CN109709547A - A kind of reality beam scanning radar acceleration super-resolution imaging method - Google Patents
A kind of reality beam scanning radar acceleration super-resolution imaging method Download PDFInfo
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- CN109709547A CN109709547A CN201910051938.9A CN201910051938A CN109709547A CN 109709547 A CN109709547 A CN 109709547A CN 201910051938 A CN201910051938 A CN 201910051938A CN 109709547 A CN109709547 A CN 109709547A
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Abstract
The present invention provides a kind of real beam scanning radars to accelerate super-resolution imaging method, belongs to radar imaging technology field.The present invention problem slower for iteration threshold contraction algorithm convergence rate, in conjunction with Taylor expansion principle, before each iterative operation, by history is iterative vectorized and its preceding two order differences information structuring predicted vector, reduce the number of iterations, algorithm the convergence speed is improved, the time needed for shortening super-resolution imaging, achievees the purpose that acceleration.
Description
Technical field
The invention belongs to radar imaging technology field, in particular to a kind of real beam scanning radar accelerates super-resolution imaging side
Method.
Background technique
Real beam scanning radar is imaged on the fields such as air-to-ground attack, terrain match, hydrospace detection, military surveillance and suffers from weight
It acts on, however the resolution ratio of its orientation is limited by antenna aperature size and distance always.In order to improve its image quality,
The azimuth resolution for improving real beam scanning radar is extremely urgent.
It, can be with since in orientation, radar return is considered as the convolution form of antenna radiation pattern and target distribution feature
Scanning radar super-resolution imaging is realized using iteration Deconvolution Method.Iteration threshold contraction algorithm due to its own simplicity and
Stability is shown one's talent in these methods.But iteration threshold contraction algorithm levels off to sublinear convergence, convergence rate is public
Think relatively slow, has seriously affected the real-time of data processing.
In order to improve its rate of convergence, iteration threshold contraction algorithm and iteration weighting compression algorithm are combined, proposed
Two step iteration threshold contraction algorithms, it is iterative vectorized in next step by being obtained to the iterative vectorized progress linear combination of the first two, change
Iteration pattern, reduces the number of iterations, improves convergence speed of the algorithm to a certain extent, but handle ill-conditioning problem when
It waits, convergence cannot be effectively ensured.
There are also a kind of iteratively faster threshold value contraction algorithm, this algorithm is used on the basis of iteration threshold contraction algorithm
Nesterov speedup gradient method thought, before each iterative operation, iterative vectorized using history iterative information construction, reduction changes
Generation number plays acceleration purpose.But since its prediction step can be intended to 1 in a short time, so that iterative process, which enters, owes resistance
Buddhist nun's state, causes objective function to vibrate, and accelerating ability is affected.
Equally based on according to the thought of the iterative vectorized linear combination structure forecast vector of history, being proposed before iterative operation
A kind of Accelerated iteration threshold value contraction algorithm passes through two iterative vectorized extrapolations of history before executing iterative operation each time
One predicted vector realizes the acceleration of algorithm.But its scale that only used sequence of iterations during structure forecast vector
Divide information, acceleration effect is not significant.
Summary of the invention
It is an object of the invention to be directed in the prior art, a kind of real beam scanning radar acceleration oversubscription is proposed
Distinguish imaging method, in conjunction with Taylor expansion principle, according to the single order of sequence of iterations and second differnce information architecture iteration predicted vector,
Reduce the number of iterations, improves algorithm the convergence speed.
A kind of reality beam scanning radar acceleration super-resolution imaging method, comprising:
S1, radar echo signal is obtained, distance is carried out to process of pulse-compression to the echo-signal, obtains echo-signal
Matrix S；
S2, antenna radiation pattern h is obtained, convolution matrix H is constructed according to the antenna radiation pattern；
S3, jth row data in the echo-signal matrix S are extracted, as echo data vector s to be processed, setting is just
Begin iterative vectorized x0It is 0；
S4, iterative model is determined；
S5, by x0It substitutes into the iterative model and obtains iteration result x1, by x1It substitutes into the iterative model and obtains iteration
As a result x2；
S6, according to the iteration result x2、x1And x0, it is based on Taylor expansion principle, structure forecast vector y, by the prediction
Vector y is substituted into the iterative model and is obtained iteration result x3；
S7, judge whether iteration result meets default stopping criterion for iteration, if meeting condition, process enters step S8；If
It is unsatisfactory for condition, by x3、x2、x1Value be assigned to x respectively2、x1、x0, the process return step S6；
S8, judge that whether all data complete by processing in the echo-signal matrix S, if processing is completed, exports super-resolution
Imaging results；If untreated completion, j=j+1 is enabled, process returns to the step S3.
