阅读说明：本技术 运动目标的sar稀疏成像方法、装置、电子设备及存储介质 (SAR sparse imaging method and device of moving target, electronic equipment and storage medium ) 是由 徐刚 陈宇智 张慧 洪伟 郭坤鹏 于 2021-09-16 设计创作，主要内容包括：本发明实施例公开了一种运动目标SAR稀疏成像方法、装置、电子设备和存储介质,所述方法包括：对采集到的运动目标线性调频信号的回波信号进行杂波抑制处理,接着使用频谱分析方法并基于所述经过杂波抑制的回波信号对所述雷达和所述运动目标进行运动补偿,得到经运动补偿的回波信号,然后基于所述经运动补偿的回波信号构建参数化表征字典并根据所述参数化表征字典表示所述经运动补偿的回波信号,并以此作为约束条件构建稀疏孔径成像和参数估计的目标函数,最后采用正交匹配追踪方法求解所述目标函数,并针对所述运动目标进行多普勒模糊数估计和相位误差校正,从而获得响应动态范围较大且聚焦性能良好的运动目标的SAR图像。(The embodiment of the invention discloses a moving target SAR sparse imaging method, a device, electronic equipment and a storage medium, wherein the method comprises the following steps: the method comprises the steps of conducting clutter suppression processing on an echo signal of a collected linear frequency modulation signal of a moving target, then conducting motion compensation on the radar and the moving target by using a frequency spectrum analysis method and based on the echo signal subjected to clutter suppression to obtain a motion compensated echo signal, then constructing a parametric representation dictionary based on the motion compensated echo signal, representing the motion compensated echo signal according to the parametric representation dictionary, constructing a target function of sparse aperture imaging and parameter estimation by using the parametric representation dictionary as a constraint condition, finally solving the target function by using an orthogonal matching tracking method, and conducting Doppler fuzzy number estimation and phase error correction on the moving target, so that an SAR image of the moving target with a large response dynamic range and good focusing performance is obtained.)

1. A SAR sparse imaging method of a moving target is characterized by comprising the following steps:

step S1, collecting echo signals of linear frequency modulation signals transmitted to a moving target by a synthetic aperture radar moving along a preset track at a preset speed;

step S2, static clutter suppression processing is carried out on the echo signal of the linear frequency modulation signal to obtain an echo signal subjected to clutter suppression;

step S3, using a frequency spectrum analysis method and based on the echo signals after clutter suppression to perform motion compensation on the radar and the moving target, so as to obtain echo signals after motion compensation;

step S4, constructing a parameterized token dictionary based on the motion compensated echo signals and representing the motion compensated echo signals according to the parameterized token dictionary;

step S5, constructing an objective function of sparse aperture imaging and parameter estimation by using the motion compensated echo signal represented by the parameterized representation dictionary as a constraint condition;

and step S6, solving the objective function by adopting an orthogonal matching tracking method, and performing Doppler fuzzy number estimation and phase error correction on the moving target to obtain an SAR image of the moving target.

2. The method according to claim 1, wherein the echo signal obtained in step S1 is:

the motion direction of the radar is defined as an x axis, and the direction vertical to the motion direction of the radar is defined as a y axis; moving object P (x)_p,y_p) At a speed (v)_x,v_y) Movement, R_p＝R₀+y_pFor radar and moving target P at azimuth time t_aV is the radar motion speed,

3. the method according to claim 2, wherein the step S2 includes: processing the echo signal using a displaced phase center antenna to obtain the clutter suppressed echo signal, wherein the clutter suppressed echo signal is obtained via:

wherein σ_pIs the retroreflection coefficient, W, of the moving object P_r(. is a distance frequency domain f_rWindow function of, W_a(. represents the azimuth time domain t_aThe window function of (a) is selected,is the center time of the observed moving object P, f_cC is the electromagnetic wave propagation velocity for the transmitted signal carrier frequency.

