A method for inverting insect wingbeat frequencies based on initial phase estimation

Abstract

This application discloses a method for inverting insect wingbeat frequency based on initial phase calculation, relating to the field of radar measurement technology. The method includes: establishing a radar echo model of insect wingbeat motion containing amplitude and phase initial phases; acquiring micro-motion signals from the collected insect echoes based on the model to generate a micro-Doppler spectrum; calculating the phase initial phase value of the insect wingbeat based on the micro-Doppler spectrum; constructing a phase compensation factor based on the phase initial phase value to obtain a wingbeat parameter plane; detecting energy peaks in the wingbeat parameter plane based on the phase compensation factor; and determining the insect's wingbeat frequency based on the position of the energy peaks. This application, by introducing phase and amplitude initial phases, recreates the dual physical processes of wing movement and body micro-motion during insect wingbeat, making the radar echo model highly consistent with the actual signal generation mechanism, reducing errors, and improving anti-interference capabilities.

Description

Technical Field

[0001] This application relates to the field of radar measurement technology, and in particular to a method for inverting insect wingbeat frequency based on initial phase estimation. Background Technology

[0002] In insect migration research, the frequency of insect wing flapping is a highly valuable key parameter. On the one hand, it directly reflects the insect's flight efficiency and endurance, serving as a core indicator for assessing its migratory ability. On the other hand, the wing flapping frequencies of different insect species vary significantly, making this parameter an important basis for identifying migratory populations and estimating their numbers. To accurately obtain this parameter, radar measurement methods based on micro-Doppler focusing (such as CN115469304A) have been developed. These methods typically calculate the wing flapping frequency by extracting the micro-Doppler signals generated by the insect's wing flapping motion, demonstrating a certain degree of feasibility and accuracy. However, in complex natural migration scenarios, the insect wing flapping model used in this method does not consider the phase difference caused by different vibration sources within the insect. This directly leads to low measurement success rates and poor data accuracy when facing scenarios with model mismatch.

[0003] Therefore, how to introduce different amplitude and phase initial phase parameters based on existing insect wing flapping models, and perform corresponding phase estimation and compensation to improve the measurement accuracy and success rate of wing flapping frequency, has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a method for inverting insect wingbeat frequency based on initial phase estimation, in order to solve the following technical problem: how to introduce different amplitude and phase initial phase parameters on the basis of existing insect wingbeat models, and perform corresponding phase estimation and compensation, thereby improving the measurement accuracy and success rate of wingbeat frequency.

[0005] In a first aspect, embodiments of this application provide a method for inverting insect wingbeat frequency based on initial phase calculation. The method includes: establishing an insect wingbeat motion radar echo model containing an amplitude initial phase and a phase initial phase; obtaining micro-motion signals from the collected insect echoes based on the insect wingbeat motion radar echo model to generate a micro-Doppler spectrum; calculating the phase initial phase value of the insect wingbeat based on the micro-Doppler spectrum; constructing a phase compensation factor based on the phase initial phase value to obtain a wingbeat parameter plane; detecting energy peaks in the wingbeat parameter plane based on the phase compensation factor; and determining the insect wingbeat frequency based on the position of the energy peaks.

[0006] In one implementation of this application, the radar echo model of insect wing flapping motion is represented by the following formula:

[0007]

[0008] 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 frequency of wingbeats. For the initial phase, This is the initial phase of the amplitude.

[0009] In one implementation of this application, based on an insect wing-beating motion radar echo model, micro-motion signals are obtained from the collected insect echoes to generate a micro-Doppler spectrum. Specifically, this includes filtering out the insect body echo component from the insect echoes to obtain the micro-motion signals, expressed by the following formula:

[0010]

[0011] in, For the time of the micro-Doppler spectrum, The frequencies in the micro-Doppler spectrum are... This serves as the time window for time-frequency analysis; time-frequency analysis is performed on the micro-motion signal to generate a micro-Doppler spectrum.

