CN105093224A - High squint synthetic aperture radar imaging processing method - Google Patents

High squint synthetic aperture radar imaging processing method Download PDF

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Publication number
CN105093224A
CN105093224A CN201510484427.8A CN201510484427A CN105093224A CN 105093224 A CN105093224 A CN 105093224A CN 201510484427 A CN201510484427 A CN 201510484427A CN 105093224 A CN105093224 A CN 105093224A
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orientation
distance
echoed signal
range
frequency
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武俊杰
孙稚超
杨建宇
黄钰林
杨海光
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University of Electronic Science and Technology of China
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University of Electronic Science and Technology of China
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S13/00Systems 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
    • G01S13/88Radar or analogous systems specially adapted for specific applications
    • G01S13/89Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/904SAR modes
    • G01S13/9041Squint mode
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S13/00Systems 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
    • G01S13/88Radar or analogous systems specially adapted for specific applications
    • G01S13/89Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/904SAR modes
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO 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
    • G01S7/00Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41Details 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
    • G01S7/411Identification of targets based on measurements of radar reflectivity

Abstract

The invention discloses a high squint synthetic aperture radar imaging processing method, which comprises the steps of S1, calculating high squint synthetic aperture radar echo signals; S2, carrying out range fast Fourier transformation (FFT) and spatial-variant range walk correction on the echo signals, and removing range spatial-variant range walk; S3, carrying out range pulse compression and high-order range migration correction on the echo signals, and removing residual range migration; S4, carrying out azimuth spatial-variant property fitting of Doppler parameters on the echo signals; S5, balancing the Doppler centroid and the slope of frequency modulation of target points of the same range cell by adopting an extended azimuth nonlinear frequency modulation scaling algorithm; and S6, carrying out azimuth focusing so as to acquire an imaging result. The high squint synthetic aperture radar imaging processing method solves a range spatial-variant property and an azimuth spatial-variant property of the Doppler parameters, can realize precise focusing of monostatic SAR echo signals under a high-squint-angle condition, and can be widely applied to the fields of earth remote sensing, resource exploration, geological mapping, military reconnaissance and the like.

Description

A kind of large scenedsmus obliquus image processing method
Technical field
The invention belongs to Radar Technology field, be specifically related to a kind of large scenedsmus obliquus image processing method.
Background technology
Synthetic-aperture radar (SAR) is a kind of round-the-clock, round-the-clock high-resolution imaging system, by launching the linear FM signal of large Timed automata, pulse compression signal is obtained through matched filtering during reception, to obtain distance to high resolving power, utilize synthetic aperture technique realize orientation to high resolving power.Image quality by impacts such as weather conditions (cloud layer, illumination), does not have the advantages that distant object to be carried out to detection and positioning.The typical application of SAR comprises disaster monitoring, resource exploration, geological mapping, military surveillance etc.
Compared with positive side-looking, the imaging of carried SAR stravismus has good dirigibility and maneuverability, by changing the sensing of wave beam, the scene objects information of carrier aircraft diagonally forward can be provided, utilize the strong correlation of RCS and angle of squint to identify man-made target, and repeatedly heavily can visit hot spot region.Therefore, the synthetic aperture radar image-forming under strabismus mode has wide application prospect.
Under stravismus mode of operation the center of antenna direction of radar not with distance to vertical, but in oblique relation, therefore, echoed signal exist distance to orientation to coupling, when angle of squint is larger, two-dimentional strong coupling can have a strong impact on image quality.In addition, under large slanting view angle machine pattern, echoed signal has the feature of large doppler centroid and large range walk.Therefore, the positive side-looking SAR imaging algorithm of tradition cannot directly apply in large slanting view angle machine SAR imaging.Range Doppler algorithm cannot process the strong two-dimentional coupled characteristic of large slanting view angle machine SAR echo data; Linear frequency modulation become mark algorithm (CS) have ignored secondary range compression with Doppler and distance to space-variant in azimuth; Omega-K algorithm needs interpolation operation consuming time in a large number just can focus on the echo data of large slanting view angle machine.Further, these algorithms all underuse the large range walk of large slanting view angle machine echoed signal and the bending feature of small distance.At document " Extendedchirpscalingalgorithmforair-andspaceborneSARdata processinginstripmapandScanSARimagingmodes " (" IEEETransactionsonGeoscienceandRemoteSensing ", vol.34, no.5, pp.1123 – 1136,1996.) in, adopt the spectral aliasing in expansion CS algorithm removal traditional C/S algorithm, carry out the echo-wave imaging of strabismus mode SAR.But, the orientation that the method have ignored secondary range compression item to distance to dependence, therefore can only be applied to angle of squint less when.At document " FocusImprovementofHighlySquintedDataBasedonAzimuthNonlin earScaling " (" IEEETransactionsonGeoscienceandRemoteSensing ", vol.49, no.6,2011), in, adopt stravismus to minimize and remove linear range unit migration.But stravismus Method for minimization make use of the range walk of reference point, have ignored the space-variant of range walk and remaining high-order range migration, cause range resolution loss and orientation to be deteriorated to focusing effect.Document " Extendednonlinearchirpscalingalgorithmforhigh-resolution highlysquintSARdatafocusing; " (" IEEETransactionsonGeoscienceandRemoteSensing ", vol.50, no.9, pp.3595 – 3609,2012.) in, adopt linear range migration correction factor (LRWC) to remove range walk, adopt orientation to carry out equilibrium to the non-linear CS of expansion to doppler frequency rate and cubic term subsequently.The orientation that the method have ignored Doppler frequency center on the impact of orientation to compression to space-variant, causes orientation and is deteriorated to focusing effect; In addition, the method have ignored the space-variant of range walk and remaining high-order range migration equally.Visible, said method all cannot carry out accurate imaging to the SAR echo signal under large strabismus mode.
