A worm echo adaptive filtering method based on micro-motion phase autocorrelation matching

Abstract

The application discloses a kind of insect echo adaptive filtering methods based on micro-motion phase autocorrelation matching, belong to radar technical field, for solving the filtering mode of the main Doppler frequency of existing insect target There is certain limitation, fixed window length is difficult to realize full scene adaptation, low frequency filtering will lead to the technical problems that low-frequency vibration flight insect target cannot be effectively extracted and identified.The method comprises the following steps: modeling the original insect echo signal to obtain the target instantaneous Doppler frequency model;The target echo phase is smoothed based on different sliding window lengths to obtain the micro-motion phase after filtering the main Doppler component;The micro-motion phase after filtering the main Doppler component is calculated for the autocorrelation function under the adaptive window length;Determine the optimal filtering window length corresponding to the current insect phase signal;The slow-time signal echo of the current insect phase signal is compensated to obtain the micro-motion signal of the current insect.

Description

Technical Field

[0001] This application relates to the field of radar technology, and in particular to an adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching. Background Technology

[0002] Insect migration is the most important animal migration phenomenon. Insects fly long distances annually to forage and reproduce, driving the global flow of energy, nutrients, reproductive bodies, pathogens, and parasites. Compared to local pests, migratory pests, due to their large-scale migration and explosive outbreaks, can cause severe economic losses and agricultural crises. Effective monitoring of migratory insects is crucial for effective pest early warning and timely formulation of control policies. The micro-movement characteristics of migratory insects are one of the key parameters for target species identification.

[0003] The dominant Doppler frequency generated by the radial motion of an insect target's body relative to the radar significantly affects the extraction of the wing-beat phase signal. Therefore, preprocessing is required to remove the dominant Doppler component before using phase to invert the insect's wing-beat frequency. Due to the complex environmental factors such as wind fields encountered by insects during actual migration, besides insect targets with a constant dominant Doppler frequency that are easy to process, some insect targets with complex radial motion patterns may have dominant Doppler frequencies that can be represented as quadratic functions, sine functions, or complex functions formed by the superposition of multiple elementary functions. The dominant Doppler frequencies of such targets are not only difficult to separate, but their fluctuation amplitude is also much higher than that of the micro-Doppler frequencies, which seriously affects the extraction and accurate measurement of micro-Doppler frequencies. Currently, the main methods for removing dominant Doppler are sliding window smoothing or low-frequency filtering. Among these, sliding window smoothing is the most widely used due to its simplicity, but this method has a fixed window length and cannot adapt to all scenarios when facing signals with different motion patterns. Low-frequency filtering, on the other hand, can prevent the effective extraction and identification of insect targets with low-frequency vibration flight.

[0004] Therefore, there is an urgent need for a method applicable to filtering out the slow phase variation caused by the main Doppler frequency component in most scenarios. Summary of the Invention

[0005] This application provides an adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching to solve the following technical problems: existing filtering methods for the main Doppler frequencies of insect targets have certain limitations. Fixed window lengths are difficult to adapt to all scenarios, and low-frequency filtering will result in the inability to effectively extract and identify insect targets that fly with low-frequency vibrations.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] On one hand, embodiments of this application provide an adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching, comprising: modeling the original insect echo signal for instantaneous Doppler frequency based on the main Doppler frequency component in the original insect echo signal to obtain a target instantaneous Doppler frequency model; smoothing the target echo phase based on different sliding window lengths using the target instantaneous Doppler frequency model to obtain a micro-motion phase after filtering out the main Doppler component; calculating the autocorrelation function of the micro-motion phase after filtering out the main Doppler component under an adaptive window length to obtain a target autocorrelation function; determining the optimal filtering window length corresponding to the current insect phase signal based on the target autocorrelation function; and compensating the current insect phase signal for slow-time signal echoes based on the optimal filtering window length to obtain the current insect micro-motion signal.

[0008] This application's embodiments employ micro-motion phase autocorrelation matching to adaptively filter insect echoes, providing an effective means of filtering insect main Doppler coupling interference. Compared to traditional insect main Doppler filtering methods, this approach can also improve the accuracy of filtering complex main Doppler targets, thus aiding in the extraction of insect wing-beat micro-motion features from radar.