4. The method according to claim 3, wherein the step S3 includes: the spectral analysis reference function is constructed according to the following formula:

multiplying the clutter suppressed echo signal with the spectral analysis reference function to obtain the motion compensated echo signal obtained by:

5. the method according to claim 4, wherein the step S4 includes:

firstly, obtaining a compression sampling matrix of a sparse aperture by using a compression random sampling method;

secondly, constructing a motion modulation parameterized representation dictionary of the moving target based on the motion compensated echo signals;

thirdly, dividing the parameterized representation dictionary into two parts, and respectively representing linear MTRC and phase modulation;

fourthly, rewriting the motion compensated echo signal by using the parameterized representation dictionary, and obtaining the motion compensated echo signal represented by the parameterized representation dictionary according to the following formula:

s＝ΦΨ(α，β)x+n，y＝Ψ₂(β)x，Ψ(α，β)＝Ψ₁(α)Ψ₂(β)；

where s, x and n are respectively represented as echo signals, SAR images and system noise, Φ is a sparsely sampled compressed sampling matrix, Ψ (α, β) is a parameterized characterizing dictionary of all moving object velocity modulations represented by vectors α and β, Ψ (α, β) is divided into Ψ₁(. alpha.) and Ψ₂(β) two parts, linear MTRC and phase modulation respectively;

therein, Ψ₁The expression of (α) is:

Ψ₁(α)＝F_a-mtrc(n_amb)F_r

in the above formula F_rFourier transform, F, representing the distance dimension_a-mtrc(n_amb) Is with a scaling factor f_rScaling transformation of the azimuth dimension of (1), n_ambIs the unknown Doppler fuzzy number vector of all moving targets, and is determined by alpha; m and N are discrete numbers in the azimuth and distance dimensions, respectively;

Ψ₂the expression of (β) is:

Ψ₂(β)＝F_a ^-1C(β)⊙F_a

wherein the content of the first and second substances,is a Fourier transform or an inverse Fourier transform, is an Adama product, C (β) < > F_aIs a chirp Fourier dictionary, gamma_mIs a discrete chirp rate of beta with a constant scaling factor.

6. The method according to claim 5, wherein the step S5 includes: constructing an objective function for the sparse aperture imaging and parameter estimation according to:

wherein | · | purple₀Is L of a vector₀-a norm.

7. The method according to claim 6, wherein the step S6 includes:

firstly, extracting a moving target component by estimating and compensating a moving target, wherein the moving target component is extracted according to the following formula:

wherein the content of the first and second substances,is the estimated blur number of the p-th moving object,andthe number of the distance and direction units of the p-th moving target;

and secondly, performing phase error estimation on the moving target component, wherein the phase error estimation is obtained according to the following formula:

wherein the content of the first and second substances,to be located atAndis/are as followsThe result of the windowing process of (a),is the estimated chirp signal of the pth moving object;andafter updating by the above equation, the signal component of the moving object can be estimated as:

wherein the content of the first and second substances,is an estimate of the p-th moving object;

the third step: the residual composition was calculated as follows: updating the echo signal overwritten by the parametric characterization dictionary in step S4 by subtracting the signal components of the moving object that have been estimated in the above-described first step and second step, to obtain an updated echo signal represented by the following equation:

wherein the content of the first and second substances,is the firstIndividual SAR image components, i.e.Subsequently, the echo signal is updated toThe number of the moving target is updated to p + 1;

the fourth step: and (3) iterative solution: and repeating the first step to the third step, and continuously extracting the next moving target by estimating the parameters until all the moving targets are extracted.

8. A SAR imaging apparatus for a moving object, comprising:

the data acquisition module is used for acquiring echo signals of linear frequency modulation signals transmitted to a moving target by a synthetic aperture radar which moves along a preset track at a preset speed;

the clutter suppression module is used for performing static clutter suppression processing on the echo signal of the linear frequency modulation signal to obtain an echo signal subjected to clutter suppression;

the compensation module is used for performing motion compensation on the radar and the moving target by using a frequency spectrum analysis method and based on the echo signals subjected to clutter suppression to obtain echo signals subjected to motion compensation;

a post-processing module, configured to construct a parameterized representation dictionary based on the motion compensated echo signals and represent the motion compensated echo signals according to the parameterized representation dictionary, and then construct an objective function of sparse aperture imaging and parameter estimation with the motion compensated echo signals represented according to the parameterized representation dictionary as constraint conditions;

and the imaging module is used for solving the target function by adopting an orthogonal matching tracking method and carrying out Doppler fuzzy number estimation and phase error correction on the moving target so as to obtain an SAR image of the moving target.

