CN103576151A - Azimuth multi-channel SAR imaging method and system based on compressed sensing - Google Patents
Azimuth multi-channel SAR imaging method and system based on compressed sensing Download PDFInfo
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Abstract
The invention discloses an azimuth multi-channel SAR imaging method and system based on compressed sensing. The azimuth multi-channel SAR imaging method based on compressed sensing comprises the steps that (1) two-dimensional random sparse sampling is carried out on signals received by channels of the azimuth multi-channel SAR imaging system, (2) phase compensation is carried out sparse sampling data of the channels according to the relative position relation between receiving antennas of the channels and a central transmitting antenna, (3) range reconstruction imaging is carried out on range data, processed through phase compensation, of each orientation unit in the corresponding azimuth channel through compressed sensing, and (4) DPCA processing is carried out the data, processed through range reconstruction imaging, of each azimuth channel, and azimuth recovery processing is carried out on azimuth data in each range gate to carry out two-dimensional compressed sensing SAR imaging. According to the technical scheme, the data volume of the azimuth multi-channel SAR imaging system is greatly reduced, the PRF of the azimuth multi-channel SAR imaging system is not limited, and accurate imaging can be carried out on observation scenes.
Description
Technical Field
The invention relates to an azimuth multi-channel Synthetic Aperture Radar (SAR) imaging technology, in particular to an azimuth multi-channel SAR imaging method and system based on compressed sensing.
Background
The azimuth multi-channel SAR imaging technology is one of the mainstream methods for realizing high-resolution and wide swath SAR imaging at present, and the azimuth multi-channel SAR imaging technology can reduce the Pulse Repetition Frequency (PRF) of azimuth transmission and realize azimuth high-resolution imaging while covering a wide swath. The azimuth multi-channel SAR imaging technology has important application in resource exploration, marine mapping, battlefield reconnaissance and environmental protection.
At present, a single transmitting antenna in the middle is used for transmitting signals by an azimuth multi-channel SAR system, and a plurality of sub-antennas in the azimuth receive echo signals from a target area respectively, so that the data volume of the azimuth multi-channel SAR system is increased sharply, and serious burden is brought to a storage link and a data transmission link of the azimuth multi-channel SAR system. Meanwhile, as the number of sub-apertures of the antenna, the sub-aperture spacing and the PRF of the azimuth multi-channel SAR system strictly meet certain conditions, when the parameters of the azimuth multi-channel SAR system do not meet the requirements of the conditions, the signals received by the azimuth multi-channel SAR system are non-uniform sampling signals, and if the non-uniform sampling signals are directly imaged by using the traditional single-channel SAR imaging algorithm, false targets appear in the imaging results, and serious azimuth ambiguity is generated.
The algorithm for solving the azimuth Doppler ambiguity mainly comprises a filter reconstruction algorithm, but the filter reconstruction algorithm relates to matrix inversion operation, when the PRF of the azimuth multi-channel SAR system works at certain specific values, the algorithm has reconstruction mismatch, so that the subsequent imaging effect is poor, and the problems of false target ambiguity caused by non-uniform sampling and the like cannot be effectively eliminated.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide an azimuth multi-channel SAR imaging method and system based on compressed sensing, which can greatly reduce the data volume of the azimuth multi-channel SAR system, and at the same time, the PRF of the azimuth multi-channel SAR system is not limited, and the observation scene can be accurately imaged.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
an azimuth multi-channel SAR imaging method based on compressed sensing is applied to an azimuth multi-channel SAR system, and the method comprises the following steps:
respectively carrying out two-dimensional random sparse sampling on signals received by each channel in the azimuth multi-channel SAR system;
respectively carrying out phase compensation on the sparse sampling data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel;
performing range direction reconstruction imaging on range direction data of each azimuth unit in each channel in the azimuth direction after phase compensation by using compressed sensing;
and performing split Phase Center Antenna (DPCA) processing on the data after the azimuth direction and each channel distance direction reconstruction imaging, and performing azimuth direction recovery processing on the azimuth direction data in each range gate to perform two-dimensional compressed sensing SAR imaging.