Further, the step S2 includes:
Obtain antenna radiation pattern h=[h0 h1 ... hl-1], l is antenna radiation pattern points；Convolution matrix H is constructed according to h
Further, the step S4 includes:
It calculates currently to the gradient measured
Wherein,X indicates target scattering system number, ()TIt indicates to carry out transposition operation to matrix；
According to steepest descent method, negative gradient direction is the most fast direction of decline, and current vector is substituted into
Wherein, t is iteration step length, byLipschitz constant inverse determine, i.e.,eigmax(HTH) representing matrix (HTH maximum eigenvalue)；
To zkThreshold value shrinkage operation is carried out, iterative model is obtained
Wherein, xkFor the iteration result of kth time, (Tλt(z))i=(zi-λt)+sgn(zi), sgn () is sign function.
Further, the step S5 includes:
By primary iteration vector x0Substitute into the iteration result x that first time is obtained in the iterative model1, then by first time
Iteration result x1It substitutes into the iterative model and obtains secondary iteration result x2。
Further, the step S6 includes:
According to the iteration result x2、x1And x0, it is based on Taylor expansion principle, structure forecast vector y
Wherein, α is prediction step；
It is substituted into the iterative model using obtained predicted vector y as iterative vectorized, obtains iteration result x3。
Further, the step S7 includes:
S71, judge whether iteration result meets default stopping criterion for iteration；
If S72, meeting condition, process enters step S8；
If S73, being unsatisfactory for condition, by x3、x2、x1Value be assigned to x respectively2、x1、x0, the process return step S6.
Further, the default stopping criterion for iteration are as follows:
||x3-x2||2< T
Wherein, T is to terminate threshold value.
Further, the step S8 includes:
S81, judge whether all data complete by processing in the echo-signal matrix S, i.e., whether is currently processed jth row
More than total line number J；
If S82, processing are completed, super-resolution imaging result is exported；
If S83, untreated completion, j=j+1 is enabled, process returns to the step S3.
Beneficial effects of the present invention: the present invention provides a kind of real beam scanning radars to accelerate super-resolution imaging method, this
Invention is for the slower problem of iteration threshold contraction algorithm convergence rate, in conjunction with Taylor expansion principle, each iterative operation it
Before, by history is iterative vectorized and its preceding two order differences information structuring predicted vector, reduces the number of iterations, improve algorithmic statement
Speed, achievees the purpose that acceleration at the time needed for shortening super-resolution imaging.
Detailed description of the invention
Fig. 1 is flow chart provided in an embodiment of the present invention.
Fig. 2 is scene figure used in the embodiment of the present invention.
Fig. 3 is the radar return sectional view that the embodiment of the present invention generates.
Fig. 4 is antenna radiation pattern used in the embodiment of the present invention.
Fig. 5 is the super-resolution result figure that 1500 iteration of prior art algorithm obtain.
Fig. 6 is the super-resolution result figure that 1500 iteration provided in an embodiment of the present invention obtain.
Specific embodiment
The present embodiment carries out simulating, verifying using MATLAB.The embodiment of the present invention is done further with reference to the accompanying drawing
Explanation.
The following table 1 is parameter used in the embodiment of the present invention.
| Parameter | Symbol | Numerical value |
| Pulse recurrence frequency | prf | 2000Hz |
| Antenna main lobe width | θ | 3o |
| Scanning speed | ω | 60°/s |
| Scanning range | θmin~θmax | - 15 °~15 ° |
1 parameter list of table
Referring to Fig. 1, a kind of real beam scanning radar proposed by the present invention accelerates super-resolution imaging method, pass through following step
It is rapid to realize:
S1, radar echo signal is obtained, distance is carried out to process of pulse-compression to echo-signal, obtains echo-signal matrix
S。
Referring to Fig. 2, Fig. 2 is scene figure used in the embodiment of the present invention, x is the scatterer that target scene is extended in Fig. 2
Coefficient.
In the present embodiment, radar echo signal R is obtained, distance is carried out to process of pulse-compression to echo-signal, is returned
Wave signal matrix S, as shown in Figure 3.
S2, antenna radiation pattern h is obtained, convolution matrix H is constructed according to antenna radiation pattern.
In the present embodiment, antenna radiation pattern h=[h as shown in Figure 4 is obtained0 h1 ... h266], l is antenna radiation pattern point
Number, l=267；Convolution matrix H is constructed according to h
S3, jth row data in echo-signal matrix S being extracted, j initial value is 1, as echo data vector s to be processed,
Primary iteration vector x is set0It is 0.
S4, iterative model is determined.
