A method, system, device and medium for extracting micro-motion features of a ballistic target

Abstract

This invention discloses a method, system, device, and medium for extracting micro-motion features of projectile targets, relating to the field of target feature extraction. It involves constructing a measured model of the projectile target based on acquired projectile echo data; using simulation software to simulate the flight data of the measured model under experimental conditions and the measured model itself, and obtaining a simulated spectrum using modal decomposition and current time-frequency analysis methods; using a data fusion method to obtain fused data based on the measured projectile echo data of the projectile target within a certain time period and the external ballistic parameter data of the projectile target obtained through optical remote sensing; processing the fused data using data analysis to obtain a measured spectrum, which is then matched with the simulated spectrum to determine if the difference value is within a set range; if not, optimizing the initial time-frequency analysis method and determining the correct time-frequency analysis method; if yes, determining the target micro-motion features; this invention enables the detection or extraction of target micro-motion features.

Description

Technical Field

[0001] This invention relates to the field of target feature extraction, and in particular to a method, system, device and medium for extracting micro-motion features of projectile targets. Background Technology

[0002] Currently, most research on the target MD (Micro-Doppler) effect is based on laboratory target modeling and simulation data. There is very little research on the actual measurement of the target micro-motion effect and the micro-motion feature extraction system. In particular, there is even less modular and practical research on the detection, extraction, and processing of target micro-motion information.

[0003] For example, when conducting live-fire tests on the micro-motion characteristics of high-speed projectiles, large radar equipment and its auxiliary guidance and tracking devices are usually used. The corresponding target micro-motion data can only be obtained in live-fire tests at professional ranges. Even for measuring micro-motion characteristic parameters such as the rotational speed of high-speed rotating projectiles, there is still no satisfactory or systematic method. This brings inconvenience to the micro-motion testing and extraction of such targets. How to modularize and miniaturize the live-fire test data extraction system for projectile target micro-motion information so that it can be easily applied to various weapon system platforms is an urgent problem to be solved.

[0004] This is because traditional optical observation, detection, and tracking equipment can no longer fully meet perception requirements. First, although the detection and tracking capabilities of automatic acoustic sensors and micro-movement target indication radar are relatively mature, their recognition capabilities are insufficient. Second, although optical, infrared, and lidar imaging sensors on UAVs and unmanned vehicles can provide recognition, they are limited by network bandwidth and cannot transmit streaming video format images. In particular, the recognition methods are not only complex in algorithm but also heavily dependent on image quality, which has become one of the world's major challenges. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, device, and medium for extracting the micro-motion features of a projectile target, so as to realize the detection or extraction of the micro-motion features of the target.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for extracting micro-motion features of a projectile target, the method comprising:

[0008] Acquire projectile echo data of the projectile target; the projectile echo data includes: volume, shape, material, and the corresponding micro-motion characteristics of the material;

[0009] A measured model of the projectile target is constructed based on the projectile echo data;

[0010] The flight data of the measured model under the test environment is obtained; the flight data includes: flight conical spin data, oscillation data and projectile translation data; the projectile translation data includes continuous wave Doppler signal data;

[0011] Based on the flight data and the measured model, simulation software is used to perform simulation to obtain simulation data; the simulation data represents the intrinsic relationship between the measured model and the flight data regarding attitude motion.

[0012] The simulation data is processed using mode decomposition and current time-frequency analysis methods to obtain a simulation spectrum.

[0013] The radar, using a predetermined deployment method, acquires measured echo data of the projectile target within a predetermined time period.

[0014] The external ballistic parameters of the projectile target are acquired using optical remote sensing; the external ballistic parameters include: flight cone spin data and oscillation data obtained by optical remote sensing.

[0015] The measured projectile echo data and the external ballistic parameter data are fused using a data fusion method to obtain fused data;

[0016] The fused data is processed using data analysis methods to obtain a measured spectrum; the data analysis methods include current time-frequency analysis methods and multi-resolution data analysis methods.

[0017] The simulated spectrum is matched with the measured spectrum to determine whether the difference between the simulated spectrum and the measured spectrum is within a set range, and the determination result is obtained.

[0018] If the judgment result is negative, then white noise with a set signal-to-noise ratio is added to the current time-frequency method to obtain an optimized time-frequency analysis method. The optimized time-frequency analysis method is then used as the next time-frequency method, and the process returns to the step of "processing the simulation data using modal decomposition and the current time-frequency analysis method to obtain a simulation spectrum".

[0019] If the judgment result is yes, then the target micro-motion characteristics of the projectile target are determined according to the measured spectrum; the target micro-motion characteristics are used to characterize the instantaneous frequency shift characteristics corresponding to the continuous wave Doppler signal data.

[0020] Optionally, the step of using data analysis to process the fused data to obtain the measured spectrum specifically includes:

[0021] The fused data is filtered to obtain the processed fused data;

[0022] The processed fused data is decomposed using current time-frequency analysis and multi-resolution analysis data methods to obtain decomposed data.

[0023] The amplitude corresponding to each frequency in the continuous wave Doppler signal data in the decomposed data is extracted using the current time-frequency analysis method.

[0024] By sequentially connecting multiple amplitude values, the measured spectrum is obtained.

[0025] Optionally, the deployment method includes: a combined tail-chase and attack deployment method, a tail-chase deployment method, and an attack deployment method.

[0026] A system for extracting micro-motion features of a projectile target, the system comprising:

[0027] The projectile echo data acquisition module is used to acquire projectile echo data of the projectile target; the projectile echo data includes: volume, shape, material, and the micro-motion characteristics corresponding to the material;

[0028] The measured model construction module is used to construct a measured model of the projectile target based on the projectile echo data;

[0029] The flight data acquisition module is used to acquire flight data of the measured model under experimental conditions; the flight data includes: flight conical spin data, oscillation data, and projectile translational data; the projectile translational data includes continuous wave Doppler signal data.

[0030] The simulation data determination module is used to perform simulation using simulation software based on the flight data and the measured model to obtain simulation data; the simulation data represents the intrinsic relationship between the measured model and the flight data regarding attitude motion.

[0031] The simulation spectrum determination module is used to process the simulation data using mode decomposition and current time-frequency analysis methods to obtain the simulation spectrum.

[0032] The measured projectile echo data acquisition module is used to acquire the measured projectile echo data of the projectile target within a set time period using a radar with a set deployment method.

