Insect flapping frequency inversion method based on initial phase calculation

Abstract

The invention discloses an insect flapping frequency inversion method based on initial phase calculation, and relates to the technical field of radar measurement. The method comprises the following steps: establishing an insect flapping motion radar echo model containing an amplitude initial phase and a phase initial phase, and obtaining a micro-motion signal from collected insect echoes based on the insect flapping motion radar echo model to generate a micro-Doppler spectrogram; based on the micro-Doppler spectrogram, calculating an initial phase value of the phase of the insect flapping wing, and based on the initial phase value, constructing a phase compensation factor to obtain a flapping wing parameter plane; and detecting an energy peak value in the flapping parameter plane based on the phase compensation factor, and determining the flapping frequency of the insect based on the position of the energy peak value. According to the method, the phase initial phase and the amplitude initial phase are introduced, the double physical processes of wing movement and body micro movement during insect flapping are restored, a radar echo model is highly matched with a real signal generation mechanism, errors are reduced, and the anti-interference capability is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of radar measurement, in particular to an insect wing beat frequency inversion method based on initial phase calculation. BACKGROUND

[0002] In insect migration research, the frequency of insect wing up-and-down flapping is a key parameter with great value. On the one hand, it directly reflects the flight efficiency and endurance of insects, and is a core index for evaluating the migration ability of insects. On the other hand, the wing beat frequency of different types of insects is significantly different, so this parameter is also an important basis for realizing the identification and quantity estimation of migration population species. In order to accurately obtain this parameter, a radar measurement method based on micro-Doppler focusing has been developed (such as CN115469304A), which usually extracts the micro-Doppler signal generated by the wing beat movement of insects to calculate the wing beat frequency, and shows certain feasibility and accuracy. However, in complex natural migration scenes, the wing beat model used by this method does not consider the phase difference caused by different vibration sources of insects, which directly leads to the problems of low measurement success rate and poor data accuracy when the method faces scenes that do not match the model.

[0003] Therefore, how to introduce different amplitude initial phase and phase initial phase parameters on the basis of the existing insect wing beat model, and perform corresponding phase estimation and compensation, so as to improve the measurement accuracy and success rate of the wing beat frequency, has become a problem to be solved. SUMMARY

[0004] The embodiment of the application provides an insect wing beat frequency inversion method based on initial phase calculation, which is used to solve the technical problem of how to introduce different amplitude initial phase and phase initial phase parameters on the basis of the existing insect wing beat model, and perform corresponding phase estimation and compensation, so as to improve the measurement accuracy and success rate of the wing beat frequency.

[0005] In a first aspect, the embodiment of the application provides an insect wing beat frequency inversion method based on initial phase calculation, which comprises: establishing an insect wing beat movement radar echo model containing amplitude initial phase and phase initial phase, and obtaining a micro-motion signal from the collected insect echo based on the insect wing beat movement radar echo model, to generate a micro-Doppler spectrum; based on the micro-Doppler spectrum, calculating the phase initial phase value of the insect wing beat, and based on the phase initial phase value, constructing a phase compensation factor to obtain a wing beat parameter plane; based on the phase compensation factor, detecting the energy peak value in the wing beat parameter plane, and determining the wing beat frequency of the insect based on the position of the energy peak value.

[0006] In an implementation manner of the application, the insect wing beat movement radar echo model is represented by the following formula:

[0007] wherein, is the radar echo of an insect, is a constant related to radar parameters, is the radar cross section (RCS) of the insect when the insect is not flapping its wings, is the ratio of the RCS fluctuation caused by the insect flapping its wings to the RCS when the insect is not flapping its wings, is a time axis, is a radar wavelength, is a flapping amplitude, is a flapping frequency, is an initial phase of the amplitude, is an initial phase of the amplitude.

[0008] In an implementation manner of the present application, based on the radar echo model of the insect flapping motion, a micro-motion signal is obtained from the collected insect echo to generate a micro-Doppler spectrum, specifically comprising: filtering out the insect body echo component from the insect echo to obtain the micro-motion signal, which is represented by the following formula:

[0009] wherein, is a micro-Doppler spectrum time, is a micro-Doppler spectrum frequency, is a time-frequency analysis window; the micro-motion signal is subjected to time-frequency analysis to generate a micro-Doppler spectrum.

