Feature detection method, device and equipment of micro-vibration target and medium

Abstract

The invention provides a feature detection method, device and equipment for a micro-vibration target and a medium, and the method comprises the steps: transmitting a frequency-modulated continuous wave to the micro-vibration target through employing a frequency-modulated continuous wave radar, receiving a time-domain voltage signal returned by the micro-vibration target, converting the time-domain voltage signal to obtain time-domain amplitude data, and transmitting the time-domain amplitude data to the micro-vibration target; fourier transform is carried out on the time domain amplitude data to obtain frequency domain amplitude data, Doppler analysis is carried out on the frequency domain amplitude data to obtain initial phase change rate data, and Doppler analysis is carried out on the initial phase change rate data to obtain target phase change rate data; and selecting data in a preset phase change rate range from the target phase change rate data as target feature data, determining feature information of the micro-vibration target according to the target feature data, and taking the feature information as a feature detection result of the micro-vibration target. The method does not need to filter the environmental noise according to a specific scene, reduces the difficulty of scene adaptation, and improves the accuracy of feature detection.

Description

Technical Field

[0001] The present application relates to the field of signal processing, and particularly to a method, device, equipment and medium for detecting the characteristics of a micro-vibration target. Background Art

[0002] FMCW (Frequency Modulated Continuous Wave Radar) radar is increasingly widely used in the field of life detection, especially excellent in detecting micro-vibration targets. Through phase analysis and Doppler analysis, FMCW radar can effectively detect and extract the micro-vibration characteristics of the target, such as vital sign parameters like breathing frequency and heartbeat. However, in practical applications, the phase change of the micro-vibration target is slow. To improve the detection accuracy, slow-time Doppler analysis is usually carried out at a low sampling rate. Although this method helps to reduce the pressure of data processing and storage, it also brings the problem of spectral aliasing, that is, the phase change beyond the Nyquist sampling frequency will be superimposed on the frequency measurement interval in the form of spectral aliasing, thus affecting the accuracy of the target detection signal.

[0003] Currently, the filtering of interference signals is mainly carried out in the target detection stage, and the interference signals are filtered by the characteristic values of specific scenarios. Although this method can reduce signal interference to a certain extent, its effect is limited by the scene recognition ability and scene coverage. In addition, this processing method also needs to extract features for each specific scenario, which is difficult to meet the requirements of complex and changeable environments, and it is difficult to achieve efficient and accurate feature detection of micro-vibration targets under limited resources. Summary of the Invention

[0004] In view of the above problems, a method, device, equipment and medium for detecting the characteristics of a micro-vibration target are provided to overcome or at least partially solve the above problems, including:

[0005] In the first aspect of the implementation of the present application, a method for detecting the characteristics of a micro-vibration target is first provided, which is characterized in that the method includes:

[0006] Transmit a frequency-modulated continuous wave to the micro-vibration target by using a frequency-modulated continuous wave radar;

[0007] Receive the time-domain voltage signal returned by the micro-vibration target in response to the frequency-modulated continuous wave;

[0008] Convert the time-domain voltage signal to obtain time-domain amplitude data;

[0009] Perform Fourier transform on the time-domain amplitude data to obtain frequency-domain amplitude data;

[0010] Performing Doppler analysis on the frequency-domain amplitude data to obtain initial phase change rate data;

[0011] Performing Doppler analysis on the initial phase change rate data to obtain target phase change rate data;

[0012] Selecting data within a preset phase change rate range from the target phase change rate data as target feature data;

[0013] Determining the feature information of the micro-vibration target according to the target feature data, and using the feature information as the feature detection result of the micro-vibration target.

[0014] Optionally, the frequency-modulated continuous wave is composed of a plurality of data frames, and each data frame includes a plurality of frequency-modulated signals. Before performing Doppler analysis on the frequency-domain amplitude data to obtain initial phase change rate data, the method further includes:

[0015] Obtaining the frequency-domain amplitude data corresponding to the data frame;

[0016] Determining DC component data according to the frequency-domain amplitude data corresponding to the data frame;

[0017] Performing DC removal processing on the frequency-domain amplitude data corresponding to the frequency-modulated signals in the data frame according to the DC component data;

[0018] Normalizing the frequency-domain amplitude data corresponding to the frequency-modulated signals after DC removal processing;

[0019] Using the normalized frequency-domain amplitude data as the new frequency-domain amplitude data.

[0020] Optionally, the initial phase change rate data includes first phase change rate data. Performing Doppler analysis on the frequency-domain amplitude data to obtain initial phase change rate data includes:

[0021] For any one of the data frames, obtaining the data frame duration corresponding to the data frame;

[0022] Determining the first Nyquist frequency according to the data frame duration;

[0023] Performing Doppler analysis on the frequency-domain amplitude data corresponding to the frequency-modulated signals in the data frame using the first Nyquist frequency to obtain the first phase change rate data.

[0024] Optionally, the initial phase change rate data further includes second phase change rate data. Before performing Doppler analysis on the initial phase change rate data to obtain target phase change rate data, the method further includes:

[0025] Obtain a preset phase change rate threshold;

[0026] Select the first phase change rate data less than the phase change rate threshold from the first phase change rate data as the second phase change rate data.

[0027] Optionally, the obtaining the target phase change rate data by performing Doppler analysis on the initial phase change rate data includes:

[0028] Obtain the number of data frames in the frequency modulated continuous wave;

[0029] Determine the second Nyquist frequency according to the data frame duration and the number of data frames;

[0030] Perform Doppler analysis on the second phase change rate data corresponding to a plurality of the data frames by using the second Nyquist frequency to obtain the target phase change rate data.

