CN105629254B - A kind of target fine motion feature coherent laser detection effect method for quantitatively evaluating - Google Patents
A kind of target fine motion feature coherent laser detection effect method for quantitatively evaluating Download PDFInfo
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The present invention provides a kind of target fine motion feature coherent laser detection effect method for quantitatively evaluating, including：Time frequency analysis processing is carried out to the target fine motion feature coherent laser detection echo-signal of collection using Smoothing Pseudo Winger Ville Distribution Algorithms, obtains the time-frequency distributions matrix of the echo-signal；Calculate the frequency domain concentration class of the echo-signal；Calculate the component resolving power of the echo-signal；Calculate the signal-to-noise ratio of the echo-signal；Calculate overall performane.The present invention gives the detection of quantitative assessment target micro-Doppler effect and the method for feature extraction effect, obtain to target fine motion feature detection effect specification, objective, quantization evaluation, solves the deficiency of big, the normative difference of existing qualitative judgement method limitation, detecting influence factor, the research of affecting laws and the selection of optimal system parameter for follow-up micro-Doppler effect lays a good foundation.
Description
Technical field
The present invention relates to target micro-Doppler feature detection technology field, is specifically a kind of target fine motion feature coherent laser
Effect on Detecting method for quantitatively evaluating.
Background technology
The accurately detecting of target and identification are always the emphasis directions of various countries' national defence research, however, with stealthy in recent years
The development and application of technology, bait technology etc., traditional detection means of identification is heavily disturbed, and task performance is had a greatly reduced quality,
It is difficult to provide and effectively judges targeted species and true and false information.At the beginning of 21 century, the V.C.Chen religions of United States Naval Research Laboratory
The concept for the micro-Doppler effect that takes the lead in proposing is awarded, opens the new way of target acquisition identification.
Target and radar can produce Doppler frequency shift there are relative radial motion, and micro-Doppler effect just refer to it is same herein
When, due to fine motion existing for target itself or some parts, and the additional frequency that echo-signal produces is modulated, finally led
The frequency bandspread effect centered on Doppler frequency shift caused.Micro-Doppler effect is considered as the unique spy for reflecting target fine motion
Sign, contains the peculiar information of target.Detected using laser radar, the characteristics of its wavelength is short can measure significantly more
Micro-Doppler effect, so as to obtain detection resolution and the sensitivity of higher.
Realize the detection of target micro-Doppler effect, it is necessary first to study detection system parameter, structure, transmission medium etc. because
The rule that element influences target echo, finds the designing scheme of optimal systematic parameter and structure and the compensation to external interference
Measure, this requires under different experimental conditions to echo in the Effect on Detecting of target micro-Doppler feature compare and analyze.
At present, most common method is exactly to carry out time frequency analysis to signal in micro-Doppler feature extraction, then from time-frequency distributions
Extract target information.The quality of system Effect on Detecting can intuitively reflect in time-frequency distributions result, thus time-frequency distributions can
The property read directly determines the accuracy of micro-Doppler feature extraction, this detection for target and identification are most important.
The echo coherent detection signal of target micro-Doppler effect is non-stationary signal, so in handling it, research
Persons generally employ Time-Frequency Analysis Method to extract target fine motion feature (micro-Doppler feature), however carry out at present to return
In the research of wave characteristic influence factor (such as frequency source phase noise, laser linewidth, detection range, detection system structure
With the factor such as parameter), in different target fine motion feature detection effect caused by analyzing different factors, researcher is mainly still
By qualitatively observing and the rule of thumb target fine motion feature time-frequency distributions result to be obtained under comparative evaluation's different condition.
This qualitatively control methods individual difference is big, normative poor, there is larger limitation, such as：It is micro- for target
Dynamic obvious two time-frequency distributions of feature detection difference on effect as a result, be can interpolate that out by observation obtain in the case of which kind of when
Effect is more preferable when frequency is distributed in extraction target fine motion feature, it can be difficult to the how much strong of the good ratio difference of effect is specifically given, can not
It is corresponding with the detection condition quantitatively changed before；And for unconspicuous two time-frequency distributions of Effect on Detecting difference as a result, only
By observing then it is difficult to which the Effect on Detecting in the case of which kind of is accurately judged to is more preferable, or even judges by accident.To solve this problem,
We need science, quantitative evaluation method to judge the detection of target micro-Doppler feature and extraction effect.