[0009] In one feasible implementation, based on the main Doppler frequency component in the original insect echo signal, a modeling process for the instantaneous Doppler frequency of the original insect echo signal is performed to obtain a target instantaneous Doppler frequency model, specifically including: based on... The instantaneous Doppler frequency model of the target was obtained from the original insect echo signal. ;in, The wavelength of the original insect echo signal, The amplitude of wingbeats of the target insect; t represents the wingbeat frequency of the target insect; t is the time variable. The target radial velocity.

[0010] In one feasible implementation, when the main Doppler signal is a linear function, the following is obtained: ;in, and These represent the acceleration and initial velocity of the target insect's main body motion, respectively; when the main Doppler signal is a sinusoidal function, we obtain... ;in, and The amplitude and frequency of the change when the insect target body is in a sinusoidal form; when the main Doppler signal is a superposition of multiple motion modes, The speed is the superimposed speed of the comprehensive motion mode.

[0011] In one feasible implementation, the target echo phase is smoothed based on different sliding window lengths using the target instantaneous Doppler frequency model to obtain a micro-motion phase after filtering out the main Doppler component. Specifically, this includes: extracting the target echo phase of the target insect; using the target instantaneous Doppler frequency model and based on the target time, smoothing the target echo phase under relevant sliding window values ​​to obtain the main Doppler component; and using the main Doppler component, performing phase filtering processing on the target echo phase under each sliding window to obtain the micro-motion phase after filtering out the main Doppler component.

[0012] In one feasible implementation, the autocorrelation function of the micro-motion phase after filtering out the main Doppler component is calculated under an adaptive window length to obtain the target autocorrelation function, specifically including: according to... The phase of the insect wing flapping micro-Doppler sampling points based on the micro-motion phase is obtained. ;in, The amplitude of wingbeats of the target insect; The wingbeat frequency of the target insect; The wavelength of the original insect echo signal; This is the first phase sampling point; according to The target autocorrelation function is obtained. ;in, The second phase sampling point is associated with the first phase sampling point; N and m are both mathematical constants.

[0013] In one feasible implementation, the optimal filtering window length corresponding to the current insect phase signal is determined based on the target autocorrelation function. Specifically, this includes: performing similarity matching between the target autocorrelation function and the phase signal function of the main Doppler to obtain a similarity matching result; calculating the difference between the main peak and the secondary peak of the autocorrelation zero point of the current insect phase signal based on the consistency information in the similarity matching result to determine the minimum difference result; and obtaining the optimal filtering window length of the current insect phase signal based on the minimum difference result and the target autocorrelation function.

[0014] In one feasible implementation, before performing slow-time signal echo compensation processing on the current insect phase signal according to the optimal filtering window length to obtain the current insect's micro-motion signal, the method further includes: according to The slow-time echo model of the target insect was obtained. ;in, These are constants related to radar parameters; The RCS results of the echo when the insect does not flap its wings; It is the wing vibration amplitude modulation coefficient; The initial distance from the insect to the radar; The amplitude of wingbeats of the target insect; The wingbeat frequency of the target insect; The radial velocity of the target insect; The imaginary unit; The wavelength of the original insect echo signal is given.

[0015] In one feasible implementation, based on the optimal filtering window length, the current insect phase signal undergoes slow-time signal echo compensation processing to obtain the current insect's micro-motion signal. Specifically, this includes: smoothing the current insect phase signal using the optimal filtering window length to identify the phase change of the main Doppler frequency component; and performing slow-time signal echo compensation processing on the current insect phase signal based on the target insect's slow-time echo model; according to... The micro-motion signal of the current insect is obtained. ;in, These are constants related to radar parameters; The RCS results of the echo when the insect does not flap its wings; It is the wing vibration amplitude modulation coefficient; The amplitude of wingbeats of the target insect; The wingbeat frequency of the target insect; The wavelength of the original insect echo signal; The imaginary unit is t; t is the time variable.

[0016] This application provides an adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching. Compared with the prior art, the embodiments of this application have the following beneficial technical effects:

[0017] This application's embodiments employ micro-motion phase autocorrelation matching to adaptively filter insect echoes, providing an effective means of filtering insect main Doppler coupling interference. Compared to traditional insect main Doppler filtering methods, this approach can also improve the accuracy of filtering complex main Doppler targets, thus aiding in the extraction of insect wing-beat micro-motion features from radar. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in 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 recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0019] Figure 1 A flowchart of an adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching is provided for an embodiment of this application;

[0020] Figure 2 A schematic diagram illustrating the smoothing process of a sliding window provided in an embodiment of this application;

[0021] Figure 3 A schematic diagram of a phase smoothing result provided in an embodiment of this application;

[0022] Figure 4 A schematic diagram of the autocorrelation function of the signal phase after removing the main Doppler signal is provided in an embodiment of this application;

[0023] Figure 5 This is a schematic diagram illustrating the change in target velocity under complex insect movement, provided in an embodiment of this application.

