CN109471105B - Rapid imaging method for compressed sensing inverse synthetic aperture radar maneuvering target deviating from grid - Google Patents

Rapid imaging method for compressed sensing inverse synthetic aperture radar maneuvering target deviating from grid Download PDF

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CN109471105B
CN109471105B CN201811360874.2A CN201811360874A CN109471105B CN 109471105 B CN109471105 B CN 109471105B CN 201811360874 A CN201811360874 A CN 201811360874A CN 109471105 B CN109471105 B CN 109471105B
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aperture radar
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CN109471105A (en
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成萍
赵家群
王新新
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Hohai University HHU
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/9021—SAR image post-processing techniques
    • G01S13/9029—SAR image post-processing techniques specially adapted for moving target detection within a single SAR image or within multiple SAR images taken at the same time
    • 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
    • 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
    • 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
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

Abstract

The invention discloses a rapid imaging method for a compressed sensing inverse synthetic aperture radar maneuvering target deviating grid, which comprises the following steps: processing the reverse synthetic aperture radar echo to obtain a range profile consisting of a group of range units; sparse representation is carried out on signals in the 1 st distance unit by utilizing a compressed sensing reconstruction algorithm, and the frequency and frequency modulation initial values of K strong scattering points are obtained; considering that the actual frequency and the frequency modulation rate of a strong scattering point generally deviate from a preset frequency grid and a preset frequency modulation rate grid, and converting the imaging of a maneuvering target deviating from the grid into the minimization problem of jointly estimating deviation grid errors and sparse coefficients; obtaining a sparse coefficient, frequency deviation and frequency modulation rate deviation by iteratively solving two minimum variance problems; and repeating the processing on the signal of each range unit, and combining the obtained sparse coefficients on all range units to obtain the final inverse synthetic aperture radar image. The method can quickly obtain the high-quality compressed sensing inverse synthetic aperture maneuvering target radar image.

Description

Rapid imaging method for compressed sensing inverse synthetic aperture radar maneuvering target deviating from grid
Technical Field
The invention relates to an imaging method of an inverse synthetic aperture radar, in particular to an imaging method of a compressed sensing inverse synthetic aperture radar deviating grid.
Background
In the inverse synthetic aperture radar imaging based on compressed sensing, since the frequency components in the signal cannot be known in advance, the fourier basis function of a preset frequency grid, that is, continuous frequencies are dispersed into the frequency grid, and the strong scattering point is considered to be at the center of the grid. However, the strong scattering point may not be on the center of the grid. Thus, there is a mismatch between the predetermined dictionary and the actual dictionary, which significantly degrades imaging performance. This is the off-grid problem.
To solve this problem, the most straightforward approach is to refine the grid, but this will increase the correlation of the basis functions, which is not conducive to reconstructing the signal reliably. Researchers have proposed methods based on kernel norm optimization and local optimization, but they have a requirement for minimum distance between scattering points. At present, most methods solve the problem of grid deviation by jointly estimating grid deviation and sparse solution, and a Bayesian method, a matching pursuit algorithm and an alternating convex search algorithm can be adopted for solving. However, they are both relatively computationally intensive. The computational burden can be reduced by using a first order taylor approximation, but the computational load of the existing methods is still large.
Some compressed sensing inverse synthetic aperture radar off-grid imaging methods have been proposed in the current field. However, they are both relatively computationally intensive and they are all directed to situations where the motion of the target is smooth. In actual conditions, most radar targets are in a maneuvering state. When the target maneuvers, deviations from the frequency modulated grid will result in a reduction of the amplitude and position of the strong scattering points, thereby significantly degrading the imaging quality. Therefore, a need exists for a method of compressed sensing inverse synthetic aperture radar moving target imaging that eliminates the effects of off-grid.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the prior art and reduce the calculated amount when the maneuvering target deviates from the grid imaging, the invention provides a rapid imaging method for the maneuvering target of the compressed sensing inverse synthetic aperture radar to deviate from the grid.