Summary of the invention
The deficiency that the precision that the distance that the object of the invention is to overcome prior art range migration causes to the orientation of space-variant and Doppler parameter to space-variant is low, a kind of vernier focusing of single base SAR echo that can realize in large slanting view angle machine situation is provided, the large scenedsmus obliquus image processing method in the fields such as earth remote sensing, resource exploration, geological mapping, military surveillance can be widely used in.
The object of the invention is to be achieved through the following technical solutions: a kind of large scenedsmus obliquus image processing method, comprises the following steps:
S1: the space geometry structure setting up large scenedsmus obliquus, and according to space geometry structure and imaging region point target position, calculate large scenedsmus obliquus echoed signal;
S2: distance is carried out to FFT conversion and space-variant Range Walk Correction to echoed signal, removes the range walk of distance to space-variant of scene objects point;
S3: distance is carried out to pulse compression to the echoed signal after step S2 corrects and carries out high-order range migration correction, remove the range migration of scene objects point remnants;
S4: the orientation of Doppler parameter is carried out to space-variant matching to the echoed signal after step S3 corrects;
S5: adopt the orientation of expansion to Non-linear chirp scaling algorithm by the Doppler frequency center of the impact point of same range unit and chirp rate equilibrium;
S6: carry out orientation to focusing, obtain imaging results.
Further, described step S1 comprises following sub-step:
S11: large scenedsmus obliquus geometry and echo generate:
The initial position of Texas tower is designated as Q, and flying height is designated as h, and synthetic-aperture radar platform is designated as V along the flying speed of direction y, and platform angle of squint is designated as θ sT, P 0be the orientation of wave beam footprint to central point, P 0coordinate is designated as (x 0, y 0, 0) ,r 0for impact point P (x, y, 0) in orientation to the instantaneous oblique distance in beam center moment, y p=Vt 0p 0with the orientation of P to distance, t 0that the beam center of P point passes through the moment;
Sensor is at the distance R (t of t distance objective P; X, y) be:
R ( t ; x , y ) = r 0 2 + V 2 ( t - t 0 ) 2 - 2 r 0 V ( t - t 0 ) sinθ s T - - - ( 1 )
Wherein, t is that orientation is to time variable;
If transmit as linear FM signal, then the echoed signal after demodulation is expressed as:
s r ( t , τ ; x , y ) = A 0 ω r ( τ - 2 R ( t ; x , y ) c ) ω a ( t - t 0 T a ) exp [ - j 4 πf c R ( t ; x , y ) c ] × exp [ jπK r ( τ - 2 R ( t ; x , y ) c ) 2 ] - - - ( 2 )
Wherein, A 0the amplitude of scattering coefficient, ω r() for distance is to envelope, ω a() orientation is to envelope, and τ is fast time variable, f cbe carrier frequency, c is the light velocity, K rbe distance to frequency modulation rate, T ait is the synthetic aperture time;
S12: by the distance R (t of sensor at t distance objective P; X, y) to orientation to time t at t=t 0moment carries out three rank Taylor expansions:
R ( t ; x , y ) ≈ r 0 + A ( t - t 0 ) + B 2 ( t - t 0 ) 2 + C 6 ( t - t 0 ) 3 - - - ( 3 )
Wherein, A, B and C are expansion coefficient, (3) formula are arranged and obtain:
R ( t ; x , y ) ≈ r 0 ′ + A ′ t + B ′ 2 t 2 + C 6 t 3 - - - ( 4 )
Wherein, r 0 ′ = R ( 0 ; x , y ) = r 0 - At 0 + B 2 t 0 2 - C 6 t 0 3 , A ′ = A - Bt 0 + C 2 t 0 2 , B ′ = B - Ct 0 , R ( 0 ; x , y ) It is zero moment point target oblique distance.
Further, described step S2 comprises following sub-step:
S21: utilize FFT conversion and principle in phase bit by echoed signal s r(t, τ; X, y) transform to distance to frequency domain, echoed signal is expressed as in the phase place of distance frequency domain orientation time domain:
Wherein, f τthat distance is to frequency variable;
S22: time domain carries out slow time change in distance frequency domain orientation, and remove the range walk along distance to space-variant, the transformation for mula of slow time change is:
t = f c t m / ( f τ + f c ) - - - ( 6 )
Wherein, t mfor the orientation after conversion is to time variable.
