CN106291494B - SAR cheating interference target identification method and system based on differential characteristics enhancing - Google Patents
SAR cheating interference target identification method and system based on differential characteristics enhancing Download PDFInfo
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- CN106291494B CN106291494B CN201610587654.8A CN201610587654A CN106291494B CN 106291494 B CN106291494 B CN 106291494B CN 201610587654 A CN201610587654 A CN 201610587654A CN 106291494 B CN106291494 B CN 106291494B
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9027—Pattern recognition for feature extraction
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/904—SAR modes
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Abstract
The invention discloses the SAR cheating interference target identification method and system enhanced based on differential characteristics, method includes：The blended data for obtaining true SAR scene echoes and SAR cheating interferences generates data matrix after correction process, generate reference signal；Second orientation one-dimensional picture of the data matrix after different disposal step generates the one-dimensional picture of first orientation comprising all information and inhibits interference characteristic respectively, obtains differential characteristics image array after being further processed；Differential characteristics image array is handled using rectangular window, generates intensity histogram diagram data and reference gray level histogram；When detecting that the matching distance of grey level histogram and reference histograms is more than pre-set threshold value, it is labeled as cheating interference target.The present invention enhances for Differential Characteristics of the imaging radar initial data under different imaging methods, is identified to cheating interference target in SAR image, improves ability of the imaging radar to anti-deceptive interference, reduce cost.
Description
Technical field
The present invention relates to signal processing technology fields, more particularly to the SAR cheating interference targets based on differential characteristics enhancing
Identification method and system.
Background technology
SAR (Synthetic Aperture Radar, synthetic aperture radar) deception jammer is according to pre-designed
For false scene to the SAR signals of intercepting and capturing into line delay and phase-modulation, the power more required than traditional compacting interference is lower, and endangers
Evil bigger.Jammer generates false target true to nature in real scene, to upset the acquisition of information and decision of SAR.Therefore
The extensive concern that scholars are caused for the research of SAR cheating interferences, also correspondingly achieves more significant achievement so that
SAR deception jammers can more life-like under the operating mode of Various Complex, more efficiently produce more false targets,
To increase the menace of cheating interference.Therefore, in order to promote the survival ability of SAR system, it is necessary to which research is answered accordingly
Method is screened and identified to cheating interference target.
Zhao Shanshan etc. are in document " Discrimination of Deception Targets in
Multistatic Radar Based on Clustering Analysis”(IEEE Sensors Journal,Vol.16,
No.8,Apr.2016:System architecture based on more base radars in 2500-2508), using layering in amplitude proportional feature space
The method of classification analysis analyzes the dispersion characteristic of cheating interference target and real goal, and passes through optimization classification number
The classification of false target and real goal is realized with the method that designs different classes of minimum cost.Although this method can be one
It is completed in a pulse recurrence interval, but needs multistatic radar system as support, system complexity is higher with cost.
Lv Gaohuan etc. are in document " Ground Moving Target Indication in SAR Images With
Symmetric Doppler Views”(IEEE Transactions on Geoscience and Remote Sensing,
Vol.54,No.1,Jan.2016:Initial data is divided into symmetrical Doppler's view in 533-543), according to moving target
Moving target is identified in the otherness being distributed in different Doppler's views from static scene echoing characteristics.It is dry due to cheating
It is similar to the doppler characterization of moving target to disturb target, therefore this method can equally be applied in cheating interference target.
But the division of symmetrical Doppler's view can cause the loss for being effectively imaged bandwidth, so as to cause the decline of imaging resolution.
Therefore, the existing technology needs to be improved and developed.
Invention content
In view of the deficiencies in the prior art, present invention aims at providing, a kind of SAR deceptions based on differential characteristics enhancing are dry
Disturb target identification method and system, it is intended to solve cheating interference target identification resolution ratio in the prior art and decline, and cheat dry
Disturb goal systems defect of high cost.
Technical scheme is as follows：
A kind of SAR cheating interference target identification methods based on differential characteristics enhancing, wherein method includes：
A, the blended data for obtaining true SAR scene echoes and SAR cheating interferences, after being corrected processing to blended data
The data matrix for generating image scene generates the reference signal of deramp processing according to SAR system parameter；
B, Fourier transformation is carried out after data matrix is multiplied point by point with reference signal, generates the one-dimensional picture of first orientation, data
Matrix is filtered after carrying out Fourier transformation, and filter result is carried out inverse Fourier transform, is inhibited after interference characteristic
Range cell data, range cell data are multiplied point by point with reference signal and carry out Fourier transformation, generates and inhibits interference
The one-dimensional picture of first orientation and the one-dimensional picture of second orientation are subtracted each other rear point-by-point Modulus of access by the one-dimensional picture of second orientation after feature, are generated
The one-dimensional picture of differential characteristics, differential characteristics are one-dimensional as being arranged in order along distance dimension, obtain differential characteristics image array；
C, the data in differential characteristics image array are handled point by point using the rectangular window of particular size, obtains and counts
The grey level histogram of the image data in window is calculated, the intensity histogram diagram data around each point is generated；
D, the histogram that grey level is calculated less than the pixel of pre-set first threshold is obtained to be averaged,
Generate reference gray level histogram；
E, the grey level histogram of arbitrary element and the matching distance of reference histograms in data matrix are calculated, when detecting
It is cheating interference target by the corresponding target label of element when being more than pre-set second threshold with distance.