9. An electronic device comprising a memory and a processor; the memory stores a computer program, and the processor is configured to execute the computer program in the memory to execute the steps of the SAR sparse imaging method of a moving object in any one of claims 1 to 7.

10. A storage medium having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, realizes the steps in the SAR sparse imaging method of moving objects of any of the claims 1 to 7.

Technical Field

The embodiment of the invention relates to a Synthetic Aperture Radar (SAR) sparse imaging method, in particular to an SAR sparse imaging method of a moving target.

Background

In recent years, as known from the Compressed Sensing (CS) theory, a sparse optimization technology signal can be used to obtain accurate reconstruction from downsampled data at a very high probability, so that the limit of nyquist sampling is broken through. Accordingly, compressed sensing is widely introduced for SAR imaging, where one important application is to downsample data, such as sparse aperture imaging. For SAR Ground Moving Target Imaging (SAR-GMTIm), motion compensation is an important issue in determining the Imaging performance of a Moving Target. In addition to the motion of the radar platform, there is additional motion of the moving object, introducing range cell migration and higher order phase modulation to the echo data (there are also cases of migration in the azimuth cell). After SAR focusing imaging, moving objects in an image often have the problem of distance and azimuth two-dimensional cross-unit migration, which is expressed as two-dimensional defocusing. In sparse aperture, SAR moving target imaging is more complex. Therefore, the sparse aperture SAR moving target imaging and compensation refocusing algorithm needs to be researched to ensure better imaging performance.

Disclosure of Invention

The invention aims to overcome the technical problems, provides a SAR sparse imaging method, a device, electronic equipment and a storage medium of a moving target aiming at sparse aperture moving target imaging, and can play a role in inhibiting the problems of defocusing, blurring, dislocation and the like of an image caused by the moving target, thereby realizing a synthetic aperture radar image of the moving target without a fuzzy moving target.

The embodiment of the invention provides an SAR sparse imaging method of a moving target, which comprises the following steps:

step S1, collecting echo signals of linear frequency modulation signals transmitted to a moving target by a synthetic aperture radar moving along a preset track at a preset speed;

step S2, static clutter suppression processing is carried out on the echo signal of the linear frequency modulation signal to obtain an echo signal subjected to clutter suppression;

step S3, using a frequency spectrum analysis method and based on the echo signals after clutter suppression to perform motion compensation on the radar and the moving target, so as to obtain echo signals after motion compensation;

step S4, constructing a parameterized representation dictionary based on the motion compensated echo signals and representing the motion compensated echo signals according to the parameterized representation dictionary;

step S5, constructing an objective function of sparse aperture imaging and parameter estimation by using the motion compensated echo signal represented by the parameterized representation dictionary as a constraint condition;

and step S6, solving the target function by adopting an orthogonal matching tracking method, and performing Doppler fuzzy number estimation and phase error correction on the moving target to obtain an SAR image of the moving target.

Correspondingly, the embodiment of the invention also provides an SAR sparse imaging device of a moving target, which comprises: the device comprises a data acquisition module, a clutter suppression module, a compensation module, a post-processing module and an imaging module.

The data acquisition module is used for acquiring echo signals of linear frequency modulation signals transmitted to a moving target by a synthetic aperture radar which moves along a preset track at a preset speed;

the clutter suppression module is used for performing static clutter suppression processing on the echo signal of the linear frequency modulation signal to obtain an echo signal subjected to clutter suppression;

the compensation module is used for performing motion compensation on the radar and the moving target by using a spectrum analysis method and based on the echo signal subjected to clutter suppression to obtain a motion compensated echo signal;

the post-processing module is used for constructing a parametric representation dictionary based on the motion compensated echo signals, representing the motion compensated echo signals according to the parametric representation dictionary, and then constructing a target function of sparse aperture imaging and parameter estimation by taking the motion compensated echo signals represented by the parametric representation dictionary as constraint conditions;

the imaging module is used for solving the target function by adopting an orthogonal matching tracking method, and performing Doppler fuzzy number estimation and phase error correction on the moving target to obtain an SAR image of the moving target.