[0031] Optionally, the frequency modulated continuous wave radar includes a plurality of receiving antennas. After obtaining the target phase change rate data by performing Doppler analysis on the initial phase change rate data, the method further includes:

[0032] Obtain a plurality of the time domain voltage signals respectively received by the plurality of receiving antennas;

[0033] Determine the receiving moments of the plurality of the time domain voltage signals, and adjust the plurality of the time domain voltage signals to a preset reference moment according to the receiving moments;

[0034] Determine the signal phases of the plurality of the time domain voltage signals, and adjust the signal phases of the plurality of the time domain voltage signals to a preset reference phase according to the signal phases;

[0035] Superimpose the adjusted plurality of the time domain voltage signals to obtain a superimposed time domain voltage signal;

[0036] Determine the superimposed target phase change rate data corresponding to the superimposed time domain voltage signal.

[0037] Optionally, the selecting the data within a preset phase change rate range from the target phase change rate data as the target feature data includes:

[0038] Obtain the to-be-detected feature of the micro-vibration target;

[0039] Determine the preset phase change rate range corresponding to the to-be-detected feature;

[0040] Select candidate feature data within the preset phase change rate range from the superimposed target phase change rate data;

[0041] Determine a noise threshold corresponding to the candidate feature data according to the candidate feature data;

[0042] Obtain amplitude data corresponding to the candidate feature data, and use the candidate feature data whose amplitude data is greater than the noise threshold as the target feature data.

[0043] In a second aspect of the implementation of the present application, a feature detection device for a micro-vibration target is provided, characterized in that the device includes:

[0044] A frequency-modulated continuous wave transmitting module, configured to transmit a frequency-modulated continuous wave to the micro-vibration target by using a frequency-modulated continuous wave radar;

[0045] A time-domain voltage signal receiving module, configured to receive a time-domain voltage signal returned by the micro-vibration target for the frequency-modulated continuous wave;

[0046] An analog-to-digital conversion module, configured to convert the time-domain voltage signal to obtain time-domain amplitude data;

[0047] A Fourier transform module, configured to perform a Fourier transform on the time-domain amplitude data to obtain frequency-domain amplitude data;

[0048] A fast-time analysis module, configured to perform Doppler analysis on the frequency-domain amplitude data to obtain initial phase change rate data;

[0049] A slow-time analysis module, configured to perform Doppler analysis on the initial phase change rate data to obtain target phase change rate data;

[0050] A target data selection module, configured to select data within a preset phase change rate range from the target phase change rate data as target feature data;

[0051] A feature detection result acquisition module, configured to determine feature information of the micro-vibration target according to the target feature data, and use the feature information as the feature detection result of the micro-vibration target.

[0052] In a third aspect of the implementation of the present application, an electronic device is provided, characterized in that it includes a processor, a memory, and a computer program stored on the memory and capable of running on the processor, and when the computer program is executed by the processor, it implements the feature detection method of the micro-vibration target as described above.

[0053] In a fourth aspect of the implementation of the present application, a computer-readable storage medium is provided, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the feature detection method of the micro-vibration target as described above.

[0054] The embodiments of the present application have the following advantages:

[0055] In the present application, a frequency-modulated continuous wave radar is used to transmit a frequency-modulated continuous wave to a micro-vibration target, and the time-domain voltage signal returned by the micro-vibration target in response to the frequency-modulated continuous wave is received. The time-domain voltage signal is converted to obtain time-domain amplitude data, the time-domain amplitude data is subjected to Fourier transform to obtain frequency-domain amplitude data, the frequency-domain amplitude data is subjected to Doppler analysis to obtain initial phase change rate data, the initial phase change rate data is subjected to Doppler analysis to obtain target phase change rate data, data within a preset phase change rate range is selected from the target phase change rate data as target feature data, the feature information of the micro-vibration target is determined based on the target feature data, and the feature information is used as the feature detection result of the micro-vibration target. By adopting the frequency-modulated continuous wave radar technology, the present application realizes high-precision detection of micro-vibration targets, and the present application does not need to filter environmental noise according to a specific scenario, thereby reducing the difficulty of scenario adaptation, reducing the pressure of data processing, and improving the utilization rate of processing resources and memory resources. Description of the Drawings

[0056] In order to more clearly illustrate the technical solutions of the present application, the drawings required for the description of the present application will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 is a flowchart of the steps of a method for detecting the characteristics of a micro-vibration target provided by an embodiment of the present application;

[0058] Figure 2 is a schematic structural diagram of a device for detecting the characteristics of a micro-vibration target provided by an embodiment of the present application. Detailed Embodiments

[0059] In order to make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0060] Due to its excellent phase detection ability and powerful Doppler analysis ability, FMCW radar is extremely sensitive to targets with velocity, making it possible to detect micro-vibration targets using FMCW radar. With its advantages such as small size, low power consumption, low cost, and less system resource occupancy, FMCW radar has gradually been widely used in the field of life detection, such as respiratory frequency detection, heart rate detection, gesture detection, and motion detection, etc.

[0061] The detection of micro-vibration targets by FMCW radar mainly relies on phase analysis. By analyzing the phase change information between multiple consecutive Chirps (frequency-modulated signals) or Frames (data frames), the micro-vibration frequency characteristics of the target are extracted, and then target recognition and the extraction of vital sign parameters are achieved.

[0062] However, in practical applications, the phase change of micro-vibration targets is usually relatively slow. When the data processing and storage capabilities are limited, in order to improve the detection accuracy, a low sampling rate is usually used for slow-time Doppler analysis. However, due to the limitation of the Nyquist frequency characteristic, the phase change beyond the Nyquist sampling frequency will be superimposed on the frequency measurement interval in the form of spectral overflow, thus affecting the target detection signal. This effect is called non-expected target vibration, which mainly includes two categories: the self-motion of the detected target and environmental superimposed vibration. The self-motion includes the movement of the target itself and the superposition of its multiple vibration frequencies, such as the superposition of gestures, breathing, and heartbeats; the environmental superimposed vibration includes the target accompanying superimposed vibration caused by the environment (such as the non-expected movement of the target in the car along with the vehicle), non-expected target vibration, and the vibration of the detection sensor itself, etc.

