CN105759267B - A kind of improvement Omega-K imaging method of large slanting view angle machine SAR - Google Patents
A kind of improvement Omega-K imaging method of large slanting view angle machine SAR Download PDFInfo
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- 238000003384 imaging method Methods 0.000 title claims abstract description 33
- 238000001228 spectrum Methods 0.000 claims abstract description 32
- 230000006835 compression Effects 0.000 claims abstract description 12
- 238000007906 compression Methods 0.000 claims abstract description 12
- 238000013507 mapping Methods 0.000 claims description 10
- 238000005070 sampling Methods 0.000 claims description 9
- 238000013139 quantization Methods 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000004422 calculation algorithm Methods 0.000 abstract description 10
- 238000000034 method Methods 0.000 abstract description 5
- 238000002474 experimental method Methods 0.000 description 6
- 208000004350 Strabismus Diseases 0.000 description 4
- 238000006073 displacement reaction Methods 0.000 description 4
- 125000004122 cyclic group Chemical group 0.000 description 3
- 238000012952 Resampling Methods 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
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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
- G01S13/9011—SAR image acquisition techniques with frequency 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
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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
- G01S13/9041—Squint mode
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Abstract
The invention proposes the improvement Omega-K imaging methods of large slanting view angle machine SAR a kind of, spectrum offset amount f ' is calculated to pulse compression, distance to inverse discrete Fourier transform, motion compensation, two-dimensional fast fourier transform and consistent compression to Fast Fourier Transform (FFT), distance by successively carrying out distance to raw radar data Sτ,minTo correct Stolt interpolation, then distance is carried out to inverse discrete Fourier transform to each data point, go forward side by side line phase compensation and orientation inverse discrete Fourier transform obtain final imaging results.Method of the invention saves hardware store resource, and operation is simple, guarantees image quality, and efficiency of algorithm is high.
Description
Technical field
The invention belongs to SAR technical field of imaging, are imaged more particularly, to the improvement Omega-K of large slanting view angle machine SAR a kind of
Method.
Background technique
Omega-K algorithm is a kind of classics of synthetic aperture radar (synthetic aperture radar, abbreviation SAR)
Imaging algorithm is inserted by carrying out consistent compression in two-dimensional frequency to complete being fully focused at reference distance, then by Stolt
Value is without being nearly completed remaining range migration correction (RCMC) at non-reference distance, remaining secondary range compression (SRC) and residual
Remaining Azimuth Compression.The mapping relations of Stolt interpolation are as follows:
Wherein, f0For carrier frequency, fτFor frequency of distance, c is the light velocity, fηFor orientation frequency, VrFor radar speed, fτ' be
Frequency of distance after mapping.Above formula is by original frequency of distance fτIt is mapped as new frequency of distance fτ', residual phase is fτ' line
Property function, thus eliminate residual phase modulation, realize the vernier focusing of the target of non-reference position.
Stolt maps the displacement and distortion that will lead to frequency spectrum, and the more big this phenomenon in angle of squint is more obvious.It is big in angle of squint
When certain value, the displacement and distortion of frequency spectrum can exceed the range of support region after Stolt mapping, cause spectrum component to lose, sternly
Important place affects image quality.
In the past by using extension Omega-k algorithm, considerably increase the computational complexity of Stolt interpolation, real-time compared with
Difference；And the method by expanding two-dimentional support region in Stolt interpolation, then image quality is exchanged for sacrifice hardware storage resource,
In today that SAR echo data is huge, certainly will make a big impact to efficiency of algorithm.
Summary of the invention
Technical problem solved by the invention is to provide the improvement Omega-K imaging method of large slanting view angle machine SAR a kind of, lead to
The offset for calculating distance to frequency spectrum after Stolt maps is crossed, interpolation front distance frequency mapping f is re-definedτ' range, come
Stolt interpolation is corrected, so that two-dimensional frequency is fallen into former support region, to save hardware store resource, guarantees image quality, mentions
High efficiency of algorithm.
The technical solution for realizing the aim of the invention is as follows:
A kind of improvement Omega-K imaging method of large slanting view angle machine SAR, comprising the following steps:
Step 1: obtaining raw radar data S；
Step 2: raw radar data S is successively carried out distance to Fast Fourier Transform (FFT) FFT, distance to pulse compress,
Distance to inverse discrete Fourier transform IFFT, motion compensation, Two-dimensional FFT and it is consistent compression；
Step 3: calculating spectrum offset amount fτ',min, correct Stolt interpolation；
Step 4: distance being carried out to IFFT to each data point, range-Dopler domain is transformed data to, obtains data point
Sik；
Step 5: according to fτ',minLinear phase compensation is carried out, distance is completed to Spectrum Correction, obtains data point S'ik；
Step 6: to data point S'ikOrientation IFFT is carried out, final imaging results are obtained.
