CN105629254A - Target micro-motion characteristic coherent laser detection effect quantitative evaluation method - Google Patents

Abstract

The invention provides a target micro-motion characteristic coherent laser detection effect quantitative evaluation method, which includes the steps of: employing a smoothed pseudo Winger-Ville distribution algorithm to perform time-frequency analysis processing on collected target micro-motion characteristic coherent laser detection echo signals, and obtaining a time-frequency distribution matrix of the echo signal; calculating a frequency domain aggregation degree of the echo signals; calculating the component resolution of the echo signal; calculating a signal-to-noise ratio of the echo signals; and calculating a total index. The invention provides a method for quantitative evaluation of target micro-doppler effect detection and characteristic extraction effect, obtains the standard, objective, quantitative evaluation of the target micro-motion characteristic detection effect, solves the deficiency of large limitation and bad normalization in the prior qualitative judgment method, and lays foundation for research of the follow-up micro-doppler effect detection influence factors and influence rules and selection of optimal system parameters.

Description

Quantitative evaluation method for target micro-motion characteristic coherent laser detection effect

Technical Field

The invention relates to the technical field of target micro Doppler feature detection, in particular to a quantitative evaluation method for a target micro motion feature coherent laser detection effect.

Background

However, with the development and application of stealth technology, decoy technology and the like in recent years, the traditional detection and identification means are seriously interfered, the working efficiency is greatly reduced, and information for effectively judging the type and the authenticity of the target is difficult to provide. At the beginning of the 21 st century, professor v.c. chen of the research laboratory of the navy of the united states firstly proposed the concept of micro-doppler effect and opened up a new way for target detection and identification.

The target and the radar have relative radial movement to generate Doppler frequency shift, and the micro Doppler effect refers to the frequency broadening effect which is finally caused by the Doppler frequency shift as the center due to the additional frequency modulation generated on the echo signal by the micro movement of the target or some components. The micro-doppler effect is considered to be the only feature reflecting the micromotion of the target, containing the specific information of the target. The laser radar is used for detection, and the characteristic of short wavelength can be used for measuring the more obvious micro Doppler effect, so that higher detection resolution and sensitivity are obtained.

In order to realize the detection of the target micro-doppler effect, firstly, the rule of influence of factors such as detection system parameters, structures and transmission media on the target echo needs to be researched, the optimal design scheme of the system parameters and the structures and the compensation measures for external interference are found, and therefore, the detection effect of the target micro-doppler characteristic in the echo needs to be contrasted and analyzed under different experimental conditions. Currently, the most common method in the extraction of the micro doppler features is to perform time-frequency analysis on signals and then extract target information from time-frequency distribution. The quality of the detection effect of the system can be intuitively reflected on the time-frequency distribution result, so the readability of the time-frequency distribution directly determines the accuracy of the extraction of the micro-Doppler characteristics, and the method is very important for the detection and identification of the target.

The echo coherent detection signal of the target micro-doppler effect is a non-stationary signal, so in the processing of the echo coherent detection signal, researchers generally adopt a time-frequency analysis method to extract target micro-motion features (micro-doppler features), however, in the currently developed research on the influence factors of the echo features (such as frequency source phase noise, laser spectral line width, detection distance, detection system structure and parameters and other factors), when analyzing the detection effects of different target micro-motion features caused by different factors, the researchers mainly compare and evaluate the target micro-motion feature time-frequency distribution results obtained under different conditions through qualitative observation and experience.

The qualitative comparison method has large individual difference, poor normalization and great limitation, such as: for two time-frequency distribution results with obvious difference of target micro-motion characteristic detection effects, the observation can judge whether the obtained time-frequency distribution has better effect when the target micro-motion characteristic is extracted, but the strength of the ratio difference with good effect is difficult to be given specifically, and the result cannot correspond to the detection condition of the prior quantitative change; for two time-frequency distribution results with unobvious detection effect difference, it is difficult to accurately judge which situation has a better detection effect and even erroneous judgment by observation. To solve this problem, a scientific and quantitative evaluation method is needed to determine the detection and extraction effects of the target micro-doppler features.

