CN108445490A - ISAR imaging methods based on time domain back projection and particle group optimizing - Google Patents
ISAR imaging methods based on time domain back projection and particle group optimizing 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
- G01S13/9021—SAR image post-processing techniques
- G01S13/9029—SAR image post-processing techniques specially adapted for moving target detection within a single SAR image or within multiple SAR images taken at the same time
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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
- G01S13/9004—SAR image acquisition techniques
- G01S13/9017—SAR image acquisition techniques with time domain processing of the SAR signals in azimuth
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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
- G01S13/9064—Inverse SAR [ISAR]
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- Radar, Positioning & Navigation (AREA)
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- General Physics & Mathematics (AREA)
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- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a kind of ISAR imaging methods based on time domain back projection and particle group optimizing.It includes being compressed into row distance to pulse to ISAR two dimensions echo data, image field sight spot is chosen according to Two dimension normal distribution in the imaged scene, using the entropy of the imaging results at image field sight spot as object function, translation and the rotation multinomial coefficient that target is iteratively solved using particle cluster algorithm, are carried out time domain back projection imaging to target and obtain the target image of entire scene.The present invention can obtain the target image of high quality in the case where traditional algorithm can not work, highly practical.
Description
Technical field
The invention belongs to radar signal processing fields more particularly to a kind of based on time domain back projection and particle group optimizing
ISAR imaging methods.
Background technology
Inverse Synthetic Aperture Radar (ISAR) is a kind of high-resolution imaging radar different from conventional radar, can it is round-the-clock,
Round-the-clock, the precise image for obtaining non-cooperative moving targets (such as aircraft, naval vessel and guided missile) at a distance have important military
With civilian value.
There are a series of effective algorithms of maturation in the links of ISAR imagings for the Aircraft Targets of smooth flight,
In terms of envelope alignment, there is the fast correlation method based on frequency domain, method, complex envelope correlation are aligned apart from nearest sliding based on norm
Method, Minimum entropy method (Wang Genyuan protects the new method electronic letters, vols of envelope alignment in polished Inverse Synthetic Aperture Radar motion compensation,
Volume 26, the 6th phase, 1998,5-8), (amplitude of envelope alignment is related complete in Wang Kun, Luo Lin .ISAR imagings for global correlation method
Office advantest method electronics science academic periodicals, Vol.20, No.3,1998,369-373) and ultra-resolution method (Wang Kun, Luo Lin protect polished and answer
Inverse synthetic aperture radar imaging envelope alignment precision Xian Electronics Science and Technology University journal is improved with super resolution technology, Vol.24 increases
Periodical, 1997).In terms of self-focusing, there are Dan Texian points method, more special aobvious point synthesis (B.D.Steinberg.Microwave
Imaging of aircraft.Proc.IEEE, Vol.76, No.12,1988,1578-1592), scattering center of gravity method (it is protect polished,
A kind of motion compensation process electronic letters, vols in Deng Wenbiao, Yang Jun .ISAR imagings, Vol.20, No.6,1992,1), it is more
General Le center tracing etc..In terms of imaging, main lateral Fourier transformation is smaller to corner and be unsatisfactory for laterally differentiating and want
When asking, with ultra-resolution methods such as Burg extrapolations, RELAX.Instantaneous-doppler imaging side also based on Radon-Wigner transformation
Method, (Cheng Ping, Jiang Yicheng, Xu Rong celebrate and are based on adaptive chirplet the ISAR imaging algorithms decomposed based on adaptive Chirplet
Fast algorithm [J] the electronics of the ISAR instantaneous imagings of transformation and information journal, 2005,27 (12):1867-1871.), and solution
(a kind of Xi'an ISAR imaging research [J] the electricity of statistics RELAX methods of is waited in Zhang Long, Li Yachao, the Soviet Army sea to frequency modulation RELAX methods
Scarabaeidae skill college journal (natural science edition), 2010,37 (6):1065-1070.).
It is not very big that method listed above, which is mostly applied to target, and scattering point does not occur greatly in the synthetic aperture time
Range walk in the case of.But when target size is larger, or movement is more fierce, has larger rotative component, is synthesizing
When Range cell migration is more serious, most of frequency domain algorithm all will be unable to work well scattering point in aperture time.One
A little time frequency analysis algorithms are imaged merge again respectively by the way that the synthetic aperture time is divided into the shorter sub- synthetic aperture time
Method come handle such case (Wu Jie Time-frequency methods high-resolution ISAR motion compensation and imaging in application [D] in
Graduate school of the academy of sciences of state (space science and application study center), 2006.), but can also have algorithm complexity, imaging effect
The problems such as bad.