[0012] In one implementation of this application, time-frequency analysis is performed on the micro-motion signal to generate a micro-Doppler spectrum. Specifically, this includes: simplifying the micro-motion signal based on a Bessel expansion to obtain the micro-Doppler spectrum, expressed by the following formula:

[0013]

[0014] in, Instantaneous Doppler spectrum of an insect target.

[0015] In one implementation of this application, the initial phase value of insect wingbeats is calculated based on a micro-Doppler spectrum, specifically including: assuming the amplitude range of insect wingbeats is... 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:

[0016] ,

[0017] ,

[0018] in, for The frequency position of the maximum amplitude in the micro-Doppler spectrum when =0. This is an estimate of the initial phase.

[0019] In one implementation of this application, after obtaining the estimated value of the initial phase, the method further includes: performing differential processing on the position of the maximum amplitude frequency in the micro-Doppler spectrum, and determining whether the estimated value of the initial phase is located in the rising or falling segment of the cosine function, so as to obtain the sign of the estimated value of the initial phase.

[0020] In one implementation of this application, before constructing the phase compensation factor, the method further includes: extracting the instantaneous time-frequency value of the insect target, expressed by the following formula:

[0021]

[0022] in, The instantaneous time-frequency value of the insect target.

[0023] In one implementation of this application, a phase compensation factor is constructed based on the initial phase value to obtain the flapping parameter plane. Specifically, this includes: constructing the phase compensation factor, which is expressed by the following formula:

[0024]

[0025] in, The phase compensation factor is used; 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 wing flapping amplitude and wing flapping frequency, and the energy accumulation is mapped to obtain the wing flapping parameter plane.

[0026] In one implementation of this application, phase compensation and integration are performed using instantaneous time-frequency values ​​and a phase compensation factor to obtain energy accumulation. Specifically, this includes energy accumulation, expressed by the following formula:

[0027]

[0028] in, This represents the cumulative energy value.

[0029] In one implementation of this application, the 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. Specifically, this includes: traversing the wing-beating parameter plane, locating the maximum point of energy accumulation, and reading the wing-beating frequency coordinates corresponding to the maximum point in the wing-beating parameter plane; and determining the wing-beating frequency of the insect based on the mapping relationship between the wing-beating frequency coordinates and a preset wing-beating frequency search range.

[0030] The insect wingbeat frequency inversion method based on initial phase estimation provided in this application has the following beneficial effects: By establishing a radar echo model of insect wingbeat motion that includes both amplitude and phase initial phases, the dual physical processes of body micro-movement and wing movement during insect wingbeat are restored, making the radar echo model highly consistent with the real signal generation mechanism, thus avoiding measurement deviations caused by incorrect model assumptions from the source; irrelevant insect body echo components are filtered out from the collected insect echoes, accurately extracting the micro-movement signals directly related to wingbeat, reducing the submersion of effective signals by background interference; a phase compensation factor is constructed based on the estimated phase initial phase value to offset the phase deviation caused by initial phase differences during signal transmission and processing, avoiding mutual cancellation of signal energy at different time points due to phase misalignment. Attached Figure Description

[0031] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0032] Figure 1 A flowchart illustrating an insect wingbeat frequency inversion method based on initial phase estimation, provided in this application embodiment;

[0033] Figure 2 A micro-Doppler spectrum provided for an embodiment of this application;

[0034] Figure 3 This application provides a parameter space search result and peak position of a micro-Doppler spectrum for embodiments of the present application.

[0035] Figure 4 An amplitude and phase diagram of an ideal simulated signal provided in an embodiment of this application;

[0036] Figure 5 A comparison of extraction results and errors between an algorithm provided in this application embodiment and a traditional algorithm. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. 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.

[0038] This application provides a method for inverting insect wingbeat frequency based on initial phase estimation, in order to solve the following technical problem: how to introduce different amplitude and phase initial phase parameters on the basis of existing insect wingbeat models, and perform corresponding phase estimation and compensation, thereby improving the measurement accuracy and success rate of wingbeat frequency.