Disclosure of Invention

The invention aims to provide a quantitative evaluation method for the coherent laser detection effect of target micro-motion characteristics, which is based on a smooth pseudo Winger-Ville distribution (SPWVD) time-frequency analysis method proven to have better performance, and provides a measurement standard and a calculation method thereof to quantitatively evaluate the detection and extraction effects of the target micro-motion characteristics so as to solve the defects of qualitative analysis.

The technical scheme of the invention is as follows:

a quantitative evaluation method for a target micro-motion characteristic coherent laser detection effect comprises the following steps:

(1) performing time-frequency analysis processing on the acquired target micro-motion characteristic coherent laser detection echo signal by adopting a smooth pseudo Winger-Ville distribution algorithm to obtain a time-frequency distribution matrix P (t, f) of the echo signal;

(2) taking the frequency domain aggregation of the echo signals as a first index of quantitative evaluation of a target micro-motion characteristic coherent laser detection effect, and calculating the frequency domain aggregation according to the time-frequency distribution matrix P (t, f);

(3) taking the component resolution of the echo signal as a second index of quantitative evaluation of the target micro-motion characteristic coherent laser detection effect, and calculating the component resolution according to the time-frequency distribution matrix P (t, f);

(4) taking the signal-to-noise ratio of the echo signal as a third index of quantitative evaluation of the coherent laser detection effect of the target micro-motion characteristic, and calculating the signal-to-noise ratio according to the time-frequency distribution matrix P (t, f);

(5) according to the frequency domain aggregation degree, the component resolution and the signal-to-noise ratio, the following formula is adopted to calculate the total index of quantitative evaluation of the target micro-motion characteristic coherent laser detection effect:

if the echo signal is a single-component signal, the calculation formula of the total index is as follows:

$Q=\frac{SNR}{a}$

if the echo signal is a multi-component signal, the calculation formula of the total index is as follows:

$Q=\frac{r}{a}*SNR$

q represents a total index of quantitative evaluation of the target micro-motion characteristic coherent laser detection effect, a represents frequency domain concentration, r represents component resolution, and SNR represents signal-to-noise ratio.

In the step (2), the frequency domain aggregation is calculated according to the time-frequency distribution matrix P (t, f), and the method comprises the following steps:

(21) calculating the instantaneous frequency domain aggregation degree:

for a single component signal, comprising:

a1, extracting t from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b1, finding P (t)_nEnergy peak A in f)_SmaxAnd its corresponding frequency f_m；

c1 finding the energy peak A_SmaxBoth sides and f_mThe corresponding energy closest to isTwo frequencies f_pAnd f_q；

d1, and finding t_n3dB bandwidth BW (t) of time signal_n) I.e. is t_nInstantaneous frequency domain concentration a (t) of time signal_n)：

a(t_n)＝BW(t_n)＝f_p-f_q，f_p＞f_q

For a multi-component signal, comprising:

a2, extracting t from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b2, finding P (t)_nF) the largest m energy peak values and the corresponding frequencies thereof, m being the number of components, m being greater than or equal to 2;

c2, sequentially obtaining m 3dB bandwidths BW corresponding to m energy peak values₁(t_n)、BW₂(t_n)、…、BW_m(t_n) Then, the average value of m 3dB bandwidths is obtained, i.e. t_nInstantaneous frequency domain concentration a (t) of time signal_n)：

$a\left(t_n\right)=\frac{1}{m}\underset{i=1}{\overset{m}{\Σ}}{\mathrm{BW}}_i\left(t_n\right),i=1,2,...,m$

(22) Calculating the integral frequency domain aggregation degree:

the instantaneous frequency domain aggregation a (t) of all time signals on the whole time axis is obtained_n) And then averaging the average values to obtain the integral frequency domain aggregation a of the signals:

$a=\frac{1}{N}\underset{n=1}{\overset{N}{\Σ}}a\left(t_n\right),n=1,2,...,N$

wherein N represents the total number of discrete time points in the whole time-frequency distribution.