[0024] Figure 6 This is a graph showing the relationship between the success rate and error of wing vibration frequency measurement and the signal-to-noise ratio, provided in an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0026] It should be noted that the adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching in this application requires establishing a criterion for minimizing the difference between the primary and secondary peaks of the autocorrelation, taking into account the characteristic that the residual primary Doppler of the insect echo affects the micro-motion phase autocorrelation. By selecting different window lengths to filter the primary Doppler of the insect echoes, the autocorrelation function is calculated, and the one that approximates the ideal form is determined as the optimal filtering window length. For ease of judgment, the criterion is to minimize the difference between the primary and secondary peaks at the autocorrelation zero point. Thus, the optimal filtering window length is adaptively determined and the primary Doppler of the insect echoes are filtered out.

[0027] This application provides an adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching, such as... Figure 1 As shown, the adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching specifically includes steps S101-S105:

[0028] S101. Based on the main Doppler frequency component in the original insect echo signal, perform instantaneous Doppler frequency modeling processing on the original insect echo signal to obtain the target instantaneous Doppler frequency model.

[0029] Specifically, based on the original insect echo signal, the preprocessed result is obtained. Due to the complex and variable motion of insect targets, their main Doppler frequency components have complex forms, which can be expressed as a single elementary function such as a linear function or a sine function, or as a superposition of multiple elementary functions. Therefore, the instantaneous Doppler frequency of the target can be modeled as: based on... The target instantaneous Doppler frequency model of the original insect echo signal was obtained. .in, The wavelength of the primitive insect echo signal, The amplitude of wingbeats of the target insect; t represents the wingbeat frequency of the target insect; t is the time variable. The target radial velocity.

[0030] As a feasible implementation method, when the main Doppler signal is a linear function, the following is obtained: .in, and These represent the acceleration and initial velocity of the target insect's main body motion, respectively. When the main Doppler signal is a sinusoidal function, we obtain... ;in, and The amplitude and frequency of the change when the insect target body is in a sinusoidal form; when the main Doppler signal is a superposition of multiple motion modes, It is the superimposed velocity of the comprehensive motion mode; that is, This cannot be represented by a simple function. This indicates that during the movement of such targets, the body is affected by external environmental factors, resulting in significant changes in speed.

[0031] S102. Using the instantaneous Doppler frequency model of the target, the target echo phase is smoothed based on different sliding window lengths to obtain the micro-motion phase after filtering out the main Doppler component.

[0032] Specifically, it is necessary to first extract the target echo phase of the target insect.

[0033] Furthermore, by using the instantaneous Doppler frequency model of the target and based on the target time, the Doppler phase value of the target echo phase is smoothed under a sliding window to obtain the main Doppler component.

[0034] Furthermore, the target echo phase is filtered out under each sliding window using the main Doppler component to obtain the micro-motion phase after filtering out the main Doppler component.

[0035] In one embodiment, the target echo phase is first extracted using a target instantaneous Doppler frequency model; then, the echo phase is smoothed based on different sliding window lengths. Figure 2This is a schematic diagram illustrating the smoothing process of a sliding window provided in an embodiment of this application, wherein the smoothing principle is as follows: Figure 2 As shown, the black curve at the top of the figure represents the original Doppler phase. The three black dashed rectangles correspond to... , and The position of the sliding window at the target time. The average value of all Doppler phase values ​​within the sliding window is obtained. , and The smoothed Doppler phase value (i.e., the estimated principal Doppler component) is obtained. By setting the step size and sliding the window from left to right, the estimated principal Doppler component is obtained (the black curve below). Finally, the micro-phase after filtering out the principal Doppler component is obtained to facilitate the subsequent selection of the optimal window size.

[0036] S103. Calculate the autocorrelation function of the micro-motion phase after filtering out the main Doppler component under the relevant adaptive window length to obtain the target autocorrelation function.