The content of the invention
It is an object of the invention to provide a kind of target fine motion feature coherent laser detection effect method for quantitatively evaluating, it is based on
Smoothing Pseudo Winger-Ville is distributed (SPWVD) this Time-Frequency Analysis Method for having been demonstrated to have preferable performance, proposes a kind of
Measurement standard and its computational methods carry out detection and the extraction effect of quantitative assessment target fine motion feature, to solve qualitative analysis not
Foot.
The technical scheme is that：
A kind of target fine motion feature coherent laser detection effect method for quantitatively evaluating, comprises the following steps：
(1) the target fine motion feature coherent laser detection echo using Smoothing Pseudo Winger-Ville Distribution Algorithms to collection
Signal carries out time frequency analysis processing, obtains the time-frequency distributions matrix P (t, f) of the echo-signal；
(2) using the frequency domain concentration class of the echo-signal as target fine motion feature coherent laser detection effect quantitatively evaluating
First index, and the frequency domain concentration class is calculated according to the time-frequency distributions matrix P (t, f)；
(3) using the component resolving power of the echo-signal as target fine motion feature coherent laser detection effect quantitatively evaluating
Second index, and the component resolving power is calculated according to the time-frequency distributions matrix P (t, f)；
(4) using the signal-to-noise ratio of the echo-signal as target fine motion feature coherent laser detection effect quantitatively evaluating
Three indexs, and the signal-to-noise ratio is calculated according to the time-frequency distributions matrix P (t, f)；
(5) according to the frequency domain concentration class, component resolving power and signal-to-noise ratio, using the following formula to target fine motion feature phase
The overall performane of dry laser acquisition effect quantitatively evaluating is calculated：
If the echo-signal is simple component signal, the calculation formula of the overall performane is：
If the echo-signal is multicomponent data processing, the calculation formula of the overall performane is：
Wherein, Q represents the overall performane of target fine motion feature coherent laser detection effect quantitatively evaluating, and a represents frequency domain aggregation
Degree, r represent component resolving power, and SNR represents signal-to-noise ratio.
The target fine motion feature coherent laser detection effect method for quantitatively evaluating, in step (2), according to the time-frequency
Distribution matrix P (t, f) calculates the frequency domain concentration class, comprises the following steps：
(21) temporal frequency domain concentration class is calculated：
For simple component signal, including：
A1, the extraction t in time-frequency distributions matrix P (t, f)nThe one-dimensional matrix P (t of frequency-Energy distribution at momentn, f)；
B1, find P (tn, f) in energy peak ASmaxAnd its corresponding frequency fm；
C1, find energy peak ASmaxBoth sides and fmClosest correspondence energy isTwo frequency fpAnd fq；
D1, obtain tnThree dB bandwidth BW (the t of time-ofday signalsn), it is tnThe temporal frequency domain concentration class a (t of time-ofday signalsn)：
a(tn)=BW (tn)=fp-fq, fp＞ fq
For multicomponent data processing, including：
A2, the extraction t in time-frequency distributions matrix P (t, f)nThe one-dimensional matrix P (t of frequency-Energy distribution at momentn, f)；
B2, find P (tn, f) in m maximum energy peak and its corresponding frequency, m is component number, m >=2；
C2, obtain the corresponding m three dB bandwidth BW of m energy peak successively1(tn)、BW2(tn)、…、BWm(tn), then obtain
The average value of m three dB bandwidth, is tnThe temporal frequency domain concentration class a (t of time-ofday signalsn)：
(22) overall frequency domain concentration class is calculated：
Obtain the temporal frequency domain concentration class a (t of all time-ofday signals on whole time shaftn), then average to it, to obtain the final product
To the overall frequency domain concentration class a of signal：
Wherein, N represents the discrete time point sum in whole time-frequency distributions.