[0033] The external ballistic parameter data acquisition module is used to acquire the external ballistic parameter data of the projectile target using optical remote sensing; the external ballistic parameter data includes: optical telemetry flight cone spin data and optical telemetry oscillation data;

[0034] The data fusion determination module is used to fuse the measured projectile echo data and the external ballistic parameter data using a data fusion method to obtain fused data;

[0035] The measured spectrum determination module is used to process the fused data using data analysis methods to obtain the measured spectrum; the data analysis methods include: current time-frequency analysis methods and multi-resolution data analysis methods;

[0036] The matching module is used to match the simulated spectrum with the measured spectrum, determine whether the difference between the simulated spectrum and the measured spectrum is within a set range, and obtain a judgment result.

[0037] The optimization module is used to add white noise with a set signal-to-noise ratio to the current time-frequency method if the judgment result is negative, so as to obtain an optimized time-frequency analysis method, and use the optimized time-frequency analysis method as the next time-frequency method, and return to the "simulation spectrum determination module".

[0038] The target micro-motion feature determination module is used to determine the target micro-motion features of the projectile target based on the measured spectrum if the judgment result is yes; the target micro-motion features are used to characterize the instantaneous frequency shift characteristics corresponding to the continuous wave Doppler signal data.

[0039] Optionally, the measured spectrum determination module includes:

[0040] The preprocessing submodule is used to filter the fused data to obtain the processed fused data.

[0041] The decomposition submodule is used to decompose the processed fused data using current time-frequency analysis methods and multi-resolution analysis data methods to obtain decomposed data.

[0042] An extraction submodule is used to extract the amplitude corresponding to each frequency in the continuous wave Doppler signal data from the decomposed data using the current time-frequency analysis method;

[0043] A submodule is defined to sequentially connect multiple amplitude values ​​to obtain the measured spectrum.

[0044] Optionally, the set deployment method in the measured projectile echo data acquisition module includes: a combined tail-chase and interception deployment method, a tail-chase deployment method, and an interception deployment method.

[0045] An electronic device includes a memory and a processor, the memory storing a computer program, and the processor running the computer program to cause the electronic device to perform the method for extracting micro-motion features of a projectile target as described above.

[0046] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for extracting micro-motion features of a projectile target as described in any of the preceding claims.

[0047] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0048] This invention provides a method, system, device, and medium for extracting the micro-motion characteristics of a projectile target. It involves constructing a measured model based on acquired projectile echo data, then using simulation software to obtain simulation data based on acquired flight data and the measured model. Modal decomposition and current time-frequency analysis methods are used to process the simulation data to obtain a simulation spectrum. Data analysis methods are then used to process the acquired measured projectile echo data and external ballistic parameter data to obtain a measured spectrum. The simulated spectrum and the measured spectrum are matched and processed to finally obtain the target's micro-motion characteristics. This application combines radar measurements, optical remote sensing, and simulation analysis to scientifically and accurately detect or extract target micro-motion characteristics. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart of a method for extracting micro-motion features of a projectile target provided in an embodiment of the present invention;

[0051] Figure 2 This is a structural diagram of a system for extracting micro-motion features of a projectile target, provided in an embodiment of the present invention.

[0052] Figure 3 A schematic diagram of the tail-chase deployment method provided in an embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram of the deployment method for counterattacking provided in an embodiment of the present invention;

[0054] Figure 5 A schematic diagram of a tail-chase and interception combined deployment method provided in an embodiment of the present invention;

[0055] Figure 6 The spectrum diagram corresponding to the projectile echo data containing clutter interference;

[0056] Figure 7 A schematic diagram of the 53-point Hamming window STFT for projectile echo;

[0057] Figure 8 A schematic diagram of the pseudo-WVD distribution of the projectile echo;

[0058] Figure 9A schematic diagram of the AOK time-frequency ridge of the projectile echo;

[0059] Figure 10 A schematic diagram of the three-dimensional time-frequency ridge of the projectile echo AOK;

[0060] Figure 11 A schematic diagram of the Hilbert spectrum of the EEMD decomposition when the projectile echo SNR = 5 dB;

[0061] Figure 12 A schematic diagram of the EEMD combined with AOK analytical algorithm for analyzing the micro-motion characteristics of projectile targets;

[0062] Figure 13 A schematic diagram of the first-order AOK time-frequency distribution of the projectile echo EEMD decomposition;

[0063] Figure 14 A schematic diagram of the second-order AOK time-frequency distribution of the projectile echo EEMD decomposition;

[0064] Figure 15 A schematic diagram of the AOK time-frequency distribution of the third order of EEMD decomposition of the projectile echo;

[0065] Figure 16 A schematic diagram of the fourth-order AOK time-frequency distribution of the projectile echo EEMD decomposition;

[0066] Figure 17 A schematic diagram illustrating the analysis of first-order and second-order systems using EMD in conjunction with AOK.

[0067] Figure 18 A schematic diagram illustrating the analysis of first-order and second-order algorithms using EEMD in conjunction with AOK.

[0068] Figure 19 This is a schematic diagram of the overall process of the experimental extraction system for the micro-motion characteristics of projectile targets provided by the present invention;

[0069] Figure 20 This is a flowchart illustrating the specific implementation process of the experimental extraction system for the micro-motion characteristics of projectile targets provided by the present invention.

[0070] Figure 21 This is a schematic diagram of the design scheme for the micro-motion feature processing and extraction software selected in the practical application of the present invention.

[0071] Figure 22 A schematic diagram of the interface for acquiring and setting up radar-measured projectile echo data in practical applications;

[0072] Figure 23 This is a schematic diagram of the power spectrum analysis display interface for radar-measured projectile echo data in practical applications.

[0073] Symbol explanation:

[0074] Projectile echo data acquisition module-1, measured model construction module-2, flight data acquisition module-3, simulation data determination module-4, simulation spectrum determination module-5, measured projectile echo data acquisition module-6, external ballistic parameter data acquisition module-7, fused data determination module-8, measured spectrum determination module-9, matching module-10, optimization module-11, target micro-motion characteristic determination module-12. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] The purpose of this invention is to provide a method, system, device, and medium for extracting the micro-motion features of a projectile target, so as to realize the detection or extraction of the micro-motion features of the target.

[0077] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0078] Example 1

[0079] like Figure 1 As shown, this embodiment of the invention provides a method for extracting micro-motion features of a projectile target, the method comprising:

[0080] Step 100: Obtain the projectile echo data of the projectile target; the projectile echo data includes: volume, shape, material, and the corresponding micro-motion characteristics of the material.

