CN103914703B - Classification and identification method for pedestrian and vehicle micro-motion targets - Google Patents

Abstract

The invention belongs to the technical field of micro-motion target classification and identification, and discloses a classification and identification method for pedestrian and vehicle micro-motion targets. The classification and identification method for the pedestrian and vehicle micro-motion targets includes the following steps that firstly, clutter rejection is carried out on pedestrian and vehicle echo signals by means of a CLEAN algorithm; secondly, time-frequency analysis is carried out on the echo signals after clutter rejection, proper spectrum energy threshold values are selected, windowing preprocessing is conducted on the proper spectrum energy threshold values, redundant information is removed, and image features are enhanced; finally, textural features of a spectrogram are extracted to serve as effective characteristic quantities, the effective characteristic quantities are combined with characteristic quantities extracted from a spectrogram obtained through pedestrian and vehicle real-time measured data by means of a support vector machine classification method, and the pedestrian and vehicle targets can be precisely classified.

Description

Classification and identification method for micro-motion targets of pedestrians and vehicles

Technical Field

The invention belongs to the technical field of micro-motion target classification and identification, and particularly relates to a classification and identification method for micro-motion targets of pedestrians and vehicles.

Background

In the moving process of the radar target, the movement of the mass center exists, and the micro-movement of rotation, swing, oscillation and the like relative to the target is included, for example, the pedestrian and the vehicle not only have the movement of the mass center, but also the arm swing and the leg movement of the pedestrian and the wheel type wheel, the crawler type wheel and the crawler rotation of the vehicle respectively generate the micro-movement relative to the mass center of the pedestrian and the mass center of the vehicle. Due to the micro-motion of the target, the radar echo signal is subjected to micro-motion modulation on the basis of Doppler modulation, and the characteristics are extracted according to the micro-motion information of the target, so that the classification and identification of pedestrians and vehicles are realized.

For ground monitoring radars, the important targets for monitoring are pedestrian and vehicle targets, and the radars have important application values in the aspects of square monitoring, airport surface monitoring, battlefield monitoring and situation perception. The method is an important development direction for identifying ground micro-motion targets and ground surveillance radar targets by using micro-motion characteristics extracted by the micro-motion effect of pedestrians and vehicles. The characteristics of strong scattering number extracted by a scanning beam radar based on short residence time by using a pedestrian and vehicle time frequency distribution spectrogram are analyzed in a document of 'Bayesian classification of humans and vehicles using Micro-Doppler signals from a scanning-beam radar' (IEEEMicrowave and Wireless Compounds Letters, Vol. 19, No. 5, 2009) by Nanzer J A and Rogers R L and the like, and a Bayesian classifier is designed for classification and identification, although a better classification result is obtained under certain conditions. However, because the dwell time is short and no fine preprocessing is performed on the fine motion target features of the spectrogram, the doppler resolution of the target and the accuracy of extracting the fine motion information are generally limited.

Disclosure of Invention

The invention aims to provide a method for classifying and identifying micro-motion targets of pedestrians and vehicles. The invention overcomes the defects that the non-stationary characteristic of a micro-motion target signal cannot be fully described only by using Doppler one-dimensional information and the resolution ratio in a time-frequency spectrogram of a scanning beam radar based on short residence time is low in the prior art, obtains the time-frequency spectrogram by using a continuous wave radar to perform short-time Fourier transform on echoes of a pedestrian and a vehicle target, performs preprocessing, extracts effective textural features, and realizes classification and identification of the pedestrian and the vehicle target by using a support vector machine classifier.

In order to achieve the technical purpose, the invention is realized by adopting the following technical scheme.

A classification and identification method for pedestrian and vehicle micro-motion targets comprises the following steps:

s1: respectively receiving pedestrian echo data and vehicle echo data by using a radar to obtain a pedestrian echo sample set and a vehicle echo sample set;

s2: selecting a pedestrian echo sample from the pedestrian echo sample set, and performing clutter suppression on the selected pedestrian echo sample to obtain clutter suppressed pedestrian echo data; selecting a vehicle echo sample from a vehicle echo sample set, and performing clutter suppression on the selected vehicle echo sample to obtain clutter suppressed vehicle echo data;

s3: carrying out short-time Fourier transform on the pedestrian echo data after clutter suppression to obtain a two-dimensional spectrogram of the pedestrian; preprocessing a two-dimensional spectrogram of the pedestrian to obtain a preprocessed two-dimensional spectrogram of the pedestrian; performing short-time Fourier transform on the clutter suppressed vehicle echo data to obtain a vehicle two-dimensional spectrogram; preprocessing a vehicle two-dimensional spectrogram to obtain a preprocessed vehicle two-dimensional spectrogram;