[0010] In an implementation manner of the present application, the micro-motion signal is subjected to time-frequency analysis to generate a micro-Doppler spectrum, specifically comprising: simplifying the micro-motion signal based on the Bessel expansion to obtain a micro-Doppler spectrum, which is represented by the following formula:

[0011] wherein, is an instantaneous Doppler spectrum of the insect target.

[0012] In an implementation manner of the present application, based on the micro-Doppler spectrum, the initial phase value of the insect flapping is calculated, specifically comprising: setting the flapping amplitude range of the insect as , the flapping frequency range of the insect as , and according to the insect radar echo, when the searched flapping amplitude is and the flapping frequency is , the insect instantaneous Doppler frequency is represented by the following formula: , ,

[0013] wherein, is a frequency position of a maximum amplitude in the micro-Doppler spectrogram when =0, is an estimated value of the initial phase of the phase.

[0014] In an implementation of the present application, after obtaining the estimated value of the initial phase of the phase, the method further comprises: differentiating the frequency position of the maximum amplitude in the micro-Doppler spectrogram, and determining whether the estimated value of the initial phase of the phase is located on a rising section or a falling section of the cosine function, to obtain a sign of the estimated value of the initial phase of the phase.

[0015] In an implementation of the present application, before constructing the phase compensation factor, the method further comprises: extracting the instantaneous time-frequency value of the insect target, which is represented by the following formula:

[0016] wherein, is the instantaneous time-frequency value of the insect target.

[0017] In an implementation of the present application, based on the initial phase value of the phase, the phase compensation factor is constructed to obtain the wing-flapping parameter plane, specifically comprising: constructing the phase compensation factor, which is represented by the following formula:

[0018] wherein, is the phase compensation factor; performing phase compensation and integration on the instantaneous time-frequency value and the phase compensation factor to obtain energy accumulation; performing two-dimensional parameter search in the search range of the wing-flapping amplitude and the search range of the wing-flapping frequency, and mapping the energy accumulation to obtain the wing-flapping parameter plane.

[0019] In an implementation of the present application, performing phase compensation and integration on the instantaneous time-frequency value and the phase compensation factor to obtain energy accumulation, specifically comprising: the energy accumulation is represented by the following formula:

[0020] wherein, is the energy accumulation value.

[0021] In an implementation of the present application, detecting the energy peak value in the wing-flapping parameter plane and determining the wing-flapping frequency of the insect based on the position of the energy peak value, specifically comprising: traversing the wing-flapping parameter plane, locating the maximum value point of the energy accumulation, and reading the wing-flapping frequency coordinate corresponding to the maximum value point in the wing-flapping parameter plane; determining the wing-flapping frequency of the insect based on the mapping relationship between the wing-flapping frequency coordinate and the preset wing-flapping frequency search range.

[0022] The insect wing beat frequency inversion method based on initial phase calculation provided by the embodiment of the application has the following beneficial effects: by establishing an insect wing beat motion radar echo model containing amplitude initial phase and phase initial phase, the double physical processes of insect body micro motion and wing motion during wing beat are restored, the radar echo model is highly consistent with the real signal generation mechanism, and measurement deviation caused by model hypothesis error is avoided from the source; irrelevant insect body echo components are filtered out from the collected insect echoes, the micro motion signals directly related to wing beat are accurately extracted, and background interference is reduced to reduce the submersion of effective signals. BRIEF DESCRIPTION OF DRAWINGS

[0023] The drawings described herein are used to provide further understanding of the application, and form a part of the application. The illustrative embodiments of the application and their descriptions serve to explain the application, and do not constitute an improper limitation on the application. In the drawings: Figure 1 A flowchart of an insect wing beat frequency inversion method based on initial phase calculation provided by the embodiment of the application is provided. Figure 2 A micro-Doppler spectrum provided by the embodiment of the application is provided. Figure 3 A parameter space search result and peak position of a micro-Doppler spectrum provided by the embodiment of the application are provided. Figure 4 An amplitude and phase diagram of an ideal simulation signal provided by the embodiment of the application is provided. Figure 5 An algorithm provided by the embodiment of the application and the extraction result and error of a traditional algorithm are compared. DETAILED DESCRIPTION

[0024] To make the purpose, technical solutions and advantages of the application clearer, the technical solutions of the application will be described clearly and completely below by combining the specific embodiments of the application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0025] The insect wing beat frequency inversion method based on initial phase calculation provided by the embodiment of the application is used to solve the following technical problem: how to introduce different amplitude initial phase and phase initial phase parameters on the basis of the existing insect wing beat model, and perform corresponding phase estimation and compensation, so as to improve the measurement accuracy and success rate of the wing beat frequency.