[0039] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0040] Figure 1 A flowchart illustrating an insect wingbeat frequency inversion method based on initial phase estimation, provided as an embodiment of this application. Figure 1 As shown in the embodiment of this application, a method for inverting insect wingbeat frequencies based on initial phase estimation is provided, which specifically includes the following steps:

[0041] Step 10: Establish a radar echo model of insect wingbeat motion that includes the initial amplitude phase and the initial phase phase, and based on the radar echo model of insect wingbeat motion, obtain micro-motion signals from the collected insect echoes to generate a micro-Doppler spectrum.

[0042] As an optional embodiment, an insect wing-beating motion radar echo model is established, including the initial amplitude phase and the initial phase phase. Based on the insect wing-beating motion radar echo model, micro-motion signals are obtained from the collected insect echoes to generate a micro-Doppler spectrum. Specifically, this may include: Step 101: The insect wing-beating motion radar echo model is represented by the following formula:

[0043]

[0044] 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 frequency of wingbeats. For the initial phase, This is the initial phase of the amplitude.

[0045] In this step, the radar cross section (RCS) fluctuations caused by wing movements during insect wing flapping and the signal initial states corresponding to body micro-movements were accurately distinguished. The physical mechanisms of the two independent movements were fully restored, avoiding fundamental deviations caused by model simplification. This laid a reliable foundation for subsequent accurate measurements. By introducing dual initial phase parameters, the model can simultaneously characterize the initial features of amplitude modulation and phase modulation, upgrading the signal representation to a three-dimensional combination of amplitude, frequency, and phase. This fully explores the key information hidden in the initial phase and provides a more comprehensive basis for subsequent time-frequency analysis and parameter calculation.

[0046] Step 102: Filter out the insect body echo component from the insect echo to obtain the micro-motion signal, expressed by the following formula:

[0047]

[0048] in, For the time of the micro-Doppler spectrum, The frequencies in the micro-Doppler spectrum are... This is the time window for time-frequency analysis.

[0049] In this step, by filtering out basic components unrelated to wingbeats in the insect echo, the focus is directly on the micro-motion signals caused by wingbeats. This reduces interference from non-target signals in subsequent analysis, making the characteristics of the effective signal more prominent and laying the foundation for the accurate generation of subsequent micro-Doppler spectra. Filtering out insect echoes essentially reduces the intensity of the background signal, indirectly improving 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 weak micro-motion signals, allowing the wingbeat signal characteristics that were originally submerged in noise to emerge, and enabling stable capture of the target signal even in complex detection environments. By selectively filtering out insect echo components, additional interference caused by changes in the insect's own state or fluctuations in the detection environment is reduced, making the entire measurement process less sensitive to the external environment. In addition, the standardized short-time Fourier transform formula ensures the consistency of the signal processing, making the measurement results more repeatable and stable in different scenarios.

[0050] Step 103: Perform time-frequency analysis on the micro-motion signal to generate a micro-Doppler spectrum.

[0051] As an optional embodiment, time-frequency analysis is performed on the micro-motion signal to generate a micro-Doppler spectrum, which may specifically include: Step 1031: Simplifying the micro-motion signal based on the Bessel expansion to obtain the micro-Doppler spectrum, expressed by the following formula:

[0052]

[0053] in, Instantaneous Doppler spectrum of an insect target.