In the present embodiment, s can be modeled as the convolution form of antenna radiation pattern h and target scattering system number x, with antenna side
To the form of picture scroll product matrix, it is expressed as s
S=Hx+n (2)
Wherein, n is noise vector.
Since radar echo signal is a convolution model, Deconvolution Method can be used, find out in original scene
The distribution characteristics of target.But directly Deconvolution Method needs to carry out inversion operation to matrix, it is low due to convolution matrix H itself
Logical effect, at cutoff frequency, noise can infinitely be amplified, and greatly affected image quality.
L is passed through using regularization method for noise-sensitive problem1The sparsity of Norm Control solution is to reduce to noise
Sensitivity, the solution of the specification linear inverse problem determines solving model are as follows:
Wherein,Indicate the optimal solution of target, λ is regularization parameter, for balancing observation data confidence and priori letter
The relationship between confidence level is ceased, it herein can be with value for 0.001；||x||1For the l of x1Norm calculates all elements absolute value in x
Sum.
The model is solved using gradient descent method or steepest descent method, iterative formula is
Due to the norm item l in objective function F (x)1Non-differentiability, gradient descent method not can solve this problem.It enables
F (x)=| | s-Hx | |2 (5)
It is solved with the approximation method of gradient descent methodIterative formula (4) can be of equal value are as follows:
Ignore the constant term in formula (6), has
Due to l1The property of the linear separability of norm, iterative formula (7) can be for iterative vectorized each component with abbreviation
One-dimensional minimization problem, i.e.,
Wherein, xkFor the iteration result of kth time；
T is iteration step length, byLipschitz constant inverse determine, i.e.,
eigmax(HTH) representing matrix (HTH maximum eigenvalue), ()TIt indicates to carry out transposition operation to matrix；
Formula (10) indicates that least square item f (x) is in iterative vectorized x in objective functionk-1The gradient at place；
(Tλt(x))i=(xi-λt)+sgn(xi) (11)
Formula (11) is threshold value shrinkage operation, and sgn () is sign function, t=5.083 × 10 threshold value λ-4Joined by regularization
Number λ=0.001 and iteration step length t=5.083 × 10-2It is common to determine.
S5, by x0It substitutes into iterative model and obtains iteration result x1, by x1It substitutes into iterative model and obtains iteration result x2。
In the present embodiment, the primary iteration vector x for being 0 by value0It substitutes into iterative model (8), first time is calculated
Iteration result x1.Again by the iteration result x of first time1It substitutes into iterative model (8) and obtains secondary iteration result x2。
S6, according to iteration result x2、x1And x0, it is based on Taylor expansion principle, structure forecast vector y, by predicted vector y generation
Enter to obtain iteration result x in iterative model3。
In the present embodiment, it is assumed that y is any vector in Iterative path, then vector y is in vector xkThe Taylor expansion at place is public
Formula are as follows:
Wherein, α indicates iterative vectorized y and xkThe distance between information, as prediction step, ΔnxkIndicate that sequence of iterations exists
xkThe n order difference information at place.According to Taylor expansion formula (12), iterative vectorized y can be by xkAnd sequence of iterations is in vector xkPlace
The higher order term of difference information approximate representation, reservation is more, and error is with regard to smaller.But with the increase of order, the information of higher order term
Fewer and fewer, the influence to error is negligible, and retains higher order term, can spend biggish memory space, causing need not
The wasting of resources wanted.Therefore, selection retains the first three items approximate representation vector y of Taylor expansion formula:
According to formula (13), according to iteration result x2、x1And x0, structure forecast vector y, i.e.,
Wherein, α is prediction step:
According to the geometrical convergence according to iterative process, constructed by iteration difference vector.For the convergence for guaranteeing algorithm, prediction
Step-length range, which is set as 0 < α < 1, enables α > 1 as α > 1.
Using obtained predicted vector y as in iterative vectorized substitution iterative model (8), iteration result x is calculated3。
S7, judge whether iteration result meets default stopping criterion for iteration, if meeting condition, process enters step S8；If
It is unsatisfactory for condition, by x3、x2、x1Value be assigned to x respectively2、x1、x0, process return step S6.
S71, judge whether iteration result meets default stopping criterion for iteration, preset stopping criterion for iteration are as follows:
||x3-x2||2< T
Wherein, T is to terminate threshold value, can be with value for 1 × 10-5；
If S72, meeting condition, process enters step S8；
If S73, being unsatisfactory for condition, by x3、x2、x1Value be assigned to x respectively2、x1、x0, process return step S6.
S8, judge that whether all data complete by processing in echo-signal matrix S, if processing is completed, exports super-resolution imaging
As a result；If untreated completion, j=j+1 is enabled, process returns to step S3.
