CN113406627B - Two-step channel error estimation and compensation method for distributed multi-channel SAR - Google Patents
Two-step channel error estimation and compensation method for distributed multi-channel SAR Download PDFInfo
- Publication number
- CN113406627B CN113406627B CN202110515560.0A CN202110515560A CN113406627B CN 113406627 B CN113406627 B CN 113406627B CN 202110515560 A CN202110515560 A CN 202110515560A CN 113406627 B CN113406627 B CN 113406627B
- Authority
- CN
- China
- Prior art keywords
- channel
- envelope
- distance
- space
- walk
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 49
- 238000012937 correction Methods 0.000 claims abstract description 26
- 238000005070 sampling Methods 0.000 claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 8
- 230000008569 process Effects 0.000 claims description 5
- 238000003384 imaging method Methods 0.000 description 13
- 238000004088 simulation Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 5
- 238000001914 filtration Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000008707 rearrangement Effects 0.000 description 2
- 238000000638 solvent extraction Methods 0.000 description 2
- 208000004350 Strabismus Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- CLOMYZFHNHFSIQ-UHFFFAOYSA-N clonixin Chemical compound CC1=C(Cl)C=CC=C1NC1=NC=CC=C1C(O)=O CLOMYZFHNHFSIQ-UHFFFAOYSA-N 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- RFHAOTPXVQNOHP-UHFFFAOYSA-N fluconazole Chemical compound C1=NC=NN1CC(C=1C(=CC(F)=CC=1)F)(O)CN1C=NC=N1 RFHAOTPXVQNOHP-UHFFFAOYSA-N 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000007493 shaping process Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9004—SAR image acquisition techniques
- G01S13/9017—SAR image acquisition techniques with time domain processing of the SAR signals in azimuth
-
- 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
-
- 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
- G01S13/9094—Theoretical aspects
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/40—Means for monitoring or calibrating
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/42—Diversity systems specially adapted for radar
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Signal Processing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention provides a two-step channel error estimation and compensation method for distributed multi-channel SAR, which belongs to the technical field of synthetic aperture radar signal processing, and comprises the following steps: based on inertial navigation data, coarse correction of envelope walk is realized; for corrected data, roughly compensating space-variant phase errors among channels based on inertial navigation data; re-ordering the data after envelope walk rough correction and inter-channel space-variant phase error rough compensation according to the correlation between the antenna sampling data; and estimating and compensating the residual envelope by adopting a distance frequency domain phase increment method, and then finely estimating and compensating the residual space-variant phase error by adopting a distance time domain phase increment method.
Description
Technical Field
The invention belongs to the technical field of synthetic aperture radar signal processing, and particularly relates to a two-step channel error estimation and compensation method for distributed multi-channel SAR.
Background
The synthetic aperture radar (Synthetic Aperture Radar, SAR) is an active microwave remote sensing device first proposed by the United states in the 50 th century, has the characteristics of all-day time, all weather, two-dimensional high resolution, strong tradition and the like, and plays an important role in the fields of disaster monitoring, map mapping and the like. Along with the improvement of stealth performance and air performance requirements of an airborne platform, the size and the weight of various sensors installed on the platform are strictly required. The distributed shaping and installing multichannel SAR system can enable the whole system to be designed with higher flexibility, and meet the requirements of stealth and aerodynamic performance of a platform.
The azimuth multi-channel SAR system can realize high-resolution wide-amplitude imaging, but the aperture of an antenna is overlarge. The multichannel distributed SAR system can divide a traditional multichannel large-aperture antenna into a plurality of small-aperture antennas, and the small-aperture antennas are distributed and shaped and arranged at all parts of the airframe, so that the influence of SAR equipment on the stealth performance and the aerodynamic performance of the platform is reduced. However, distributed SAR channels are sparse and there is a large vertical track baseline. The channel sparseness causes poor correlation among channels, and the traditional phase increment method cannot estimate channel amplitude-phase errors and sampling time errors, and has larger computation amount. The vertical track base line can generate inter-channel skew difference, so that inter-channel data envelope walk and phase error are caused, azimuth Doppler reconstruction cannot be directly carried out, and compensation is needed. When the imaging breadth is large, the large distance null change exists in the skew distance difference, only the phase error distance null change is considered in the traditional compensation method, the distance null change of envelope walking is ignored, and the imaging quality is affected. There is therefore a need to further investigate distributed multi-channel SAR channel error estimation and compensation methods.
