CN103278819A - Onboard high-resolution strabismus bunching synthetic aperture radar (SAR) imaging method based on sliding receiving window - Google Patents

Abstract

The invention discloses an onboard high-resolution strabismus bunching synthetic aperture radar (SAR) imaging method based on a sliding receiving window. The onboard high-resolution strabismus bunching SAR imaging method comprises the following steps of 1, reading original echo data and relevant imaging parameters; 2, performing azimuth linear frequency demodulation processing; 3, performing azimuth Fourier transformation processing; 4, performing azimuth linear frequency demodulation residual phase error compensation processing; 5, performing range Fourier transformation processing; 6, performing uniform compression processing; 7, performing stolt interpolation processing; 8, performing azimuth inverse Fourier transformation processing; 9, performing geometric correction processing; and 10, performing range Fourier transformation processing. The invention provides the onboard high-resolution strabismus bunching SAR imaging method based on the sliding receiving window in order to solve the problem that a method for imaging the onboard high-resolution strabismus bunching SAR original echo data based on a sliding receiving window technology does not exist at present.

Description

Airborne High-resolution stravismus Spotlight SAR Imaging formation method based on the slip receiver window

Technical field

The invention belongs to the signal process field, particularly a kind of Airborne High-resolution stravismus spot beam SAR synthetic-aperture radar (Synthetic Aperture Radar, SAR) formation method based on the slip receiver window.

Background technology

SAR is a kind of active remote sensing device that is operated in microwave frequency band, overcome the defective that optical imagery is limited by weather and illumination condition, can round-the-clock, round-the-clock, carry out remote sensing of the earth observation at a distance, and can penetrate natural vegetation, artificial camouflage etc., improved the information capture ability of radar greatly.Therefore, SAR has become the popular research field of Radar Technology, by more and more countries is paid attention to.Than traditional side-looking SAR system, the Airborne Squint SAR imaging has very high dirigibility and maneuverability in actual applications, and by the adjustment controlling antenna wave beam to point, but the observation area is selected on SAR system freedom and flexibility ground, and can heavily visit the sensitizing range fast, improve the observing capacity of SAR greatly.In addition, along with the raising of SAR resolution, make SAR target reconnaissance and recognition capability be significantly improved.Therefore, in the last few years, the imaging of high resolving power stravismus had become an important developing direction.

But the big Squint SAR imaging of high resolving power has also brought new technological difficulties.On the one hand, along with the increase of stravismus angle, SAR original echo orientation causes the high precision imaging more difficult to more serious to coupling phenomenon with distance; On the other hand, the range migration amount forms geometric growth with the increase of angle of squint and the increase of resolution, causes the remarkable increase of SAR original echo data volume, has strengthened the difficulty of data storage with real time imagery.Slip receiver window technology refers to open constantly by changing the echo window, eliminates once item, i.e. range walk in the range migration.Slip receiver window technology can reduce SAR original echo range migration amount, and then reduces SAR original echo data volume.But, the technological adjustment of slip receiver window the admission zero-time of echo data, in the observation area Doppler's course of each target also with the orientation to variation has taken place, make traditional formation method no longer suitable.

Summary of the invention

To the objective of the invention is in order addressing the above problem, based on slip receiver window technical characterstic, in conjunction with conventional wave number field formation method, to have proposed a kind of Airborne High-resolution stravismus Spotlight SAR Imaging formation method based on the slip receiver window.

A kind of Airborne High-resolution stravismus Spotlight SAR Imaging formation method based on the slip receiver window comprises following step:

Step 1: read in original echo data and dependent imaging parameter;

Read in the Airborne High-resolution stravismus Spotlight SAR Imaging two dimension original echo emulation complex data S based on the slip receiver window _StartAnd corresponding imaging parameters, specifically comprise: the orientation is to sampling number N _a, distance is to sampling number N _r, signal sampling rate f _s, signal bandwidth Bw, pulse width τ, chirp rate b, pulse repetition rate PRF is with reference to oblique distance R _Ref, doppler centroid fd ₀, doppler frequency rate f _R0, satellite velocities P _v, equivalent angle of squint Signal wavelength lambda, signal carrier frequency f ₀, signal velocity c;

Step 2: the orientation is handled to separating linear frequency modulation;

With two-dimentional echo simulation complex data S _StartCarrying out the orientation handles to separating linear frequency modulation: complex data S at first _StartWith factor H ₁Multiply each other, obtain complex data S _{1_1}Secondly to complex data S _{1_1}Do the orientation to Fourier transform, namely carry out fast Fourier along each distance to (by row) and change (FFT), obtain complex data S _{1_2}At last with complex data S _{1_2}With factor H ₂Multiply each other, obtain final orientation to the complex data S that separates behind the linear frequency modulation ₁

Step 3: the orientation is handled to Fourier transform;

The complex data S that step 2 is obtained ₁Carry out Fast Fourier Transform (FFT) (FFT) along each distance to (by row), obtain the orientation to frequency spectrum complex data S ₂

Step 4: the orientation is handled to separating linear frequency modulation residual phase error compensation;

The complex data S that step 3 is obtained ₂With orientation constantly, corresponding orientation to separating linear frequency modulation residual phase error compensation factor Ω ₁Multiply each other the complex data S after being compensated ₃

Step 5: distance is handled to Fourier transform;

