequently. The entire conversion process is coherent and efficient, significantly improving the sensitivity and reliability of magnetic field signal capture.

[0035] S103. Based on the magnetic field characteristic reference, the voltage signal is decomposed into time-frequency features to generate electrical signal components that characterize the evolution of magnetic field energy over time in different frequency bands. Optionally, such as Figure 2 As shown, step S103 may specifically include the following steps: S1031. Based on the characteristic time scale and characteristic frequency range included in the magnetic field characteristic reference, set the time window length corresponding to the characteristic time scale, and determine multiple analysis frequency bands according to the characteristic frequency range; S1032. Based on the time window length, the voltage signal is segmented to obtain a series of signal segments, and the frequency components in each signal segment are separated by a bandpass filter bank. The step S1032, "separating the frequency components in each signal segment using a bandpass filter bank," specifically includes the following processes: constructing a filter bank composed of multiple parallel bandpass filters, wherein the passband boundary of each bandpass filter coincides with the boundary of the corresponding analysis frequency band, and there is a preset width of overlap between adjacent analysis frequency bands; simultaneously inputting the signal segment into the filter bank, and calculating the instantaneous energy distribution of the output signal in the overlap region based on the output signals of two adjacent bandpass filters in the overlap region; determining the energy allocation weight based on the ratio of the instantaneous energy distribution, and redistributing the energy in the overlap region based on the energy allocation weight; detecting the signal attenuation characteristics at the boundary of each analysis frequency band, and dynamically adjusting the boundary position of the analysis frequency band and updating the passband parameters of the corresponding bandpass filter when abnormal attenuation is detected at the boundary; and determining the output of each channel after energy redistribution and boundary adjustment as the separated frequency components.

[0036] S1033. Obtain the signal amplitude in each analysis frequency band based on the amplitude of the frequency components, and calculate the energy value of each analysis frequency band in a continuous time window based on the signal amplitude. S1034. Arrange the energy values ​​of each analysis frequency band in chronological order to construct an energy distribution structure with time as the horizontal axis and frequency band as the vertical axis. Use the energy distribution structure as an electrical signal component characterizing the evolution of magnetic field energy over time in different frequency bands.

[0037] In the above steps, time-frequency feature decomposition is a processing technique that decomposes a one-dimensional voltage signal into multi-dimensional features containing both time and frequency information. Its purpose is to reveal the distribution patterns of signal energy across different times and frequency bands. The magnetic field feature benchmark is the set of previously extracted characteristics of the aircraft's eddy current magnetic field variations in time and frequency dimensions, including a characteristic time scale and a characteristic frequency range. The characteristic time scale is the time interval at which the magnetic field signal exhibits significant changes, and the characteristic frequency range is the frequency interval where the magnetic field signal energy is concentrated. The time window length is the time interval used for segmenting the voltage signal and must be adapted to the characteristic time scale. The analysis frequency band is a series of continuous frequency intervals divided according to the characteristic frequency range, used to analyze the signal characteristics of different frequency bands. The bandpass filter bank is a parallel processing unit composed of multiple bandpass filters, each capable of filtering signals in a specific frequency band. The preset width overlap region is the frequency range overlapping between adjacent analysis frequency bands, used to avoid signal loss at band boundaries. The instantaneous energy distribution is the energy distribution state of the signal within the overlap region at a certain moment. The energy allocation weight is an allocation coefficient determined based on the ratio of the output energy of adjacent filters, used to rationally divide the energy within the overlap region. Signal attenuation characteristics describe the pattern of energy reduction at frequency band boundaries; abnormal attenuation is a sudden drop in energy that deviates from the normal pattern. Signal amplitude is the magnitude of the frequency component waveform, while energy value is a quantitative indicator of signal strength, calculated from signal amplitude. The energy distribution structure is a two-dimensional data structure, with the horizontal axis representing time and the vertical axis representing frequency band; the value at each position represents the signal energy at the corresponding time and frequency band.

[0038] In this embodiment, the time window length and analysis frequency band are first set in step S1031. This step requires extracting the characteristic time scale and characteristic frequency range from the magnetic field characteristic reference, and setting the time window length to a value close to the characteristic time scale to ensure complete capture of signal changes in the time dimension. Then, an equal bandwidth division method is used to divide the signal into multiple continuous analysis frequency bands based on the characteristic frequency range. For example, in the A-domain security scenario, the characteristic time scale obtained from the magnetic field characteristic reference is 0.1 seconds, and the characteristic frequency range is 10 Hz to 100 Hz. The time window length is then set to 0.1 seconds, and the range of 10 Hz to 100 Hz is divided into 9 analysis frequency bands according to the standard of one frequency band per 10 Hz.