The target fine motion feature coherent laser detection effect method for quantitatively evaluating, in step (3), according to the time-frequency
Distribution matrix P (t, f) calculates the component resolving power, comprises the following steps：
(31) transient component resolving power is calculated：
For the multicomponent data processing containing m component, m >=2, including：
A, for two of which component, the extraction t in time-frequency distributions matrix P (t, f)nFrequency-the Energy distribution one at moment
Tie up matrix P (tn, f)；
B, in P (tn, f) in find maximum two energy peak ASmax1And ASmax2And its corresponding frequency f1、f2；
C, energy peak A is obtainedSmax2Corresponding three dB bandwidth BW2(tn)；
D, two components are obtained in t according to the following formulanThe resolving power at moment：
E, according to step a~d, obtain important instantaneous resolving power r between any twod(tn), then to all rd(tn) ask
Average value, that is, obtain the transient component resolving power r (t of signaln)：
Wherein,2 obtained number of combinations are selected in expression from m component；
(32) overall component resolving power is calculated：
Obtain the transient component resolving power r (t of all time-ofday signals on whole time shaftn), then average to it, to obtain the final product
To the overall component resolving power r of signal：
Wherein, N represents the discrete time point sum in whole time-frequency distributions.
The target fine motion feature coherent laser detection effect method for quantitatively evaluating, in step (4), according to the time-frequency
Distribution matrix P (t, f) calculates the signal-to-noise ratio, comprises the following steps：
(41) instantaneous signal-to-noise ratio is calculated：
For simple component signal, including：
A1, the extraction t in time-frequency distributions matrix P (t, f)nThe one-dimensional matrix P (t of frequency-Energy distribution at momentn, f)；
B1, find P (tn, f) in energy peak ASmaxAnd its corresponding frequency；
C1, obtain tnThree dB bandwidth BW (the t of time-ofday signalsn), find the corresponding frequency coordinate of signal in three dB bandwidth；
D1, obtain P (t againn, f) in corresponding average energy E (A of all frequency coordinates beyond three dB bandwidthnoise), as
Noise energy；
E1, using the following formula calculate tnInstantaneous signal-to-noise ratio SNR (the t of time-ofday signalsn)：
For multicomponent data processing, including：
A2, the extraction t in time-frequency distributions matrix P (t, f)nThe one-dimensional matrix P (t of frequency-Energy distribution at momentn, f)；
B2, find P (tn, f) in maximum m energy peak ASmax1、ASmax2、…、ASmaxmAnd its corresponding frequency, m are
Component number, m >=2；
C2, obtain the corresponding m three dB bandwidth BW of m energy peak successively1(tn)、BW2(tn)、…、BWm(tn), find m
The corresponding frequency coordinate of signal in a three dB bandwidth；
D2, obtain P (t againn, f) in corresponding average energy E (A of all frequency coordinates beyond m three dB bandwidthnoise),
As noise energy；
E2, using the following formula calculate tnInstantaneous signal-to-noise ratio SNR (the t of time-ofday signalsn)：
(42) overall signal-to-noise ratio is calculated：
Obtain the instantaneous signal-to-noise ratio SNR (t of all time-ofday signals on whole time shaftn), then average to it, that is, obtain
The overall Signal to Noise Ratio (SNR) of signal：
Wherein, N represents the discrete time point sum in whole time-frequency distributions.
Beneficial effects of the present invention are：
As shown from the above technical solution, The present invention gives the detection of quantitative assessment target micro-Doppler effect and feature extraction
The method of effect, obtains, to target fine motion feature detection effect specification, objective, quantization evaluation, solving existing qualitative judgement
The deficiency of big, the normative difference of method limitation, influence factor, the research of affecting laws and most are detected for follow-up micro-Doppler effect
The selection of excellent systematic parameter is laid a good foundation.
Brief description of the drawings
The evaluation criterion that Fig. 1 is the present invention quantifies calculation flow chart；
Fig. 2 is method for solving of the present invention definition to simple component signal evaluation index, wherein, Fig. 2 (a) is time-frequency distributions,
Fig. 2 (b) is each index calculating parameter explanation；
Fig. 3 is method for solving of the present invention definition to multicomponent data processing evaluation index, wherein, Fig. 3 (a) is time-frequency distributions,
Fig. 3 (b) is each index calculating parameter explanation.
Embodiment
Below in conjunction with the accompanying drawings the present invention is further illustrated with specific embodiment.