The quantitative evaluation method for the coherent laser detection effect of the target micro-motion characteristic comprises the following steps of (3) calculating the component resolution according to the time-frequency distribution matrix P (t, f):

(31) calculating the instantaneous component resolution:

for a multi-component signal having m components, m ≧ 2, comprising:

a. for two of the components, t is extracted from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b. At P (t)_nFinding the largest two energy peaks A in f)_Smax1And A_Smax2And its corresponding frequency f₁、f₂；

c. Finding the energy peak A_Smax2Corresponding 3dB bandwidth BW₂(t_n)；

d. Two components at t are found according to the following formula_nResolution of time:

$r_d\left(t_n\right)=(f_2-\frac{{\mathrm{BW}}_2\left(t_n\right)}{2})-f_1$

e. according to the steps a to d, the instantaneous resolution r between every two components is calculated_d(t_n) Then for all r_d(t_n) Averaging to obtain the instantaneous component resolution r (t) of the signal_n)：

$r\left(t_n\right)=\frac{1}{C_m^2}\underset{i=1}{\overset{C_m^2}{\Σ}}{r}_{di}\left(t_n\right),i=1,2,...,C_m^2$

Wherein,represents the number of combinations of 2 out of m components;

(32) calculating the integral component resolution:

the instantaneous component resolution r (t) of all time signals on the whole time axis is obtained_n) And then averaging the signals to obtain the integral component resolution r of the signal:

$r=\frac{1}{N}\underset{n=1}{\overset{N}{\Σ}}r\left(t_n\right),n=1,2,...,N$

wherein N represents the total number of discrete time points in the whole time-frequency distribution.

The quantitative evaluation method for the coherent laser detection effect of the target micro-motion characteristic comprises the following steps of (4) calculating the signal-to-noise ratio according to the time-frequency distribution matrix P (t, f):

(41) calculating the instantaneous signal-to-noise ratio:

for a single component signal, comprising:

a1, extracting t from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b1, finding P (t)_nEnergy peak A in f)_SmaxAnd its corresponding frequency;

c1, finding t_n3dB bandwidth BW (t) of time signal_n) Finding out a frequency coordinate corresponding to a signal in a 3dB bandwidth;

d1, and then obtaining P (t)_nF) average value E (A) of the energy corresponding to all frequency coordinates except the 3dB bandwidth_noise) As noise energy;

e1, calculating t by using the following formula_nInstantaneous signal-to-noise ratio SNR (t) of time-of-day signal_n)：

$SNR\left(t_n\right)=10lg\frac{A_{S\max }}{E\left(A_{noise}\right)}$

For a multi-component signal, comprising:

a2, extracting t from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b2, finding P (t)_nThe largest m energy peaks A in f)_Smax1、A_Smax2、…、A_SmaxmAnd the corresponding frequency, m is the number of components, m is more than or equal to 2;

c2, sequentially obtaining m 3dB bandwidths BW corresponding to m energy peak values₁(t_n)、BW₂(t_n)、…、BW_m(t_n) Finding out frequency coordinates corresponding to m signals in the 3dB bandwidth;

d2, and then obtaining P (t)_nF) average value E (A) of the energy corresponding to all frequency coordinates except the m 3dB bandwidths_noise) As noise energy;

e2, calculating t by using the following formula_nInstantaneous signal-to-noise ratio SNR (t) of time-of-day signal_n)：

$SNR\left(t_n\right)=10lg\frac{{\mathrm{\Σ}}_{i=1}^m{P}_{S\max i}}{m*E\left(A_{noise}\right)},i=1,2,...,m$

(42) Calculating the overall signal-to-noise ratio:

the instantaneous signal-to-noise ratio SNR (t) of the signal at all time points on the whole time axis is obtained_n) And then averaging the average values to obtain the integral signal-to-noise ratio SNR of the signal:

$SNR=\frac{1}{N}\underset{n=1}{\overset{N}{\Σ}}SNR\left(t_n\right),n=1,2,...,N$

wherein N represents the total number of discrete time points in the whole time-frequency distribution.