Correspondingly, the embodiment of the invention also provides the electronic equipment, which comprises a memory and a processor; the memory stores a computer program, and the processor is configured to execute the computer program in the memory to perform the steps in the SAR sparse imaging of the moving object in any of the above embodiments.

Furthermore, an embodiment of the present invention further provides a storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps in the SAR rarefaction imaging of the moving object described in any of the above embodiments.

The embodiment of the invention adopts the technical scheme that the echo signal of the collected linear frequency modulation signal of the moving target is subjected to the spurious suppression treatment, then using a frequency spectrum analysis method and performing motion compensation on the radar and the moving target based on the clutter suppressed echo signal to obtain a motion compensated echo signal, then constructing a parameterized representation dictionary based on the motion compensated echo signals and representing the motion compensated echo signals according to the parameterized representation dictionary, constructing a target function of sparse aperture imaging and parameter estimation by taking the target function as a constraint condition, solving the target function by adopting an orthogonal matching tracking method, performing Doppler fuzzy number estimation and phase error correction on the moving target, therefore, the SAR image of the moving target with large response dynamic range and good focusing performance is obtained.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flow chart of a SAR imaging method for a moving target according to an embodiment of the present invention;

fig. 2 is a specific structural diagram for constructing a SAR imaging geometry according to an embodiment of the present invention;

FIG. 3 is a graph of simulation experiment results for an embodiment of the present invention;

FIG. 4 is a two-dimensional imaging effect diagram of measured data according to an embodiment of the present invention;

FIG. 5 is a sparsely reconstructed image of an embodiment of the invention;

FIG. 6 is a graph of simulation experiment results for an embodiment of the present invention;

FIG. 7 is a schematic structural diagram of a single-channel synthetic aperture radar imaging apparatus according to an embodiment of the present invention;

fig. 8 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without inventive exercise, are within the scope of the present invention.

The terms "first," "second," and the like in the description and in the claims, and in the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein.

In view of the problem that a moving target in the prior art is often blurred in an SAR image domain, the SAR sparse imaging method of the moving target provided by the invention has a motion compensation process which is roughly divided into two steps: firstly, carrying out spectrum analysis processing in a distance-frequency domain and an orientation-time domain, then carrying out residual component correction in sparse imaging, and aiming at the problems of Doppler blurring and phase errors of a moving target, providing a moving target parameter sparse imaging method. In this method, a parameterized token dictionary is used to solve the two previous key problems. On the basis, an improved Orthogonal Matching Pursuit (OMP) method is utilized to carry out Doppler fuzzy number estimation and phase error correction on the moving target so as to realize high-quality imaging, and meanwhile, the method provided by the invention can process the conditions of a plurality of moving targets, so that a multi-moving-target SAR image with a large response dynamic range and good focusing performance is obtained.

The SAR sparse imaging method, apparatus, electronic device and storage medium of a moving target according to an embodiment of the present invention are described below with reference to fig. 1 to 8.

Fig. 1 is a schematic flowchart of a SAR imaging method for a moving target according to an embodiment of the present invention, as shown in fig. 1. A method of SAR imaging of a moving object, comprising:

step S1, collecting echo signals of linear frequency modulation signals transmitted to a moving target by a synthetic aperture radar moving along a preset track at a preset speed;

step S2, static clutter is carried out on the echo signal of the linear frequency modulation signal to obtain an echo signal subjected to clutter suppression;

step S3, using a frequency spectrum analysis method and carrying out motion compensation on the radar and the moving target based on the echo signal subjected to clutter suppression to obtain a motion compensated echo signal;

step S4, constructing a parameterized representation dictionary based on the motion compensated echo signals and representing the motion compensated echo signals according to the parameterized representation dictionary;

step S5, constructing a target function of sparse aperture imaging and parameter estimation by using the echo signal subjected to motion compensation represented by the parameterized representation dictionary as a constraint condition;

and step S6, solving the target function by adopting an orthogonal matching tracking method, and performing Doppler fuzzy number estimation and phase error correction on the moving target to obtain an SAR image of the moving target.

Compared with the prior art, the technical scheme provided by the embodiment of the invention has the following advantages:

most sparse SAR imaging algorithms of the prior art only consider the motion of the radar platform and ignore the motion of moving targets. Meanwhile, most sparse SAR moving target imaging methods only consider azimuth cross-unit migration introduced by moving targets, but do not consider the existence of range unit migration. The embodiment of the invention can realize two-dimensional unit migration correction by using a parametric sparse method under a sparse aperture, thereby ensuring high-quality imaging of a moving target.

The above steps S1 to S6 are specifically described below.

Fig. 2 is a specific structural diagram for constructing a SAR imaging geometry according to an embodiment of the present invention. As shown in fig. 2, in the step S1, an echo signal of a chirp signal transmitted by the synthetic aperture radar moving along a predetermined trajectory at a preset speed to a moving target is collected, the predetermined trajectory is shown as a trajectory in fig. 2, the airborne radar moves along the predetermined trajectory at a speed v and transmits a chirp (LFM) signal at a certain pulse repetition frequency, and the collected echo signal can be represented as:

the motion direction of the radar is defined as an x axis, and the motion direction perpendicular to the radar is defined as a y axis; moving object P (x)_p,y_p) At a speed (v)_x,v_y) Movement, R_p＝R₀+y_pAs a radarAt azimuth time t with moving object P_aV is the radar motion speed,

in the step S2, the echo signal is processed by using a phase-shifted central antenna to obtain the clutter suppressed echo signal, wherein the clutter suppressed echo signal is obtained by the following formula:

wherein σ_pIs the retroreflection coefficient, W, of the moving object P_r(. is a distance frequency domain f_rWindow function of, W_a(. represents the azimuth time domain t_aThe window function of (a) is selected,is the center time of the observed moving object P, f_cC is the electromagnetic wave propagation velocity for the transmitted signal carrier frequency.

In the above step in step S3, the spectral analysis reference function is constructed according to the following formula:

multiplying the clutter suppressed echo signal with the spectral analysis reference function to obtain the motion compensated echo signal obtained via:

specifically, echo signals that have undergone phase-shifted center antenna (DPCA) clutter suppression are multiplied by a SPECAN reference function to achieve motion compensation for the radar and moving targets.

The step S4 specifically includes:

firstly, obtaining a compression sampling matrix of a sparse aperture by using a compression random sampling method;

secondly, constructing a motion modulation parameterized representation dictionary of the moving target based on the motion compensated echo signals;

thirdly, dividing the parameterized representation dictionary into two parts, which respectively represent linear MTRC (Migration Through Resolution Cell) and phase modulation;

fourthly, rewriting the echo signal after motion compensation by using the parameterized representation dictionary to obtain:

s＝ΦΨ(α,β)x+n，y＝Ψ₂(β)x，Ψ(α,β)＝Ψ₁(α)Ψ₂(β)；

wherein s, x and n are respectively represented as echo signals, SAR images and system noise, phi is a compressed sampling matrix of sparse sampling, psi (alpha, beta) is a parameterized representation dictionary of all moving object velocity modulation represented by vectors alpha and beta, psi (alpha, beta) is divided into psi₁(. alpha.) and Ψ₂(β) two parts, linear MTRC and phase modulation respectively;

therein, Ψ₁The expression of (α) is:

Ψ₁(α)＝F_a-mtrc(n_amb)F_r

in the above formula F_rFourier transform, F, representing the distance dimension_a-mtrc(n_amb) Is with a scaling factor f_rScaling transformation of the azimuth dimension of (1), n_ambIs the unknown Doppler fuzzy number vector of all moving targets, and is determined by alpha; m and N are discrete numbers in the azimuth and distance dimensions, respectively;

Ψ₂the detailed form of (β) is:

Ψ₂(β)＝F_a ^-1C(β)⊙F_a

wherein the content of the first and second substances,is a Fourier transform or an inverse Fourier transform, is an Adama product, C (β) < > F_aIs a

It should be noted that the purpose of the step S4 is to obtain a Sparse Aperture (SA) SAR image, where a spectral analysis (SPECAN) imaging algorithm is an imaging algorithm widely used in SAR fast look processing, and is mainly characterized in that-Deramp processing is performed in the azimuth direction, and the computation amount is small.