Invention content
The present invention goal of the invention be：In order to solve existing ISAR imaging methods can not it is larger in target size or
The problem of movement is more fierce, corner is larger, scattering point works when Range cell migration is more serious, the present invention carries
A kind of ISAR imaging methods based on time domain back projection and particle group optimizing are gone out.
The technical scheme is that：A kind of ISAR imaging methods based on time domain back projection and particle group optimizing, including
Following steps：
A, the ISAR two dimensions echo data of acquisition is compressed into row distance to pulse, is obtained compressed to pulse through distance
Echo-signal；
B, estimate the distance of target to size to the compressed echo-signal of pulse through distance according in step A, and at
Image field sight spot is chosen according to Two dimension normal distribution in image field scape；
C, it using the entropy of the imaging results at image field sight spot in step B as object function, is changed using particle cluster algorithm
In generation, solves, and obtains translation and the rotation multinomial coefficient of target under mount model；
D, according to the translation of the target obtained to the compressed echo-signal of pulse and step C through distance in step A with turn
Dynamic multinomial coefficient carries out time domain back projection imaging to target, obtains the target image of entire scene.
Further, it is expressed as to the compressed echo-signal of pulse through distance in the step A：
Wherein, Sr(ts,tf) for through distance, to the compressed echo-signal of pulse, λ is transmitting signal wavelength, and c is the light velocity, j
For imaginary unit.tsFor orientation slow time, tfIt is distance to fast time, AiFor the scattering strength of scattering point i, Ri(ts) it is scattering
For point i to radar apart from history, w () is sinc functions.
Further, the step B according in step A through distance to the compressed echo-signal of pulse estimate target away from
Descriscent size, and image field sight spot is chosen according to Two dimension normal distribution in the imaged scene, specifically include it is following step by step：
B1, in step through distance to extraction of each orientation moment of the compressed echo-signal of pulse target envelope simultaneously
Envelope size is calculated, according to the distance of envelope length estimate target to size；
B2, centered on scene center point, set target distribution range, N number of image field chosen using Two dimension normal distribution
Sight spot.
Further, the step C is used using the entropy of the imaging results at image field sight spot in step B as object function
Particle cluster algorithm is iterated solution, obtains translation and the rotation multinomial coefficient of target under mount model, specifically includes following
Step by step：
C1, according in step A through distance to the compressed echo-signal of pulse, be mount model by target kinematic configuration；
C2, it is a translation and a rotation by target Kinematic Decomposition, and builds the polynomial module of translation and rotation respectively
Type；
C3, using the entropy of the imaging results at image field sight spot in step B as object function, changed using particle cluster algorithm
In generation, solves, and obtains optimal translation and rotation multinomial coefficient.
Further, the step D is obtained through distance to the compressed echo-signal of pulse and step C according in step A
Target translation with rotation multinomial coefficient to target carry out time domain back projection imaging, obtain the target image of entire scene,
Specifically include it is following step by step：
D1, according in step A through distance to the compressed echo-signal of pulse, by image scene according to the imaging essence of setting
Degree is divided into a grid；
D2, translation and rotation multinomial coefficient using the obtained targets of step C, it is anti-to carry out time domain to each grid point
Projection imaging obtains the target image of entire scene.
The beneficial effects of the invention are as follows：By the present invention in that being made with the entropy for the part scene point imaging results being imaged based on BP
For object function, iteratively solve to obtain the optimal translation of target and rotation multinomial coefficient using particle cluster algorithm, further according to
The multinomial arrived is imaged entire image scene using BP algorithm, obtains the target image of high quality；The present invention can be
Traditional algorithm obtains the target image of high quality in the case of can not working, highly practical.
Description of the drawings
Fig. 1 is the flow diagram of the ISAR imaging methods based on time domain back projection and particle group optimizing of the present invention.
Fig. 2 is target scene schematic diagram in the embodiment of the present invention.
Fig. 3 is through distance in the embodiment of the present invention to the compressed echo-signal schematic diagram of pulse.
Fig. 4 is the schematic diagram for choosing N number of point in the embodiment of the present invention in the imaged scene using Two dimension normal distribution.
Fig. 5 is intermediate station model schematic of the embodiment of the present invention.