The invention has the beneficial effects that:

according to the technical scheme, the method for quantitatively evaluating the target micro Doppler effect detection and characteristic extraction effects is provided, the standard, objective and quantitative evaluation on the target micro characteristic detection effect is obtained, the defects of large limitation and poor normalization of the conventional qualitative judgment method are overcome, and a foundation is laid for the research of subsequent micro Doppler effect detection influence factors and influence rules and the selection of optimal system parameters.

Drawings

FIG. 1 is a flow chart of the evaluation criterion quantitative calculation of the present invention;

FIG. 2 is a method for defining the solution of the evaluation index of the single component signal according to the present invention, wherein FIG. 2(a) is a time-frequency distribution, and FIG. 2(b) is a parameter description for each index calculation;

fig. 3 is a method for defining the solution of the multi-component signal evaluation index according to the present invention, wherein fig. 3(a) is a time-frequency distribution, and fig. 3(b) is a parameter description for each index calculation.

Detailed Description

The invention is further described with reference to the following figures and specific examples.

As shown in fig. 1, a method for quantitatively evaluating the coherent laser detection effect of the target micro-motion feature comprises the following steps:

s1, carrying out time-frequency analysis processing on the collected echo signals containing the target micro Doppler effect by using a smooth pseudo Winger-Ville distribution algorithm to obtain a signal time-frequency distribution diagram containing target micro-motion characteristic information and a time-frequency distribution matrix P (t, f).

The principle of the SPWVD is to respectively perform windowing smoothing on a frequency domain and a time domain on the basis of Winger-Ville distribution so as to inhibit the influence of cross terms. The SPWVD has a good effect of inhibiting cross terms, and in a time-frequency distribution result, the cross terms basically do not influence the extraction of signal characteristics, so that the cross terms are ignored when a performance evaluation index is considered, but the windowing is smooth, so that the energy distribution is widened, the time-frequency aggregation is reduced, and the two indexes of the aggregation degree and the resolution need to be subjected to key analysis.

The calculation formula of SPWVD is as follows:

${\mathrm{SPWVD}}_s(t,f)={\∫}_{-\mathrm{\∞}}^{\mathrm{\∞}}{\∫}_{-\mathrm{\∞}}^{\mathrm{\∞}}g\left(\mathrm{\μ}\right)h\left(\mathrm{\τ}\right)s(t-\mathrm{\μ}+\frac{\mathrm{\τ}}{2})s^{*}(t-\mathrm{\μ}-\frac{\mathrm{\τ}}{2})e^{-j2\mathrm{\π}f\mathrm{\τ}}d\mathrm{\τ}d\mathrm{\μ}$

where g (μ) is a window function added to frequency, h (τ) is a window function added to time, both of which are real even window functions, and g (0) ═ h (0) ═ 1; s (-) s^*Denotes the echo signal and its conjugate, so s (-) s^*(. cndot.) represents an autocorrelation function of some past time signal and a future time signal.

The calculated time-frequency distribution matrix P (t, f) ═ SPWVD_s(t, f) with the abscissa representing time and the ordinate representing frequency, and the values in the matrix representing the energy distribution of the signal over the time-frequency plane.

S2, calculating a frequency domain concentration which is a first index of the quantitative evaluation of the target micro-motion characteristic coherent laser detection effect:

the frequency domain aggregation, which is defined as the extent of broadening of signal energy in the time-frequency distribution, reflects the uncertainty of the instantaneous frequency.