[0037] It should be noted that the window length has a significant impact on the smoothing result during processing. A larger window length results in a smoother smoothing result, but it also deviates from the true value when the principal Doppler variation is large, leading to greater principal Doppler residue. Figure 3 A schematic diagram of a phase smoothing result provided in an embodiment of this application is shown below. Figure 3 As shown in the upper half, the smaller the window length, the closer the smoothing result is to the echo phase value, resulting in the filtering out of some components of the micro-Doppler signal, such as... Figure 3 (See the lower half). Due to the complexity of the actual situation, the effective signal duration of insect radar echoes will fluctuate. Using a fixed window length will cause the filtering effect to deviate from the actual situation, ultimately leading to a decrease in the success rate of wing flapping inversion.

[0038] Specifically, to obtain a suitable window length for the actual signal and achieve optimal removal of the main Doppler component, the signal characteristics were studied. It was found that the autocorrelation function of the insect wing-beat micro-Doppler signal exhibits periodicity, and the main Doppler significantly affects the characteristics of the autocorrelation function. Therefore, the characteristics of the autocorrelation function were used to adaptively determine the suitable window length. Thus, the phase of the insect wing-beat micro-Doppler sampling points was first calculated, i.e., based on… The phase of insect wing flapping micro-Doppler sampling points based on micro-motion phase was obtained. .in, The amplitude of wingbeats of the target insect; The wingbeat frequency of the target insect; The wavelength of the original insect echo signal; This is the first phase sampling point.

[0039] Furthermore, based on The target autocorrelation function is obtained. .in, The second phase sampling point is associated with the first phase sampling point; N and m are both mathematical constants.

[0040] S104. Based on the target autocorrelation function, determine the optimal filter window length corresponding to the current insect phase signal.

[0041] Specifically, it is also necessary to perform similarity matching between the target autocorrelation function and the phase signal function of the main Doppler signal to obtain the similarity matching result. Then, based on the consistency information in the similarity matching result, the difference between the main peak and the secondary peak of the autocorrelation zero point of the current insect phase signal is calculated to determine the minimum difference result.

[0042] Furthermore, based on the minimum difference result and the target autocorrelation function, the optimal filter window length for the current insect phase signal is finally obtained.

[0043] In one embodiment, Figure 4 A schematic diagram of the autocorrelation function of the signal phase after removing the main Doppler effect is provided for an embodiment of this application, as shown below. Figure 4 As shown in the upper half, by selecting an appropriate window length, the autocorrelation function of the phase signal, which completely removes the main Doppler, exhibits periodic changes (consistent with the target autocorrelation function). Figure 4 (Bottom half) The autocorrelation function of the phase signal, where a suitable window length was not selected and the main Doppler effect was not completely removed, exhibits irregular fluctuations. Therefore, by selecting different window lengths to filter out the main Doppler effect from the insect echo, and observing the autocorrelation function to determine the optimal filtering window length as approximating the formula, the criterion for easy judgment is to minimize the difference between the main peak and the secondary peak of the autocorrelation zero point. Finally, based on the minimum difference result and the target autocorrelation function, the optimal filtering window length for the current insect phase signal is obtained.

[0044] S105. Based on the optimal filter window length, perform slow-time signal echo compensation processing on the current insect phase signal to obtain the current insect's micro-motion signal.

[0045] Specifically, it is necessary to first establish a slow-time echo model of insects, that is: based on The slow-time echo model of the target insect was obtained. .in, These are constants related to radar parameters; The RCS results of the echo when the insect does not flap its wings; It is the wing-beat amplitude modulation coefficient, that is, the change in RCS caused by wing-beat behavior compared to the RCS when there is no wing-beating. The ratio; The initial distance from the insect to the radar; The amplitude of wingbeats of the target insect; The wingbeat frequency of the target insect; The radial velocity of the target insect; The imaginary unit; The wavelength of the primitive insect echo signal.

[0046] As a feasible implementation method, when the main Doppler is a linear function, , and The acceleration and initial velocity of the target body at the moment of motion are separated; when the main Doppler is a sine function, , and The amplitude and frequency of the change when the main Doppler is sinusoidal. When the main Doppler is a superposition of multiple motion modes, This cannot be represented by a simple function. This indicates that during the movement of such targets, the body is affected by external environmental factors, resulting in significant changes in speed. Therefore, it is impossible to compensate for this with a simple formula fitting, and the principal Doppler will severely affect the extraction of micro-motion features.

[0047] Furthermore, the phase change of the main Doppler frequency component of the current insect phase signal is identified through smoothing processing with the optimal filtering window length. And based on the slow-time echo model of the target insect, slow-time signal echo compensation processing is performed on the current insect phase signal.