[0054] In this step, the Bessel expansion can decompose and simplify the complex trigonometric function combinations in the micro-motion signal, eliminate redundant calculation terms, and generate... Figure 2 The micro-Doppler spectra shown directly focus on the core signal features related to insect wingbeats, avoiding interference from irrelevant components in the interpretation of the spectra, making the frequency trajectory corresponding to wingbeats clearer and more identifiable. The simplification process only removes redundant terms from the signal, without losing the characteristic information corresponding to key parameters such as wingbeat frequency, amplitude, and double initial phase, ensuring that the micro-Doppler spectra can fully reflect the signal essence of insect wingbeat motion, providing accurate data support for subsequent initial phase calculation, phase compensation, and GRFT parameter space search. For weak wingbeat micro-motion signals, the Bessel expansion simplification process can effectively suppress noise amplification, improve the distinction between the target signal and noise, and make the wingbeat features that were originally submerged in noise stand out. Even in complex detection environments, usable micro-Doppler spectra can be generated stably, ensuring the reliability of subsequent measurement procedures.

[0055] Step 20: Based on the micro-Doppler spectrum, calculate the initial phase value of the insect's wingbeats, and construct a phase compensation factor based on the initial phase value to obtain the wingbeat parameter plane.

[0056] As an optional embodiment, based on the micro-Doppler spectrum, the initial phase value of insect wingbeats is calculated, and based on the initial phase value, a phase compensation factor is constructed to obtain the wingbeat parameter plane. Specifically, this may include: Step 201: Let the range of insect 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:

[0057] ,

[0058] ,

[0059] in, for The frequency position of the maximum amplitude in the micro-Doppler spectrum when =0. This is an estimate of the initial phase.

[0060] In this step, the initial phase estimate is integrated into the instantaneous Doppler frequency calculation, allowing the frequency trajectory to accurately match the true phase characteristics of insect wingbeats. This avoids trajectory deviations caused by phase bias, making the signal extracted from the micro-Doppler spectrum more closely resemble the essence of wingbeat motion, laying the foundation for subsequent parameter matching. The search boundaries for wingbeat amplitude and frequency are clearly defined, avoiding indiscriminate traversal of invalid parameter intervals and reducing redundant computation. Simultaneously, the frequency trajectory combined with the initial phase estimate is more directional, allowing the GRFT parameter space search to focus on effective parameter combinations, significantly 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-beating signal clearer. Even in scenarios with strong noise interference, it can effectively distinguish the target trajectory from the interference signal, ensuring the stability of signal extraction. The instantaneous Doppler frequency obtained based on the formula contains the initial phase information. The phase compensation factor constructed subsequently can more accurately offset the phase deviation in signal transmission, avoid the energy cancellation of wing-beating signals at different time points due to phase misalignment, make the energy concentration in the GRFT transform more concentrated, and improve the identification of the wing-beating parameter plane peak.

[0061] Step 202: Perform differential processing on the position of the maximum amplitude frequency in the micro-Doppler spectrum, and determine whether the initial phase estimate is located in the rising or falling segment of the cosine function, so as to obtain the sign of the initial phase estimate.

[0062] In this step, firstly, the frequency position corresponding to the maximum amplitude is located from the micro-Doppler spectrum. This position is the core reference point reflecting the signal characteristics of the initial stage of insect wing flapping and is directly related to the basic calculation of the initial phase. Subsequently, by comparing the signal amplitude changes at adjacent time points or adjacent frequency points, the increase or decrease pattern of the signal at this frequency position is determined. This differential processing can effectively capture the local dynamic characteristics of the signal, thereby clarifying the cosine function change segment corresponding to the estimated initial phase value. If the signal shows an increasing trend, it can be determined that the estimated initial phase value is located in the rising segment of the cosine function; if the signal shows a decreasing trend, it can be 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 estimated initial phase value can be determined, ensuring that the estimated initial phase value not only conforms to the calculation range numerically, but also is physically consistent with the actual initial state of insect wing flapping. This provides an accurate and reliable initial phase basis for subsequent steps such as phase compensation and GRFT parameter space search, ensuring the accuracy of the entire wing flapping frequency inversion process.

[0063] Step 203: Extract the instantaneous time-frequency value of the insect target, expressed by the following formula:

[0064]

[0065] in, The instantaneous time-frequency value of the insect target.

