Insect echo self-adaptive filtering method based on micro-motion phase self-correlation matching

Abstract

The invention discloses an insect body echo self-adaptive filtering method based on micro-motion phase self-correlation matching, belongs to the technical field of radars, and is used for solving the problems that an existing filtering mode of the main Doppler frequency of an insect target has certain limitation, full-scene adaptation is difficult to realize by a fixed window length, and the filtering efficiency is low. And low-frequency filtering causes that the insect target in low-frequency vibration flight cannot be effectively extracted and identified. The method comprises the following steps: carrying out instantaneous Doppler frequency model modeling processing on an original insect echo signal to obtain a target instantaneous Doppler frequency model; performing smoothing processing based on different sliding window lengths on the target echo phase to obtain a micro-motion phase after the main Doppler component is filtered; carrying out self-correlation function calculation under a related self-adaptive window length on the micro-motion phase after the main Doppler component is filtered out; determining an optimal filtering window length corresponding to the current insect phase signal; and performing slow-time signal echo compensation processing on the current insect phase signal to obtain a micro-motion signal of the current insect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar, in particular to a moth echo adaptive filtering method based on micro-motion phase autocorrelation matching. BACKGROUND

[0002] Insect migration is the most important animal migration phenomenon. Insects fly long distances every year to forage and reproduce, driving the global flow of energy, nutrients, propagules, pathogens, and parasites. Compared with local pests, migratory pests have characteristics such as wide migration and outbreak, which can cause serious economic losses and agricultural crisis to agricultural production. Effective monitoring of migratory insects is the key to achieving effective early warning of insect pests and timely formulation of prevention and control policies. The micro-motion characteristics of migratory insects are one of the key parameters for identifying target species.

[0003] The main Doppler frequency generated by the radial motion of the insect target body relative to the radar will seriously affect the extraction of the wing-flapping micro-motion phase signal. Therefore, before using the phase to invert the insect wing-flapping frequency, the main Doppler component needs to be removed. Due to the influence of complex environmental factors such as wind field in the actual migration process of insects, in addition to the main Doppler constant insect targets that are easy to process, there are some insect targets with complex radial motion forms, whose main Doppler frequency forms may be represented as a quadratic function, a sine function, or a complex function formed by the superposition of multiple elementary functions. The main Doppler frequency of such targets is not only difficult to separate, but also has a much higher fluctuation amplitude than the micro-Doppler frequency, which will seriously affect the extraction and accurate measurement of the micro-Doppler frequency. At present, the methods for removing the main Doppler are mainly sliding window smoothing processing or low-frequency filtering. Among them, the sliding window smoothing filtering method is the most widely used due to its simple operation, but this method has a fixed window length, and cannot achieve full-scene adaptation when facing signals with different motion forms. Low-frequency filtering, on the other hand, cannot effectively extract and identify insect targets that fly at low-frequency vibrations.

[0004] Therefore, there is an urgent need for a method that can filter out the slowly varying phase caused by the main Doppler frequency component in most scenarios. SUMMARY

[0005] The embodiment of the present application provides a moth echo adaptive filtering method based on micro-motion phase autocorrelation matching, which is used to solve the following technical problems: the existing main Doppler frequency filtering method for insect targets has certain limitations, fixed window length cannot achieve full-scene adaptation, and low-frequency filtering cannot effectively extract and identify insect targets that fly at low-frequency vibrations.

[0006] The embodiment of the present application adopts the following technical scheme: In one aspect, the embodiment of the present application provides a worm echo adaptive filtering method based on micro-motion phase autocorrelation matching, comprising: according to a main Doppler frequency component in an original insect echo signal, performing model modeling processing on an instantaneous Doppler frequency of the original insect echo signal to obtain a target instantaneous Doppler frequency model; through the target instantaneous Doppler frequency model, performing smoothing processing on a target echo phase under different sliding window lengths to obtain a micro-motion phase after filtering the main Doppler component; performing autocorrelation function calculation on the micro-motion phase after filtering the main Doppler component under a related adaptive window length to obtain a target autocorrelation function; based on the target autocorrelation function, determining an optimal filtering window length corresponding to a current insect phase signal; and according to the optimal filtering window length, performing compensation processing on a slow-time signal echo of the current insect phase signal to obtain a micro-motion signal of the current insect.