Preferably, the two-dimensional random sparse sampling is performed on the signals received by each channel in the azimuth multi-channel SAR system, and includes:
and randomly receiving signals in the azimuth direction through each channel in the azimuth direction multi-channel SAR system, and randomly and sparsely sampling the range direction signals in the range direction at a sampling frequency lower than the Nyquist frequency.
Preferably, the distance direction reconstruction imaging of the distance direction data of each azimuth unit in each channel after the phase compensation by using compressed sensing includes:
constructing a corresponding distance direction observation matrix according to the position of the distance direction sparse sampling data;
according to a minimum of1And (3) performing distance direction reconstruction imaging on the distance direction observation data of each channel by adopting an orthogonal matching tracking algorithm according to the norm criterion.
Preferably, the DPCA processing of the data after the directional orientation of each channel range direction reconstruction imaging, and the directional orientation recovery processing of the directional orientation data in each range gate include:
rearranging the data of the azimuth channels after distance direction reconstruction imaging according to the DPCA principle;
constructing a sub-observation matrix of each channel according to the position relation of the randomly sampled data of each channel in the azimuth direction;
carrying out corresponding arrangement and recombination on each sub-observation matrix to construct an azimuth total observation matrix;
using a minimum of1And performing azimuth recovery processing on azimuth data in each range gate by using a norm criterion and an orthogonal matching pursuit algorithm.
Preferably, the performing phase compensation on the sparsely sampled data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel includes:
using phase compensation factors
Performing phase compensation on sparse sampling data of each channel;
wherein,
i=1,2,…,N，△xiis the distance between the receiving antenna of the i-th channel and the central transmitting antenna, N represents the number of azimuth channels, i represents the number of the receiving antenna of each channel, dazRepresenting the adjacent subaperture spacing, λ represents the carrier wavelength, R0Representing the center slope of the observed scene.
An azimuthally multi-channel SAR imaging system based on compressed sensing, the system comprising: the device comprises a two-dimensional random sparse sampling module, a phase compensation module, a distance direction reconstruction imaging module and an orientation direction recovery processing module; wherein,
the two-dimensional random sparse sampling module is used for respectively carrying out two-dimensional random sparse sampling on signals received by each channel in the azimuth multi-channel SAR system;
the phase compensation module is used for respectively performing phase compensation on the sparse sampling data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel;
the range direction reconstruction imaging module is used for performing range direction reconstruction imaging on range direction data of each azimuth unit in each channel after phase compensation by utilizing compressed sensing;
and the azimuth recovery processing module is used for performing the DPCA processing on the data after the range direction reconstruction imaging of each channel in the azimuth direction, and performing the azimuth recovery processing on the azimuth data in each range gate so as to perform the two-dimensional compressed sensing SAR imaging.
Preferably, the two-dimensional random sparse sampling module is further configured to randomly receive signals in an azimuth direction through each channel in the azimuth-direction multi-channel SAR system, and randomly sparsely sample the range-direction signals in a range direction at a sampling frequency lower than the nyquist frequency.
Preferably, the distance direction reconstruction imaging module is further configured to construct a corresponding distance direction observation matrix according to the position of the distance direction sparse sampling data; according to a minimum of1And (3) performing distance direction reconstruction imaging on the distance direction observation data of each channel by adopting an orthogonal matching tracking algorithm according to the norm criterion.
Preferably, the azimuth recovery processing module is further configured to rearrange the data after the azimuth channels pass through the distance direction reconstruction imaging according to the DPCA principle; constructing a sub-observation matrix of each channel according to the position relation of the randomly sampled data of each channel in the azimuth direction; carrying out corresponding arrangement and recombination on each sub-observation matrix to construct an azimuth total observation matrix; using a minimum of1And performing azimuth recovery processing on azimuth data in each range gate by using a norm criterion and an orthogonal matching pursuit algorithm.
Preferably, the phase compensation module is further configured to use a phase compensation factor
Performing phase compensation on sparse sampling data of each channel;
wherein,
i=1,2,…,N，△xiis the distance between the receiving antenna of the i-th channel and the central transmitting antenna, N represents the number of azimuth channels, i represents the number of the receiving antenna of each channel, dazRepresenting the adjacent subaperture spacing, λ represents the carrier wavelength, R0Representing the center slope of the observed scene.