Fig. 6 is calculating schematic diagram of the scattering point apart from history under intermediate station model of the embodiment of the present invention.
Fig. 7 is time domain back projection imaging schematic diagram in the embodiment of the present invention.
Fig. 8 is finally obtained target image schematic diagram in the embodiment of the present invention.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.
As shown in Figure 1, the flow for the ISAR imaging methods based on time domain back projection and particle group optimizing of the present invention is shown
It is intended to.A kind of ISAR imaging methods based on time domain back projection and particle group optimizing, include the following steps：
A, the ISAR two dimensions echo data of acquisition is compressed into row distance to pulse, is obtained compressed to pulse through distance
Echo-signal；
B, estimate the distance of target to size to the compressed echo-signal of pulse through distance according in step A, and at
Image field sight spot is chosen according to Two dimension normal distribution in image field scape；
C, it using the entropy of the imaging results at image field sight spot in step B as object function, is changed using particle cluster algorithm
In generation, solves, and obtains translation and the rotation multinomial coefficient of target under mount model；
D, according to the translation of the target obtained to the compressed echo-signal of pulse and step C through distance in step A with turn
Dynamic multinomial coefficient carries out time domain back projection imaging to target, obtains the target image of entire scene.
In an alternate embodiment of the present invention where, image scene is radar and target about distance 10km, and target is with one
Determine velocity and acceleration to be moved.Radar transmitting wave uses pulsed linear FM signal, carrier frequency f in above-mentioned steps Ac=
10GHz, burst length tr=2 μ s, bandwidth B=200MHz, signal frequency modulation rate are Kr=B/tr=1014.The pulse of radar work
Frequency prf=2000, orientation sampling number ns=512, distance is to sample frequency fs=600MHz.After receiving echo
To echo data into row distance to FFT (Fast Fourier Transform (FFT)), be then multiplied to matched filter function in distance, most
IFFT (inverse fast Fourier transform) is carried out to the signal Jing Guo above-mentioned processing afterwards, to obtain being returned after distance is compressed to pulse
The signal forms of time and space of wave, is expressed as：
Wherein, Sr(ts,tf) for through distance, to the compressed echo-signal of pulse, λ is transmitting signal wavelength, λ=c/fc=
0.03m, c are the light velocity, and j is imaginary unit, tsFor orientation slow time, tfIt is distance to fast time, AiFor the scattering of scattering point i
Intensity determined by target scene, Ri(ts) be scattering point i to radar apart from history, w () is sinc functions.Such as Fig. 2 institutes
Show, is target scene schematic diagram in the embodiment of the present invention.
Matched filtering function is：
Wherein, frIt is distance to frequency, variation range isThat is [- 300300] MHz, j are imaginary unit.rect
(*) and exp (*) are respectively rectangular function and using e as the exponential function at bottom.As shown in figure 3, in the embodiment of the present invention through distance
To the compressed echo-signal schematic diagram of pulse.
In an alternate embodiment of the present invention where, above-mentioned steps B specifically include it is following step by step：
B1, in step through distance to extraction of each orientation moment of the compressed echo-signal of pulse target envelope simultaneously
Envelope size is calculated, according to the distance of envelope length estimate target to size；
The present invention according in step A through distance to the compressed echo-signal of pulse, extract target at each orientation moment
The width number n of envelope, obtain target each orientation moment distance to developed width l=nc/fs.Take wherein maximum value work
For target approximate size l0, in the present embodiment, calculate l0=107.6m.
B2, centered on scene center point, within the scope of setting target distribution ranging from its 3 σ, σ is its variance, using two
It ties up normal distribution and chooses N number of image field sight spot.
The present invention is with target approximate size l01.5~2 times as BP imaging scene sizes.One is established with image field
Centered on scape central point, 1/6th l0For the dimensional gaussian distribution of variance, with this Two dimension normal distribution on BP image scenes
Take N number of coordinate points P1~PN.Depending on hardware computing capability, the bigger final calculation results of N are more accurate but calculate speed for the selection of N
Degree is slower.General N needs to take 200 or more.As shown in figure 4, to use variances sigma=l in the embodiment of the present invention0The two of/6=17.9
The schematic diagram of N=200 point is chosen in dimension normal distribution in the imaged scene.Dash area is the target area of signal in figure, and
Non-genuine target.
