A method for imaging moving targets in the absence of data

Abstract

The present invention discloses a method for imaging a moving target in the absence of data, using missing echoes and estimating the two-dimensional motion parameters of a non-cooperative moving target; after obtaining the two-dimensional motion parameters of the non-cooperative moving target, using the estimated values ​​of the two-dimensional motion parameters to construct a phase compensation function to ensure the sparsity of the signal after phase compensation, so as to meet the prerequisites required for the subsequent use of a compressed sensing method; using the missing echo signal after phase compensation, using a compressed sensing method to reconstruct and estimate the complete echo of the moving target; after the two-dimensional motion parameters of the non-cooperative moving target and the complete echo of the moving target are effectively estimated, using the estimated value of the complete echo of the moving target to perform cooperative imaging on the moving target using an imaging algorithm, and obtaining a focusing result of the moving target. The present invention can effectively focus on imaging a moving target in the absence of data, and obtain an imaging result that is almost equivalent to an ideal moving target imaging result.

Description

Technical Field

[0001] The invention belongs to the technical field of radar imaging, and in particular relates to a method for imaging a moving target in the absence of data. Background Art

[0002] Nowadays, there are two main ways to observe unknown scenes. One is to use an optical system to perform optical imaging of the observed scene to obtain detailed scene photos. However, optical imaging has obvious defects, such as only being able to work under strong light during the day and being easily blocked by clouds. Therefore, the Synthetic Aperture Radar (SAR) imaging system, which is not affected by weather and light and can image the observed scene all day and all day, has been widely concerned in the fields of military defense, civil surveying and mapping since it was proposed in 1951.

[0003] The working mode of the classic single-station SAR system is to load a radar system on a mobile platform, use the small antenna on the radar system as a single radiation unit, move the radar platform continuously along a straight line, receive the echo signal of the same object at different positions and perform relevant demodulation and compression processing. A small antenna can be equivalent to a large antenna along its movement direction by moving. We define the movement direction of the radar as the azimuth direction, so that a high azimuth resolution image can be obtained. The classic SAR imaging system usually uses the matched filtering technology to realize the imaging task of the original scene to be imaged by the pulse echo collected at different azimuth positions. In order to ensure that the scene to be imaged can be accurately focused, the phase relationship between the scene to be imaged and the moving radar needs to be strictly and accurately known, which means that the slant range relationship between each grid point in the scene to be imaged and the moving radar needs to be strictly and accurately known. When the targets in the scene to be imaged are all stationary targets, we assume that the motion trajectory of the moving radar platform can be accurately known by GPS, Beidou, inertial navigation and other systems, and the phase relationship between the two is determined, which means that the scene to be imaged can be accurately imaged by the classic SAR imaging algorithm. However, once there are some moving targets in the scene, the premise of determining the phase relationship between the non-cooperative moving targets and the moving radar is broken. At this time, directly using the classic SAR imaging algorithm based on stationary targets on the moving targets will naturally make the imaging results defocused, and the moving targets cannot be accurately focused.

[0004] Based on this, in recent decades, moving target imaging has been an important research direction in the field of SAR imaging. Various moving target imaging methods and algorithms have been actively studied for different SAR system configurations and increasingly complex moving target motion types. The classic mainstream moving target imaging algorithm mainly uses various interpolation ideas and time-frequency analysis methods to eliminate the extra distance movement and extra distance curvature caused by the two-dimensional non-cooperative motion parameters of the moving target, so as to achieve accurate focusing on the moving target when the motion information is unknown. Usually, when the echo signal along the azimuth direction is complete and ideal, a well-focused moving target can be obtained by using the existing classic mainstream moving target imaging algorithm. However, in the actual operation of the SAR system, there may be some missing data in the echo signal along the azimuth direction. See Figure 1 , the missing data will lead to a significant reduction in the imaging performance of the original classic mainstream moving target imaging algorithm. More specifically, directly using the classic mainstream moving target imaging algorithm in the absence of data will cause energy defocusing and diffusion in the final imaging result. On the one hand, the azimuth resolution will decrease, and on the other hand, many false targets will appear in the image, causing new difficulties for the observer to distinguish the real target.