In the embodiment of the invention, two-dimensional random sparse sampling is respectively carried out on signals received by each channel in the azimuth multi-channel SAR system; respectively carrying out phase compensation on the sparse sampling data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel; performing range direction reconstruction imaging on range direction data of each azimuth unit in each channel in the azimuth direction after phase compensation by using compressed sensing; and performing DPCA processing on the data after the distance direction reconstruction imaging of each channel in the azimuth direction, and performing azimuth direction recovery processing on the azimuth direction data in each range gate to perform two-dimensional compressed sensing SAR imaging. Therefore, the data volume of the azimuth multi-channel SAR system is greatly reduced, and meanwhile, the PRF of the azimuth multi-channel SAR system is not limited and can accurately image an observation scene.
Drawings
FIG. 1 is a schematic flow chart of an azimuth multi-channel SAR imaging method based on compressed sensing according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a geometric imaging model of a satellite-borne azimuth multi-channel SAR according to an embodiment of the invention;
FIG. 3 is a signal processing flow chart of an azimuth multi-channel SAR imaging method based on compressed sensing according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of two-dimensional random sparse sampling of each channel of an azimuth multi-channel SAR system according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a simulation result of an azimuth multi-channel SAR imaging method based on compressed sensing according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of simulation results of a conventional single-channel imaging algorithm for imaging full-sample data;
FIG. 7 is a schematic diagram of a simulation result of one-dimensional imaging of azimuth full-sampling data by using a matched filtering method after PRF works at a singular point and performs spectrum reconstruction by using a filter bank method;
FIG. 8 is a schematic diagram of a one-dimensional reconstruction imaging simulation result of azimuth triple-fold sampled data directly by using a compressed sensing method when a PRF works at a singular point in the embodiment of the present invention;
fig. 9 is a schematic structural composition diagram of an azimuth multi-channel SAR imaging system based on compressed sensing in an embodiment of the present invention.
Detailed Description
So that the manner in which the features and aspects of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings.
The basic idea of the embodiment of the invention is as follows: firstly, the azimuth multi-channel SAR system performs two-dimensional random sparse sampling on each channel, and performs phase compensation according to the position relation between each receiving antenna and a central transmitting antenna; then, performing distance direction reconstruction imaging on the distance direction data of each channel after the phase compensation; and carrying out DPCA processing on the data of each channel after the distance direction reconstruction imaging, and then carrying out azimuth direction recovery on the data in each distance gate, thereby realizing two-dimensional compressed sensing imaging.
Fig. 1 is a schematic flow diagram of an azimuth multi-channel SAR imaging method based on compressed sensing according to an embodiment of the present invention, where the azimuth multi-channel SAR imaging method based on compressed sensing in the embodiment of the present invention is applied to an azimuth multi-channel SAR system, and in a preferred embodiment of the present invention, the method includes the following steps:
step 101: and respectively carrying out two-dimensional random sparse sampling on signals received by each channel in the azimuth multi-channel SAR system.
Preferably, the two-dimensional random sparse sampling is performed on the signals received by each channel in the azimuth multi-channel SAR system, and includes:
and randomly receiving signals in the azimuth direction through each channel in the azimuth direction multi-channel SAR system, and randomly and sparsely sampling the range direction signals in the range direction at a sampling frequency lower than the Nyquist frequency.
Specifically, when two-dimensional random sparse sampling is performed on signals received by each channel in the direction, a central transmitting antenna randomly transmits a limited number of pulse signals, and receiving antennas corresponding to each channel respectively receive a limited number of echo signals; and in the upward distance, random sparse sampling is carried out on the signals received by each channel by using the frequency far lower than the Nyquist frequency, so that the two-dimensional random sparse sampling of each channel is realized.
Step 102: and respectively carrying out phase compensation on the sparse sampling data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel.