In an alternate embodiment of the present invention where, above-mentioned steps C specifically include it is following step by step：
C1, according in step A through distance to the compressed echo-signal of pulse, be mount model by target kinematic configuration；
As shown in figure 5, being intermediate station model schematic of the embodiment of the present invention.
C2, it is a translation and a rotation by target Kinematic Decomposition, and builds the polynomial module of translation and rotation respectively
Type；
Target is moved to C from A and is decomposed into from A to B again to the process of C by the present invention；Wherein the movement of A to B to echo not
Then generating influences, can ignore；Target has carried out putting down in target and radar line in the motion process of B to C
A dynamic and rotation around the rotation of target center.Translation and rotation are modeled as two multinomials respectively：
θ (t)=θ0+a1t+a2t2+...+amtm
R0(t)=R0+b1t+b2t2+…+bntn
Wherein, θ0For initial angle, R0It is initial time target's center point at a distance from radar.aiAnd biIt is polynomial
Coefficient, t are run duration.M and n is two polynomial exponent numbers.
The present invention sets the translation to be iteratively solved, rotation polynomial order n and m according to hardware computing capability.Exponent number is got over
Height is ultimately imaged that result is more accurate but calculation amount is bigger.General translation polynomial order n takes 4 ranks or 4 ranks or more, rotates multinomial
Exponent number m takes 2 ranks or 2 ranks or more.At this point, translation can be expressed as above formula with rotation multinomial.Wherein, θ00, R can be taken0It indicates
Initial time target's center point can be estimated at a distance from radar from echo.R0Value can only influence to be ultimately imaged
When target apart from upward position, therefore only need to carry out a rough estimate.In the present embodiment, n takes 4, m to take 2, R0It takes
10km。
C3, using the entropy of the imaging results at image field sight spot in step B as object function, changed using particle cluster algorithm
In generation, solves, and obtains optimal translation and rotation multinomial coefficient.
Objective function of the present invention is：Input variable is vector v=[a that a length is m+n1 a2 … am b1 b2
… bn], the first m polynomial m term coefficient of target rotational for indicating estimation, the latter n target translation multinomial for indicating estimation
N term coefficients.Output valve is the entropy according to the multinomial coefficient of input to the BP imaging results for the N number of point chosen in S2, i.e.,piFor i-th point of BP imaging results of selection, it is assumed that i-th of point coordinates is (xi,yi), then it expresses
Formula is：
Wherein, tsIt is orientation slow time, TsIt is the synthetic aperture time,Pass through the echo obtained to S1
Specifically it is worth into row interpolation.Ri(t) it is to indicate point (x apart from history functioni,yi) to radar distance and time pass
System, expression formula are：
Wherein θ (t) and R0(t) it is determined with the multinomial coefficient provided in rotation multinomial and input variable according to translation.
As shown in fig. 6, for calculating schematic diagram of the scattering point apart from history under intermediate station model of the embodiment of the present invention.
Based on object function f (v) defined above, the present invention is iterated solution using particle cluster algorithm (PSO) and obtains
So that the input variable v of f (v) minimumsoptTo get to the optimal translation of target under mount model with rotation multinomial coefficient.It is preferred that
Ground, the present invention can also use other optimization algorithms such as genetic algorithm (GA).In the present embodiment, finally obtained so that f
(v) minimum input variable voptFor vopt=[0.014 2.7*10-4 -127.65 1.23 0.021 5.4*10-4]。
In an alternate embodiment of the present invention where, above-mentioned steps D specifically include it is following step by step：
D1, according in step A through distance to the compressed echo-signal of pulse, by image scene according to the imaging essence of setting
Degree is divided into a grid；
As shown in fig. 7, for time domain back projection imaging schematic diagram in the embodiment of the present invention.
D2, translation and rotation multinomial coefficient using the obtained targets of step C, it is anti-to carry out time domain to each grid point
Projection imaging obtains the target image of entire scene.
Assuming that i-th of point coordinates is (xi,yi), then imaging results piExpression formula can be obtained according to step C.Wherein distance
History function Ri(t) it is calculated by above formula, θ (t) therein and R0(t) it is obtained with rotation multinomial and step C according to translation
Multinomial coefficient determines.As shown in figure 8, for finally obtained target image schematic diagram in the embodiment of the present invention.