Further, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention, raw radar data in step 1
The size of S is Na × Nr, wherein Na is orientation sampling number, and Nr is distance to sampling number.
Further, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention, by consistent compression in step 2
Data afterwards are stored in the form of two-dimensional matrix.
Further, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention, step 3 specifically include following step
It is rapid:
Step 3-1: frequency of distance f of the computer azimuth to unitτF is mapped to by Stoltτ' axis minimum value, quantization takes
F is arrived in storage after wholeτ',minIn；
Step 3-2: with fτ',minAs initial value, withFor frequency interval, the frequency of distance for calculating each data point is reflected
Penetrate fτ' value；
Step 3-3: by Stolt mapping equation, the f of each data point is calculatedτ' it is worth correspondence in fτThe position of axis, and calculate
Interpolation result out.
Further, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention, i-th of orientation in step 3-1
To the frequency of distance f of unitτF is mapped to by Stoltτ' axis quantization be rounded after minimum value calculation method are as follows:
Wherein, fτ',min[i] indicates the f of i-th of orientation unitτ' quantify the minimum value after being rounded, fτ[0] distance is indicated
Frequency initial value, fη[i] indicates the orientation frequency of i-th of orientation unit, fsIndicate sample frequency, Nr is original echo number
According to the distance of S to sampling number, f0Indicate that carrier frequency, c indicate the light velocity, VrIndicate radar speed.
Further, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention, i-th of orientation in step 3-3
To the f of unitτ' it is worth correspondence in fτThe position of axis are as follows:
Wherein, i is orientation coordinate, and k is distance to coordinate, fτ',ikIndicate position coordinates be (i, k) data point away from
Off-frequency rate is mapped in fτ' axis value, fτ,ikIndicate fτ',ikIt corresponds in fτThe position of axis.
Further, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention is inserted using sinc in step 3-3
Value calculates interpolation result.
Further, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention, linear phase compensation in step 5
Data point afterwards are as follows:
Wherein, fτ',min[i] indicates the f of i-th of orientation unitτ' quantifying the minimum value after being rounded, Nr is original echo
For the distance of data S to sampling number, k is positive integer.
The invention adopts the above technical scheme compared with prior art, has following technical effect that
1, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention does not need to expand support region, saves hardware and deposits
Store up resource；
2, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention can prevent Stolt interpolation from making frequency spectrum oblique pull
Distortion exceeds support region, sufficiently possesses all spectrum components；
3, the improvement Omega-K imaging method of large slanting view angle machine SAR of the invention is while guaranteeing image quality, operation letter
It is single, improve efficiency of algorithm.
Detailed description of the invention
Fig. 1 is the improvement Omega-K imaging method flow chart of large slanting view angle machine SAR of the invention；
Fig. 2 is the frequency spectrum before and after traditional Omega-K algorithm Stolt interpolation, wherein (a) is the frequency spectrum before Stolt interpolation,
It (b) is the frequency spectrum after Stolt interpolation；
Fig. 3 is the distribution map of emulation experiment point target of the invention；
Fig. 4 is the frequency spectrum improved in Omega-K algorithm of the invention, wherein (a) is the frequency spectrum corrected before Stolt interpolation,
(b) it is the frequency spectrum after amendment Stolt interpolation, (c) is the compensated frequency spectrum of linear phase；
Fig. 5 is the image of emulation experiment of the invention；
Fig. 6 is contour map of the invention, wherein (a) is the contour map of center point target, it is (b) upper right point target
Contour map.
Specific embodiment
Embodiments of the present invention are described below in detail, the example of the embodiment is shown in the accompanying drawings, wherein from beginning
Same or similar element or element with the same or similar functions are indicated to same or similar label eventually.Below by ginseng
The embodiment for examining attached drawing description is exemplary, and for explaining only the invention, and is not construed as limiting the claims.
A kind of flow chart of the improvement Omega-K imaging method of large slanting view angle machine SAR proposed by the present invention is as shown in Figure 1.It is main
To include pulse compression, motion compensation, two-dimensional fast fourier transform FFT, consistent compression, amendment Stolt interpolation, distance to from
Dissipate inverse Fourier transform IFFT, linear phase compensation, orientation IFFT.With traditional Omega-K algorithm the difference is that
With amendment Stolt interpolation and linear phase compensation instead of traditional Stolt interpolation.Below by from the angle of signal processing to this two
A step is further explained.