Disclosure of Invention
In view of the above, the invention provides a two-step channel error estimation and compensation method for a distributed multi-channel SAR, which effectively solves the problems that the distributed multi-channel SAR is sparse and has difficult error estimation and compensation caused by a vertical track baseline, and is suitable for high-resolution wide-amplitude imaging of the distributed multi-channel SAR.
The technical scheme for realizing the invention is as follows:
a two-step channel error estimation and compensation method for distributed multi-channel SAR comprises the following steps:
based on inertial navigation data, coarse correction of envelope walk is realized; for corrected data, roughly compensating space-variant phase errors among channels based on inertial navigation data;
re-ordering the data after envelope walk rough correction and inter-channel space-variant phase error rough compensation according to the correlation between the antenna sampling data; and estimating and compensating the residual envelope by adopting a distance frequency domain phase increment method, and then finely estimating and compensating the residual space-variant phase error by adopting a distance time domain phase increment method.
Preferably, the envelope walk of the present invention includes a distance space variant envelope walk and an inter-channel space variant envelope walk.
Preferably, the specific process for realizing coarse correction of envelope walk based on inertial navigation data in the invention is as follows:
calculating the slant distance differences between targets at different distances in a scene and each channel and a reference channel by using inertial navigation information, and performing linear fitting on the slant distance differences; and carrying out CS operation based on the obtained fitting parameters, correcting the space-variant part of the envelope walk, multiplying the signal obtained after the CS operation is completed by a linear phase on a distance frequency domain, correcting the space-variant part of the envelope walk, and finishing the coarse correction of the envelope walk based on inertial navigation data.
Preferably, the CS operation of the present invention is: and setting a scaling equation and a frequency domain matching function based on the obtained fitting parameters, processing echo signals, and multiplying the processed result by the residual phase to realize space-variant correction of the envelope walking distance.
Preferably, the transformation equation of the present invention is:
s sc (t)＝exp{jπα m K r (t-t ref ) 2 }
wherein ,
K r representing the frequency modulation slope of the transmitted signal, t ref ＝2r 0 C represents a reference time, c represents a speed of light, t represents a time, beta m Is the slope in the fitting parameters.
Preferably, the transformation equation of the present invention is:
wherein ,fτ Represent distance frequency, K r Representing the chirp rate of the transmitted signal,
β m is the slope in the fitting parameters.
The beneficial effects are that:
firstly, the invention provides a two-step channel error estimation and compensation method for a distributed multi-channel SAR, which reorders data after envelope walk coarse correction and inter-channel space-variant phase error coarse compensation according to the correlation between antenna sampling data, so that the correlation between adjacent data is good, and the accurate estimation error can be adopted by a traditional time domain correlation method, thereby realizing the channel error estimation and compensation of the distributed multi-channel SAR space-variant.
Secondly, the invention provides a two-step channel error estimation and compensation method for distributed multi-channel SAR, which corrects the linear change of the envelope walk through CS operation without dividing the image, and greatly reduces the operation complexity compared with the traditional mode of dividing the image to correct the envelope walk.
Drawings
FIG. 1 is a flow chart of a two-step channel error estimation and compensation method for distributed multi-channel SAR;
FIG. 2 is a schematic diagram of distributed SAR multi-channel data spatial sampling;
FIG. 3 is a azimuthal cross-section of the results of point target simulation imaging;
fig. 4 is a view of the results of simulation imaging of a scene.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be noted that, without conflict, the following embodiments and features in the embodiments may be combined with each other; and, based on the embodiments in this disclosure, all other embodiments that may be made by one of ordinary skill in the art without inventive effort are within the scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the following claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present disclosure, one skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. In addition, such apparatus may be implemented and/or such methods practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
As shown in fig. 1, an embodiment of the present application provides a two-step channel error estimation and compensation method for a distributed multi-channel SAR, which includes the following steps:
s1: based on inertial navigation data, envelope walk of space variation among echo data channels is compensated.