[0066] In this step, the extracted instantaneous time-frequency values ​​integrate the signal characteristics corresponding to core parameters such as wing flapping amplitude, frequency, and dual initial phase. They include not only the radar cross-section fluctuation information caused by wing movement, but also the phase change information corresponding to body micro-movements, without omitting any key physical characteristics of wing flapping motion, ensuring that subsequent analysis can be carried out based on comprehensive and accurate signal data. The formula explicitly incorporates the assumed wing flapping amplitude, frequency, and instantaneous Doppler frequency information, so that the extracted instantaneous time-frequency values ​​can accurately correspond to the preset wing flapping parameter combination, avoiding interference from irrelevant signals. The extraction process of instantaneous time-frequency values ​​fully considers the influence of the dual initial phase, reducing signal misalignment caused by phase deviation, and providing a highly consistent signal foundation for subsequent phase compensation.

[0067] Step 204: Construct the phase compensation factor, expressed by the following formula:

[0068]

[0069] in, This is the phase compensation factor.

[0070] In this step, the phase compensation factor incorporates hypothetical flapping amplitude, frequency, initial phase estimate, and instantaneous Doppler frequency information. This accurately locates and compensates for phase deviations in the flapping signal during transmission and processing, preventing interference between signals at different time points due to phase misalignment. This ensures high phase consistency, laying the foundation for subsequent energy accumulation. With precise phase compensation, weak flapping signals dispersed across different time dimensions can be directionally superimposed, efficiently concentrating energy at the corresponding position on the flapping parameter plane. This significantly improves the distinguishability between the target signal and noise, making the flapping signal characteristics prominent even in low signal-to-noise ratio scenarios. This effectively solves the problem of weak signal identification. Simultaneously, the phase compensation factor makes the flapping signal trajectory in the micro-Doppler spectrum clearer and more prominent, reducing invalid search paths and the probability of mismatches. This shortens search time while significantly improving parameter matching accuracy, providing reliable support for subsequent peak detection.

[0071] Step 205: Use the instantaneous time-frequency value and phase compensation factor to perform phase compensation and integration to obtain energy accumulation.

[0072] As an optional embodiment, phase compensation and integration are performed using instantaneous time-frequency values ​​and phase compensation factors to obtain energy accumulation. Specifically, this may include: Step 2051: Energy accumulation, expressed by the following formula:

[0073]

[0074] in, This represents the accumulated energy value. Step 206: Perform a two-dimensional parameter search within the search range of wing flapping amplitude and wing flapping frequency, and map the energy accumulation to obtain the wing flapping parameter plane.

[0075] In this step, the energy accumulation process deeply integrates instantaneous time-frequency values ​​with a precisely constructed phase compensation factor. Through integral calculations, the energy of wingbeat signals from different time dimensions is directionally superimposed, effectively avoiding energy loss caused by phase deviation. This highly concentrates the energy of the weak target signal that was originally submerged in noise, significantly improving the distinguishability between the target signal and noise. Furthermore, the two-dimensional parameter search is strictly limited to the effective range of wingbeat amplitude and frequency, avoiding interference from invalid parameter combinations. This allows the energy accumulation results to be accurately mapped to the corresponding parameter coordinates, resulting in a sharper energy peak corresponding to the actual wingbeat parameters in the final wingbeat parameter plane. The boundaries are clearer, effectively reducing issues such as peak blurring and sidelobe interference. The energy accumulation formula integrates core parameter features such as wing amplitude, frequency, and dual initial phase, allowing the two-dimensional parameter search to focus directly on the matching and verification of effective parameter combinations without the need for additional filtering of invalid information. This significantly reduces redundant computation, improving search speed while ensuring the accuracy of parameter matching. It enables the stable extraction of wing-related energy features even in complex interference environments or weak signal scenarios, avoiding measurement failures caused by weak signals or noise interference. This ensures the quality of the generated wing parameter plane and provides reliable support for subsequent peak detection.