Further, described step S3 comprises following sub-step:
S31: after the conversion in (6) formula, the phase place of echoed signal is expressed as:
Wherein, Section 1 be orientation to phase place, Section 2 is range migration, and Section 3 is that distance is to frequency modulation item; Can find out, the range walk of the space-variant after conversion completely eliminated;
S32: remaining high-order range migration is expressed as:
RCM r e s = B ′ t m 2 2 + Ct m 3 3 - - - ( 8 )
In addition, distance to chirp rate there occurs change, new distance is expressed as to chirp rate:
K ′ ( x , y ) = 1 1 K r + 2 B ′ ( x , y ) t m 2 cf c + 2 C ( x , y ) t m 3 cf c - - - ( 9 ) ;
S33: with scene center point P 0structure distance as a reference point is to pulse compression function:
ψ R c o m ( f τ ; x 0 , y 0 ) = exp { j π f τ 2 K ′ ( x 0 , y 0 ) } - - - ( 10 ) ;
S34: the echoed signal after formula (6) conversion is multiplied by ψ rcomobtain accurate distance to the echoed signal after pulse compression, remove the single order range migration in echoed signal;
S35: the echoed signal obtained step S34, is multiplied by following phase factor, to remove remaining high-order range migration:
ψ H R C M ( t m , f τ ) = exp { - j 4 π ( B ′ ( x 0 , y 0 ) t m 2 2 c + C ( x 0 , y 0 ) t m 3 3 c ) f τ } - - - ( 11 ) ;
S36: carry out distance and convert to IFFT, echo data is switched back to two-dimensional time-domain.
After high-order range migration correction, the different azimuth of same range unit to the Doppler parameter of impact point along orientation to space-variant, therefore need to carry out the orientation of Doppler parameter to space-variant matching to echoed signal, approximating method specifically comprises following sub-step:
S41: the Doppler frequency center of the impact point of same range unit and Doppler frequency modulation slope are expressed as the function of orientation to position, obtain:
f d c ( t 0 ) = 2 Vsinθ s T λ = 2 Vy 0 λ r 0 2 - ( Vt 0 + y 0 ) 2 + y 0 2 - - - ( 12 )
f d r ( t 0 ) = 2 V 2 cos 2 θ s T λr 0 = - 2 V 2 [ r 0 2 - ( Vt 0 + y 0 ) 2 ] λ [ r 0 2 - ( Vt 0 + y 0 ) 2 + y 0 2 ] 3 2 - - - ( 13 ) ;
S42: by Doppler frequency center and orientation from orientation to chirp rate to space-variant carry out single order matching and second-order fit respectively, obtain:
f dc=f dc0+at 0(14)
f d r = f d r 0 + bt 0 + dt 0 2 - - - ( 15 )
F dc0for the Doppler frequency center of reference point, f dr0for the Doppler frequency modulation slope of reference point, a, b and d are respectively fitting coefficient.
Because the echoed signal orientation under strabismus mode is comparatively large to barycenter, therefore adopt the orientation of expansion to Non-linear chirp scaling algorithm by the Doppler frequency center of the impact point of same range unit and chirp rate equilibrium; Specifically comprise following sub-step:
S51: to time domain, following frequency spectrum shift function is multiplied by phase of echo in orientation, the azimuth spectrum with reference to point moves to the center of reference pulse repetition frequency:
Ψ DC(t m)=exp{-j2πf dc0t m}(16);
S52: structure orientation is to four filter function ψ f(f a) and Non-linear chirp scaling function ψ nLCS(t m):
ψ F ( f a ) = exp { j π ( Y 3 f a 3 + Y 4 f a 4 ) } - - - ( 17 )
Ψ N L C S ( t m ) = exp { jπq 2 t m 2 + jπq 3 t m 3 + jπq 4 t m 4 } - - - ( 18 )
Wherein, Y 3, Y 4, q 2, q 3, q 4be respectively four filtering and non-linear CS coefficient, utilize the parameter f obtained in step S4 dc0, f dr0, a, b and d calculate:
q 2 = - 2 a α + ( 2 α - 1 ) f d r 0 , q 3 = L 3 ( f d r 0 - a ) 2 q 4 = M / 4 - ( f d r 0 - a ) f d r 0 3 q 2 Y 4 a - f d r 0 Y 3 = b ( 2 q 2 + a + f d r 0 ) - f d 3 ( a + q 2 ) 3 ( f d r 0 - a ) 2 q 2 f d r 0 Y 4 = M / 6 - N / 4 ( f d r 0 - a ) 2 q 2 f d r 0 2 ( q 2 + f d r 0 )
Wherein, α is for expansion orientation is to Non-linear chirp scaling parameter;
Wherein, L=2b (q 2+ a) (q 2+ f dr0)-f d3(a+q 2) 2-q 2[b (2q 2+ a+f dr0)-f d3(a+q 2)]
M=-[d(q 2+f dr0) 2-b 2(q 2+f dr0)]-3f d3b(a+q 2)+3Y 3q 2bf dr0(3f dr0q 2-2aq 2+f dr0a)-3q 3b(q 2-2f dr0+3a)
N = ( - 3 f d 3 b + 3 Y 3 q 2 bf d r 0 2 - 3 q 3 b ) ( a - f d r 0 ) ;
S53: the echoed signal after frequency spectrum shift is transformed to distance time domain orientation frequency domain through orientation to FFT;
S54: by the echoed signal quadruplication time filter function ψ of distance time domain orientation frequency f(f a);
S55: the signal obtained by step S55 carries out orientation and switches back to two-dimensional time-domain to IFFT;
S56: the echoed signal of two-dimensional time-domain is multiplied by Non-linear chirp scaling function ψ nLCS(t m).
Further, described step S6 comprises following sub-step:
S61: through expansion orientation after Non-linear chirp scaling, the orientation of echoed signal is expressed as to phase place:
φ a z N L C S ( f a ) = - π α t 0 f a - π f a 2 q 2 + f d r 0 + π ( f d 3 / 3 + Y 3 f d r 0 3 + q 3 ) f a 3 ( q 2 + f d r 0 ) 3 + π ( Y 4 f d r 0 4 + q 4 ) f a 4 ( q 2 + f d r 0 ) 4 - - - ( 19 ) ;
S62: be constructed as follows orientation to frequency domain reference function:
Ψ A C ( f a ) = exp { - jΦ a z N L C S ( f a ) - j π α t 0 f a } - - - ( 20 ) ;
S63: the echoed signal obtained by step S56 transforms to orientation to frequency domain by orientation to FFT;
S64: will orientation be transformed to the echoed signal of frequency domain and reference function ψ aC(f a) be multiplied;
S65: the signal obtained that is multiplied by step S64 transforms to two-dimensional time-domain by orientation IFFT, obtains the large scenedsmus obliquus image after focusing on.
The invention has the beneficial effects as follows: first remove the range walk of distance to space-variant by slow time change, then remaining high-order range migration correction method is proposed, finally, expansion orientation is used to Non-linear chirp scaling algorithm to by the Doppler parameter of orientation to space-variant; Compared with the SAR frequency domain imaging method of existing large slanting view angle machine list base, the distance solving range migration is to the orientation of space-variant and Doppler parameter to space-variant, the vernier focusing of the single base SAR echo in large slanting view angle machine situation can be realized, imaging precision is higher, can be widely used in the fields such as earth remote sensing, resource exploration, geological mapping, military surveillance.
Accompanying drawing explanation
Fig. 1 is geometrized structure graph of the present invention;
Fig. 2 is formation method calculation flow chart of the present invention;
Fig. 3 be in embodiments of the invention same range gate different azimuth to the two-dimensional time-domain echographic image of 5 impact points of position;
Fig. 4 is the two-dimensional time-domain echographic image of 1 impact point in Fig. 3;
Fig. 5 is the final imaging results of 2 points in embodiments of the invention.
Embodiment
Content of the present invention for convenience of description, first makes an explanation to following term:
Large Squint SAR: as shown in Figure 1, large Squint SAR refers to that the beam position of platform is the diagonally forward of carrier aircraft or oblique rear, belongs to large slanting view angle machine pattern when angle of squint is greater than 40 degree.
The present invention mainly adopts the method for emulation experiment to verify, institute in steps, conclusion all on Matlab2012 checking correct.Technical scheme of the present invention is further illustrated below in conjunction with the drawings and specific embodiments.
As shown in Figure 2, a kind of large scenedsmus obliquus image processing method, comprises the following steps:
S1: the space geometry structure setting up large scenedsmus obliquus, and according to space geometry structure and imaging region point target position, calculate large scenedsmus obliquus echoed signal; Specifically comprise following sub-step:
S11: large scenedsmus obliquus geometry and echo generate:
The initial position of Texas tower is designated as Q, and flying height is designated as h, and synthetic-aperture radar platform is designated as V along the flying speed of direction y, and platform angle of squint is designated as θ sT, P 0be the orientation of wave beam footprint to central point, P 0coordinate is designated as (x 0, y 0, 0), r 0for impact point P (x, y, 0) in orientation to the instantaneous oblique distance in beam center moment, y p=Vt 0p 0with the orientation of P to distance, t 0that the beam center of P point passes through the moment;
Sensor is at the distance R (t of t distance objective P; X, y) be:
R ( t ; x , y ) = r 0 2 + V 2 ( t - t 0 ) 2 - 2 r 0 V ( t - t 0 ) sinθ s T - - - ( 1 )
Wherein, t is that orientation is to time variable;
If transmit as linear FM signal, then the echoed signal after demodulation is expressed as:
s r ( t , τ ; x , y ) = A 0 ω r ( τ - 2 R ( t ; x , y ) c ) ω a ( t - t 0 T a ) exp [ - j 4 πf c R ( t ; x , y ) c ] × exp [ jπK r ( τ - 2 R ( t ; x , y ) c ) 2 ] - - - ( 2 )
Wherein, A 0it is the amplitude of scattering coefficient ,ω r() for distance is to envelope, ω a() orientation is to envelope, and τ is fast time variable, f cbe carrier frequency, c is the light velocity, K rbe distance to frequency modulation rate, T ait is the synthetic aperture time;
S12: by the distance R (t of sensor at t distance objective P; X, y) to orientation to time t at t=t 0moment carries out three rank Taylor expansions:
R ( t ; x , y ) ≈ r 0 + A ( t - t 0 ) + B 2 ( t - t 0 ) 2 + C 6 ( t - t 0 ) 3 - - - ( 3 )
Wherein, A, B and C are expansion coefficient, (3) formula are arranged and obtain:
R ( t ; x , y ) ≈ r 0 ′ + A ′ t + B ′ 2 t 2 + C 6 t 3 - - - ( 4 )
Wherein, r 0 ′ = R ( 0 ; x , y ) = r 0 - At 0 + B 2 t 0 2 - C 6 t 0 3 , A ′ = A - Bt 0 + C 2 t 0 2 , B ′ = B - Ct 0 , R ( 0 ; x , y ) It is zero moment point target oblique distance.