In particular, a phase compensation factor is employed
Performing phase compensation on sparse sampling data of each channel;
wherein,i=1,2,…,N，△xiis the distance between the receiving antenna of the i-th channel and the central transmitting antenna, N represents the number of azimuth channels, i represents the number of the receiving antenna of each channel, dazRepresenting the adjacent subaperture spacing, λ represents the carrier wavelength, R0Representing the center slope of the observed scene.
The above parameters in this embodiment can be understood by referring to the schematic diagram of the geometric imaging model of the multi-channel SAR in the satellite-borne azimuth direction shown in fig. 2.
Step 103: and performing range direction reconstruction imaging on range direction data of each azimuth unit in each channel after the phase compensation by using compressed sensing.
Preferably, the distance direction reconstruction imaging of the distance direction data of each azimuth unit in each channel after the phase compensation by using compressed sensing includes:
constructing a corresponding distance direction observation matrix according to the position of the distance direction sparse sampling data;
according to a minimum of1And (3) performing distance direction reconstruction imaging on the distance direction observation data of each channel by adopting an orthogonal matching tracking algorithm according to the norm criterion.
Specifically, firstly, an observation dictionary is constructed by using a transmitting signal waveform as an atom, a corresponding distance direction observation matrix is constructed according to the position of distance direction sparse sampling data of each channel, and then the distance direction observation data and the distance direction observation matrix of each channel are utilized based on the minimum l1A norm criterion, wherein an orthogonal matching pursuit algorithm is adopted to realize distance direction reconstruction imaging;
the constructed distance direction observation matrix is as follows: phir(p,q)=ST((ω(p)-q)×△τ)；
Wherein p and q are respectively the number of rows and columns of the observation matrix, STFor range-wise transmit waveforms, ω (p) is a sequence of locations of range-wise randomly sampled data, and Δ τ is the distanceTime interval to nyquist sampling.
The minimum l1The norm criterion is:
s.t.y=Θα
wherein,
and (3) obtaining a backscattering coefficient estimated value of each distance unit, wherein alpha is the real backscattering coefficient of each distance unit, argmin is the minimum value of the taking function, y is random observation data of the distance direction in each azimuth unit, and theta is a corresponding distance direction observation matrix.
Step 104: and carrying out DPCA processing on the data after the azimuth direction and each channel range direction reconstruction imaging, and carrying out azimuth direction recovery processing on the azimuth direction data in each range gate so as to carry out two-dimensional compressed sensing SAR imaging.
Preferably, the DPCA processing of the data after the directional orientation of each channel range direction reconstruction imaging, and the directional orientation recovery processing of the directional orientation data in each range gate include:
rearranging the data of the azimuth channels after distance direction reconstruction imaging according to the DPCA principle;
constructing a sub-observation matrix of each channel according to the position relation of the randomly sampled data of each channel in the azimuth direction;
carrying out corresponding arrangement and recombination on each sub-observation matrix to construct an azimuth total observation matrix;
using a minimum of1And performing azimuth recovery processing on azimuth data in each range gate by using a norm criterion and an orthogonal matching pursuit algorithm.
Specifically, firstly, according to the position of the random emission pulse of the azimuth direction, the azimuth direction sub-observation matrix of each channel is constructed, and then the azimuth direction of each channel for completing the distance direction reconstructionRearranging observation data according to DPCA principle, rearranging each azimuth sub-observation matrix correspondingly to construct an azimuth total observation matrix, and finally, based on minimum l1And (3) completing the recovery of the azimuth observation scene by utilizing an orthogonal matching tracking algorithm according to a norm criterion, thereby realizing the two-dimensional compressed sensing imaging of the azimuth multi-channel SAR.
The azimuth sub-observation matrix of each channel is as follows:
i=1,2,…,N
wherein k and l are respectively the row number and the column number of the sub-observation matrix of the ith channel, rect is a rectangular window function, omega (k) is a position sequence of the azimuth random emission pulse, N is the number of the azimuth channels,
Tafor azimuthal synthetic aperture time, f0Is the carrier wavelength, RiRound-trip skew, n, from transmitting antenna to ith receiving antenna for transmitting signals0For the range gate sequence, c represents the speed of light.
The total observation matrix of the azimuth direction is as follows: <math>
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wherein
Represents the ith sub-observation matrixk"Rongji]TRepresenting the transpose of the matrix.
Fig. 3 is a signal processing flow chart of a compressed sensing-based azimuth multi-channel SAR imaging method according to an embodiment of the present invention, and in a preferred embodiment of the present invention, the method includes the following steps:
step 301: performing two-dimensional random sparse sampling on signals received by each channel in the azimuth multi-channel SAR system; and performing phase compensation on the sparse sampling data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel.
Here, the two-dimensional random sparse sampling result of the signal received by each channel is as shown in fig. 4, and the black cells represent the positions of the actually sampled data.
Step 302: and performing distance direction reconstruction imaging on the distance direction data of each azimuth unit in each channel of the azimuth direction after the phase compensation by using a compressed sensing method.
Step 303: carrying out DPCA processing on the data after the azimuth direction and each channel distance direction reconstruction imaging; and performing azimuth recovery on azimuth data in each range gate to realize two-dimensional compressed sensing SAR imaging.
The main difference between the present embodiment and the conventional azimuth multi-channel SAR imaging algorithm is that: according to the method, the sampling data volume of the system is greatly reduced through two-dimensional random sparse sampling of each receiving channel, meanwhile, accurate imaging of an observation scene can be directly achieved through a compressed sensing method for non-uniform signals, spectrum reconstruction preprocessing is not needed through a filter bank method, and effective suppression of the blurring of the azimuth false target can be achieved. The simulation system parameters of this embodiment are shown in table 1:
| number of azimuth subapertures N | 3 |
| Carrier wave length lambda | 0.03m |
| Radar platform height H | 495km |
| Slope distance R | 700km |
| Length L of azimuth antenna | 12.6m |
| Transmission signal bandwidth Br | 66.4MHz |
| Azimuth subaperture length daz | 4.15m |
| Transmission pulse time width Tr | 5μs |
| Platform velocity Vs | 7500m/s |
| Pulse repetition frequency PRF | 1500Hz |
| Incident angle theta | 45° |
| Distance down-sampling rate | 3 times of |
| Azimuth down-sampling rate | 3 times of |
TABLE 1
As can be seen from table 1, the system parameters are in the non-uniform sampling operation mode. FIG. 5 is a simulation result of an azimuth multi-channel SAR imaging method based on compressed sensing. The simulation result of directly using the conventional single-channel RD algorithm to perform imaging without performing filter bank spectrum reconstruction on the full-sampling data is shown in fig. 6. The azimuth multi-channel SAR imaging algorithm based on compressed sensing can effectively inhibit false target problems caused by non-uniform sampling and realize accurate imaging of an observation scene.
It should be noted that, for non-uniformly sampled signals, when the PRF operates at a specific value, aliasing may occur to the sampled data in the actual azimuth direction of the system, and the conventional filter bank spectrum reconstruction algorithm becomes unstable due to the matrix inversion operation, that is, the PRF of the system has singular points for the filter bank reconstruction method. When the PRF operates at a singular point, the spectral reconstruction using the filter bank method will fail, resulting in an increase in side lobes. The compressed sensing-based azimuth multi-channel SAR imaging algorithm has no limit requirement on PRF, and can realize accurate recovery of an observation scene under the condition of PRF at a singular point.
When the PRF works near a singular point 1807Hz and other parameters are as in table 1, fig. 7 is a simulation result of performing one-dimensional imaging on full-sampling data by using a matched filtering method after spectral reconstruction by a filter bank, and fig. 8 is an azimuth recovery result of performing one-dimensional imaging by directly using a compressed sensing-based method by using triple-time down-sampling data.
From simulation results, when the PRF of the system works at a singular point, the filter bank spectrum reconstruction method is obviously invalid, and the method based on compressed sensing can realize accurate recovery of the azimuth observation scene by using data volume far lower than full sampling.
Fig. 9 is a schematic structural composition diagram of an azimuth-direction multi-channel SAR imaging system based on compressed sensing in an embodiment of the present invention, and as shown in fig. 9, the system includes: the system comprises a two-dimensional random sparse sampling module 91, a phase compensation module 92, a distance direction reconstruction imaging module 93 and an orientation direction recovery processing module 94; wherein,
the two-dimensional random sparse sampling module 91 is used for respectively performing two-dimensional random sparse sampling on signals received by each channel in the azimuth multi-channel SAR system;
the phase compensation module 92 is configured to perform phase compensation on the sparse sampling data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel;
the range direction reconstruction imaging module 93 is configured to perform range direction reconstruction imaging on the range direction data of each azimuth unit in each channel after the phase compensation by using compressed sensing;
the azimuth recovery processing module 94 is configured to perform the DPCA processing on the data after the range direction reconstruction imaging of each channel in the azimuth direction, and perform the azimuth recovery processing on the azimuth data in each range gate to perform the two-dimensional compressed sensing SAR imaging.
Preferably, the two-dimensional random sparse sampling module 91 is further configured to randomly receive signals in an azimuth direction through each channel in the azimuth-direction multi-channel SAR system, and randomly sparsely sample the range-direction signals in a range direction at a sampling frequency lower than the nyquist frequency.
Preferably, the distance direction reconstruction imaging module 93 is further configured to construct a corresponding distance direction observation matrix according to the position of the distance direction sparse sampling data; according to a minimum of1And (3) performing distance direction reconstruction imaging on the distance direction observation data of each channel by adopting an orthogonal matching tracking algorithm according to the norm criterion.
Preferably, the azimuth recovery processing module 94 is further configured to rearrange the data after the azimuth channels pass through the range direction reconstructed imaging according to the DPCA principle; constructing a sub-observation matrix of each channel according to the position relation of the randomly sampled data of each channel in the azimuth direction; carrying out corresponding arrangement and recombination on each sub-observation matrix to construct an azimuth total observation matrix; using a minimum of1And performing azimuth recovery processing on azimuth data in each range gate by using a norm criterion and an orthogonal matching pursuit algorithm.
Preferably, the phase compensation module 92 is further configured to apply a phase compensation factor
Performing phase compensation on sparse sampling data of each channel;
wherein,i=1,2,…,N，△xiis the distance between the receiving antenna of the i-th channel and the central transmitting antenna, N represents the number of azimuth channels, i represents the number of the receiving antenna of each channel, dazRepresenting the adjacent subaperture spacing, λ represents the carrier wavelength, R0Representing the center slope of the observed scene.
It should be understood by those skilled in the art that the functions of the modules in the compressed sensing-based azimuth-multi-channel SAR imaging system shown in fig. 9 can be understood by referring to the related description of the compressed sensing-based azimuth-multi-channel SAR imaging method. The functions of the modules in the compressed sensing-based azimuth-oriented multi-channel SAR imaging system shown in fig. 9 can be realized by a program running on a processor, and can also be realized by a specific logic circuit.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (10)
1. An azimuth multi-channel Synthetic Aperture Radar (SAR) imaging method based on compressed sensing is applied to an azimuth multi-channel SAR system, and is characterized by comprising the following steps:
respectively carrying out two-dimensional random sparse sampling on signals received by each channel in the azimuth multi-channel SAR system;
respectively carrying out phase compensation on the sparse sampling data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel;
performing range direction reconstruction imaging on range direction data of each azimuth unit in each channel in the azimuth direction after phase compensation by using compressed sensing;
and performing DPCA processing on the data after the distance direction reconstruction imaging of each channel in the azimuth direction, and performing azimuth direction recovery processing on the azimuth direction data in each range gate to perform two-dimensional compressed sensing SAR imaging.
2. The method of claim 1, wherein the performing two-dimensional random sparse sampling on the signals received by each channel in the multi-channel SAR system comprises:
and randomly receiving signals in the azimuth direction through each channel in the azimuth direction multi-channel SAR system, and randomly and sparsely sampling the range direction signals in the range direction at a sampling frequency lower than the Nyquist frequency.