S81, judge in echo-signal matrix S whether all data complete by processing, i.e., currently processed jth row whether be more than
Total line number J；
If S82, processing are completed, super-resolution imaging result is exported；
If S83, untreated completion, j=j+1 is enabled, process returns to step S3.
Fig. 5 is the super-resolution that 1500 iteration of prior art algorithm obtain as a result, Fig. 6 is that 1500 iteration of the invention obtain
Super-resolution result.
Those of ordinary skill in the art will understand that embodiment here be to help reader understand it is of the invention
Principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.This field it is common
Technical staff disclosed the technical disclosures can make the various various other tools for not departing from essence of the invention according to the present invention
Body variations and combinations, these variations and combinations are still within the scope of the present invention.
Claims (8)
1. a kind of reality beam scanning radar accelerates super-resolution imaging method characterized by comprising
S1, radar echo signal is obtained, distance is carried out to process of pulse-compression to the echo-signal, obtains echo-signal matrix
S；
S2, antenna radiation pattern h is obtained, convolution matrix H is constructed according to the antenna radiation pattern；
S3, jth row data in the echo-signal matrix S are extracted, as echo data vector s to be processed, setting is initial repeatedly
For vector x0It is 0；
S4, iterative model is determined；
S5, by x0It substitutes into the iterative model and obtains iteration result x1, by x1It substitutes into the iterative model and obtains iteration result
x2；
S6, according to the iteration result x2、x1And x0, it is based on Taylor expansion principle, structure forecast vector y, by the predicted vector
Y is substituted into the iterative model and is obtained iteration result x3；
S7, judge whether iteration result meets default stopping criterion for iteration, if meeting condition, process enters step S8；If discontented
Sufficient condition, by x3、x2、x1Value be assigned to x respectively2、x1、x0, the process return step S6；
S8, judge that whether all data complete by processing in the echo-signal matrix S, if processing is completed, exports super-resolution imaging
As a result；If untreated completion, j=j+1 is enabled, process returns to the step S3.
2. reality beam scanning radar as described in claim 1 accelerates super-resolution imaging method, which is characterized in that the step S2
Include:
Obtain antenna radiation pattern h=[h0 h1...hl-1], l is antenna radiation pattern points；Convolution matrix H is constructed according to h
3. reality beam scanning radar as described in claim 1 accelerates super-resolution imaging method, which is characterized in that the step S4
Include:
It calculates currently to the gradient measured
Wherein,X indicates target scattering system number, ()TIt indicates to carry out transposition operation to matrix；
According to steepest descent method, negative gradient direction is the most fast direction of decline, and current vector is substituted into
Wherein, t is iteration step length, byLipschitz constant inverse determine, i.e.,
eigmax(HTH) representing matrix (HTH maximum eigenvalue)；
To zkThreshold value shrinkage operation is carried out, iterative model is obtained
Wherein, xkFor the iteration result of kth time, (Tλt(z))i=(zi-λt)+sgn(zi), sgn () is sign function.
4. reality beam scanning radar as described in claim 1 accelerates super-resolution imaging method, which is characterized in that the step S5
Include:
By primary iteration vector x0Substitute into the iteration result x that first time is obtained in the iterative model1, then by the iteration of first time
As a result x1It substitutes into the iterative model and obtains secondary iteration result x2。
5. reality beam scanning radar as described in claim 1 accelerates super-resolution imaging method, which is characterized in that the step S6
Include:
According to the iteration result x2、x1And x0, it is based on Taylor expansion principle, structure forecast vector y
Wherein, α is prediction step；
It is substituted into the iterative model using obtained predicted vector y as iterative vectorized, obtains iteration result x3。
6. reality beam scanning radar as described in claim 1 accelerates super-resolution imaging method, which is characterized in that the step S7
Include:
S71, judge whether iteration result meets default stopping criterion for iteration；
If S72, meeting condition, process enters step S8；
If S73, being unsatisfactory for condition, by x3、x2、x1Value be assigned to x respectively2、x1、x0, the process return step S6.
7. reality beam scanning radar as claimed in claim 6 accelerates super-resolution imaging method, which is characterized in that described to preset repeatedly
For termination condition are as follows:
||x3-x2||2< T
Wherein, T is to terminate threshold value.
8. reality beam scanning radar as described in claim 1 accelerates super-resolution imaging method, which is characterized in that the step S8
Include:
S81, judge in the echo-signal matrix S whether all data complete by processing, i.e., currently processed jth row whether be more than
Total line number J；
If S82, processing are completed, super-resolution imaging result is exported；
If S83, untreated completion, j=j+1 is enabled, process returns to the step S3.
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