[0007] The embodiment of the present application can provide an effective filtering means for insect main Doppler coupling interference by performing adaptive filtering on the worm echo through micro-motion phase autocorrelation matching. Compared with the traditional insect main Doppler filtering method, the embodiment can also improve the precision of filtering a complex main Doppler target, and will be helpful for extracting the wing-flapping micro-motion characteristics of the radar.

[0008] In a feasible implementation, according to a main Doppler frequency component in an original insect echo signal, model modeling processing is performed on an instantaneous Doppler frequency of the original insect echo signal to obtain a target instantaneous Doppler frequency model, and the model modeling processing specifically comprises: obtaining the target instantaneous Doppler frequency model of the original insect echo signal ; wherein, is a wavelength of the original insect echo signal, is a wing-flapping amplitude of a target insect; is a wing-flapping frequency of the target insect; and t is a time variable. is a target radial velocity.

[0009] In a feasible implementation, when the main Doppler signal is a linear function, the target instantaneous Doppler frequency model is obtained as ; wherein, and are an acceleration and a starting speed of a target insect body motion, respectively; when the main Doppler signal is a sinusoidal function, the target instantaneous Doppler frequency model is obtained as ; wherein, and are a transformation amplitude and a frequency when the insect target body is in a sinusoidal form; and when the main Doppler signal is a superposition of multiple motion modes, is a superposition acceleration of the comprehensive motion mode.

[0010] In an implementable embodiment, the target echo phase is smoothed based on different sliding window lengths by the target instantaneous Doppler frequency model to obtain a micro-motion phase after filtering out a main Doppler component, specifically including: extracting a target echo phase of a target insect; smoothing the target echo phase based on a target time and a sliding window by the target instantaneous Doppler frequency model to obtain a main Doppler component; and performing phase filtering on the target echo phase in each sliding window based on the main Doppler component to obtain the micro-motion phase after filtering out the main Doppler component.

[0011] In an implementable embodiment, a self-correlation function is calculated for the micro-motion phase based on an adaptive window length to obtain a target self-correlation function, specifically including: obtaining a wing-flapping micro-Doppler sampling point phase of the insect based on the micro-motion phase according to ; wherein, is a wing-flapping amplitude of the target insect; is a wing-flapping frequency of the target insect; is a wavelength of the original insect echo signal; is a first phase sampling point; and obtaining the target self-correlation function according to ; wherein, is a second phase sampling point associated with the first phase sampling point; and N and m are both mathematical constants.

[0012] In an implementable embodiment, an optimal filtering window length corresponding to a current insect phase signal is determined based on the target self-correlation function, specifically including: performing similarity matching on the target self-correlation function and a phase signal function of a main Doppler to obtain a similarity matching result; performing difference calculation on a self-correlation zero point main peak and a secondary peak of the current insect phase signal based on consistency information in the similarity matching result to determine a 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 self-correlation function.

[0013] In an implementable embodiment, before performing compensation processing on the current insect phase signal according to the optimal filtering window length to obtain a micro-motion signal of the current insect, the method further includes: obtaining a slow-time echo model of the target insect according to ; wherein, is a constant related to a radar parameter; is an echo RCS result when the insect is not wing-flapping; is a wing-flapping amplitude modulation coefficient; is an initial distance of the insect to the radar.​​ is the wing beat amplitude of the target insect; is the wing beat 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.

[0014] In an implementable embodiment, according to the optimal filter window length, the current insect phase signal is subjected to a slow-time signal echo compensation process to obtain a micro-motion signal of the current insect, specifically including: through a smoothing process of the optimal filter window length, a phase change identification of a main Doppler frequency component is performed on the current insect phase signal; and based on a slow-time echo model of the target insect, the current insect phase signal is subjected to a slow-time signal echo compensation process; according to , the micro-motion signal of the current insect is obtained ; wherein, is a constant related to radar parameters; is the echo RCS result when the insect does not flap its wings; is the wing beat amplitude modulation coefficient; is the wing beat amplitude of the target insect; is the wing beat frequency of the target insect; is the wavelength of the original insect echo signal; is the imaginary unit; t is a time variable.