The complex data S that step 4 is obtained ₃(by row) carries out Fast Fourier Transform (FFT) (FFT) along each orientation constantly, obtains 2-d spectrum complex data S ₄

Step 6: consistently compress processing;

The complex data S that step 5 is obtained ₄With corresponding consistent compressibility factor Ω ₂Multiply each other, slightly focused on complex data S ₅

Step 7: Stolt (stolt) interpolation processing;

The complex data S that obtains for step 6 ₅, utilize Singh (sinc) method of interpolation to carry out the stolt interpolation processing, obtain being mapped to by two-dimensional frequency the complex data S of two-dimentional wavenumber domain ₆

Step 8: the orientation is handled to inverse Fourier transform;

The complex data S that step 7 is obtained ₆Carry out inverse fast Fourier transform (IFFT) along each distance to (by row), obtain the orientation time domain apart from wavenumber domain complex data S ₇

Step 9: geometry correction is handled;

The complex data S that step 8 is obtained ₇With geometry correction factor Ω ₃Multiply each other, obtain the complex data S after geometry correction ₈

Step 10: distance is handled to Fourier transform;

The complex data S that step 9 is obtained ₉(by row) carries out inverse fast Fourier transform (IFFT) along each orientation constantly, obtains final imaging results S _End

The invention has the advantages that:

(1) the present invention proposes a kind of Airborne High-resolution stravismus Spotlight SAR Imaging formation method based on the slip receiver window, solved the present situation that does not have formation method at present based on the Airborne High-resolution stravismus Spotlight SAR Imaging original echo data of slip receiver window technology.

(2) the present invention proposes a kind of Airborne High-resolution stravismus Spotlight SAR Imaging formation method based on the slip receiver window, have the characteristics of high precision focal imaging.Because the formation method that the present invention proposes is a kind of improved wavenumber domain formation method, and the advantage of wavenumber domain formation method is, as long as satisfy this condition of constant airspeed (carried SAR satisfies this condition just), just can realize that high precision focuses on.Therefore, utilize the present invention can realize scene high precision focal imaging.

(3) the present invention proposes a kind of Airborne High-resolution stravismus Spotlight SAR Imaging formation method based on the slip receiver window, have characteristic of strong applicability.On the one hand, because the formation method that the present invention proposes is a kind of improved wavenumber domain formation method, and the wavenumber domain formation method is not looked side ways the restriction of angle, therefore, under the very big condition of stravismus angle, the present invention can realize the vernier focusing of scene equally, obtains high-quality SAR image.On the other hand, the present invention can realize the imaging of 0.1m ultrahigh resolution, and obtains vernier focusing, and therefore, the present invention is applicable to the imaging requirements of present various resolution.

Description of drawings

Fig. 1 is a kind of Airborne High-resolution stravismus Spotlight SAR Imaging formation method process flow diagram based on the slip receiver window that the present invention proposes;

Fig. 2 is embodiment simulating scenes synoptic diagram;

Fig. 3 is the embodiment imaging results;

Fig. 4 is the upper left point target sectional view of embodiment;

Fig. 5 is embodiment intermediate point object profile figure;

Fig. 6 is embodiment lower-right most point object profile figure.

Embodiment

The present invention is described in further detail below in conjunction with drawings and Examples.

The present invention proposes a kind of airborne resolution stravismus Spotlight SAR Imaging formation method based on the slip receiver window, and the object of processing is based on the Airborne High-resolution stravismus Spotlight SAR Imaging original echo data of slip receiver window, and the result who obtains is a panel height resolution oblique view picture.

The slip receiver window refers to SAR when receiving echo, and the echo receiver window is opened constantly and constantly changed with the orientation, thereby reduces the range migration of target, eliminates the range walk (linear segment in the range migration) of target in other words.The SAR system of employing slip receiver window is each orientation echo window unlatching constantly moment T'(i at work) be:

$T^{\&prime;}\left(i\right)=T_0-\frac{\mathrm{\&lambda;}\&CenterDot;{\mathrm{<figure-callout\; id="0"\; label="fd"\; filenames="HDA00003160526000031.png,HDA00003160526000032.png"\; state="\{\{state\}\}">fd</figure-callout>}}_0\&CenterDot;t\left(i\right)}{c}---\left(1\right)$

Wherein, T ₀Be traditional SAR system's fixed echo window unlatching moment under the same terms, fd ₀Refer to doppler centroid, λ refers to signal wavelength, and c refers to signal velocity, and t (i) refers to the orientation constantly, and , i=0,1,2 ..., N _a-1.Slip receiver window technology has reduced the range migration amount of echo, and then has reduced the echo data amount, has alleviated the pressure of data storage and real time imagery.