Amendment Stolt is illustrated first.
Step 1: frequency of distance f of the computer azimuth to unitτF is mapped to by Stoltτ' axis minimum value, quantization be rounded
F is arrived in storage afterwardsτ',minIn.
After consistent compression, the remaining phase theta of 2-d spectrumREF(fτ,fη) be approximately:
Wherein, R0It is target range to position, RrefFor reference distance, fτFor the frequency of distance of orientation unit, f0It indicates
Carrier frequency, c indicate the light velocity, VrIndicate radar speed.
Doppler centroid fηcExpression formula is as follows:
Wherein, θr,cFor the angle of squint of beam center, λ is wavelength.fηcMake fηGreater than actual value.As shown in Fig. 2, wherein (a)
It is (b) frequency spectrum after Stolt interpolation for the frequency spectrum before Stolt interpolation, in strabismus, f after Stolt mappingτ' compared with fτHave compared with
Big displacement, and it is different for the value of different direction to the displacement of unit, there are oblique pulls and twisted phenomena.Therefore by distance to
Spectral range estimates the f of each orientation unitτIt is mapped to fτ' the minimum value on axis quantifies as initial value, and to it
It is rounded:
Wherein, fτ',min[i] indicates the f of i-th of orientation unitτ' quantify the minimum value after being rounded, fτ[0] distance is indicated
Frequency initial value, fη[i] indicates the orientation frequency of i-th of orientation unit, fsIndicate sample frequency, Nr is original echo number
According to the distance of S to sampling number.
Step 2: with fτ',minAs initial value, withFor frequency interval, the frequency of distance for calculating each data point is reflected
Penetrate fτ' value.
The frequency of distance of i-th of orientation unit maps fτ',iIt may be defined as:
Consistent compressed data are stored in the form of two-dimensional matrix, if the position coordinates of data point are (i, k),
Middle i is orientation coordinate, and k is distance to coordinate, then formula (5) can be expressed as:
Step 3: calculating the f of each data point by Stolt mapping equationτ' it is worth correspondence in fτThe position of axis, and calculate
Interpolation result out.
Acquire fτ',ikAfterwards, it is substituted into (1) formula:
After equation converts, it is acquired in fτThe mapping value of axis:
fτ,ikAfter acquiring, resampling is carried out on frequency spectrum in distance to it, in order to guarantee that precision generally uses sinc interpolation
Carry out resampling.The frequency of distance of each orientation unit maps fτ' initial position it is all different, this is done to correct
Frequency spectrum guarantees that all spectrum components are both fallen in former support region, increases support region utilization rate.
Next linear phase compensation is illustrated.
After correcting Stolt interpolation, the f of each orientation unitτ' initial position be different, this will be in distance
Linear phase compensation is carried out to data after to IFFT, so that the f of each orientation unitτ' alignment.
Discrete Fourier transform property frequency shift property are as follows:
Wherein, x (n) is time domain discrete sequence, X (ejω) it is the corresponding frequency spectrum of x (n), ω0For spectrum offset amount, ω is number
Word angular frequency, ω and simulation angular frequency Ω and sample frequency fsRelationship are as follows:
Coordinate position be (i, k) data point amendment Stolt interpolation in offset from distance to frequency domain are as follows:
Formula (11) are substituted into formula (9) and formula (10), this is equivalent in distance to time domain multiplied by following phase:
Therefore need distance to after IFFT to data point SikThis phase is filled, expression formula is as follows:
Wherein, m is positive integer.
Cyclic shift is done in frequency domain since the phase multiplication of time domain is equivalent to, thus after phase compensation, each side
F of the position to unitτ' be aligned, although 2-d spectrum has restored oblique pull characteristic, but since cyclic shift carries out replicate, no
Support region can be exceeded.
Orientation IFFT is finally carried out, then available imaging results.
Effectiveness of the invention is further illustrated below by point target emulation experiment.
Software platform used in emulation experiment of the present invention is MATLAB.
The distribution map of point target is as shown in Figure 3 in emulation experiment.It is as shown in the table for radar parameter:
Fig. 4 (a) is consistent compressed 2-d spectrum, and Fig. 4 (b) is the frequency spectrum after correcting Stolt interpolation, it can be seen that
Frequency spectrum after interpolation falls into the support region of script substantially.Fig. 4 (c) is compensated in range-Dopler domain progress linear phase
2-d spectrum, this step have restored traditional Stolt interpolation bring spectral distortion and oblique pull, but since time domain is multiplied by linear phase
Position is equivalent to the cyclic shift in frequency domain, therefore frequency spectrum has carried out replicate in Fig. 4 (c), without departing from supporting domain.
Fig. 5 is the imaging results figure of emulation experiment.Fig. 6 (a) is the contour map of center point target, and 6 (b) be upper right point mesh
Target contour map.It can be seen that method proposed by the present invention can obtain good imaging results in large slanting view angle machine.
The above is only some embodiments of the invention, it is noted that for the ordinary skill people of the art
For member, without departing from the principle of the present invention, several improvement can also be made, these improvement should be regarded as guarantor of the invention
Protect range.
Claims (8)
1. the improvement Omega-K imaging method of large slanting view angle machine SAR a kind of, which comprises the following steps:
Step 1: obtaining raw radar data S；
Step 2: distance is successively carried out to raw radar data S to Fast Fourier Transform (FFT) FFT, distance to pulse compression, distance
To inverse discrete Fourier transform IFFT, motion compensation, Two-dimensional FFT and consistent compression；
Step 3: calculating spectrum offset amount f 'τ,min, correct Stolt interpolation；
Step 4: distance being carried out to IFFT to each data point, range-Dopler domain is transformed data to, obtains data point Sik；
Step 5: according to f 'τ,minLinear phase compensation is carried out, distance is completed to Spectrum Correction, obtains data point S 'ik；
Step 6: to data point S 'ikOrientation IFFT is carried out, final imaging results are obtained.
2. the improvement Omega-K imaging method of large slanting view angle machine SAR according to claim 1, which is characterized in that in step 1
The size of raw radar data S is Na × Nr, wherein Na is orientation sampling number, and Nr is distance to sampling number.
3. the improvement Omega-K imaging method of large slanting view angle machine SAR according to claim 1 or 2, which is characterized in that step 2
It is middle to be stored in the form of two-dimensional matrix by consistent compressed data.
4. the improvement Omega-K imaging method of large slanting view angle machine SAR according to claim 2, which is characterized in that step 3 tool
Body the following steps are included:
Step 3-1: frequency of distance f of the computer azimuth to unitτF ' is mapped to by StoltτThe minimum value of axis, after quantization is rounded
Store f 'τ,minIn；
Step 3-2: with f 'τ,minAs initial value, withFor frequency interval, the frequency of distance mapping f ' of each data point is calculatedτ
Value；
Step 3-3: by Stolt mapping equation, the f ' of each data point is calculatedτValue is corresponding in fτThe position of axis, and calculate slotting
It is worth result.
5. the improvement Omega-K imaging method of large slanting view angle machine SAR according to claim 4, which is characterized in that step 3-1
In i-th of orientation unit frequency of distance fτF ' is mapped to by StoltτThe calculation method of minimum value after axis quantization rounding
Are as follows:
Wherein, f 'τ,min[i] indicates the f ' of i-th of orientation unitτMinimum value after quantization rounding, fτ[0] frequency of distance is indicated
Initial value, fη[i] indicates the orientation frequency of i-th of orientation unit, fηFor orientation frequency, fsIndicate sample frequency, Nr is
The distance of raw radar data S is to sampling number, f0Indicate that carrier frequency, c indicate the light velocity, VrIndicate radar speed.
6. the improvement Omega-K imaging method of large slanting view angle machine SAR according to claim 5, which is characterized in that step 3-3
In i-th of orientation unit f 'τValue is corresponding in fτThe position of axis are as follows:
Wherein, i is orientation coordinate, and k is distance to coordinate, f 'τ,ikIndicate the distance frequency that position coordinates are the data point of (i, k)
Rate is mapped in f 'τThe value of axis, fτ,ikIndicate f 'τ,ikIt corresponds in fτThe position of axis.
7. the improvement Omega-K imaging method of large slanting view angle machine SAR according to claim 4, which is characterized in that step 3-3
It is middle that interpolation result is calculated using sinc interpolation.
8. the improvement Omega-K imaging method of large slanting view angle machine SAR according to claim 6, which is characterized in that in step 5
The compensated data point of linear phase are as follows:
Wherein, f 'τ,min[i] indicates the f ' of i-th of orientation unitτMinimum value after quantization rounding, Nr are raw radar data S
Distance to sampling number, m is positive integer.
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