[0076] Step 30: Based on the phase compensation factor, detect the energy peak in the wingbeat parameter plane, and determine the insect's wingbeat frequency based on the position of the energy peak.

[0077] As an optional embodiment, based on the phase compensation factor, the 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. Specifically, it may include: Step 301: Traversing the wing-beating parameter plane, locating the maximum point of energy accumulation, and reading the wing-beating frequency coordinates corresponding to the maximum point in the wing-beating parameter plane.

[0078] In this step, the flapping parameter plane is formed through energy accumulation and two-dimensional parameter search mapping. Its horizontal and vertical axes correspond to the preset flapping frequency and flapping amplitude search range, respectively. The value of each coordinate point on the plane represents the energy accumulation result corresponding to that set of flapping parameters. During the traversal, the energy accumulation values ​​of all coordinate points in the plane are checked and compared one by one. The core objective is to select the maximum energy accumulation point. This point is formed because the assumed flapping parameters and the actual flapping state of the insect are accurately matched, resulting in a high concentration of signal energy after phase compensation. After locating this maximum point, its corresponding flapping frequency coordinates in the flapping parameter plane are further read, such as... Figure 3As shown, the coordinates represent the inversion result of the insect's true wingbeat frequency. Through objective traversal comparison and coordinate reading, the accuracy and objectivity of the wingbeat frequency measurement are ensured, providing direct and reliable data support for subsequent applications such as insect species identification and migratory ability assessment.

[0079] Step 302: Determine the wingbeat frequency of the insect based on the mapping relationship between the wingbeat frequency coordinates and the preset wingbeat frequency search range.

[0080] In this step, after locating the energy peak of the wing-beating parameter plane and reading the corresponding wing-beating frequency coordinates, the true wing-beating frequency of the insect needs to be determined by mapping these frequency coordinates to a preset wing-beating frequency search range. The preset wing-beating frequency search range is a reasonable interval defined based on the general characteristics of insect wing-beating behavior and the actual needs of radar detection, providing a clear reference boundary for frequency determination. The core of the mapping process is to verify whether the read frequency coordinates fall within this preset range. Simultaneously, combined with the energy distribution logic of the wing-beating parameter plane, only frequency coordinates matching the insect's true wing-beating state can correspond to valid values ​​within the preset range in the mapping. Through this mapping relationship, abnormal coordinate values ​​caused by noise interference or parameter search deviations can be eliminated, ensuring that the finally determined wing-beating frequency not only conforms to the preset reasonable range but also matches the true wing-beating state reflected by the energy peak. The entire process, relying on the constraints of the preset range and the verification of coordinate mapping, achieves accurate screening and determination of the wing-beating frequency. Figure 5 As shown, compared with traditional methods, this application can still extract wing flapping parameters stably and accurately in low signal-to-noise ratio scenarios, with higher success rate and smaller error, providing reliable core parameters for subsequent applications such as species identification and migration ability assessment in insect migration monitoring; Figure 4 The study demonstrates the changes in amplitude and phase of an ideal simulated signal over time. It shows that there are significant differences in the initial points of signal amplitude and phase, intuitively presenting the amplitude and phase modulation characteristics of insect wingbeat echo signals, and providing basic data support for subsequent algorithm verification.

[0081] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0082] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for inverting insect wingbeat frequency 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. 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 frequency of wingbeats. For the initial phase, This is the initial phase of the amplitude; 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. 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.

2. The method for inverting insect wingbeat frequencies based on initial phase estimation according to claim 1, 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.

3. The method for inverting insect wingbeat frequency based on initial phase estimation according to claim 2, 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.

4. The method for inverting insect wingbeat frequencies based on initial phase estimation according to claim 1, 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.

5. The method for inverting insect wingbeat frequencies based on initial phase estimation according to claim 1, 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.

6. The method for inverting insect wingbeat frequencies based on initial phase estimation according to claim 1, 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.

7. The method for inverting insect wingbeat frequency based on initial phase estimation according to claim 1, 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.

Images