[0048] Furthermore, it is necessary to utilize the optimal filtering window length selected based on the criterion, smooth the phase change caused by the main Doppler frequency component, and then compensate by inverting the main Doppler phase and multiplying it by the slow-time signal echo to ultimately filter out the main Doppler frequency component and obtain the insect's micro-motion signal. That is: according to... The micro-movement signals of the insects were obtained. .in, These are constants related to radar parameters; The RCS results of the echo when the insect does not flap its wings; It is the wing vibration amplitude modulation coefficient; The amplitude of wingbeats of the target insect; The wingbeat frequency of the target insect; The wavelength of the original insect echo signal; The imaginary unit is t; t is the time variable.

[0049] As a feasible implementation method, in the experimental stage, to verify the correctness of the main Doppler filtering of the extraction method described above, based on the radar reflection insect echo model, an insect echo adaptive filtering method based on micro-motion phase autocorrelation matching of this application was used to complete the measurement of insect wingbeat frequency, and compared with the traditional method. The simulation data processing results are described below. In this embodiment, the simulation parameters are shown in Table 1. White noise was added to the simulation signal, and the echo signal-to-noise ratio was set to increase from 0dB to 20dB, with a signal-to-noise ratio step of 1dB. 1000 Monte Carlo simulations were performed at each signal-to-noise ratio. Figure 5 This application provides a schematic diagram illustrating the change in target velocity under complex insect motion, as shown in the embodiment of the present application. Figure 5 As shown, the success rate of wingbeat frequency inversion is used as the performance index of the method. A successful measurement is defined as the absolute difference between the measured value and the true value being less than 10% of the wingbeat frequency (3Hz). The error is measured by calculating the root mean square error of all measured results compared to the true value. Finally, the ratio of the number of successful measurements to the total number of simulations is calculated as the measurement success rate, and the measurement error is also calculated.

[0050] Table 1 Simulation parameters of wingbeat frequency

[0051]

[0052] In one embodiment, based on the simulation parameters in Table 1, Figure 6 This application provides a graph showing the relationship between the success rate and error of wing vibration frequency measurement and the signal-to-noise ratio, and as an embodiment of the present application. Figure 6 The curves shown visualize the relationship between the success rate of wingbeat frequency measurement, root mean square error, and signal-to-noise ratio for both methods. Figure 6 Simulation results show that the proposed adaptive filtering method for insect echoes based on micro-motion phase autocorrelation matching can effectively achieve adaptive filtering of the main Doppler of the insect.

[0053] In addition, this application embodiment also provides an adaptive filtration device for insect echoes based on micro-motion phase autocorrelation matching. The adaptive filtration device for insect echoes based on micro-motion phase autocorrelation matching specifically includes:

[0054] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform:

[0055] Based on the main Doppler frequency component in the original insect echo signal, the instantaneous Doppler frequency of the original insect echo signal is modeled to obtain the instantaneous Doppler frequency model of the target.

[0056] By using the instantaneous Doppler frequency model of the target, the target echo phase is smoothed based on different sliding window lengths to obtain the micro-motion phase after filtering out the main Doppler component;

[0057] The autocorrelation function of the micro-motion phase after filtering out the main Doppler component is calculated under the relevant adaptive window length to obtain the target autocorrelation function.

[0058] Based on the target autocorrelation function, the optimal filter window length corresponding to the current insect phase signal is determined;

[0059] Based on the optimal filtering window length, the current insect phase signal is compensated for with slow-time signal echo to obtain the current insect's micro-motion signal.

[0060] This application's embodiments employ micro-motion phase autocorrelation matching to adaptively filter insect echoes, providing an effective means of filtering insect main Doppler coupling interference. Compared to traditional insect main Doppler filtering methods, this approach can also improve the accuracy of filtering complex main Doppler targets, thus aiding in the extraction of insect wing-beat micro-motion features from radar.

[0061] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0062] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0063] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0065] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0067] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0068] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0069] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0070] 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.

[0071] 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 this specification.