[0026] The technical solutions of the embodiments of the present application are described in detail below with reference to the drawings.

[0027] Figure 1 A flow chart of an insect wing beat frequency inversion method based on initial phase calculation is provided in the embodiments of the present application. Figure 1 As shown in the flow chart, the insect wing beat frequency inversion method based on initial phase calculation provided in the embodiments of the present application specifically includes the following steps: Step 10: Establish an insect wing beat motion radar echo model containing amplitude initial phase and phase initial phase, and based on the insect wing beat motion radar echo model, obtain micro-motion signals from the collected insect echo to generate a micro-Doppler spectrum.

[0028] As an optional embodiment, the insect wing beat motion radar echo model containing amplitude initial phase and phase initial phase is established, and based on the insect wing beat motion radar echo model, micro-motion signals are obtained from the collected insect echo to generate a micro-Doppler spectrum, which specifically can include: step 101: the insect wing beat motion radar echo model is represented by the following formula:

[0029] wherein, is the insect radar echo, is a constant related to radar parameters, is the radar scattering cross section (RCS) of the insect when the wings are not beating, is the ratio of the RCS fluctuation caused by the insect wing beat to the RCS when the wings are not beating, is the time axis, is the radar wavelength, is the wing beat amplitude, is the wing beat frequency, is the phase initial phase, is the amplitude initial phase.

[0030] In this step, the signal starting states corresponding to the radar scattering cross section (RCS) fluctuation caused by the wing movement of the insect when the wings are beating and the body micro-motion are accurately distinguished, the physical mechanisms of the two independent movements are completely restored, the basic deviation caused by model simplification is avoided, a reliable foundation is laid for subsequent accurate measurement, by introducing the double initial phase parameters, the model can depict the starting characteristics of amplitude modulation and phase modulation at the same time, the signal representation is upgraded to three-dimensional combination of amplitude, frequency and phase, the key information hidden in the initial phase is fully excavated, and more comprehensive basis is provided for subsequent time-frequency analysis and parameter calculation.

[0031] Step 102: filter out the insect body echo component from the insect echo to obtain micro-motion signals, which are represented by the following formula:

[0032] wherein, is the micro-Doppler spectrogram time, is the micro-Doppler spectrogram frequency, is the time-frequency analysis window.

[0033] In this step, by filtering out the basic components in the insect echo unrelated to wing movement, the micro-motion signal caused by insect wing movement is directly focused on, reducing the interference of non-target signals on subsequent analysis, making the characteristics of effective signals more prominent, and laying the foundation for the accurate generation of subsequent micro-Doppler spectrograms; the operation of filtering out the insect echo essentially reduces the strength of the background signal, indirectly improves the relative signal-to-noise ratio of the micro-motion signal; at the same time, the local analysis characteristics of the short-time Fourier transform can effectively accumulate the energy of the weak micro-motion signal, so that the characteristics of the wing signal originally submerged in noise can be revealed, and the target signal can still be stably captured in a complex detection environment; by filtering out the insect echo component, the additional interference caused by changes in the state of the insect itself or fluctuations in the detection environment is reduced, reducing the sensitivity of the entire measurement process to the external environment; at the same time, the standardized short-time Fourier transform formula ensures the consistency of the signal processing process, making the measurement results in different scenarios more repeatable and stable.

[0034] Step 103: performing time-frequency analysis on the micro-motion signal to generate a micro-Doppler spectrogram.

[0035] As an optional embodiment, the time-frequency analysis on the micro-motion signal to generate a micro-Doppler spectrogram can specifically include: step 1031: simplifying the micro-motion signal based on the Bessel expansion formula to obtain a micro-Doppler spectrogram, which is represented by the following formula:

[0036] wherein, is the instantaneous Doppler spectrogram of the insect target.