As shown in Figure 1, a kind of target fine motion feature coherent laser detection effect method for quantitatively evaluating, comprises the following steps：
S1, the letter of the echo comprising target micro-Doppler effect using Smoothing Pseudo Winger-Ville Distribution Algorithms to collection
Number carry out time frequency analysis processing, obtain the signal time frequency distribution map containing target fine motion characteristic information and time-frequency distributions matrix P (t,
f)。
The principle of SPWVD is that frequency domain and time domain difference adding window smoothly, come in Winger-Ville distributed basis
The influence of suppressing crossterms.Since SPWVD is preferable to the inhibition of cross term, in time-frequency distributions result, cross term is basic
Influence will not be brought on the extraction of signal characteristic, so cross term this index has been ignored as when considering Performance Evaluating Indexes,
But adding window smoothly makes energy distribution broadening, time-frequency locality has declined, so concentration class and resolving power the two indexs needs
Selective analysis.
The calculation formula of SPWVD is：
Wherein, g (μ) is the window function added to frequency, and h (τ) is the window function added to the time, they are all real even window letters
Number, and have g (0)=h (0)=1；s(·)s*() represents echo-signal and its conjugation, so s () s*() represents a certain mistake
Remove the auto-correlation function of time signal and future time signal.
Time-frequency distributions matrix P (t, the f)=SPWVD being calculateds(t, f), its abscissa represent the time, and ordinate represents
Frequency, the numerical value in matrix represent Energy distribution of the signal on time frequency plane.
S2, first index for calculating target fine motion feature coherent laser detection effect quantitatively evaluating --- frequency domain is assembled
Degree：
Frequency domain concentration class, its definition is broadening degree of the signal energy in time-frequency distributions, reflects the not true of instantaneous frequency
Fixed degree.
S21, calculate temporal frequency domain concentration class：
For simple component signal, that is, the signal of one-component is comprised only, Fig. 2 (a) gives its SPWVD distributions, it is corresponded to
Time-frequency distributions matrix P (t, f), the extraction t in P (t, f)nThe one-dimensional matrix P (t of frequency-Energy distribution on time sectionn,
F), i.e., P (t are found as shown in Fig. 2 (b) perpendicular to the straight line of time shaft, specific distribution in figuren, f) in energy peak ASmaxAnd
Its corresponding frequency fm；Find peak value both sides and fmClosest correspondence energy isTwo frequency fpAnd fq, the two
Spacing is tnThe temporal frequency domain concentration class a (t of time-ofday signalsn), equivalent to the three dB bandwidth BW (t of the time-ofday signalsn)：
a(tn)=BW (tn)=fp-fq, fp＞ fq
For multicomponent data processing, the i.e. signal containing multiple components, its temporal frequency domain concentration class asks method to believe in simple component
Be extended on the basis of number, in peaking by it is original ask a peak value to be changed to ask maximum preceding m peak value (m is component
Number, m >=2), the corresponding three dB bandwidth BW of m peak value is then sought successively1(tn)、BW2(tn)、…、BWm(tn), tnTime-ofday signals
Temporal frequency domain concentration class a (tn) be each component 3 dB bandwidth average value：
Fig. 3 (a) gives the SPWVD distributions of two component signals, according to the computational methods of simple component signal, obtains Fig. 3 (b)
In BW1(tn)、BW2(tn), the temporal frequency domain concentration class of signal is at this time：
S22, calculate overall frequency domain concentration class：
The temporal frequency domain concentration class a of all time-ofday signals on whole time shaft is obtained according to the method described in step S21
(tn), then average to it, that is, obtain the overall frequency domain concentration class a of signal：
Wherein, N represents the discrete time point sum in whole time-frequency distributions.
S3, second index for calculating target fine motion feature coherent laser detection effect quantitatively evaluating --- component is differentiated
Power：
Component resolving power, refer to multicomponent data processing from item in the resolution capability of frequency domain, there is no component point for simple component signal
Situation about distinguishing.
S31, calculate transient component resolving power：
By taking two component signals as an example, the extraction t in Fig. 3 (a)nThe one-dimensional square of frequency-Energy distribution on time section
Battle array P (tn, f), i.e. figure cathetus position, shown in Energy distribution such as Fig. 3 (b) of section, in P (tn, f) in find maximum two
Peak ASmax1And ASmax2And the corresponding frequency values f of peak value1、f2, according to the described methods of step S21, obtain f2Corresponding point
Amount is in tnThe three dB bandwidth BW at moment2(tn), two components are obtained in t further according to the following formulanThe resolving power at moment：
As the peak value of component 1 and three dB bandwidth of the peak distance of component 2 less than component 2, i.e. rd(tn) ＜ 0 when, it is believed that two
A component can not be differentiated.