When the baseline vertical component is large and the imaging breadth is wide, the envelope due to the inter-channel vertical baseline shifts by a distance that is not negligible. Over a large range, the envelope walk can be approximated by a linear fit. The traditional method is used for carrying out envelop walk correction by partitioning, and when the number of the partitioning is large, the operation is complex. The CS operation can correct the envelope walk of linear change, so that the CS operation is introduced in the step to perform one-time space-variant envelope walk correction on the whole wide scene. The process of the step is as follows: calculating the slant distance differences between targets at different distances in a scene and each channel and a reference channel by using inertial navigation information, and performing linear fitting on the slant distance differences; based on the obtained fitting parameters, CS operation is carried out, the space-variant part of the envelope walking distance is corrected, the CS operation is to set a scaling equation and a frequency domain matching function, echo signals are processed, and then the processed result is multiplied by the residual phase, so that space-variant correction of the envelope walking distance is realized; and multiplying the signal obtained after CS operation is completed by a linear phase on a distance frequency domain, and finishing envelope walk rough correction based on inertial navigation data.
The method comprises the following steps:
firstly, obtaining the flight altitude and the flight attitude of a platform according to inertial navigation information, calculating the coordinates of each channel of a radar, and calculating the skew difference delta R between targets at different distances in a scene and each channel and a reference channel m (r), wherein m represents the channel number and r is the skew of the target to the reference channel.
For DeltaR m (r) performing a linear fit:
ΔR m (r)≈ΔR m,0 +β m (r-r 0 )
wherein ,ΔRm,0 And beta m For fitting the resulting parameters ΔR m,0 Is a constant term, beta m Is the slope, r 0 Is the skew from the center of the scene to the reference channel.
Secondly, CS operation is carried out on echo signals;
the specific process of CS operation is:
let the required transformation equation be:
s sc (t)＝exp{jπα m K r (t-t ref ) 2 }
wherein ,
K r representing the frequency modulation slope of the transmitted signal, t ref ＝2r 0 And/c represents a reference time, c represents a speed of light, and t represents time.
Multiplying the echo signal and the scaling equation, performing Fourier transform (FFT), and multiplying the result with a frequency domain matching function to realize matched filtering, wherein the frequency domain matching function is that
wherein ,fτ Representing distance frequency;
the frequency domain signal after matching and filtering is converted into a time domain signal by inverse Fourier transform and then is multiplied by the residual phase
And (3) finishing CS operation, and correcting the distance space variant part of the envelope walking.
Again, in the distance frequency domain, the signal obtained after the CS operation is completed is multiplied by the linear phase
Finishing envelope walk rough correction based on inertial navigation data to obtain data s m,1 (t, η), η represents slow time.
S2: ΔR calculated from inertial navigation data m (r) data s m,1 (t, eta) multiplied by the phase
Realize coarse correction of space-variant phase errors among channels based on inertial navigation data,obtaining data s m,2 (t, eta), wherein>
Thus also the phase can be->
Denoted as->
S3: and reordering the data after envelope walk rough correction and inter-channel space-variant phase error rough compensation according to the correlation between the antenna sampling data.
After envelope walk and phase coarse correction are carried out according to inertial navigation information, channel amplitude-phase errors, distance sampling time delay and residual skew difference caused by inertial navigation measurement errors are remained. A fine estimation and correction is required. However, the channel spacing is sparse, the correlation is poor, and the error cannot be estimated by adopting the traditional time domain correlation method. Data rearrangement is required. FIG. 2 is a schematic diagram of distributed SAR multichannel data spatial sampling, p m,n Sample data representing the nth PRT (pulse repetition time) of the mth antenna. Assuming that the radar has 3 antennas, the data sequence is [ 1,n-1 ,p 2,n-1 ,p 3,n-1 ,p 1,n ,p 2,n ,p 3,n ,p 1,n+1 ,p 2,n+1 ,p 3,n+1 ,...]The adjacent data has poor correlation, assuming p 1,n And p is as follows 2,n-1 The data correlation between the two is larger, p 3,n-2 And p is as follows 1,n+1 The data with larger correlation are adjacently arranged, and the rearranged data in this embodiment is [ 1,n ,p 2,n-1 ,p 3,n-2 ,p 1,n+1 ,p 2,n ,p 3,n-1 ,p 1,n+2 ,p 2,n+1 ,p 3,n ,...]The method comprises the steps of carrying out a first treatment on the surface of the And (3) reordering the data processed in the step (S2) according to the arrangement sequence.