[0081] Step 200: Construct a measured model of the projectile target based on the projectile echo data.

[0082] Step 300: Obtain flight data of the test model under the test environment; the flight data includes: flight cone rotation data, oscillation data and projectile translation data; the projectile translation data includes continuous wave Doppler signal data.

[0083] Step 400: Based on the flight data and the measured model, simulation software is used to perform simulation to obtain simulation data; the simulation data represents the intrinsic relationship between the measured model and the flight data regarding attitude motion.

[0084] Step 500: The simulation data is processed using modal decomposition and current time-frequency analysis methods to obtain the simulation spectrum.

[0085] Step 600: The radar, with a set deployment method, acquires the measured echo data of the projectile target within a set time period.

[0086] Step 700: Use optical remote sensing to acquire external ballistic parameter data of the projectile target; the external ballistic parameter data includes: flight cone spin data and oscillation data from optical remote sensing.

[0087] Step 800: Use the data fusion method to fuse the measured projectile echo data and external ballistic parameter data to obtain fused data.

[0088] Step 900: Use data analysis methods to process the fused data to obtain the measured spectrum; the data analysis methods include: current time-frequency analysis methods and multi-resolution data analysis methods.

[0089] Step 1000: Match the simulated spectrum with the measured spectrum, determine whether the difference between the simulated spectrum and the measured spectrum is within the set range, and obtain the judgment result.

[0090] Step 1001: If the judgment result is negative, add white noise with a set signal-to-noise ratio to the current time-frequency method to obtain an optimized time-frequency analysis method. Use the optimized time-frequency analysis method as the next time-frequency method and return to the step of "processing the simulation data using modal decomposition and the current time-frequency analysis method to obtain the simulation spectrum".

[0091] Step 1002: If the judgment result is yes, then determine the target micro-motion characteristics of the projectile target based on the measured spectrum diagram; the target micro-motion characteristics are used to characterize the instantaneous frequency shift characteristics corresponding to the continuous wave Doppler signal data.

[0092] Specifically, step 900 includes:

[0093] The fused data is filtered to obtain the processed fused data.

[0094] The processed fused data is decomposed using current time-frequency analysis and multi-resolution analysis methods to obtain decomposed data.

[0095] The amplitude corresponding to each frequency in the continuous wave Doppler signal data in the decomposed data is extracted using the current time-frequency analysis method.

[0096] By connecting multiple amplitude values ​​sequentially, the measured spectrum is obtained.

[0097] The deployment methods include: a combination of tail-chase and interception deployment, a tail-chase deployment, and an interception deployment.

[0098] Specifically, this invention starts with the study of the electromagnetic scattering characteristics of projectile targets. Based on the electromagnetic scattering model of the projectile, it carefully and scientifically designs the site layout for live-fire tests in order to effectively obtain measured echo data.

[0099] Studies on the electromagnetic scattering characteristics of the projectile target surface and radar measurement environments show that when the radar is positioned to measure from the side and rear of the projectile target, the bottom of the projectile flying in the optical zone forms strong scattering off from the center of the projectile base. If the radar velocity measurement accuracy is not high (e.g., 0.05 m / s), the projectile scattering center can be considered to be at the center of the projectile base. This scattering model is used when analyzing micro-motions such as projectile spin and precession. For projectiles with multiple tail fins, the equivalent scattering center is located at the midpoint of the fins.

[0100] Therefore, a station layout scheme for the field measurement of the micro-motion characteristics of the projectile target was designed, mainly including two measurement methods: tail-chase and head-on attack. Figure 3 and Figure 4 As shown.

[0101] In a tail-chase deployment, the radar is positioned to the side and rear of the missile launch system, such as... Figure 3 As shown. Depending on the type of projectile, the longitudinal distance ranges from tens of meters to several kilometers, while the lateral distance is generally tens of meters. This deployment scheme ensures that the radar can detect and acquire the target within seconds of the projectile's launch, enabling effective measurement. In the interception deployment, the radar is positioned outside the impact zone, below and to the side of the trajectory.

[0102] In particular, during projectile firing and testing, it is essential to scientifically deploy radar stations by calculating radar power and other parameters, considering the actual projectile type and testing environment, and supplementing this with telemetry equipment, including theodolites, high-speed video recorders, and other auxiliary testing equipment, to collect data on the projectile's micro-motions. Taking all these factors into account, the actual measurement scheme for projectile target micro-motions adopts a combined tail-chase and head-on attack deployment method, such as... Figure 5 As shown. The specific site layout design and analysis principles are as follows:

[0103] According to the range equation of CW radar:

[0104]

[0105] Where P t·avg Let G be the average transmit power of the radar, λ be the radar antenna gain, λ be the radar operating wavelength, RCS be the effective radar cross-section of the target, and K be the Boltzmann constant, k = 1.38 × 10⁻⁶. -23 J / °K, T0 is the system noise temperature, T0 = 290°K, B is the radar system noise bandwidth, F is the radar system noise figure, L is the system loss, and SNR is the corresponding R max The signal-to-noise ratio.

[0106] If a radar has an average transmit power of 400W, an antenna gain of 40dB, an operating wavelength of 0.0285m, and a noise figure F of 1.8dB, by combining the radar-related parameters and constants in the radar range equation, we obtain the radar's antenna constants:

[0107]

[0108] Substituting the radar antenna constant into the radar equation, the radar equation for a certain radar can be expressed as follows:

[0109]

[0110] This shows that the maximum effective range of a radar is related to the radar's antenna constant, system bandwidth, target's cross-sectional area, and minimum detectable signal-to-noise ratio.

[0111] For CW radar, system bandwidth refers to the radar's video bandwidth, and its relationship with observation time is B = 1 / T. obs CW radar transmits continuously, allowing for continuous target illumination during tracking. Observation time primarily depends on signal processing time; longer observation times result in greater radar power. However, the observation time is not infinitely long, limited by signal acquisition and processing capabilities. Since radar speed, range, and angle measurements are based on spectrum analysis, and the target's speed is continuously changing, the observation time T... obs Excessive observation time can cause signal ambiguity, thus reducing the radar's effective range. In real-time tracking mode, long observation times mean a reduction in real-time data rate, leading to angle tracking problems.