For multicomponent data processing, the instantaneous resolving power r of each component between any two is first obtainedd(tn), then to all rd(tn)
Average, that is, obtain the transient component resolving power r (t of signaln)：
Wherein, m is component number, m >=2,2 obtained number of combinations are selected in expression from m component.
S32, calculate overall component resolving power：
The transient component resolving power r of all time-ofday signals on whole time shaft is obtained according to the method described in step S31
(tn), then average to it, that is, obtain the overall component resolving power r of signal：
Wherein, N represents the discrete time point sum in whole time-frequency distributions.
S4, the 3rd index --- signal-to-noise ratio for calculating target fine motion feature coherent laser detection effect quantitatively evaluating：
Signal-to-noise ratio, it is defined as the ratio between signal component peak energy and noise contribution average energy in time frequency distribution map, instead
The degree of purity and clarity of signal component in time-frequency distributions are reflected, directly determines the levels of precision of micro-Doppler feature extraction.
S41, calculate instantaneous signal-to-noise ratio：
For simple component signal, as shown in Fig. 2 (b), the extraction t in its time-frequency distributions matrix P (t, f)nTime is cutd open
The one-dimensional matrix P (t of frequency-Energy distribution on facen, f), find P (tn, f) in energy peak ASmax, retouched according to step S21
The method stated, obtains tnThree dB bandwidth BW (the t of time-ofday signalsn), find the corresponding frequency coordinate of signal in three dB bandwidth；P is obtained again
(tn, f) in corresponding average energy E (A of all frequency coordinates beyond three dB bandwidthnoise), it is believed that it is noise energy.Use with
Lower formula calculates tnInstantaneous signal-to-noise ratio SNR (the t of time-ofday signalsn)：
For the multicomponent data processing containing m component (m >=2), its instantaneous signal-to-noise ratio is defined as m signal component peak value energy
The ratio between average and noise contribution average energy of amount：
By taking two component signals as an example, as shown in Fig. 3 (b), P (t are foundn, f) in energy peak ASmax1And ASmax2, at this time
The instantaneous signal-to-noise ratio of signal is：
S42, calculate overall signal-to-noise ratio：
The instantaneous signal-to-noise ratio SNR of all time-ofday signals on whole time shaft is obtained according to the method described in step S41
(tn), then average to it, that is, obtain the overall Signal to Noise Ratio (SNR) of signal：
Wherein, N represents the discrete time point sum in whole time-frequency distributions.
S5, the overall performane for calculating target fine motion feature coherent laser detection effect quantitatively evaluating：
For simple component signal, since there is no the resolution to component, so the calculation formula of overall performane Q is：
For multicomponent data processing, the calculation formula of overall performane Q is：
Q values are bigger, illustrate that the effect that target fine motion feature can be extracted from time-frequency distributions result is better.
The influence detected for studying a certain parameter alpha to target micro-Doppler effect, is asked successively in the range of the alterable of α
Go out its corresponding Q value, maximum Q values are corresponding be exactly the parameter in target fine motion feature detection optimal value.
Embodiment described above is only that the preferred embodiment of the present invention is described, not to the model of the present invention
Enclose and be defined, on the premise of design spirit of the present invention is not departed from, technical side of the those of ordinary skill in the art to the present invention
The various modifications and improvement that case is made, should all fall into the protection domain that claims of the present invention determines.