[0063] In the related technology, the filtering method of interference signals is mainly carried out in the target detection stage, and the interference signals are filtered by the characteristic values of specific scenarios. Although this method can reduce signal interference to a certain extent, its effect is limited by the scene recognition ability and scene coverage. In addition, this processing method also needs to extract features for each specific scenario, which is difficult to meet the requirements of complex and changeable environments, and it is difficult to achieve efficient and accurate feature detection of micro-vibration targets under limited resources.

[0064] Therefore, this patent provides a method for feature detection of micro-vibration targets, which screens the signals to a greater extent in the signal processing stage, retains effective targets, so as to obtain a higher proportion of effective targets before the data processing stage, and then improves the accuracy of feature detection of micro-vibration targets.

[0065] Referring to Figure 1 , a flowchart of the steps of a method for feature detection of a micro-vibration target provided by an embodiment of the present application is shown. The method may specifically include the following steps:

[0066] S101: Transmit a frequency - modulated continuous - wave (FMCW) to a micro - vibration target.

[0067] In the embodiment of the present application, an FMCW radar can be used to transmit an FMCW to a micro - vibration target.

[0068] Among them, the micro - vibration target can be a target that generates minute vibrations or weak movements. These targets usually have low vibration frequencies and small vibration amplitudes, so they may be difficult to accurately detect and identify in traditional radar systems. The body vibrations caused by breathing and heartbeat belong to micro - vibrations. Other such vibrations include gestures, the swaying of hanging objects, alarm clocks, and mobile phone vibrations. The detection of micro - vibration targets is of great significance in many applications, especially in the field of life detection, such as respiratory rate detection, heartbeat detection, gesture detection, and motion detection.

[0069] The FMCW can be a continuous frequency - modulated signal, encoded in the form of data frames. Among them, a data frame can be the basic unit for an FMCW radar to perform a complete measurement. Each data frame is composed of multiple frequency - modulated signals, and these frequency - modulated signals are transmitted and received in a certain order and time interval.

[0070] Each frequency - modulated signal can be a basic unit in the data frame. The frequency of the frequency - modulated signal linearly increases from a low frequency to a high frequency within a certain time, and then linearly decreases back to the low frequency. Usually, only monotonic frequency modulation is performed. The frequency change range (bandwidth) and duration (period) of the frequency - modulated signal determine the range resolution and measurement accuracy of the FMCW radar.

[0071] S102: Receive the time - domain voltage signal returned by the micro - vibration target in response to the FMCW.

[0072] In the embodiment of the present application, the time - domain voltage signal returned by the micro - vibration target in response to the FMCW can be received. Among them, the time - domain voltage signal can be obtained when the FMCW radar transmits an FMCW signal to the target, and the signal reflected by the target is mixed (multiplied) with the current transmitted signal to obtain a difference - frequency signal. The frequency of this difference - frequency signal is proportional to the distance of the target and can be represented as a time - domain voltage signal at the receiving end. These time - domain voltage signals contain the distance, speed, and micro - vibration information of the target, and the characteristic information of the target can be extracted by analyzing these signals.

[0073] S103: Convert the time - domain voltage signal to obtain time - domain amplitude data.

[0074] In the embodiment of the present application, the time - domain voltage signal can be converted to obtain time - domain amplitude data. Specifically, ADC data sampling can be performed on the time - domain voltage signal to obtain the time - domain amplitude data.

[0075] ADC (Analog-to-Digital Converter) data sampling is a key step in the FMCW radar system, which is used to convert the received analog signal into a digital signal for subsequent signal processing and analysis. The quality and efficiency of ADC data sampling directly affect the performance and detection accuracy of the radar system.

[0076] Among them, a single time-domain amplitude data can be in the form of a complex number (a + i*b) with phase information, which contains amplitude information a and phase information b, referring to the signal phase and energy sampled at each time point. The storage method of single-channel time-domain amplitude data is stored in units of time (i.e., the abscissa is time). Because there are multiple frequency-modulated signals, the time-domain amplitude data can be understood as a three-dimensional graph with the x-axis as time, the z-axis as amplitude, and the y-axis as the frequency-modulated signal number in graphical form. It should be noted that the three-dimensional graph can only express the amplitude information. In fact, the time-domain amplitude data is a complex number form that contains both phase and amplitude.

[0077] In practical applications, the frequency-modulated signal is usually monotonically frequency-modulated (either monotonically increasing or monotonically decreasing). Since the measurement range is three-dimensional in space, it can be understood that all targets within the range will be measured. Therefore, the actual time-domain waveform is an aliasing of multiple single-target waveforms.

[0078] S104: Perform Fourier transform on the time-domain amplitude data to obtain the frequency-domain amplitude data.

[0079] In the embodiment of the present application, the time-domain amplitude data can be subjected to Fourier transform to obtain the frequency-domain amplitude data. The time-domain amplitude data can be Fourier-analyzed in units of each frequency-modulated signal to obtain the frequency-domain amplitude data in units of the frequency-modulated signal. The frequency-domain amplitude data can reflect the distance information of the micro-vibration target.

[0080] Among them, a single frequency-domain amplitude data can also be in the form of a complex number (a + i*b), which contains amplitude information a and phase information b, referring to the energy of each frequency and its signal phase respectively. The storage method of single-channel frequency-domain amplitude data is stored in units of frequency (i.e., the abscissa is frequency, which also represents distance). Because there are multiple frequency-modulated signals, the frequency-domain amplitude data can be understood as a three-dimensional graph with the x-axis as frequency (distance), the z-axis as amplitude, and the y-axis as the frequency-modulated signal number in graphical form. It should be noted that the three-dimensional graph can only express the amplitude information. In fact, the frequency-domain amplitude data is a complex number form that contains both phase and amplitude.

[0081] S105: Perform Doppler analysis on the frequency-domain amplitude data to obtain the initial phase change rate data.

[0082] In the embodiments of the present application, Doppler analysis can be performed on the frequency-domain amplitude data to obtain the initial phase change rate data. Among them, the Doppler analysis performed on the frequency-domain amplitude data can be fast-time Doppler analysis, that is, taking data frames as units, and performing fast Fourier transform processing on each data from the frequency modulation signal dimension (that is, the y-axis frequency modulation signal number dimension in the frequency-domain amplitude data). The initial phase change rate data can reflect the velocity information of the micro-vibration target.