S21, calculating the instantaneous frequency domain concentration:

for a single-component signal, i.e. a signal containing only one component, fig. 2(a) shows its SPWVD distribution, which corresponds to a time-frequency distribution matrix P (t, f), where t is extracted_nOne-dimensional matrix P (t) of frequency-energy distribution on time-of-day time profile_nF), i.e. a straight line perpendicular to the time axis in the figure, the concrete distribution is as shown in fig. 2(b), and P (t) is found_nEnergy peak A in f)_SmaxAnd its corresponding frequency f_m(ii) a Find both sides of the peak and f_mThe corresponding energy closest to isTwo frequencies f_pAnd f_qThe distance between the two is t_nInstantaneous frequency domain concentration a (t) of time signal_n) Equivalent to the 3dB bandwidth BW (t) of the signal at that time_n)：

a(t_n)＝BW(t_n)＝f_p-f_q，f_p＞f_q

For multi-component signals, i.e. signals containing multiple components, the method for solving the instantaneous frequency domain aggregation degree is expanded on the basis of single-component signals, and when the peak value is solved, the original peak value is solved instead of solving the maximum first m peak values (m is the number of components, and m is not less than2) Then, sequentially obtaining the 3dB bandwidth BW corresponding to m peak values₁(t_n)、BW₂(t_n)、…、BW_m(t_n)，t_nInstantaneous frequency domain concentration a (t) of time signal_n) Average of the 3dB bandwidths for each component:

$a\left(t_n\right)=\frac{1}{m}\underset{i=1}{\overset{m}{\Σ}}{\mathrm{BW}}_i\left(t_n\right),i=1,...,m$

FIG. 3(a) shows the SPWVD distribution of two component signals, and the BW in FIG. 3(b) is calculated according to the calculation method of the single component signal₁(t_n)、BW₂(t_n) The instantaneous frequency domain concentration of the signal at this time is:

$a\left(t_n\right)=\frac{{\mathrm{BW}}_1\left(t_n\right)+{\mathrm{BW}}_2\left(t_n\right)}{2}$

s22, calculating the integral frequency domain concentration:

the instantaneous frequency domain concentrations a (t) of the signals at all times on the entire time axis are obtained in accordance with the method described in step S21_n) And then averaging the average values to obtain the integral frequency domain aggregation a of the signals:

$a=\frac{1}{N}\underset{n=1}{\overset{N}{\Σ}}a\left(t_n\right),n=1,2,...,N$

wherein N represents the total number of discrete time points in the whole time-frequency distribution.

S3, calculating a second index of quantitative evaluation of the target micro-motion characteristic coherent laser detection effect, namely component resolution:

the component resolution refers to the resolution capability of the terms of the multi-component signal in the frequency domain, and the single-component signal has no component resolution.

S31, calculating the resolution of the transient component:

taking the two-component signal as an example, t is extracted from FIG. 3(a)_nOne-dimensional matrix P (t) of frequency-energy distribution on time-of-day time profile_nF), i.e., the straight line position in the figure, the energy distribution of the cross section is shown in FIG. 3(b) at P (t)_nFinding the largest two peaks A in f)_Smax1And A_Smax2And peak valueCorresponding frequency value f₁、f₂F is obtained according to the method described in step S21₂Corresponding component at t_n3dB bandwidth BW at time₂(t_n) Then, the following formula is used to calculate the two components at t_nResolution of time:

$r_d\left(t_n\right)=(f_2-\frac{{\mathrm{BW}}_2\left(t_n\right)}{2})-f_1$

when the distance between the peak of component 1 and the peak of component 2 is less than the 3dB bandwidth of component 2, i.e. r_d(t_n) When < 0, the two components are considered to be indistinguishable.

For multi-component signals, the instantaneous resolution r between each two components is firstly calculated_d(t_n) Then for all r_d(t_n) Averaging to obtain the instantaneous component resolution r (t) of the signal_n)：

$r\left(t_n\right)=\frac{1}{C_m^2}\underset{i=1}{\overset{C_m^2}{\&Sigma;}}{r}_{di}\left(t_n\right),i=1,2,...,C_m^2$

Wherein m is the number of components, m is more than or equal to 2,the number of combinations obtained by selecting 2 out of m components is shown.