Claims (4)
1. a kind of target fine motion feature coherent laser detection effect method for quantitatively evaluating, it is characterised in that comprise the following steps：
(1) the target fine motion feature coherent laser detection echo-signal using Smoothing Pseudo Winger-Ville Distribution Algorithms to collection
Time frequency analysis processing is carried out, obtains the time-frequency distributions matrix P (t, f) of the echo-signal；
(2) using the frequency domain concentration class of the echo-signal as target fine motion feature coherent laser detection effect quantitatively evaluating
One index, and the frequency domain concentration class is calculated according to the time-frequency distributions matrix P (t, f)；
(3) using the component resolving power of the echo-signal as target fine motion feature coherent laser detection effect quantitatively evaluating
Two indexs, and the component resolving power is calculated according to the time-frequency distributions matrix P (t, f)；
(4) the 3rd using the signal-to-noise ratio of the echo-signal as target fine motion feature coherent laser detection effect quantitatively evaluating
Index, and the signal-to-noise ratio is calculated according to the time-frequency distributions matrix P (t, f)；
(5) according to the frequency domain concentration class, component resolving power and signal-to-noise ratio, it is concerned with using the following formula to target fine motion feature sharp
The overall performane of optical detection effect quantitatively evaluating is calculated：
If the echo-signal is simple component signal, the calculation formula of the overall performane is：
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If the echo-signal is multicomponent data processing, the calculation formula of the overall performane is：
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Wherein, Q represents the overall performane of target fine motion feature coherent laser detection effect quantitatively evaluating, and a represents frequency domain concentration class, r
Represent component resolving power, SNR represents signal-to-noise ratio.
2. target fine motion feature coherent laser detection effect method for quantitatively evaluating according to claim 1, it is characterised in that
In step (2), the frequency domain concentration class is calculated according to the time-frequency distributions matrix P (t, f), is comprised the following steps：
(21) temporal frequency domain concentration class is calculated：
For simple component signal, including：
A1, the extraction t in time-frequency distributions matrix P (t, f)nThe one-dimensional matrix P (t of frequency-Energy distribution at momentn, f)；
B1, find P (tn, f) in energy peak ASmaxAnd its corresponding frequency fm；
C1, find energy peak ASmaxBoth sides and fmClosest correspondence energy isTwo frequency fpAnd fq；
D1, obtain tnThree dB bandwidth BW (the t of time-ofday signalsn), it is tnThe temporal frequency domain concentration class a (t of time-ofday signalsn)：
a(tn)=BW (tn)=fp-fq, fp＞ fq
For multicomponent data processing, including：
A2, the extraction t in time-frequency distributions matrix P (t, f)nThe one-dimensional matrix P (t of frequency-Energy distribution at momentn, f)；
B2, find P (tn, f) in m maximum energy peak and its corresponding frequency, m is component number, m >=2；
C2, obtain the corresponding m three dB bandwidth BW of m energy peak successively1(tn)、BW2(tn)、…、BWm(tn), then obtain m
The average value of three dB bandwidth, is tnThe temporal frequency domain concentration class a (t of time-ofday signalsn)：
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(22) overall frequency domain concentration class is calculated：
Obtain the temporal frequency domain concentration class a (t of all time-ofday signals on whole time shaftn), then average to it, that is, obtain letter
Number overall frequency domain concentration class a：
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Wherein, N represents the discrete time point sum in whole time-frequency distributions.
3. target fine motion feature coherent laser detection effect method for quantitatively evaluating according to claim 1, it is characterised in that
In step (3), the component resolving power is calculated according to the time-frequency distributions matrix P (t, f), is comprised the following steps：
(31) transient component resolving power is calculated：
For the multicomponent data processing containing m component, m >=2, including：
A, for two of which component, the extraction t in time-frequency distributions matrix P (t, f)nThe one-dimensional matrix of frequency-Energy distribution at moment
P(tn, f)；
B, in P (tn, f) in find maximum two energy peak ASmax1And ASmax2And its corresponding frequency f1、f2；
C, energy peak A is obtainedSmax2Corresponding three dB bandwidth BW2(tn)；
D, two components are obtained in t according to the following formulanThe resolving power at moment：
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<mo>)</mo>
</mrow>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>f</mi>
<mn>2</mn>
</msub>
<mo>-</mo>
<mfrac>
<mrow>
<msub>
<mi>BW</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>n</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mn>2</mn>
</mfrac>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>f</mi>
<mn>1</mn>
</msub>
</mrow>
E, according to step a~d, obtain important instantaneous resolving power r between any twod(tn), then to all rd(tn) be averaging
Value, that is, obtain the transient component resolving power r (t of signaln)：
<mrow>
<mi>r</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>n</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msubsup>
<mi>C</mi>
<mi>m</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<msubsup>
<mi>C</mi>
<mi>m</mi>
<mn>2</mn>
</msubsup>
</munderover>
<msub>
<mi>r</mi>
<mrow>
<mi>d</mi>
<mi>i</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>n</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msubsup>
<mi>C</mi>
<mi>m</mi>
<mn>2</mn>
</msubsup>
</mrow>
Wherein,2 obtained number of combinations are selected in expression from m component；
(32) overall component resolving power is calculated：
Obtain the transient component resolving power r (t of all time-ofday signals on whole time shaftn), then average to it, that is, obtain letter
Number overall component resolving power r：
<mrow>
<mi>r</mi>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>n</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mi>r</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>n</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>n</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>N</mi>
</mrow>
Wherein, N represents the discrete time point sum in whole time-frequency distributions.
