CN108562901A - ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion - Google Patents
ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion Download PDFInfo
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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
-
- 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
-
- 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
- G01S13/9064—Inverse SAR [ISAR]
Abstract
The invention discloses a kind of ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion, realize in clutter and noise circumstance and are imaged to target ISAR, obtain and focus good ISAR two dimensional images.Scheme includes：Wideband correlation obtains target and environmental information；Waveform optimization is carried out to the signal of perception information based on maximum letter miscellaneous noise ratio criterion, and designs receiving filter；Using optimization waveform and receiving filter, optimization waveform echo is obtained；The distance of constitution optimization waveform echo is to dictionary, to the sparse reconstruct of target distance image；The orientation dictionary of constitution optimization waveform echo obtains the ISAR two-dimensional imaging results of target to target bearing as sparse reconstruct.The present invention optimizes transmitted waveform using maximum letter miscellaneous noise ratio criterion, target sparse is reconstructed again, it is realized in clutter and noise circumstance and focuses good ISAR two dimensions high-resolution imaging, there is better clutter recognition effect, be used under clutter and noise circumstance to target ISAR two-dimensional imagings.
Description
Technical field
The invention belongs to Radar Technology fields, further relate to the high-resolution two dimension of maximum letter miscellaneous noise ratio design waveform
ISAR is imaged, and specifically a kind of ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion can be used for complicated electromagnetism ring
Target high-resolution ISAR is imaged under border.
Background technology
The waveform of conventional radar transmitting is fixed, does not make full use of environmental information to design transmitted waveform so that thunder
The imaging performance of target is restricted up under to complex electromagnetic environment.With the hair of electronic technology and Radar Signal Processing Technology
Exhibition, advanced radar system utilize knowledge-based signal and data processing technique, change by the way that priori is adaptive
Become the characteristic of itself.In this context, Canadian scholar Simon Haykin formally proposed cognition radar in 2006
Concept, cognition radar come into being.Recognize radar can by the adaptive interrogation link information of various prioris, and
Corresponding waveform library is built, and then according to the different waveform of the transmitting of different ambient intelligences.It recognizes radar and utilizes environment and mesh
The relevant information such as mark make radar system can adapt to electromagnetism increasingly complicated and changeable to design and emit radar optimum waveform
Environment, to improve the performance of radar system.
Paper " the Mutual information and that Guo D, Shamai S, Verdu S et al. is delivered at it
minimum mean-square error in channels”(IEEE Transactions on Information
Theory,2005,51(4):In 1261-1282) under the background of white Gaussian noise, least-mean-square-error criterion and mutual information are utilized
Relationship between theory recognizes radar emission waveform to optimize.Paper " the Optimal that Haykin S, Xue Y et al. is delivered at it
waveform design for cognitive radar”(Asilomar Conference on Signals,Systems
and Computers,2008:Using signal-to-noise ratio as constraints in 3-7), using interior point method come maximize target and echo it
Between mutual information, the optimal transmitted waveform of Design cognition radar is come with this.The paper that Hu Xu, Jia Xin et al. are delivered at it is " based on most
(marine electronic fights .2013,36 (3) to the cognition radar waveform optimization algorithm of big signal-to-noise ratio (SNR) Criterion ":Radar is directed in 59-62)
The cognition radar waveform optimization algorithm based on maximum signal to noise ratio is proposed in echo-signal the problem of signal-to-noise ratio, this algorithm improves
The signal-to-noise ratio for recognizing radar return, to improve detection probability of the cognition radar to target.
More design method all only considered influence of the noise to echo, and the influence of clutter is not discussed, cannot inhibit empty
Between in clutter.
The paper that RIC A.ROMERO et al. are delivered at it " Theory and Application of SNR and
Mutual Information Matched Illumination Waveforms”(IEEE Transactions On
Aerospace And Electronic Systems,2011:Vol.47, No.2) in complex electromagnetic environment target
The problem of echo is by environmental disturbances, it is proposed that based on maximum letter miscellaneous noise ratio waveform optimization method, based on maximum letter miscellaneous noise ratio
Waveform optimization method analyzes the relationship of matched filter output signal-to-noise ratio and transmitted waveform, and is designed using maximum signal noise ratio principle
Transmitted waveform, it can make matched filter output letter miscellaneous noise ratio reach maximum.But the waveform of this method design is to be based on
Signal detection, although directly by this optimization waveform for ISAR imagings can effectively clutter reduction and noise, there are secondary lobe compared with
The problem that height causes image focusing effect bad.
In conclusion existing waveform optimization method mostly inhibits noise by designing transmitted waveform, clutter is not accounted for
Influence, have no the effect of clutter reduction.And existing clutter suppression method is all based on the waveform optimization of signal detection, directly makes
Can there is a problem of that image focusing effect is bad with these optimization waveforms to carry out ISAR imagings to target.
Invention content
It is an object of the invention to propose that one kind preferably clutter reduction and image focusing effect can be preferably based on most
The cognition ISAR high-resolution imaging methods of big letter miscellaneous noise ratio criterion.