The technical scheme is as follows: a method for quickly imaging a maneuvering target of a compressed sensing inverse synthetic aperture radar deviating from a grid comprises the following steps:
s10, processing the reverse synthetic aperture radar echo to obtain a range profile composed of a group of range units;
s20, performing sparse representation on signals in the 1 st distance unit by using a compressed sensing reconstruction algorithm to obtain the frequency of K strong scattering points and the initial value of the frequency modulation rate;
s30, considering that the actual frequency and the frequency modulation rate of the strong scattering point generally deviate from a preset frequency grid and a preset frequency modulation rate grid, and converting the imaging of the maneuvering target deviating from the grid into the minimization problem of jointly estimating deviation grid errors and sparse coefficients;
s40, obtaining a sparse coefficient, a frequency deviation and a frequency modulation deviation of the combined minimization problem by iteratively solving two minimum variance problems;
and S50, repeating the processing of S20-S40 on the signals of each other range unit, and finally combining the sparse coefficients S obtained on all the range units to obtain a final inverse synthetic aperture radar image.
Preferably, in step S10, the inverse synthetic aperture radar echo is subjected to de-chirp, range compression, and motion compensation to obtain a range profile consisting of L range cells.
Preferably, the step S20 includes: supposing that a measurement signal in a 1 st distance unit is y, a measurement matrix is psi, a basis function is a linear frequency modulation basis function phi of a preset grid, sparsely representing the signal by adopting a compressed sensing reconstruction algorithm, and obtaining an initial frequency value f ═ f of K strong scattering points1,f2,…fi,…fK]And initial value of frequency modulation rate gamma ═ gamma1,γ2,…γi,…γK]，fiAnd gammaiIndicating the frequency and frequency modulation of the ith strong scattering point.
Preferably, the minimization problem in step S30 is expressed as:
Figure BDA0001867302930000021
where i is the scattering point number, siIs the coefficient of the ith scattering point, sign δ fiAnd δ γiIs the unknown deviation between the ith scattering point and the nearest frequency grid and the FM grid, subject to represents the limiting condition, ΔfAnd ΔγThe size of the frequency grid and the frequency-modulated grid, g (f), respectivelyi+δfi,γi+δγi) Is that the parameter in the recovery matrix G ═ Ψ Φ is (f)i+δfi,γi+δγi) The column vector of (2).
Preferably, in the step S40, the sparse coefficient S ═ S is solved1,s2,…si,…sK]And frequency deviation δ f ═ δ f1,δf2,…δfi,…δfK]And frequency deviation δ γ ═ δ γ [ [ δ γ ] ]1,δγ2,…δγi,…δγK]The method comprises the following steps:
first f(1)Initialized to f, gamma(1)Initialized to gamma to obtain initial estimation of sparse coefficient
Figure BDA0001867302930000022
Wherein the superscript 1 denotes the 1 st iterationGeneration;
then, the iteration number is represented by n, and iteration is carried out according to the following formula from n to 1 until a convergence condition is met:
(δf(n),δγ(n))＝Re{((P(n))HP(n))-1(P(n))Hy(n)}
f(n+1)＝f(n)+δf(n)
γ(n+1)＝γ(n)+δγ(n)
s(n+1)＝((Q(n+1))HQ(n+1))-1(Q(n+1))Hy
wherein Re represents the real part, is defined
Figure BDA0001867302930000031
Wherein
Figure BDA0001867302930000032
And
Figure BDA0001867302930000033
respectively represent
Figure BDA0001867302930000034
To fi (n)And
Figure BDA0001867302930000035
the partial derivative of (a) of (b),
Figure BDA0001867302930000036
preferably, the method comprises
Figure BDA0001867302930000037
To fi (n)In the case of the partial derivative of (c),
Figure BDA0001867302930000038
in the request of
Figure BDA0001867302930000039
To pair
Figure BDA00018673029300000310
In the case of the partial derivative of (c),
Figure BDA00018673029300000311
preferably, the offset (δ f) is updated iteratively(n),δγ(n)) Frequency f(n+1)Frequency of gamma(n+1)And a sparse coefficient s(n +1)When the convergence condition is the residual amount
Figure BDA00018673029300000312
2 norm of or deviation of a certain frequency | fi (n+1)-fi|＞Δf2, or deviation from a certain tuning frequency
Figure BDA00018673029300000313
Has the advantages that: the existing method for solving the problem of grid deviation in compressed sensing inverse synthetic aperture radar imaging is only limited to the condition that the target moves stably, the method breaks through the limitation, realizes the compressed sensing inverse synthetic aperture radar grid deviation imaging of the maneuvering target, and has simple algorithm and high speed. The imaging algorithm of the compressed sensing inverse synthetic aperture radar maneuvering target deviating from the grid can quickly obtain a high-quality radar image.