4. target fine motion feature coherent laser detection effect method for quantitatively evaluating according to claim 1, it is characterised in that
In step (4), the signal-to-noise ratio is calculated according to the time-frequency distributions matrix P (t, f), is comprised the following steps：
(41) instantaneous signal-to-noise ratio is calculated：
For simple component signal, including：
A1, the extraction t in time-frequency distributions matrix P (t, f)nThe one-dimensional matrix P (t of frequency-Energy distribution at momentn, f)；
B1, find P (tn, f) in energy peak ASmaxAnd its corresponding frequency；
C1, obtain tnThree dB bandwidth BW (the t of time-ofday signalsn), find the corresponding frequency coordinate of signal in three dB bandwidth；
D1, obtain P (t againn, f) in corresponding average energy E (A of all frequency coordinates beyond three dB bandwidthnoise), as noise
Energy；
E1, using the following formula calculate tnInstantaneous signal-to-noise ratio SNR (the t of time-ofday signalsn)：
<mrow>
<mi>S</mi>
<mi>N</mi>
<mi>R</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>n</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mn>10</mn>
<mi>lg</mi>
<mfrac>
<msub>
<mi>A</mi>
<mrow>
<mi>S</mi>
<mi>max</mi>
</mrow>
</msub>
<mrow>
<mi>E</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>A</mi>
<mrow>
<mi>n</mi>
<mi>o</mi>
<mi>i</mi>
<mi>s</mi>
<mi>e</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
</mrow>
For multicomponent data processing, including：
A2, the extraction t in time-frequency distributions matrix P (t, f)nThe one-dimensional matrix P (t of frequency-Energy distribution at momentn, f)；
B2, find P (tn, f) in maximum m energy peak ASmax1、ASmax2、…、ASmaxmAnd its corresponding frequency, m are component
Number, m >=2；
C2, obtain the corresponding m three dB bandwidth BW of m energy peak successively1(tn)、BW2(tn)、…、BWm(tn), find m 3dB
The corresponding frequency coordinate of signal in bandwidth；
D2, obtain P (t againn, f) in corresponding average energy E (A of all frequency coordinates beyond m three dB bandwidthnoise), as
Noise energy；
E2, using the following formula calculate tnInstantaneous signal-to-noise ratio SNR (the t of time-ofday signalsn)：
<mrow>
<mi>S</mi>
<mi>N</mi>
<mi>R</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>n</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mn>10</mn>
<mi>lg</mi>
<mfrac>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>m</mi>
</msubsup>
<msub>
<mi>A</mi>
<mrow>
<mi>S</mi>
<mi>max</mi>
<mi>i</mi>
</mrow>
</msub>
</mrow>
<mrow>
<mi>m</mi>
<mo>*</mo>
<mi>E</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>A</mi>
<mrow>
<mi>n</mi>
<mi>o</mi>
<mi>i</mi>
<mi>s</mi>
<mi>e</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>m</mi>
</mrow>
(42) overall signal-to-noise ratio is calculated：
Obtain the instantaneous signal-to-noise ratio SNR (t of all time-ofday signals on whole time shaftn), then average to it, that is, obtain signal
Overall Signal to Noise Ratio (SNR)：
<mrow>
<mi>S</mi>
<mi>N</mi>
<mi>R</mi>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>n</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mi>S</mi>
<mi>N</mi>
<mi>R</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>n</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>n</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>N</mi>
</mrow>
Wherein, N represents the discrete time point sum in whole time-frequency distributions.
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