Claims

1. A method for adaptive filtering of insect echoes based on micro-motion phase self-correlation matching, characterized in that, The method comprises: According to the main Doppler frequency component in the original insect echo signal, the original insect echo signal is subjected to model modeling of instantaneous Doppler frequency to obtain a target instantaneous Doppler frequency model, specifically comprising: According to , the target instantaneous Doppler frequency model regarding the original insect echo signal is obtained; wherein, is the wavelength of the original insect echo signal, is the wing amplitude of the target insect; is the wing frequency of the target insect; t is the time variable; is the target radial velocity; When the main Doppler signal is a linear function, we get ; where, and are the acceleration and initial velocity of the main body of the target insect; when the main Doppler signal is a sinusoidal function, we get ; where, and are the amplitude and frequency of the transformation when the main body of the insect target is in a sinusoidal form; when the main Doppler signal is a superposition of multiple motion modes, is the superposition acceleration of the comprehensive motion mode; t is the time variable; Through the target instantaneous Doppler frequency model, target echo phase is subjected to smoothing processing based on different sliding window lengths to obtain micro-motion phase after filtering out the main Doppler component; The micro-motion phase after filtering out the main Doppler component is subjected to autocorrelation function calculation under an adaptive window length to obtain a target autocorrelation function; Based on the target autocorrelation function, the optimal filtering window length corresponding to the current insect phase signal is determined; According to the optimal filtering window length, the slow-time signal echo compensation processing is performed on the current insect phase signal to obtain the micro-motion signal of the current insect.

2. The insect echo adaptive filtering method based on micro-motion phase autocorrelation matching according to claim 1, characterized in that, Through the target instantaneous Doppler frequency model, target echo phase is subjected to smoothing processing based on different sliding window lengths to obtain micro-motion phase after filtering out the main Doppler component, specifically comprising: Extract the target echo phase of the target insect; Through the target instantaneous Doppler frequency model and based on the target time, the target echo phase is subjected to smoothing Doppler phase value under the sliding window to obtain the main Doppler component; Through the main Doppler component, the target echo phase is subjected to phase filtering processing under each sliding window to obtain the micro-motion phase after filtering out the main Doppler component.

3. The method of claim 1, wherein the method is characterized by, The micro-motion phase after filtering out the main Doppler component is subjected to autocorrelation function calculation under an adaptive window length to obtain a target autocorrelation function, specifically comprising: According to , a wing-flapping micro-Doppler sampling point phase of an insect under the micro-motion phase is obtained ; wherein, is the wing-flapping amplitude of the target insect; is the wing-flapping frequency of the target insect; is the wavelength of the original insect echo signal; is the first phase sampling point; According to , the target autocorrelation function is obtained; wherein, is the second phase sampling point associated with the first phase sampling point; N and m are both mathematical constants.

4. The insect echo adaptive filtering method based on micro-motion phase autocorrelation matching according to claim 1, characterized in that, Based on the target autocorrelation function, the optimal filtering window length corresponding to the current insect phase signal is determined, specifically comprising: The target autocorrelation function and the phase signal function of the main Doppler are subjected to similarity matching to obtain a similarity matching result; According to the consistency information in the similarity matching result, the difference value calculation is performed between the autocorrelation zero main peak and the secondary peak of the current insect phase signal to determine the minimum difference value result; Based on the minimum difference value result and the target autocorrelation function, the optimal filtering window length of the current insect phase signal is obtained.

5. The insect echo adaptive filtering method based on micro-motion phase autocorrelation matching according to claim 1, characterized in that, Before the slow-time signal echo compensation processing is performed on the current insect phase signal according to the optimal filtering window length to obtain the micro-motion signal of the current insect, the method further comprises: According to , a slow-time echo model of the target insect is obtained ; wherein, is a constant related to radar parameters; is the echo RCS result when the insect is not flapping wings; is the flapping amplitude modulation coefficient; is the initial distance of the insect to the radar; is the flapping amplitude of the target insect; is the flapping frequency of the target insect; is the radial velocity of the target insect; is the imaginary unit; is the wavelength of the original insect echo signal.

6. The insect echo adaptive filtering method based on micro-motion phase autocorrelation matching according to claim 5, characterized in that, According to the optimal filtering window length, the slow-time signal echo compensation processing is performed on the current insect phase signal to obtain the micro-motion signal of the current insect, specifically comprising: Through the smoothing processing of the optimal filtering window length, the phase change of the main Doppler frequency component of the current insect phase signal is identified; and based on the slow-time echo model of the target insect, the slow-time signal echo compensation processing is performed on the current insect phase signal; According to , the micro-motion signal to the current insect is obtained ; wherein, is a constant related to radar parameters; is the echo RCS result when the insect is not flapping wings; is the wing amplitude modulation coefficient; is the wing amplitude of the target insect; is the wing frequency of the target insect; is the wavelength of the original insect echo signal; is the imaginary unit; t is a time variable.

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