[0005] The specific reasons for data loss are as follows: Data loss is often caused by various interferences and shieldings during the movement of the target and the radar. For example, when multiple systems are working simultaneously, due to the increasing number of different communication devices, more communication devices working simultaneously will bring strong interference to the SAR system in operation. At this time, during the movement of the radar platform, some azimuth positions may have strong interference, resulting in the radar platform being unable to receive available echo signals at these azimuth positions, resulting in the loss of some data. For example, in order to avoid the opponent's reconnaissance and destruction of our SAR system, we actively control the working mode of the SAR system flexibly, and actively turn off the radar system after turning on the radar transmitter for a certain period of time, so that the energy generated during a single working time does not trigger the detection threshold of the opponent's reconnaissance system. Obviously, this working mode will cause intermittent missing of the data received by the SAR system. For example, in urban remote sensing observation, when the SAR system moves in a complex urban environment, the shielding of some buildings along the road causes the SAR system to only receive the echo of the building at these positions but not the echo of the target behind the building, resulting in the loss of some echoes.

[0006] When using SAR to image ground scenes, the radar carrier moves along the azimuth direction to collect ground echoes. Due to strong interference from the other party in certain directions, temporary failure of the radar transmitter, and frequent start and stop of the radar transmitter actively controlled by our side to resist interference, the echo actually received by the radar receiver will have some aperture data missing. In order to accurately image stationary targets in the absence of data, in 2018, Y. Qian et al. published a Doppler recovery estimation method based on sparse constraints (High-resolution SAR Imaging from Azimuth Periodically Gapped Raw Data via GOMP) in Electronics letters. This method estimates and recovers a phase-compensated Doppler domain signal, transforms it to the two-dimensional time domain to obtain an estimated value of the complete echo, and finally performs matched filtering imaging on the estimated value of the complete echo to improve the signal-to-noise ratio, so that accurate images can be obtained using missing data. In 2021, a similar method extended to the demultiplexed frequency-modulated SAR system was published in IEEE Geoscience and Remote Sensing Letters (Parameter Design and Imaging Method of Spaceborne Azimuth Interrupted FMCW SAR).

[0007] The disadvantage of the prior art is that it can only accurately image stationary targets in the absence of data. Once the target is a non-cooperative moving target, the existing imaging method cannot accurately focus on the moving target. The reason is that in the imaging of stationary targets, the target and the radar platform are cooperative. As the radar platform moves, the phase relationship between the stationary target and the moving radar at different azimuth positions is known. In the imaging of moving targets, the moving target is non-cooperative, and only the radar platform is cooperative. Therefore, since the actual motion information of the moving target is unknown, the phase relationship between the non-cooperative moving target and the moving radar at different azimuth positions is also unknown. Therefore, the existing method cannot directly accurately image the moving target in the absence of data.

[0008] The Chinese patent with publication number CN103901429B discloses a method for inverse synthetic aperture radar imaging of maneuvering targets based on sparse aperture, receiving the original echo data of the inverse synthetic aperture radar with sparse aperture; range compression and envelope alignment processing; accurate phase correction; reconstruction of sparse aperture echo signal; fast Fourier transform to achieve range Doppler imaging. The Chinese patent with publication number CN107340518A discloses an ISAR radar imaging method for signal loss. This patent is also ISAR imaging, using the sparse characteristics of the target to directly derive the ISAR sparse imaging algorithm, making full use of the sparsity brought by the imaging space on the transmitted signal, sparsely sampling the echo of the partially missing dual-base ISAR to obtain measurement data, constructing a sparse basis in the imaging plane, and obtaining a high-precision estimate of the spatial distribution of the imaging points through the reconstruction algorithm. However, there are differences in the problems faced by SAR imaging and ISAR imaging, that is, the imaging mechanisms and algorithms of the two are also different. Therefore, the above patent cannot solve the problem of deriving moving target imaging based on the SAR system under missing aperture.