Drawings
FIG. 1 is a flow chart of a rapid imaging method of the present invention;
FIG. 2 is a result of imaging based on an original signal according to an embodiment of the present invention;
FIG. 3 is a diagram of results of orientation imaging using Fourier-based basis functions based on 20% signals, according to an embodiment of the present invention;
fig. 4 is a result of azimuth imaging using chirped basis functions based on 20% signal according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
The invention provides a rapid imaging method for compressively sensing a mobile target of an inverse synthetic aperture radar to deviate from a grid, which aims to perform compressive sensing imaging on the mobile target of the inverse synthetic aperture radar and eliminate the influence of the deviation frequency grid and a frequency modulation grid on imaging quality. Referring to fig. 1, the present invention is further described in detail below, specifically including the following steps:
(1) and performing de-chirp, distance compression and motion compensation on the inverse synthetic aperture radar echo to obtain a group of range images consisting of L range units.
(2) Assume that the measured signal in the 1 st range bin is y and the measurement matrix is Ψ. For stationary targets, the signal within each range bin can be viewed as a linear sum of a series of sinusoidal signals. However, this model is no longer adapted to the maneuvering target. For a mobile target, the signal within each range cell can be viewed as being made up of a series of chirps whose frequencies and frequencies are unknown. The basis function thus adopts a chirp basis function phi of a predetermined grid. Because the linear frequency modulation basis function which is more consistent with the characteristics of the maneuvering target signal is adopted in imaging, the imaging quality can be obviously improved. The initial values of the frequency of the strong scattering points and the modulation frequency can be obtained by using a sparse reconstruction algorithm (such as an orthogonal matching pursuit algorithm, a basis pursuit algorithm, a FOCUSS algorithm or a sparse bayes algorithm). Since the computation complexity of the orthogonal matching pursuit algorithm is low, the orthogonal matching pursuit algorithm is adopted here. The orthogonal matching pursuit algorithm uses the idea of iteration, firstly, the signal is projected to all atoms in the basis function, and the atom with the largest inner product is selected as a matching atom; and eliminating the projection value from the original signal to obtain a residual signal, continuing to project the residual signal and selecting the most matched atom, and repeating the steps until a given condition is met. The frequency and frequency modulation parameters of the matched atoms are the frequency and frequency modulation of the strong scattering points, so that the initial frequency f ═ f of the K strong scattering points can be obtained1,f2,…fi,…fK]HarmonyInitial frequency value gamma ═ gamma1,γ2,…γi,…γK]Where f isiAnd gammaiIndicating the frequency and frequency modulation of the ith strong scattering point.