The present invention is a kind of Airborne High-resolution stravismus Spotlight SAR Imaging formation method based on the slip receiver window, and idiographic flow may further comprise the steps as shown in Figure 1:

Step 1: read in the Airborne High-resolution stravismus Spotlight SAR Imaging two dimension original echo complex data S based on the slip receiver window _StartAnd corresponding imaging parameters.Wherein, S _StartBe a two-dimentional plural groups, size is N _a* N _r, and the imaging parameter specifically comprises: the orientation is to sampling number N _a, the distance to sampling number N _r, signal sampling rate f _s, signal bandwidth Bw, chirp rate b, pulse repetition rate PRF, with reference to oblique distance R _Ref, doppler centroid fd ₀, doppler frequency rate f _R0, satellite velocities P _v, equivalent angle of squint

, signal wavelength lambda, signal carrier frequency f ₀, signal velocity c;

Step 2: with two-dimentional original echo complex data S _StartCarry out the orientation and handle to separating linear frequency modulation, specifically can be divided into following step:

(a) two one-dimensional sequence i of structure, j, wherein i represents the orientation to sequence (OK), and j represents distance to sequence (row);

i＝[1,2,…,N _a] （2）

j＝[1,2,…,N _r]

(b) obtain two-dimentional original echo complex data S _StartThe orientation moment t (i) that each row is corresponding;

$t\left(i\right)=\frac{i-N_a/2}{\mathrm{PRF}}---\left(3\right)$

(c) with two-dimentional original echo complex data S _StartWith removing twiddle factor H ₁Multiply each other, obtain data S _{1_1}(i, j), factor H wherein ₁(i) be that size is N _a* 1 one dimension plural groups, formula is:

H ₁(i)＝exp{jπ(fr ₀·t ²(i)+2·fd ₀·t(i))} （4）

Two-dimentional plural groups S then _{1_1}Can be drawn by following formula:

S _{1_1}(i,j)＝S _start(i,j)·H ₁(i) （5）

(d) to complex data S _{1_1}Carry out fast Fourier along each distance to (by row) and change (FFT), obtain complex data S _{1_2}

S _{1_2}(:,j)＝FFT(S _{1_1}(:,j)) （6）

Wherein, S _{1_2}(:, j) expression S _{1_2}N row, S _{1_1}(:, j) expression S _{1_1}N row, FFT () expression is carried out Fast Fourier Transform (FFT) to one-dimension array.

(e) obtain postrotational equivalent pulse repetition frequency PRF';

${\mathrm{PRF}}^{\&prime;}=\frac{N_r\&CenterDot;{\mathrm{fr}}_0}{\mathrm{PRF}}---\left(7\right)$

(g) convolution (7) obtains the corresponding orientation of the every row of two-dimentional complex data t constantly ₁(i);

$t_1\left(i\right)=\frac{i-N_a/2}{{\mathrm{PRF}}^{\&prime;}}---\left(8\right)$

(h) with complex data S _{1_2}With factor H ₂Multiply each other, obtain final orientation to the data S that separates behind the linear frequency modulation ₁Factor H wherein ₂(i) be that size is N _a* 1 one dimension plural groups, its formula is:

$H_2\left(i\right)=\exp \{\mathrm{j\&pi;}\&CenterDot;{\mathrm{fr}}_0\&CenterDot;t_1^2\left(i\right)\}---\left(9\right)$

Two-dimentional plural groups S then ₁Can be drawn by following formula:

S ₁(i,j)＝S _{1_2}(i,j)·H ₂(i) （10）

Step 3: the complex data S that step 2 is obtained ₁(i j) carries out Fast Fourier Transform (FFT) (FFT) along each distance to (by row), obtains the orientation to frequency spectrum complex data S ₂(i, j);

S ₂(:,j)＝FFT(S ₁(:,j)) （11）

Wherein, S ₂(:, j) expression S ₂J row, S ₁(:, j) expression S ₁J row, FFT () expression is carried out Fast Fourier Transform (FFT) to one-dimension array.

Step 4: the complex data S that step 3 is obtained ₂(i, j) with orientation constantly, corresponding orientation to separating linear frequency modulation residual phase error compensation factor Ω ₁(i) multiply each other the complex data S after being compensated ₃(i, j);

(a) convolution (7) obtain two-dimentional orientation to frequency domain distance to time domain complex data S ₂(i, j) the corresponding orientation frequency f of every row _a(i);

$f_a\left(i\right)=\frac{i-N_a/2}{N_a}\&CenterDot;{\mathrm{PRF}}^{\&prime;}---\left(12\right)$

(b) convolution (12) obtains the big or small N that is _a* 1 one dimension compensating factor Ω ₁(i);

${\mathrm{\&Omega;}}_1\left(i\right)=\exp \{\mathrm{j\&pi;}\&CenterDot;\frac{f_a^2\left(i\right)}{{\mathrm{fr}}_0}\}---\left(13\right)$

(c) obtain two-dimentional complex data S after the compensation ₃(i, j);

S ₃(i,j)＝S ₂(i,j)·Ω ₁(i) （14）

Step 5: the complex data S that step 4 is obtained ₃(i, j) (by row) carries out Fast Fourier Transform (FFT) (FFT) along each orientation constantly, obtains 2-d spectrum complex data S ₄(i, j);

S ₄(i,:)＝FFT(S ₃(i,:)) （15）

Wherein, S ₃(i :) expression S ₃I capable, S ₄(i :) expression S ₄I capable.

Step 6: the complex data S that step 5 is obtained ₄(i is j) with corresponding consistent compressibility factor Ω ₂(i j) multiplies each other, and is slightly focused on complex data S ₅(i, j).