[0112] Substituting a radar antenna constant, observation time, target cross-section, and minimum detectable level into the radar equation, we get... R max =76973m.

[0113] This allows us to theoretically determine the effectiveness of a radar system, which can then be applied in actual testing and deployment.

[0114] In actual measurements, the data measured by a certain radar is first statistically analyzed to estimate the radar's maximum effective range against various targets. Since the actual measured RCS of various targets is unknown, only statistical analysis of the measurement data can be used to estimate the radar's power. From the radar range equation, we know:

[0115]

[0116]

[0117] In the formula, SNR min This is the minimum detectable level for radar; data with a signal-to-noise ratio below this value is invalid. Comparing the two equations, we get:

[0118]

[0119] SNR during real-time target tracking min =12dB, SNR during post-processing min =7dB, but post-processing is based on the Doppler signal recorded during real-time tracking measurement, so it is only meaningful if the radar memory tracking is effective. From this, the maximum range estimate of the radar based on the measured data can be obtained.

[0120] Taking the measurement data of a certain projectile target as an example, due to the change in radar illumination angle, the cross-sectional area of ​​the projectile target also changes with time. Therefore, when calculating the statistical results, only the data with a relatively gradual change in the maximum effective distance are counted. Data from 15 seconds to 50 seconds is selected for statistical analysis. The result is that the actual maximum effective distance is 59,523m, which is consistent with the statistical estimate.

[0121] Then, the accuracy of each test device is analyzed. For example, in radar speed measurement, the echo signal is not a continuous wave of a single frequency; its Doppler signal contains a certain spectral width. Measuring the frequency of the Doppler signal is to estimate the Doppler frequency of the signal's center spectral line, which is the maximum value of the spectral envelope. The maximum likelihood method is used to estimate the maximum value of the spectral envelope, and the random error of the speed measurement is:

[0122]

[0123] It can be seen that the velocity measurement error is inversely proportional to the square root of the echo signal-to-noise ratio and also inversely proportional to the observation time of signal processing. For a given projectile target, once its RCS and velocity are given, the radar's velocity measurement error can be estimated.

[0124] When the signal-to-noise ratio of the target echo signal is greater than 40dB, the velocity measurement error is less than 0.008m / s, which meets the radar performance requirements, and the effective range is approximately 15 kilometers. When the signal-to-noise ratio of the target echo signal is greater than 30dB, the velocity measurement error is less than 0.2m / s, which meets the military standard requirements for velocity measurement accuracy, and the effective range is approximately 27 kilometers.

[0125] Similarly, the ranging accuracy can also be estimated. When the radar uses the time delay of the difference frequency signal cos(2πΔft) for ranging, the estimated variance of the target echo time delay corresponding to the signal cos(2πΔft) is:

[0126]

[0127] Therefore, the root mean square error of the distance measurement is:

[0128]

[0129] It is evident that the higher the signal-to-noise ratio (SNR) of the echo signal, the higher the ranging accuracy. Therefore, the SNR of the echo signal and the distance are related as follows:

[0130]

[0131] With radar and target parameters remaining constant, the greater the distance, the worse the signal-to-noise ratio (SNR) of the echo signal, and the larger the corresponding root mean square (RMS) error in ranging. When the target echo signal SNR is greater than 40 dB, the ranging error is less than 0.2 meters, and the effective range is approximately 15 kilometers. When the target echo signal SNR is greater than 30 dB, the ranging error is less than 0.75 meters, and the effective range is approximately 27 kilometers. When the target echo signal SNR is greater than 20 dB, the ranging error is less than 2.4 meters, and the effective range is approximately 47 kilometers.

[0132] Furthermore, considering the characteristics of the target rotation speed of the projectile, the test equipment should be rationally arranged so that test equipment such as launch area and impact area radars can acquire ballistic parameter data and video images of each segment of the projectile during flight, that is, the entire motion state of the projectile from ignition to impact, including the time-varying coordinates of the projectile's head and tail, and the center of mass velocity (V). x V y ,V), acceleration (a x ,a y a) and other trajectory parameters, and transmit the test data to the command and display system in real time to complete the radar test and auxiliary test of the ballistic parameters, so as to extract micro-motion information data such as the projectile rotation speed from the target echo signal and form a micro-motion characteristic database.

[0133] Time-frequency analysis and multi-resolution data analysis methods were used to process the measured projectile echo data and external ballistic parameter data to obtain the measured spectrum. The specific operation steps are as follows:

[0134] For the acquired measured micro-motion data of the projectile target (i.e., measured projectile echo data and external ballistic parameter data), advanced HHT combined with multi-resolution analysis data processing technology is used. First, the local transient echo signal of the complex superimposed projectile target MD information is decomposed into a finite number of physically meaningful intrinsic mode eigenfunctions (IMFs). Then, through HHT, the mean curve of the local transient MD signal of the non-stationary data of the projectile target is extracted from the radar echo signal and envelope signal to obtain meaningful instantaneous frequencies and Hilbert time spectra, i.e., projectile target micro-motion characteristics. In this way, by measuring the micro-motion characteristics of the projectile target, the time spectra of the projectile velocity, rotation speed, etc. are extracted from the radar measurement data R, A, E, to obtain the micro-motion velocity, rotation speed, etc., and then the projectile's orbital motion attitude is analyzed to achieve the estimation of the projectile's micro-motion parameters.

[0135] The innovation of this method lies in the introduction of the concept of inherent modes of projectile target micro-motion data and the empirical mode decomposition (EMD) method. Its characteristics are that there are no Wigner-Ville distribution cross terms, and it has higher time-frequency resolution than wavelet analysis. It is a new method that is particularly suitable for the analysis of non-stationary data.

[0136] In actual testing of projectile target micro-motion data, the projectile target echo signal inevitably experiences strong clutter interference, and the projectile target echo spectrum has several side lobes, making it difficult to separate the required micro-motion echo spectrum. Figure 6 As shown.

[0137] How to reduce the impact of noise and clearly separate the micro-motion characteristics of the projectile target in time-frequency analysis? Based on the above analysis, the interference caused by noise can be addressed through time-domain filtering or time-frequency domain signal smoothing. Here, to analyze the noise suppression effect of different time-frequency methods, we still analyze the projectile target echo signal. Under low signal-to-noise ratio, when analyzing the simulated projectile signal of a point target, we add Gaussian white noise with an SNR of 1 and compare the noise suppression effects of different time-frequency analysis methods, such as... Figures 7-10 As shown.