S32, calculating the resolution of the whole component:

the temporal component resolution r (t) of the signal at all times over the entire time axis is found in accordance with the method described in step S31_n) And then averaging the signals to obtain the integral component resolution r of the signal:

$r=\frac{1}{N}\underset{n=1}{\overset{N}{\&Sigma;}}r\left(t_n\right),n=1,2,...,N$

wherein N represents the total number of discrete time points in the whole time-frequency distribution.

S4, calculating a third index of quantitative evaluation of the target micro-motion characteristic coherent laser detection effect, namely signal-to-noise ratio:

and the signal-to-noise ratio is defined as the ratio of the peak energy of the signal component in the time-frequency distribution diagram to the average energy of the noise component, reflects the purity and definition of the signal component in the time-frequency distribution, and directly determines the accuracy of the micro Doppler feature extraction.

S41, calculating the instantaneous signal-to-noise ratio:

for a single component signal, t is extracted from its time-frequency distribution matrix P (t, f), as shown in FIG. 2(b)_nOne-dimensional matrix P (t) of frequency-energy distribution on time-of-day time profile_nF), find P (t)_nEnergy peak A in f)_SmaxIn accordance with the method described in step S21, t is obtained_n3dB bandwidth BW (t) of time signal_n) Finding out a frequency coordinate corresponding to a signal in a 3dB bandwidth; then, P (t) is obtained_nF) average value E (A) of the energy corresponding to all frequency coordinates except the 3dB bandwidth_noise) And is considered noise energy. Calculate t using the following formula_nInstantaneous signal-to-noise ratio SNR (t) of time-of-day signal_n)：

$SNR\left(t_n\right)=10lg\frac{A_{S\max }}{E\left(A_{noise}\right)}$

For a multi-component signal containing m components (m ≧ 2), the instantaneous signal-to-noise ratio is defined as the ratio of the mean of the peak energies of the m signal components to the mean energy of the noise component:

$SNR\left(t_n\right)=10lg\frac{{\mathrm{\&Sigma;}}_{i=1}^m{P}_{S\max i}}{m*E\left(A_{noise}\right)},i=1,2,...,m$

taking the two-component signal as an example, as shown in FIG. 3(b), P (t) is found_nEnergy peak A in f)_Smax1And A_Smax2The instantaneous snr of the signal at this time is:

$SNR\left(t_n\right)=10lg\frac{A_{S\max 1}+A_{S\max 2}}{2*E\left(A_{noise}\right)}$

s42, calculating the overall signal-to-noise ratio:

the instantaneous signal-to-noise ratio SNR (t) of the signal at all times over the time axis is found in accordance with the method described in step S41_n) And then averaging the average values to obtain the integral signal-to-noise ratio SNR of the signal:

$SNR=\frac{1}{N}\underset{n=1}{\overset{N}{\&Sigma;}}SNR\left(t_n\right),n=1,2,...,N$

wherein N represents the total number of discrete time points in the whole time-frequency distribution.

S5, calculating a total index of quantitative evaluation of the target micro-motion characteristic coherent laser detection effect:

for a single component signal, since there is no resolution of the components, the overall indicator Q is calculated as:

$Q=\frac{SNR}{a}$

for a multi-component signal, the overall indicator Q is calculated as:

$Q=\frac{r}{a}*SNR$

the larger the Q value is, the better the effect of extracting the target micro-motion characteristics from the time-frequency distribution result is.

For researching the influence of a certain parameter alpha on target micro Doppler effect detection, the corresponding Q value is sequentially solved within the variable range of alpha, and the maximum Q value corresponds to the optimal value of the parameter in target micro motion characteristic detection.