(a) according to reference oblique distance R _RefObtain the shortest oblique distance R _Min

$R_{\min }=R_{\mathrm{ref}}-\frac{c}{{2f}_s}\&CenterDot;\frac{N_r}{2}---\left(16\right)$

(b) obtain two-dimensional frequency complex data S ₄(i, j) every row correspondence apart from frequency domain f _τ(j);

$f_{\mathrm{\&tau;}}\left(j\right)=\frac{j-N_r/2}{\mathrm{Nr}}\&CenterDot;f_s---\left(17\right)$

(c) convolution (12) obtains two-dimensional frequency complex data S with formula (17) ₄(i, j) orientation of every row correspondence is to wave number k _x(i) corresponding with every row distance is to wave number k _Rc(j);

$k_x\left(i\right)=\frac{2\mathrm{\&pi;}{f}_a\left(i\right)}{P_v}$ $\left(18\right)$

$k_{\mathrm{rc}}\left(j\right)=\frac{4\mathrm{\&pi;}({\mathrm{<figure-callout\; id="0"\; label="f"\; filenames="HDA00003160526000031.png,HDA00003160526000032.png"\; state="\{\{state\}\}">f</figure-callout>}}_0+f_{\mathrm{\&tau;}}\left(j\right))}{c}$

(d) convolution (16)～formula (18) is obtained the big or small N that is _a* N _rTwo-dimentional consistent compressibility factor Ω ₂(i, j);

(e) convolution (19) obtains the two-dimentional complex data S after consistent the compression ₅(i, j);

S ₅(i,j)＝S ₄(i,j)·Ω ₂(i,j) （20）

Step 7: the complex data S that obtains for step 6 ₅(i j), utilizes the sinc method of interpolation to carry out the stolt interpolation processing, obtains being mapped to by two-dimensional frequency the complex data S of two-dimentional wavenumber domain ₆(i, j);

(a) according to the distance frequency domain to apart from the mapping relations of wavenumber domain, convolution (18) obtains apart from wavenumber domain wave number k' _Rc(i, j);

(b) traversal is obtained apart from wavenumber domain wave number k' _Rc(i, maximal value k' j) _{Rc, max}With minimum value k' _{Rc, max}, and obtain apart from branch interval delta k' such as wavenumber domain wave numbers _Rc

$\mathrm{\&Delta;}{k}_{\mathrm{rc}}^{\&prime;}=\frac{k_{\mathrm{rc},\max }^{\&prime;}-k_{\mathrm{rc},\min }^{\&prime;}}{N_r}---\left(22\right)$

(c) obtain two-dimentional wavenumber domain data uniformly apart from the wavenumber domain wave number

$k_{\mathrm{rc}}^e\left(j\right)=k_{\mathrm{rc}}^{\&prime;}\min +j\&CenterDot;{\mathrm{\&Delta;k}}_{\mathrm{rc}}^{\&prime;}---\left(23\right)$

(d) obtain two-dimentional wavenumber domain complex data each uniformly apart from the wavenumber domain wave number

At the corresponding inhomogeneous k' of every row _RcPosition p in (i :) (i, j).

Method is for carrying out following operation by row: be example with the position p (1,1) that obtains first row, first row, at first obtain absolute difference

N=[1,2 ..., N _r], obtain minimum absolute difference Δ k _MinWith the n of correspondence position, if $k_{\mathrm{rc}}^e\left(1\right)<k_{\mathrm{rc}}^{\&prime;}(1,n),$ P (1,1)=n-1, if $k_{\mathrm{rc}}^e\left(1\right)\&GreaterEqual;k_{\mathrm{rc}}^{\&prime;}(1,n),$ p(1,1)＝n，

By that analogy, obtain each position p (i, j).

(e) the position p that obtained in conjunction with the last step (i, j), obtain the required sampled point of sinc interpolation at interval q (i, j, n);

$q(i,j,n)=\frac{k_{\mathrm{rc}}^e\left(j\right)-k_{\mathrm{rc}}^{\&prime;}(i,(p(i,j)+n))}{\frac{4\mathrm{\&pi;}{f}_s}{c}\&CenterDot;\frac{1}{N_r}},$ n＝[-N/2,-N/2+1,…,N/2-1] （24）

(f) convolution (24) utilizes the sinc method of interpolation, obtains out the 2-D data S after the stolt interpolation ₆, because 2-D data is complex data, need respectively to S ₆(i, real part S j) _{6_re}(i is j) with imaginary part S _{6_im}(i j) carries out the sinc method of interpolation respectively and obtains and draw.

Wherein, N is interpolation kernel length, and sinc () refers to interpolating function

S _{5_re}(i j) refers to 2-D data S _{5_re}The real part of the capable j row of i, S _{5_im}(i j) refers to 2-D data S _{5_im}The imaginary part of the capable j row of i.

Step 8: the complex data S that step 7 is obtained ₆(i j) carries out inverse fast Fourier transform (IFFT) along each distance to (by row), obtains the orientation time domain apart from wavenumber domain complex data S ₇(i, j);

S ₇(:,j)＝IFFT(S ₆(:,j)) （27）

Wherein, S ₆(:, j) expression S ₆J row, S ₇(:, j) expression S ₇J row, IFFT () expression is carried out inverse fast Fourier transform to one-dimension array.

Step 9: the complex data S that step 8 is obtained ₇(i is j) with geometry correction factor Ω ₃(i j) multiplies each other, and obtains the complex data S after geometry correction ₈(i, j);

(a) convolution (8) and formula (12) obtain geometry correction factor Ω ₄(i, j);

${\mathrm{\&Omega;}}_4(i,j)=\exp \{-j2\mathrm{\&pi;}\&CenterDot;f_a\left(i\right)\&CenterDot;\frac{\mathrm{\&lambda;}\&CenterDot;{\mathrm{<figure-callout\; id="0"\; label="fd"\; filenames="HDA00003160526000031.png,HDA00003160526000032.png"\; state="\{\{state\}\}">fd</figure-callout>}}_0\&CenterDot;t_1\left(i\right)}{c}\}---\left(28\right)$

(b) utilize formula (30) to obtain complex data S after geometry correction ₈(i, j);

S ₈(i,j)＝S ₇(i,j)·Ω ₃(i,j) （29）

Step 10: the complex data S that step 9 is obtained ₈(i, j) (by row) carries out inverse fast Fourier transform (IFFT) along each orientation constantly, obtains final imaging results S _End(i, j);

S _end(i,:)＝IFFT(S ₈(i,:)) （30）

Wherein, S ₈(i :) expression S ₈I capable, S _End(i :) expression S _EndI capable.

Embodiment:

Present embodiment proposes a kind of Airborne High-resolution stravismus Spotlight SAR Imaging formation method based on the slip receiver window, simulating scenes is 3 * 3 dot matrix, its point and the 100m that is spaced apart that puts, finally respectively upper left in the simulating scenes, middle, three points in bottom right are assessed, concrete simulating scenes as shown in Figure 2, the imaging parameters that relates in its imaging process is as shown in table 1.

Table 1 embodiment parameter

Present embodiment specifically may further comprise the steps:

Step 1: read in the Airborne High-resolution stravismus Spotlight SAR Imaging two dimension original echo complex data S based on the slip receiver window _StartAnd corresponding imaging parameters.Wherein, S _StartBe two-dimentional plural groups, size is 65536 * 16384, and concrete imaging parameters is as shown in table 1;

Step 2: with two-dimentional original echo complex data S _StartCarry out the orientation and handle to separating linear frequency modulation, the concrete operations step is:

(a) structuring one-dimensional sequence, as the formula (2), i=[1,2 ..., 65536], j=[1,2 ..., 16384];

(b) obtain two-dimentional original echo complex data S _StartThe orientation moment t (i) that every row is corresponding, detailed process is undertaken by formula (3);

(c) with data S _StartWith removing twiddle factor H ₁(i) multiply each other, obtain data S _{1_1}(i, j).Factor H wherein ₁Be that size is 65536 * 1 one dimension plural groups, concrete acquisition process is undertaken by formula (4), and two-dimentional plural groups S _{1_1}(i, j) acquisition process is undertaken by formula (5);

(d) to complex data S _{1_1}(i j) changes (FFT) along each distance to carrying out fast Fourier by formula (6), obtains complex data S _{1_2}(i, j);

(e) obtain postrotational equivalent pulse repetition frequency PRF', acquisition process is undertaken by formula (7);

(g) convolution (7) obtains the corresponding orientation of the every row of two-dimentional complex data t constantly ₁(i), acquisition process is undertaken by formula (8);

(h) with data S _{1_2}(i is j) with factor H ₂(i) multiply each other, obtain final orientation to the data S that separates behind the linear frequency modulation ₁(i, j).Factor H wherein ₂(i) be that size is 65536 * 1 one dimension plural groups, its acquisition process is undertaken by formula (9), and two-dimentional plural groups S ₁Acquisition process is undertaken by formula (10);

Step 3: the complex data S that step 2 is obtained ₁(i j) carries out Fast Fourier Transform (FFT) (FFT) along each distance to (by row), obtains the orientation to frequency spectrum complex data S ₂(i, j), specific operation process is undertaken by formula (11);

Step 4: the complex data S that step 3 is obtained ₂(i, j) with orientation constantly, corresponding orientation to separating linear frequency modulation residual phase error compensation factor Ω ₁(i) multiply each other the complex data S after being compensated ₃(i, j);

(a) obtain two-dimentional orientation to frequency domain distance to time domain complex data S ₂(i, j) the corresponding orientation frequency f of every row _a, concrete acquisition process is undertaken by formula (12);

(b) to obtain size be 65536 * 1 one dimension compensating factor Ω to convolution (7) ₁(i), concrete acquisition process is undertaken by formula (13);

(c) obtain two-dimentional complex data S after the compensation ₃(i, j), concrete acquisition process is undertaken by formula (14);

Step 5: the complex data S that step 4 is obtained ₃(i, j) (by row) carries out Fast Fourier Transform (FFT) (FFT) along each orientation constantly, obtains 2-d spectrum complex data S ₄(i, j), specific operation process is undertaken by formula (15);

Step 6: the complex data S that step 5 is obtained ₄(i is j) with corresponding consistent compressibility factor Ω ₂(i j) multiplies each other, and is slightly focused on complex data S ₅(i, j).

(a) according to reference oblique distance R _Ref=19.31km obtains the shortest oblique distance R _Min, concrete acquisition process is undertaken by formula (16);

(b) obtain two-dimensional frequency complex data S ₄(i, j) every row correspondence apart from frequency domain f _τ(j), concrete acquisition process is undertaken by formula (17);

(c) convolution (7) obtains two-dimensional frequency complex data S with formula (11) ₄(i, j) orientation of every row correspondence is to wave number k _x(i) corresponding with every row distance is to wave number k _Rc(j), concrete acquisition process is undertaken by formula (18);

(d) to obtain size be 65536 * 16384 two-dimentional consistent compressibility factor Ω to convolution (10)～formula (12) ₂(i, j), concrete acquisition process is undertaken by formula (19);

(e) convolution (13) obtains the two-dimentional complex data S after consistent the compression ₅(i, j), concrete acquisition process is undertaken by formula (20);

Step 7: the complex data S that obtains for step 6 ₅(i j), utilizes the sinc method of interpolation to carry out the stolt interpolation processing, obtains being mapped to by two-dimensional frequency the complex data S of two-dimentional wavenumber domain ₆(i, j);

(a) according to the distance frequency domain to apart from the mapping relations of wavenumber domain, convolution (18) obtains apart from wavenumber domain wave number k' _Rc(i, j), concrete acquisition process is undertaken by formula (21);

(b) traversal is obtained apart from wavenumber domain wave number k' _Rc(i, maximal value k' j) _{Rc, max}=69.12rad/s and minimum value k' _{Rc, max}=-105.06rad/s, and obtain apart from branch interval delta k' such as wavenumber domain wave numbers _Rc, concrete operations are undertaken by formula (22);

(c) obtain two-dimentional wavenumber domain complex data uniformly apart from the wavenumber domain wave number Concrete operations are undertaken by formula (23);

(d) obtain two-dimentional wavenumber domain complex data each uniformly apart from the wavenumber domain wave number

At the corresponding inhomogeneous k' of every row _RcPosition p in (i :) (i, j).Method is for carrying out following operation by row: obtain the position p (1,1) of first row, first row earlier, obtain absolute difference

N=[1,2 ..., 16384], obtain minimum absolute difference Δ k _MinWith the n of correspondence position, if $k_{\mathrm{rc}}^e\left(1\right)<k_{\mathrm{rc}}^{\&prime;}(1,n),$ P (1,1)=n-1, if $k_{\mathrm{rc}}^e\left(1\right)\&GreaterEqual;k_{\mathrm{rc}}^{\&prime;}(1,n),$ p(1,1)＝n，

By that analogy, obtain each position p (i, j).

(e) the position p that obtained in conjunction with the last step (i, j), obtain the required sampled point of sinc interpolation at interval q (n), concrete operations are undertaken by formula (24) for i, j;

(f) convolution (18) utilizes the sinc method of interpolation, and selecting sinc interpolation kernel length is N=8, obtains out the two-dimentional complex data S after the stolt interpolation ₆(i j), because 2-D data is complex data, needs respectively to S ₆(i, real part S j) _{6_re}(i is j) with imaginary part S _{6_im}(i j) carries out the sinc method of interpolation respectively and obtains and draw, and concrete operations are undertaken by formula (25) (26).

Step 8: the complex data S that step 7 is obtained ₆(i j) carries out inverse fast Fourier transform (IFFT) along each distance to (by row), obtains the orientation time domain apart from wavenumber domain complex data S ₇(i, j), concrete operations are undertaken by formula (27);

Step 9: the complex data S that step 8 is obtained ₇(i is j) with geometry correction factor Ω ₃(i j) multiplies each other, and obtains the complex data S after geometry correction ₈(i, j);

(a) convolution (8) and formula (12) obtain geometry correction factor Ω ₃(i, j), concrete operations are undertaken by formula (28);

(b) utilize formula (28) to obtain complex data S after geometry correction ₈(i, j), concrete operations are undertaken by formula (29);

Step 10: the complex data S that step 9 is obtained ₈(i, j) (by row) carries out inverse fast Fourier transform (IFFT) along each orientation constantly, obtains final imaging results S _End(i, j), concrete operations are undertaken by formula (30);

Imaging processing through above-mentioned steps obtains final scene imaging results as shown in Figure 3.Table 2 has provided that scene is upper left, middle, the Imaging Evaluation result of three point targets in bottom right, and Fig. 4, Fig. 5, Fig. 6 have provided respectively that scene is upper left, middle, the sectional view of three point targets in bottom right.

Table two Imaging Evaluation result

According to table 2 assessment result and Fig. 4～sectional view shown in Figure 6, can draw: on the one hand, this formation method is 70 still can vernier focusing when spending in the stravismus angle, illustrate that the method for the present invention's proposition do not looked side ways the restriction of angle; On the other hand, this formation method still can vernier focusing for the 0.1m super-resolution, illustrates that the method that the present invention proposes can realize vernier focusings to present various resolution.Therefore, method proposed by the invention can realize the Airborne High-resolution angle of squint Spotlight SAR Imaging accurately image based on the slip receiver window, has obtained high-precision imaging results.

Claims (1)

1. the Airborne High-resolution based on the slip receiver window is looked side ways the Spotlight SAR Imaging formation method, may further comprise the steps:

Step 1: read in the Airborne High-resolution stravismus Spotlight SAR Imaging two dimension original echo complex data S based on the slip receiver window _StartAnd corresponding imaging parameters;

S _StartBe a two-dimentional plural groups, size is N _a* N _r, imaging parameters comprises: the orientation is to sampling number N _a, the distance to sampling number N _r, signal sampling rate f _s, signal bandwidth Bw, chirp rate b, pulse repetition rate PRF, with reference to oblique distance R _Ref, doppler centroid fd ₀, doppler frequency rate f _R0, satellite velocities P _v, equivalent angle of squint

Signal wavelength lambda, signal carrier frequency f ₀, signal velocity c;

Step 2: with two-dimentional original echo complex data S _StartCarry out the orientation and handle to separating linear frequency modulation, specifically comprise following step:

(a) two one-dimensional sequence i of structure, j, wherein i represents the orientation to sequence, and j represents distance to sequence;

i＝[1,2,…,N _a] （2）

j＝[1,2,…,N _r]

(b) obtain two-dimentional original echo complex data S _StartThe orientation moment t (i) that each row is corresponding;

$t\left(i\right)=\frac{i-N_a/2}{\mathrm{PRF}}---\left(3\right)$

(c) with two-dimentional original echo complex data S _StartWith remove twiddle factor H ₁Multiply each other, obtain data S _{1_1}(i, j), factor H wherein ₁(i) be that size is N _a* 1 one dimension plural groups, formula is:

H ₁(i)＝exp{jπ(fr ₀·t ²(i)+2·fd ₀·t(i))} （4）

Two-dimentional plural groups S then _{1_1}Drawn by following formula:

S _{1_1}(i,j)＝S _start(i,j)·H ₁(i) （5）

(d) to complex data S _{1_1}Change to carrying out fast Fourier along each distance, obtain complex data S _{1_2}

S _{1_2}(:,j)＝FFT(S _{1_1}(:,j)) （6）

Wherein, S _{1_2}(:, j) expression S _{1_2}N row, S _{1_1}(:, j) expression S _{1_1}N row, FFT () expression is carried out Fast Fourier Transform (FFT) to one-dimension array;

(e) obtain postrotational equivalent pulse repetition frequency PRF';

${\mathrm{PRF}}^{\&prime;}=\frac{N_r\&CenterDot;{\mathrm{fr}}_0}{\mathrm{PRF}}---\left(7\right)$

(g) convolution (7) obtains the corresponding orientation of the every row of two-dimentional complex data t constantly ₁(i);

$t_1\left(i\right)=\frac{i-N_a/2}{{\mathrm{PRF}}^{\&prime;}}---\left(8\right)$

(h) with complex data S _{1_2}With factor H ₂Multiply each other, obtain final orientation to the data S that separates behind the linear frequency modulation ₁Factor H wherein ₂(i) be that size is N _a* 1 one dimension plural groups, its formula is:

$H_2\left(i\right)=\exp \{\mathrm{j\&pi;}\&CenterDot;{\mathrm{fr}}_0\&CenterDot;t_1^2\left(i\right)\}---\left(9\right)$

Two-dimentional plural groups S then ₁Drawn by following formula:

S ₁(i,j)＝S _{1_2}(i,j)·H ₂(i) （10）

Step 3: the complex data S that step 2 is obtained ₁(i, j) along each the distance to carrying out Fast Fourier Transform (FFT), obtain the orientation to frequency spectrum complex data S ₂(i, j);

S ₂(:,j)＝FFT(S ₁(:,j)) （11）

Wherein, S ₂(:, j) expression S ₂J row, S ₁(:, j) expression S ₁J row, FFT () expression is carried out Fast Fourier Transform (FFT) to one-dimension array;

Step 4: the complex data S that step 3 is obtained ₂(i, j) with orientation constantly, corresponding orientation to separating linear frequency modulation residual phase error compensation factor Ω ₁(i) multiply each other the complex data S after being compensated ₃(i, j);

(a) convolution (7) obtain two-dimentional orientation to frequency domain distance to time domain complex data S ₂(i, j) the corresponding orientation frequency f of every row _a(i);

$f_a\left(i\right)=\frac{i-N_a/2}{N_a}\&CenterDot;{\mathrm{PRF}}^{\&prime;}---\left(12\right)$

(b) convolution (12) obtains the big or small N that is _a* 1 one dimension compensating factor Ω ₁(i);

${\mathrm{\&Omega;}}_1\left(i\right)=\exp \{\mathrm{j\&pi;}\&CenterDot;\frac{f_a^2\left(i\right)}{{\mathrm{fr}}_0}\}---\left(13\right)$

(c) obtain two-dimentional complex data S after the compensation ₃(i, j);

S ₃(i,j)＝S ₂(i,j)·Ω ₁(i) （14）

Step 5: the complex data S that step 4 is obtained ₃(i j) carries out Fast Fourier Transform (FFT) constantly along each orientation, obtain 2-d spectrum complex data S ₄(i, j);

S ₄(i,:)＝FFT(S ₃(i,:)) （15）

Wherein, S ₃(i :) expression S ₃I capable, S ₄(i :) expression S ₄I capable;

Step 6: the complex data S that step 5 is obtained ₄(i is j) with corresponding consistent compressibility factor Ω ₂(i j) multiplies each other, and is slightly focused on complex data S ₅(i, j);

(a) according to reference oblique distance R _RefObtain the shortest oblique distance R _Min

$R_{\min }=R_{\mathrm{ref}}-\frac{c}{{2f}_s}\&CenterDot;\frac{N_r}{2}---\left(16\right)$

(b) obtain two-dimensional frequency complex data S ₄(i, j) every row correspondence apart from frequency domain f _τ(j);

$f_{\mathrm{\&tau;}}\left(j\right)=\frac{j-N_r/2}{\mathrm{Nr}}\&CenterDot;f_s---\left(17\right)$

