CN106371095A - Pulse compression technique-based range imaging method and range imaging system - Google Patents

Abstract

The present invention discloses a pulse compression technique-based range imaging method. The range imaging method includes the following steps that: a zero-filled transmitted signal sequence is adopted to carry out matched filtering on a received signal sequence, so that an initialized range image estimation value can be obtained; under the MMSE criterion, the existing range image estimation value is utilized to obtain a new filter weight vector; and the new filter weight vector is utilized to update the range image estimation value; and the above operation is executed repeatedly until a preset judgment condition is satisfied, so that range imaging is completed. According to the method, all received data are utilized to carry out the adaptive design of the filter weight vector; the degree of freedom of self-adaption is not limited by the length of transmitted signals; the range image of a target scene of any size can be estimated; and the estimated length of the range image in an iteration process will not be decreased. Compared with an existing method, the method has higher utilization rate of sampling data.

Description

Distance based on pulse compression technique is to imaging method and distance to imaging system

Technical field

The present invention relates to Radar Signal Processing Technology field is and in particular to pulse compression based on iteration fully adaptive technology The distance of technology is to imaging method and distance to imaging system.

Background technology

Pulse compression technique is widely used in radar imagery, in supersonic sounding and medical imaging.Pulse compression technique Detection system can be made to obtain higher range resolution ratio while keeping big average emitted power.Traditional pulse compression To be completed by matched filter, but often to suffer from impact and the resolution of high secondary lobe using the Range Profile that matched filtering obtains Rate is limited, and therefore application in pulse compression for the matched filtering technique is restricted.

Self adaptation pulse compression technique is a kind of emerging pulse compression technique, and initial document of delivering specifically includes that " the adaptive pulse of " ieee transactions on aerospace and electronic system " Compression via mmse estimation " and " multistatic adaptive pulse compression ", from Adapt to pulse compression technique and wave filter weight vector and Range Profile valuation are updated by iteration, can effectively suppress between adjacent objects Interfere, obtain high-precision Range Profile.Paper " the gain- delivering in ieee radar meeting in 2009 Contrained adaptive pulse compression via an mvdr framework " and " electronics and informaticss Report " publish the article in " the self adaptation pulse compression technique based on maximum output signal-to-noise ratio criterion ", have studied different Adaptive Criterion Application in self adaptation pulse compression, obtains the pulse compression result of optimum under different criterions.Document " multistage adaptive pulse compression》、《fast implementation of adaptive multi-pulse compression via dimensionality reduction technique》、《adaptive pulse compression of orthogonal transmitted waveforms based on mpdr-mwf》、 " dimensionality reduction techniques for adaptive pulse compression " with " based on many The mimo radar self-adaption impulse compression method of level Wiener filtering " concentrate to have studied how to improve self adaptation pulse compression technique Operation efficiency, by dimension-reduction treatment or multistage wiener filter form it is achieved that the quick realization of self adaptation pulse compression.

But existing self adaptation pulse compression technique yet suffers from two aspect problems: imageable distance is sent out to length Ejected wave shape length limitation and each iteration all can reduce the distance of imaging region to length.Existing self adaptation pulse compression technique The part in receipt signal (length is usually transmitted waveform length) is only make use of to update weight vector, to belong to partial adaptivity Technology, its self adaptation degree of freedom is subject to transmitted waveform length limitation, it is estimated that distance to number of targets limited it is impossible to for big Scene imaging；In addition, all being estimated with the Range Profile losing two ends certain length during each iteration of this kind of self adaptation pulse compression technique It is worth for cost, the distance of imaging region quickly reduces to length with the increase of iterationses, is equivalent to the process in iteration In waste substantial portion of sampled data.These defects all limit existing partial adaptivity pulse compression technique in reality Application in border.

Content of the invention

The present invention provides a kind of distance based on pulse compression technique to imaging method and distance to imaging system, can estimate Count out the Range Profile of arbitrary size target scene, the Range Profile length estimating in an iterative process will not reduce, sampled data Utilization rate higher.

For achieving the above object, the present invention provides a kind of distance based on pulse compression technique to imaging method, its feature It is that this distance comprises to imaging method:

S1, using the transmission signal sequence after zero paddingTo receipt signal sequenceCarry out matched filtering, obtain initial The Range Profile valuation changed

S2, under mmse criterion, obtain new wave filter weight vector using existing Range Profile valuation x (l)

S3, update Range Profile valuation using new wave filter weight vector w (l)

S4, repeat s2 and s3, complete distance to imaging until meeting default Rule of judgment.

Above-mentioned s1 comprises:

If radar emission waveform has sampling length n, irradiation area is made up of l range cell, l-th range cell Target backscattering coefficient is expressed as x (l), and the receipt signal sampling length of radar receiver is not less than l+n-1；

Consider receipt signal length and effect of noise, radar return signal such as formula (1):

$\tilde{y}=\underset{l=1}{\overset{l}{\σ}}{\tilde{s}}_{l}x\left(l\right)+v---\left(1\right)$

In formula (1), v is additive white Gaussian noise,After zero padding Transmission signal vector,For transmission signal, 0_lRepresent the zero vector of l × 1 dimension；

Receipt signal using overall length carries out matched filtering, obtains initialized Range Profile valuation, as shown in formula (2):

$x\left(l\right)={\tilde{s}}^h\left(l\right)\tilde{y}---\left(2\right).$

Above-mentioned s2 comprises:

It is described using mmse criterion, the mmse cost function such as formula (3) of standard:

$j\left(l\right)=e\[{|x\left(l\right)-{\mathrm{\ω}}^h\left(l\right)\tilde{y}|}^2\]---\left(3\right)$

In formula (3), e [] represents expectation computing symbol；

Using formula (3) to w (l) derivation, and make it be equal to zero, optimum weight vector can be obtained, as formula (4):

$w\left(l\right)={(e\[\tilde{y}{\tilde{y}}^h\])}^{-1}-e\[\tilde{y}{x}^{*}\left(l\right)\]---\left(4\right)$

Assume that backscattering coefficient x (l) keeps constant in irradiation time, the backscattering coefficient in different distance unit Orthogonal, backscattering coefficient and noise are also orthogonal simultaneously, through deriving, formula (4) can be reduced to as formula (5) institute Show:

$w\left(l\right)=\mathrm{\ρ}\left(l\right){(c_m+r_v)}^{-1}{\tilde{s}}_l---\left(5\right)$

In formula (5), intermediate variable ρ (l)=| x (l) |², intermediate variableNoise covariance matrix r_v =e [vv^h], r under white Gaussian noise is assumed_v=σ²I, σ²It is noise variance, i is unit battle array.

It is above-mentioned that carry out during adaptive updates weight vector can be using based on iteration mmse criterion or mvdr criterion.

In above-mentioned s3, re-start distance to imaging using optimum weight vector w (l) obtaining according to mmse criterion, represent As formula (7):

$x\left(l\right)=w^h\left(l\right)\tilde{y}---\left(6\right).$

The Rule of judgment of above-mentioned s4 is:

Repeat s2 and s3 according to default iterationses, stop after reaching default iterationses, finally obtain Range Profile valuation is as optimal value；Or,

Repeat s2 and s3, the norm of the difference of Range Profile valuation producing after adjacent iteration twice is less than default threshold During value, stop iterative process, the Range Profile valuation taking last circulation is optimal value.

A kind of distance based on pulse compression technique to imaging system, is characterized in, this distance comprises to imaging system:

To initialization module, it carries out coupling filter using the transmission signal sequence pair receipt signal sequence after zero padding to distance Ripple, obtains initialized Range Profile valuation；

Wave filter weight vector update module, its input connects distance to initialization module, using initialization obtain away from Obtain new wave filter weight vector from as valuation；

Range Profile update module, its input connects wave filter weight vector update module, using the wave filter power arrow updating Amount updates Range Profile valuation；

Determination module, it connects Range Profile update module and wave filter weight vector update module, according to default judgement bar Part iteration, obtains final Range Profile valuation.

The distance based on pulse compression technique for the present invention is to imaging method and distance to imaging system and existing self adaptation arteries and veins The technology of punching is compared, and has an advantage in that, self adaptation degree of freedom in the impulse compression method based on fully adaptive technology for the present invention etc. In the sampling length of receipt signal, in the case that receipt signal completely includes irradiation area echo, self adaptation pulse compression skill The degree of freedom of art always greater than irradiation area range cell number it is possible to obtain any distance length target scene away from From picture；

The present invention under identical iterationses, using certain length receipt signal fully adaptive pulse compression technique often Longer target scene Range Profile can be obtained, in the case of identical iterationses m, institute of the present invention extracting method can be than existing Self adaptation impulse compression method obtains the backscattering coefficient valuation of more range cells.

Brief description

Fig. 1 is the flow chart of the distance based on pulse compression technique of the present invention to imaging method；

Fig. 2 be the present invention the distance based on pulse compression technique to the embodiment two of imaging method, there is a single point mesh The pulse compression result figure of timestamp；

Fig. 3 rushes the graph of a relation of compression result mse and iterationses and noise variance for embodiment illustrated in fig. 2 two middle arteries；

Fig. 4 is the embodiment three of the distance based on pulse compression technique of the present invention to imaging method, and it is little that number of targets is more than 1 Pulse compression result figure when transmission signal length；

Fig. 5 rushes the graph of a relation of compression result mse and iterationses and noise variance for embodiment illustrated in fig. 4 three middle arteries；

Fig. 6 is the example IV of the distance based on pulse compression technique of the present invention to imaging method, and number of targets is more than to be sent out Penetrate pulse compression result figure during signal length；

Fig. 7 rushes the graph of a relation of compression result mse and iterationses and noise variance for embodiment illustrated in fig. 6 four middle arteries.

Specific embodiment

Below in conjunction with accompanying drawing, further illustrate the specific embodiment of the present invention.

As shown in figure 1, the invention discloses a kind of distance of the pulse compression technique based on iteration fully adaptive technology to The embodiment one of imaging method, this distance comprises the steps of to imaging method

Transmission signal sequence after s1, matched filter receipt signal sequence and zero padding.Range Profile initializes, using zero padding Transmission signal sequence afterwardsTo receipt signal sequenceCarry out matched filtering, obtain initialized Range Profile valuation

If radar emission waveform has sampling length n, irradiation area is made up of l range cell, l-th range cell Target backscattering coefficient is expressed as x (l), and the receipt signal sampling length of radar receiver is not less than l+n-1, the present embodiment one In, choose receipt signal sampling length and be equal to l+n-1.

Consider receipt signal length and effect of noise, radar return signal such as formula (1):

$\tilde{y}=\underset{l=1}{\overset{l}{\σ}}{\tilde{s}}_{l}x\left(l\right)+v---\left(1\right)$

In formula (1), v is additive white Gaussian noise,After zero padding Transmission signal vector,For transmission signal, 0_lRepresent the zero vector of l × 1 dimension.

Receipt signal using overall length carries out matched filtering, obtains initialized Range Profile valuation, as shown in formula (2):

$x\left(l\right)={\tilde{s}}^h\left(l\right)\tilde{y}---\left(2\right).$

Matched filter is often affected by high secondary lobe and low resolution, so needing design more suitably weight vector w L () is to replace the weight vector in matched filterOnly utilize receipt signal different from existing self adaptation impulse compression method Middle length be one section of n updating and to design weight vector w (l), the present invention carried fully adaptive pulse compression technique make use of entirely The receipt signal of length carries out self-adaptive processing, to obtain more preferable distance to imaging results.

S2, under mmse criterion, obtain new wave filter weight vector using existing Range Profile valuation x (l)

The present invention can be using based on iteration mmse criterion or mvdr criterion when carrying out adaptive updates weight vector.

In the present embodiment one, it is described using mmse criterion, the mmse cost function such as formula (3) of standard:

$j\left(l\right)=e\[{|x\left(l\right)-{\mathrm{\ω}}^h\left(l\right)\tilde{y}|}^2\]---\left(3\right)$

In formula (3), e [] represents expectation computing symbol.

Using formula (3) to w (l) derivation, and make it be equal to zero, optimum weight vector can be obtained, as formula (4):

$w\left(l\right)={(e\[\tilde{y}{\tilde{y}}^h\])}^{-1}e\[\tilde{y}{x}^{*}\left(l\right)\]---\left(4\right).$

Assume that backscattering coefficient x (l) keeps constant in irradiation time, the backscattering coefficient in different distance unit Orthogonal, backscattering coefficient and noise are also orthogonal simultaneously, through deriving, formula (4) can be reduced to as formula (5) institute Show:

$w\left(l\right)=\mathrm{\ρ}\left(l\right){(c_m+r_v)}^{-1}{\tilde{s}}_l---\left(5\right)$

In formula (5), intermediate variable ρ (l)=| x (l) |², intermediate variableNoise covariance matrix r_v =e [vv^h], r under white Gaussian noise is assumed_v=σ²I, σ²It is noise variance, i is unit battle array；

Wherein, time domain expression-form and the array received of single-input single-output radar return signal is can be seen that from formula (2) The spatial domain representation of signal has similarity, and wherein sampling instant/range cell l is corresponding with spatial domain angle, and zero padding skew is sent out Penetrate signal phasorCorresponding with steering vector, the signal source in the corresponding space of x (l), sampling length is the receipt signal phase of l+n-1 When in array number for the signal of l+n-1 array received it is seen that all available self adaptation degree of freedom be l+n-1.

In addition, from hereafter formula (5) and formula (6) it can be seen that iteration self-adapting method is updated as valuation using current distance Wave filter weight vector, thus obtain preferably estimate performance.

S3, update Range Profile valuation using new wave filter weight vector w (l)

Re-start distance to imaging using optimum weight vector w (l) obtaining according to mmse criterion, represent as formula (7):

$x\left(l\right)=w^h\left(l\right)\tilde{y}---\left(6\right).$

S4, repeat s2 and s3, complete distance to imaging until meeting default Rule of judgment.

Rule of judgment has following two:

Repeat s2 and s3 according to default iterationses, stop after reaching default iterationses, finally obtain Range Profile valuation is as optimal value；Or,

Repeat s2 and s3, the norm of the difference of Range Profile valuation producing after adjacent iteration twice is less than default threshold During value, stop iterative process, the Range Profile valuation taking last circulation is optimal value.

The invention also discloses a kind of distance based on pulse compression technique is to imaging system, this distance is to imaging system bag Contain:

To initialization module, it carries out coupling filter using the transmission signal sequence pair receipt signal sequence after zero padding to distance Ripple, obtains initialized Range Profile valuation；

Wave filter weight vector update module, its input connects distance to initialization module, using initialization obtain away from Obtain new wave filter weight vector from as valuation；

Range Profile update module, its input connects wave filter weight vector update module, using the wave filter power arrow updating Amount updates Range Profile valuation；

Determination module, it connects Range Profile update module and wave filter weight vector update module, according to default judgement bar Part iteration, obtains final Range Profile valuation.

As shown in Fig. 2 being the embodiment two to imaging method for the distance based on pulse compression technique for the present invention, only 1 There is situation during target in range cell.

Radar emission phase-coded signal, sampling length is 100.Observation area is made up of 200 range cells.Entirely adaptive Radar return signal sampling length during pulse compression is answered to be 299；During partial adaptivity pulse compression receipt signal sampling length with The iterationses m setting is relevant, is 200+99 (2m+1), and it is 993 that such as 3 times iteration need receipt signal length, far more than entirely certainly Adapt to the situation of pulse compression.Noise is additive white Gaussian noise, and variance is 10^-4.

Assume to comprise only a point target in observation area, range cell is 100, backscattering coefficient is 1.3 iteration The result of fully adaptive pulse compression and partial adaptivity pulse compression and matched filtering result are as shown in Figure 2 afterwards.Left in Fig. 2 Top square frame is the enlarged drawing existing at the range cell of target, notes this little in figure in order to highlight Range Profile amplitude error So not taking the logarithm, and big in figure employs logarithmic coordinates for the side lobe levels comparing pulse compression.

As shown in Fig. 2 for the backscattering coefficient (i.e. Range Profile) at range cell 100, matched filtering valuation is 0.9992, partial adaptivity valuation is 0.9982, and fully adaptive valuation is 0.9997；The maximum side lobe levels of matched filtering be- 16.05db, partial adaptivity maximum side lobe levels are -53.44db, and fully adaptive maximum side lobe levels are -54.62db.Permissible Find out, in the case of only existing single point target, three kinds of compressions can estimate the backscattering coefficient of target exactly, but The side lobe levels of matched filtering will all be far above other two methods and reach -16db, easily adjacency unit is produced dry Disturb or form false target.In addition the highest side lobe levels of two methods are all below -50db, can effectively suppress different away from Interfering between unit, the side lobe levels outline of fully adaptive pulse compression technique is better than partial adaptivity pulse compression Technology.

For the convergence of parser and affected by noise, keep radar parameter constant with target information, not Same noise variance 10^-6、10^-4、10^-2Under the conditions of 1, respectively to fully adaptive pulse compression and partial adaptivity pulse compression Perform 20 iteration, the mean square deviation (mse) of Range Profile and the actual range profile estimated after each iteration is as shown in figure 3, all Variance defines as formula (7):

$mse=\sqrt{\frac{1}{l}\underset{l=1}{\overset{l}{\σ}}{\left(\hat{x}\right(l)-x(l\left)\right)}^2}---\left(7\right)$

As shown in figure 3, two kinds of iteration self-adapting method estimated accuracies in the 1st～3 iteration improve rapidly, 5～10 All tend to Complete Convergence after secondary iteration.During single target, estimated accuracy after two kinds of adaptive approach Complete Convergences is all with making an uproar The reduction of sound variance and improve.Under same noise variance, the precision of partial adaptivity method is higher than fully adaptive method, this When being because that number of targets is little, the degree of freedom of two kinds of adaptive approachs all has enough degree of freedom and disinthibites interference, so Less, noise becomes the main of impact estimated accuracy for the now impact increasing to raising Range Profile estimated accuracy of degree of freedom quantity Factor.Partial adaptivity carries out weight vector renewal using the data of n length every time, and fully adaptive adopts the number of l+n-1 length According to section, in the case that noise variance is the same, the when width of fully adaptive processing data is longer so introducing noise is more, is equivalent to Have lost certain signal to noise ratio, lead to estimated accuracy less better compared with partial adaptivity, but both when noise variance is less It is more or less the same, and precision is all very high.

As shown in figure 4, being the embodiment three to imaging method for the distance based on pulse compression technique, there is the distance of target Unit number is more than 1 but the situation less than transmission signal sampling length.

Radar parameter is identical with embodiment two.Assume in continuous 90 range cells 51～140 in observation area all Containing point target, backscattering coefficient is all 1.Iterationses are set to 3, pulse compression result such as Fig. 4 institute that distinct methods obtain Show, the little figure in upper left is the enlarged drawing that there is target phase, wherein amplitude is not taken the logarithm.

As shown in figure 4, there is the distance segment of target, the maximum deviation of matched filtering gained Range Profile valuation is 0.775, The maximum deviation of partial adaptivity valuation is 0.013, and the maximum deviation of fully adaptive valuation is 0.005；In secondary lobe region, mate The highest side lobe levels of filtering are -5.5db, and the highest secondary lobe of partial adaptivity is -44db, the highest secondary lobe of fully adaptive is - 55.48db.It can be seen that, have that number of targets is more but during less than transmission signal length, matched filtering method performance degradation is serious, institute Obtain Range Profile distortion, in addition two methods can effectively estimate Range Profile, and the precision of fully adaptive method is more Height, secondary lobe is lower.

For the convergence of parser and affected by noise, keep radar parameter constant with target information, not Same noise variance 10^-6、10^-4、10^-2Under the conditions of 1, respectively to fully adaptive pulse compression and partial adaptivity pulse compression Perform 20 iteration, after each iteration, the mean square deviation of Range Profile valuation is as shown in Figure 5.

As shown in figure 5, to reach Complete Convergence, and the reduction with noise variance after 3～5 iteration, two kinds certainly The precision of adaptive method is all improved.From unlike the situation (as shown in Figure 3) of single target, now fully adaptive method Precision exceeded partial adaptivity method, this is because number of targets more but no more than transmitted waveform length when, partly adaptive Should be disinthibited interference using most of degree of freedom, the degree of freedom of the remaining noise that disinthibites tails off, so partial adaptivity is now Although being estimated that Range Profile, precision is poor compared with fully adaptive method.

As shown in fig. 6, to the example IV of imaging method, there is the distance list of target in the distance based on pulse compression technique First number is more than the situation of transmission signal sampling length.

Radar parameter is identical with embodiment two.Assume in continuous 150 range cells 31～180 in observation area all Contain point target, backscattering coefficient is all 1.Iterationses are 3, pulse compression result such as Fig. 6 institute that distinct methods obtain Show exist shown in the amplification picture figure as little in lower middle portion of target range section, wherein amplitude is not taken the logarithm.

As shown in fig. 6, there is the distance segment of target, the maximum deviation of matched filtering gained Range Profile valuation is 0.65, The maximum deviation of partial adaptivity is 0.49, and the maximum deviation of fully adaptive is 0.004；In secondary lobe region, matched filtering is High side lobe levels are -5.5db, and the highest side lobe levels of partial adaptivity are -44db, the highest side lobe levels of fully adaptive are - 52db.As can be seen that when number of targets is more than transmission signal length, partial adaptivity technology cannot accurately at estimation each The backscattering coefficient of range cell, this be partial adaptivity technology degree of freedom be subject to transmitted waveform length limitation, work as number of targets When being more than transmitted waveform length, do not have enough degree of freedom to disinthibite interfering between different distance unit, led to Relatively large deviation in Range Profile valuation.And fully adaptive method carries out adaptive updates weight vector using all collection signals, it Degree of freedom and receipt signal equal length, in once complete echo wave signal acquisition, receipt signal length is always not less than quilt Target area apart from length, thus fully adaptive pulse compression technique can carry out height to the target area of arbitrary size Precision distance is to imaging.

For the convergence of parser and affected by noise, it is more than the situation of transmitted waveform length in number of targets Under, in different noise variances 10^-6、10^-4、10^-2Under the conditions of 1, respectively to fully adaptive pulse compression and partial adaptivity arteries and veins Punching press contracting performs 20 iteration, and after each iteration, the mean square deviation of Range Profile valuation is as shown in Figure 7.

As shown in fig. 7, when number of targets is more than transmission signal length, fully adaptive method reaches after 2～3 iteration completely Convergence, imaging precision reduces with noise variance and improves, and is far superior to partial adaptivity method；And partial adaptivity side Method is 10 in noise variance^-6、10^-4With 10^-2When, Range Profile estimates that mse rests near -12db, or even in noise variance is Divergent Phenomenon occurs when 1.It can be seen that, when number of targets is more than transmission signal length, partial adaptivity method cannot obtain essence True Range Profile valuation, this is that the degree of freedom of partial adaptivity method limited causes.

It can also be seen that with there is increasing of target range unit number, fully adaptive method in relatively Fig. 3, Fig. 5 and Fig. 7 Convergence rate speed, this is because iteration self-adapting method updates needing using last Range Profile valuation during weight vector, Some do not exist only has random noise, the back scattering system of these noise range cells after each iteration in the range cell of target Number valuations all can change, and the more convergence rates of such range cell number are slower.This also makes actual range as can in imaging applications Using less iterationses, to be normally set up iterationses for 3, so both can obtain sufficiently high estimated accuracy, reduce again Operation time.

Although present disclosure has been made to be discussed in detail by above preferred embodiment, but it should be appreciated that above-mentioned Description is not considered as limitation of the present invention.After those skilled in the art have read the above, for the present invention's Multiple modifications and substitutions all will be apparent from.Therefore, protection scope of the present invention should be limited to the appended claims.

Claims (7)

1. a kind of distance based on pulse compression technique is to imaging method it is characterised in that this distance comprises to imaging method:

S1, using the transmission signal sequence after zero paddingTo receipt signal sequenceCarry out matched filtering, obtain initialized Range Profile valuation

S2, under mmse criterion, obtain new wave filter weight vector using existing Range Profile valuation x (l)

S3, update Range Profile valuation using new wave filter weight vector w (l)

S4, repeat s2 and s3, complete distance to imaging until meeting default Rule of judgment.

2. the distance based on pulse compression technique as claimed in claim 1 is to imaging method it is characterised in that described s1 bag Contain:

If radar emission waveform has sampling length n, irradiation area is made up of l range cell, the target of l-th range cell Backscattering coefficient is expressed as x (l), and the receipt signal sampling length of radar receiver is not less than l+n-1；

Consider receipt signal length and effect of noise, radar return signal such as formula (1):

$\tilde{y}=\underset{l=1}{\overset{l}{\σ}}{\tilde{s}}_{l}x\left(l\right)+v---\left(1\right)$

In formula (1), v is additive white Gaussian noise,For the transmitting after zero padding Signal phasor,For transmission signal, 0_lRepresent the zero vector of l × 1 dimension；

Receipt signal using overall length carries out matched filtering, obtains initialized Range Profile valuation, as shown in formula (2):

$x\left(l\right)={\tilde{s}}^h\left(l\right)\tilde{y}---\left(2\right).$

3. the distance based on pulse compression technique as claimed in claim 2 is to imaging method it is characterised in that described s2 bag Contain:

It is described using mmse criterion, the mmse cost function such as formula (3) of standard:

$j\left(l\right)=e\[|x\left(l\right)-{\mathrm{\ω}}^h\left(l\right)\tilde{y}{|}^2\]---\left(3\right)$

In formula (3), e [] represents expectation computing symbol；

Using formula (3) to w (l) derivation, and make it be equal to zero, optimum weight vector can be obtained, as formula (4):

$w\left(l\right)={(e\[\tilde{y}{\tilde{y}}^h\])}^{-1}-e\[\tilde{y}{x}^{*}\left(l\right)\]---\left(4\right)$

Assume that backscattering coefficient x (l) keeps constant in irradiation time, the backscattering coefficient in different distance unit is mutually not Correlation, backscattering coefficient and noise are also orthogonal simultaneously, and through deriving, formula (4) can be reduced to as shown in formula (5):

$w\left(l\right)=\mathrm{\ρ}\left(l\right){(c_m+r_v)}^{-1}{\tilde{s}}_l---\left(5\right)$

In formula (5), intermediate variable ρ (l)=| x (l) |², intermediate variableNoise covariance matrix r_v =e [vv^h], r under white Gaussian noise is assumed_v=σ²I, σ²It is noise variance, i is unit battle array.

4. the distance based on pulse compression technique as described in claim 1 or 2 or 3 is to imaging method it is characterised in that described Carrying out can be using based on iteration mmse criterion or mvdr criterion during adaptive updates weight vector.

5. the distance based on pulse compression technique as claimed in claim 3 is to imaging method it is characterised in that in described s3, Re-start distance to imaging using optimum weight vector w (l) obtaining according to mmse criterion, represent as formula (7):

$x\left(l\right)=w^h\left(l\right)\tilde{y}---\left(6\right).$

6. the distance based on pulse compression technique as claimed in claim 2 is to imaging method it is characterised in that described s4's sentences Broken strip part is:

Repeat s2 and s3 according to default iterationses, stop after reaching default iterationses, the distance finally obtaining As valuation is as optimal value；Or,

Repeat s2 and s3, the norm of the difference of Range Profile valuation producing after adjacent iteration twice is less than default threshold value When, stop iterative process, the Range Profile valuation taking last circulation is optimal value.

7. a kind of distance based on pulse compression technique is to imaging system it is characterised in that this distance comprises to imaging system:

To initialization module, it carries out matched filtering using the transmission signal sequence pair receipt signal sequence after zero padding, obtains distance To initialized Range Profile valuation；

Wave filter weight vector update module, its input connects distance to initialization module, the Range Profile obtaining using initialization Valuation obtains new wave filter weight vector；

Range Profile update module, its input connects wave filter weight vector update module, using the wave filter weight vector updating more New Range Profile valuation；

Determination module, it connects Range Profile update module and wave filter weight vector update module, according to default Rule of judgment weight Multiple iteration, obtains final Range Profile valuation.