s4: respectively extracting texture features of the preprocessed pedestrian two-dimensional spectrogram and the preprocessed vehicle two-dimensional spectrogram;

s5: repeating the steps S2 to S4 until all pedestrian echo samples in the pedestrian echo sample set and all vehicle echo samples in the vehicle echo sample set are traversed to obtain a pedestrian two-dimensional spectrogram texture feature sample set and a vehicle two-dimensional spectrogram texture feature sample set;

s6: and training and testing through a support vector machine according to the pedestrian two-dimensional spectrogram texture feature sample set and the vehicle two-dimensional spectrogram texture feature sample set to obtain a corresponding classification recognition result.

The invention has the beneficial effects that:

firstly, the invention adopts a time-frequency domain joint analysis method of short-time Fourier transform to describe the target echo non-stationary signal, overcomes the defect that the correct recognition rate is low under the condition of low signal-to-noise ratio by only using a Doppler one-dimensional signal in the prior art, and has the advantages of comprehensiveness and stability.

Secondly, the two-dimensional spectrogram is subjected to windowing processing, redundant information is eliminated, and the defects that the extraction features are not obvious and the calculation amount is large due to the fact that the whole image is used in the prior art are overcome, so that the two-dimensional spectrogram has the advantages of being obvious in features and reducing the calculation amount.

Thirdly, the method extracts the spectrum entropy from the preprocessed two-dimensional spectrogram, counts texture features of the third moment and the directivity of the histogram and fully utilizes the spatial information of the image, so that the extracted texture features have the advantages of higher accuracy and accordance with the identification rule of biological vision.

Drawings

FIG. 1 is a flow chart of a method for classifying and identifying micro-motion targets of pedestrians and vehicles according to the present invention;

FIG. 2a is a two-dimensional spectrogram of a pedestrian of measured data;

FIG. 2b is a two-dimensional spectrogram of the measured data of the vehicle;

FIG. 2c is a diagram of a two-dimensional pedestrian spectrogram of measured data after being preprocessed;

fig. 2d is a schematic diagram of the classification result of the measured data.

Detailed Description

The invention will be further described with reference to the accompanying drawings in which:

referring to fig. 1, a flow chart of a method for classifying and identifying a micro-motion target of a pedestrian and a vehicle according to the present invention is shown. The classification and identification method of the pedestrian and vehicle micro-motion target comprises the following steps:

s1: and respectively receiving pedestrian echo data and vehicle echo data by utilizing a radar to obtain a target echo sample set, wherein the target echo sample set comprises a pedestrian echo sample set and a vehicle echo sample set. Specifically, narrow-band linear frequency modulation continuous wave signals are respectively transmitted to pedestrians and vehicles by utilizing a radar, and pedestrian echo data and vehicle echo data are respectively received by adopting a Dechirp receiving system.

S2: selecting a pedestrian echo sample from the pedestrian echo sample set, and performing clutter suppression on the selected pedestrian echo sample to obtain clutter suppressed pedestrian echo data; and selecting a vehicle echo sample from the vehicle echo sample set, and performing clutter suppression on the selected vehicle echo sample to obtain clutter suppressed vehicle echo data. The concrete description is as follows:

selecting a pedestrian echo sample from the pedestrian echo sample set, performing range-wise pulse compression processing on the selected pedestrian echo sample to obtain pedestrian echo data after pulse pressure processing, and then performing filtering processing on the pedestrian echo data after pulse pressure processing by using a Moving Target Indication (MTI) method to obtain the pedestrian echo data after filtering processing. Selecting a vehicle echo sample from the vehicle echo sample set, performing range-wise pulse compression processing on the selected vehicle echo sample to obtain vehicle echo data after pulse pressure processing, and then performing filtering processing on the vehicle echo data after pulse pressure processing by using a moving target display method to obtain the vehicle echo data after filtering processing.

In the pedestrian echo data after filtering processing, performing azimuth Fourier transform on data of a distance unit with the maximum energy to obtain pedestrian azimuth frequency domain data; and in the filtered vehicle echo data, performing azimuth Fourier transform on the data of the distance unit with the maximum energy to obtain vehicle azimuth frequency domain data.

Clutter is removed from the pedestrian azimuth frequency domain data by adopting a CLEAN algorithm, and then inverse Fourier transform is carried out on the pedestrian azimuth frequency domain data after the clutter removal to obtain pedestrian echo data after clutter suppression; and removing clutter from the vehicle azimuth frequency domain data by adopting a CLEAN algorithm, and performing inverse Fourier transform on the vehicle azimuth frequency domain data after the clutter removal to obtain vehicle echo data after clutter suppression.

S3: carrying out short-time Fourier transform on the pedestrian echo data after clutter suppression to obtain a two-dimensional spectrogram of the pedestrian; preprocessing a two-dimensional spectrogram of the pedestrian to obtain a preprocessed two-dimensional spectrogram of the pedestrian; performing short-time Fourier transform on the clutter suppressed vehicle echo data to obtain a vehicle two-dimensional spectrogram; and preprocessing the vehicle two-dimensional spectrogram to obtain a preprocessed vehicle two-dimensional spectrogram. The concrete description is as follows:

carrying out short-time Fourier transform on the pedestrian echo data after clutter suppression to obtain a two-dimensional spectrogram of the pedestrian; and performing short-time Fourier transform on the clutter suppressed vehicle echo data to obtain a vehicle two-dimensional spectrogram.

Accumulating the energy of the two-dimensional spectrogram of the pedestrian in the time dimension according to the following formula to obtain the frequency dimension energy distribution E of the two-dimensional spectrogram of the pedestrian_x(n_x)：

Wherein S is_x(m_x,n_x) Representing the energy distribution of a two-dimensional spectrogram of a pedestrian, m_xNumber of sampling points in time dimension corresponding to two-dimensional spectrogram of pedestrian, n_xThe number of sampling points of the frequency dimension corresponding to the two-dimensional spectrogram of the pedestrian is obtained.

According to the following formula, energy accumulation is carried out on the two-dimensional spectrogram of the vehicle in the time dimension to obtain the frequency dimension energy distribution E of the two-dimensional spectrogram of the vehicle_c(n_c)：

Wherein S is_c(m_c,n_c) Representing the energy distribution of a two-dimensional map of the vehicle, m_cThe number of sampling points in the time dimension corresponding to the two-dimensional spectrogram of the vehicle, n_cIs a vehicleAnd sampling point numbers of frequency dimensions corresponding to the vehicle two-dimensional spectrogram.

Frequency dimension energy distribution E of pedestrian two-dimensional spectrogram_x(n_x) Peak position of (F)_xRespectively selecting frequency units symmetrically towards two sides for a symmetry axis, and performing energy accumulation until the energy sum exceeds 60% of the total energy of the frequency dimension of the pedestrian two-dimensional spectrogram, at the moment, recording the minimum frequency and the maximum frequency in all the selected frequency units, and constructing a rectangular window function corresponding to the pedestrian two-dimensional spectrogram on the basis of the recorded minimum frequency and maximum frequency; and windowing the two-dimensional spectrogram of the pedestrian according to the rectangular window function corresponding to the two-dimensional spectrogram of the pedestrian to obtain the preprocessed two-dimensional spectrogram of the pedestrian.

Frequency dimension energy distribution E of vehicle two-dimension spectrogram_c(n_c) Peak position of (F)_cRespectively selecting frequency units symmetrically towards two sides for a symmetry axis, and performing energy accumulation until the energy sum exceeds 60% of the total energy of the frequency dimension of the two-dimensional spectrogram of the vehicle, at the moment, recording the minimum frequency and the maximum frequency in all the selected frequency units, and constructing a rectangular window function corresponding to the two-dimensional spectrogram of the vehicle on the basis of the recorded minimum frequency and maximum frequency; and windowing the vehicle two-dimensional spectrogram according to the rectangular window function corresponding to the vehicle two-dimensional spectrogram to obtain the preprocessed vehicle two-dimensional spectrogram.

S4: and respectively extracting texture features of the preprocessed pedestrian two-dimensional spectrogram and the preprocessed vehicle two-dimensional spectrogram. The method specifically comprises the following steps of extracting the textural features of the preprocessed two-dimensional spectrogram of the pedestrian:

accumulating the energy of the preprocessed pedestrian two-dimensional spectrogram in a time dimension to obtain frequency dimension energy distribution of the preprocessed pedestrian two-dimensional spectrogram, normalizing the frequency dimension energy distribution of the preprocessed pedestrian two-dimensional spectrogram to obtain normalized data p of the frequency dimension energy distribution of the preprocessed pedestrian two-dimensional spectrogram_x(n_x) Then, the entropy E of the two-dimensional spectrogram of the pedestrian is calculated according to the following formula_x：

According to the energy distribution of the preprocessed two-dimensional spectrogram of the pedestrian, a plurality of equally divided energy intervals are obtained through energy equally dividing processing (such as ten equally dividing processing); obtaining a pedestrian two-dimensional spectrogram energy statistical histogram based on the plurality of equally divided energy intervals, and then calculating a third moment M of the pedestrian two-dimensional spectrogram energy statistical histogram_x。

Defining two mutually perpendicular directions H_xAnd V_xCalculating the gradient vector magnitude of the preprocessed two-dimensional spectrogram of the pedestrian according to the following formulaAnd gradient vector angle theta_x：

Wherein,the energy of the pixel points of the preprocessed two-dimensional spectrogram of the pedestrian is H_xThe gradient in the direction of the magnetic field,the energy of pixel points of a two-dimensional spectrogram of the preprocessed pedestrian is V_xA gradient in direction.

Gradient vector angle theta of preprocessed pedestrian two-dimensional spectrogram_xDiscretizing and equally dividing the vector data to obtain a plurality of equally divided gradient vector angle sections, wherein each equally divided gradient vector angle section isi is taken from 2 to L,is theta_xA discretized maximum; obtaining a pedestrian two-dimensional spectrogram directivity statistical histogram based on the gradient vector angle intervals of the plurality of equal divisions

Finding out the gradient vector angle α corresponding to the peak point of the directional statistical histogram of the two-dimensional spectrogram of the pedestrian_xCalculating the directional characteristic F of the preprocessed two-dimensional spectrogram of the pedestrian according to the following formula_x：

Extraction of E_x、M_x、And F_xAnd the obtained data is used as the texture feature of the preprocessed two-dimensional spectrogram of the pedestrian.

In step S4, the step of extracting the texture features of the preprocessed vehicle spectrogram specifically includes the following steps:

accumulating the energy of the preprocessed vehicle two-dimensional spectrogram in a time dimension to obtain frequency dimension energy distribution of the preprocessed vehicle two-dimensional spectrogram, normalizing the frequency dimension energy distribution of the preprocessed vehicle two-dimensional spectrogram to obtain normalized data p of the frequency dimension energy distribution of the preprocessed vehicle two-dimensional spectrogram_c(n_c) Then, the entropy value E of the two-dimensional spectrogram of the vehicle is calculated according to the following formula_x：

According to the energy distribution of the preprocessed vehicle two-dimensional spectrogram, a plurality of equally divided energy intervals are obtained through energy equally dividing processing; based on the plurality of equally divided energy intervals, obtaining a vehicle two-dimensional spectrogram energy statistical histogram, and then calculating a third moment M of the vehicle two-dimensional spectrogram energy statistical histogram_c。

Defining two mutually perpendicular directions H_cAnd V_cCalculating the gradient vector magnitude of the preprocessed vehicle two-dimensional spectrogram according to the following formulaAnd gradient vector angle theta_c：

Wherein,the energy of the pixel points of the two-dimensional spectrogram of the preprocessed vehicle is H_cThe gradient in the direction of the magnetic field,the energy of pixel points of a two-dimensional spectrogram of the preprocessed vehicle is V_cA gradient in direction.

Gradient vector angle theta of preprocessed vehicle two-dimensional spectrogram_cDiscretizing and equally dividing the vector data to obtain a plurality of equally divided gradient vector angle sections, wherein each equally divided gradient vector angle section isi is taken from 2 to L,is theta_cA discretized maximum; obtaining a two-dimensional spectrogram directivity statistical histogram of the vehicle based on the gradient vector angle intervals of the plurality of equal divisions

Finding out the corresponding gradient vector angle α of the peak point of the directional statistical histogram of the two-dimensional spectrogram of the vehicle_cCalculating the directional characteristic F of the preprocessed vehicle two-dimensional spectrogram according to the following formula_c：

Extraction of E_c、M_c、And F_cAnd the texture features are used as the texture features of the preprocessed vehicle two-dimensional spectrogram.

S5: and repeating the steps S2 to S4 until all pedestrian echo samples in the pedestrian echo sample set and all vehicle echo samples in the vehicle echo sample set are traversed to obtain a pedestrian two-dimensional spectrogram texture feature sample set and a vehicle two-dimensional spectrogram texture feature sample set.

In step S5, the sample set of the two-dimensional spectrogram texture features of the pedestrian is: a set of texture features of the preprocessed pedestrian two-dimensional spectrogram corresponding to all the pedestrian echo samples in the pedestrian echo sample set; the texture feature sample set of the two-dimensional spectrogram of the vehicle is as follows: and the texture feature set of the preprocessed vehicle two-dimensional spectrogram corresponding to all the vehicle echo samples in the vehicle echo sample set.

S6: and training and testing through a support vector machine according to the pedestrian two-dimensional spectrogram texture feature sample set and the vehicle two-dimensional spectrogram texture feature sample set to obtain a corresponding classification recognition result. The concrete description is as follows:

selecting 30% of samples from the pedestrian two-dimensional spectrogram texture feature sample set and the vehicle two-dimensional spectrogram texture feature sample set to form a target two-dimensional spectrogram training sample set; the target two-dimensional spectrogram test sample set comprises: the rest samples of the pedestrian two-dimensional spectrogram texture feature sample set and the rest samples of the vehicle two-dimensional spectrogram texture feature sample set;

sending the target two-dimensional spectrogram training sample set into a Support Vector Machine (SVM), and carrying out training and learning; and then, sequentially sending each test sample of the target two-dimensional spectrogram test sample set into a support vector machine for testing (sending the test sample into an SVM classifier for testing) to obtain a corresponding test result, wherein each test result is a corresponding classification recognition result.

After the corresponding classification recognition results are obtained, the correct recognition rates of the pedestrian target and the vehicle target in the classification recognition results are respectively calculated, and weighted average is carried out to obtain the average recognition rate.

The effects of the present invention can be further illustrated by the following experiments:

in the experiment, the radar transmitting signal is a narrow-band linear frequency modulation continuous wave signal, the radar receiving system adopts a Dechirp receiving system, and the sampling frequency of the Dechirp receiving system is 3.125 MHz. Target sample parameters referring to table 1, targets include both pedestrians and vehicles.

TABLE 1

Referring to fig. 2a, it is a two-dimensional spectrogram of the measured data; wherein, the abscissa is time, and the unit is second; the ordinate is frequency in Hz. Referring to fig. 2b, the two-dimensional vehicle spectrogram is measured data. In fig. 2b, the abscissa is time in seconds; the ordinate is frequency in Hz. Referring to fig. 2c, the two-dimensional spectrogram of the pedestrian is preprocessed from the measured data, wherein the abscissa is time and the unit is second; the ordinate is frequency in Hz. Referring to fig. 2d, a diagram of the classification result of the measured data is shown. In fig. 2d, the triangle represents a vehicle object and the circle represents a pedestrian object, it can be seen that the two types of objects have been substantially separated. In this experiment, the recognition rate of the pedestrian target was found to be 100%, while the recognition rate of the vehicle target was 98.67%, and the average recognition rate was 99.33%.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (7)

1. A classification and identification method for micro-motion targets of pedestrians and vehicles is characterized by comprising the following steps:

s1: respectively receiving pedestrian echo data and vehicle echo data by using a radar to obtain a pedestrian echo sample set and a vehicle echo sample set;

s2: selecting a pedestrian echo sample from the pedestrian echo sample set, and performing clutter suppression on the selected pedestrian echo sample to obtain clutter suppressed pedestrian echo data; selecting a vehicle echo sample from a vehicle echo sample set, and performing clutter suppression on the selected vehicle echo sample to obtain clutter suppressed vehicle echo data;

s3: carrying out short-time Fourier transform on the pedestrian echo data after clutter suppression to obtain a two-dimensional spectrogram of the pedestrian; preprocessing a two-dimensional spectrogram of the pedestrian to obtain a preprocessed two-dimensional spectrogram of the pedestrian; performing short-time Fourier transform on the clutter suppressed vehicle echo data to obtain a vehicle two-dimensional spectrogram; preprocessing a vehicle two-dimensional spectrogram to obtain a preprocessed vehicle two-dimensional spectrogram;

the step S3 specifically includes the following steps:

s31: carrying out short-time Fourier transform on the pedestrian echo data after clutter suppression to obtain a two-dimensional spectrogram of the pedestrian; performing short-time Fourier transform on the clutter suppressed vehicle echo data to obtain a vehicle two-dimensional spectrogram;

s32: accumulating the energy of the two-dimensional spectrogram of the pedestrian in the time dimension according to the following formula to obtain the frequency dimension energy distribution E of the two-dimensional spectrogram of the pedestrian_x(n_x)：

$E_x\left(n_x\right)=\underset{m=-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\Σ}}\left|S_x(m_x,n_x)\right|$

Wherein S is_x(m_x，n_x) Representing the energy distribution of a two-dimensional spectrogram of a pedestrian, m_xNumber of sampling points in time dimension corresponding to two-dimensional spectrogram of pedestrian, n_xSampling point numbers of frequency dimensions corresponding to the two-dimensional spectrogram of the pedestrian;

for a vehicle according to the following formulaAccumulating the energy of the two-dimensional spectrogram in the time dimension to obtain the frequency dimension energy distribution E of the two-dimensional spectrogram of the vehicle_c(n_c)：

$E_c\left(n_c\right)=\underset{m=-\mathrm{\∞}}{\overset{\mathrm{\∞}}{\Σ}}\left|S_c(m_c,n_c)\right|$

Wherein S is_c(m_c，n_c) Representing the energy distribution of a two-dimensional map of the vehicle, m_cThe number of sampling points in the time dimension corresponding to the two-dimensional spectrogram of the vehicle, n_cThe sampling point number of the frequency dimension corresponding to the vehicle two-dimensional spectrogram is obtained; m is a time dimension coordinate of the pedestrian two-dimensional spectrogram and the vehicle two-dimensional spectrogram;

s33: frequency dimension energy distribution E of pedestrian two-dimensional spectrogram_x(n_x) Peak position of (F)_xRespectively selecting frequency units symmetrically towards two sides for a symmetry axis, and performing energy accumulation until the energy sum exceeds 60% of the total energy of the frequency dimension of the pedestrian two-dimensional spectrogram, at the moment, recording the minimum frequency and the maximum frequency in all the selected frequency units, and constructing a rectangular window function corresponding to the pedestrian two-dimensional spectrogram on the basis of the recorded minimum frequency and maximum frequency; windowing the two-dimensional spectrogram of the pedestrian according to the rectangular window function corresponding to the two-dimensional spectrogram of the pedestrian to obtain a preprocessed two-dimensional spectrogram of the pedestrian;

frequency dimension energy distribution E of vehicle two-dimension spectrogram_c(n_c) Peak position of (F)_cSelecting frequencies symmetrically to both sides for the symmetry axisThe unit carries out energy accumulation until the energy sum exceeds 60% of the total energy of the frequency dimension of the two-dimensional spectrogram of the vehicle, at the moment, the minimum frequency and the maximum frequency are recorded in all selected frequency units, and a rectangular window function corresponding to the two-dimensional spectrogram of the vehicle is constructed on the basis of the recorded minimum frequency and maximum frequency; windowing the vehicle two-dimensional spectrogram according to the rectangular window function corresponding to the vehicle two-dimensional spectrogram to obtain a preprocessed vehicle two-dimensional spectrogram;

s4: respectively extracting texture features of the preprocessed pedestrian two-dimensional spectrogram and the preprocessed vehicle two-dimensional spectrogram;

s5: repeating the steps S2 to S4 until all pedestrian echo samples in the pedestrian echo sample set and all vehicle echo samples in the vehicle echo sample set are traversed to obtain a pedestrian two-dimensional spectrogram texture feature sample set and a vehicle two-dimensional spectrogram texture feature sample set;

s6: and training and testing through a support vector machine according to the pedestrian two-dimensional spectrogram texture feature sample set and the vehicle two-dimensional spectrogram texture feature sample set to obtain a corresponding classification recognition result.

2. The method for classifying and identifying the micro-moving objects of the pedestrians and the vehicles according to claim 1, wherein in step S1, the radar is used to transmit narrow-band chirp continuous wave signals to the pedestrians and the vehicles, respectively, and a Dechirp receiving system is used to receive the echo data of the pedestrians and the echo data of the vehicles, respectively.

3. The method for classifying and identifying the micro-motion object of the pedestrian and the vehicle as claimed in claim 1, wherein the step S2 specifically comprises the steps of:

s21: selecting a pedestrian echo sample from a pedestrian echo sample set, performing range-wise pulse compression processing on the selected pedestrian echo sample to obtain pedestrian echo data after pulse pressure processing, and then performing filtering processing on the pedestrian echo data after pulse pressure processing by using a moving target display method to obtain the pedestrian echo data after filtering processing;

selecting a vehicle echo sample from a vehicle echo sample set, performing range-wise pulse compression processing on the selected vehicle echo sample to obtain vehicle echo data after pulse pressure processing, and then performing filtering processing on the vehicle echo data after the pulse pressure processing by using a moving target display method to obtain the vehicle echo data after the filtering processing;

s22: in the pedestrian echo data after filtering processing, performing azimuth Fourier transform on data of a distance unit with the maximum energy to obtain pedestrian azimuth frequency domain data;

in the filtered vehicle echo data, performing azimuth Fourier transform on the data of the distance unit with the maximum energy to obtain vehicle azimuth frequency domain data;

s23: clutter is removed from the pedestrian azimuth frequency domain data by adopting a CLEAN algorithm, and then inverse Fourier transform is carried out on the pedestrian azimuth frequency domain data after the clutter removal to obtain pedestrian echo data after clutter suppression;

and removing clutter from the vehicle azimuth frequency domain data by adopting a CLEAN algorithm, and performing inverse Fourier transform on the vehicle azimuth frequency domain data after the clutter removal to obtain vehicle echo data after the clutter suppression.

4. The method for classifying and identifying the micro-motion target of the pedestrian and the vehicle as claimed in claim 1, wherein in the step S4, the step of extracting the texture features of the preprocessed two-dimensional spectrogram of the pedestrian specifically comprises the steps of:

s411: accumulating the energy of the preprocessed pedestrian two-dimensional spectrogram in a time dimension to obtain frequency dimension energy distribution of the preprocessed pedestrian two-dimensional spectrogram, normalizing the frequency dimension energy distribution of the preprocessed pedestrian two-dimensional spectrogram to obtain normalized data p of the frequency dimension energy distribution of the preprocessed pedestrian two-dimensional spectrogram_x(n_x) Then, the entropy E of the two-dimensional spectrogram of the pedestrian is calculated according to the following formula_x：

$E_x=-{\mathrm{\Σ}}_{n_x}{p}_x\left(n_x\right)\ln p_x\left(n_x\right);$

Wherein n is_xSampling point numbers of frequency dimensions corresponding to the two-dimensional spectrogram of the pedestrian;

s412: according to the energy distribution of the preprocessed two-dimensional spectrogram of the pedestrian, a plurality of equally divided energy intervals are obtained through energy equally dividing processing; obtaining a pedestrian two-dimensional spectrogram energy statistical histogram based on the plurality of equally divided energy intervals, and then calculating a third moment M of the pedestrian two-dimensional spectrogram energy statistical histogram_x；

S413: defining two mutually perpendicular directions H_xAnd V_xCalculating the gradient vector magnitude of the preprocessed two-dimensional spectrogram of the pedestrian according to the following formulaAnd gradient vector angle theta_x：

$\▿G_x=(\▿V_x+\▿H_x)/2$

${\mathrm{\θ}}_x={\tan }^{-1}(\▿V_x/\▿H_x)$

Wherein,the energy of the pixel points of the preprocessed two-dimensional spectrogram of the pedestrian is H_xThe gradient in the direction of the magnetic field,the energy of pixel points of a two-dimensional spectrogram of the preprocessed pedestrian is V_xA gradient in direction;

s414: gradient vector angle theta of preprocessed pedestrian two-dimensional spectrogram_xDiscretizing and equally dividing the vector data to obtain a plurality of equally divided gradient vector angle sections, wherein each equally divided gradient vector angle section isi is taken from 2 to L,is theta_xA discretized maximum; obtaining a pedestrian two-dimensional spectrogram directivity statistical histogram based on the plurality of equally divided gradient vector angle intervalsL-1 is the number of the pedestrian two-dimensional spectrogram gradient vector angle interval and the vehicle two-dimensional spectrogram gradient vector angle interval;

s415, finding out a gradient vector angle α corresponding to the peak point of the directional statistical histogram of the two-dimensional spectrogram of the pedestrian_xCalculating the directional characteristic F of the preprocessed two-dimensional spectrogram of the pedestrian according to the following formula_x：

$F_x=\underset{i=1}{\overset{L}{\Σ}}{({\mathrm{\θ}}_i^{\left(x\right)}-{\mathrm{\α}}_x)}^2{H}_x\left({\mathrm{\θ}}_i^{\left(x\right)}\right);$

S416: extraction of E_x、M_x、And F_xAnd the obtained data is used as the texture feature of the preprocessed two-dimensional spectrogram of the pedestrian.