[0039] Secondly, the voltage signal is segmented and its frequency components are separated in step S1032. First, based on a set time window length, the voltage signal is segmented using a sliding window method, with the sliding step size set to half the time window length, resulting in a series of continuous and partially overlapping signal segments. Then, the frequency components are separated according to the following process: First, a filter bank consisting of multiple parallel bandpass filters is constructed, ensuring that the passband boundary of each bandpass filter coincides with the corresponding analysis frequency band boundary, and a preset width overlap area is set between adjacent analysis frequency bands. Second, the signal segments are simultaneously input into the filter bank. Based on the output signals of two adjacent bandpass filters within the overlap area, the instantaneous energy distribution is obtained by calculating the instantaneous value of the square of the signal amplitude. Third, the energy allocation weight is determined based on the ratio of the instantaneous energy distributions, and the weight is multiplied by the energy of the overlap area to achieve redistribution. Fourth, the signal attenuation characteristics at the boundary of each analysis frequency band are detected using an energy monitoring algorithm. When abnormal attenuation is detected at the boundary, the boundary position of the analysis frequency band is dynamically modified through iterative adjustment, and the passband parameters of the corresponding bandpass filter are updated. Fifth, the output of each channel after energy redistribution and boundary adjustment is determined as the separated frequency components.

[0040] For example, in a security scenario in airspace A, the voltage signal is segmented based on a time window of 0.1 seconds, resulting in signal segments with a duration of 0.1 seconds and 50% overlap. Nine bandpass filters are constructed, with each passband corresponding to one of the nine analysis frequency bands. Adjacent frequency bands have a 1 Hz overlap region. After inputting a signal segment into the filter bank, the instantaneous energy distribution of the output signals of adjacent filters within the overlap region is calculated. If the instantaneous energy of the preceding filter is 4 units and that of the following filter is 6 units, the energy allocation weights are 0.4 and 0.6, respectively, and the energy in the overlap region is allocated according to these weights. When an abnormal attenuation with a sudden drop in energy is detected at the boundary of a frequency band, the boundary is adjusted to a higher frequency direction by 0.5 Hz, and the passband parameters of the corresponding filter are updated synchronously, ultimately yielding the frequency components of the nine separated frequency bands.

[0041] Next, step S1033 calculates the energy value of each analysis frequency band within a continuous time window. First, the amplitude of each frequency component is extracted using a peak detection algorithm, and this amplitude is taken as the signal amplitude within the corresponding analysis frequency band. Then, based on the signal amplitude, a time-domain energy calculation method is used to calculate the energy value. The calculation formula is as follows: ,in, Represents energy value. Represents the signal amplitude at discrete time points The value, This represents the number of sampling points within each time window. For example, in In airspace security scenarios, the amplitude sequence of frequency components in a certain analysis frequency band is extracted as follows: The number of sampling points within this time window is 5. The energy value is calculated according to the formula as follows: This value represents the energy level of the analyzed frequency band within that time window.

[0042] Finally, the energy distribution structure is constructed through step S1034. First, the energy values ​​of each analysis frequency band in different time windows are arranged sequentially to form multiple energy sequences. Then, with time as the horizontal axis and the analysis frequency band as the vertical axis, the energy sequences of each analysis frequency band are mapped to the corresponding positions on the vertical axis to construct a two-dimensional energy distribution structure. This structure represents the electrical signal components characterizing the evolution of magnetic field energy over time in different frequency bands. For example, in the security scenario of airspace A, each of the 9 analysis frequency bands has 10 energy values ​​in 10 time windows. The 10 energy values ​​of each frequency band are arranged in chronological order, with the horizontal axis labeled with time window numbers 1 to 10 and the vertical axis labeled with analysis frequency band numbers 1 to 9. The corresponding energy value is filled in at each intersection point to form a clear energy distribution structure.

[0043] In practical applications, within a low-altitude security scenario in Area A, personnel first extract the characteristic time scale and frequency range from the magnetic field characteristic reference. Based on this, a time window length of 0.1 seconds is set, and the frequency range from 10 Hz to 100 Hz is divided into 9 analysis frequency bands. Subsequently, based on this time window length, the voltage signal output by the magnetoelectric sensing unit is segmented to obtain a series of signal segments. Frequency components are then separated using a filter bank composed of 9 bandpass filters. During this process, energy is allocated to the overlapping region of adjacent frequency bands within 1 Hz, and the boundaries of frequency bands with abnormal attenuation are dynamically adjusted. The amplitude of each frequency component is then extracted as the signal amplitude. The energy value of each analysis frequency band within the continuous time window is calculated using a formula. Finally, these energy values ​​are arranged in chronological order to construct an energy distribution structure with time as the horizontal axis and frequency band as the vertical axis, serving as the electrical signal components for subsequent analysis.

[0044] In the overall scheme of step S103 above, by setting the time window and analysis frequency band based on the magnetic field characteristic benchmark, the targeted nature of signal processing is ensured, avoiding feature loss caused by indiscriminate processing. By separating frequency components through a bandpass filter bank combined with energy distribution in overlapping regions and dynamic boundary adjustment, the problem of signal loss or distortion at frequency band boundaries is effectively solved, improving the completeness and accuracy of frequency component separation. Energy values ​​are obtained through standardized energy calculation methods, providing a unified standard for signal strength analysis. By constructing an energy distribution structure, the complex signal variation patterns are transformed into an intuitive two-dimensional distribution, clearly presenting the evolution characteristics of magnetic field energy in different frequency bands over time, significantly improving the precision of signal processing.

[0045] S104. Select characteristic magnetic field change components from the electrical signal components, match and compare the electrical signal characteristics of the magnetic field change components with the standard magnetic field patterns of known aircraft in the preset feature database, and identify the aircraft type based on the matching results. Optionally, step S104 may specifically include the following steps: S1041. Select candidate components whose energy values ​​exceed a preset energy threshold from different frequency bands of the electrical signal components, determine the peak region based on the energy distribution structure of the electrical signal components, and determine the candidate components located in the peak region as characteristic magnetic field change components. S1042. Extract the oscillation period characteristics, bandwidth characteristics, and energy concentration characteristics of the characteristic magnetic field change components to form a set of electrical signal characteristics to be matched. S1043. The electrical signal feature to be matched is compared item by item with the standard magnetic field patterns of various known aircraft stored in the preset feature database, and the feature similarity score between the electrical signal feature to be matched and each of the standard magnetic field patterns is calculated. S1044. Determine the type of the currently detected aircraft based on the aircraft model corresponding to the standard magnetic field pattern with the highest feature similarity score.

[0046] In the above steps, the electrical signal components are energy distribution structures representing the evolution of magnetic field energy over time in different frequency bands, obtained through time-frequency feature decomposition. Characteristic magnetic field variation components refer to signal components that reflect the unique properties of the aircraft's eddy current magnetic field and are distinct from environmental interference. The preset energy threshold is a judgment boundary set based on the energy of environmental interference signals, used to filter components that may contain the target signal. The peak region is a local area where the energy of the electrical signal component is significantly higher than the surrounding area; it is the region where the target signal energy is concentrated. Candidate components are signal segments whose energy values ​​exceed the preset energy threshold. The oscillation period characteristic is the time interval of the periodic fluctuation of the characteristic magnetic field variation component over time; the bandwidth characteristic is the width of the frequency range covered by the component's energy distribution; and the energy concentration characteristic is the degree of energy concentration of the component within a specific region. These three characteristics together constitute the electrical signal characteristics to be matched. The preset feature database is a database storing characteristic parameters related to the eddy current magnetic fields of various known aircraft. The standard magnetic field pattern within it is a set of typical electrical signal characteristics corresponding to each known aircraft. The feature similarity score is a quantitative indicator that measures the similarity between the electrical signal characteristics to be matched and the standard magnetic field pattern; a higher value indicates a higher similarity. The aircraft type is the specific model or category of the currently detected aircraft, determined based on the matching results.

[0047] In this embodiment, characteristic magnetic field variation components are first screened through step S1041. This step requires setting a preset energy threshold based on the energy level of the environmental interference signal, traversing different frequency bands of the electrical signal component, and marking signal segments with energy values ​​exceeding the threshold as candidate components. Then, a regional maximum detection algorithm is used to analyze the energy distribution structure of the electrical signal component, identify peak regions with energy significantly higher than the surrounding areas, and finally determine the candidate components located within the peak regions as characteristic magnetic field variation components. For example, in a security scenario in airspace A, the preset energy threshold is set to a reasonable value based on historical interference data. After traversing 9 analysis frequency bands, 15 candidate components with energy exceeding the threshold are screened. Regional maximum detection reveals 3 peak regions in the energy distribution structure, and finally, 6 candidate components located within these peak regions are determined as characteristic magnetic field variation components.

[0048] Secondly, the electrical signal features to be matched are extracted in step S1042. For the selected characteristic magnetic field variation components, the zero-crossing detection algorithm is used to extract the oscillation period feature, calculating the time interval between two adjacent zero-crossing points and taking the average as the oscillation period; the half-power bandwidth method is used to extract the bandwidth feature, calculating the frequency difference corresponding to when the signal energy drops to half of its peak value as the bandwidth; and the energy entropy calculation method is used to extract the energy concentration feature, calculating the energy entropy based on the uniformity of signal energy distribution within the bandwidth, with a smaller entropy value indicating higher energy concentration. For example, in a security scenario in airspace A, six characteristic magnetic field variation components are processed. The average oscillation period is obtained as a certain value through zero-crossing detection, the bandwidth is measured as a certain range through the half-power bandwidth method, and the energy concentration feature parameters are obtained through energy entropy calculation. These three sets of parameters are integrated into the electrical signal features to be matched.

[0049] Next, the feature similarity score is calculated in step S1043. First, standard magnetic field patterns of all known aircraft are retrieved from the preset feature database. The cross-correlation method is used to align the electrical signal features to be matched with each standard magnetic field pattern. Then, the Pearson correlation coefficient algorithm is used to calculate the similarity of the aligned features. The calculation formula is as follows: ,in The Pearson correlation coefficient represents the feature similarity score. and These are the characteristics of the electrical signal to be matched and the standard magnetic field mode at the 1st... The value of each sample point and These are the mean values ​​of the electrical signal characteristics to be matched and the standard magnetic field mode, respectively. This represents the total number of sample points.

[0050] For example, in a security scenario in airspace A, standard magnetic field patterns of three known aircraft types (B, C, and D) are retrieved from a pre-defined feature database. The electrical signal features to be matched are then aligned with these three patterns, and the similarity is calculated using a formula. Taking the model comparison of a type of aircraft as an example, the feature sample value to be matched is The mean value is 3; the sample value for the B-type pattern is... The mean is 3.06; the numerator is calculated as follows: The result is ; Calculate the square root of the sum of squares of the features to be matched in the denominator. The result is 2; the square root of the sum of squares in the standard model is The result was approximately 1.98; the final similarity score was... Similarly, the similarity scores with the C-type and D-type patterns are calculated.

[0051] Finally, the aircraft type is determined in step S1044. All calculated feature similarity scores are sorted, and the standard magnetic field pattern with the highest score is selected. The aircraft type corresponding to this pattern is determined as the currently detected aircraft type. For example, in the security scenario of airspace A, the similarity scores between the feature to be matched and the standard patterns of aircraft types B, C, and D are calculated to be 0.72, 0.45, and 0.38, respectively. Type B has the highest score, therefore the currently detected aircraft type is determined to be type B.

[0052] In practical applications, within a low-altitude security scenario in Area A, personnel first set a preset energy threshold based on environmental interference. Candidate components exceeding the threshold are then selected from the electrical signal components. Combined with the peak regions of the energy distribution structure, six characteristic magnetic field variation components are identified. Subsequently, the oscillation period, bandwidth, and energy concentration characteristics of these components are extracted to form the electrical signal features to be matched. Standard magnetic field patterns of various known aircraft are retrieved from a preset feature database. Similarity scores between the features to be matched and each pattern are obtained through cross-correlation alignment and Pearson correlation coefficient calculation. Based on the score ranking, the aircraft type B corresponding to the highest score is identified as the currently detected aircraft type.

[0053] In the overall scheme of step S104 above, the combination of energy threshold screening and peak region localization effectively eliminates environmental interference signals and accurately identifies the magnetic field variation components that reflect the characteristics of the aircraft, laying a high-quality foundation for subsequent feature extraction. By extracting multi-dimensional electrical signal features, the unique properties of the aircraft's eddy current magnetic field are comprehensively captured, improving the completeness of feature representation. The matching method using cross-correlation alignment and Pearson correlation coefficient calculation ensures the accuracy of feature comparison and reduces the risk of misjudgment. The method of determining the aircraft type by similarity scoring and ranking is logically clear and highly reliable, achieving efficient connection from signal screening to type recognition, and significantly improving the accuracy and reliability of aircraft identification.

[0054] S105. Based on the type of aircraft, select the corresponding motion characteristic parameters, and combine them with the temporal characteristics of the magnetic field change components to analyze the change law of the aircraft motion state in order to predict the flight trajectory. Based on the relative positional relationship between the flight trajectory and the preset safety area, generate a graded early warning signal.

[0055] Optionally, step S105 may specifically include the following steps: S1051. Select the corresponding typical motion characteristic parameters from the parameter library according to the type of the aircraft, and calculate the instantaneous velocity vector and motion direction change rate of the aircraft by combining the temporal characteristics of the characteristic magnetic field change components. S1052. Based on the instantaneous velocity vector and the rate of change of motion direction, the position sequence of future time points is extrapolated using a kinematic model to form a predicted flight trajectory; Specifically, step S1052 may include the following process: based on the instantaneous velocity vector and the rate of change of the motion direction, construct the aircraft state vector and establish a state recursion relationship for the aircraft state vector, wherein the state recursion relationship defines the transformation rules from the current moment to the next moment; based on the aircraft state vector at the current moment, calculate the aircraft state vector at the next moment through the state recursion relationship, and update the calculated aircraft state vector to the aircraft state vector at the current moment; repeat the calculation and update operation until a preset number of recursions is reached, extract the position component from the aircraft state vector obtained in each calculation, store and connect the position components in chronological order to form a predicted flight trajectory.

[0056] S1053. Calculate the shortest distance between the predicted flight trajectory and the boundary of the preset safe area, and generate graded early warning signals of different levels based on the shortest distance and the distance threshold range.

[0057] In the above steps, motion characteristic parameters are typical motion attribute parameters related to a specific aircraft type, including maximum speed and acceleration range, used to describe the aircraft's motion capabilities. Temporal characteristics are the changing patterns of characteristic magnetic field components over time, such as the fluctuation trend of amplitude over time. The instantaneous velocity vector is a vector describing the speed and direction of the aircraft at a certain moment, containing information on velocity magnitude and direction. The rate of change of motion direction is the rate at which the aircraft's motion direction changes over time, reflecting the speed of turning. The kinematic model is a mathematical model built based on kinematic principles to predict the motion state of an object. The aircraft state vector is a set of vectors containing information such as the aircraft's position, velocity, and motion direction, used to completely describe the aircraft's motion state. The state recursion relationship defines the rules for the transformation of the aircraft's state vector from the current moment to the next moment, established based on kinematic laws. The preset recursion number is the number of state vector updates set for predicting future positions, determining the prediction time length. The position component is the part of the aircraft's state vector representing its spatial position. The predicted flight trajectory is a sequence of the aircraft's positions over a future period obtained through extrapolation, reflecting possible motion paths. The preset safe zone is a pre-defined area of ​​airspace that needs protection, and its boundary is the spatial limit of that area. The shortest distance is the minimum distance from each point on the predicted flight path to the boundary of the preset safe zone. The distance threshold range is a standard range of distance intervals used to classify warning levels; different intervals correspond to different warning levels. The graded warning signal is a warning signal of different levels generated based on the positional relationship between the predicted trajectory and the safe zone, used to indicate the degree of risk.

[0058] In this embodiment, the instantaneous velocity vector and rate of change of motion direction of the aircraft are first calculated in step S1051. This step requires retrieving typical motion characteristic parameters from the parameter library based on the identified aircraft type, such as the velocity characteristic range of that type of aircraft; then, the temporal characteristics of the characteristic magnetic field change components are analyzed. By correlating the time interval of magnetic field changes with the corresponding spatial position changes, and combining the motion characteristic parameters, a velocity estimation algorithm is used to calculate the instantaneous velocity vector. The velocity calculation formula is as follows: ,in Represents the magnitude of instantaneous velocity. This represents the change in spatial location corresponding to a change in the magnetic field. The time interval represents the change in the magnetic field; simultaneously, the rate of change of the direction of motion is calculated by the ratio of the change in the angle between adjacent moments to the time interval, using the following formula: ,in Represents the rate of change of the direction of motion. This represents the change in the angle between the directions of motion at adjacent moments. Represents a time interval.

[0059] For example, in In airspace security scenarios, the aircraft type was identified as... After the model was completed, typical motion characteristic parameters of the B-type aircraft were retrieved from the parameter library, and the temporal characteristics of its characteristic magnetic field variation components were analyzed. It was found that the time interval between the occurrence of magnetic field peaks shortened from 2 seconds to 1 second. Combined with the speed characteristics of this type of aircraft, through... Calculations show that if the spatial position change corresponding to the two peaks is 50 meters and the time interval is 1 second, then the instantaneous velocity magnitude is 50 meters per second, and the direction is northeast, meaning the instantaneous velocity vector is 50 meters per second in the northeast direction. The calculations also consider the angle between the motion directions at three consecutive moments; if the angle changes between moments 1 and 2... Changes in the included angle at times 2 and 3 If the time interval is 1 second, then the rate of change of the direction of motion is deflection per second. about.

[0060] Secondly, the predicted flight trajectory is formed in step S1052. This is achieved through the following process: First, based on the instantaneous velocity vector and the rate of change of motion direction, a trajectory including position is constructed. velocity components , direction angle of motion aircraft state vector Based on the assumption of uniform motion, a recursive relationship for the state is established. For the position in the x-direction, the recursive formula is: For the position in the y-direction, the recursive formula is: For the direction angle of motion, the recursive formula is: ,in Representing the next moment Location, Representing the current moment Location, represent The velocity component in the direction, Represents a time interval. The angle representing the direction of motion at the next moment. The angle representing the direction of motion at the current moment. The first step represents the rate of change of motion direction; this relationship defines the rules for calculating the state vector at the next moment from the current state vector using parameters such as velocity and rate of change of direction. The second step substitutes the current aircraft state vector into the state recursion relation to calculate the next aircraft state vector and updates it to the new current state vector. The third step repeats the calculation and update operations until a preset number of recursions is reached, extracting the relevant parameters from the state vector after each calculation. The position components are then connected in chronological order to form the predicted flight trajectory.

[0061] For example, in the security scenario of airspace A, the constructed aircraft state vector includes The coordinate position, velocity components in the x and y directions, and motion direction angle are given by the initial state vector. The directional velocity component is obtained by decomposing it at 50 m / s in the northeast direction), and the state recursion relationship is set as follows: , , where the time interval Second, rate of change of direction of motion The preset recursion count is 10, starting from the initial state vector. Initially, the first recursive calculation yielded... Calculate the 10 state vectors for the next 10 seconds in sequence, and extract the state vectors for each time period. The coordinates are connected in chronological order to form the predicted flight trajectory.

[0062] Finally, a graded early warning signal is generated in step S1053. First, the distance from each point on the predicted flight trajectory to the boundary of the preset safe zone is calculated using the Euclidean distance formula. If the safe zone is circular, the center of the boundary is... , radius is Then a point on the trajectory The formula for the distance to the boundary is: ,in This represents the distance from the point to the boundary. Represents the coordinates of a point on the trajectory. Represents the coordinates of the boundary circle center. Represents the boundary radius; select all The minimum value in the range is used as the shortest distance. Then take the shortest distance The system compares the shortest distance with a preset distance threshold range. If the shortest distance falls within the threshold range of a relatively far distance, a low-level warning signal is generated; if it falls within the threshold range of a medium distance, a medium-level warning signal is generated; and if it falls within the threshold range of a relatively close distance or into a safe area, a high-level warning signal is generated.

[0063] For example, in a security scenario in airspace A, the preset safe zone is defined as follows: The central circular region, with a boundary radius The distance threshold range is set as follows: greater than 300 meters is low level, 100 to 300 meters is medium level, and less than 100 meters is high level; a point on the predicted trajectory is selected. Through formula (Negative values ​​indicate that the value is within the region), another point The shortest distance can be calculated. meters (a point) Calculated The example here has been corrected to match the 200 meters mentioned earlier. ,correspond (meters), which falls within the threshold range of 100 to 300 meters, thus generating a medium-level warning signal.

[0064] In the overall scheme of step S105 above, by combining the motion characteristic parameters of the aircraft type with the temporal characteristics of the magnetic field change components, key parameters reflecting the real-time motion state of the aircraft are accurately calculated, providing a reliable basis for trajectory prediction. The trajectory prediction method based on state recursion can reasonably extrapolate the future motion path of the aircraft, ensuring the rationality and foresight of the prediction results. By calculating the shortest distance between the predicted trajectory and the safe zone and matching the corresponding threshold to generate an early warning signal, risk classification is achieved, making prevention and control measures more targeted. The entire process, from motion state analysis to trajectory prediction to early warning generation, forms a complete safety prevention and control chain, effectively improving the ability to predict and respond to potential risks of aircraft, and ensuring airspace safety.

[0065] The following is a complete embodiment for steps S101 to S105: like Figure 3 As shown, in the low-altitude security scenario of area A, it is necessary to accurately detect and provide safety warnings for aircraft that intrude into the airspace. The specific implementation is as follows: First, deploy a superconducting quantum interference device (SQU) sensor array to collect data on the alternating magnetic field generated by the eddy currents of passing aircraft. Continuous wavelet transform is used to extract the fluctuation period (e.g., 2 seconds / cycle) in the time dimension and the energy concentration band (10-50Hz) in the frequency dimension, constructing a magnetic field characteristic benchmark. Second, install a magnetoelectric sensing unit at the monitoring point, aligning the long axis of the magnetostrictive sensing rod with the direction of magnetic field change, and attaching a fiber optic grating along the rod. When an aircraft enters the airspace, its eddy current magnetic field causes periodic deformation of the sensing rod, shifting the center wavelength of the reflected light from the fiber optic grating. The photoelectric conversion component converts this shift into a voltage signal. Next, based on the magnetic field characteristic benchmark, a 0.2-second time window and five analysis frequency bands are set. The voltage signal is segmented, and frequency components are separated using a bandpass filter bank. The energy of each frequency band is calculated, and an energy distribution structure is constructed to generate electrical signal components. Subsequently, magnetic field variation components with energy exceeding the threshold and located in the peak region were screened, and their oscillation period, bandwidth, and other features were extracted. These were compared with the standard magnetic field patterns of Type B and Type C aircraft in the preset feature database. The pattern with the highest similarity to Type B was determined, and the aircraft was identified as Type B. Finally, the motion characteristic parameters of Type B aircraft were retrieved from the parameter library, and the instantaneous velocity vector (40 m / s in the northeast direction) and the rate of change of motion direction (3° / s) were calculated in combination with the time series features. The flight trajectory for the next 10 seconds was predicted using a state recursion formula. The shortest distance between the calculated trajectory and the boundary of the circular safety area (radius 500 m) was 150 m, generating a medium-level warning signal.

[0066] The aircraft detection method based on eddy current-induced fiber optic micro-vibration provided in this application avoids the susceptibility to environmental interference in traditional acoustic detection by acquiring the stable characteristic signal of the aircraft's eddy current magnetic field. It combines magnetostriction and fiber optic sensing technologies to achieve precise conversion of magnetic field changes into electrical signals, significantly improving signal acquisition sensitivity. The time-frequency feature decomposition and feature matching steps effectively filter interference signals and accurately identify aircraft types, solving the problem of target misjudgment in complex environments. Based on motion characteristic parameters and state recursion-based trajectory prediction, it can proactively grasp aircraft dynamics. Combined with a tiered early warning mechanism, it significantly improves target detection accuracy and risk response efficiency in low-altitude security scenarios, providing reliable technical support for airspace safety.