Simulation parameter shown in the present embodiment employing table one emulates.
Table one
S2: distance is carried out to FFT conversion and space-variant Range Walk Correction to echoed signal, removes the range walk of distance to space-variant of scene objects point; Specifically comprise following sub-step:
S21: utilize FFT conversion and principle in phase bit by echoed signal s r(t, τ; X, y) transform to distance to frequency domain, echoed signal is expressed as in the phase place of distance frequency domain orientation time domain:
Wherein, f τthat distance is to frequency variable;
S22: time domain carries out slow time change in distance frequency domain orientation, and remove the range walk along distance to space-variant, the transformation for mula of slow time change is:
t=f ct m/(f τ+f c)(6)
Wherein, t mfor the orientation after conversion is to time variable.
S3: distance is carried out to pulse compression to the echoed signal after step S2 corrects and carries out high-order range migration correction, remove the range migration of scene objects point remnants; Specifically comprise following sub-step:
S31: after the conversion in (6) formula, the phase place of echoed signal is expressed as:
Wherein, Section 1 be orientation to phase place, Section 2 is range migration, and Section 3 is that distance is to frequency modulation item; Can find out, the range walk of the space-variant after conversion completely eliminated;
S32: remaining high-order range migration is expressed as:
RCM r e s = B ′ t m 2 2 + Ct m 3 3 - - - ( 8 )
In addition, distance to chirp rate there occurs change, new distance is expressed as to chirp rate:
K ′ ( x , y ) = 1 1 K r + 2 B ′ ( x , y ) t m 2 cf c + 2 C ( x , y ) t m 3 cf c - - - ( 9 ) ;
S33: with scene center point P 0structure distance as a reference point is to pulse compression function:
ψ R c o m ( f τ ; x 0 , y 0 ) = exp { j π f τ 2 K ′ ( x 0 , y 0 ) } - - - ( 10 ) ;
S34: the echoed signal after formula (6) conversion is multiplied by ψ rcomobtain accurate distance to the echoed signal after pulse compression, remove the single order range migration in echoed signal;
S35: the echoed signal obtained step S34, is multiplied by following phase factor, to remove remaining high-order range migration:
ψ H R C M ( t m , f τ ) = exp { - j 4 π ( B ′ ( x 0 , y 0 ) t m 2 2 c + C ( x 0 , y 0 ) t m 3 3 c ) f τ } - - - ( 11 ) ;
S36: carry out distance and convert to IFFT, echo data is switched back to two-dimensional time-domain.
Fig. 3 is the two-dimensional time-domain echo data image after the present embodiment carries out high-order range migration correction, and be the two-dimensional time-domain echographic image of same range unit different azimuth to 5 impact points of position in Fig. 3, the coordinate of these 5 points is respectively: P 1(0,0) rice, P 2(-1029.5,500) rice, P 3(884.588 ,-500) rice, P 4(-493,250) rice, P 5(457.3 ,-250) rice; Wherein, P 2the two-dimensional time-domain echographic image that (-1029.5,500) put as shown in Figure 4.
S4: the orientation of Doppler parameter is carried out to space-variant matching to the echoed signal after step S3 corrects: after high-order range migration correction, the different azimuth of same range unit to the Doppler parameter of impact point along orientation to space-variant, therefore need to carry out the orientation of Doppler parameter to space-variant matching to echoed signal, approximating method specifically comprises following sub-step:
S41: the Doppler frequency center of the impact point of same range unit and Doppler frequency modulation slope are expressed as the function of orientation to position, obtain:
f d c ( t 0 ) = 2 Vsinθ s T λ = 2 Vy 0 λ r 0 2 - ( Vt 0 + y 0 ) 2 + y 0 2 - - - ( 12 )
f d r ( t 0 ) = 2 V 2 cos 2 θ s T λr 0 = - 2 V 2 [ r 0 2 - ( Vt 0 + y 0 ) 2 ] λ [ r 0 2 - ( Vt 0 + y 0 ) 2 + y 0 2 ] 3 2 - - - ( 13 ) ;
S42: by Doppler frequency center and orientation from orientation to chirp rate to space-variant carry out single order matching and second-order fit respectively, obtain:
f dc=f dc0+at 0(14)
f d r = f d r 0 + bt 0 + dt 0 2 - - - ( 15 )
F dc0for the Doppler frequency center of reference point, f dr0for the Doppler frequency modulation slope of reference point, a, b and d are respectively fitting coefficient.