The present invention is a kind of cognition ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion, which is characterized in that
Including having the following steps：
(1) target and environmental information are perceived：Inverse Synthetic Aperture Radar perception target and the environmental information where target are recognized,
Obtain the frequency spectrum G (f), noise power spectral density P of target impact responsen(f), clutter power spectrum density Pc(f), wherein f indicates frequency
Rate；
(2) it is based on maximum letter miscellaneous noise ratio criterion and optimizes transmitted waveform：The environment and target information perceived using radar,
The wideband correlation of perception information is optimized according to maximum letter miscellaneous noise ratio criterion, the transmitted waveform after being optimized
S and receiving filter h；
(3) optimization waveform echo-signal is obtained：The transmitted waveform s after Inverse Synthetic Aperture Radar transmitting optimization is recognized, will be connect
The echo-signal received obtains optimization waveform echo matrix S by receiving filter hr, SrDimension is Nr×Na, NrIt is single for distance
First number, NaFor localizer unit number；
(4) distance of optimization waveform echo is generated to dictionary：To optimization waveform echo into row distance to sparse modeling, generate
Optimize the distance of waveform echo to dictionary, which is ψr, ψrDimension is Nr× Q, Q be by scene distance to discretization it
Corresponding meshes number afterwards takes Q ＞ Nr, ψr=[sr1,…srq,…,srQ] it is that all scattering point positions correspond on ordinate grid
Optimize the matrix that waveform echo vector is constituted,Correspond to what optimization waveform echo was constituted for q-th of scattering point
Vector, dimension Nr× 1, t are the fast time, and c is the light velocity, RqThe distance of q-th of scattering point, q ∈ [1, Q] are scattering point serial number；
(5) using optimization waveform echo matrix SrTo the sparse reconstruct of target distance image：First with the method for sparse reconstruct
Its Range Profile is solved to the optimization waveform echo of single localizer unit, same method again returns the optimization waveform of all localizer units
Wave carries out the sparse reconstruct of Range Profile, and the Range Profile for gathering the optimization waveform echo of all localizer units obtains target distance image matrix
S1；
(6) the orientation dictionary of optimization waveform echo is generated：The sparse modeling of orientation is carried out to optimization waveform echo, is generated
Optimize the orientation dictionary of waveform echo, which is ψa, ψaDimension is Na× P, P be by scene orientation discretization it
Corresponding meshes number afterwards takes P ＞ Na,Optimization is corresponded to by being possible to frequency point on Doppler's grid
The matrix that waveform echo vector is constituted, p ∈ [1, P] they are frequency point serial number, wherein
PRF is pulse recurrence frequency, tmFor the column vector lined up by the slow time；
(7) using the target distance image matrix S of optimization waveform echo reconstruct1To target bearing as sparse reconstruct：It is sharp first
Its orientation picture is solved to the single range cell for optimizing waveform echo restructuring distance picture with the method for sparse reconstruct, same method is again
Orientation is carried out as sparse reconstruct to all range cells of optimization waveform echo reconstruct, set forms the orientation of all range cells
Picture obtains the target ISAR two dimension high-resolution imaging matrixes S of optimization waveform echo reconstruct3, complete is pairs of target based on maximum
Believe the cognition ISAR high-resolution imagings of miscellaneous noise ratio criterion.
Traditional ISAR echoes letter miscellaneous noise ratio is relatively low lead to that target ISAR cannot be imaged in the case of, the present invention can be real
Now to target ISAR two dimension high-resolution imagings, obtains and focus good ISAR two dimensional images.
Compared with prior art, the invention has the advantages that：
Conventional radar emits fixed waveform, does not utilize target and environmental information, and echo is believed under complex electromagnetic environment
Miscellaneous noise ratio is low, and radar system performance is bad, and the present invention makes full use of the target and environmental information that cognition radar obtains, using maximum
The method of letter miscellaneous noise ratio optimizes radar emission waveform, and the power of target echo can be improved using this transmitted waveform,
The power of clutter reduction improves the letter miscellaneous noise ratio of echo, solves under complex electromagnetic environment, and clutter and noise lead to tradition
Radar return believes the relatively low problem of miscellaneous noise ratio, can effectively inhibit the clutter in environment and noise.
The present invention to optimization waveform echo into row distance to and the sparse modeling of orientation, it is proposed that based on maximum letter miscellaneous noise ratio
Criterion optimizes the distance of waveform echo to the building method of dictionary and orientation dictionary, is passed through using optimization waveform echo sparse heavy
The method of structure carries out Range Profile and orientation as sparse reconstruct to target respectively, so obtain the ISAR two dimensions high-resolution of target at
Picture.It is this to the ISAR imaging results that are obtained using sparse reconstructing method of optimization waveform echo compared to using matching matrix to obtain
To ISAR imaging results have more good image focusing effect, this is because optimization waveform there are problems that main-side lobe ratio reduction
It is poor to result in the ISAR imaging results focusing effects obtained using matching matrix.
Description of the drawings
Fig. 1 is the implementation flow chart of the present invention；
Fig. 2 is target Equivalent scattering center distribution map；
Fig. 3 is transmitted waveform ISAR two-dimensional imaging result figures before present invention optimization；
Fig. 4 is transmitted waveform spectrogram before present invention optimization；
Fig. 5 is transmitted waveform spectrogram after present invention optimization；
Fig. 6 is the ISAR imaging results figures of present invention optimization waveform echo matched filtering；
Fig. 7 is the ISAR imaging results figures of the present invention optimization sparse reconstruct of waveform echo；
Fig. 8 is radar emission of the present invention-reception channel mapping.
Specific implementation mode
Technical scheme of the present invention is described in detail below in conjunction with the drawings and specific embodiments.
Embodiment 1
Existing waveform optimization method is mostly to design transmitted waveform to inhibit noise, does not account for the influence of clutter, this
A little optimization waveforms do not have the effect of clutter reduction, to the target under noise and clutter environment, since clutter is not inhibited,
The letter miscellaneous noise ratio of echo is still relatively low, the problem of still leading to the ISAR two dimensional images that cannot obtain target.And these are existing
There is method to be all based on the waveform optimization of signal detection, these optimization waveforms have that main-side lobe ratio is relatively low, directly to this
A little optimization waveform echoes are imaged using matching matrix, and obtained ISAR imaging results focusing effects are bad.For this existing
Shape, the present invention propose that a kind of cognition ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion include referring to Fig. 1
Following steps：
(1) target and environmental information are perceived：Recognize Inverse Synthetic Aperture Radar transmitting wideband correlation s0(t) it perceives
Target and the environmental information where target, obtain the frequency spectrum G (f), noise power spectral density P of target impact responsen(f), clutter
Power spectral density Pc(f), wherein f indicates frequency.The frequency spectrum G (f) of wherein target impact response is target information, noise power spectrum
Density Pn(f), clutter power spectrum density Pc(f) it is environmental information.With target and environmental information are generally obtained not by narrow band signal
Together, the present invention obtains target and environmental information using broadband signal.
(2) it is based on maximum letter miscellaneous noise ratio criterion and optimizes transmitted waveform：Radar emission-is established under clutter and noise circumstance to connect
Channel model is received, radar return signal is made of target echo, clutter with noise, as shown in figure 8, s (t) is that transmitting is believed in figure
Number, g (t) responds for target pulse, and c (t) responds for noise pulse, and it is a random process, n (t) that noise pulse, which responds c (t),
For noise, h (t) is matched filter impulse response, and y (t) is to receive signal.The environment perceived using radar is believed with target
Breath finds out the optimal transmitted waveform and reception of radar in radar emission-reception channel model according to maximum letter miscellaneous noise ratio criterion
Filter h optimizes the wideband correlation of perception information further according to the frequency spectrum of optimal transmitted waveform, is optimized
Transmitted waveform s afterwards.It is different it to be unable to clutter reduction from existing most optimization waveform, based on maximum letter miscellaneous noise ratio criterion in the present invention
The optimization waveform of design plays the role of clutter reduction.