[0067] Figure 4 This is a schematic diagram illustrating a specific implementation of an aircraft detection system based on eddy current-induced fiber optic micro-vibration, as provided in this application. (Refer to...) Figure 4 The system may include: Extraction module 41 is used to collect alternating magnetic field data generated by the eddy current of the aircraft, and analyze the alternating magnetic field data to extract the variation characteristics in the time and frequency dimensions as a magnetic field feature benchmark. Conversion module 42 is used to install a magnetoelectric sensing unit. When the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod in the magnetoelectric sensing unit, the magnetostrictive sensing rod converts the magnetic field change into a voltage signal by combining with a fiber optic grating. The decomposition module 43 is used to perform time-frequency feature decomposition on the voltage signal based on the magnetic field feature reference, so as to generate electrical signal components that characterize the evolution of magnetic field energy over time in different frequency bands. The matching module 44 is used to filter out characteristic magnetic field change components from the electrical signal components, match and compare the electrical signal characteristics of the magnetic field change components with the standard magnetic field patterns of known aircraft in the preset feature database, and identify the aircraft type based on the matching results. The generation module 45 is used to select corresponding motion characteristic parameters based on the aircraft type, and combine the temporal characteristics of the magnetic field change components to analyze the change law of the aircraft motion state to predict the flight trajectory, and generate graded early warning signals according to the relative positional relationship between the flight trajectory and the preset safety area.

[0068] The aircraft detection system based on eddy current induced fiber optic micro-vibration in this application embodiment is used to implement the aforementioned aircraft detection method based on eddy current induced fiber optic micro-vibration. Therefore, the specific implementation of the aircraft detection system based on eddy current induced fiber optic micro-vibration can be found in the embodiment section of the aircraft detection method based on eddy current induced fiber optic micro-vibration mentioned above. The specific implementation can be referred to the description of the corresponding embodiment, and will not be repeated here.

[0069] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described aircraft detection methods based on eddy current-induced fiber micro-vibration.

[0070] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described aircraft detection methods based on eddy current-induced fiber micro-vibration.

[0071] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0072] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the aircraft detection method based on eddy current induced fiber micro-vibration.

[0073] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0074] The above provides a detailed description of the aircraft detection method and system based on eddy current-induced fiber optic micro-vibration provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for detecting aircraft based on eddy current-induced fiber optic micro-vibrations, characterized in that, include: Data on alternating magnetic fields generated by eddy currents in aircraft are collected, and the alternating magnetic field data is analyzed to extract the variation characteristics in the time and frequency dimensions, which serve as the magnetic field characteristic benchmark. When a magnetoelectric sensing unit is installed, and the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod in the magnetoelectric sensing unit, the magnetostrictive sensing rod converts the magnetic field change into a voltage signal by combining with a fiber optic grating. Based on the magnetic field characteristic reference, the voltage signal is decomposed into time-frequency features to generate electrical signal components that characterize the evolution of magnetic field energy over time in different frequency bands. Characteristic magnetic field change components are selected from the electrical signal components. The electrical signal characteristics of the magnetic field change components are matched and compared with the standard magnetic field patterns of known aircraft in a preset feature database. The aircraft type is identified based on the matching results. Based on the type of aircraft, select the corresponding motion characteristic parameters, and combine them with the temporal characteristics of the magnetic field change components to analyze the change law of the aircraft's motion state in order to predict the flight trajectory. Based on the relative positional relationship between the flight trajectory and the preset safety area, generate graded early warning signals.

2. The method according to claim 1, characterized in that, Based on the magnetic field characteristic reference, the voltage signal is decomposed into time-frequency features to generate electrical signal components characterizing the evolution of magnetic field energy over time in different frequency bands, including: Based on the characteristic time scale and characteristic frequency range included in the magnetic field characteristic reference, the length of the time window corresponding to the characteristic time scale is set, and multiple analysis frequency bands are determined according to the characteristic frequency range; Based on the time window length, the voltage signal is segmented to obtain a series of signal segments, and the frequency components in each signal segment are separated by a bandpass filter bank. The signal amplitude within each analysis frequency band is obtained based on the amplitude of the frequency components, and the energy value of each analysis frequency band within a continuous time window is calculated based on the signal amplitude. The energy values ​​of each analysis frequency band are arranged in chronological order to construct an energy distribution structure with time as the horizontal axis and frequency band as the vertical axis. The energy distribution structure is used as an electrical signal component to characterize the evolution of magnetic field energy over time in different frequency bands.

3. The method according to claim 2, characterized in that, Separating the frequency components in each of the signal segments using a bandpass filter bank includes: Construct a filter bank consisting of multiple parallel bandpass filters, wherein the passband boundary of each bandpass filter is consistent with the corresponding analysis frequency band boundary, and there is an overlap region of a preset width between adjacent analysis frequency bands; The signal segment is simultaneously input into the filter bank, and the instantaneous energy distribution of the output signal in the overlapping region is calculated based on the output signals of two adjacent bandpass filters in the overlapping region. The energy allocation weight is determined based on the ratio of the instantaneous energy distribution, and the energy in the overlapping area is redistributed based on the energy allocation weight. The signal attenuation characteristics at the boundary of each analysis frequency band are detected. When abnormal attenuation is detected at the boundary, the boundary position of the analysis frequency band is dynamically adjusted and the passband parameters of the corresponding bandpass filter are updated. The outputs of each channel after energy redistribution and boundary adjustment are determined as the separated frequency components.