5. The method for classifying and identifying the micro-motion target of the pedestrian and the vehicle as claimed in claim 1, wherein in the step S4, the step of extracting the texture feature of the preprocessed vehicle spectrogram specifically comprises the steps of:

s421: accumulating the energy of the preprocessed vehicle two-dimensional spectrogram in a time dimension to obtain frequency dimension energy distribution of the preprocessed vehicle two-dimensional spectrogram, normalizing the frequency dimension energy distribution of the preprocessed vehicle two-dimensional spectrogram to obtain normalized data p of the frequency dimension energy distribution of the preprocessed vehicle two-dimensional spectrogram_c(n_c) Then, the entropy value E of the two-dimensional spectrogram of the vehicle is calculated according to the following formula_x：

$E_c=-{\mathrm{\Σ}}_{n_c}{p}_c\left(n_c\right)\ln p_c\left(n_c\right);$

Wherein n is_cThe sampling point number of the frequency dimension corresponding to the vehicle two-dimensional spectrogram is obtained;

s422: according to the energy distribution of the preprocessed vehicle two-dimensional spectrogram, a plurality of equally divided energy intervals are obtained through energy equally dividing processing; obtaining a vehicle two-dimensional spectrogram energy statistical histogram based on the plurality of equally divided energy intervals, and then calculating a third moment M of the vehicle two-dimensional spectrogram energy statistical histogram_c；

S423: defining two mutually perpendicular directions H_cAnd V_cCalculating the gradient vector magnitude of the preprocessed vehicle two-dimensional spectrogram according to the following formulaAnd gradient vector angle theta_c：

$\▿G_c=(\▿V_c+\▿H_c)/2$

${\mathrm{\θ}}_c={\tan }^{-1}(\▿V_c/\▿H_c)$

Wherein,the energy of the pixel points of the two-dimensional spectrogram of the preprocessed vehicle is H_cThe gradient in the direction of the magnetic field,the energy of pixel points of a two-dimensional spectrogram of the preprocessed vehicle is V_cA gradient in direction;

s424: gradient vector angle theta of preprocessed vehicle two-dimensional spectrogram_cDiscretizing and equally dividing the vector data to obtain a plurality of equally divided gradient vector angle sections, wherein each equally divided gradient vector angle section isi is taken from 2 to L,is theta_cA discretized maximum; obtaining a two-dimensional spectrogram directivity statistical histogram of the vehicle based on the plurality of equally divided gradient vector angle intervalsL-1 is the number of the pedestrian two-dimensional spectrogram gradient vector angle interval and the vehicle two-dimensional spectrogram gradient vector angle interval;

s425, finding out a gradient vector angle α corresponding to a peak point of a directional statistical histogram of a two-dimensional spectrogram of the vehicle_cCalculating the directional characteristic F of the preprocessed vehicle two-dimensional spectrogram according to the following formula_c：

$F_c=\underset{i=1}{\overset{L}{\Σ}}{({\mathrm{\θ}}_i^{\left(c\right)}-{\mathrm{\α}}_c)}^2{H}_c\left({\mathrm{\θ}}_i^{\left(c\right)}\right);$

S426: extraction of E_c、M_c、And F_cAnd the texture features are used as the texture features of the preprocessed vehicle two-dimensional spectrogram.

6. The method for classifying and identifying the micro-motion object between the pedestrian and the vehicle as claimed in claim 1, wherein in step S5, the sample set of texture features of the two-dimensional spectrogram of the pedestrian is as follows: the pedestrian echo sample set comprises a set of texture features of a preprocessed pedestrian two-dimensional spectrogram corresponding to all pedestrian echo samples;

the texture feature sample set of the vehicle two-dimensional spectrogram is as follows: and the set of texture features of the preprocessed vehicle two-dimensional spectrogram corresponding to all vehicle echo samples in the vehicle echo sample set.

7. The method for classifying and identifying the micro-motion object of the pedestrian and the vehicle as claimed in claim 1, wherein the step S6 specifically comprises the steps of:

s61: selecting 30% of samples from the pedestrian two-dimensional spectrogram texture feature sample set and the vehicle two-dimensional spectrogram texture feature sample set to form a target two-dimensional spectrogram training sample set; the target two-dimensional spectrogram test sample set comprises: the rest samples of the pedestrian two-dimensional spectrogram texture feature sample set and the rest samples of the vehicle two-dimensional spectrogram texture feature sample set;

s62: sending the target two-dimensional spectrogram training sample set into a support vector machine for training and learning; and then, sequentially sending each test sample of the target two-dimensional spectrogram test sample set into a support vector machine for testing to obtain a corresponding test result, wherein each test result is a corresponding classification recognition result.