(3) optimization waveform echo-signal is obtained：The transmitted waveform s after Inverse Synthetic Aperture Radar transmitting optimization is recognized, will be connect
The echo-signal received obtains optimization waveform echo matrix S by receiving filter hr, SrDimension is Nr×Na, NrIt is single for distance
First number, NaFor localizer unit number.Echo matrix SrIt is to be obtained with the radar emission waveform s after optimization, the radar emission after optimization
Waveform s referred to as optimization waveforms.
(4) distance of optimization waveform echo is generated to dictionary：To optimization waveform echo into row distance to sparse modeling, generate
Optimize the distance of waveform echo to dictionary, which is ψr, ψrDimension is Nr× Q, Q be by scene distance to discretization it
Corresponding meshes number afterwards takes Q ＞ Nr, ψr=[sr1,…srq,…,srQ] it is that all scattering point positions correspond on ordinate grid
Optimize the matrix that waveform echo vector is constituted,Correspond to what optimization waveform echo was constituted for q-th of scattering point
Vector, dimension Nr× 1, t are the fast time, and c is the light velocity, RqThe distance of q-th of scattering point, q ∈ [1, Q] are scattering point serial number.This
Invention reconstructs target distance image to optimization waveform echo using the method for sparse reconstruct, needs the distance of constitution optimization waveform echo
To dictionary.
(5) using optimization waveform echo matrix SrTo the sparse reconstruct of target distance image：First with the method for sparse reconstruct
Its Range Profile is solved to the optimization waveform echo of single localizer unit, same method again returns the optimization waveform of all localizer units
Wave carries out the sparse reconstruct of Range Profile, and the Range Profile for gathering the optimization waveform echo of all localizer units obtains target distance image matrix
S1.Since transmitted waveform main-side lobe ratio is relatively low after optimization, to the target range that is obtained using matching matrix of optimization waveform echo
As that can there is a problem of that focusing effect is bad.The present invention for this problem structure optimization waveform echo distance to dictionary, it is right
Optimize waveform echo and solve target distance image using the method for sparse reconstruct, reduces optimization waveform main-side lobe ratio to target range
The influence of image focu degree can improve the image quality of the Range Profile of target.
(6) the orientation dictionary of optimization waveform echo is generated：The sparse modeling of orientation is carried out to optimization waveform echo, is generated
Optimize the orientation dictionary of waveform echo, which is ψa, ψaDimension is Na× P, P be by scene orientation discretization it
Corresponding meshes number afterwards takes P ＞ Na,Optimization is corresponded to by being possible to frequency point on Doppler's grid
The matrix that waveform echo vector is constituted, p ∈ [1, P] they are frequency point serial number, wherein
PRF is pulse recurrence frequency, tmFor the column vector lined up by the slow time.Target range of the present invention to optimization waveform echo reconstruct
As reconstructing target bearing picture using the method for sparse reconstruct, the orientation dictionary of constitution optimization waveform echo is needed.
(7) using the target distance image matrix S of optimization waveform echo reconstruct1To target bearing as sparse reconstruct：It is sharp first
Its orientation picture is solved to the single range cell for optimizing waveform echo restructuring distance picture with the method for sparse reconstruct, same method is again
Orientation is carried out as sparse reconstruct to the Range Profile of all range cells of optimization waveform echo reconstruct, it is single that set forms all distances
The orientation picture of member obtains the target ISAR two dimension high-resolution imaging matrixes S of optimization waveform echo reconstruct3, complete the base to target
In the cognition ISAR high-resolution imagings of maximum letter miscellaneous noise ratio criterion.The target that optimization waveform echo is obtained using matching matrix
Orientation picture can have that focusing effect is bad, and the present invention uses the method for optimization waveform echo reconstruct target bearing picture, structure
Build the orientation dictionary of optimization waveform echo, and using the target distance image of optimization waveform echo reconstruct to target bearing to sparse
Reconstruct, improves the image quality of the orientation picture of target.
The present invention makes full use of cognition radar to obtain target and environmental information, using the method for maximum letter miscellaneous noise ratio to radar
Be emitted into row waveform optimization, improve the letter miscellaneous noise ratio of echo, to optimization waveform echo into row distance to and orientation is sparse builds
Mould, construction distance carry out ISAR by the method for sparse reconstruct to dictionary and orientation dictionary, to optimization waveform echo to target
Two-dimentional high-resolution imaging obtains the good imaging results of target focusing effect under clutter and noise circumstance.
Embodiment 2
ISAR high-resolution imagings method based on maximum letter miscellaneous noise ratio criterion is with embodiment 1, the acquisition described in step (2)
Transmitted waveform after optimization and receiving filter, including have the following steps：
(2a) finds out the frequency spectrum of optimal transmitted waveform with maximum letter miscellaneous noise ratio criterion, in channel model shown in Fig. 8,
Clutter maximizes the letter miscellaneous noise ratio for receiving signal with maximum letter miscellaneous noise ratio criterion, find out makes reception as the following formula under noise circumstance
Emit the frequency spectrum of signal when Signal-to-Noise maximumWherein S (f) is
The frequency spectrum of optimal transmitted waveform, α1To limit constant by transmitting signal energy, the constant is true by the energy of transmitting signal before optimizing
Fixed, the energy of the front and back transmitted waveform of limitation optimization is consistent, and G (f) is the frequency spectrum of target impact response, Pn(f) it is noise work(
Rate spectrum density, Pc(f) it is clutter power spectrum density, f indicates that frequency, max expressions are maximized.
(2b) is by the wideband correlation s of perception information0(t) it is fourier transformed into frequency domain, obtains S0(f), to S0
(f) formula is pressedIt is modulated into line amplitude, the frequency spectrum S of radar emission waveform after being optimized1(f), in this way
Obtain | S1(f) |=| S (f) |.
(2c) finds out the frequency spectrum of receiving filter, the frequency spectrum of receiving filter with maximum letter miscellaneous noise ratio criterionIn formula, α2For non-zero constant, symbol () * expressions take conjugation, S1(f) it is after optimizing
The frequency spectrum of radar emission waveform, G (f) are the frequency spectrum of target impact response, Pn(f) it is noise power spectral density, Pc(f) it is clutter
Power spectral density, f indicate frequency.
(2d) is by S1(f) inverse Fourier transform is to time domain, the radar emission waveform s after being optimized, by receiving filter
Spectrum H (f) inverse Fourier transform to time domain, obtain receiving filter h, optimization waveform echo is by optimization waveform as emitting
Waveform will receive what signal was obtained by receiving filter.
The present invention establishes radar emission-reception channel model under clutter and noise circumstance, accurate with maximum letter miscellaneous noise ratio
Optimal transmitted waveform is then found out, under conditions of transmitting signal energy is constant before and after limiting optimization, then the broadband to perceiving environment
Linear FM signal optimizes, the radar emission signal after being optimized.The optimization signal obtained in this way can inhibit environment
In clutter and noise, reduce radar return in clutter and noise power, enhancing target to emit signal back scattering, carry
The power of target echo in high radar return, and then improve the letter miscellaneous noise ratio of echo.
Embodiment 3
ISAR high-resolution imagings method based on maximum letter miscellaneous noise ratio criterion is with embodiment 1-2, the life described in step (4)
At the distance of optimization waveform echo to dictionary, including have the following steps：
(4a) is with distance interval Δ RrTarget is divided into the upward one dimensional network of row distance, it is equidistant between adjacent mesh and
Distance is Δ Rr, meshes number Q.
(4b), as transmitted waveform, is received signal by receiving filter h, obtained using the radar emission waveform s after optimization
The optimization waveform echo of each scattering point divided to distance to network.
Be possible to scattering point position on one-dimensional ordinate grid is corresponded to the vectorial s that optimization waveform echo is constituted by (4c)r1,
sr2..., srQRespectively as optimization waveform echo distance to dictionary matrix ψrThe 1st, 2 ..., Q row, obtain the dictionary matrix
ψr.The position of these scattering points is on ordinate grid with distance interval Δ RrIt obtains.
The present invention to the optimization waveform echo obtained based on maximum letter miscellaneous noise ratio criterion into row distance to sparse modeling, will be excellent
Change waveform s as transmitted waveform, h obtains the excellent of each target point divided apart from upward one dimensional network as receiving filter
Change waveform echo, using these optimization waveform echoes as distance to each row of dictionary come the distance of constitution optimization waveform echo to
Dictionary.
Embodiment 4
ISAR high-resolution imagings method based on maximum letter miscellaneous noise ratio criterion makes with embodiment 1-3 described in step (5)
With optimization waveform echo matrix SrTo the sparse reconstruct of target distance image, including have the following steps：
There are the localizer unit serial numbers of echo for (5a) initialization first, enable first localizer unit serial number there are echo
D=1.
(5b) is rightIt is obtained using the method for sparse reconstructTo optimize waveform echo matrix SrTake localizer unit serial number
For an echo of d,The Range Profile for being d for localizer unit.
(5c) enables localizer unit serial number d=d+1, judges whether d ＞ Na, if not satisfied, thening follow the steps (5b).If full
Foot, then stop iteration, by the Range Profile of the optimization waveform echo of all localizer unitsIt is combined by row, obtains target distance image square
Battle arrayS1Dimension be Q × Na, matrix S1Line number indicate range cell number, columns indicate orientation list
First number.
The method that the present invention utilizes sparse reconstruct, successively to the optimization waveform echo of each localizer unit to target into line-spacing
From as sparse reconstruct, the Range Profile of target is obtained.This method to the sparse reconstruct of target distance image can overcome optimization waveform
The bad disadvantage of the target distance image focusing effect that is obtained using matching matrix, because there are main-side lobe ratio reductions for optimization waveform
The problem of can reduce the quality of the target distance image image obtained using matching matrix.
Embodiment 5
ISAR high-resolution imagings method based on maximum letter miscellaneous noise ratio criterion is with embodiment 1-4, the life described in step (6)
At the orientation dictionary of optimization waveform echo, including have the following steps：
(6a) is to target with Doppler frequency interval Δ fdThe one dimensional network carried out in orientation divides, meshes number P.
(6b) is by slow time tm, m=1,2 ..., NaIt is arranged in column vector tm, find out and be possible to scatter on abscissa grid
The corresponding Doppler frequency value in point position Wherein between Doppler frequency
EveryPRF is pulse recurrence frequency.
(6c) willRespectively as the 1st, 2 of dictionary matrix ψ a the ..., P row obtain optimization waveform and return
The orientation dictionary matrix ψ of wavea。
The present invention carries out the sparse modeling of orientation, construction to the optimization waveform echo obtained based on maximum letter miscellaneous noise ratio criterion
The orientation dictionary for optimizing waveform echo carries out the one dimensional network in orientation to target and divides, will be each on abscissa grid
Each row of the corresponding Doppler frequency value in a scattering point position as orientation dictionary.
Embodiment 6
ISAR high-resolution imagings method based on maximum letter miscellaneous noise ratio criterion makes with embodiment 1-5 described in step (7)
With the target distance image matrix S of optimization waveform echo reconstruct1To target bearing as sparse reconstruct, including have the following steps：
(7a) is by Range Profile matrix S1Transposition is carried out, S is obtained2=(S1)T, ()TExpression turns order, matrix S2Dimension is Na×
Q, there are the range cell serial number b=1 of echo for initialization first.
(7b) use and same method in step (5b), by ψaAs dictionary matrix, K2It is right as orientation degree of rareficationIt is obtained using the method for sparse reconstructFor Range Profile matrix S2It is the echo of b to take range cell,Range cell is b
Orientation picture.
(7c) enables b=b+1, judges whether b ＞ Nr, if not satisfied, thening follow the steps (7b).If satisfied, then stop iteration,
By the orientation picture of all range cellsIt is combined by row, obtains the target ISAR two dimension high-resolution imagings of optimization waveform echo reconstruct
MatrixS3Dimension is P × Q, matrix S3Line number indicate localizer unit, columns indicate range cell.
The method that the present invention utilizes sparse reconstruct carries out orientation as dilute to the Range Profile of each range cell to target successively
Reconstruct is dredged, the orientation picture of target is obtained.It is this that waveform echo weight is optimized as the method for sparse reconstruct can overcome to target bearing
The target distance image of structure using the bad disadvantage of the target bearing image focu effect that matching matrix obtains, improve target bearing to
Image quality.
A more detailed example is given below, the present invention is further described.
Embodiment 7
ISAR high-resolution imagings method based on maximum letter miscellaneous noise ratio criterion is the same as embodiment 1-6, ISAR high-resolution of the present invention
Imaging method is to believe miscellaneous noise ratio criterion based on maximum, includes having the following steps referring to Fig. 1 concrete implementations：
Step 1, optimization front signal echo is obtained：
Optimize front signal echo, refers to that transmitting signal, obtained echo are used as using wideband correlation.Purpose is
When recognizing radar circumstances not known information, perception imaging circumstances are gone as transmitting signal using wideband correlation.
Step 2, perceive and obtain target and environmental information：
(2.1) target echo s is obtained under conditions of low interferencer0(t), the frequency spectrum of target impact responseWherein Sr0(f) it is transmitting signal s0(t) frequency spectrum, Sr0(f) it is target echo sr0(t) frequency spectrum.
(2.2) certain clutter sample function is s in spacec0(t), clutter PSD of respoase is Pc(f), using formulaCome approximate clutter PSD of respoase, wherein Sc0(f) it is sc0(t) frequency spectrum, | | indicate modulus.
(2.3) it is white noise, noise power spectral density P in ambient noisen(f) it is constant.
Step 3, it is based on maximum letter miscellaneous noise ratio criterion design optimization transmitted waveform：
(3.1) under clutter and noise circumstance, the letter miscellaneous noise ratio for receiving signal is maximized with maximum letter miscellaneous noise ratio criterion, is asked
The frequency spectrum for emitting signal when snr of received signal maximum of sening as an envoy to, by formula
The frequency spectrum of optimal transmitted waveform is found out, wherein S (f) is the frequency spectrum of optimal transmitted waveform, α1It is limited by transmitting signal energy
Constant.
(3.2) by the wideband correlation s of perception information0(t) it is fourier transformed into frequency domain, obtains S0(f), to S0
(f) formula is pressedIt is modulated into line amplitude, the frequency spectrum S of radar emission waveform after being optimized1(f)。
(3.3) frequency spectrum of receiving filter, the frequency spectrum of receiving filter are found out with maximum letter miscellaneous noise ratio criterionα in formula2For non-zero constant, symbol ()*Expression takes conjugation, S1(f) it is thunder after optimizing
Up to the frequency spectrum of transmitted waveform, G (f) is the frequency spectrum of target impact response, Pn(f) it is noise power spectral density, Pc(f) it is clutter work(
Rate spectrum density, f indicate frequency.
(3.4) by S1(f) inverse Fourier transform is to time domain, the radar emission waveform after being optimizeds, by receiving filter
Spectrum H (f) inverse Fourier transform to time domain, obtain receiving filter h.
Step 4, using the transmitted waveform s after optimization, receiving filter h, optimization waveform echo is obtained.
Step 5, distance is generated to dictionary：
(5.1) with distance interval Δ RrTarget is divided into the upward one dimensional network of row distance, meshes number Q.
(5.2) using the s after optimization as transmitted waveform, h obtains echo as receiving filter.
(5.3) be possible to scattering point position on ordinate grid is corresponded to the vectorial s of echo compositionr1, sr2..., srQ,
Respectively as dictionary matrix ψrThe 1st, 2 ..., Q row, obtain dictionary matrix ψr。
Step 6, the sparse reconstruct of target distance image：
(6.1) there are the localizer unit serial number d=1 of echo for initialization first.
(6.2) rightIt is obtained using the method for sparse reconstructTo optimize waveform echo matrix SrTake localizer unit sequence
Number be d an echo,The Range Profile for being d for localizer unit.
(6.2.1) is initialized：Localizer unit serial number d=1, residual error For echo matrix SrTake orientation list
An echo of first serial number d, matching matrix For empty set, iterations k=1；
(6.2.2) residual error rk-1With matrix ψrEach row do inner product, find out the corresponding columns l of maximum inner productk, i.e. lk=arg
maxQ=1 ... Q|<rt-1,(ψr)q>|,<·,·>Indicate inner product, argmaxQ=1 ... Q() indicates traversal q=1, and 2 ..., Q are obtained
The maximum value of (), then makes The l of representing matrix ψ rkRow；
(6.2.3) uses formulaUpdate matching matrix；
(6.2.4) solves optimization problem：2 norms of representing matrix use minimum two
Multiply solutionAs the solution of optimization problem, enableRepresenting matrix is inverted,It indicates
ПkHermit matrixes；
(6.2.5) enables k=k+1, updates residual error
(6.2.6) judges whether k ＞ K1, K1It is distance to degree of rarefication, if satisfied, then stopping iteration, if not satisfied, then holding
Row step (6.2.2).
(6.3) d=d+1 is enabled, judges whether d ＞ Na, if not satisfied, thening follow the steps (6.2).If satisfied, then stopping changing
Generation, by the Range Profile of the optimization waveform echo of all localizer unitsIt is combined by row, obtains target distance image matrixS1Dimension be Q × Na, matrix S1Line number indicate range cell number, columns indicate localizer unit
Number.
Step 7, orientation dictionary is generated：
(7.1) it carries out the one dimensional network in orientation to target to divide, meshes number P.
(7.2) by slow time tm, m=1,2 ..., M are arranged in column vector tm, find out and be possible to scatter on abscissa grid
The corresponding Doppler frequency value in point position Wherein
PRF is pulse recurrence frequency.
(7.3) willRespectively as dictionary matrix ψaThe 1st, 2 ..., P row, obtain dictionary matrix
ψa。
Step 8, target bearing is as sparse reconstruct：
(8.1) by Range Profile matrix S1Transposition is carried out, S is obtained2=(S1)T, ()TIndicate transposition, matrix S2Dimension be M ×
Q。
(8.2) use and same method in step (6.2), with S2As observing matrix, ψaAs dictionary matrix, use
Sparse reconstructing method obtains the orientation picture of each range cellIndicate range cell serial number.
(8.3) by the orientation picture of all range cellsIt is combined by row, obtains the matrix of echo reconstruct target two-dimensional imageS3Dimension is P × Q, matrix S3Line number indicate orientation, columns indicate distance to.
The present invention basic ideas be：On the basis of recognizing ISAR acquisition targets with environmental information, first with maximum
Believe that miscellaneous noise ratio criterion carries out waveform optimization, inhibit clutter and noise in space, improve the letter miscellaneous noise ratio of echo, then to optimization
Waveform echo into row distance to the sparse modeling of orientation, the distance of constitution optimization waveform echo to orientation dictionary, finally
ISAR two dimension high-resolution imagings are carried out to target by the method for sparse reconstruct to optimization waveform echo, obtain clutter with make an uproar
The good imaging results of target focusing effect under acoustic environment.
The technique effect of the present invention is verified and is illustrated by following emulation and its experimental result：
Embodiment 8
Cognition ISAR high-resolution imagings method based on maximum letter miscellaneous noise ratio criterion is the same as embodiment 1-7.
Simulation parameter
Using the radar for being operated in X-band, corresponding carrier frequency is 10GHz, bandwidth 1GHz, pulse recurrence frequency 1kHz,
Imaging accumulation angle is 0.075rad.The radical length of target is 15m, and tangential length is 18m, and target includes 274 scattering points, mesh
Mark is covered by panus, and clutter is generated by panus.
Emulation content
Emulation 1：It is equivalent that center is scattered to target, draws its equivalent scattering center distribution, as a result such as Fig. 2, Fig. 2 is mesh
Equivalent scattering center distribution map is marked, which is specially the satellite in space.
Emulation 2：Transmitting is not optimised transmitted waveform, and ISAR bis- is carried out to target shown in Fig. 2 under clutter and noise circumstance
Dimension imaging draws its range-azimuth to imaging results, as a result if Fig. 3, Fig. 3 are transmitted waveform ISAR two dimensions before the present invention optimizes
Imaging results figure.
From figure 3, it can be seen that under strong clutter and noise circumstance, using being not optimised echo that transmitted waveform obtains by miscellaneous
The influence of wave and noise cannot carry out ISAR two-dimensional imagings to target.
It can be obtained by Fig. 2 and Fig. 3 comparisons, under strong clutter and noise circumstance, be sent out as radar using transmitted waveform is not optimised
Signal is penetrated, ISAR two-dimensional imagings cannot be carried out to target, this is because target emits letter before in clutter and noise circumstance, optimizing
It number cannot inhibit the clutter in space and noise, cause echo letter miscellaneous noise ratio relatively low.
Embodiment 9
Cognition ISAR high-resolution imagings method based on maximum letter miscellaneous noise ratio criterion is the same as embodiment 1-7, emulation content and ginseng
Number is the same as embodiment 8.
Emulation 3：Fourier transformation is carried out to being not optimised transmitted waveform in emulation 2, its frequency spectrum is drawn, as a result such as Fig. 4, Fig. 4
It is the preceding transmitted waveform frequency spectrum of optimization.
Emulation 4：Fourier transformation is optimized and carried out to transmitted waveform, draws its frequency spectrum, and as a result such as Fig. 5, Fig. 5 is excellent
Transmitted waveform frequency spectrum after change.
Transmitted waveform frequency spectrum is almost constant in whole bandwidth before optimizing as can be seen from Figure 4, is not concentrated to clutter
Frequency band inhibited, not to where target response frequency band enhance, transmitted waveform is in clutter and noise ring before this optimization
The echo letter miscellaneous noise ratio obtained under border is relatively low.
Transmitted waveform frequency spectrum after optimizing as can be seen from Figure 5 is the frequency that clutter response is concentrated compared with the frequency band where low amplitude value
Band, can clutter reduction, frequency band where higher magnitude is the frequency band that target response is concentrated, and can enhance target echo, this hair
The echo letter miscellaneous noise ratio that transmitted waveform obtains under clutter and noise circumstance after bright this optimization is improved.
It can be obtained by Fig. 4 and Fig. 5 comparisons, the frequency spectrum for being not optimised transmitted waveform is similar to a constant in whole bandwidth, does not have
There is the effect of clutter reduction and noise.It is low that range value of the optimization waveform that the present invention obtains in whole bandwidth has height to have, and is taking
Target echo can be enhanced in the high frequency range of value, is improved the letter of echo with clutter reduction by being worth in low frequency range
Miscellaneous noise ratio.
Embodiment 10
Cognition ISAR high-resolution imagings method based on maximum letter miscellaneous noise ratio criterion is the same as embodiment 1-7, emulation content and ginseng
Number is the same as embodiment 8 and embodiment 9.
Emulation 5：ISAR imagings are carried out using traditional matched filtering method to optimizing waveform echo shown in Fig. 5, as a result such as
Fig. 6, Fig. 6 are the imaging results of existing matched filtering.
Emulation 6：ISAR imagings are carried out to optimizing waveform echo sparse reconstructing method using the present invention shown in Fig. 5, as a result
If Fig. 7, Fig. 7 are the imaging results of the sparse reconstruct of the present invention.
It can be obtained by Fig. 6 and Fig. 2 comparisons, although the ISAR of target can be obtained using traditional matched filtering to optimization waveform echo
Two-dimensional imaging as a result, but the obtained Range Profile of ISAR two dimensional images and orientation picture have the fuzzy existing of clarity and accuracy
As the focusing effect of target imaging result is poor, there is a problem of that image quality is poor.
It can be obtained by Fig. 7 and Fig. 2 comparisons, obtained target two dimensional image is reconstructed using the present invention is sparse to optimization waveform echo
Almost the same with the range-azimuth image of target Equivalent scattering center, image is more clear, and illustrates clutter reduction and figure of the present invention
Image focu effect is more preferable, and more accurately reconstruct can be carried out to target.
Although visual image can also tell certain clarity, the data of quantization can more reflect image comparison
Essence.It can be obtained by Fig. 6 and Fig. 7 comparisons, the ISAR imaging results image entropys that Fig. 6 matched filterings obtain are 1.84, and focusing effect is not
It is good, this is because the secondary lobe of optimization waveform is higher.The present invention is sparse, and the obtained ISAR imaging results image entropys that reconstruct are 0.114,
Focusing effect is improved.The target ISAR obtained by sparse reconstructing method is used to optimization waveform echo using what the present invention was carried
Two-dimensional imaging result has higher resolution ratio compared with the target ISAR two-dimensional imaging results that existing matched filtering method obtains,
Image is more clear, and clutter reduction and image focusing effect are more preferable.