4. The method according to claim 1, characterized in that, Based on the aircraft type, corresponding motion characteristic parameters are selected, and combined with the temporal characteristics of the magnetic field variation components, the change law of the aircraft's motion state is analyzed to predict the flight trajectory. Based on the relative positional relationship between the flight trajectory and the preset safety area, a graded early warning signal is generated, including: Based on the type of aircraft, select the corresponding typical motion characteristic parameters from the parameter library, and combine them with the temporal characteristics of the characteristic magnetic field change components to calculate the instantaneous velocity vector and the rate of change of motion direction of the aircraft. Based on the instantaneous velocity vector and the rate of change of motion direction, the position sequence of future time points is extrapolated using a kinematic model to form a predicted flight trajectory; Calculate the shortest distance between the predicted flight trajectory and the boundary of the preset safe area, and generate graded early warning signals of different levels based on the shortest distance and the distance threshold range.

5. The method according to claim 4, characterized in that, Based on the instantaneous velocity vector and the rate of change of motion direction, a kinematic model is used to extrapolate the position sequence at future time points to form a predicted flight trajectory, including: Based on the instantaneous velocity vector and the rate of change of the motion direction, a spacecraft state vector is constructed, and a state recursion relationship of the spacecraft state vector is established. The state recursion relationship defines the transition rules from the current moment to the next moment. Based on the current aircraft state vector, the aircraft state vector at the next moment is calculated through the state recursion relationship, and the calculated aircraft state vector is updated to the current aircraft state vector. The calculation and update operations are repeated until a preset number of iterations are reached. The position components in the aircraft state vector obtained from each calculation are extracted, and the position components are stored and connected in chronological order to form a predicted flight trajectory.

6. The method according to claim 1, characterized in that, A magnetoelectric sensing unit is installed. When the magnetic field generated by the eddy currents of the aircraft acts on the magnetostrictive sensing rod in the magnetoelectric sensing unit, the magnetostrictive sensing rod converts the magnetic field change into a voltage signal by combining with a fiber optic grating, including: The magnetoelectric sensing unit is fixed in the monitoring area, and the long axis of the magnetostrictive sensing rod in the magnetoelectric sensing unit is aligned with the expected direction of magnetic field change. The fiber grating is then attached to the surface of the rod along the long axis of the magnetostrictive sensing rod. When the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod, the change in the magnetic field induces the rod to produce periodic deformation. The periodic deformation of the fiber optic grating caused by the sensing rod causes a change in the grating period, thereby obtaining the center wavelength shift of the reflected light signal. The reflected light signal from the fiber optic grating is received using a photoelectric conversion component, and the center wavelength offset is converted into a voltage signal.

7. The method according to claim 1, characterized in that, Characteristic magnetic field variation components are selected from the electrical signal components. The electrical signal characteristics of these magnetic field variation components are matched and compared with the standard magnetic field patterns of known aircraft in a preset feature database. The aircraft type is then identified based on the matching results, including: Candidate components with energy values ​​exceeding a preset energy threshold are selected from different frequency bands of the electrical signal components, and peak regions are determined based on the energy distribution structure of the electrical signal components. The candidate components located within the peak regions are identified as components with characteristic magnetic field changes. The oscillation period characteristics, bandwidth characteristics, and energy concentration characteristics of the characteristic magnetic field variation components are extracted to form a set of electrical signal characteristics to be matched. The electrical signal features to be matched are compared item by item with the standard magnetic field patterns of various known aircraft stored in the preset feature database, and the feature similarity score between the electrical signal features to be matched and each of the standard magnetic field patterns is calculated. The type of aircraft being detected is determined based on the aircraft model corresponding to the standard magnetic field pattern with the highest feature similarity score.

8. A spacecraft detection system based on eddy current-induced fiber optic micro-vibration, characterized in that, include: The extraction module is used to collect alternating magnetic field data generated by the eddy current of the aircraft, and analyze the alternating magnetic field data to extract the variation characteristics in the time and frequency dimensions as a magnetic field feature benchmark. The conversion module is used to install the magnetoelectric sensing unit. When the magnetic field generated by the eddy current of the aircraft acts on the magnetostrictive sensing rod in the magnetoelectric sensing unit, the magnetostrictive sensing rod converts the magnetic field change into a voltage signal by combining with the fiber optic grating. The decomposition module is used to perform time-frequency feature decomposition on the voltage signal based on the magnetic field feature reference, so as to generate electrical signal components that characterize the evolution of magnetic field energy over time in different frequency bands. The matching module is used to filter out characteristic magnetic field change components from the electrical signal components, match and compare the electrical signal characteristics of the magnetic field change components with the standard magnetic field patterns of known aircraft in a preset feature database, and identify the aircraft type based on the matching results. The generation module is used to select corresponding motion characteristic parameters based on the aircraft type, and combine the temporal characteristics of the magnetic field change components to analyze the change law of the aircraft motion state in order to predict the flight trajectory. Based on the relative positional relationship between the flight trajectory and the preset safety area, a graded early warning signal is generated.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the aircraft detection method based on eddy current induced fiber micro-vibration as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the aircraft detection method based on eddy current-induced fiber micro-vibration as described in any one of claims 1 to 7.

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