[0083] "Fast time" can be a concept regarding the collection time length of data, and the processing of signals within a data frame can all be referred to as "fast time".

[0084] Among them, a single initial phase change rate data can also be in complex form (a + i*b), which contains amplitude information a and phase information b, respectively referring to the energy and signal phase of a certain frequency and a certain frequency change rate. The initial phase change rate data can be understood graphically as having the frequency (distance) on the x-axis, the amplitude on the z-axis, and the phase change rate (which can also be understood as the Doppler frequency and also refers to the target velocity) on the y-axis. It should be noted that the three-dimensional graph can only express the amplitude information, and actually the initial phase change rate data is a complex form that contains both phase and amplitude.

[0085] S106: Perform Doppler analysis on the initial phase change rate data to obtain the target phase change rate data.

[0086] In the embodiments of the present application, Doppler analysis can be performed on the initial phase change rate data to obtain the target phase change rate data. Among them, the Doppler analysis performed on the initial phase change rate data can be slow-time Doppler analysis, that is, taking multiple data frames as units, and processing each data from the data frame dimension. After performing fast Fourier transform on each data frame, there is an additional fourth dimension in the data dimension, that is, the data frame number. Performing fast Fourier transform on the data frame number dimension is the slow-time analysis, and the target phase change rate data can reflect the vibration frequency information of the micro-vibration target.

[0087] The finally obtained data is four-dimensional data, with the frequency (distance) on the x-axis, the amplitude on the z-axis, the Doppler frequency (which can also be understood as the phase change rate and also refers to the target velocity) on the y-axis, and the fourth dimension being the vibration frequency. The target phase change rate data can reflect the vibration frequency information of the micro-vibration target.

[0088] "Slow time" can be a concept regarding the collection time length of data, and the processing of signals between data frames can all be referred to as "slow time".

[0089] Among them, the target phase change rate data can be graphically understood as having the frequency (distance) on the x-axis, the amplitude on the z-axis, the Doppler frequency (which can also be understood as the phase change rate and also refers to the target speed) on the y-axis, and the vibration frequency as the fourth dimension. It should be noted that the graph can only express the amplitude information. In fact, the target phase change rate data contains both phase and amplitude in complex form.

[0090] S107: Select the data within the preset phase change rate range from the target phase change rate data as the target feature data.

[0091] In the embodiment of the present application, the data within the preset phase change rate range can be selected from the target phase change rate data as the target feature data. The preset phase change rate range can be the frequency interval corresponding to the feature to be detected of the micro-vibration target. The target feature data can be the data within the frequency interval corresponding to the feature to be detected.

[0092] S108: Determine the feature information of the micro-vibration target according to the target feature data, and use the feature information as the feature detection result of the micro-vibration target.

[0093] In the embodiment of the present application, the feature information of the micro-vibration target can be determined according to the target feature data, and the feature information is used as the feature detection result of the micro-vibration target.

[0094] Among them, the target feature data is selected from the target phase change rate data. According to the target feature data, the energy amplitude, phase, distance, speed, and vibration frequency of the micro-vibration target can be obtained. For the feature detection of the micro-vibration target, the most useful information is the distance, speed, and vibration frequency. The feature detection result of the micro-vibration target can be determined according to the information such as the distance, speed, and vibration frequency of the micro-vibration target.

[0095] In the present application, a frequency-modulated continuous-wave radar is used to transmit a frequency-modulated continuous wave to the micro-vibration target, receive the time-domain voltage signal returned by the micro-vibration target for the frequency-modulated continuous wave, convert the time-domain voltage signal to obtain the time-domain amplitude data, perform Fourier transform on the time-domain amplitude data to obtain the frequency-domain amplitude data, perform Doppler analysis on the frequency-domain amplitude data to obtain the initial phase change rate data, perform Doppler analysis on the initial phase change rate data to obtain the target phase change rate data, select the data within the preset phase change rate range from the target phase change rate data as the target feature data, determine the feature information of the micro-vibration target according to the target feature data, and use the feature information as the feature detection result of the micro-vibration target. By adopting the frequency-modulated continuous-wave radar technology, the present application realizes high-precision detection of the micro-vibration target, and the present application does not need to filter the environmental noise according to a specific scenario, thereby reducing the difficulty of scenario adaptation, reducing the pressure of data processing, and improving the utilization rate of processing resources and memory resources.

[0096] In an FMCW radar system, due to the differences in distance variation and target radar cross - section (RCS), the signal - to - noise ratio (SNR) of weak targets may be significantly reduced, making it difficult to detect these targets. This signal energy difference mainly stems from the following aspects: First, the radar signal is attenuated by distance during propagation. According to the radar equation, the power of the signal is inversely proportional to the fourth power of the distance. Therefore, the farther the distance, the lower the signal energy. Second, the RCS (radar cross - section) of the target directly affects the intensity of the reflected signal. Targets with a smaller RCS reflect weaker signal energy, resulting in a lower SNR. In addition, environmental noise and multipath effects will further weaken the target signal, making it difficult to detect these targets in complex background noise. To improve the detection ability of weak targets, a series of signal - processing techniques are required to increase the SNR and detection accuracy.

[0097] In an alternative embodiment of the present application, the frequency - modulated continuous wave is composed of several data frames, and each data frame includes several frequency - modulated signals. Before step S105, the method further includes the following steps:

[0098] S11: Obtain the frequency - domain amplitude data corresponding to the data frame;

[0099] S12: Determine the DC component data according to the frequency - domain amplitude data corresponding to the data frame.

[0100] In an embodiment of the present application, the frequency - domain amplitude data corresponding to the data frame can be obtained, and the DC component data can be determined according to the frequency - domain amplitude data corresponding to the data frame.