[0037] In this step, the Bessel expansion formula can decompose and simplify the complex combination of trigonometric functions in the micro-motion signal, eliminate redundant calculation items, and generate Figure 2The micro-Doppler spectrogram shown focuses directly on the core signal characteristics related to insect wing vibration, avoids interference of irrelevant components on spectrogram interpretation, and makes the frequency track corresponding to wing vibration clearer and more identifiable; the simplification process only removes redundant items in the signal without losing characteristic information of key parameters such as wing vibration frequency, amplitude and double initial phase, ensuring that the micro-Doppler spectrogram can fully reflect the signal nature of insect wing vibration and provide accurate data support for subsequent initial phase calculation, phase compensation and GRFT parameter space search; for weak wing vibration micro-motion signals, the simplification processing of the Bessel expansion formula can effectively suppress noise amplification, improve the discrimination degree of the target signal and noise, highlight the wing vibration characteristics that were originally submerged in noise, and stably generate a usable micro-Doppler spectrogram even in a complex detection environment, ensuring the reliability of the subsequent measurement process.

[0038] Step 20: Based on the micro-Doppler spectrogram, the initial phase value of the insect wing vibration phase is calculated, and based on the initial phase value, a phase compensation factor is constructed to obtain a wing vibration parameter plane.

[0039] As an optional embodiment, based on the micro-Doppler spectrogram, the initial phase value of the insect wing vibration phase is calculated, and based on the initial phase value, a phase compensation factor is constructed to obtain a wing vibration parameter plane, which can specifically include: step 201: assuming that the wing vibration amplitude range of the insect is , the wing vibration frequency range of the insect is , according to the insect radar echo, when the searched wing vibration amplitude is , the wing vibration frequency is , the insect instantaneous Doppler frequency is represented by the following formula: , ,

[0040] wherein, is the frequency position of the maximum amplitude in the micro-Doppler spectrogram when =0, is the estimated value of the initial phase.

[0041] In this step, the phase initial phase estimate is integrated into the instantaneous Doppler frequency calculation, so that the frequency trajectory can accurately match the true phase characteristics of insect wing vibration, avoid trajectory deviation caused by phase deviation, and make the signal extracted from the micro-Doppler spectrum more consistent with the essence of wing vibration, laying a foundation for subsequent parameter matching; The search boundary of the wing amplitude and frequency is clear, avoiding indiscriminate traversal of invalid parameter intervals and reducing redundant computation; At the same time, the frequency trajectory combined with the initial phase estimate is more directional, allowing the GRFT parameter space search to focus on effective parameter combinations, greatly improving search speed and matching accuracy; The formula for accurate estimation of the initial phase can offset the interference of phase ambiguity on the frequency trajectory, making the frequency trajectory corresponding to the weak wing vibration signal clearer, and effectively distinguishing the target trajectory from the interference signal even in a strong noise interference scene, ensuring the stability of signal extraction; The instantaneous Doppler frequency obtained based on the formula contains the initial phase information, and the subsequent phase compensation factor can more accurately offset the phase deviation in signal transmission, avoid energy cancellation of wing vibration signals at different time points due to phase misalignment, and make the energy aggregation in the GRFT transformation more concentrated, improving the recognition of the wing parameter plane peak.

[0042] Step 202: Differentiate the maximum amplitude frequency position in the micro-Doppler spectrum, and determine whether the phase initial phase estimate is located in the rising segment or the falling segment of the cosine function to obtain the sign of the phase initial phase estimate.

[0043] In this step, first, the frequency position corresponding to the maximum amplitude in the micro-Doppler spectrum is located, which is a core reference point reflecting the signal characteristics of the initial stage of insect wing vibration and is directly related to the basic calculation of the initial phase; Then, by comparing the signal amplitude changes of adjacent time points or adjacent frequency points, the increasing or decreasing law of the signal at this frequency position is determined; This differential processing can effectively capture the local dynamic characteristics of the signal, and then determine the change segment of the cosine function corresponding to the phase initial phase estimate. If the signal shows an increasing trend, it can be determined that the phase initial phase estimate is located in the rising segment of the cosine function; If the signal shows a decreasing trend, it is determined that it is located in the falling segment of the cosine function. Based on the judgment result of this change segment, the sign of the phase initial phase estimate can be determined, ensuring that the phase initial phase estimate not only meets the calculation range in terms of numerical value, but also is consistent with the true initial state of insect wing vibration in terms of physical meaning, providing accurate and reliable initial phase basis for subsequent steps such as phase compensation and GRFT parameter space search, and ensuring the accuracy of the entire wing frequency inversion process.