ISAR target motion modelings are first mount model by the present invention, and translation component is modeled as two with rotative component
Multinomial optimizes estimation to the two polynomial coefficients using particle cluster algorithm and solves, and using based on BP algorithm
Part image field sight spot coherent superposition result entropy as the valuation functions in particle cluster algorithm；It is asked when by particle cluster algorithm
Go out after the two polynomial coefficients, entire image scene is imaged to obtain high quality graphic using BP algorithm.Wherein,
Particle cluster algorithm can also be changed to other optimization algorithms such as genetic algorithm.
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.This field
Those of ordinary skill can make according to the technical disclosures disclosed by the invention various does not depart from the other each of essence of the invention
The specific variations and combinations of kind, these variations and combinations are still within the scope of the present invention.
Claims (5)
1. a kind of ISAR imaging methods based on time domain back projection and particle group optimizing, which is characterized in that include the following steps：
A, the ISAR two dimensions echo data of acquisition is compressed into row distance to pulse, is obtained through distance to the compressed echo of pulse
Signal；
B, the distance of target is estimated to size, and in image field to the compressed echo-signal of pulse through distance according in step A
Image field sight spot is chosen according to Two dimension normal distribution in scape；
C, it using the entropy of the imaging results at image field sight spot in step B as object function, is iterated and is asked using particle cluster algorithm
Solution obtains translation and the rotation multinomial coefficient of target under mount model；
D, more according to the translation of the target obtained to the compressed echo-signal of pulse and step C through distance in step A and rotation
Binomial coefficient carries out time domain back projection imaging to target, obtains the target image of entire scene.
2. the ISAR imaging methods based on time domain back projection and particle group optimizing as described in claim 1, which is characterized in that institute
It states in step A and is expressed as to the compressed echo-signal of pulse through distance：
Wherein, Sr(ts,tf) for through distance, to the compressed echo-signal of pulse, λ is transmitting signal wavelength, c is the light velocity, and j is void
Number unit.tsFor orientation slow time, tfIt is distance to fast time, AiFor the scattering strength of scattering point i, Ri(ts) it is scattering point i
To radar apart from history, w () is sinc functions.
3. the ISAR imaging methods based on time domain back projection and particle group optimizing as claimed in claim 2, which is characterized in that institute
It states step B and estimates the distance of target to size, and in image field to the compressed echo-signal of pulse through distance according in step A
Image field sight spot is chosen according to Two dimension normal distribution in scape, specifically include it is following step by step：
B1, target envelope is extracted to each orientation moment of the compressed echo-signal of pulse through distance in step and calculated
Envelope size, according to the distance of envelope length estimate target to size；
B2, centered on scene center point, set target distribution range, N number of image field sight spot chosen using Two dimension normal distribution.
4. the ISAR imaging methods based on time domain back projection and particle group optimizing as claimed in claim 3, which is characterized in that institute
Step C is stated using the entropy of the imaging results at image field sight spot in step B as object function, is iterated and is asked using particle cluster algorithm
Solution obtains the translation of target under mount model and rotation multinomial coefficient, specifically include it is following step by step：
C1, according in step A through distance to the compressed echo-signal of pulse, be mount model by target kinematic configuration；
C2, it is a translation and a rotation by target Kinematic Decomposition, and builds the multinomial model of translation and rotation respectively；
C3, using the entropy of the imaging results at image field sight spot in step B as object function, be iterated and asked using particle cluster algorithm
Solution obtains optimal translation and rotation multinomial coefficient.
5. the ISAR imaging methods based on time domain back projection and particle group optimizing as claimed in claim 4, which is characterized in that institute
It is more according to the translation and rotation of the target obtained to the compressed echo-signal of pulse and step C through distance in step A to state step D
Binomial coefficient to target carry out time domain back projection imaging, obtain the target image of entire scene, specifically include it is following step by step：
D1, according in step A through distance to the compressed echo-signal of pulse, image scene is drawn according to the imaging precision of setting
It is divided into a grid；
D2, translation and rotation multinomial coefficient using the obtained targets of step C, time domain back projection is carried out to each grid point
Imaging, obtains the target image of entire scene.
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| CN113310582A (en) * | 2020-02-27 | 2021-08-27 | 来达科技股份有限公司 | System and method for a deep thermal imaging module |
| CN113608191A (en) * | 2021-07-30 | 2021-11-05 | 上海无线电设备研究所 | Near-field target orientation identification method |
| CN114966693A (en) * | 2022-07-20 | 2022-08-30 | 南京信息工程大学 | Airborne ship target ISAR refined imaging method based on deep learning |
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