[0138] Figure 7 When SNR=1, the 53-point Hamming window STFT, which performs well in the absence of noise, shows that the window function has a significant effect on suppressing noise components in the time-frequency image, but the time-frequency clustering becomes blurred. Figure 8 In the analysis of projectile echo signals using pseudo-WVD distribution, the noise component is distributed across the entire time-frequency plane, and the MD signal characteristics are severely contaminated, especially in the time range of 50-150, where the time-frequency aggregation is severely reduced. Figure 9 and Figure 10 The time-frequency ridges and 3D time-frequency images of the projectile signal are analyzed for AOK time-frequency distribution. It can be seen that the time-frequency ridges obtained from local search extrema also fill the entire time-frequency domain. However, the ridges of noise appear as noise points or discontinuous, while the time-frequency ridges of the echo signal appear continuous and regular. From this, it is easy to derive the MD time-frequency image with high time-frequency clustering.

[0139] Combining several time-frequency analysis methods, the EEMD combined with AOK time-frequency ridge method can more accurately analyze signal characteristics under low signal-to-noise ratio conditions. Specifically, by decomposing the noise signal using EEMD, several IMFs with effectively suppressed mode aliasing are obtained. To analyze the MD characteristics in the echo, the MD characteristic patterns need to be found in the time-frequency surface, and the HT (time-frequency ridge) of each order of IMF obtained from EEMD decomposition is calculated. When SNR = 5dB, the HT of the projectile echo EEMD decomposition is as follows: Figure 11 As shown.

[0140] use Figure 11Compared to the HT obtained from EMD decomposition, noise is somewhat suppressed, but the time-frequency image is still severely interfered with, making it difficult to extract useful information. Considering that EEMD combined with AOK time-frequency analysis can yield a clearer time-frequency image of the mD features, the following section describes AOK time-frequency analysis of each IMF obtained from EEMD decomposition. The algorithm is as follows: Figure 12 As shown.

[0141] By adding Gaussian white noise to the projectile target's micro-motion echo, EMD is first decomposed into 1st-4th order IMFs. Then, the average of each IMF is used for EEMD improvement. Finally, AOK time-frequency analysis is used to obtain the time spectrum of each IMF, such as... Figures 13-16 As shown.

[0142] Figures 13-16 In the analysis, the approximate equality of the time-frequency images in IMF1 and IMF2, along with their mD frequencies, indicates that the first two signals are generated by the modulation of the radar echo by the same micro-motion. Similarly, IMF3 and IMF4 can also be considered as eigenmode components generated by the same micro-motion. Through AOK time-frequency analysis, the noise in the time-frequency plane of the modal function components is effectively suppressed, and the mD characteristics are relatively obvious, laying the foundation for analyzing their physical characteristics.

[0143] For the first two components (imf1+imf2) of the echo signal from a noisy projectile target after mode decomposition, the time-frequency images of the first two modes are obtained using EEMD combined with AOK time-frequency analysis, resulting in a time-frequency domain mD feature comparison. Figures 17-18 As shown.

[0144] Compare Figures 17-18 The MD echo time-frequency distributions obtained by the two different methods clearly show that the EEMD combined with AOK time-frequency analysis method has better detailed time-frequency images when characterizing MD features, especially in the region with severe mode aliasing in the 0-1500 time series, where it exhibits better time-frequency clustering and integrity. In the region with less severe mode aliasing, both methods can obtain good MD time-frequency images. The EEMD combined with AOK time-frequency analysis method has better anti-mode aliasing and noise reduction capabilities, and can better estimate the micro-motion characteristic parameters of the projectile target using the IMF component time-frequency images.

[0145] This invention, based on a research approach combining projectile target micro-motion theory with live-fire testing, constructs several modules: simulation calculation of electromagnetic scattering characteristics of the projectile target and test background based on a small CW (continuous wave) radar; mathematical modeling and analytical derivation of projectile micro-motion characteristics; acquisition of measured data of projectile micro-motion characteristics and digital filtering of transient signals; and analytical extraction of projectile micro-motion characteristics using EMD (Inherent Mode Decomposition) and HHT (Hilbert-Huang Trasform, HHT). It employs a three-pronged approach: live-fire testing of the projectile based on a small CW radar, auxiliary optical telemetry testing, and model laboratory simulation. This scientifically, accurately, and reliably acquires raw micro-motion data of the projectile target based on a small CW radar, better utilizing the raw measured data to support micro-motion theory analysis, and completing the acquisition and mining of raw micro-motion data of the projectile target. Figure 19 As shown.

[0146] exist Figure 19 In this scenario, a projectile target flying at a certain speed will experience a Doppler frequency shift in its radar echo signal under the influence of CW radar. If the projectile target or its components simultaneously exhibit minute attitude movements such as mechanical vibration or rotation, this will electromagnetically modulate the radar echo signal, resulting in a sideband signal in the Doppler signal, a phenomenon known as the micro-Doppler phenomenon. Furthermore, in addition to translational motion, projectile targets in space also experience tumbling, precession, and rotational micro-motions, all of which generate MD modulation. Therefore, a corresponding mathematical model of the projectile target's micro-motions is constructed to explore the intrinsic relationship between the projectile target's micro-motion characteristics and attitude motion, and to derive the analytical relationship between the projectile target's MD frequency and the radar radial velocity, in order to obtain the projectile target's micro-motion information.

[0147] In practical applications, it is necessary to establish the functional relationship between the parameters measured by the CW radar and the MD modulation, analyze the transient change law of Doppler frequency shift, and then use the envelope modulation signal or velocity curve as useful information. Multi-resolution EMD combined with HHT is used to study the time domain, frequency domain, M-DS and RCS of the micro-motion modulation characteristics, so as to estimate and extract the micro-motion parameters of the projectile and thus construct a measured database of the micro-motion characteristics of the projectile target.

[0148] This invention, based on a combination of theory and experimental research, presents a novel and practical system for studying the micro-motion characteristics of projectile targets. Therefore, the core technologies mainly comprise two aspects: theoretical modeling research on the micro-motion characteristics of projectile targets and a live-fire, measured MD effect analysis and extraction system based on CW radar. Figure 20As shown. This invention is not only for studying the micro-motion characteristics of a certain type of missile in space flight, but more importantly, it aims to establish a complete research system and method for detecting and processing the micro-motion characteristics of missile targets in space flight, based on the concept of practical application. That is, in the space flight test of the missile target, the missile echo signal is tested by CW radar, and new information and features of the missile target's micro-motion are extracted from the missile echo signal through multi-resolution analysis and HHT analysis.