(3) When the target maneuvers, the parameters that need to be discretized are not only the frequency but also the tuning frequency. Taking into account the deviation of the actual values of the frequency and the modulation frequency of the strong scattering point from the predetermined frequency grid and the modulation frequency grid in order to better match the measured values, i.e. to match the measured values
Figure BDA0001867302930000041
Where i is the scattering point number, siIs the coefficient of the ith scattering point, sign δ fiAnd δ γiRepresenting the unknown deviation between the actual frequency and frequency modulation of the ith scattering point and the frequency grid and frequency modulation grid closest to it. If the actual frequency and frequency modulation of the scattering point exactly fall on the predetermined grid, δ fi＝0，δγi0; if not on the preset grid, the grid is necessarily between two adjacent grids, and when the grid is in the middle of the two grids, the offset is the largest and is half of the grid size, namely | δ fi|＝Δf/2，|δγi|＝Δγ2, otherwise the offset is less than half the grid size, i.e. | δ fi|＜Δf/2，|δγi|＜Δγ/2. subject to represents the limiting condition, ΔfAnd ΔγThe size of the frequency grid and the frequency-modulated grid, g (f), respectivelyi+δfi,γi+δγi) Is that the parameter in the recovery matrix G ═ Ψ Φ is (f)i+δfi,γi+δγi) The column vector of (2).
To obtain the above joint minimization, the following procedure is used to estimate the sparse coefficient s ═ s1,s2,…si,…sK]Frequency deviation δ f ═ δ f1,δf2,…δfi,…δfK]Deviation from frequency of modulation delta gamma ═ delta gamma1,δγ2,…δγi,…δγK]。
① first f(1)Initialized to f, gamma(1)Initialized to gamma to obtain initial estimation of sparse coefficient
Figure BDA0001867302930000051
Where the superscript 1 denotes the 1 st iteration.
② the number of iterations is represented by n, and the iteration is started from n-1, when the residual quantity is
Figure BDA0001867302930000052
Stopping the iteration when the 2 norm is minimum, or the deviation | f of a certain frequencyi (n+1)-fi|＞Δf2, or deviation from a certain tuning frequency
Figure BDA0001867302930000053
The iteration is stopped.
(δf(n),δγ(n))＝Re{((P(n))HP(n))-1(P(n))Hy(n)} (1)
f(n+1)＝f(n)+δf(n)
γ(n+1)＝γ(n)+δγ(n)
s(n+1)＝((Q(n+1))HQ(n+1))-1(Q(n+1))Hy (2)
Wherein the superscripts n and n +1 represent the iteration times, and Re represents the real part and is taken
Figure BDA0001867302930000054
Wherein
Figure BDA0001867302930000055
The method converts the off-grid imaging problem of the maneuvering target into two minimum variance problem equations (1) and (2), and is simple and easy to solve. Compared with the orthogonal matching pursuit algorithm, the method increases the calculation amount in the calculation formula (1), the formula (A)2) And formula (3). Let K be the sparsity of the N-dimensional signal and M be the dimension of the measured value. Because of P(n)And Q(n)Has dimension of M × K, y(n)Since the dimension of the sum y is M × 1, the computational complexity of the formulae (1) and (2) is O (MK)2). If to P(n)And Q(n)QR decomposition is carried out, and the complexity can be further reduced to O (MK). Since the formula (3) has 2K components, each of which requires M-order multiplication, the computational complexity of the formula (3) is o (2 MK). Here, K is the sparsity of the signal and has a small value. Therefore, the method has low calculation complexity and can be quickly solved.
(4) And if the number of the distance units is less than L, adding 1 to the number of the distance units, and repeating the steps (2) to (3). If the number of range units is equal to L, the obtained sparse coefficients s on all range units are combined to form the final inverse synthetic aperture radar image.