The SAR cheating interference target identification methods based on differential characteristics enhancing, wherein the A is specifically included：
A1, the blended data for obtaining true SAR scene echoes and SAR cheating interferences carry out Range compress, range migration school
After positive processing, the data matrix of K × L dimension image scenes is generated, s (t are denoted asr,ta), column direction indicates distance dimension in data matrix,
Line direction indicates azimuth dimension,；trIt is distance to fast time, taFor the orientation slow time；
A2, the reference signal that azimuth dimension deramp processing is generated according to SAR system parameter, are denoted as s0(ta)；Specially：
Wherein γaFor doppler frequency rate.
The SAR cheating interference target identification methods based on differential characteristics enhancing, wherein the B is specifically included：
B1, data matrix s (t are obtainedr,ta) in row k data sk(ta), with reference signal s0(ta) be multiplied point by point, it is right
Result after multiplication carries out Fourier transformation, generates the one-dimensional picture of first orientation for including real scene and cheating interference all information
WhereinIndicate Fourier transformation；
B2, data matrix s (t are obtainedr,ta) in row k data sk(ta), Fourier transformation is carried out,
Obtain frequency spectrum dataObtain frequency domain bandpass filter H (fa)
Wherein BaFor the doppler bandwidth of signal, rect () is rectangular window function, according to H (fa) to frequency domain data Sk
(fa) be filtered, filter result is subjected to inverse Fourier transform, generates k-th of range cell data after inhibiting interference characteristic
WhereinInverse Fourier transform is indicated, by k-th of the range cell data and s after inhibition interference characteristic0(ta)
It is point-by-point to be multiplied, and Fourier transformation, the one-dimensional picture of second orientation after the interference characteristic that is inhibited are carried out to result
B3, by q1k(x) and q2k(x) subtract each other, and to the point-by-point Modulus of access of the complex data subtracted each other, differential spy is calculated
Levy one-dimensional picture
Δqk(x)=norm (q1k(x)-q2k(x))
Wherein norm () indicates the operation of point-by-point modulus value；
B4, k=1,2 ..., K are enabled, repeats step B1~B3, obtained differential characteristics are one-dimensional as Δ qk(x) along distance
Dimension is arranged in order, and obtains differential characteristics image array Δ I.
The SAR cheating interference target identification methods based on differential characteristics enhancing, wherein the C is specifically included：
C1, the data in difference image are handled point by point using the rectangular window of W × W, wherein W is the length of side point of window
Number, with Δ IwIt indicates the image data in window, calculates its grey level histogram
H=hist (vec (Δ Iw))
Wherein vec () is indicated matrix Δ IwThe operation of vectorization, hist () indicate to calculate the fortune of grey level histogram
It calculates,For grey level histogram vector, G indicates the quantization level of histogram, rectangular window is slided point by point on Δ I, generates
The intensity histogram diagram data of each point.
The SAR cheating interference target identification methods based on differential characteristics enhancing, wherein the D is specifically included：
Grey level is less than first threshold ε in D1, acquisition Δ IrThe histogram that is calculated of pixel be averaged, obtain
To reference gray level histogram
Wherein huIt indicates to be located at (ku,lu) at point histogram, grey level in Differential Characteristics figure is less than threshold value
εr, i.e. Δ I (ku,lu) < εr, U represents less than the number of all the points of the threshold value.
The SAR cheating interference target identification methods based on differential characteristics enhancing, wherein the E is specifically included：
E1, the grey level histogram h for calculating arbitrary element in data matrixaWith the matching distance of reference histograms
dM(ho,ha)=‖ ho-ha‖1
Wherein | | | |1The operation for indicating to ask vectorial 1 norm, to the individual element point in image calculate Histogram Matching away from
From generation Histogram Matching matrix
E2, when detection detect matching distance be more than pre-set second threshold when, by the corresponding target label of element
For cheating interference target, it is otherwise labeled as real scene.
A kind of SAR cheating interference target identification systems based on differential characteristics enhancing, wherein system includes：
Reference signal generation module, the blended data for obtaining true SAR scene echoes and SAR cheating interferences, to mixed
The data matrix that data are corrected generation image scene after processing is closed, the ginseng of deramp processing is generated according to SAR system parameter
Examine signal；
Differential characteristics image array generation module carries out Fourier's change after being multiplied point by point with reference signal for data matrix
It changes, generates the one-dimensional picture of first orientation, data matrix is filtered after carrying out Fourier transformation, and filter result is carried out in inverse Fu
Leaf transformation, the range cell data after the interference characteristic that is inhibited, range cell data are multiplied point by point with reference signal and are gone forward side by side
Row Fourier transformation generates the one-dimensional picture of second orientation after inhibiting interference characteristic, by the one-dimensional picture of first orientation and second orientation one
Dimension generates the one-dimensional picture of differential characteristics, the one-dimensional picture of differential characteristics is arranged in order along distance dimension, is obtained as subtracting each other rear point-by-point Modulus of access
Differential characteristics image array；
Grey level histogram generation module, for the rectangular window using particular size to the data in differential characteristics image array
It is handled point by point, obtains the grey level histogram of the image data in simultaneously calculation window, generate the grey level histogram around each point
Data；
Reference gray level histogram generation module is less than the pixel of pre-set first threshold for obtaining grey level
The histogram being calculated is averaged, and reference gray level histogram is generated；
Mark module, for calculate the matchings of the grey level histogram of arbitrary element and reference histograms in data matrix away from
From, when detect matching distance be more than pre-set second threshold when, by the corresponding target label of element be cheating interference mesh
Mark.
The SAR cheating interference target identification systems based on differential characteristics enhancing, wherein the reference signal generates
Module specifically includes:
Correction unit, the blended data for obtaining true SAR scene echoes and SAR cheating interferences, progress Range compress,
After range migration correction processing, the data matrix of K × L dimension image scenes is generated, s (t are denoted asr,ta), column direction in data matrix
Indicate that distance dimension, line direction indicate azimuth dimension,；trIt is distance to fast time, taFor the orientation slow time；
Computing unit, the reference signal for generating azimuth dimension deramp processing according to SAR system parameter, is denoted as s0(ta)；
Specially：
Wherein γaFor doppler frequency rate.