In brief, a kind of ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion disclosed by the invention, it is main
Solve traditional ISAR echoes letter miscellaneous noise ratio is relatively low lead to that target ISAR cannot be imaged in the case of, can be through the invention
It realizes to target ISAR two dimension high-resolution imagings, obtains and focus good ISAR two dimensional images.Its scheme includes：1) pass through broadband
Linear FM signal obtains target and environmental information；2) based on maximum letter miscellaneous noise ratio criterion to the wide-band LFM of perception information
Signal carries out waveform optimization, and designs receiving filter；3) it uses optimization waveform as transmitted waveform, the echo received is believed
Number by receiving filter, optimization waveform echo is obtained；4) for the distance of constitution optimization waveform echo to dictionary matrix, use is sparse
The method of reconstruct is to the sparse reconstruct of target distance image；5) the orientation dictionary matrix of constitution optimization waveform echo, using sparse heavy
The method of structure, as sparse reconstruct, obtains the ISAR two-dimensional imaging results of target to target bearing.The present invention makes an uproar using maximum letter is miscellaneous
Optimize transmitted waveform than criterion, target sparse is reconstructed using optimization waveform echo, is realized under clutter and noise circumstance poly-
Burnt good high-resolution two dimension ISAR imagings, there is better clutter reduction.
Claims (6)
1. a kind of ISAR high-resolution imaging methods based on maximum letter miscellaneous noise ratio criterion, which is characterized in that including having the following steps：
(1) target and environmental information are perceived：Inverse Synthetic Aperture Radar perception target and the environmental information where target are recognized, is obtained
The frequency spectrum G (f), noise power spectral density P of target impact responsen(f), clutter power spectrum density Pc(f), wherein f indicates frequency；
(2) it is based on maximum letter miscellaneous noise ratio criterion and optimizes transmitted waveform：The environment and target information perceived using radar, according to
Maximum letter miscellaneous noise ratio criterion the wideband correlation of perception information is optimized, the transmitted waveform s after being optimized with
Receiving filter h；
(3) optimization waveform echo-signal is obtained：The transmitted waveform s after Inverse Synthetic Aperture Radar transmitting optimization is recognized, will be received
Echo-signal by receiving filter h, obtain optimization waveform echo matrix Sr, SrDimension is Nr×Na, NrFor range cell number,
NaFor localizer unit number；
(4) distance of optimization waveform echo is generated to dictionary：To optimization waveform echo into row distance to sparse modeling, optimization is generated
For the distance of waveform echo to dictionary, which is ψr, ψrDimension is Nr× Q, Q are by scene distance to right after discretization
The meshes number answered takes Q ＞ Nr, ψr=[sr1,…srq,…,srQ] it is that all scattering point positions correspond to optimization on ordinate grid
The matrix that waveform echo vector is constituted,The vector that optimization waveform echo is constituted is corresponded to for q-th of scattering point,
Dimension is Nr× 1, t are the fast time, and c is the light velocity, RqThe distance of q-th of scattering point, q ∈ [1, Q] are scattering point serial number；
(5) using optimization waveform echo matrix SrTo the sparse reconstruct of target distance image：First with the method for sparse reconstruct to single
The optimization waveform echo of localizer unit solves its Range Profile, and same method again carries out the optimization waveform echo of all localizer units
The sparse reconstruct of Range Profile, the Range Profile for gathering the optimization waveform echo of all localizer units obtain target distance image matrix S1；
(6) the orientation dictionary of optimization waveform echo is generated：The sparse modeling of orientation is carried out to optimization waveform echo, generates optimization
The orientation dictionary of waveform echo, the dictionary matrix are ψa, ψaDimension is Na× P, P are will be right after scene orientation discretization
The meshes number answered takes P ＞ Na,Optimization waveform is corresponded to by being possible to frequency point on Doppler's grid
The matrix that echo vector is constituted, p ∈ [1, P] they are frequency point serial number, whereinPRF
For pulse repetition period, tmFor the column vector lined up by the slow time；
(7) using the target distance image matrix S of optimization waveform echo reconstruct1To target bearing as sparse reconstruct：First with sparse
The method of reconstruct solves its orientation picture to the Range Profile of the single range cell of optimization waveform echo reconstruct, and same method is again to excellent
The Range Profile for changing all range cells of waveform echo reconstruct carries out orientation as sparse reconstruct, and set forms all range cells
Orientation picture obtains the target ISAR two dimension high-resolution imaging matrixes S of optimization waveform echo reconstruct3, complete to target based on most
The cognition ISAR high-resolution imagings of big letter miscellaneous noise ratio criterion.
2. the ISAR high-resolution imaging methods according to claim 1 based on maximum letter miscellaneous noise ratio criterion, which is characterized in that
Transmitted waveform after being optimized described in step (2) and receiving filter, including have the following steps：
(2a) finds out the frequency spectrum of optimal transmitted waveform, the frequency spectrum of optimal transmitted waveform with maximum letter miscellaneous noise ratio criterionWherein α1For the constant limited by transmitting signal energy；
(2b) is by the wideband correlation s of perception information0(t) it is fourier transformed into frequency domain, obtains S0(f), to S0(f) it presses
FormulaThe frequency spectrum S of radar emission waveform after being optimized is modulated into line amplitude1(f)；
(2c) finds out the frequency spectrum of receiving filter, the frequency spectrum of receiving filter with maximum letter miscellaneous noise ratio criterionα2For non-zero constant, symbol ()*Expression takes conjugation；
(2d) is by S1(f) inverse Fourier transform is to time domain, the radar emission waveform s after being optimized, by the frequency spectrum of receiving filter
H (f) inverse Fourier transforms obtain receiving filter h to time domain.