[0015] The present application provides a worm body echo adaptive filtering method based on micro-motion phase autocorrelation matching, compared with the prior art, the embodiments of the present application have the following beneficial technical effects: The embodiments of the present application can provide an effective filtering means for insect main Doppler coupling interference by adaptively filtering the worm body echo through micro-motion phase autocorrelation matching. Compared with the traditional insect main Doppler filtering method, the accuracy of filtering complex main Doppler targets can also be improved, which will help to extract the insect wing beat micro-motion characteristics of the radar. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments described in the present application, and those skilled in the art can also obtain other drawings according to these drawings without creating any creative labor. In the drawings: Figure 1 is a worm body echo adaptive filtering method flowchart based on micro-motion phase autocorrelation matching provided by the embodiments of the present application; Figure 2A sliding window smoothing processing schematic diagram provided for an embodiment of the present application; Figure 3 A phase smoothing result schematic diagram provided for an embodiment of the present application; Figure 4 A self-correlation function schematic diagram of a signal phase after removing a main Doppler provided for an embodiment of the present application; Figure 5 A target motion speed change schematic diagram under complex insect motion provided for an embodiment of the present application; Figure 6 A wing beat frequency measurement success rate and error change with signal-to-noise ratio graph provided for an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order for those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0018] It should be noted that the insect body echo adaptive filtering method based on micro-motion phase self-correlation matching of the present application needs to establish a minimum criterion evaluation of the difference between the primary and secondary peaks of the self-correlation based on the characteristics that the main Doppler residual of the insect body will affect the micro-motion phase self-correlation. By selecting different window lengths to filter the main Doppler of the insect echo, the self-correlation function is calculated, and the determination of the optimal filtering window length is close to the ideal form. For easy judgment, the minimum criterion is used for the difference between the primary and secondary peaks of the self-correlation zero point. Thus, the optimal filtering window length is adaptively determined and the main Doppler of the insect body is filtered.

[0019] The embodiment of the present application provides an insect body echo adaptive filtering method based on micro-motion phase self-correlation matching, as shown in Figure 1 The insect body echo adaptive filtering method based on micro-motion phase self-correlation matching specifically includes steps S101-S105: S101, according to the main Doppler frequency component in the original insect echo signal, performing model modeling processing on the instantaneous Doppler frequency of the original insect echo signal to obtain a target instantaneous Doppler frequency model.

[0020] Specifically, based on the original insect echo signal, a preprocessed result is obtained. Due to the complex and variable motion of the insect target, the form of the main Doppler frequency component is complex, which can be expressed as a single elementary function such as a linear function or a sine function, or superimposed by multiple elementary functions. Therefore, the instantaneous Doppler frequency of the target can be modeled as: according to , to obtain the target instantaneous Doppler frequency model of the original insect echo signal . Wherein, is the wavelength of the original insect echo signal, is the wing beat amplitude of the target insect; is the wing beat frequency of the target insect; t is the time variable; is the target radial velocity.

[0021] As a feasible implementation, when the main Doppler signal is a linear function, the target instantaneous Doppler frequency model is obtained as . Wherein, and are the acceleration and initial speed of the target insect body movement respectively. When the main Doppler signal is a sinusoidal function, the target instantaneous Doppler frequency model is obtained as ; wherein, and are the transformation amplitude and frequency when the insect target body 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; that is, cannot be characterized by a simple function. It is illustrated that in the motion process of such target, the body is affected by external environmental factors, and the speed changes greatly.

[0022] S102, through the target instantaneous Doppler frequency model, the target echo phase is smoothed based on different sliding window lengths to obtain the micro-motion phase filtered by the main Doppler component.

[0023] Specifically, the target echo phase of the target insect needs to be extracted first.

[0024] Further, the target echo phase is smoothed by the target instantaneous Doppler frequency model and based on the target time to obtain the main Doppler component.

[0025] Further, the target echo phase is filtered by the main Doppler component in each sliding window to obtain the micro-motion phase filtered by the main Doppler component.