The effect of the present invention is verified by an example. The method is used for direction dimension imaging of one-dimensional ISAR simulation data, and comprises the following steps:
(1) generating a signal z in a range bin: the signal length is 320, and is composed of 4 chirp signals, the amplitude is randomly generated from a complex unit circle, the frequency is randomly selected from 0 to 320Hz, the frequency modulation rates are respectively 30,40,50 and 60, and the sampling frequency is 320 Hz. Observation is performed using the measurement matrix Ψ, which is a gaussian random matrix of size 64 × 320, resulting in a measurement value y of dimension 64. White gaussian noise is added so that the signal to noise ratio of the signal is 25 dB. The basic function phi adopts a discrete linear frequency modulation basic matrix with the size of 320 multiplied by 320, the initial value of the frequency modulation rate is 0, and the size of the frequency grid is deltafTaking 1, the frequency-modulated grid size Δ γ1 is taken. Obtaining sparse coefficients and initial frequency f ═ f of 4 linear frequency modulation components by adopting an orthogonal matching pursuit algorithm1,f2,f3,f4]And initial value of frequency modulation rate gamma ═ gamma1,γ2,γ3,γ4]。
(2) Taking into account the actual frequencies of the 4 chirp components and the deviation of the tuning frequency from the corresponding initial value to better match the measured values, i.e.
Figure BDA0001867302930000061
To obtain the above joint minimization, the following procedure is used to estimate the sparse coefficient s ═ s1,s2,s3,s4]Frequency deviation δ f ═ δ f1,δf2,δf3,δf4]Deviation from frequency of modulation delta gamma ═ delta gamma1,δγ2,δγ3,δγ4]。
① first f(1)Initialized to f, gamma(1)Initialized to gamma to obtain initial estimation of sparse coefficient
Figure BDA0001867302930000062
Where the superscript 1 denotes the 1 st iteration.
② the number of iterations is represented by n, and the iteration is started from n-1, when the residual quantity is
Figure BDA0001867302930000071
Stopping the iteration when the 2 norm is minimum, or the deviation | f of a certain frequencyi (n+1)-fi| 1/2, or some deviation from the tuning frequency
Figure BDA0001867302930000072
(δf(n),δγ(n))＝Re{((P(n))HP(n))-1(P(n))Hy(n)} (1)
f(n+1)＝f(n)+δf(n)
γ(n+1)＝γ(n)+δγ(n)
s(n+1)＝((Q(n+1))HQ(n+1))-1(Q(n+1))Hy (2)
Wherein the superscripts n and n +1 represent the iteration times, and Re represents the real part and is taken
Figure BDA0001867302930000073
Wherein
Figure BDA0001867302930000074
Fig. 2 is an imaging result based on the original signal, and fig. 3 and 4 are results of azimuth imaging using the fourier basis function-based orthogonal matching pursuit algorithm and the present invention, respectively, based on 20% of the signal. As can be seen from the figure, the present invention obtains a more accurate imaging result compared to the existing algorithm because it employs the chirp basis function and solves the problem of grid deviation.

Claims (7)

1. A method for fast imaging of a maneuvering target of a compressed sensing inverse synthetic aperture radar deviating from a grid is characterized by comprising the following steps:
s10, processing the reverse synthetic aperture radar echo to obtain a range profile composed of a group of range units;
s20, performing sparse representation on the signals in the 1 st distance unit by using a compressed sensing reconstruction algorithm to obtain initial values of the frequency and the frequency modulation rate of K strong scattering points, wherein the initial values comprise:
supposing that a measurement signal in a 1 st distance unit is y, a measurement matrix is psi, a basis function is a linear frequency modulation basis function phi of a preset grid, sparsely representing the signal by adopting a compressed sensing reconstruction algorithm, and obtaining an initial frequency value f ═ f of K strong scattering points1,f2,…fi,…fK]And initial value of frequency modulation rate gamma ═ gamma1,γ2,…γi,…γK]，fiAnd gammaiRepresenting the frequency and the frequency modulation rate of the ith strong scattering point;