[0044] Step 203: Extract the instantaneous time-frequency value of the insect target, which is represented by the following formula:

[0045] Wherein, is the instantaneous time-frequency value of the insect target.

[0046] In this step, the extracted instantaneous time-frequency value integrates the signal characteristics corresponding to the core parameters of wing amplitude, frequency and double initial phase, including both the radar scattering cross section fluctuation information caused by wing movement and the phase change information corresponding to body micro movement, without missing the key physical characteristics of wing movement, ensuring that the subsequent analysis can be based on comprehensive and true signal data; the formula explicitly incorporates the assumed wing amplitude, frequency and instantaneous Doppler frequency information, so that the extracted instantaneous time-frequency value can accurately correspond to the preset wing parameters, avoiding irrelevant signal interference; the extraction process of the instantaneous time-frequency value fully considers the influence of the double initial phase relative phase, reduces the signal misplacement caused by phase deviation, and provides a highly consistent signal basis for subsequent phase compensation.

[0047] Step 204: Constructing a phase compensation factor, represented by the following formula:

[0048] Wherein, is the phase compensation factor.

[0049] In this step, the phase compensation factor incorporates the assumed wing amplitude, frequency, phase initial phase estimate and instantaneous Doppler frequency information, can accurately locate and offset the phase deviation of the wing signal generated in the transmission and processing process, avoid the mutual interference of signals at different time points due to phase misplacement, keep the signals highly consistent in phase, and lay a foundation for subsequent energy accumulation; with accurate phase compensation, the weak wing signals dispersed in different time dimensions can be directionally superimposed, the energy is efficiently gathered to the corresponding position of the wing parameter plane, greatly improving the discrimination degree of target signals and noise, even in a low signal-to-noise ratio scenario, the wing signal characteristics can be highlighted, effectively solving the problem of weak signal identification, at the same time, the phase compensation factor makes the wing signal trajectory in the micro-Doppler spectrum clearer and the characteristics more prominent, reduces the invalid search path and the probability of false matching, shortens the search time while greatly improving the accuracy of parameter matching, and provides reliable support for subsequent peak detection.

[0050] Step 205: Phase compensation and integration using the instantaneous time-frequency value and the phase compensation factor to obtain energy accumulation.

[0051] As an optional embodiment, phase compensation and integration using the instantaneous time-frequency value and the phase compensation factor to obtain energy accumulation can specifically include: step 2051: energy accumulation, represented by the following formula:

[0052] Wherein, is the energy accumulation value. Step 206: Perform two-dimensional parameter search in the search range of wing amplitude and the search range of wing frequency, and map the energy accumulation to obtain a wing parameter plane.

[0053] In this step, the energy accumulation process deeply integrates the instantaneous time-frequency value with the precisely constructed phase compensation factor, and through integral operation, the wingbeat signal energy of different time dimensions is directionally superimposed, effectively avoiding energy loss caused by phase deviation, highly gathering the energy of the weak target signal originally submerged in noise, and significantly improving the discrimination degree of the target signal and noise; and the two-dimensional parameter search is strictly limited within the effective range of the wingbeat amplitude and frequency, avoiding the interference of invalid parameter combinations, so that the energy accumulation result can be accurately mapped to the corresponding parameter coordinates, and in the finally formed wingbeat parameter plane, the energy peak corresponding to the real wingbeat parameter is more sharp and the boundary is more clear, effectively reducing the problems of peak ambiguity and sidelobe interference; the energy accumulation formula integrates the core parameter characteristics of wingbeat amplitude, frequency, and double initial phase, so that the two-dimensional parameter search does not need to filter out invalid information, and can directly focus on the matching and verification of valid parameter combinations, greatly reducing the redundant calculation amount, improving the search speed while ensuring the accuracy of parameter matching, so that in complex interference environment or weak signal scene, the wingbeat related energy features can still be stably extracted, avoiding the measurement failure problem caused by weak signal or noise interference, and ensuring the generation quality of the wingbeat parameter plane to provide reliable support for subsequent peak detection.