In the above step S5, the objective function of the sparse aperture imaging and parameter estimation is constructed according to the following formula. The target function expression is:

wherein₀Is L of a vector₀-a norm.

In the above step S6, the step S6 includes:

firstly, extracting a moving target component by estimating and compensating a moving target, wherein the moving target component is extracted according to the following formula:

wherein the content of the first and second substances,is the estimated blur number of the p-th moving object,andthe distance and the number of azimuth units of the p-th moving target;

and secondly, performing phase error estimation on the moving target component, wherein the phase error estimation is obtained according to the following formula:

wherein the content of the first and second substances,to be located atAndis/are as followsThe result of the windowing process of (a),is the estimated chirp signal of the p-th moving object,andafter being updated by the above formula, the signal component of the moving object can be estimated as;

wherein the content of the first and second substances,is an estimate of the p-th moving object;

the third step: the residual composition was calculated as follows: updating the echo signal overwritten by the parametric characterization dictionary in step S4 by subtracting the signal components of the moving object that have been estimated in the above-described first step and second step, to obtain an updated echo signal represented by the following equation:

wherein the content of the first and second substances,is the firstIndividual SAR image components, i.e.Subsequently, the echo signal is updated toThe number of the moving target is updated to p + 1;

the fourth step: and (3) iterative solution: and repeating the first step to the third step, and continuously extracting the next moving target through the estimation parameters until all the moving targets are extracted so as to obtain SAR images of all the moving targets.

To illustrate the effectiveness of the invention in motion compensation of sparse imaging of moving target synthetic aperture radar, further demonstration is carried out by simulation data and actual measurement data experiment as follows:

based on MATLAB software platform, the system parameters of single-channel SAR simulation experiment are as follows:

SAR system simulation parameter setting

Parameter(s)	Value of	Parameter(s)	Value of
				Center frequency of radar	10GHz	Bandwidth of	360MHz
Sampling frequency	400MHz	Center distance of scene	20km
				Radar Pulse Repetition Frequency (PRF)	500Hz	Speed of motion of radar platform	100m/s

The two-dimensional imaging effect of the simulation data of the present invention will be further explained with reference to fig. 3.

Fig. 3 is a diagram of simulation experiment results according to an embodiment of the present invention, in which the abscissa of fig. 3(a) -3 (c) is the pulse number, and the ordinate is the distance direction sampling unit. In fig. 3(d) -3 (f), the abscissa represents the azimuth sampling unit, and the ordinate represents the distance sampling unit.

Fig. 3(a) shows the range profile of the radar to indicate the presence of a significant MTRC. The linear MTRC is mainly derived from the range velocity of the moving object, and the secondary MTRC is mainly derived from the motion modulation of the radar platform. In order to compensate for motion modulation in the envelope and phase terms, motion compensation is applied to the radar and the moving object using a spectral analysis method and based on the clutter suppressed echo signal as proposed in step S3, resulting in a motion compensated echo signal, and the range profile of the processed radar is shown in fig. 3 (b). As shown in fig. 3(b), the MTRC is in a linear form after the completion of the SPECAN process. Then using the Keystone transform (distance walk can be compensated for), trying to correct the residual linear MTRC, resulting in the result shown in fig. 3 (c); as shown in fig. 3(c), the distance profile of the low-speed moving object has been successfully corrected. However, for two moving objects that are doppler blurred, there is still a large MTRC. Then, a phase error correction-free SAR image is obtained by fourier transform in the azimuth direction, as shown in fig. 3 (d).

For two fast moving objects there is a significant 2D (two dimensional) blur. Therefore, motion compensation for moving objects is required, including MTRC and phase error correction. In the full aperture case, the moving object parameter sparse algorithm proposed by the embodiment of the present invention is used for a well-focused moving object image, as shown in fig. 3 (e). The dashed rectangular area in fig. 3(e) is enlarged for clarity to be shown in fig. 3 (f). As is clear from fig. 3(e) and 3(f), perfect motion compensation for the moving object can be achieved by performing MTRC correction and phase error correction to overcome the doppler ambiguities of the radar and the moving object.