s30, considering that the actual frequency and the frequency modulation rate of the strong scattering point generally deviate from a preset frequency grid and a preset frequency modulation rate grid, converting the imaging of the maneuvering target deviating from the grid into a minimization problem of jointly estimating deviating grid errors and sparse coefficients, wherein the minimization problem is expressed as:
Figure FDA0002420954650000011
where i is the scattering point number, siIs the coefficient of the ith scattering point, sign δ fiAnd δ γiIs the unknown deviation between the ith scattering point and the nearest frequency grid and the FM grid, subject to represents the limiting condition, ΔfAnd ΔγThe size of the frequency grid and the frequency-modulated grid, g (f), respectivelyi+δfi,γi+δγi) Is that the parameter in the recovery matrix G ═ Ψ Φ is (f)i+δfi,γi+δγi) A column vector of (a);
s40, obtaining a sparse coefficient S ═ S of the joint minimization problem by iteratively solving two minimum variance problems1,s2,…si,…sK]Frequency deviation δ f ═ δ f1,δf2,…δfi,…δfK]Deviation from frequency of modulation delta gamma ═ delta gamma1,δγ2,…δγi,…δγK]The method comprises the following steps:
first f(1)Initialized to f, gamma(1)Initialized to gamma to obtain initial estimation of sparse coefficient
Figure FDA0002420954650000012
Where superscript 1 denotes iteration 1;
then, the iteration number is represented by n, and iteration is carried out according to the following formula from n to 1 until a convergence condition is met:
(δf(n),δγ(n))＝Re{((P(n))HP(n))-1(P(n))Hy(n)}
f(n+1)＝f(n)+δf(n)
γ(n+1)＝γ(n)+δγ(n)
s(n+1)＝((Q(n+1))HQ(n+1))-1(Q(n+1))Hy
wherein Re represents the real part, is defined
Figure FDA0002420954650000021
Wherein
Figure FDA0002420954650000022
And
Figure FDA0002420954650000023
respectively represent g (f)i (n),γi (n)) To fi (n)And gammai (n)The partial derivative of (a) of (b),
Figure FDA0002420954650000024
and S50, repeating the processing of S20-S40 on the signals of each other range unit, and finally combining the sparse coefficients S obtained on all the range units to obtain a final inverse synthetic aperture radar image.
2. The method for fast imaging of the maneuvering target of the compressed sensing inverse synthetic aperture radar from the grid according to claim 1, characterized in that the inverse synthetic aperture radar echo is processed by de-chirp, range compression and motion compensation in step S10 to obtain a set of range profiles composed of L range units.
3. The method for rapid imaging of a maneuvering target of a compressed sensing inverse synthetic aperture radar according to claim 1, characterized in that the compressed sensing reconstruction algorithm employs an orthogonal matching pursuit algorithm.
4. The compressed sensing inverse synthetic aperture radar maneuvering target off-grid fast imaging method according to claim 1, characterized by: then, g (f) is calculatedi (n),γi (n)) To fi (n)In the case of the partial derivative of (c),
Figure FDA0002420954650000025
5. the compressed sensing inverse synthetic aperture radar maneuvering target off-grid fast imaging method according to claim 1, characterized by: then, g (f) is calculatedi (n),γi (n)) For gammai (n)In the case of the partial derivative of (c),
Figure FDA0002420954650000026
6. the compressed sensing inverse synthetic aperture radar maneuvering target off-grid fast imaging method according to claim 1, characterized by: in step S40, the offset (δ f) is iteratively updated(n),δγ(n)) Frequency f(n+1)Frequency of gamma(n+1)And a sparse coefficient s(n+1)When the convergence condition is the residual amount
Figure FDA0002420954650000027
The 2 norm of (c) is minimal.
7. The compressed sensing inverse synthetic aperture radar maneuvering target off-grid fast imaging method according to claim 1, characterized by: in step S40, the offset (δ f) is iteratively updated(n),δγ(n)) Frequency f(n+1)Frequency of gamma(n+1)And a sparse coefficient s(n+1)The convergence condition is a deviation | f of a certain frequencyi (n+1)-fi|＞Δf2, or deviation of a certain tuning frequency | γi (n+1)-γi|＞Δγ/2。
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