[0149] Theoretically, based on the rigid body attitude dynamics of projectiles and the principles of radar detection, and adhering to the principle of organic correlation, a research system for the micro-motion characteristics of projectile targets based on CW radar is proposed. This system mainly includes three aspects: First, the organic connection between the projectile target and various internal factors of the radar detection system; second, the organic connection between the radar and the projectile target micro-motion characteristic extraction and analysis system and the external measured environment, meaning that the radar's detection and analysis of projectile micro-motion depends not only on the performance parameters of the radar detector itself, but also on the characteristic parameters of the projectile target and the characteristic parameters of the measured environment; third, the entire research system depends not only on the independent parameters of these three elements, but also on the correlation parameters among them. That is, to obtain projectile target micro-motion characteristic data, a projectile target model must first be established, and characteristic parameters such as volume, shape, and vibration characteristics must be selected. Similarly, the performance parameters of the radar and other detectors used, the environmental characteristic parameters, and the correlation parameters between the radar and the projectile target must all be given corresponding models, and parameters should be appropriately selected to maximize radar detection accuracy. Only in this way can the obtained projectile target characteristic parameter data best reflect the target characteristics.

[0150] Based on the analysis and establishment of the above research system, a mathematical model of the micro-motion states such as conical rotation and oscillation of the missile target in space flight is constructed. The modulation effect of the micro-motion electromagnetic waves superimposed in the Doppler signal of the missile echo is studied. The analytical relationship between the missile body MD and the radar radial velocity is derived. The connection between the missile body motion attitude and the MD effect is analyzed. The electromagnetic simulation system is used to simulate the electromagnetic scattering characteristics and RCS of the missile target.

[0151] Example 2

[0152] This invention provides a system for extracting the micro-motion characteristics of a projectile target. The system includes: a projectile echo data acquisition module 1, a measured model construction module 2, a flight data acquisition module 3, a simulation data determination module 4, a simulation spectrum determination module 5, a measured projectile echo data acquisition module 6, an external ballistic parameter data acquisition module 7, a fusion data determination module 8, a measured spectrum determination module 9, a matching module 10, an optimization module 11, and a target micro-motion characteristic determination module 12.

[0153] Projectile echo data acquisition module 1 is used to acquire projectile echo data of the projectile target; the projectile echo data includes: volume, shape, material and the micro-motion characteristics corresponding to the material.

[0154] The measured model construction module 2 is used to construct a measured model of the projectile target based on the projectile echo data.

[0155] Flight data acquisition module 3 is used to acquire flight data of the test model under test environment; the flight data includes: flight cone rotation data, oscillation data and projectile translation data; the projectile translation data includes continuous wave Doppler signal data.

[0156] The simulation data determination module 4 is used to perform simulation using simulation software based on flight data and the measured model to obtain simulation data; the simulation data represents the intrinsic relationship between the measured model and the flight data regarding attitude motion.

[0157] The simulation spectrum determination module 5 is used to process the simulation data using modal decomposition and current time-frequency analysis methods to obtain the simulation spectrum.

[0158] The measured projectile echo data acquisition module 6 is used to acquire measured projectile echo data of the projectile target within a set time period using a radar with a set deployment method.

[0159] The external ballistic parameter data acquisition module 7 is used to acquire external ballistic parameter data of the projectile target using optical remote sensing; the external ballistic parameter data includes: flight cone spin data and oscillation data from optical remote sensing.

[0160] The data fusion determination module 8 is used to fuse the measured projectile echo data and external ballistic parameter data using a data fusion method to obtain fused data.

[0161] The measured spectrum determination module 9 is used to process the fused data using data analysis methods to obtain the measured spectrum. The data analysis methods include current time-frequency analysis methods and multi-resolution data analysis methods.

[0162] The matching module 10 is used to match the simulated spectrum diagram with the measured spectrum diagram, determine whether the difference between the simulated spectrum diagram and the measured spectrum diagram is within a set range, and obtain the judgment result.

[0163] The optimization module 11 is used to add white noise with a set signal-to-noise ratio to the current time-frequency method if the judgment result is negative, so as to obtain an optimized time-frequency analysis method, use the optimized time-frequency analysis method as the next time-frequency method, and return to the "simulation spectrum determination module".

[0164] The target micro-motion feature determination module 12 is used to determine the target micro-motion features of the projectile target based on the measured spectrum if the judgment result is yes; the target micro-motion features are used to characterize the instantaneous frequency shift characteristics corresponding to the continuous wave Doppler signal data.

[0165] Specifically, the measured spectrum determination module 9 includes: a preprocessing submodule, a decomposition submodule, an extraction submodule, and a determination submodule.

[0166] The preprocessing submodule is used to filter the fused data to obtain the processed fused data.

[0167] The decomposition submodule is used to decompose the processed fused data using current time-frequency analysis methods and multi-resolution analysis data methods to obtain decomposed data.

[0168] The extraction submodule is used to extract the amplitude corresponding to each frequency in the continuous wave Doppler signal data from the decomposed data using the current time-frequency analysis method.

[0169] The determination submodule is used to connect multiple amplitudes sequentially to obtain the measured spectrum.

[0170] As an optional implementation method, the set layout in the measured projectile echo data acquisition module 6 includes: a combined tail-chase and interception layout, a tail-chase layout, and an interception layout.

[0171] The solution provided in this application can be implemented using micro-motion feature processing and extraction software in practical applications. The design scheme of this software is as follows: Figure 21 As shown.

[0172] The selected software mainly consists of four sets of software: data acquisition, initial velocity, rotational speed, precession processing, and host computer control. This data processing software utilizes the VC++ and MATLAB development environments, using Microsoft Office Access and TXT as databases, configured through an ODBC data source manager. It was designed and developed to acquire, analyze, and extract the micro-motion characteristics of radar echo signals from projectile targets. Through digital filtering, autocorrelation, and other processing, and using FFT, adaptive algorithms combined with EMD and HHT analysis, a database of target characteristics such as electromagnetic scattering distribution and RCS of the projectile echoes, as well as instantaneous frequency, power spectrum, rotational speed, initial velocity, and tumble characteristics of the projectile target's micro-motions, is generated.