[0054] Step 30: detecting the energy peak in the wingbeat parameter plane based on the phase compensation factor, and determining the wingbeat frequency of the insect based on the position of the energy peak.

[0055] As an optional embodiment, detecting the energy peak in the wingbeat parameter plane based on the phase compensation factor, and determining the wingbeat frequency of the insect based on the position of the energy peak, can specifically include: step 301: traversing the wingbeat parameter plane, locating the maximum value point of energy accumulation, and reading the wingbeat frequency coordinate corresponding to the maximum value point in the wingbeat parameter plane.

[0056] In this step, the wingbeat parameter plane is formed by energy accumulation and two-dimensional parameter search mapping, and the horizontal and vertical coordinate axes correspond to the preset wingbeat frequency and wingbeat amplitude search range, respectively. The value of each coordinate point on the plane represents the energy accumulation result corresponding to the wingbeat parameter. During the traversal process, the energy accumulation values of all coordinate points in the plane will be checked and compared one by one, and the core target is to select the maximum value point of energy accumulation. The formation of this point is due to the precise matching of the assumed wingbeat parameters and the real wingbeat state of the insect, so that the signal energy is highly gathered after phase compensation. After locating this maximum value point, the wingbeat frequency coordinate corresponding to it in the wingbeat parameter plane is further read, such as Figure 3The coordinates are the inversion results of the real wing beat frequency of the insects; through objective traversal comparison and coordinate reading, the accuracy and objectivity of the wing beat frequency measurement are ensured, and direct and reliable data support is provided for subsequent insect species identification, migration ability evaluation and other applications.

[0057] Step 302: determining the wing beat frequency of the insect based on the mapping relationship between the wing beat frequency coordinates and the preset wing beat frequency search range.

[0058] In this step, after completing the energy peak positioning of the wing beat parameter plane and reading the corresponding wing beat frequency coordinates, the real wing beat frequency of the insect needs to be finally determined through the mapping relationship between the frequency coordinates and the preset wing beat frequency search range; the preset wing beat frequency search range is a reasonable interval based on the general characteristics of insect wing beat behavior and the actual needs of radar detection, which provides a clear reference boundary for frequency judgment; the core of the mapping process is to verify whether the read frequency coordinates fall within the preset range, and at the same time, combined with the energy distribution logic of the wing beat parameter plane, only the frequency coordinates matching the real wing beat state of the insect can correspond to the effective value within the preset range in the mapping; through this mapping relationship, abnormal coordinate values caused by noise interference or parameter search deviation can be excluded, ensuring that the finally determined wing beat frequency not only conforms to the preset reasonable range, but also is consistent with the real wing beat state reflected by the energy peak; the whole process relies on the constraint of the preset range and the verification of the coordinate mapping, realizing the accurate selection and determination of the wing beat frequency. Figure 5 As shown, compared with the traditional method, the application can still stably and accurately extract the wing beat parameters in a low signal-to-noise ratio scene, with higher success efficiency and smaller error, providing reliable core parameters for subsequent applications such as species identification and migration ability evaluation in insect migration monitoring. Figure 4 The amplitude and phase of the ideal simulation signal are shown to change with time, and it can be seen that the initial points of the signal amplitude and phase are obviously different, which intuitively presents the amplitude modulation and phase modulation characteristics of the insect wing beat echo signal, providing basic data support for subsequent algorithm verification.

[0059] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed, or include elements inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0060] The above merely provides an example of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the scope of claims of the present application.

Claims

1. A method for inverting insect wingbeat frequencies based on initial phase estimation, characterized in that, The method includes: A radar echo model of insect wingbeat motion, including amplitude initial phase and phase initial phase, is established. Based on the radar echo model of insect wingbeat motion, micro-motion signals are obtained from the collected insect echoes to generate micro-Doppler spectra. Based on the microDoppler spectrum, the initial phase value of the insect's wingbeats is calculated, and based on the initial phase value, a phase compensation factor is constructed to obtain the wingbeat parameter plane. Based on the phase compensation factor, an energy peak is detected in the wing-beating parameter plane, and the wing-beating frequency of the insect is determined based on the position of the energy peak.