[0101] In a specific implementation, the frequency - domain amplitude data can be X k (f), where k represents the frequency - modulated signal coding, f represents the frequency, and the DC component data DC k can be expressed as:

[0102]

[0103] where N is the number of sampling points of the fast Fourier transform.

[0104] S13: Perform DC removal processing on the frequency - domain amplitude data corresponding to the frequency - modulated signals in the data frame according to the DC component data.

[0105] In an embodiment of the present application, the frequency - domain amplitude data corresponding to the frequency - modulated signals in the data frame can be subjected to DC removal processing according to the DC component data.

[0106] In a specific implementation, the DC removal processing can be expressed as:

[0107] X′ k (f)=X k (f)-DCk

[0108] Among them, X′ k (f) is the frequency-domain amplitude data after DC removal processing.

[0109] S14: Normalize the frequency-domain amplitude data corresponding to the frequency-modulated signal after DC removal processing;

[0110] S15: Use the normalized frequency-domain amplitude data as the new frequency-domain amplitude data.

[0111] In the embodiment of the present application, the frequency-domain amplitude data corresponding to the frequency-modulated signal after DC removal processing can be normalized, and the normalized frequency-domain amplitude data is used as the new frequency-domain amplitude data. Normalization can eliminate the amplitude differences between different signals, making the signals have a consistent reference benchmark in subsequent processing. At least one of maximum normalization, mean normalization, fractional normalization, and logarithmic normalization can be used.

[0112] Through DC removal processing and normalization processing, the present application can filter out the static energy information in the data, retain the phase change information, eliminate the signal energy differences caused by distance and RCS (radar cross section), highlight weak targets, and obtain higher-quality data.

[0113] The Nyquist frequency refers to that when performing signal sampling, in order to avoid spectral aliasing, the sampling frequency must be at least twice the highest frequency of the signal. In an FMCW radar system, the Nyquist frequency has an important impact on the analysis effects of fast time (distance) and slow time (speed). If the sampling frequency is lower than the Nyquist frequency, it will cause spectral aliasing, making the frequency components of the signal unable to be accurately restored, thus affecting the measurement accuracy of distance and speed. Therefore, selecting an appropriate sampling frequency to meet the Nyquist frequency requirement is the key to ensuring the analysis effects of fast and slow time.

[0114] In an alternative embodiment of the present application, the initial phase change rate data includes first phase change rate data, and step S105 further includes the following sub-steps:

[0115] S21: For any data frame, obtain the data frame duration corresponding to the data frame.

[0116] S22: Determine the first Nyquist frequency according to the data frame duration.

[0117] In the embodiment of the present application, for any data frame, the data frame duration corresponding to the data frame can be obtained, and the first Nyquist frequency is determined according to the data frame duration. Among them, the first Nyquist frequency is the frequency used for fast-time Doppler analysis of the frequency-domain amplitude data. The first Nyquist frequency can be the reciprocal of the data frame duration.

[0118] S23: Perform Doppler analysis on the frequency-domain amplitude data corresponding to the frequency-modulated signal in the data frame at the first Nyquist frequency to obtain the first phase change rate data.

[0119] In the embodiment of the present application, Doppler analysis can be performed on the frequency-domain amplitude data corresponding to the frequency-modulated signal in the data frame at the first Nyquist frequency to obtain the first phase change rate data. The first phase change rate data is the data obtained by performing fast-time Doppler analysis on the frequency-domain amplitude data at the first Nyquist frequency.

[0120] In an alternative embodiment of the present application, the initial phase change rate data further includes second phase change rate data. Before step S106, the method further includes the following steps:

[0121] S31: Obtain a preset phase change rate threshold;

[0122] S32: Select the first phase change rate data less than the phase change rate threshold from the first phase change rate data as the second phase change rate data.

[0123] In the embodiment of the present application, a preset phase change rate threshold can be obtained, and the first phase change rate data less than the phase change rate threshold is selected from the first phase change rate data as the second phase change rate data. The preset phase change rate threshold can be set according to the actual situation.

[0124] Among them, according to the actual situation, data with a speed lower than a certain threshold can be screened, that is, after fast-time Doppler analysis, all data with a y-axis speed (i.e., Doppler frequency, phase change rate) lower than a certain threshold.

[0125] In an alternative embodiment of the present application, step S106 further includes the following sub-steps:

[0126] S41: Obtain the number of data frames in the frequency-modulated continuous wave;

[0127] S42: Determine the second Nyquist frequency according to the data frame duration and the number of data frames.

[0128] In the embodiment of the present application, the number of data frames in the frequency-modulated continuous wave can be obtained, and the second Nyquist frequency is determined according to the data frame duration and the number of data frames. Among them, the second Nyquist frequency is the frequency used for slow-time Doppler analysis of the second phase change rate data. The second Nyquist frequency can be the reciprocal of the product of the data frame duration and the number of data frames.

[0129] S43: Perform Doppler analysis on the second phase change rate data corresponding to a number of data frames at the second Nyquist frequency to obtain the target phase change rate data.

[0130] In the embodiment of the present application, the Doppler analysis can be performed on the second phase change rate data corresponding to a plurality of data frames by using the second Nyquist frequency to obtain the target phase change rate data.

[0131] In the present application, through a step-by-step Doppler analysis method, first, the first Nyquist frequency is determined by using the data frame duration, and the fast-time Doppler analysis is performed on the frequency-modulated signal to obtain the first phase change rate data; then, the second phase change rate data is screened out by setting a phase change rate threshold, and then the second Nyquist frequency is determined according to the number and duration of the data frames, and the slow-time Doppler analysis is performed to finally obtain the target phase change rate data. This method not only improves the accuracy and reliability of the Doppler analysis, but also reduces the interference of noise and irrelevant data through threshold screening, improving the accuracy and reliability of the analysis results. In addition, this method solves the problem of spectral overflow caused by insufficient Nyquist frequency, can quickly separate high-speed and high-frequency vibration targets, reduces the data volume and processing resource occupancy, and at the same time provides a higher phase change resolution, which helps to detect lower-frequency targets.