(c) convolution (12) obtains two-dimensional frequency complex data S with formula (17) ₄(i, j) orientation of every row correspondence is to wave number k _x(i) corresponding with every row distance is to wave number k _Rc(j);

$k_x\left(i\right)=\frac{2\mathrm{\&pi;}{f}_a\left(i\right)}{P_v}$ $\left(18\right)$

$k_{\mathrm{rc}}\left(j\right)=\frac{4\mathrm{\&pi;}(f_0+f_{\mathrm{\&tau;}}\left(j\right))}{c}$

(d) convolution (16)～formula (18) is obtained the big or small N that is _a* N _rTwo-dimentional consistent compressibility factor Ω ₂(i, j);

(e) convolution (19) obtains the two-dimentional complex data S after consistent the compression ₅(i, j);

S ₅(i,j)＝S ₄(i,j)·Ω ₂(i,j) （20）

Step 7: the complex data S that obtains for step 6 ₅(i j), utilizes the sinc method of interpolation to carry out the stolt interpolation processing, obtains being mapped to by two-dimensional frequency the complex data S of two-dimentional wavenumber domain ₆(i, j);

(a) according to the distance frequency domain to apart from the mapping relations of wavenumber domain, convolution (18) obtains apart from wavenumber domain wave number k' _Rc(i, j);

(b) traversal is obtained apart from wavenumber domain wave number k' _Rc(i, maximal value k' j) _{Rc, max}With minimum value k' _{Rc, max}, and obtain apart from branch interval delta k' such as wavenumber domain wave numbers _Rc

$\mathrm{\&Delta;}{k}_{\mathrm{rc}}^{\&prime;}=\frac{k_{\mathrm{rc},\max }^{\&prime;}-k_{\mathrm{rc},\min }^{\&prime;}}{N_r}---\left(22\right)$

(c) obtain two-dimentional wavenumber domain data uniformly apart from the wavenumber domain wave number

$k_{\mathrm{rc}}^e\left(j\right)=k_{\mathrm{rc}}^{\&prime;}\min +j\&CenterDot;\mathrm{\&Delta;}{k}_{\mathrm{rc}}^{\&prime;}---\left(23\right)$

(d) obtain two-dimentional wavenumber domain complex data each uniformly apart from the wavenumber domain wave number

At the corresponding inhomogeneous k' of every row _RcPosition p in (i :) (i, j);

Be specially: be example with the position p (1,1) that obtains first row first row, at first obtain absolute difference

N=[1,2 ..., N _r], obtain minimum absolute difference Δ k _MinWith the n of correspondence position, if

P (1,1)=n-1, if

P (1,1)=n, in like manner, by that analogy, obtain each position p (i, j);

(e) the position p that obtained in conjunction with the last step (i, j), obtain the required sampled point of sinc interpolation at interval q (i, j, n);

$q(i,j,n)=\frac{k_{\mathrm{rc}}^e\left(j\right)-k_{\mathrm{rc}}^{\&prime;}(i,(p(i,j)+n))}{\frac{4\mathrm{\&pi;}{f}_s}{c}\&CenterDot;\frac{1}{N_r}},$ n＝[-N/2,-N/2+1,…,N/2-1] （24）

(f) convolution (24) utilizes the sinc method of interpolation, obtains out the 2-D data S after the stolt interpolation ₆, because 2-D data is complex data, need respectively to S ₆(i, real part S j) _{6_re}(i is j) with imaginary part S _{6_im}(i j) carries out the sinc method of interpolation respectively and obtains and draw;

Wherein, N is interpolation kernel length, and sinc () refers to interpolating function S _{5_re}(i j) refers to 2-D data S _{5_re}The real part of the capable j row of i, S _{5_im}(i j) refers to 2-D data S _{5_im}The imaginary part of the capable j row of i;

Step 8: the complex data S that step 7 is obtained ₆(i, j) along each the distance to carrying out inverse fast Fourier transform, obtain the orientation time domain apart from wavenumber domain complex data S ₇(i, j);

S ₇(:,j)＝IFFT(S ₆(:,j)) （27）

Wherein, S ₆(:, j) expression S ₆J row, S ₇(:, j) expression S ₇J row, IFFT () expression is carried out inverse fast Fourier transform to one-dimension array;

Step 9: the complex data S that step 8 is obtained ₇(i is j) with geometry correction factor Ω ₃(i j) multiplies each other, and obtains the complex data S after geometry correction ₈(i, j);

(a) convolution (8) and formula (12) obtain geometry correction factor Ω ₄(i, j);

${\mathrm{\&Omega;}}_4(i,j)=\exp \{-j2\mathrm{\&pi;}\&CenterDot;f_a\left(i\right)\&CenterDot;\frac{\mathrm{\&lambda;}\&CenterDot;{\mathrm{fd}}_0\&CenterDot;t_1\left(i\right)}{c}\}---\left(28\right)$

(b) utilize formula (30) to obtain complex data S after geometry correction ₈(i, j);

S ₈(i,j)＝S ₇(i,j)·Ω ₃(i,j) （29）

Step 10: the complex data S that step 9 is obtained ₈(i j) carries out inverse fast Fourier transform constantly along each orientation, obtain final imaging results S _End(i, j);

S _end(i,:)＝IFFT(S ₈(i,:)) （30）

Wherein, S ₈(i :) expression S ₈I capable, S _End(i :) expression S _EndI capable.

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