2. The method for inverting insect wingbeat frequencies based on initial phase estimation according to claim 1, characterized in that, The radar echo model of insect wingbeat motion is expressed by the following formula: in, For insect radar echoes, A constant related to radar parameters. The radar cross-section of an insect when its wings are not flapping. The ratio of the RCS fluctuation caused by wing flapping to the RCS when the insect is not flapping its wings. It's a timeline. It is the radar wavelength. This refers to the amplitude of wingbeats. The wingbeat frequency, The initial phase, This is the initial phase of the amplitude.

3. The method for inverting insect wingbeat frequency based on initial phase estimation according to claim 2, characterized in that, Based on the aforementioned insect wing-beating radar echo model, micro-motion signals are obtained from the collected insect echoes to generate micro-Doppler spectra, specifically including: The micro-motion signal is obtained by filtering out the insect body echo component from the insect echo, as expressed by the following formula: in, For the time of the micro-Doppler spectrum, The frequencies in the micro-Doppler spectrum are... For time-frequency analysis; Time-frequency analysis is performed on the micro-motion signal to generate the micro-Doppler spectrum.

4. The method for inverting insect wingbeat frequency based on initial phase estimation according to claim 3, characterized in that, Performing time-frequency analysis on the micro-motion signal to generate the micro-Doppler spectrum specifically includes: The micro-motion signal is simplified based on Bessel expansion to obtain the micro-Doppler spectrum, which is expressed by the following formula: in, Instantaneous Doppler spectrum of an insect target.

5. The method for inverting insect wingbeat frequency based on initial phase estimation according to claim 2, characterized in that, Based on the micro-Doppler spectrum, the initial phase value of insect wing flapping is calculated, specifically including: Let the range of the insect's wingbeat amplitude be . The range of insect wingbeat frequencies is Based on the insect radar echo, the amplitude of the wingbeats being searched is... wingbeat frequency is At that time, the instantaneous Doppler frequency of insects It can be expressed by the following formula: ， , in, for The frequency position of the maximum amplitude in the micro-Doppler spectrum when =0. This is an estimate of the initial phase.

6. The method for inverting insect wingbeat frequency based on initial phase estimation according to claim 5, characterized in that, After obtaining the estimated value of the initial phase, the method further includes: The position of the maximum amplitude frequency in the micro-Doppler spectrum is differentially processed, and the initial phase estimate is determined to be located in the rising or falling segment of the cosine function, so as to obtain the sign of the initial phase estimate.

7. The method for inverting insect wingbeat frequency based on initial phase estimation according to claim 5, characterized in that, Before constructing the phase compensation factor, the method further includes: The instantaneous time-frequency value of the insect target is extracted and expressed by the following formula: in, The instantaneous time-frequency value of the insect target.

8. The method for inverting insect wingbeat frequency based on initial phase estimation according to claim 7, characterized in that, Based on the initial phase value, a phase compensation factor is constructed to obtain the flapping parameter plane, specifically including: The phase compensation factor is constructed using the following formula: in, This is the phase compensation factor; Phase compensation and integration are performed using the instantaneous time-frequency value and the phase compensation factor to obtain energy accumulation; A two-dimensional parameter search is performed within the search range of the wing flapping amplitude and the search range of the wing flapping frequency, and the energy accumulation is mapped to obtain the wing flapping parameter plane.

9. The method for inverting insect wingbeat frequency based on initial phase estimation according to claim 8, characterized in that, Phase compensation and integration are performed using the instantaneous time-frequency value and the phase compensation factor to obtain energy accumulation, specifically including: The energy accumulation is expressed by the following formula: in, This represents the cumulative energy value.

10. The method for inverting insect wingbeat frequencies based on initial phase estimation according to claim 8, characterized in that, Detecting energy peaks in the wing-beating parameter plane and determining the insect's wing-beating frequency based on the position of the energy peaks, specifically includes: Traverse the flapping parameter plane, locate the maximum point of energy accumulation, and read the flapping frequency coordinates corresponding to the maximum point in the flapping parameter plane; The wingbeat frequency of an insect is determined based on the mapping relationship between the wingbeat frequency coordinates and the preset wingbeat frequency search range.

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