[0132] In an alternative embodiment of the present application, the frequency-modulated continuous wave radar includes a plurality of receiving antennas. After step S106, the method further includes the following steps:

[0133] S51: Obtain a plurality of time-domain voltage signals respectively received by a plurality of receiving antennas.

[0134] In the embodiment of the present application, a plurality of time-domain voltage signals respectively received by a plurality of receiving antennas can be obtained.

[0135] Among them, MIMO (Multiple Input Multiple Output) is a wireless communication technology that improves the performance of a communication system by using multiple transmitting antennas and multiple receiving antennas. The MIMO technology can significantly improve the data transmission rate and system capacity without increasing the bandwidth or total transmission power, while enhancing the reliability and coverage of the signal. Due to the existence of MIMO, the time-domain voltage signals returned by the micro-vibration target received in the present application for the frequency-modulated continuous wave can be multi-channel data. Since the phases of each channel are not equal, coherent superposition is required to maximize the energy. Here, it can be understood as signal superposition, so the form of the signal after superposition will not change. The data form finally obtained by processing the time-domain voltage signals is the same as the target phase change rate data after slow-time Doppler analysis, except that it changes from single-channel data to multi-channel data superposition.

[0136] S52: Determine the reception times of the plurality of time-domain voltage signals, and adjust the plurality of time-domain voltage signals to a preset reference time according to the reception times.

[0137] In the embodiments of the present application, the reception times of a plurality of time-domain voltage signals can be determined, and the plurality of time-domain voltage signals are adjusted to a preset reference time according to the reception times. The preset reference time can be set according to the actual situation.

[0138] Before coherent superposition, it is first necessary to ensure that all signals are synchronized in time. If the signals are not correctly aligned, the phase information will not be correctly matched, resulting in poor coherent superposition effect. In the embodiments of the present application, time delay estimation (TDE) technology can be used to determine the time difference between signals and make corresponding adjustments to align all signals in time.

[0139] S53: Determine the signal phases of a plurality of time-domain voltage signals, and adjust the signal phases of the plurality of time-domain voltage signals to a preset reference phase according to the signal phases.

[0140] In the embodiments of the present application, the signal phases of a plurality of time-domain voltage signals can be determined, and the signal phases of the plurality of time-domain voltage signals are adjusted to a preset reference phase according to the signal phases. The preset reference phase can be set according to the actual situation.

[0141] After aligning the signals, phase correction can be performed. Phase correction is to ensure that all signals have the same phase when superimposed, so as to achieve the maximum effect of coherent superposition. In the embodiments of the present application, the phase difference of each signal can be calculated and the corresponding phase correction factor can be applied to make the phases of all signals consistent. This usually involves the phase adjustment of complex signals.

[0142] S54: Superimpose the adjusted plurality of time-domain voltage signals to obtain a superimposed time-domain voltage signal;

[0143] S55: Determine the superimposed target phase change rate data corresponding to the superimposed time-domain voltage signal.

[0144] In the embodiments of the present application, the adjusted plurality of time-domain voltage signals can be superimposed to obtain a superimposed time-domain voltage signal, and then the superimposed target phase change rate data corresponding to the superimposed time-domain voltage signal can be determined.

[0145] After signal time alignment and phase correction, all signals that have been time-aligned and phase-corrected can be linearly superimposed. Since the phases of the signals have been aligned, the intensity of the superimposed signal will increase significantly, while the noise will not be superimposed in the same way, thus improving the signal-to-noise ratio.

[0146] In an alternative embodiment of the present application, step S107 further includes the following steps:

[0147] S61: Obtain the features to be detected of the micro-vibration target;

[0148] S62: Determine the preset phase change rate range corresponding to the feature to be detected;

[0149] S63: Select candidate feature data within the preset phase change rate range from the superimposed target phase change rate data.

[0150] In the embodiments of the present application, the feature to be detected of the micro-vibration target can be obtained, the preset phase change rate range corresponding to the feature to be detected can be determined, and then candidate feature data within the preset phase change rate range can be selected from the superimposed target phase change rate data. The candidate feature data is the data within the frequency interval of the feature to be detected.

[0151] Among them, different phase change rates can be classified and screened to distinguish the frequency intervals corresponding to different features to be detected, and then the data within the frequency interval of the feature to be detected can be extracted as the candidate feature data.

[0152] S64: Determine the noise threshold corresponding to the candidate feature data according to the candidate feature data.

[0153] In the embodiments of the present application, the noise threshold corresponding to the candidate feature data can be determined according to the candidate feature data.

[0154] Constant False Alarm Rate (CFAR) is a target detection technology used in radar signal processing, aiming to maintain a constant false alarm rate in different noise environments. The CFAR technology ensures that the false alarm rate (i.e., the probability of misdetecting a target) remains constant by dynamically adjusting the detection threshold. In the embodiments of the present application, the CFAR technology can be used for noise estimation, threshold calculation, and target detection.

[0155] A reference window can be used to estimate the noise level. The reference window can contain multiple range cells, which are considered background noise and do not contain target signals. By statistically analyzing the signal strength of these cells (such as mean, median, or Gaussian distribution estimation), an estimated value of the noise level can be obtained.

[0156] After obtaining the noise estimated value, the noise threshold can be calculated. The calculation of the threshold needs to consider the required false alarm rate to ensure that the false alarm rate remains constant in different noise levels.

[0157] S65: Obtain the amplitude data corresponding to the candidate feature data, and use the candidate feature data with the amplitude data greater than the noise threshold as the target feature data.

[0158] In an embodiment of the present application, amplitude data corresponding to candidate feature data can be obtained, and candidate feature data with amplitude data greater than the noise threshold is used as target feature data.

[0159] Apply the calculated noise threshold to actual signal detection. If the signal intensity of the candidate feature data exceeds the noise threshold, it is considered that there is target feature data in the candidate feature data.