In order to illustrate the effectiveness of the embodiment of the invention on the SAR sparse imaging of the moving target, the echo signal of the SAR sparse imaging of the moving target is further proved by an algorithm provided by the embodiment of the invention:

experiment in order to verify the robustness of the algorithm, the following describes the two-dimensional imaging effect of the measured data according to the embodiment of the present invention with reference to fig. 4.

Illustratively, complex random gaussian noise is added to the acquired echo signals to different degrees, and the signal-to-noise ratios of the noise are respectively set to be 20db, 10db and 5 db. In the simulation, the sa (synthetic aperture) data was used with the data volume being half and quarter of the full aperture, as shown in fig. 4(a) and 4(c), respectively. Then, using a conventional SAR imaging algorithm, azimuth focusing is performed in the azimuth dimension, and the processing results of the two data sets are respectively shown in fig. 4(b) and fig. 4 (d).

As shown in fig. 4(b) and 4(d), an excessive number of artifacts (ghosts) are present in the image. The reason is mainly divided into two aspects: one is sparse data and the other is potential residual MTRC and phase error modulation.

Under two sparse sampling conditions, simulation experiments are respectively carried out on 6 data sets with signal-to-noise ratios of 20db, 10db and 5db, and experimental results show that the algorithm provided by the embodiment of the invention can simultaneously overcome the influence of synthetic aperture and motion modulation. The resulting sparsely reconstructed image is shown in fig. 5. As shown in fig. 5, the first row of fig. 5(a) -5 (b) is the result of using half-pulses with full aperture, and the second row of fig. 5(d) -5 (f) is the result of using quarter-pulses with full aperture, and experimental results show that the algorithm can obtain well-focused images from compressed sample data, which benefits from the combination of sparse imaging and motion compensation.

In order to further verify the effectiveness of the algorithm, a simulation experiment is carried out by utilizing measured data of a certain airborne platform.

The radar has a transmitter and three receivers, and operates in the X band with a signal bandwidth of 18mhz and a Pulse Repetition Frequency (PRF) of approximately 830 hz. Here, 1024 pulses are used for SAR GMTIM with a position resolution of about 6.0 m. After channel alignment and channel balancing, SAR focusing, including SPECAN processing and Keystone transformation, is applied to obtain a coarsely focused SAR image, as shown in fig. 6 (a). There is 2D or at least azimuth dimension ambiguity for most moving objects. The range ambiguity is the presence of the MTRC due to doppler ambiguity. While the azimuth ambiguities are caused by phase error modulation of moving objects.

Therefore, the residual MTRC correction and the phase error correction are required for the SAR image with good focus. Then, based on the algorithm proposed by the embodiment of the present invention, the sparsely reconstructed image is shown in fig. 6(b), where it can be seen from the graph that both the MTRC and the phase error are successfully removed. Next, the SA data is used for sparse imaging by applying the algorithm proposed by the embodiment of the present invention. Fig. 6(c) and 6f show the data used with half a quarter full aperture. Meanwhile, fig. 6(d) and 6(f) are sparse reconstructed images in these two cases, respectively. Experimental results show that the algorithm can well solve the problems of SA and motion compensation, and can obtain a high-quality SAR image even in the presence of Doppler blur. This advantage mainly benefits from the parameter sparseness method proposed by the embodiment of the present invention. The simulation experiment verifies the effectiveness and feasibility of the method provided by the embodiment of the invention.

According to yet another aspect of the invention, an embodiment of the invention provides a moving target synthetic aperture radar sparse imaging device.

Fig. 7 is a schematic structural diagram of a single-channel synthetic aperture radar imaging apparatus according to an embodiment of the present invention. As shown in fig. 7, a synthetic aperture radar imaging apparatus 1000 includes a data acquisition module 1010, a clutter suppression module 1020, a compensation module 1030, a post-processing module 1040, and an imaging module 1050.

The data acquisition module 1010 is configured to acquire an echo signal of a chirp signal transmitted to a moving target by a synthetic aperture radar moving along a predetermined trajectory at a preset speed.

And a clutter suppression module 1020, configured to perform stationary clutter suppression processing on the echo signal of the chirp signal to obtain a clutter suppressed echo signal.

A compensation module 1030, configured to perform motion compensation on the radar and the moving target based on the clutter suppressed echo signal by using a spectrum analysis method, so as to obtain a motion compensated echo signal.

A post-processing module 1040, configured to construct a parametric representation dictionary based on the motion compensated echo signals, and represent the motion compensated echo signals according to the parametric representation dictionary, and then construct an objective function of sparse aperture imaging and parameter estimation with the motion compensated echo signals represented by the parametric representation dictionary as constraint conditions.

An imaging module 1050, configured to solve the objective function by using an orthogonal matching pursuit method, and perform doppler ambiguity number estimation and phase error correction on the moving target to obtain an SAR image of the moving target.

Other aspects of the specific steps of the post-preprocessing module 1040 are the same as or similar to the aforementioned method for sparse imaging of a moving target with a synthetic aperture radar, and are not described herein again.

Fig. 8 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 8, the electronic device may include: a processor (processor)1110, a communication interface (communication interface)1120, a memory (memory)1130, and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other via the communication bus 1140. The processor 1110 may invoke logic instructions in the memory 1130 to perform the single channel synthetic aperture radar sparse imaging method described above. Other aspects provided by the present embodiment are the same as or similar to those of the single-channel synthetic aperture radar sparse imaging method described above, and are not described herein again.

In addition, the logic instructions in the memory 1130 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, and a floppy disk.

According to yet another aspect of the present invention, an embodiment of the present invention further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer is capable of executing the synthetic aperture radar sparse imaging method provided by the above embodiments. Other aspects provided by the present embodiment are the same as or similar to the aforementioned method for sparse imaging of synthetic aperture radar of a moving target, and are not described herein again.

It will be understood by those skilled in the art that all or part of the steps in the methods of the above embodiments may be performed by instructions or by instructions controlling associated hardware, and the instructions may be stored in a computer readable storage medium and loaded and executed by a processor.

To this end, according to yet another aspect of the present invention, an embodiment of the present invention further provides a storage medium, on which a computer program is stored, which when executed by a processor is implemented to execute the method for sparse imaging of synthetic aperture radar for a moving target provided in the above embodiments. For example, the computer program may perform the steps of:

step S1, collecting echo data of a linear frequency modulation signal transmitted to a moving target by a synthetic aperture radar flying along a preset route at a preset speed;

step S2, based on the echo data of the linear frequency modulation signal, adopting a displacement phase center antenna method to suppress static clutter under the condition of sparse aperture, and obtaining the echo data processed by the displacement phase center antenna;

step S3, performing motion compensation on the radar platform and the moving target by using a SPECAN method to obtain echo data after the SPECAN compensation;

step S4, establishing a discrete sparse aperture signal model by using a parameterized representation dictionary to obtain a sparse aperture SAR image;

step S5, constructing a target function of sparse aperture imaging and parameter estimation by taking the SPECAN processing result represented by the parameterized representation dictionary as a constraint condition;

and step S6, solving the target function by adopting an orthogonal matching tracking method, and performing Doppler fuzzy number estimation and phase error correction on the target function to obtain SAR images of all moving targets.

Other aspects provided by the embodiment of the present invention are the same as or similar to those of the synthetic aperture radar sparse imaging method described above, and are not described herein again.

The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.

Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.

Since the computer program stored in the storage medium may execute the steps in any moving target synthetic aperture radar sparse imaging method provided in the embodiment of the present invention, the beneficial effects that can be achieved by any moving target synthetic aperture radar sparse imaging method provided in the embodiment of the present invention may be achieved, which are detailed in the foregoing embodiments and will not be described herein again.

The moving target synthetic aperture radar sparse imaging method, the moving target synthetic aperture radar sparse imaging device, the electronic device and the storage medium provided by the embodiment of the invention are described in detail, a specific embodiment is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in specific embodiments and application ranges, and in summary, the content of the present specification should not be construed as a limitation to the present invention.