[0026] In one embodiment, the target echo phase is extracted by using the target instantaneous Doppler frequency model; then the echo phase is smoothed based on different sliding window lengths, Figure 2 is a sliding window smoothing processing schematic diagram provided by the embodiment of the application, wherein the smoothing principle is as shown in Figure 2 The black curve on the top of the figure represents the original Doppler phase. The three black dotted rectangulars 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] S104. Based on the target autocorrelation function, determine the optimal filter window length corresponding to the current insect phase signal.

[0032] Specifically, the target autocorrelation function is also needed to be similarity matched with the phase signal function of the main Doppler, to obtain a similarity matching result. Then, according to the consistency information in the similarity matching result, a difference value is calculated between the autocorrelation zero point main peak and the secondary peak of the current insect phase signal, to determine the minimum difference value result.

[0033] Further, based on the minimum difference value result and the target autocorrelation function, the optimal filtering window length of the current insect phase signal is finally obtained.

[0034] In one embodiment, Figure 4 An autocorrelation function diagram of a signal phase after removing the main Doppler provided by the embodiment of the present application is shown in FIG. 1. Figure 4 As shown in the upper half of FIG. 1, a suitable window length is selected, and the autocorrelation function of the phase signal after removing the main Doppler presents a periodic change (consistent with the target autocorrelation function), while Figure 4 As shown in the lower half of FIG. 1, the autocorrelation function of the phase signal after removing the main Doppler presents irregular fluctuations. Therefore, by selecting different window lengths, the main Doppler of the insect echo can be removed, and by observing the autocorrelation function, the optimal filtering window length is determined in the form of the formula. For the convenience of judgment, the minimum difference value between the autocorrelation zero point main peak and the secondary peak is used as the criterion. Finally, 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.

[0035] S105, according to the optimal filtering window length, the slow time signal echo of the current insect phase signal is compensated and processed, to obtain the micro-motion signal of the current insect.

[0036] Specifically, the slow time echo model of the insect needs to be established first, that is, according to , the slow time echo model of the target insect is obtained . Wherein, is a constant related to the radar parameters; is the echo RCS result when the insect does not flap its wings; is the wing amplitude modulation coefficient, that is, the ratio of the change of RCS caused by the wing behavior to the RCS when the insect does not flap its wings; is the initial distance of the insect to the radar; is the wing amplitude of the target insect; is the wing frequency of the target insect; is the radial velocity of the target insect; is an imaginary unit; is the wavelength of the original insect echo signal.

[0037] As a feasible implementation manner, 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.

[0038] 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.

[0039] 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 current insect 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.

[0040] 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 The success rate of wing beat frequency inversion is used as the performance indicator of the method. When the absolute difference between the measured value and the true value is less than 10% of the wing beat frequency (3Hz), the measurement is considered successful. The root mean square error of all measurement results and the true value is calculated as the error measurement standard. 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 calculated.

[0041] Table 1: Simulation parameters of wing beat frequency

[0042] In one embodiment, based on the simulation parameters in Table 1, Figure 6 A graph showing the relationship between the wing beat frequency measurement success rate and the error provided by the embodiments of the present application with respect to the signal-to-noise ratio is shown in FIG. 6, and the curve relationship is shown in FIG. 7. Figure 6 The relationship between the wing beat frequency measurement success rate, the root mean square error, and the signal-to-noise ratio of the two methods can be visualized, i.e. Figure 6 The simulation data results in FIG. 8 show that the insect body echo adaptive filtering method based on micro-motion phase autocorrelation matching proposed in the present application can effectively achieve adaptive filtering of the main Doppler of the insect body.

[0043] In addition, the embodiments of the present application also provide an insect body echo adaptive filtering device based on micro-motion phase autocorrelation matching, which specifically comprises: 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, so that the at least one processor can execute: According to the main Doppler frequency component in the original insect echo signal, the original insect echo signal is subjected to model modeling of the instantaneous Doppler frequency to obtain a target instantaneous Doppler frequency model; The target echo phase is subjected to smoothing processing based on different sliding window lengths through the target instantaneous Doppler frequency model to obtain a micro-motion phase after filtering the main Doppler component; The micro-motion phase after filtering the main Doppler component is subjected to autocorrelation function calculation with respect to an adaptive window length to obtain a target autocorrelation function; Based on the target autocorrelation function, an optimal filtering window length corresponding to the current insect phase signal is determined; According to the optimal filtering window length, the current insect phase signal is subjected to compensation processing of the slow-time signal echo to obtain a micro-motion signal of the current insect.