[0160] In the present application, by obtaining the features to be detected of the micro-vibration target, determining the preset phase change rate range, and screening out candidate feature data from the superimposed target phase change rate data. Then, determine the noise threshold according to the candidate feature data, and finally use the candidate feature data with amplitude data greater than the noise threshold as the target feature data. This method can effectively improve the accuracy and reliability of micro-vibration target detection through fine feature screening and noise threshold calculation. By classifying and screening data with different phase change rates, the frequency intervals corresponding to different features to be detected can be distinguished, so as to extract candidate feature data. Further, by dynamically adjusting the noise threshold, ensure that the false alarm rate remains constant under different noise levels, thereby improving the stability and reliability of target detection. This method can not only reduce noise interference, but also improve the extraction accuracy of target feature data, and is applicable to micro-vibration target detection in complex noise environments.

[0161] Refer to Figure 2 , which shows a schematic structural diagram of a feature detection device for a micro-vibration target provided by an embodiment of the present application, and specifically may include the following modules:

[0162] A frequency-modulated continuous wave transmitting module 201, configured to transmit a frequency-modulated continuous wave to the micro-vibration target by using a frequency-modulated continuous wave radar;

[0163] A time-domain voltage signal receiving module 202, configured to receive the time-domain voltage signal returned by the micro-vibration target for the frequency-modulated continuous wave;

[0164] An analog-to-digital conversion module 203, configured to convert the time-domain voltage signal to obtain time-domain amplitude data;

[0165] A Fourier transform module 204, configured to perform a Fourier transform on the time-domain amplitude data to obtain frequency-domain amplitude data;

[0166] A fast-time analysis module 205, configured to perform Doppler analysis on the frequency-domain amplitude data to obtain initial phase change rate data;

[0167] A slow-time analysis module 206, configured to perform Doppler analysis on the initial phase change rate data to obtain target phase change rate data;

[0168] A target data selection module 207, configured to select data within a preset phase change rate range from the target phase change rate data as target feature data;

[0169] A feature detection result acquisition module 208, configured to determine feature information of the micro-vibration target according to the target feature data, and use the feature information as the feature detection result of the micro-vibration target.

[0170] Optionally, the frequency-modulated continuous wave is composed of a plurality of data frames, and each data frame includes a plurality of frequency-modulated signals. The apparatus further includes:

[0171] A frequency-domain amplitude data acquisition module, configured to acquire the frequency-domain amplitude data corresponding to the data frame;

[0172] A DC component determination module, configured to determine DC component data according to the frequency-domain amplitude data corresponding to the data frame;

[0173] A DC removal processing module, configured to perform DC removal processing on the frequency-domain amplitude data corresponding to the frequency-modulated signals in the data frame according to the DC component data;

[0174] A normalization module, configured to normalize the frequency-domain amplitude data corresponding to the frequency-modulated signals after DC removal processing;

[0175] A frequency-domain amplitude data update module, configured to use the normalized frequency-domain amplitude data as the new frequency-domain amplitude data.

[0176] Optionally, the fast time analysis module 205 includes:

[0177] A data frame duration acquisition module, configured to acquire the data frame duration corresponding to any one of the data frames;

[0178] A first Nyquist frequency acquisition module, configured to determine a first Nyquist frequency according to the data frame duration;

[0179] A first phase change rate data acquisition module, configured to perform Doppler analysis on the frequency-domain amplitude data corresponding to the frequency-modulated signals in the data frame by using the first Nyquist frequency to obtain the first phase change rate data.

[0180] Optionally, the initial phase change rate data further includes second phase change rate data. The apparatus further includes:

[0181] A phase change rate threshold acquisition module, configured to acquire a preset phase change rate threshold;

[0182] A second phase change rate data acquisition module, configured to select, from the first phase change rate data, the first phase change rate data that is less than the phase change rate threshold as the second phase change rate data.

[0183] Optionally, the slow time analysis module 206 includes:

[0184] A data frame number acquisition module, configured to acquire the number of data frames in the frequency modulated continuous wave;

[0185] A second Nyquist frequency acquisition module, configured to determine a second Nyquist frequency according to the data frame duration and the number of data frames;

[0186] A target phase change rate data acquisition module, configured to perform Doppler analysis on the second phase change rate data corresponding to a plurality of the data frames by using the second Nyquist frequency to obtain the target phase change rate data.

[0187] Optionally, the frequency modulated continuous wave radar includes a plurality of receiving antennas, and the apparatus further includes:

[0188] A multi-channel data acquisition module, configured to acquire a plurality of the time domain voltage signals respectively received by the plurality of receiving antennas;

[0189] A reference time adjustment module, configured to determine the reception time of the plurality of time domain voltage signals, and adjust the plurality of time domain voltage signals to a preset reference time according to the reception time;

[0190] A reference phase adjustment module, configured to determine the signal phase of the plurality of time domain voltage signals, and adjust the signal phases of the plurality of time domain voltage signals to a preset reference phase according to the signal phase;

[0191] A signal superposition module, configured to superpose the adjusted plurality of time domain voltage signals to obtain a superposed time domain voltage signal;

[0192] A superposed target phase change rate data acquisition module, configured to determine the superposed target phase change rate data corresponding to the superposed time domain voltage signal.

[0193] Optionally, the target data selection module 207 includes:

[0194] A to-be-detected feature acquisition module, configured to acquire the to-be-detected features of the micro-vibration target;

[0195] A phase change rate range acquisition module, configured to determine the preset phase change rate range corresponding to the to-be-detected features;

[0196] A candidate feature selection module, configured to select candidate feature data within the preset phase change rate range from the superimposed target phase change rate data;

[0197] A noise threshold acquisition module, configured to determine the noise threshold corresponding to the candidate feature data according to the candidate feature data;

[0198] A target feature data determination module, configured to obtain the amplitude data corresponding to the candidate feature data, and use the candidate feature data whose amplitude data is greater than the noise threshold as the target feature data.

[0199] For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For related parts, please refer to the partial description of the method embodiment.

[0200] An embodiment of the present application further provides an electronic device, which is characterized by including a processor, a memory, and a computer program stored on the memory and capable of running on the processor. When the computer program is executed by the processor, the feature detection method of the micro-vibration target as described above is implemented.