S4: and performing correlation operation on the reordered adjacent data, namely, correlating between closest points of the spatial sampling positions to obtain a phase increment, calculating Doppler center frequency according to the result of the correlation operation, calculating residual envelope quantity walk and compensating based on the Doppler center frequency.
Based on the sorting example listed in the step S3, the specific process implemented in this step is as follows:
after space-variant envelope walk coarse correction and space-variant phase error coarse correction, respectively performing distance Fourier transform on the multi-channel data to obtain distance frequency domain data;
wherein ,θm Superimposed phase for point object in scene, phi m For the channel intrinsic phase error, d m For the separation from the reference channel, v is the stage velocity, θ 0 For beam azimuth center squint angle, m=1, 2 … M-1.
The phase increment of the m and m+1 th channels is
Let C M (f τ η) is the cross-correlation of channel 1 at η+PRT time and channel M at η - (M-1) PRT time, M being the total number of channels, then
And further obtaining Doppler center frequency as follows:
the phase error of channel m and the reference channel is:
m＝2,3,...,M
by solving for
Can obtain a refined estimate of the residual envelope running quantity +.>
Data S m,2 (f τ η) compensation
After that, data S is obtained m,3 (f τ ,η)。
S5: for the data S m,3 (f τ η) to estimate the residual space-variant phase error and compensate by means of a distance time domain phase increment method.
The method comprises the following steps: for S m,3 (f τ η) to obtain s m,3 (t, eta) where there is only a residual space-variant phase difference due to channel phase error and inertial measurement error. The phase error of the local area channel can be considered as a constant, the scene is partitioned, and each block adopts a phase increment method to estimate the phase error in the block. Similar to the correlation method in S4, the m and m+1 th channels have phase increments of
wherein ,ΔRm,e And (τ) is the residual pitch difference.
The remaining channel errors are:
because the oblique distance is different from the delay distance to the space variant, the sub-block phase difference can be estimated in a blocking way, and the phase error of the whole scene along with the space variant distance is estimated by adopting a fitting mode of a weighted least square method
Final compensation
The compensated data can be subjected to imaging processing after spectrum reconstruction by adopting a traditional azimuth inverse filtering method.
The effects of the present invention will be further described below through point target simulation and scene simulation experiments.
The simulation system is used for 4 channels with mixed baselines which are linearly and uniformly distributed, the parameters are shown in table 1, the inclined distance of a point target from a reference channel is 140km, and the point target is positioned near the near end of a scene.
| Platform speed | 1700m/s |
| Channel spacing (x, y, z) | 10m，4.8m，4m |
| Antenna azimuth dimension | 1.5m |
| Transmission bandwidth | 200MHz |
| Pulse repetition frequency | 900Hz |
| Platform height | 20km |
| Swath range | 130km-200km |
And the effectiveness of the distance space-variant envelope walk compensation method based on the CS principle is verified by evaluating the azimuth ambiguity side lobe of the imaging result. And in the comparison experiment, when the compensation envelope moves, CS operation is carried out by taking the scene center as a reference distance, the linear phase is directly multiplied in the distance frequency domain by taking the scene center slope distance as the reference distance, and the linear phase is directly multiplied in the distance frequency domain by taking the point target slope distance as the reference distance. After compensation, at the target position, the residual envelope walk between adjacent channels is respectively as follows: 0.0026m,0.0577m,0m. The three experiments are consistent with each other except for the envelope walk correction, the processing flow is carried out according to the flow chart shown in fig. 1, the processing result is shown in fig. 3, the corresponding fuzzy sidelobe heights are-67 dB, -43dB and-73 dB, and the CS method considering the envelope walk distance space variant is improved by 24dB compared with the unified correction method taking the scene center as a reference in the simulation experiment. The result after the envelope walk is accurately corrected close to the target position as a reference.