The SAR cheating interference target identification systems based on differential characteristics enhancing, wherein the differential characteristics image
Matrix generation module specifically includes：
First orientation is one-dimensional as generation unit, for obtaining data matrix s (tr,ta) in row k data sk(ta), with
Reference signal s0(ta) be multiplied point by point, Fourier transformation is carried out to the result after multiplication, it includes real scene and cheating interference to generate
The one-dimensional picture of first orientation of all information
WhereinIndicate Fourier transformation；
Second orientation is one-dimensional as generation unit, for obtaining data matrix s (tr,ta) in row k data sk(ta), into
Row Fourier transformation, obtains frequency spectrum dataObtain frequency domain bandpass filter H (fa)
Wherein BaFor the doppler bandwidth of signal, rect () is rectangular window function, according to H (fa) to frequency domain data Sk
(fa) be filtered, filter result is subjected to inverse Fourier transform, generates k-th of range cell data after inhibiting interference characteristic
WhereinInverse Fourier transform is indicated, by k-th of the range cell data and s after inhibition interference characteristic0(ta)
It is point-by-point to be multiplied, and Fourier transformation, the one-dimensional picture of second orientation after the interference characteristic that is inhibited are carried out to result
Differential characteristics are one-dimensional as generation unit, are used for q1k(x) and q2k(x) subtract each other, and the complex data to subtracting each other by
Point Modulus of access, is calculated the one-dimensional picture of differential characteristics
Δqk(x)=norm (q1k(x)-q2k(x))
Wherein norm () indicates the operation of point-by-point modulus value；
Differential image array generation unit computes repeatedly the one-dimensional picture of differential characteristics, by gained for enabling k=1,2 ..., K
The differential characteristics arrived are one-dimensional as Δ qk(x) it is arranged in order along distance dimension, obtains differential characteristics image array Δ I.
The SAR cheating interference target identification systems based on differential characteristics enhancing, wherein
The grey level histogram generation module is specifically used for carrying out the data in difference image using the rectangular window of W × W
Point-by-point processing, wherein W is that the length of side of window is counted, with Δ IwIt indicates the image data in window, calculates its grey level histogram
H=hist (vec (Δ Iw))
Wherein vec () is indicated matrix Δ IwThe operation of vectorization, hist () indicate to calculate the fortune of grey level histogram
It calculates,For grey level histogram vector, G indicates the quantization level of histogram, rectangular window is slided point by point on Δ I, generates
The intensity histogram diagram data of each point.
The present invention provides a kind of SAR cheating interference target identification method and system based on differential characteristics enhancing, this hairs
The bright method enhanced by Differential Characteristics can not improve existing single channel radar system complexity and not reduce imaging resolution
Under the premise of, enhanced for Differential Characteristics of the imaging radar initial data under different imaging methods, in SAR image
In cheating interference target is identified, improve the ability of conventional one-channel broadband imaging radar electronic warfare cheating interference, method is kept away
The high expense for having exempted from single channel scalable multi channel system upgrade greatly reduces SAR system anti-deceptive interference capacity upgrade
Cost.
Description of the drawings
Fig. 1 is a kind of preferable implementation of SAR cheating interference target identification methods based on differential characteristics enhancing of the present invention
The flow chart of example.
Fig. 2 is a kind of concrete application of SAR cheating interference target identification methods based on differential characteristics enhancing of the present invention
The real scene schematic diagram of embodiment.
Fig. 3 is a kind of concrete application of SAR cheating interference target identification methods based on differential characteristics enhancing of the present invention
The cheating interference scene template schematic diagram of embodiment.
Fig. 4 is a kind of concrete application of SAR cheating interference target identification methods based on differential characteristics enhancing of the present invention
Embodiment there are the SAR imaging results schematic diagrames of cheating interference.
Fig. 5 is a kind of concrete application of SAR cheating interference target identification methods based on differential characteristics enhancing of the present invention
The cheating interference target identification result schematic diagram of embodiment.
Fig. 6 is a kind of preferable implementation of SAR cheating interference target identification systems based on differential characteristics enhancing of the present invention
The functional schematic block diagram of example.
Specific implementation mode
To make the purpose of the present invention, technical solution and effect clearer, clear and definite, below to the present invention further specifically
It is bright.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
The present invention provides a kind of preferred embodiments of the SAR cheating interference target identification methods based on differential characteristics enhancing
Flow chart, as shown in Figure 1, wherein method includes：
Step S100, the blended data for obtaining true SAR scene echoes and SAR cheating interferences, school is carried out to blended data
The data matrix that image scene is generated after positive processing, the reference signal of deramp processing is generated according to SAR system parameter.
When it is implemented, step S100 is specifically included：
Step S101, the blended data of true SAR scene echoes and SAR cheating interferences is obtained, Range compress, distance are carried out
After migration correction process, the data matrix of K × L dimension image scenes is generated, s (t are denoted asr,ta), column direction indicates in data matrix
Distance dimension, line direction indicate azimuth dimension,；trIt is distance to fast time, taFor the orientation slow time；
Step S102, the reference signal that azimuth dimension deramp processing is generated according to SAR system parameter, is denoted as s0(ta)；Specifically
For：
Wherein γaFor doppler frequency rate.
Wherein, during range migration refers to synthetic aperture, the oblique distance variation between radar and target be more than one away from
From resolution cell so that the echo-signal from same target, to being distributed in different range cells, causes letter in distance
Number orientation and distance to coupling.As previously mentioned, the two dimension of imaging, which is moved change process, becomes two one-dimensional shiftings not
Change process needs to carry out range migration correction to eliminate distance to the coupling with orientation.So-called range migration correction, seeks to
Range migration curvilinear path is corrected to the straight line for being parallel to orientation, precision will reach a synthetic aperture time
Interior, the variation of oblique distance is less than the half of Range resolution unit.In Space-borne SAR Imaging, echo-signal generally entails big distance
Migration, thus range migration correction becomes the important link in imaging directly affects the design of imaging algorithm and final
Image quality.
Step S200, Fourier transformation is carried out after data matrix is multiplied point by point with reference signal, and it is one-dimensional to generate first orientation
Picture, data matrix is filtered after carrying out Fourier transformation, and filter result is carried out inverse Fourier transform, and be inhibited interference
Range cell data are multiplied with reference signal and carry out Fourier transformation, generate suppression by the range cell data after feature point by point
The one-dimensional picture of first orientation and the one-dimensional picture of second orientation are subtracted each other rear point-by-point modulus by the one-dimensional picture of second orientation after interference characteristic processed
Value generates the one-dimensional picture of differential characteristics, and differential characteristics are one-dimensional as being arranged in order along distance dimension, obtains differential characteristics image array.
When it is implemented, step S200 is specifically included:
Step S201, data matrix s (t are obtainedr,ta) in row k data sk(ta), with reference signal s0(ta) point-by-point phase
Multiply, Fourier transformation is carried out to the result after multiplication, generates the first orientation for including real scene and cheating interference all information
One-dimensional picture
WhereinIndicate Fourier transformation；
Step S202, data matrix s (t are obtainedr,ta) in row k data sk(ta), Fourier transformation is carried out, frequency is obtained
Modal dataObtain frequency domain bandpass filter H (fa)
Wherein BaFor the doppler bandwidth of signal, rect () is rectangular window function, according to H (fa) to frequency domain data Sk
(fa) be filtered, filter result is subjected to inverse Fourier transform, generates k-th of range cell data after inhibiting interference characteristic
WhereinInverse Fourier transform is indicated, by k-th of the range cell data and s after inhibition interference characteristic0(ta)
It is point-by-point to be multiplied, and Fourier transformation, the one-dimensional picture of second orientation after the interference characteristic that is inhibited are carried out to result
Step S203, by q1k(x) and q2k(x) subtract each other, and to the point-by-point Modulus of access of the complex data subtracted each other, be calculated
The one-dimensional picture of differential characteristics
Δqk(x)=norm (q1k(x)-q2k(x))
Wherein norm () indicates the operation of point-by-point modulus value；
Step S204, k=1,2 ..., K are enabled, step S201~S203 is repeated, obtained differential characteristics are one-dimensional as Δ
qk(x) it is arranged in order along distance dimension, obtains differential characteristics image array Δ I.
Step S300, the data in differential characteristics image array are handled point by point using the rectangular window of particular size,
The grey level histogram for obtaining the image data in simultaneously calculation window, generates the intensity histogram diagram data around each point.
When it is implemented, step S300 is specifically included：
Step S301, the data in difference image are handled point by point using the rectangular window of W × W, wherein W is the side of window
Long points.By taking the pixel of the row k l row in image as an example, it is assumed that the length of side points W=3 of window, with Δ IwIndicate the figure in window
As data, then have
Wherein, Δ Ik,lIndicate the data at the row k l row of differential characteristics image array Δ I.To Δ IwCalculate its gray scale
Histogram
H=hist (vec (Δ Iw))
Wherein vec () is indicated matrix Δ IwThe operation of vectorization, hist () indicate to calculate the fortune of grey level histogram
It calculates,For grey level histogram vector, G indicates the quantization level of histogram, rectangular window is slided point by point on Δ I, generates
The intensity histogram diagram data of each point.
Step S400, obtain grey level be less than the histogram that is calculated of pixel of pre-set first threshold into
Row is average, generates reference gray level histogram.
When it is implemented, step S400 is specifically included：
Step S401, it obtains grey level in Δ I and is less than first threshold εrThe histogram that is calculated of pixel carry out
It is average, obtain reference gray level histogram
First threshold ε is setrMax { Δ I } equal to 5%, wherein max { Δ I } indicate the maximum of all elements in Δ I
Value.Wherein huIt indicates to be located at (ku,lu) at point histogram, grey level in Differential Characteristics figure is less than threshold epsilonr, i.e. Δ
I(ku,lu) < εr, U represents less than the number of all the points of the threshold value.
Step S500, the grey level histogram of arbitrary element and the matching distance of reference histograms in data matrix are calculated.When
It is cheating interference target by the corresponding target label of element when detecting that matching distance is more than pre-set second threshold.With
In differential image between position of orientation and jammer distance be less than 5% length of synthetic aperture element grey level histogram match away from
From average value as second threshold,
When it is implemented, step S500 is specifically included：
Step S501, the grey level histogram ha of arbitrary element and the matching distance of reference histograms in data matrix are calculated
dM(ho,ha)=‖ ho-ha‖1
Wherein | | | |1The operation for indicating to ask vectorial 1 norm, to the individual element point in image calculate Histogram Matching away from
From generation Histogram Matching matrix
Step S502, when detection detects that matching distance is more than pre-set second threshold, by the corresponding mesh of element
Mark is labeled as cheating interference target, is otherwise labeled as real scene.
Further, the present invention is emulated using MATLAB softwares, and the parameter for emulating data is as follows：Fig. 2 is shown not
There are the real scene SAR imaging results of cheating interference, Fig. 3 to show cheating interference target, devised in cheating interference multigroup
Vehicle target (is respectively designated as " I " " III "), keeps it interlaced mixed to generate with the real vehicles target of depletion region
Confuse.False target " II " " IV " is then covered and has been cheated to the landform in real scene, to true scene and mesh
Mark is protected.Fig. 4 show cheating interference as a result, real goal is interlaced with false target, it is difficult to distinguish, reach
The purpose of cheating interference.Fig. 5 show using the method for the present invention to cheating interference be identified as a result, effective mark
The information such as the shape of false scene and target, position, although in the range of length of synthetic aperture 5% centered on jammer
There is the case where indicating failure, but by the identification information outside the region, it still can be to the false building mesh in the region
Mark is identified.
The present invention also provides a kind of preferable implementations of the SAR cheating interference target identification systems based on differential characteristics enhancing
The functional schematic block diagram of example, as shown in fig. 6, system includes：
Reference signal generation module 100, the blended data for obtaining true SAR scene echoes and SAR cheating interferences are right
Blended data is corrected the data matrix of generation image scene after processing, and deramp processing is generated according to SAR system parameter
Reference signal；Specifically as described in embodiment of the method.
Differential characteristics image array generation module 200 carries out after being multiplied point by point with reference signal for data matrix in Fu
Leaf transformation generates the one-dimensional picture of first orientation, and data matrix is filtered after carrying out Fourier transformation, and filter result is carried out inverse
Fourier transformation, the range cell data after the interference characteristic that is inhibited, range cell data are multiplied point by point with reference signal
And Fourier transformation is carried out, the one-dimensional picture of second orientation after inhibiting interference characteristic is generated, by the one-dimensional picture of first orientation and second party
The one-dimensional picture in position subtracts each other rear point-by-point Modulus of access, generates the one-dimensional picture of differential characteristics, and differential characteristics are one-dimensional as being arranged in order along distance dimension,
Obtain differential characteristics image array；Specifically as described in embodiment of the method.
Grey level histogram generation module 300, for the rectangular window using particular size in differential characteristics image array
Data are handled point by point, obtain the grey level histogram of the image data in simultaneously calculation window, and the gray scale generated around each point is straight
Square diagram data；Specifically as described in embodiment of the method.
Reference gray level histogram generation module 400 is less than the picture of pre-set first threshold for obtaining grey level
The histogram that vegetarian refreshments is calculated is averaged, and reference gray level histogram is generated；Specifically as described in embodiment of the method.
Mark module 500, the matching for calculating the grey level histogram of arbitrary element and reference histograms in data matrix
The corresponding target label of element is cheating interference when detecting that matching distance is more than pre-set second threshold by distance
Target；Specifically as described in embodiment of the method.
The SAR cheating interference target identification systems based on differential characteristics enhancing, wherein the reference signal generates
Module specifically includes:
Correction unit, the blended data for obtaining true SAR scene echoes and SAR cheating interferences, progress Range compress,
After range migration correction processing, the data matrix of K × L dimension image scenes is generated, s (t are denoted asr,ta), column direction in data matrix
Indicate that distance dimension, line direction indicate azimuth dimension,；trIt is distance to fast time, taFor the orientation slow time；Specific such as method is implemented
Described in example.
Computing unit, the reference signal for generating azimuth dimension deramp processing according to SAR system parameter, is denoted as s0(ta)；
Specially：
Wherein γaFor doppler frequency rate；Specifically as described in embodiment of the method.
The SAR cheating interference target identification systems based on differential characteristics enhancing, wherein the differential characteristics image
Matrix generation module specifically includes：
First orientation is one-dimensional as generation unit, for obtaining data matrix s (tr,ta) in row k data sk(ta), with
Reference signal s0(ta) be multiplied point by point, Fourier transformation is carried out to the result after multiplication, it includes real scene and cheating interference to generate
The one-dimensional picture of first orientation of all information
WhereinIndicate Fourier transformation；Specifically as described in embodiment of the method.
Second orientation is one-dimensional as generation unit, for obtaining data matrix s (tr,ta) in row k data sk(ta), into
Row Fourier transformation, obtains frequency spectrum dataObtain frequency domain bandpass filter H (fa)
Wherein BaFor the doppler bandwidth of signal, rect () is rectangular window function, according to H (fa) to frequency domain data Sk
(fa) be filtered, filter result is subjected to inverse Fourier transform, generates k-th of range cell data after inhibiting interference characteristic
WhereinInverse Fourier transform is indicated, by k-th of the range cell data and s after inhibition interference characteristic0(ta)
It is point-by-point to be multiplied, and Fourier transformation, the one-dimensional picture of second orientation after the interference characteristic that is inhibited are carried out to result
Specifically as described in embodiment of the method.
Differential characteristics are one-dimensional as generation unit, are used for q1k(x) and q2k(x) subtract each other, and the complex data to subtracting each other by
Point Modulus of access, is calculated the one-dimensional picture of differential characteristics
Δqk(x)=norm (q1k(x)-q2k(x))
Wherein norm () indicates the operation of point-by-point modulus value；Specifically as described in embodiment of the method.
Differential image array generation unit computes repeatedly the one-dimensional picture of differential characteristics, by gained for enabling k=1,2 ..., K
The differential characteristics arrived are one-dimensional as Δ qk(x) it is arranged in order along distance dimension, obtains differential characteristics image array Δ I；Specific such as method
Described in embodiment.
The SAR cheating interference target identification systems based on differential characteristics enhancing, wherein
The grey level histogram generation module is specifically used for carrying out the data in difference image using the rectangular window of W × W
Point-by-point processing, wherein W is that the length of side of window is counted, with Δ IwIt indicates the image data in window, calculates its grey level histogram
H=hist (vec (Δ Iw))
Wherein vec () is indicated matrix Δ IwThe operation of vectorization, hist () indicate to calculate the fortune of grey level histogram
It calculates,For grey level histogram vector, G indicates the quantization level of histogram, rectangular window is slided point by point on Δ I, generates
The intensity histogram diagram data of each point；Specifically as described in embodiment of the method.
The reference gray level histogram generation module is specifically used for obtaining grey level in Δ I and is less than first threshold εrPicture
The histogram that vegetarian refreshments is calculated is averaged, and reference gray level histogram is obtained
Wherein huIt indicates to be located at (ku,lu) at point histogram, grey level in Differential Characteristics figure is less than threshold value
εr, i.e. Δ I (ku,lu) < εr, U represents less than the number of all the points of the threshold value；Specifically as described in embodiment of the method.
The mark module specifically includes：
Matching distance computing unit, the grey level histogram h for calculating arbitrary element in data matrixaWith reference histograms
Matching distance
dM(ho,ha)=‖ ho-ha‖1
Wherein | | | |1The operation for indicating to ask vectorial 1 norm, to the individual element point in image calculate Histogram Matching away from
From generation Histogram Matching matrixSpecifically as described in embodiment of the method.
Marking unit, for when detection detects that matching distance is more than pre-set second threshold, element to be corresponded to
Target label be cheating interference target, otherwise be labeled as real scene；Specifically as described in embodiment of the method.
File transfers unit, if being used for including timestamp and keyword input by user, execute according to log input by user
The journal file after operation is transferred in corresponding operation；Specifically as described in embodiment of the method.
In conclusion the present invention provides it is a kind of based on differential characteristics enhancing SAR cheating interference target identification methods and
System, method include：The blended data for obtaining true SAR scene echoes and SAR cheating interferences generates data square after correction process
Battle array generates reference signal；Data matrix generates the one-dimensional picture of first orientation for including all information respectively by different disposal step
The one-dimensional picture of second orientation with after inhibition interference characteristic, obtains differential characteristics image array after being further processed；Using rectangular window
Differential characteristics image array is handled, intensity histogram diagram data and reference gray level histogram are generated；When detecting that gray scale is straight
When side's figure and the matching distance of reference histograms are more than pre-set threshold value, it is labeled as cheating interference target.The present invention is directed to
Differential Characteristics of the imaging radar initial data under different imaging methods are enhanced, to cheating interference target in SAR image
It is identified, improves ability of the imaging radar to anti-deceptive interference, reduce cost.
It should be understood that the application of the present invention is not limited to the above for those of ordinary skills can
With improvement or transformation based on the above description, all these modifications and variations should all belong to the guarantor of appended claims of the present invention
Protect range.
Claims (10)
1. a kind of SAR cheating interference target identification methods based on differential characteristics enhancing, which is characterized in that the method includes：
A, the blended data for obtaining true SAR scene echoes and SAR cheating interferences generates after being corrected processing to blended data
The data matrix of image scene generates the reference signal of deramp processing according to SAR system parameter；
B, Fourier transformation is carried out after data matrix is multiplied point by point with reference signal, generates the one-dimensional picture of first orientation, data matrix
Be filtered after carrying out Fourier transformation, and filter result be subjected to inverse Fourier transform, after the interference characteristic that is inhibited away from
From cell data, range cell data are multiplied point by point with reference signal and carry out Fourier transformation, generates and inhibits interference characteristic
The one-dimensional picture of first orientation and the one-dimensional picture of second orientation are subtracted each other rear point-by-point Modulus of access, generate differential by the one-dimensional picture of second orientation afterwards
The one-dimensional picture of feature, differential characteristics are one-dimensional as being arranged in order along distance dimension, obtain differential characteristics image array；
C, the data in differential characteristics image array are handled point by point using the rectangular window of particular size, obtains and calculates window
The grey level histogram of image data in mouthful generates the intensity histogram diagram data around each point；
D, it obtains the histogram that grey level is calculated less than the pixel of pre-set first threshold to be averaged, generate
Reference gray level histogram；
E, calculate data matrix in the grey level histogram of arbitrary element and the matching distance of reference histograms, when detect matching away from
It is cheating interference target by the corresponding target label of element when from more than pre-set second threshold.
2. the SAR cheating interference target identification methods according to claim 1 based on differential characteristics enhancing, feature exist
In the A is specifically included：
A1, the blended data for obtaining true SAR scene echoes and SAR cheating interferences, at progress Range compress, range migration correction
After reason, the data matrix of K × L dimension image scenes is generated, s (t are denoted asr,ta), column direction indicates distance dimension, row side in data matrix
To expression azimuth dimension, trIt is distance to fast time, taFor the orientation slow time；
A2, the reference signal that azimuth dimension deramp processing is generated according to SAR system parameter, are denoted as s0(ta)；Specially：
Wherein γaFor doppler frequency rate.
3. the SAR cheating interference target identification methods according to claim 2 based on differential characteristics enhancing, feature exist
In the B is specifically included：
B1, data matrix s (t are obtainedr,ta) in row k data sk(ta), with reference signal s0(ta) be multiplied point by point, to being multiplied
Result afterwards carries out Fourier transformation, generates the one-dimensional picture of first orientation for including real scene and cheating interference all information
q1k(x)=F (sk(ta)s0(ta))
Wherein F () indicates Fourier transformation；
B2, data matrix s (t are obtainedr,ta) in row k data sk(ta), Fourier transformation is carried out, frequency spectrum data S is obtainedk
(fa)=F (sk(ta)), obtain frequency domain bandpass filter H (fa)
Wherein BaFor the doppler bandwidth of signal, rect () is rectangular window function, according to H (fa) to frequency domain data Sk(fa) carry out
Filter result is carried out inverse Fourier transform, generates k-th of range cell data after inhibiting interference characteristic by filtering
skf(ta)=F-1(Sk(fa)Hf(fa))
Wherein F-1() indicates inverse Fourier transform, by k-th of the range cell data and s after inhibition interference characteristic0(ta) point-by-point
It is multiplied, and Fourier transformation, the one-dimensional picture of second orientation after the interference characteristic that is inhibited is carried out to result
q2k(x)=F (skf(ta)s0(ta))；
B3, by q1k(x) and q2k(x) subtract each other, and to the point-by-point Modulus of access of the complex data subtracted each other, it is one-dimensional that differential characteristics are calculated
Picture
Δqk(x)=norm (q1k(x)-q2k(x))
Wherein norm () indicates the operation of point-by-point modulus value；
B4, k=1,2 ..., K are enabled, repeats step B1~B3, obtained differential characteristics are one-dimensional as Δ qk(x) along distance dimension according to
Secondary arrangement obtains differential characteristics image array Δ I.
4. the SAR cheating interference target identification methods according to claim 3 based on differential characteristics enhancing, feature exist
In the C is specifically included：
C1, the data in difference image are handled point by point using the rectangular window of W × W, wherein W is that the length of side of window is counted, and is used
ΔIwIt indicates the image data in window, calculates its grey level histogram
H=hist (vec (Δ Iw))
Wherein vec () is indicated matrix Δ IwThe operation of vectorization, hist () indicate to calculate the operation of grey level histogram, h ∈
R1×GFor grey level histogram vector, G indicates the quantization level of histogram, rectangular window is slided point by point on Δ I, generates each point
Intensity histogram diagram data.
5. the SAR cheating interference target identification methods according to claim 4 based on differential characteristics enhancing, feature exist
In the D is specifically included：
Grey level is less than first threshold ε in D1, acquisition Δ IrThe histogram that is calculated of pixel be averaged, joined
Examine grey level histogram
Wherein huIt indicates to be located at (ku,lu) at point histogram, grey level in Differential Characteristics figure is less than threshold epsilonr, i.e. Δ
I(ku,lu) < εr, U represents less than the number of all the points of the threshold value.
6. the SAR cheating interference target identification methods according to claim 5 based on differential characteristics enhancing, feature exist
In the E is specifically included：
E1, the grey level histogram h for calculating arbitrary element in data matrixaWith the matching distance of reference histograms
dM(ho,ha)=‖ ho-ha‖1
Wherein | | | |1The operation of vectorial 1 norm is asked in expression, and Histogram Matching distance is calculated to the individual element point in image, raw
At Histogram Matching matrix DM∈RK×L；
E2, when detection detect matching distance be more than pre-set second threshold when, by the corresponding target label of element be take advantage of
Jamming target is deceived, real scene is otherwise labeled as.
7. a kind of SAR cheating interference target identification systems based on differential characteristics enhancing, which is characterized in that system includes：
Reference signal generation module, the blended data for obtaining true SAR scene echoes and SAR cheating interferences, to mixed number
According to the data matrix for generating image scene after processing is corrected, the reference that deramp processing is generated according to SAR system parameter is believed
Number；
Differential characteristics image array generation module carries out Fourier transformation after being multiplied point by point with reference signal for data matrix,
The one-dimensional picture of first orientation is generated, data matrix is filtered after carrying out Fourier transformation, and filter result is carried out inverse Fourier
It converts, range cell data are multiplied and are carried out with reference signal by the range cell data after the interference characteristic that is inhibited point by point
Fourier transformation generates the one-dimensional picture of second orientation after inhibiting interference characteristic, and the one-dimensional picture of first orientation and second orientation is one-dimensional
As subtracting each other rear point-by-point Modulus of access, the one-dimensional picture of differential characteristics is generated, the one-dimensional picture of differential characteristics is arranged in order along distance dimension, is obtained micro-
Dtex levies image array；
Grey level histogram generation module carries out the data in differential characteristics image array for the rectangular window using particular size
Point-by-point processing obtains the grey level histogram of the image data in simultaneously calculation window, generates the intensity histogram diagram data around each point；
Reference gray level histogram generation module is calculated for obtaining grey level less than the pixel of pre-set first threshold
Obtained histogram is averaged, and reference gray level histogram is generated；
Mark module, the matching distance for calculating the grey level histogram of arbitrary element and reference histograms in data matrix, when
It is cheating interference target by the corresponding target label of element when detecting that matching distance is more than pre-set second threshold.
8. the SAR cheating interference target identification systems according to claim 7 based on differential characteristics enhancing, feature exist
In the reference signal generation module specifically includes:
Unit is corrected, the blended data for obtaining true SAR scene echoes and SAR cheating interferences carries out Range compress, distance
After migration correction process, the data matrix of K × L dimension image scenes is generated, s (t are denoted asr,ta), column direction indicates in data matrix
Distance dimension, line direction indicate azimuth dimension, trIt is distance to fast time, taFor the orientation slow time；
Computing unit, the reference signal for generating azimuth dimension deramp processing according to SAR system parameter, is denoted as s0(ta)；Specifically
For：
Wherein γaFor doppler frequency rate.
9. the SAR cheating interference target identification systems according to claim 8 based on differential characteristics enhancing, feature exist
In the differential characteristics image array generation module specifically includes：
First orientation is one-dimensional as generation unit, for obtaining data matrix s (tr,ta) in row k data sk(ta), believe with reference
Number s0(ta) be multiplied point by point, Fourier transformation is carried out to the result after multiplication, generates and all believes comprising real scene and cheating interference
The one-dimensional picture of first orientation of breath
q1k(x)=F (sk(ta)s0(ta))
Wherein F () indicates Fourier transformation；
Second orientation is one-dimensional as generation unit, for obtaining data matrix s (tr,ta) in row k data sk(ta), it carries out in Fu
Leaf transformation obtains frequency spectrum data Sk(fa)=F (sk (ta)), obtain frequency domain bandpass filter H (fa)
Wherein BaFor the doppler bandwidth of signal, rect () is rectangular window function, according to H (fa) to frequency domain data Sk(fa) carry out
Filter result is carried out inverse Fourier transform, generates k-th of range cell data after inhibiting interference characteristic by filtering
skf(ta)=F-1(Sk(fa)Hf(fa))
Wherein F-1() indicates inverse Fourier transform, by k-th of the range cell data and s after inhibition interference characteristic0(ta) point-by-point
It is multiplied, and Fourier transformation, the one-dimensional picture of second orientation after the interference characteristic that is inhibited is carried out to result
q2k(x)=F (skf(ta)s0(ta))；
Differential characteristics are one-dimensional as generation unit, are used for q1k(x) and q2k(x) subtract each other, and the complex data to subtracting each other takes point by point
The one-dimensional picture of differential characteristics is calculated in modulus value
Δqk(x)=norm (q1k(x)-q2k(x))
Wherein norm () indicates the operation of point-by-point modulus value；
Differential image array generation unit computes repeatedly the one-dimensional picture of differential characteristics for enabling k=1,2 ..., K, will be obtained
Differential characteristics are one-dimensional as Δ qk(x) it is arranged in order along distance dimension, obtains differential characteristics image array Δ I.
10. the SAR cheating interference target identification systems according to claim 9 based on differential characteristics enhancing, feature exist
In,
The grey level histogram generation module is specifically used for carrying out the data in difference image using the rectangular window of W × W point-by-point
Processing, wherein W is that the length of side of window is counted, with Δ IwIt indicates the image data in window, calculates its grey level histogram
H=hist (vec (Δ Iw))
Wherein vec () is indicated matrix Δ IwThe operation of vectorization, hist () indicate to calculate the operation of grey level histogram, h ∈
R1×GFor grey level histogram vector, G indicates the quantization level of histogram, rectangular window is slided point by point on Δ I, generates each point
Intensity histogram diagram data.
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| WO2018139947A1 (en) * | 2017-01-24 | 2018-08-02 | Huawei Technologies Co., Ltd. | Apparatus and method for data compression |
| CN111257835B (en) * | 2020-02-17 | 2022-02-18 | 森思泰克河北科技有限公司 | Interference suppression method for radar and terminal equipment |
| CN112485792B (en) * | 2020-11-26 | 2023-08-08 | 中国人民解放军空军军医大学 | Three-dimensional enhanced imaging method for human body target |
| CN112598632A (en) * | 2020-12-16 | 2021-04-02 | 北京卫星制造厂有限公司 | Appearance detection method and device for contact element of crimp connector |
| CN112859005B (en) * | 2021-01-11 | 2023-08-29 | 成都圭目机器人有限公司 | Method for detecting metal straight cylinder structure in multichannel ground penetrating radar data |
| CN112859006B (en) * | 2021-01-11 | 2023-08-29 | 成都圭目机器人有限公司 | Method for detecting metal bending cylindrical structure in multichannel ground penetrating radar data |
| CN113093185B (en) * | 2021-03-31 | 2022-03-08 | 电子科技大学 | Method for matching gray scale between video SAR imaging frames |
| CN113203991B (en) * | 2021-04-29 | 2022-05-31 | 电子科技大学 | Anti-deception jamming method of multi-base SAR (synthetic aperture radar) in multi-jammer environment |
| CN113311431B (en) * | 2021-05-27 | 2023-09-05 | 深圳大学 | Deception jamming inhibition method and system based on single-channel SAR single imaging |
| CN115407282B (en) * | 2022-08-24 | 2024-04-26 | 北京航空航天大学 | SAR active deception jamming detection method based on interference phase under short base line |
| CN117496000B (en) * | 2023-12-29 | 2024-05-17 | 北京宏锐星通科技有限公司 | Method and device for generating interference template image |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102721948A (en) * | 2012-07-06 | 2012-10-10 | 西安电子科技大学 | Large-scene SAR deception jamming implementation method |
| CN103675769A (en) * | 2013-12-06 | 2014-03-26 | 西安电子科技大学 | Squinting SAR deception jamming method based on distributed receivers |
| CN105467368A (en) * | 2015-11-25 | 2016-04-06 | 深圳大学 | Multi-receiver equidistant rectangular distribution-based SAR deception jamming method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9069066B2 (en) * | 2013-05-20 | 2015-06-30 | Faran Awais Butt | Radar deception jamming prevention using bi-static and mono-static radars |
-
2016
- 2016-07-21 CN CN201610587654.8A patent/CN106291494B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102721948A (en) * | 2012-07-06 | 2012-10-10 | 西安电子科技大学 | Large-scene SAR deception jamming implementation method |
| CN103675769A (en) * | 2013-12-06 | 2014-03-26 | 西安电子科技大学 | Squinting SAR deception jamming method based on distributed receivers |
| CN105467368A (en) * | 2015-11-25 | 2016-04-06 | 深圳大学 | Multi-receiver equidistant rectangular distribution-based SAR deception jamming method and system |
Non-Patent Citations (2)
| Title |
|---|
| Improved method for synthetic aperture radar scattered wave deception jamming;Bo Zhao et al.;《The Institution of Engineering and Technology 2014》;20141231;第971-976页 * |
| 一种虚假大场景 SAR 快速转发式欺骗干扰方法研究;赵博 等;《电子与信息学报》;20120430;第34卷(第4期);第963-968页 * |
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