The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims (4)

1. A quantitative evaluation method for a target micro-motion characteristic coherent laser detection effect is characterized by comprising the following steps:

(1) performing time-frequency analysis processing on the acquired target micro-motion characteristic coherent laser detection echo signal by adopting a smooth pseudo Winger-Ville distribution algorithm to obtain a time-frequency distribution matrix P (t, f) of the echo signal;

(2) taking the frequency domain aggregation of the echo signals as a first index of quantitative evaluation of a target micro-motion characteristic coherent laser detection effect, and calculating the frequency domain aggregation according to the time-frequency distribution matrix P (t, f);

(3) taking the component resolution of the echo signal as a second index of quantitative evaluation of the target micro-motion characteristic coherent laser detection effect, and calculating the component resolution according to the time-frequency distribution matrix P (t, f);

(4) taking the signal-to-noise ratio of the echo signal as a third index of quantitative evaluation of the coherent laser detection effect of the target micro-motion characteristic, and calculating the signal-to-noise ratio according to the time-frequency distribution matrix P (t, f);

(5) according to the frequency domain aggregation degree, the component resolution and the signal-to-noise ratio, the following formula is adopted to calculate the total index of quantitative evaluation of the target micro-motion characteristic coherent laser detection effect:

if the echo signal is a single-component signal, the calculation formula of the total index is as follows:

$Q=\frac{SNR}{a}$

if the echo signal is a multi-component signal, the calculation formula of the total index is as follows:

$Q=\frac{r}{a}*SNR$

q represents a total index of quantitative evaluation of the target micro-motion characteristic coherent laser detection effect, a represents frequency domain concentration, r represents component resolution, and SNR represents signal-to-noise ratio.

2. The method for quantitatively evaluating the coherent laser detection effect of the target micro-motion feature according to claim 1, wherein in the step (2), the frequency domain concentration is calculated according to the time-frequency distribution matrix P (t, f), and the method comprises the following steps:

(21) calculating the instantaneous frequency domain aggregation degree:

for a single component signal, comprising:

a1, extracting t from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b1, finding P (t)_nEnergy peak A in f)_SmaxAnd its corresponding frequency f_m；

c1 finding the energy peak A_SmaxBoth sides and f_mThe corresponding energy closest to isTwo frequencies f_pAnd f_q；

d1, and finding t_n3dB bandwidth BW (t) of time signal_n) I.e. is t_nInstantaneous frequency domain concentration a (t) of time signal_n)：

a(t_n)＝BW(t_n)＝f_p-f_q，f_p＞f_q

For a multi-component signal, comprising:

a2, extracting t from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b2, finding P (t)_nF) the largest m energy peak values and the corresponding frequencies thereof, m being the number of components, m being greater than or equal to 2;

c2, sequentially obtaining m 3dB bandwidths BW corresponding to m energy peak values₁(t_n)、BW₂(t_n)、…、BW_m(t_n) Then, the average value of m 3dB bandwidths is obtained, i.e. t_nInstantaneous frequency domain concentration a (t) of time signal_n)：

$a\left(t_n\right)=\frac{1}{m}\underset{i=1}{\overset{m}{\&Sigma;}}{\mathrm{BW}}_i\left(t_n\right),i=1,2,...,m$

(22) Calculating the integral frequency domain aggregation degree:

the instantaneous frequency domain aggregation a (t) of all time signals on the whole time axis is obtained_n) And then averaging the average values to obtain the integral frequency domain aggregation a of the signals:

$a=\frac{1}{N}\underset{n=1}{\overset{N}{\&Sigma;}}a\left(t_n\right),n=1,2,...,N$

wherein N represents the total number of discrete time points in the whole time-frequency distribution.