The scene simulation is a swath edge scene, the distance sampling point is 2200, and the azimuth sampling point is 10000. The imaging results are shown in fig. 4, fig. 4a is the imaging result estimated and compensated by the conventional method, and fig. 4b is the processing result of the method of this embodiment. The boxes in fig. 4 identify the surface targets for strong scattering coefficients in the scene. Region a is the blurring component of the surface target. In fig. 4a, the blurring component is very pronounced. In fig. 4b, the blurring component is hardly visible. Region B is a river region, and the scattering coefficient is weak. A blurred component, which is usually mentioned by some strong scattering on shore, is present. The blurring component of the lower image is significantly stronger than above. The imaging result blurring inhibition capability of the method provided by the patent is obviously enhanced, and the imaging quality is good.
Therefore, the invention provides a two-step channel error estimation and compensation method for the distributed multi-channel SAR, which adopts coarse compensation based on inertial navigation data and fine estimation and compensation based on data to correct channel amplitude-phase errors, sampling time delay and inter-channel distance space variation skew. And the CS method is adopted to realize space variant envelope motion compensation. And the residual error is estimated by adopting a phase increment method after data rearrangement is provided, so that the defects of the prior art are overcome.
Of course, the present invention is capable of other various embodiments and its several details are capable of modification and variation in light of the present invention by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (4)
1. The two-step channel error estimation and compensation method for the distributed multi-channel SAR is characterized by comprising the following steps of:
based on inertial navigation data, coarse correction of envelope walk is realized; for corrected data, roughly compensating space-variant phase errors among channels based on inertial navigation data;
the envelope walking comprises distance space variant envelope walking and inter-channel space variant envelope walking; the specific process for realizing coarse correction of envelope walk based on inertial navigation data comprises the following steps:
calculating the slant distance differences between targets at different distances in a scene and each channel and a reference channel by using inertial navigation information, and performing linear fitting on the slant distance differences; performing CS operation based on the obtained fitting parameters, correcting a distance space-variant part of the envelope walk, multiplying a signal obtained after the CS operation is completed by a linear phase on a distance frequency domain, correcting an inter-channel space-variant part of the envelope walk, and finishing the envelope walk rough correction based on inertial navigation data;
re-ordering the data after envelope walk rough correction and inter-channel space-variant phase error rough compensation according to the correlation between the antenna sampling data; and estimating and compensating the residual envelope by adopting a distance frequency domain phase increment method, and then finely estimating and compensating the residual space-variant phase error by adopting a distance time domain phase increment method.
2. The two-step channel error estimation and compensation method for distributed multi-channel SAR according to claim 1, wherein said CS operation is: and setting a scaling equation and a frequency domain matching function based on the obtained fitting parameters, processing echo signals, and multiplying the processed result by the residual phase to realize space-variant correction of the envelope walking distance.
3. The two-step channel error estimation and compensation method for distributed multi-channel SAR according to claim 2, wherein said scaling equation is:
s sc (t)＝exp{jπα m K r (t-t ref ) 2 }
wherein ,
K r representing the frequency modulation slope of the transmitted signal, t ref ＝2r 0 C represents a reference time, c represents a speed of light, t represents a time, beta m Is the slope in the fitting parameters.