S5: because the echoed signal orientation under strabismus mode is comparatively large to barycenter, therefore adopts the orientation of expansion to Non-linear chirp scaling algorithm by the Doppler frequency center of the impact point of same range unit and chirp rate equilibrium; Specifically comprise following sub-step:
S51: to time domain, following frequency spectrum shift function is multiplied by phase of echo in orientation, the azimuth spectrum with reference to point moves to the center of reference pulse repetition frequency:
Ψ DC(t m)=exp{-j2πf dc0t m}(16);
S52: structure orientation is to four filter function ψ f(f a) and Non-linear chirp scaling function ψ nLCS(t m):
ψ F ( f a ) = exp { j π ( Y 3 f a 3 + Y 4 f a 4 ) } - - - ( 17 )
Ψ N L C S ( t m ) = exp { jπq 2 t m 2 + jπq 3 t m 3 + jπq 4 t m 4 } - - - ( 18 )
Wherein, Y 3, Y 4, q 2, q 3, q 4be respectively four filtering and non-linear CS coefficient, utilize the parameter f obtained in step S4 dc0, f dr0, a, b and d calculate:
q 2 = - 2 a α + ( 2 α - 1 ) f d r 0 , q 3 = L 3 ( f d r 0 - a ) 2 q 4 = M / 4 - ( f d r 0 - a ) f d r 0 3 q 2 Y 4 a - f d r 0 Y 3 = b ( 2 q 2 + a + f d r 0 ) - f d 3 ( a + q 2 ) 3 ( f d r 0 - a ) 2 q 2 f d r 0 Y 4 = M / 6 - N / 4 ( f d r 0 - a ) 2 q 2 f d r 0 2 ( q 2 + f d r 0 )
Wherein, α is for expansion orientation is to Non-linear chirp scaling parameter, and in order to avoid orientation to distortion, the present embodiment gets α=0.5.
Wherein, L=2b (q 2+ a) (q 2+ f dr0)-f d3(a+q 2) 2-q 2[b (2q 2+ a+f dr0)-f d3(a+q 2)]
M=-[d(q 2+f dr0) 2-b 2(q 2+f dr0)]-3f d3b(a+q 2)+3Y 3q 2bf dr0(3f dr0q 2-2aq 2+f dr0a)-3q 3b(q 2-2f dr0+3a)
N = ( - 3 f d 3 b + 3 Y 3 q 2 bf d r 0 2 - 3 q 3 b ) ( a - f d r 0 ) ;
S53: the echoed signal after frequency spectrum shift is transformed to distance time domain orientation frequency domain through orientation to FFT;
S54: by the echoed signal quadruplication time filter function ψ of distance time domain orientation frequency f(f a);
S55: the signal obtained by step S55 carries out orientation and switches back to two-dimensional time-domain to IFFT;
S56: the echoed signal of two-dimensional time-domain is multiplied by Non-linear chirp scaling function ψ nLCS(t m).
S6: carry out orientation to focusing, obtain imaging results, specifically comprise following sub-step:
S61: through expansion orientation after Non-linear chirp scaling, the orientation of echoed signal is expressed as to phase place:
φ a z N L C S ( f a ) = - π α t 0 f a - π f a 2 q 2 + f d r 0 + π ( f d 3 / 3 + Y 3 f d r 0 3 + q 3 ) f a 3 ( q 2 + f d r 0 ) 3 + π ( Y 4 f d r 0 4 + q 4 ) f a 4 ( q 2 + f d r 0 ) 4 - - - ( 19 ) ;
S62: be constructed as follows orientation to frequency domain reference function:
Ψ A C ( f a ) = exp { - jΦ a z N L C S ( f a ) - j π α t 0 f a } - - - ( 20 ) ;
S63: the echoed signal obtained by step S56 transforms to orientation to frequency domain by orientation to FFT;
S64: will orientation be transformed to the echoed signal of frequency domain and reference function ψ aC(f a) be multiplied;
S65: the signal obtained that is multiplied by step S64 transforms to two-dimensional time-domain by orientation IFFT, obtains the large scenedsmus obliquus image after focusing on.Fig. 5 is P in the present embodiment 2, P 3the imaging results of point, wherein (a) is P 2point, (b) is P 3point.
As can be seen from Figure 5, the present invention can realize the vernier focusing of the single base SAR echo in large slanting view angle machine situation, obtains accurate radar image.
Those of ordinary skill in the art will appreciate that, embodiment described here is to help reader understanding's principle of the present invention, should be understood to that protection scope of the present invention is not limited to so special statement and embodiment.Those of ordinary skill in the art can make various other various concrete distortion and combination of not departing from essence of the present invention according to these technology enlightenment disclosed by the invention, and these distortion and combination are still in protection scope of the present invention.

Claims (7)

1. a large scenedsmus obliquus image processing method, is characterized in that, comprise the following steps:
S1: the space geometry structure setting up large scenedsmus obliquus, and according to space geometry structure and imaging region point target position, calculate large scenedsmus obliquus echoed signal;
S2: distance is carried out to FFT conversion and space-variant Range Walk Correction to echoed signal, removes the range walk of distance to space-variant of scene objects point;
S3: distance is carried out to pulse compression to the echoed signal after step S2 corrects and carries out high-order range migration correction, remove the range migration of scene objects point remnants;
S4: the orientation of Doppler parameter is carried out to space-variant matching to the echoed signal after step S3 corrects;
S5: adopt the orientation of expansion to Non-linear chirp scaling algorithm by the Doppler frequency center of the impact point of same range unit and chirp rate equilibrium;
S6: carry out orientation to focusing, obtain imaging results.
2. large scenedsmus obliquus image processing method according to claim 1, it is characterized in that, described step S1 comprises following sub-step:
S11: large scenedsmus obliquus geometry and echo generate:
The initial position of Texas tower is designated as Q, and flying height is designated as h, and synthetic-aperture radar platform is designated as V along the flying speed of direction y, and platform angle of squint is designated as θ sT, P 0be the orientation of wave beam footprint to central point, P 0coordinate is designated as (x 0, y 0, 0), r 0for impact point P (x, y, 0) in orientation to the instantaneous oblique distance in beam center moment, y p=Vt 0p 0with the orientation of P to distance, t 0that the beam center of P point passes through the moment;
Sensor is at the distance R (t of t distance objective P; X, y) be:
R ( t ; x , y ) = r 0 2 + V 2 ( t - t 0 ) 2 - 2 r 0 V ( t - t 0 ) sinθ s T - - - ( 1 )
Wherein, t is that orientation is to time variable;
If transmit as linear FM signal, then the echoed signal after demodulation is expressed as:
s r ( t , τ ; x , y ) = A 0 ω r ( τ - 2 R ( t ; x , y ) c ) ω a ( t - t 0 T a ) exp [ - j 4 πf c R ( t ; x , y ) c ] × exp [ jπK r ( τ - 2 R ( t ; x , y ) c ) 2 ] - - - ( 2 )
Wherein, A 0the amplitude of scattering coefficient, ω r() for distance is to envelope, ω a() orientation is to envelope, and τ is fast time variable, f cbe carrier frequency, c is the light velocity, K rbe distance to frequency modulation rate, T ait is the synthetic aperture time;
S12: by the distance R (t of sensor at t distance objective P; X, y) to orientation to time t at t=t 0moment carries out three rank Taylor expansions:
R ( t ; x , y ) ≈ r 0 + A ( t - t 0 ) + B 2 ( t - t 0 ) 2 + C 6 ( t - t 0 ) 3 - - - ( 3 )
Wherein, A, B and C are expansion coefficient, (3) formula are arranged and obtain:
R ( t ; x , y ) ≈ r 0 ′ + A ′ t + B ′ 2 t 2 + C 6 t 3 - - - ( 4 )
Wherein, r 0 ′ = R ( 0 ; x , y ) = r 0 - At 0 + B 2 t 0 2 - C 6 t 0 3 , A ′ = A - Bt 0 + C 2 t 0 2 , B '=B-Ct 0, R (0; X, y) be zero moment point target oblique distance.
3. large scenedsmus obliquus image processing method according to claim 2, it is characterized in that, described step S2 comprises following sub-step:
S21: utilize FFT conversion and principle in phase bit by echoed signal s r(t, τ; X, y) transform to distance to frequency domain, echoed signal is expressed as in the phase place of distance frequency domain orientation time domain:
Wherein, f τthat distance is to frequency variable;
S22: time domain carries out slow time change in distance frequency domain orientation, and remove the range walk along distance to space-variant, the transformation for mula of slow time change is:
t=f ct m/(f τ+f c)(6)
Wherein, t mfor the orientation after conversion is to time variable.
4. large scenedsmus obliquus image processing method according to claim 3, it is characterized in that, described step S3 comprises following sub-step:
S31: after the conversion in (6) formula, the phase place of echoed signal is expressed as:
Wherein, Section 1 be orientation to phase place, Section 2 is range migration, and Section 3 is that distance is to frequency modulation item;
S32: remaining high-order range migration is expressed as:
RCM r e s = B ′ t m 2 2 + Ct m 3 3 - - - ( 8 )
New distance is expressed as to chirp rate:
K ′ ( x , y ) = 1 1 K r + 2 B ′ ( x , y ) t m 2 cf c + 2 C ( x , y ) t m 3 cf c - - - ( 9 ) ;
S33: with scene center point P 0structure distance as a reference point is to pulse compression function:
ψ R c o m ( f τ ; x 0 , y 0 ) = exp { j π f τ 2 K ′ ( x 0 , y 0 ) } - - - ( 10 ) ;
S34: the echoed signal after formula (6) conversion is multiplied by ψ rcomobtain accurate distance to the echoed signal after pulse compression, remove the single order range migration in echoed signal;
S35: the echoed signal obtained step S34, is multiplied by following phase factor, to remove remaining high-order range migration:
ψ H R C M ( t m , f τ ) = exp { - j 4 π ( B ′ ( x 0 , y 0 ) t m 2 2 c + C ( x 0 , y 0 ) t m 3 3 c ) f τ } - - - ( 11 ) ;
S36: carry out distance and convert to IFFT, echo data is switched back to two-dimensional time-domain.