3. The method for quantitatively evaluating the coherent laser detection effect of the target micro-motion feature according to claim 1, wherein in the step (3), the component resolution is calculated according to the time-frequency distribution matrix P (t, f), and the method comprises the following steps:

(31) calculating the instantaneous component resolution:

for a multi-component signal having m components, m ≧ 2, comprising:

a. for two of the components, t is extracted from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b. At P (t)_nFinding the largest two energy peaks A in f)_Smax1And A_Smax2And its corresponding frequency f₁、f₂；

c. Finding the energy peak A_Smax2Corresponding 3dB bandwidth BW₂(t_n)；

d. Two components at t are found according to the following formula_nResolution of time:

$r_d\left(t_n\right)=(f_2-\frac{{\mathrm{BW}}_2\left(t_n\right)}{2})-f_1$

e. according to the steps a to d, the instantaneous resolution r between every two components is calculated_d(t_n) Then for all r_d(t_n) Averaging to obtain the instantaneous component resolution r (t) of the signal_n)：

$r\left(t_n\right)=\frac{1}{C_m^2}\underset{i=1}{\overset{C_m^2}{\&Sigma;}}{r}_{di}\left(t_n\right),i=1,2,...,C_m^2$

Wherein,represents the number of combinations of 2 out of m components;

(32) calculating the integral component resolution:

the instantaneous component resolution r (t) of all time signals on the whole time axis is obtained_n) And then averaging the signals to obtain the integral component resolution r of the signal:

$r=\frac{1}{N}\underset{n=1}{\overset{N}{\&Sigma;}}r\left(t_n\right),n=1,2,...,N$

wherein N represents the total number of discrete time points in the whole time-frequency distribution.

4. The method for quantitatively evaluating the coherent laser detection effect of the target micro-motion feature according to claim 1, wherein in the step (4), the signal-to-noise ratio is calculated according to the time-frequency distribution matrix P (t, f), and the method comprises the following steps:

(41) calculating the instantaneous signal-to-noise ratio:

for a single component signal, comprising:

a1, extracting t from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b1, finding P (t)_nEnergy peak A in f)_SmaxAnd its corresponding frequency;

c1, finding t_n3dB bandwidth BW (t) of time signal_n) Finding out a frequency coordinate corresponding to a signal in a 3dB bandwidth;

d1, and then obtaining P (t)_nF) average value E (A) of the energy corresponding to all frequency coordinates except the 3dB bandwidth_noise) As noise energy;

e1, calculating t by using the following formula_nInstantaneous signal-to-noise ratio SNR (t) of time-of-day signal_n)：

$SNR\left(t_n\right)=10\lg \frac{A_{S\max }}{E\left(A_{noise}\right)}$

For a multi-component signal, comprising:

a2, extracting t from the time-frequency distribution matrix P (t, f)_nOne-dimensional matrix P (t) of the frequency-energy distribution at a time_n，f)；

b2, finding P (t)_nThe largest m energy peaks A in f)_Smax1、A_Smax2、…、A_SmaxmAnd the corresponding frequency, m is the number of components, m is more than or equal to 2;

c2, sequentially obtaining m 3dB bandwidths BW corresponding to m energy peak values₁(t_n)、BW₂(t_n)、…、BW_m(t_n) Finding out frequency coordinates corresponding to m signals in the 3dB bandwidth;

d2, and then obtaining P (t)_nF) average value E (A) of the energy corresponding to all frequency coordinates except the m 3dB bandwidths_noise) As noise energy;

e2, calculating t by using the following formula_nInstantaneous signal-to-noise ratio SNR (t) of time-of-day signal_n)：

$SNR\left(t_n\right)=10\lg \frac{{\mathrm{\&Sigma;}}_{i=1}^m{A}_{S\max i}}{m*E\left(A_{noise}\right)},i=1,2,...,m$

(42) Calculating the overall signal-to-noise ratio:

the instantaneous signal-to-noise ratio SNR (t) of the signal at all time points on the whole time axis is obtained_n) Then, the average value of the two values is calculated,i.e. the overall signal-to-noise ratio SNR of the signal is obtained:

$SNR=\frac{1}{N}\underset{n=1}{\overset{N}{\&Sigma;}}SNR\left(t_n\right),n=1,2,...,N$

wherein N represents the total number of discrete time points in the whole time-frequency distribution.