[0173] In particular, this database acquisition software adopts a brand-new technical design, which is a hardware and software system device designed with dual embedded systems and virtual instrument technology. It is a small instrument suitable for signal recording and analysis. It can not only perform signal data acquisition, but also communicate with a computer and transmit data through the LAN and RS232 interfaces on the device. And through the accompanying SWView dedicated analysis software, it can realize functions such as device control, parameter setting, waveform display, and data reading.

[0174] Moreover, the software has low requirements for online operation: a PC or industrial computer can be used as the main control unit; a LAN interface is required (a computer with a network interface supporting 100M or higher is required when real-time functions are needed); a monitor with a resolution of 800×600 or higher; 2GB of RAM or more (4GB or more is recommended when using Windows 7 or higher); more than 1TB of hard disk space; Windows 7 or higher (some functions also require the MATLAB MCRinstaller.exe environment variable to be installed).

[0175] Since the acquisition of measured radar echo data from projectile targets is a crucial step in analyzing their micro-motion characteristics, the initial implementation used a JV52118 series data acquisition card to sample the Doppler signal output from a small radar antenna and store the sampled data in a specific format on an industrial control computer. The software design was based on a Windows operating system and developed using Microsoft Visual C++ 6.0, the MatlAB signal processing toolbox, and Matcom 4.5 software. The main system interface is based on Microsoft MFC multi-document classes, featuring a graphical user interface that is fully visual and easy to operate. The designed system interface includes the following components: parameter settings, data acquisition control and display, and signal time-frequency analysis.

[0176] After reading the stored measured sampling data from the radar antenna head, the waveform of the original measured signal can be displayed. Furthermore, after performing a Fourier transform on the original measured signal, it can be displayed and analyzed in various formats such as power spectrum, phase spectrum, amplitude spectrum, and amplitude-frequency characteristic spectrum. The analyzed data can then be stored in graphical or text format for easy post-analysis and comparison. See the comparison details below. Figures 22-23 .

[0177] This target micro-motion data acquisition instrument can be programmed independently or via a networked computer, offering convenient and flexible signal processing. It also enables filtering, demodulation, and time-frequency analysis. During live-fire testing, the signal acquisition trigger source utilizes infrared control for synchronous triggering, facilitating data acquisition synchronization using the muzzle flash from the projectile. The minimum key technical specifications of this portable target micro-motion data acquisition instrument are as follows:

[0178] (1) Number of input channels: 2 channels, synchronous parallel acquisition.

[0179] (2) Maximum sampling rate per channel: 1MSps, adjustable in multiple levels by programmable down.

[0180] (3) Display screen: It comes with a 3.5-inch TFT color LCD touch screen, which allows for on-site adjustment of the monitoring instrument parameters. After the measurement and control is completed, the complete test waveform and parameters such as maximum wave velocity and main frequency can be read immediately. It does not require connection to a computer and can achieve true offline operation.

[0181] (4) Data storage depth: 8G bytes / unit (SD card, capacity expandable).

[0182] (5) Resolution: 16-bit (1 / 65536 of the range).

[0183] (6) Input signal bandwidth: 0Hz-400KHz.

[0184] (7) Signal-to-noise ratio: ≥62dB.

[0185] (8) Data interface: Supports RS232 and 10 / 100M adaptive Ethernet.

[0186] (9) Triggering methods: manual trigger, out-of-window trigger, in-window trigger, external trigger.

[0187] (10) Range: Amplitude range ±10V.

[0188] (11) Data recording method: maximum 2048 segments for storage and automatic recording; single or multiple segment triggers, supporting automatic saving of the current segment and automatic segment continuation function.

[0189] (12) Number of input channels: 2 channels, synchronous parallel acquisition.

[0190] (13) The data acquisition instrument uses embedded data acquisition and control software, which has good human-computer interaction and is very convenient to build a test database of the micro-motion characteristics of the projectile target.

[0191] Example 3

[0192] An electronic device includes a memory and a processor. The memory stores a computer program, and the processor runs the computer program to cause the electronic device to perform any of the methods for extracting micro-motion features of a projectile target as described in Embodiment 1.

[0193] As an optional implementation, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements any one of the methods for extracting micro-motion features of a projectile target in Embodiment 1.

[0194] This invention follows a main thread: "mathematical model of micro-motion – new characteristics of micro-motion – measured radar echo – non-stationary signal processing." It theoretically derives analytical relationships reflecting the micro-motion characteristics of the projectile and obtains target micro-motion measurement data from various experiments. This process yields new information and features about target micro-motion based on radar echo estimation. The key aspects of this invention are mainly reflected in the following:

[0195] 1. For the first time, a practical data acquisition and processing system for the micro-motion characteristics of small CW radar-based projectile targets was systematically constructed.

[0196] 2. A multi-parameter mathematical model of the projectile target's micro-motion was established, which accurately characterizes the analytical relationship between the projectile target's flight attitude and micro-motion.

[0197] 3. The multi-resolution EMD and HHT of the measured data of the projectile target micro-motion effectively solved the problem of extracting the local micro-motion signal of the projectile target.

[0198] 4. The designed software and instrument for acquiring and extracting the micro-motion characteristics of projectile targets show good agreement with the establishment of the measured database of micro-motion characteristics of projectile targets and simulation and experimental verification.

[0199] This invention combines projectile target micro-motion testing with theoretical research, possessing systematicity, scientific rigor, and practicality. The research content involves cutting-edge theories from multiple fields, including radar target detection, flight mechanics, attitude dynamics, electromagnetic scattering, data mining and fusion, and modern information processing. It is characterized by high research difficulty, strong fundamental requirements, and the difficulty in obtaining live-fire micro-motion data. The results achieved are as follows:

[0200] 1. To make the projectile target micro-motion information projectile measurement data extraction system modular, miniaturized, economical, reliable, and easy to promote and apply on various target characteristic acquisition system platforms.

[0201] 2. The innovative design of the projectile target micro-motion data measurement station layout provides a reference for similar tests.

[0202] 3. The constructed missile target MD measured data acquisition and extraction system is essentially a target micro-motion new feature mining and identification system. It is a development direction for research on using new target M-DS for non-imaging classification and identification. It is also an emerging target identification technology that does not rely on imaging at home and abroad. Accelerating the materialization of research results will surely bring the existing radar target classification and identification technology to a new level.

[0203] 4. The Inherent Mode Form (IMF) concept and Empirical Mode Decomposition (EMD) method are introduced into the analysis of projectile target micro-motion data. It does not have Wigner-Ville distribution cross terms and has higher time-frequency resolution than wavelet analysis. It is a new method that is particularly suitable for the analysis of non-stationary data.

[0204] 5. Through live-fire tests, the effectiveness of the estimation of the projectile target's micro-motion parameters was verified, and a method for extracting the projectile target's micro-motion feature database was explored.

[0205] 6. The establishment of a measured database of micro-motion of projectile targets has guiding significance for the analysis and design of guidance and control systems, and also promotes the development of modern signal processing technology and has practical application value.

[0206] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0207] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for extracting micro-motion features of a projectile target, characterized in that, The method includes: Acquire projectile echo data of the projectile target; the projectile echo data includes: volume, shape, material, and the corresponding micro-motion characteristics of the material; A measured model of the projectile target is constructed based on the projectile echo data; The flight data of the measured model under the test environment is obtained; the flight data includes: flight conical spin data, oscillation data and projectile translation data; the projectile translation data includes continuous wave Doppler signal data; Based on the flight data and the measured model, simulation software is used to perform simulation to obtain simulation data; the simulation data represents the intrinsic relationship between the measured model and the flight data regarding attitude motion. The simulation data is processed using mode decomposition and current time-frequency analysis methods to obtain a simulation spectrum. The radar, using a predetermined deployment method, acquires measured echo data of the projectile target within a predetermined time period. The external ballistic parameters of the projectile target are acquired using optical remote sensing; the external ballistic parameters include: flight cone spin data and oscillation data obtained by optical remote sensing. The measured projectile echo data and the external ballistic parameter data are fused using a data fusion method to obtain fused data; The fused data is processed using data analysis methods to obtain a measured spectrum; the data analysis methods include current time-frequency analysis methods and multi-resolution data analysis methods. The simulated spectrum is matched with the measured spectrum to determine whether the difference between the simulated spectrum and the measured spectrum is within a set range, and the determination result is obtained. If the judgment result is negative, then white noise with a set signal-to-noise ratio is added to the current time-frequency method to obtain an optimized time-frequency analysis method. The optimized time-frequency analysis method is then used as the next time-frequency method, and the process returns to the step of "processing the simulation data using modal decomposition and the current time-frequency analysis method to obtain a simulation spectrum". If the judgment result is yes, then the target micro-motion characteristics of the projectile target are determined according to the measured spectrum; the target micro-motion characteristics are used to characterize the instantaneous frequency shift characteristics corresponding to the continuous wave Doppler signal data.

2. The method for extracting micro-motion features of a projectile target according to claim 1, characterized in that, The process of using data analysis to process the fused data to obtain the measured spectrum includes: The fused data is filtered to obtain the processed fused data; The processed fused data is decomposed using current time-frequency analysis and multi-resolution analysis data methods to obtain decomposed data. The amplitude corresponding to each frequency in the continuous wave Doppler signal data in the decomposed data is extracted using the current time-frequency analysis method. By sequentially connecting multiple amplitude values, the measured spectrum is obtained.

3. The method for extracting micro-motion features of a projectile target according to claim 1, characterized in that, The deployment methods include: a combined tail-chase and attack deployment method, a tail-chase deployment method, and an attack deployment method.

4. A system for extracting micro-motion features of a projectile target, characterized in that, The system includes: The projectile echo data acquisition module is used to acquire projectile echo data of the projectile target; the projectile echo data includes: volume, shape, material, and the micro-motion characteristics corresponding to the material; The measured model construction module is used to construct a measured model of the projectile target based on the projectile echo data; The flight data acquisition module is used to acquire flight data of the measured model under experimental conditions; the flight data includes: flight conical spin data, oscillation data, and projectile translational data; the projectile translational data includes continuous wave Doppler signal data. The simulation data determination module is used to perform simulation using simulation software based on the flight data and the measured model to obtain simulation data; the simulation data represents the intrinsic relationship between the measured model and the flight data regarding attitude motion. The simulation spectrum determination module is used to process the simulation data using mode decomposition and current time-frequency analysis methods to obtain the simulation spectrum. The measured projectile echo data acquisition module is used to acquire the measured projectile echo data of the projectile target within a set time period using a radar with a set deployment method. The external ballistic parameter data acquisition module is used to acquire the external ballistic parameter data of the projectile target using optical remote sensing; the external ballistic parameter data includes: optical telemetry flight cone spin data and optical telemetry oscillation data; The data fusion determination module is used to fuse the measured projectile echo data and the external ballistic parameter data using a data fusion method to obtain fused data; The measured spectrum determination module is used to process the fused data using data analysis methods to obtain the measured spectrum; the data analysis methods include: current time-frequency analysis methods and multi-resolution data analysis methods; The matching module is used to match the simulated spectrum with the measured spectrum, determine whether the difference between the simulated spectrum and the measured spectrum is within a set range, and obtain a judgment result. The optimization module is used to add white noise with a set signal-to-noise ratio to the current time-frequency method if the judgment result is negative, to obtain an optimized time-frequency analysis method, and to use the optimized time-frequency analysis method as the next time-frequency method, and return to the "simulation spectrum determination module"; The target micro-motion feature determination module is used to determine the target micro-motion features of the projectile target based on the measured spectrum if the judgment result is yes; the target micro-motion features are used to characterize the instantaneous frequency shift characteristics corresponding to the continuous wave Doppler signal data.

5. The system for extracting micro-motion features of a projectile target according to claim 4, characterized in that, The measured spectrum determination module includes: The preprocessing submodule is used to filter the fused data to obtain the processed fused data. The decomposition submodule is used to decompose the processed fused data using current time-frequency analysis methods and multi-resolution analysis data methods to obtain decomposed data. An extraction submodule is used to extract the amplitude corresponding to each frequency in the continuous wave Doppler signal data from the decomposed data using the current time-frequency analysis method; A submodule is defined to sequentially connect multiple amplitude values ​​to obtain the measured spectrum.

6. The system for extracting micro-motion features of a projectile target according to claim 4, characterized in that, The set deployment methods in the measured projectile echo data acquisition module include: a combined tail-chase and intercept deployment method, a tail-chase deployment method, and an intercept deployment method.

7. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the method for extracting micro-motion features of a projectile target as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method for extracting micro-motion features of a projectile target as described in any one of claims 1 to 3.

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