[0201] An embodiment of the present application further provides a computer-readable storage medium, which is characterized in that a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the feature detection method of the micro-vibration target as described above is implemented.

[0202] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties. And the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0203] Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0204] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a device, or a computer program product. Therefore, the embodiments of the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of 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 memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0205] Embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate means for implementing the specified functions in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0206] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0207] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable terminal device provide steps for implementing the specified functions in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or multiple blocks.

[0208] Although the preferred embodiments of the embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present application.

[0209] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or terminal device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or terminal device comprising the above elements.

[0210] The above has introduced in detail the feature detection method, device, equipment and medium of the provided micro-vibration target. In this text, specific examples are used to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for detecting characteristics of a micro-vibration target, characterized in that: The method comprises: Using a frequency modulated continuous wave radar to transmit a frequency modulated continuous wave to the micro-vibration target; Receiving a time domain voltage signal returned by the micro-vibration target in response to the frequency modulated continuous wave; Converting the time-domain voltage signal to obtain time-domain amplitude data; Performing Fourier transform on the time domain amplitude data to obtain frequency domain amplitude data; Performing Doppler analysis on the frequency domain amplitude data to obtain initial phase change rate data; Performing Doppler analysis on the initial phase change rate data to obtain target phase change rate data; Selecting data in a preset phase change rate range from the target phase change rate data as target feature data; The characteristic information of the micro-vibration target is determined according to the target characteristic data, and the characteristic information is used as a characteristic detection result of the micro-vibration target.

2. The method according to claim 1, characterized in that The frequency modulated continuous wave is composed of a plurality of data frames, each of which includes a plurality of frequency modulated signals. Before performing Doppler analysis on the frequency domain amplitude data to obtain initial phase change rate data, the method further includes: Acquire the frequency domain amplitude data corresponding to the data frame; Determine DC component data according to the frequency domain amplitude data corresponding to the data frame; Performing DC removal processing on the frequency domain amplitude data corresponding to the frequency modulation signal in the data frame according to the DC component data; Normalizing the frequency domain amplitude data corresponding to the frequency modulation signal after the DC removal process; The normalized frequency domain amplitude data is used as new frequency domain amplitude data.

3. The method according to claim 2, characterized in that The initial phase change rate data includes first phase change rate data, and the performing of Doppler analysis on the frequency domain amplitude data to obtain the initial phase change rate data includes: For any of the data frames, obtaining a data frame duration corresponding to the data frame; determining a first Nyquist frequency according to the data frame duration; The first Nyquist frequency is used to perform Doppler analysis on the frequency domain amplitude data corresponding to the frequency modulation signal in the data frame to obtain the first phase change rate data.

4. The method according to claim 3, characterized in that The initial phase change rate data also includes second phase change rate data. Before performing Doppler analysis on the initial phase change rate data to obtain target phase change rate data, the method further includes: Obtaining a preset phase change rate threshold; The first phase change rate data that is less than the phase change rate threshold is selected from the first phase change rate data as the second phase change rate data.

5. The method according to claim 4, characterized in that The performing Doppler analysis on the initial phase change rate data to obtain target phase change rate data includes: Obtaining the number of the data frames in the frequency modulated continuous wave; determining a second Nyquist frequency based on the data frame duration and the number of data frames; The target phase change rate data is obtained by performing Doppler analysis on the second phase change rate data corresponding to a number of the data frames using the second Nyquist frequency.

6. The method according to claim 1, characterized in that The frequency modulated continuous wave radar includes a plurality of receiving antennas. After performing Doppler analysis on the initial phase change rate data to obtain target phase change rate data, the method further includes: Acquire a plurality of the time domain voltage signals respectively received by a plurality of the receiving antennas; Determining a receiving time of the plurality of time-domain voltage signals, and adjusting the plurality of time-domain voltage signals to a preset reference time according to the receiving time; Determining signal phases of the plurality of time-domain voltage signals, and adjusting the signal phases of the plurality of time-domain voltage signals to preset reference phases according to the signal phases; Superimposing the adjusted time-domain voltage signals to obtain a superimposed time-domain voltage signal; Determine superimposed target phase change rate data corresponding to the superimposed time-domain voltage signal.

7. The method according to claim 6, characterized in that The step of selecting data in a preset phase change rate range from the target phase change rate data as target feature data comprises: Acquiring a feature to be detected of the micro-vibration target; Determining the preset phase change rate range corresponding to the feature to be detected; Selecting candidate feature data within the preset phase change rate range from the superimposed target phase change rate data; Determine a noise threshold corresponding to the candidate feature data according to the candidate feature data; Amplitude data corresponding to the candidate feature data is acquired, and the candidate feature data whose amplitude data is greater than the noise threshold is used as the target feature data.

8. A feature detection device for a micro-vibration target, characterized in that: The device comprises: A frequency modulated continuous wave transmitting module, used for transmitting a frequency modulated continuous wave to the micro-vibration target by using a frequency modulated continuous wave radar; A time domain voltage signal receiving module, used for receiving the time domain voltage signal returned by the micro-vibration target in response to the frequency modulated continuous wave; An analog-to-digital conversion module, used for converting the time-domain voltage signal to obtain time-domain amplitude data; A Fourier transform module, used for performing Fourier transform on the time domain amplitude data to obtain frequency domain amplitude data; A fast time analysis module, used for performing Doppler analysis on the frequency domain amplitude data to obtain initial phase change rate data; A slow time analysis module, used for performing Doppler analysis on the initial phase change rate data to obtain target phase change rate data; A target data selection module, used to select data in a preset phase change rate range from the target phase change rate data as target feature data; The feature detection result acquisition module is used to determine the feature information of the micro-vibration target according to the target feature data, and use the feature information as the feature detection result of the micro-vibration target.

9. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the characteristic detection method of the micro-vibration target according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the feature detection method for a micro-vibration target according to any one of claims 1 to 7 is implemented.

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