3. the ISAR high-resolution imaging methods according to claim 1 based on maximum letter miscellaneous noise ratio criterion, which is characterized in that
The distance of generation optimization waveform echo described in step (4) is to dictionary, including has the following steps：
(4a) is with distance interval Δ RrTarget is divided into the upward one dimensional network of row distance, meshes number Q；
(4b) using the radar emission waveform s after optimization as transmitted waveform, h obtains optimization waveform and returns as receiving filter
Wave；
Be possible to scattering point position on ordinate grid is corresponded to the vectorial s that optimization waveform echo is constituted by (4c)r1, sr2...,
srQRespectively as generating the distance of optimization waveform echo to dictionary matrix ψrThe 1st, 2 ..., Q row, obtain dictionary matrix ψr。
4. the ISAR high-resolution imaging methods according to claim 1 based on maximum letter miscellaneous noise ratio criterion, which is characterized in that
Use optimization waveform echo matrix S described in step (5)rTo the sparse reconstruct of target distance image, including have the following steps：
(5a) initializes first, and there are the localizer unit serial number d=1 of echo；
(5b) is rightIt is obtained using the method for sparse reconstruct To optimize waveform echo matrix SrTake localizer unit serial number d's
Echo,The Range Profile for being d for localizer unit；
(5c) enables d=d+1, judges whether d ＞ Na, if not satisfied, thening follow the steps (5b).If satisfied, then stopping iteration, by institute
There is the Range Profile of the optimization waveform echo of localizer unitIt is combined by row, obtains target distance image matrixS1Dimension be Q × Na, matrix S1Line number indicate range cell number, columns indicate localizer unit
Number.
5. the ISAR high-resolution imaging methods according to claim 1 based on maximum letter miscellaneous noise ratio criterion, which is characterized in that
The orientation dictionary of generation optimization waveform echo described in step (6), including have the following steps：
(6a) carries out the one dimensional network in orientation to target and divides, meshes number P；
(6b) is by slow time tm, m=1,2 ..., M are arranged in column vector tm, find out be possible to scattering point position on abscissa grid
Set corresponding Doppler frequency valueWhereinPRF is
Pulse recurrence frequency；
(6c) willRespectively as dictionary matrix ψaThe 1st, 2 ..., P row obtain optimization waveform echo
Orientation dictionary matrix ψa。
6. the ISAR high-resolution imaging methods according to claim 1 based on maximum letter miscellaneous noise ratio criterion, which is characterized in that
The target distance image matrix S of use optimization waveform echo reconstruct described in step (7)1To target bearing as sparse reconstruct, packet
It includes and has the following steps：
(7a) is by Range Profile matrix S1Transposition is carried out, S is obtained2=(S1)T, ()TExpression turns order, matrix S2Dimension is Na× Q, just
There are the range cell serial number b=1 of echo for beginningization first；
(7b) use and same method in step (5b), by ψaAs dictionary matrix, K2It is right as orientation degree of rareficationMake
It is obtained with the method for sparse reconstruct For Range Profile matrix S2It is the echo of b to take range cell,Range cell is the side of b
Position picture；
(7c) enables b=b+1, judges whether b ＞ Nr, if not satisfied, thening follow the steps (7b).If satisfied, then stopping iteration, by institute
There is the orientation picture of range cellIt is combined by row, obtains the target ISAR two dimension high-resolution imaging matrixes of optimization waveform echo reconstructS3Dimension is P × Q, matrix S3Line number indicate localizer unit, columns indicate range cell.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110471034A (en) * | 2019-09-19 | 2019-11-19 | 上海无线电设备研究所 | A kind of ULTRA-WIDEBAND RADAR waveform design method |
| CN111781598A (en) * | 2020-07-10 | 2020-10-16 | 西安电子科技大学 | DSN-based high-resolution two-dimensional ISAR imaging method |
| CN114594425A (en) * | 2022-03-14 | 2022-06-07 | 电子科技大学 | Clutter interference resistant short-time pulse train waveform design method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1727913A (en) * | 2004-07-26 | 2006-02-01 | 电子科技大学 | Method for compensating relative motion of mobile multiple objective for reverse synthetic aperture radar |
| US20130009807A1 (en) * | 2011-07-07 | 2013-01-10 | Lamb Brian M | Apparatus & Method for Short Dwell Inverse Synthetic Aperture Radar (ISAR) Imaging of Turning Moving Vehicles |
| CN106777679A (en) * | 2016-12-14 | 2017-05-31 | 中国人民解放军空军工程大学 | A kind of ISAR imaging radar resource-adaptive dispatching methods based on pulse interlacing |
| CN108008385A (en) * | 2017-11-20 | 2018-05-08 | 西安电子科技大学 | Interference environment ISAR high-resolution imaging methods based on management loading |
-
2018
- 2018-05-14 CN CN201810456090.3A patent/CN108562901B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1727913A (en) * | 2004-07-26 | 2006-02-01 | 电子科技大学 | Method for compensating relative motion of mobile multiple objective for reverse synthetic aperture radar |
| US20130009807A1 (en) * | 2011-07-07 | 2013-01-10 | Lamb Brian M | Apparatus & Method for Short Dwell Inverse Synthetic Aperture Radar (ISAR) Imaging of Turning Moving Vehicles |
| CN106777679A (en) * | 2016-12-14 | 2017-05-31 | 中国人民解放军空军工程大学 | A kind of ISAR imaging radar resource-adaptive dispatching methods based on pulse interlacing |
| CN108008385A (en) * | 2017-11-20 | 2018-05-08 | 西安电子科技大学 | Interference environment ISAR high-resolution imaging methods based on management loading |
Non-Patent Citations (2)
| Title |
|---|
| E.GIUSTI等: "Cognitive Multichannel ISAR Imaging for Maritime Coastal Surveillance and Ground B order Control", 《2015 IEEE 6TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP)》 * |
| 孟迪等: "基于脉冲交错的ISAR成像雷达资源自适应调度算法", 《空军工程大学学报（自然科学版）》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110471034A (en) * | 2019-09-19 | 2019-11-19 | 上海无线电设备研究所 | A kind of ULTRA-WIDEBAND RADAR waveform design method |
| CN110471034B (en) * | 2019-09-19 | 2021-08-31 | 上海无线电设备研究所 | Ultra-wideband radar waveform design method |
| CN111781598A (en) * | 2020-07-10 | 2020-10-16 | 西安电子科技大学 | DSN-based high-resolution two-dimensional ISAR imaging method |
| CN111781598B (en) * | 2020-07-10 | 2023-03-14 | 西安电子科技大学 | DSN-based high-resolution two-dimensional ISAR imaging method |
| CN114594425A (en) * | 2022-03-14 | 2022-06-07 | 电子科技大学 | Clutter interference resistant short-time pulse train waveform design method |
| CN114594425B (en) * | 2022-03-14 | 2023-05-16 | 电子科技大学 | Short-time pulse train waveform design method for resisting clutter interference |
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