4. The two-step channel error estimation and compensation method for distributed multi-channel SAR according to claim 2, wherein said scaling equation is:
wherein ,
f τ represent distance frequency, K r Representing the frequency modulation slope, beta, of the transmitted signal m Is the slope in the fitting parameters. />
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110515560.0A CN113406627B (en) | 2021-05-12 | 2021-05-12 | Two-step channel error estimation and compensation method for distributed multi-channel SAR |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110515560.0A CN113406627B (en) | 2021-05-12 | 2021-05-12 | Two-step channel error estimation and compensation method for distributed multi-channel SAR |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113406627A CN113406627A (en) | 2021-09-17 |
| CN113406627B true CN113406627B (en) | 2023-05-16 |
Family
ID=77678274
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110515560.0A Active CN113406627B (en) | 2021-05-12 | 2021-05-12 | Two-step channel error estimation and compensation method for distributed multi-channel SAR |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113406627B (en) |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5179383A (en) * | 1991-07-15 | 1993-01-12 | Raney R K | Synthetic aperture radar processor to handle large squint with high phase and geometric accuracy |
| DE102013214676A1 (en) * | 2013-07-26 | 2015-01-29 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Method for correcting the time and phase references of non-synchronous SAR data |
| CN104777479B (en) * | 2015-05-05 | 2017-03-29 | 西安电子科技大学 | Front side based on multi-core DSP regards SAR realtime imaging methods |
| CN104931967B (en) * | 2015-06-12 | 2017-07-18 | 西安电子科技大学 | A kind of improved High Resolution SAR Imaging self-focusing method |
| CN107085213A (en) * | 2017-05-19 | 2017-08-22 | 中国人民解放军63892部队 | The moving target ISAR imaging methods designed based on random Based on Modulated Step Frequency Waveform |
| CN108627831B (en) * | 2018-05-11 | 2020-06-09 | 西安空间无线电技术研究所 | ISAR (inverse synthetic aperture radar) imaging method for medium and high orbit satellite target with ultralow signal-to-noise ratio |
| CN111381217B (en) * | 2020-04-01 | 2022-10-18 | 上海无线电设备研究所 | Missile-borne SAR motion compensation method based on low-precision inertial navigation system |
-
2021
- 2021-05-12 CN CN202110515560.0A patent/CN113406627B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113406627A (en) | 2021-09-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gogineni et al. | Bed topography of Jakobshavn Isbræ, Greenland, and Byrd Glacier, Antarctica | |
| CN105759263B (en) | A kind of spaceborne Squint SAR radar imaging method under high-resolution large scene | |
| US11454702B2 (en) | Synthetic aperture radar method and synthetic aperture radar device | |
| CN110146889B (en) | Large-rotation-angle ISAR imaging method based on optimal echo sub-region selection | |
| CN108490443B (en) | Multi-subarray synthetic aperture sonar omega-k imaging algorithm based on analytic solution and NUFFT | |
| CN107271997B (en) | Airborne multi-channel CSSAR ground moving target motion parameter estimation method | |
| CN114545411B (en) | Polar coordinate format multimode high-resolution SAR imaging method based on engineering realization | |
| CN107942295B (en) | Sparse antenna of forward-looking array SAR system | |
| CN109581362B (en) | Signal processing method of synthetic aperture radar altimeter in variable pulse cluster mode | |
| CN102879784A (en) | Unified imaging method for synthetic aperture radar (SAR) in four modes | |
| CN110146884B (en) | Maneuvering track front-side-looking synthetic aperture radar tomography method | |
| CN110554391B (en) | Low-altitude wind shear wind speed estimation method based on DDD-GMB | |
| CN110488283B (en) | Error correction method for multi-channel HRWS-SAR channel | |
| CN112034459A (en) | Linear synthetic aperture radar imaging method and system based on antenna directional diagram compensation | |
| CN110824473B (en) | Subspace-based high-resolution wide swath SAR-GMTI clutter suppression method | |
| CN114089333B (en) | SAR vibration error estimation and compensation method based on helicopter platform | |
| CN110988873A (en) | Single-channel SAR ship speed estimation method and system based on energy center extraction | |
| CN114609629A (en) | GEO satellite-machine bistatic synchronization method based on direct wave and clutter subspace | |
| CN107271996B (en) | Airborne CSSAR (compact spherical synthetic aperture radar) ground moving target imaging method | |
| CN113900099A (en) | Sparse aperture ISAR maneuvering target imaging and calibrating method | |
| CN113406627B (en) | Two-step channel error estimation and compensation method for distributed multi-channel SAR | |
| CN111707996B (en) | GEO satellite-borne SAR moving target detection method based on improved GRFT-STAP | |
| CN113466797A (en) | Bistatic SAR space-time clutter suppression method based on clutter ridge matching sparse recovery | |
| CN112198490A (en) | Ground clutter suppression method and device for airborne millimeter wave cloud detection radar and radar | |
| Saeedi et al. | Improved navigation-based motion compensation for LFMCW synthetic aperture radar imaging |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |