CN113176565B - Multi-channel SAR distance ambiguity suppression method and device - Google Patents
Multi-channel SAR distance ambiguity suppression method and device Download PDFInfo
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Abstract
The embodiment of the application provides a multichannel SAR distance ambiguity suppression method and device, wherein the method comprises the following steps: carrying out azimuth phase encoding on each transmission pulse of the multi-channel SAR system to obtain a phase-weighted transmission pulse; performing azimuth phase demodulation on the echo of each phase-weighted transmitting pulse to obtain a demodulation signal; constructing a reconstruction optimization model taking the azimuth fuzzy power of the demodulation signal as a quadratic constraint and the distance fuzzy power of the demodulation signal as a target function; solving the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter; and restraining the distance ambiguity of the multi-channel SAR system through the multi-channel SAR reconstruction filter. Therefore, the distance ambiguity suppression method provided by the embodiment of the application can be applied to an APC multi-channel SAR system, and meanwhile, the distance ambiguity can be effectively suppressed through a multi-channel SAR reconstruction filter.
Description
Technical Field
The embodiment of the application relates to the technical field of radar, and relates to but is not limited to a method and a device for multi-channel SAR distance ambiguity suppression.
Background
A multi-channel Synthetic Aperture Radar (SAR) system is a SAR system with single transmission and multiple reception along the azimuth direction, and the system replaces time sampling with azimuth space sampling, so that the requirement of system design on Pulse Repetition Frequency (PRF) is reduced, and the problems of insufficient space freedom, contradiction between resolution and mapping bandwidth, fuzzy relation constraint and the like of the traditional SAR system are solved. Azimuth Phase encoding (APC) refers to a technique for Phase-encoding each transmission pulse in the Azimuth direction, which is low in system complexity and capable of suppressing the distributed target range ambiguity, but requires a large PRF. The APC multi-channel SAR system is characterized in that an APC technology is applied to the multi-channel SAR system, and the distance ambiguity performance of the multi-channel SAR system is further improved through multi-channel phase coding.
At present, the traditional reconstruction algorithm in the APC multi-channel SAR system can cause the reduction of the APC inhibition capability on the range ambiguity, and even worsen the range ambiguity of the multi-channel SAR system.
Disclosure of Invention
Based on the above problems in the related art, embodiments of the present application provide a method and an apparatus for multi-channel SAR distance ambiguity suppression, which can enhance the capability of APC for suppressing distance ambiguity through a reconstruction optimization model.
The technical scheme of the embodiment of the application is realized as follows:
the embodiment of the application provides a multichannel SAR distance ambiguity suppression method, which comprises the following steps:
carrying out azimuth phase encoding on each transmission pulse of the multi-channel SAR system to obtain a transmission pulse after phase weighting;
performing azimuth phase demodulation on the echo of each phase-weighted transmission pulse to obtain a demodulation signal;
constructing a reconstruction optimization model taking the azimuth fuzzy power of the demodulation signal as quadratic constraint and distance fuzzy power as a target function;
solving the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter;
and inhibiting the distance ambiguity through the multi-channel SAR reconstruction filter.
The embodiment of the application provides a multichannel SAR range ambiguity suppression device, includes:
the azimuth phase coding module is used for carrying out azimuth phase coding on each transmitting pulse of the multi-channel SAR system to obtain a phase-weighted transmitting pulse;
the azimuth phase demodulation module is used for carrying out azimuth phase demodulation on the echo of the emission pulse after each phase weighting to obtain a demodulation signal;
the construction module is used for constructing a reconstruction optimization model which takes the azimuth fuzzy power of the demodulation signal as a secondary constraint and takes the distance fuzzy power as a target function;
the solving module is used for solving the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter;
and the suppression module is used for suppressing the range ambiguity through the multi-channel SAR reconstruction filter.
In the embodiment of the application, azimuth phase coding is carried out on each transmitting pulse of a multi-channel SAR system to obtain the transmitting pulse after phase weighting, azimuth phase demodulation is carried out on the echo of the transmitting pulse after each phase weighting to obtain a demodulation signal, a reconstruction optimization model taking azimuth fuzzy power of the demodulation signal as secondary constraint and distance fuzzy power as a target function is constructed, the reconstruction optimization model is solved to obtain a multi-channel SAR reconstruction filter, and distance fuzzy is restrained through the multi-channel SAR reconstruction filter. Therefore, the distance ambiguity suppression method provided by the embodiment of the application can be applied to an APC multi-channel SAR system, and meanwhile, the distance ambiguity can be effectively suppressed through a multi-channel SAR reconstruction filter.
Drawings
Fig. 1 is an alternative flow chart diagram of a multi-channel SAR distance ambiguity suppression method provided in an embodiment of the present application;
FIG. 2 is a spectral envelope of an azimuth useful signal and an azimuth distance blurred signal after a conventional reconstruction method;
fig. 3 is a frequency spectrum envelope of an azimuth useful signal and an azimuth distance ambiguity signal obtained by a multi-channel SAR distance ambiguity suppression method according to an embodiment of the present application;
FIG. 4 is a graph comparing APC gains with PRF changes of a conventional reconstruction method and the multi-channel SAR range ambiguity suppression method provided by the embodiment of the present application;
fig. 5 is a schematic structural diagram of a composition of a multi-channel SAR distance ambiguity suppression apparatus provided in an embodiment of the present application.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of the present application belong. The terminology used in the embodiments of the present application is for the purpose of describing the embodiments of the present application only and is not intended to be limiting of the present application.
Based on the above problems in the related art, an embodiment of the present application provides a multi-channel SAR distance ambiguity suppression method, referring to fig. 1, where fig. 1 is an optional flow diagram of the multi-channel SAR distance ambiguity suppression method provided in the embodiment of the present application, and will be described with reference to the steps shown in fig. 1.
And S101, carrying out azimuth phase coding on each transmitting pulse of the multi-channel SAR system to obtain a phase-weighted transmitting pulse.
Here, azimuth phase encoding refers to phase weighting of the pulses transmitted at each azimuth time.
In some embodiments, the transmit pulse at the nth azimuth time in the multi-channel SAR system is phase weighted to obtain a weighted phase of the transmit pulse; and obtaining the phase-weighted transmission pulse according to the weighted phase of the transmission pulse.
And step S102, performing azimuth phase demodulation on the echo of each phase-weighted transmitting pulse to obtain a demodulation signal.
In some embodiments, a receiving pulse time n + m corresponding to an nth azimuth time of a transmitting pulse is obtained, and azimuth phase demodulation is performed on an echo of each transmitting pulse according to the receiving pulse time n + m to obtain a demodulated signal.
In some embodiments, for the SAR, the electromagnetic wave propagation time is much longer than the pulse repetition time (PRI), so the transmit pulse n corresponds to a receive pulse time n + m (where m is a fixed constant).
The purpose of the azimuth demodulation here is to compensate for the weighted phase modulated on the useful signal, the azimuth residual phase of the demodulated useful signal and the even-order range ambiguity signals being 0, while the azimuth residual phase of the odd-order range ambiguity signals alternating between pi/2 and-pi/2.
In some embodiments, the range ambiguity performance of the space-borne SAR system is mainly based on the first-order range ambiguity, and therefore, the embodiments of the present application mainly discuss the influence of the first-order range ambiguity on a useful signal.
And S103, constructing a reconstruction optimization model taking the azimuth fuzzy power of the demodulation signal as a secondary constraint and the distance fuzzy power of the demodulation signal as an objective function.
In the embodiment of the present application, step S103 may be implemented by the following steps:
and step S1031, determining the frequency spectrum of the actually sampled signal according to the geometric relation between the demodulated signal and the azimuth multi-channel.
In the embodiment of the application, the azimuth signal is determined according to the geometrical relationship among the azimuth multiple channels of the demodulation signal, and the azimuth signal comprises an azimuth useful signal and an azimuth distance fuzzy signal; carrying out azimuth phase demodulation on the azimuth useful signal and the azimuth distance fuzzy signal to determine a useful signal after actual sampling; determining the frequency spectrum of the actually sampled useful signal according to Fourier transform properties; determining an actually sampled azimuth distance fuzzy signal according to the change of the residual phase of the azimuth distance fuzzy signal; determining the frequency spectrum of the actually sampled azimuth distance fuzzy signal according to Fourier transform properties; and determining the frequency spectrum of the actually sampled useful signal and the frequency spectrum of the actually sampled azimuth distance fuzzy signal as the frequency spectrum of the actually sampled signal.
Step S1032 determines a spectrum vector of the actually sampled signal and a covariance matrix of the actually sampled signal according to the spectrum of the actually sampled signal.
In the embodiment of the application, according to the frequency spectrum of the actually sampled useful signal and the frequency spectrum of the actually sampled azimuth distance fuzzy signal, determining a frequency spectrum vector of the actually sampled useful signal and a frequency spectrum vector of the actually sampled azimuth distance fuzzy signal, and a covariance matrix of the actually sampled useful signal and a covariance matrix of the actually sampled distance fuzzy signal; determining the frequency spectrum vector of the actually sampled useful signal and the frequency spectrum vector of the actually sampled azimuth distance fuzzy signal as the frequency spectrum vector of the actually sampled signal; and determining the covariance matrix of the actually sampled useful signal and the covariance matrix of the actually sampled distance fuzzy signal as the covariance matrix of the actually sampled signal.
Step S1033, determining a reconstructed filtered signal, a reconstructed azimuth ambiguity power, and a reconstructed range ambiguity signal power by reconstructing a filter bank, a spectrum vector of the actually sampled signal, and a covariance matrix of the actually sampled signal.
In some embodiments, reconstructing the filtered signal comprises: a useful signal and a range ambiguity signal. Wherein the useful signal comprises: the useful signal is azimuthally ambiguous and the desired useful signal.
And S1034, constructing the reconstruction optimization model by taking the azimuth fuzzy power as quadratic constraint and the distance fuzzy signal power as a target function.
And step S104, solving the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter.
In some embodiments, a solution formula is obtained by solving the reconstruction optimization model, and a multi-channel SAR reconstruction filter is obtained by solving the solution formula.
In the embodiment of the application, the reconstruction optimization model is a quadratic constraint quadratic programming convex optimization problem.
And S105, restraining the range ambiguity of the multi-channel SAR system through the multi-channel SAR reconstruction filter.
The method comprises the steps of carrying out azimuth phase coding on each transmission pulse of a multi-channel SAR system to obtain the transmission pulse after phase weighting, carrying out azimuth phase demodulation on an echo of the transmission pulse after each phase weighting to obtain a demodulation signal, constructing a reconstruction optimization model taking azimuth fuzzy power of the demodulation signal as secondary constraint and distance fuzzy signal power as a target function, solving the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter, and inhibiting distance fuzzy through the multi-channel SAR reconstruction filter. Therefore, the distance ambiguity suppression method provided by the embodiment of the application can be applied to an APC multi-channel SAR system, and can effectively suppress distance ambiguity.
Based on the foregoing embodiments, the embodiments of the present application further provide a multi-channel SAR distance ambiguity suppression method, which will be described with reference to the steps shown in fig. 1.
And S101, carrying out azimuth phase coding on each transmitting pulse of the multi-channel SAR system to obtain a phase-weighted transmitting pulse.
In some embodiments, azimuth phase encoding refers to phase weighting of the pulses transmitted at each azimuth time instant, where the weighted phase of the transmit pulse at the nth azimuth time instant
Expressed as the following formula (1):
wherein M is an integer and M is not less than 2; j is an imaginary unit; assuming that PRF represents the pulse repetition frequency, this weighted phase will cause the azimuthal Doppler spectrum to shift by PRF/M, hence the term M is the APC frequency shift factor.
In some embodiments, to maximize the amount of frequency shift, we take M =2, and at this time, the weighted phase of the transmit pulse at the nth azimuth instant may be further reduced to equation (2) below:
here, the encoding method for performing azimuth phase encoding on each transmission pulse of the multi-channel SAR system is 0, -pi/2,0, -pi/2, ….
And step S102, performing azimuth phase demodulation on the echo of each phase-weighted transmitting pulse to obtain a demodulation signal.
In some embodiments, for on-board SAR, the electromagnetic wave propagation time is much longer than the pulse repetition time, so the receive pulse time instant for the transmit pulse n is n + m (where m is a fixed constant).
The purpose of the azimuth demodulation here is to compensate for the weighted phase modulated on the useful signal, and therefore the azimuth demodulation phase
Can be expressed as the following equation (3):
here, the azimuth residual phase of the demodulated useful signal and the even-order range ambiguity signal are both 0, while the azimuth residual phase of the odd-order range ambiguity signal alternates between pi/2 and-pi/2.
In some embodiments, the range ambiguity performance of the space-borne SAR system is mainly based on first-order range ambiguity, and therefore, the embodiments of the present application mainly discuss the influence of the first-order range ambiguity on a useful signal.
Here, the echo of each transmit pulse is azimuth phase demodulated to a demodulation phase of 0, π/2,0, π/2, ….
And S103, constructing a reconstruction optimization model taking the azimuth fuzzy power of the demodulation signal as a secondary constraint and the distance fuzzy power of the demodulation signal as an objective function.
In some embodiments, the orientation signal is determined based on a geometric relationship with the orientation multichannel; wherein the azimuth signal comprises an azimuth useful signal and an azimuth distance ambiguity signal in s j 0 (η) and s j r (η) represents the useful azimuth signal and the range ambiguity signal of the jth channel, respectively, and is expressed by the following formula (4):
wherein σ 0 、σ r Respectively representing scattering coefficients corresponding to the azimuth useful signal and the azimuth distance fuzzy signal; s (η) represents the reference signal, η is the azimuth time,
representing right shift on the basis of signal s (eta)
A length; d j = (j-1) d, representing the spacing between the jth channel and the reference channel; d is the adjacent channel spacing; v. of s Is the satellite platform velocity.
In some embodiments, the actual sampled useful signal is generated after APC modulation or demodulation of the azimuth signal
Can be expressed as the following formula (5):
wherein, T p Represents the pulse repetition interval, δ (η) being the dirac function; delta (. Eta. -kT) p ) Representing a right shift kT on the basis of a function delta (eta) p A length; k is an integer;
in some embodiments, the Fourier transform property is usedDetermining the frequency spectrum of the actually sampled useful signal
Expressed as the following equation (6):
wherein f represents frequency; f. of p Represents the pulse repetition frequency; s (f) represents the spectrum of S (η).
Here, the actually sampled distance-blurred signal is due to the fact that the residual phase of the distance-blurred signal alternates between π/2 and- π/2
Expressed as the following equation (7):
accordingly, the frequency spectrum of the azimuth distance blur signal is expressed as the following formula (8):
in some embodiments, the spectral vector of the azimuth useful signal is based on the results of equations (6) and (8)
Spectral vector of sum-azimuth distance ambiguity signal
The forms are expressed as the following formulas (9), respectively:
wherein the matrix in the formula (9) can be expressed as the following formula (10):
wherein diag (·) represents a diagonal operator, and n (f) is white gaussian noise;
representing the actually sampled useful signal spectrum vector;
representing the distance fuzzy signal spectrum vector after actual sampling; s (f) represents an ideal Doppler spectrum vector; a (f) represents a channel matrix; a is k (f) Representing a channel vector; c r The matrix is modulated for APC.
In some embodiments, the covariance matrix of the actual sampled useful signal
Covariance matrix of distance fuzzy signal after actual sampling
Expressed as the following formula (11), respectively:
wherein R is r (f)＝A(f)Λ(f-f p /2)A H (f) (ii) a E (-) represents the desired operator; sigma n 2 Representing the noise power; i is N Is an N-order identity matrix; a. The H (f) Is a matrix after conjugate transpose of A (f); Λ (f) is a diagonal matrix associated with the antenna pattern, and Λ (f) is expressed as the following equation (12):
Λ(f)＝diag([…,|S(f)| 2 ,…,|S(f-kf p )| 2 ,…] T ) (12)；
in some embodiments, let ω l (f) L =1,2, N is the reconstruction filter bank and satisfies ω l H (f)a l (f) =1, so that the filtered signal S is reconstructed c,l (f) Expressed as the following equation (13):
wherein c is a real number.
In the embodiment of the application, the azimuth fuzzy power P of the reconstructed useful signal am Expressed as the following equation (14):
wherein, a k (f) Representing a channel vector; s (f-kf) p ) Representing a right shift kf on the basis of the function S (f) p Length.
In the embodiment of the application, the reconstructed distance fuzzy signal power
Expressed as the following equation (15):
wherein, C r H Is C r Conjugating and transposing the matrix; omega l H (f) Is omega l (f) The vector after transposition is conjugated.
In the embodiment of the present application, the azimuth-distance blur signal power P without using APC r Expressed as the following equation (16):
in some embodimentsIn the middle, APC gain refers to the ratio of distance blur signal power before and after APC, APC gain G APC Expressed as the following formula (17):
in some embodiments, the conventional reconstruction filter ω l (f) Expressed as the following equation (18):
ω l (f)＝(A 0 -1 (f)) H e l (18)；
wherein A is 0 (f) Is an N matrix, is a subset of matrix A (f); a. The 0 -1 (f) Is A 0 (f) The inverse matrix of (d); e.g. of the type l Representing an N x 1 th order zero vector divided by 1 th position.
In some embodiments, the conventional reconstruction filter and matrix C are given by equation (18) r Irrelevant, but range-blurred signals contain matrix C r Therefore, the reconstruction filter cannot suppress the range ambiguity to the maximum extent, and even deteriorates the range ambiguity performance.
In the embodiment of the present application, in order to solve the problem that the conventional reconstruction filter cannot suppress the range ambiguity to the maximum extent, the embodiment of the present application imposes a feasibility constraint on the azimuth ambiguity power of the useful signal, where the feasibility constraint is expressed by the following formula (19):
ω l H (f)R am (f)ω l (f)≤∈ 2 (f) (19)；
wherein e is 2 (f) And representing an orientation ambiguity constraint function used for controlling the orientation ambiguity performance of the APC multi-channel SAR system.
In the embodiment of the present application, in order to suppress the distance ambiguity to the maximum extent, the embodiment of the present application establishes a reconstruction optimization model according to the following formula (20):
here, the equation constraint in equation (20) is used to ensure that the useful signal is not distorted within the doppler bandwidth.
And step S104, solving the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter.
In some embodiments, equation (20) is a quadratic constraint quadratic programming convex optimization problem, and the reconstruction filter can be obtained by solving equation (21), where equation (21) is expressed as:
wherein, λ and μ are intermediate variables, λ is a real number greater than or equal to 0, and μ is any real number.
Here, the solution of equation (21) can be expressed as the following equation (22):
wherein,
is an intermediate variable;
is a real solution to equation (23), equation (23) being expressed as:
here, of the formula (23)
The method can be obtained by a Newton iteration method (Newton iteration method), and the equation (23) can also be converted into a one-dimensional multiple equation to solve the equation root
In some embodiments of the present invention, the,
and S105, restraining the range ambiguity of the multi-channel SAR system through the multi-channel SAR reconstruction filter.
In the embodiment of the present application, the parameters of the multi-channel SAR system are shown in table 1 below.
TABLE 1 multichannel SAR System parameters
| Parameter(s) | Value of |
| Doppler processing bandwidth | 3500Hz |
| Platform velocity | 7500m/s |
| Carrier frequency | 9.6GHz |
| Total antenna length | 12m |
| Length of transmitting antenna | 5m |
| Number of receiving antennas | 8 |
| Sub-aperture length | 1.5m |
| Pulse repetition frequency | |
| PRF range | |
| 1300～2000Hz |
In the embodiment of the present application, fig. 2 is a spectrum envelope of an azimuth useful signal and an azimuth distance blurred signal after a conventional reconstruction method; fig. 3 is a spectrum envelope of an azimuth useful signal and an azimuth distance ambiguity signal obtained by a multi-channel SAR distance ambiguity suppression method according to an embodiment of the present application. Compared with the traditional reconstruction method, the multi-channel SAR distance ambiguity suppression method provided by the embodiment of the application can effectively suppress the distance ambiguity of the multi-channel SAR.
Fig. 4 is a graph comparing APC gains with PRF variation for the conventional reconstruction method and the multi-channel SAR range ambiguity suppression method provided in the embodiment of the present application. As can be seen from fig. 4, compared with the conventional reconstruction method, the multi-channel SAR distance ambiguity suppression method provided by the embodiment of the present application can have a higher APC gain (i.e. G) apc ) And the APC gain increases with increasing PRF.
Fig. 5 is a schematic structural diagram of a composition of a multi-channel SAR distance ambiguity suppression apparatus provided in an embodiment of the present application, and as shown in fig. 5, the multi-channel SAR distance ambiguity suppression apparatus 500 includes:
the azimuth phase encoding module 501 is configured to perform azimuth phase encoding on each transmit pulse of the multi-channel SAR system to obtain a phase-weighted transmit pulse; an azimuth phase demodulation module 502, configured to perform azimuth phase demodulation on the echo of each phase-weighted transmit pulse to obtain a demodulated signal; a constructing module 503, configured to construct a reconstruction optimization model with the azimuth fuzzy power of the demodulated signal as a quadratic constraint and the range fuzzy power of the demodulated signal as a target function; a solving module 504, configured to solve the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter; a suppression module 505, configured to suppress the range ambiguity of the multi-channel SAR system through the multi-channel SAR reconstruction filter.
In some embodiments, the azimuth phase encoding module is further to: carrying out phase weighting on the transmitted pulse at the nth azimuth moment in the multi-channel SAR system to obtain a weighted phase of the transmitted pulse; wherein the weighted phase of the transmit pulse at the nth azimuth time
Expressed as:
wherein M is an azimuth phase encoding frequency shift factor, M is an integer and M is greater than or equal to 2;
wherein, when M is 2, the weighting phase of the transmitting pulse at the nth azimuth moment
Comprises the following steps:
and obtaining the phase-weighted transmission pulse according to the weighted phase of the transmission pulse.
In some embodiments, when M is 2, the azimuth phase demodulation module is further configured to: acquiring a receiving pulse time n + m corresponding to the nth azimuth time of the phase-weighted transmitting pulse;
according to the pulse receiving time, the azimuth phase demodulation is carried out on the echo of each phase-weighted transmitting pulse to obtain the demodulation signal; wherein, when M is 2, the azimuth demodulation phase
Expressed as:
in some embodiments, the build module is further to: obtaining the azimuth fuzzy power of the reconstructed useful signal and the reconstructed distance fuzzy signal power according to the demodulation signal, wherein the azimuth fuzzy power P of the reconstructed useful signal am Expressed as:
the distance-blurred signal power
Expressed as:
applying a feasibility constraint to the azimuth ambiguity power, expressed as:
ω l H (f)R am (f)ω l (f)≤∈ 2 (f)；
constructing the reconstruction optimization model by taking the azimuth fuzzy power as a secondary constraint and the distance fuzzy signal power as an objective function; wherein the reconstruction optimization model is represented as:
in some embodiments, the build module is further to: determining the frequency spectrum of the actually sampled signal according to the geometric relationship between the demodulated signal and the azimuth multi-channel; determining a frequency spectrum vector of the actually sampled signal and a covariance matrix of the actually sampled signal according to the frequency spectrum of the actually sampled signal; and determining the azimuth fuzzy power of the reconstructed filtered signal, the reconstructed useful signal and the reconstructed distance fuzzy signal power through a reconstruction filter bank, the frequency spectrum vector of the actually sampled signal and the covariance matrix of the actually sampled signal.
In some embodiments, the build module is further to: determining an azimuth signal according to the geometrical relationship between the demodulation signal and the azimuth multichannel; wherein the azimuth signal comprises an azimuth useful signal and an azimuth distance fuzzy signal;
by s j 0 (η) and s j r (η) respectively representing the orientation useful signal and the orientation distance fuzzy signal of the jth channel, wherein s j 0 (η) and s j r (η) is represented by:
wherein σ 0 、σ r Respectively representing scattering coefficients corresponding to the azimuth useful signal and the azimuth distance fuzzy signal; s (η) represents a reference signal; eta is azimuth time; d j = (j-1) d, representing the spacing between the jth channel and the reference channel; d is the adjacent channel spacing; v. of s Is the satellite platform velocity;
performing azimuth phase demodulation on the azimuth signal to determine an actually sampled useful signal, wherein the actually sampled useful signal is represented as:
wherein, T p Represents the pulse repetition interval, δ (η) is the dirac function;
determining the frequency spectrum of the actually sampled useful signal according to the Fourier transform property, wherein the frequency spectrum of the actually sampled useful signal is represented as:
wherein, f p Represents the pulse repetition frequency;
determining an actually sampled azimuth distance fuzzy signal according to the change of the residual phase of the azimuth distance fuzzy signal, wherein the actually sampled azimuth distance fuzzy signal is represented as:
determining a frequency spectrum of the actually sampled azimuth distance fuzzy signal according to Fourier transform properties, wherein the frequency spectrum of the azimuth distance fuzzy signal is represented as:
and determining the frequency spectrum of the actually sampled useful signal and the frequency spectrum of the actually sampled azimuth distance fuzzy signal as the frequency spectrum of the actually sampled signal.
In some embodiments, the build module is further to: determining a frequency spectrum vector of the actually sampled useful signal and a frequency spectrum vector of the actually sampled azimuth distance fuzzy signal, and a covariance matrix of the actually sampled useful signal and a covariance matrix of the actually sampled distance fuzzy signal according to the frequency spectrum of the actually sampled useful signal and the frequency spectrum of the actually sampled azimuth distance fuzzy signal;
determining the frequency spectrum vector of the actually sampled useful signal and the actually sampled distance fuzzy signal as the frequency spectrum vector of the actually sampled signal;
determining the covariance matrix of the actually sampled useful signal and the actually sampled distance fuzzy signal as the covariance matrix of the actually sampled signal;
wherein the spectral vector of the actual sampled signal is represented as:
the covariance matrix of the actually sampled signal is expressed as:
wherein E (-) represents the desired operator; sigma n 2 Representing the noise power; i is N Is an N-order identity matrix; Λ (f) is the diagonal matrix associated with the antenna pattern.
In some embodiments, the build module is further to: determining a reconstructed filtered signal, an orientation fuzzy power of a reconstructed useful signal and a reconstructed distance fuzzy signal power through a reconstruction filter bank, a frequency spectrum vector of the actually sampled signal and a covariance matrix of the actually sampled signal, and reconstructing a filtered signal S c,l (f) Expressed as:
the azimuth fuzzy power P of the reconstructed useful signal am Expressed as:
the reconstructed distance-blurred signal power
Expressed as:
in some embodiments, the solving module is further configured to: obtaining a solving formula by solving the reconstruction optimization model, wherein the solving formula is expressed as:
obtaining the multi-channel SAR reconstruction filter by solving the solving formula, wherein the multi-channel SAR reconstruction filter
Expressed as:
it should be noted that the description of the apparatus in the embodiment of the present application is similar to that of the method embodiment described above, and has similar beneficial effects to the method embodiment, and therefore, the description is not repeated. For technical details not disclosed in the embodiments of the apparatus, reference is made to the description of the embodiments of the method of the present application for understanding.
The above description is only an example of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, and improvement made within the spirit and scope of the present application are included in the protection scope of the present application. It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises", "comprising" or any other variation thereof are intended to cover a non-exclusive inclusion, so that a process, a method or an apparatus including a series of elements includes not only those elements but also other elements not explicitly listed or inherent to such process, method or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element. In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present application, and shall cover the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A multi-channel SAR distance ambiguity suppression method is characterized by comprising the following steps:
carrying out azimuth phase encoding on each transmission pulse of the multi-channel SAR system to obtain a phase-weighted transmission pulse;
performing azimuth phase demodulation on the echo of each phase-weighted transmitting pulse to obtain a demodulation signal;
constructing a reconstruction optimization model taking the azimuth fuzzy power of the demodulation signal as quadratic constraint and the distance fuzzy power of the demodulation signal as a target function;
solving the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter;
and restraining the distance ambiguity of the multi-channel SAR system through the multi-channel SAR reconstruction filter.
2. The method of claim 1, wherein performing azimuth phase encoding on each transmit pulse of the multi-channel SAR system to obtain a phase-weighted transmit pulse comprises:
carrying out phase weighting on the transmitted pulse at the nth azimuth moment in the multi-channel SAR system to obtain a weighted phase of the transmitted pulse; wherein the weighted phase of the transmit pulse at the nth azimuth time
Expressed as:
wherein M is an azimuth phase encoding frequency shift factor, M is an integer and M is greater than or equal to 2; j is an imaginary unit;
wherein, when M is 2, the weighting phase of the transmitting pulse at the nth azimuth moment
Comprises the following steps:
and obtaining the phase-weighted transmission pulse according to the weighted phase of the transmission pulse.
3. The method of claim 2, wherein when M is 2, performing azimuth-phase demodulation on the echo of each phase-weighted transmit pulse to obtain a demodulated signal, includes:
acquiring a receiving pulse time n + m corresponding to the nth azimuth time of the phase-weighted transmitting pulse;
according to the pulse receiving time, the azimuth phase demodulation is carried out on the echo of each phase-weighted transmitting pulse to obtain the demodulation signal; wherein, when M is 2, the azimuth demodulation phase
Expressed as:
4. the method of claim 1, wherein constructing a reconstruction optimization model with quadratic constraint on the azimuth ambiguity power of the demodulated signal and objective function on the range ambiguity power of the demodulated signal comprises:
obtaining the azimuth fuzzy power of the reconstructed useful signal and the reconstructed distance fuzzy signal power according to the demodulation signal, wherein the azimuth fuzzy power P of the reconstructed useful signal am Expressed as:
wherein, a k (f) Representing a channel vector; s (f-kf) p ) Representing a right shift kf on the basis of the function S (f) p A length;
the distance-blurred signal power
Expressed as:
wherein, C r H Is C r Conjugating and transposing the matrix; omega l H (f) Is omega l (f) Conjugating the transposed vector;
applying a feasibility constraint to the azimuth ambiguity power, expressed as:
ω l H (f)R am (f)ω l (f)≤∈ 2 (f)；
constructing the reconstruction optimization model by taking the azimuth fuzzy power as a secondary constraint and the distance fuzzy signal power as an objective function; wherein the reconstruction optimization model is represented as:
5. the method of claim 4, wherein obtaining the azimuth ambiguity power of the reconstructed useful signal and the reconstructed range ambiguity signal power from the demodulated signal comprises:
determining the frequency spectrum of the actually sampled signal according to the geometric relationship between the demodulated signal and the azimuth multi-channel;
determining a frequency spectrum vector of the actually sampled signal and a covariance matrix of the actually sampled signal according to the frequency spectrum of the actually sampled signal;
and determining the azimuth fuzzy power of the reconstructed filtered signal, the reconstructed useful signal and the reconstructed distance fuzzy signal power through a reconstruction filter bank, the frequency spectrum vector of the actually sampled signal and the covariance matrix of the actually sampled signal.
6. The method of claim 5, wherein determining the frequency spectrum of the actual sampled signal based on the geometric relationship between the demodulated signal and the azimuthal multi-channel comprises:
determining an azimuth signal according to a geometrical relationship between the demodulation signal and the azimuth multichannel; wherein the azimuth signal comprises an azimuth useful signal and an azimuth distance fuzzy signal;
by s j 0 (η) and s j r (η) respectively representing the orientation useful signal and the orientation distance fuzzy signal of the jth channel, wherein s j 0 (η) and s j r (η) is represented by:
wherein σ 0 、σ r Respectively representing scattering coefficients corresponding to the azimuth useful signal and the azimuth distance fuzzy signal; s (η) represents a reference signal; eta is the azimuth moment of time,
representing right-shift on the basis of the signal s (eta)
A length; d j = (j-1) d, representing the spacing between the jth channel and the reference channel; d is the adjacent channel spacing; v. of s Is the satellite platform velocity;
performing azimuth phase demodulation on the azimuth signal to determine an actually sampled useful signal, wherein the actually sampled useful signal is represented as:
wherein, T p Represents the pulse repetition interval, δ (η) being the dirac function; delta (. Eta. -kT) p ) Representing a right shift kT on the basis of a function delta (eta) p A length; k is an integer;
determining the frequency spectrum of the actually sampled useful signal according to the Fourier transform property, wherein the frequency spectrum of the actually sampled useful signal is represented as:
wherein f represents frequency; f. of p Represents the pulse repetition frequency; s (f) represents the spectrum of S (η);
determining an actually sampled azimuth distance fuzzy signal according to the change of the residual phase of the azimuth distance fuzzy signal, wherein the actually sampled azimuth distance fuzzy signal is represented as:
determining a frequency spectrum of the actually sampled azimuth distance fuzzy signal according to Fourier transform properties, wherein the frequency spectrum of the azimuth distance fuzzy signal is represented as:
and determining the frequency spectrum of the actually sampled useful signal and the frequency spectrum of the actually sampled azimuth distance fuzzy signal as the frequency spectrum of the actually sampled signal.
7. The method of claim 6, wherein determining the spectral vector of the actual sampled signal and the covariance matrix of the actual sampled signal from the spectrum of the actual sampled signal comprises:
determining a frequency spectrum vector of the actually sampled useful signal and a frequency spectrum vector of the actually sampled azimuth distance fuzzy signal, and a covariance matrix of the actually sampled useful signal and a covariance matrix of the actually sampled distance fuzzy signal according to the frequency spectrum of the actually sampled useful signal and the frequency spectrum of the actually sampled azimuth distance fuzzy signal;
determining the frequency spectrum vector of the actually sampled useful signal and the frequency spectrum vector of the actually sampled azimuth distance fuzzy signal as the frequency spectrum vector of the actually sampled signal;
determining the covariance matrix of the actually sampled useful signal and the covariance matrix of the actually sampled distance fuzzy signal as the covariance matrix of the actually sampled signal;
wherein the spectral vector of the actual sampled signal is represented as:
the covariance matrix of the actually sampled signal is expressed as:
wherein E (·) represents the desired operator; sigma n 2 Representing the noise power; i is N Is an N-order identity matrix; Λ (f) is a diagonal matrix associated with the antenna pattern;
representing the actually sampled useful signal spectrum vector;
representing a distance blurred signal spectrum vector after actual sampling; s (f) represents an ideal Doppler spectrum vector; a (f) represents a channel matrix; a. The H (f) Is a matrix after conjugate transpose of A (f); c r Modulating the matrix for APC; r r (f)＝A(f)Λ(f-f p /2)A H (f)。
8. The method of claim 7, wherein determining the reconstructed filtered signal, the reconstructed useful signal azimuth ambiguities power, and the reconstructed range ambiguities signal power by the reconstruction filter bank, the actual sampled signal spectral vector, and the actual sampled signal covariance matrix comprises:
determining the azimuth fuzzy power and the range fuzzy signal power of the reconstructed filtered signal and the reconstructed useful signal through a reconstruction filter bank, the frequency spectrum vector of the actually sampled signal and the covariance matrix of the actually sampled signal, and reconstructing the filtered signal S c,l (f) Expressed as:
the azimuth fuzzy power P of the reconstructed useful signal am Expressed as:
wherein, a k (f) Representing a channel vector;
the reconstructed distance-blurred signal power
Expressed as:
9. the method of claim 4, wherein solving the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter comprises:
obtaining a solving formula by solving the reconstruction optimization model, wherein the solving formula is expressed as:
wherein, λ and μ are intermediate variables, λ is a real number greater than or equal to 0, and μ is any real number;
obtaining the multi-channel SAR reconstruction filter by solving the solving formula, wherein the multi-channel SAR reconstruction filter
Expressed as:
10. a multi-channel SAR distance ambiguity suppression apparatus, the apparatus comprising:
the azimuth phase coding module is used for carrying out azimuth phase coding on each transmitting pulse of the multi-channel SAR system to obtain a phase-weighted transmitting pulse;
the azimuth phase demodulation module is used for carrying out azimuth phase demodulation on the echo of the emission pulse after each phase weighting to obtain a demodulation signal;
the construction module is used for constructing a reconstruction optimization model which takes the azimuth fuzzy power of the demodulation signal as a secondary constraint and the distance fuzzy power of the demodulation signal as a target function;
the solving module is used for solving the reconstruction optimization model to obtain a multi-channel SAR reconstruction filter;
and the suppression module is used for suppressing the range ambiguity of the multi-channel SAR system through the multi-channel SAR reconstruction filter.
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