5. large scenedsmus obliquus image processing method according to claim 4, it is characterized in that, described step S4 comprises following sub-step:
S41: the Doppler frequency center of the impact point of same range unit and Doppler frequency modulation slope are expressed as the function of orientation to position, obtain:
f d c ( t 0 ) = 2 Vsinθ s T λ = 2 Vy 0 λ r 0 2 - ( Vt 0 + y 0 ) 2 + y 0 2 - - - ( 12 )
f d r ( t 0 ) = 2 V 2 cos 2 θ s T λr 0 = - 2 V 2 [ r 0 2 - ( Vt 0 + y 0 ) 2 ] λ [ r 0 2 - ( Vt 0 + y 0 ) 2 + y 0 2 ] 3 2 - - - ( 13 ) ;
S42: by Doppler frequency center and orientation from orientation to chirp rate to space-variant carry out single order matching and second-order fit respectively, obtain:
f dc=f dc0+at 0(14)
f d r = f d r 0 + bt 0 + dt 0 2 - - - ( 15 )
F dc0for the Doppler frequency center of reference point, f dr0for the Doppler frequency modulation slope of reference point, a, b and d are respectively fitting coefficient.
6. large scenedsmus obliquus image processing method according to claim 5, it is characterized in that, described step S5 comprises following sub-step:
S51: to time domain, following frequency spectrum shift function is multiplied by phase of echo in orientation, the azimuth spectrum with reference to point moves to the center of reference pulse repetition frequency:
Ψ DC(t m)=exp{-j2πf dc0t m}(16);
S52: structure orientation is to four filter function ψ f(f a) and Non-linear chirp scaling function ψ nLCS(t m):
ψ F ( f a ) = exp { j π ( Y 3 f a 3 + Y 4 f a 4 ) } - - - ( 17 )
ψ N L C S ( t m ) = exp { jπq 2 t m 2 + jπq 3 t m 3 + jπq 4 t m 4 } - - - ( 18 )
Wherein, Y 3, Y 4, q 2, q 3, q 4be respectively four filtering and non-linear CS coefficient, utilize the parameter f obtained in step S4 dc0, f dr0, a, b and d calculate:
q 2 = - 2 a α + ( 2 α - 1 ) f d r 0 , q 3 = L 3 ( f d r 0 - a ) 2 q 4 = M / 4 - ( f d r 0 - a ) f d r 0 3 q 2 Y 4 a - f d r 0 Y 3 = b ( 2 a 2 + a + f d r 0 ) - f d 3 ( a + q 2 ) 3 ( f d r 0 - a ) 2 q 2 f d r 0 Y 4 = M / 6 - N / 4 ( f d r 0 - a ) 2 q 2 f d r 0 2 ( q 2 + f d r 0 )
Wherein, α is for expansion orientation is to Non-linear chirp scaling parameter;
Wherein, L=2b (q 2+ a) (q 2+ f dr0)-f d3(a+q 2) 2-q 2[b (2q 2+ a+f dr0)-f d3(a+q 2)]
M=-[d(q 2+f dr0) 2-b 2(q 2+f dr0)]-3f d3b(a+q 2)
+3Y 3q 2bf dr0(3f dr0q 2-2aq 2+f dr0a)-3q 3b(q 2-2f dr0+3a)
N = ( - 3 f d 3 b + 3 Y 3 q 2 bf d r 0 2 - 3 q 3 b ) ( a - f d r 0 ) ;
S53: the echoed signal after frequency spectrum shift is transformed to distance time domain orientation frequency domain through orientation to FFT;
S54: by the echoed signal quadruplication time filter function ψ of distance time domain orientation frequency f(f a);
S55: the signal obtained by step S55 carries out orientation and switches back to two-dimensional time-domain to IFFT;
S56: the echoed signal of two-dimensional time-domain is multiplied by Non-linear chirp scaling function ψ nLCS(t m).
7. large scenedsmus obliquus image processing method according to claim 6, it is characterized in that, described step S6 comprises following sub-step:
S61: through expansion orientation after Non-linear chirp scaling, the orientation of echoed signal is expressed as to phase place:
Φ a z N L C S ( f a ) = - π α t 0 f a - π f a 2 q 2 + f d r 0 + π ( f d 3 / 3 + Y 3 f d r 0 3 + q 3 ) f a 3 ( q 2 + f d r 0 ) 3 + π ( Y 4 f d r 0 4 + q 4 ) f a 4 ( q 2 + f d r 0 ) 4 - - - ( 19 ) ;
S62: be constructed as follows orientation to frequency domain reference function:
Ψ A C ( f a ) = exp { - jΦ a z N L C S ( f a ) - j π α t 0 f a } - - - ( 20 ) ;
S63: the echoed signal obtained by step S56 transforms to orientation to frequency domain by orientation to FFT;
S64: will orientation be transformed to the echoed signal of frequency domain and reference function ψ aC(f a) be multiplied;
S65: the signal obtained that is multiplied by step S64 transforms to two-dimensional time-domain by orientation IFFT, obtains the large scenedsmus obliquus image after focusing on.
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