[0044] The embodiment of the present application can filter out the insect echo adaptively by micro-motion phase autocorrelation matching, and can provide an effective filtering method for the insect main Doppler coupling interference. Compared with the traditional insect main Doppler filtering method, the embodiment of the present application can also improve the filtering precision of the complex main Doppler target, and will be helpful for the extraction of the insect wing flutter micro-motion characteristics of the radar.

[0045] The embodiments of the present application are described in a progressive manner, and the same or similar parts of the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments. Especially, the device and medium embodiments are basically similar to the method embodiments, and thus the description is relatively simple, and the related parts can be referred to the description of the method embodiments.

[0046] The device and medium provided by the embodiments of the present application are one-to-one corresponding to the method, and thus the device and medium also have the similar beneficial technical effects as the method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be described here.

[0047] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0048] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device implemented in the flowcharts and / or block diagrams. Figure 1 The device that implements the function specified in one flow or multiple flows and / or blocks. Figure 1 The device that implements the function specified in one flow or multiple flows and / or blocks.

[0049] These computer program instructions can also be stored in a computer readable storage medium that can guide the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a manufactured product including instruction devices that implement the flowcharts and / or block diagrams. Figure 1 The device that implements the function specified in one flow or multiple flows and / or blocks.Figure 1 the function specified in the one or more blocks.

[0050] These computer program instructions can also be loaded into computer or other programmable data processing devices to cause a series of operational steps to be performed on the computer or other programmable devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable devices provide steps for implementing the flowchart block or blocks. Figure 1 the flowchart block or blocks. Figure 1 the function specified in the one or more blocks.

[0051] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0052] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the computer stores information about an operating system, application software, and / or the like. Memory is an example of computer readable media.

[0053] Computer readable media includes permanent and non-permanent, moveable and non- moveable media that can be implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules 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 technology, compact disc read only memory (CD-ROM), digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that is accessible to a computing device. According to the definition provided herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0054] It is also noted that the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0055] The above merely provides an example of the present application, but is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of the present application.

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; Through the target instantaneous Doppler frequency model, target echo phase is subjected to smoothing processing under 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 current insect phase signal is subjected to compensation processing of slow-time signal echoes 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, 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.

3. The insect echo adaptive filtering method based on micro-motion phase autocorrelation matching according to claim 2, characterized in that, 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.

4. 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 under different sliding window lengths to obtain micro-motion phase after filtering out the main Doppler component, specifically comprising: Extracting 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 a sliding window to obtain a 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.

5. The method of claim 1, wherein the method is based on a micro-motion phase self-correlation matching for echo adaptive filtering of insects. 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.

6. 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: Similarity matching is performed between the target autocorrelation function and the phase signal function of the main Doppler to obtain a similarity matching result; According to the consistency information in the similarity matching result, difference calculation is performed between the autocorrelation zero main peak and the secondary peak of the current insect phase signal to determine a minimum difference result; Based on the minimum difference result and the target autocorrelation function, the optimal filtering window length of the current insect phase signal is obtained.

7. The method of claim 1, wherein the method is based on a micro-motion phase self-correlation matching for insect echo adaptive filtering. Before the current insect phase signal is subjected to compensation processing of slow-time signal echoes 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 a 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 an imaginary unit; is the wavelength of the original insect echo signal.

8. The insect echo adaptive filtering method based on micro-motion phase autocorrelation matching according to claim 7, characterized in that, According to the optimal filtering window length, the current insect phase signal is subjected to compensation processing of slow-time signal echoes to obtain the micro-motion signal of the current insect, specifically comprising: Through smoothing processing of the optimal filtering window length, phase change identification of the main Doppler frequency component is performed on the current insect phase signal; and based on the slow-time echo model of the target insect, compensation processing of slow-time signal echoes 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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