3. The method of claim 1, wherein the performing range-wise reconstruction imaging on the range-wise data of the phase-compensated azimuth direction to each azimuth cell in each channel by using compressed sensing comprises:
constructing a corresponding distance direction observation matrix according to the position of the distance direction sparse sampling data;
according to a minimum of1And (3) performing distance direction reconstruction imaging on the distance direction observation data of each channel by adopting an orthogonal matching tracking algorithm according to the norm criterion.
4. The method according to claim 1, wherein the performing DPCA processing on the data after the directional-direction distance-direction reconstruction imaging on the directional-direction individual channel distance-direction data, and performing directional-direction recovery processing on the directional-direction data in each range gate comprises:
rearranging the data of the azimuth channels after distance direction reconstruction imaging according to the DPCA principle;
constructing a sub-observation matrix of each channel according to the position relation of the randomly sampled data of each channel in the azimuth direction;
carrying out corresponding arrangement and recombination on each sub-observation matrix to construct an azimuth total observation matrix;
using a minimum of1And performing azimuth recovery processing on azimuth data in each range gate by using a norm criterion and an orthogonal matching pursuit algorithm.
5. The method according to any one of claims 1 to 4, wherein the performing phase compensation on the sparsely sampled data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel comprises:
using phase compensation factorsPerforming phase compensation on sparse sampling data of each channel;
wherein,
i=1,2,…,N，△xiis the distance between the receiving antenna of the i-th channel and the central transmitting antenna, N represents the number of azimuth channels, i represents the number of the receiving antenna of each channel, dazRepresenting the adjacent subaperture spacing, λ represents the carrier wavelength, R0Representing the center slope of the observed scene.
6. An azimuth multi-channel SAR imaging system based on compressed sensing, characterized in that the system comprises: the device comprises a two-dimensional random sparse sampling module, a phase compensation module, a distance direction reconstruction imaging module and an orientation direction recovery processing module; wherein,
the two-dimensional random sparse sampling module is used for respectively carrying out two-dimensional random sparse sampling on signals received by each channel in the azimuth multi-channel SAR system;
the phase compensation module is used for respectively performing phase compensation on the sparse sampling data of each channel according to the relative position relationship between the receiving antenna and the central transmitting antenna of each channel;
the range direction reconstruction imaging module is used for performing range direction reconstruction imaging on range direction data of each azimuth unit in each channel after phase compensation by utilizing compressed sensing;
and the azimuth recovery processing module is used for performing the DPCA processing on the data after the range direction reconstruction imaging of each channel in the azimuth direction, and performing the azimuth recovery processing on the azimuth data in each range gate so as to perform the two-dimensional compressed sensing SAR imaging.
7. The system of claim 6, wherein the two-dimensional random sparse sampling module is further configured to randomly receive signals in an azimuth direction through each channel in the azimuth-direction multi-channel SAR system, and randomly sparsely sample range-direction signals in a range direction at a sampling frequency lower than a Nyquist frequency.
8. The system of claim 6, wherein the range-wise reconstruction imaging module is further configured to construct a corresponding range-wise observation matrix according to the location of the range-wise sparsely sampled data; according to a minimum of1And (3) performing distance direction reconstruction imaging on the distance direction observation data of each channel by adopting an orthogonal matching tracking algorithm according to the norm criterion.
9. The system of claim 6, wherein the azimuth restoration processing module is further configured to rearrange the data after the azimuth channels are subjected to the range-wise reconstruction imaging according to the DPCA principle; constructing a sub-observation matrix of each channel according to the position relation of the randomly sampled data of each channel in the azimuth direction; carrying out corresponding arrangement and recombination on each sub-observation matrix to construct an azimuth total observation matrix; using a minimum of1And performing azimuth recovery processing on azimuth data in each range gate by using a norm criterion and an orthogonal matching pursuit algorithm.
10. The system of any of claims 6 to 9, wherein the phase compensation module is further configured to use a phase compensation factorPerforming phase compensation on sparse sampling data of each channel;
wherein,
i=1,2,…,N，△xiis the distance between the receiving antenna of the i-th channel and the central transmitting antenna, N represents the number of azimuth channels, i represents the number of the receiving antenna of each channel, dazRepresenting the adjacent subaperture spacing, λ represents the carrier wavelength, R0Representing the center slope of the observed scene.
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