[0009] In the absence of data, directly using the existing classic SAR imaging algorithm based on stationary targets to image non-cooperative moving targets will cause the target to be defocused and offset, and the lack of data will lead to the appearance of false targets; in the absence of data, directly using the classic SAR moving target imaging algorithm. Moving targets can be relatively focused, but false targets will also be generated, resulting in poor performance of the final image results; in the absence of data, if the existing missing data imaging algorithm based on stationary targets is used, false targets can be well suppressed, but an accurate moving target imaging result cannot be obtained. Summary of the invention

[0010] In order to solve the problem that existing methods cannot directly perform accurate SAR imaging of moving targets under missing data, the present invention proposes a new method for imaging moving targets under missing data to solve the problem of how to effectively focus on moving targets in ground scenes under missing aperture.

[0011] The present invention discloses a method for imaging a moving target in the absence of data, comprising the following steps:

[0012] Acquire the echo of a moving target with a missing part of the signal, and estimate the two-dimensional motion parameters of the non-cooperative moving target by using the missing echo and the parameter estimation method;

[0013] After obtaining the two-dimensional motion parameters of the non-cooperative moving target, a phase compensation function is constructed using the estimated values ​​of the two-dimensional motion parameters to ensure the sparsity of the signal after phase compensation and meet the prerequisites required for the subsequent use of the compressed sensing method;

[0014] The missing echo signal after phase compensation is used to reconstruct and estimate the complete echo of the moving target using the compressed sensing recovery method.

[0015] After the two-dimensional motion parameters of the non-cooperative moving target and the complete echo of the moving target are effectively estimated, the SAR imaging algorithm is used to perform cooperative imaging on the moving target using the complete echo estimation value of the moving target to obtain the focusing result of the moving target.

[0016] Furthermore, when the moving target echo has missing data along the azimuth, the received azimuth sampling points are no longer complete, and the set of azimuth complete sampling point positions is The set of azimuth sampling point positions actually received is , the specific sampling position of each sampling point is expressed by the following formula:

[0017] (1)

[0018] in , , are the m1, m2,…,Mth sampling points;

[0019] In the case of data loss, the radar received echo is de-carriered, de-FMed, de-skewed and Fourier transformed in the range direction to obtain the signal after pulse compression in the range direction. As shown below:

[0020] (2)

[0021] in Represents the distance frequency value, is the signal center frequency, represents the imaginary unit, For slow time, represents the distance window function, is the azimuth window function, c is the speed of light;

[0022] The distance slope distance is expressed as follows:

[0023] (3)

[0024] By performing Taylor approximation expansion on formula (3), we can obtain:

[0025] (4)

[0026] in is the shortest slant distance to the moving target, Respectively represent the initial position of the moving target in azimuth and distance upwards, Represents the operating altitude of the sports radar platform, Represents the speed of the radar platform, and Respectively represent the speed of the moving target along the azimuth and distance directions;

[0027] Because the orientation to the initial position is much smaller than the shortest slope distance Therefore, the distance slope distance is simplified to

[0028] (5)

[0029] Substitute the simplified distance slope distance calculation formula into formula (2) and extract its exponential term to obtain:

[0030] (6)

[0031] in, , .

[0032] Furthermore, the parameter estimation method is a second-order keystone transformation method, and the expression of the second-order keystone transformation method is as follows:

[0033]

[0034] The method of estimating the two-dimensional motion parameters of the non-cooperative moving target using the parameter estimation method includes:

[0035] Substituting the second-order keystone transform expression into formula (6) to eliminate the second-order coupling, we obtain

[0036] (7)

[0037] in is the exponential term obtained after the second-order Keystone transformation;

[0038] Perform time-frequency analysis on the results to obtain the Doppler modulation frequency of the moving target echo , and thus use the following two formulas to get The estimated value of and azimuth velocity Estimated value:

[0039] (8)

[0040] (9)

[0041] v x Simplifying gives:

[0042] (10).

[0043] Furthermore, the phase compensation function is designed to eliminate the distance bending effect caused by the quadratic coupling term between fast time frequency and slow time, and we have

[0044] (11)

[0045] in The phase compensation function corresponding to the quadratic coupling term between fast time frequency and slow time is eliminated;

[0046] Multiplying equation (11) with equation (7) cancels out the third phase term and we get

[0047] (12)

[0048] In formula (12), there is a coupling between fast time frequency and slow time in the second phase term. Therefore, using the second-order keystone transform again, formula (12) becomes

[0049] (13)

[0050] is the exponential term obtained after the second Keystone transformation;

[0051] At this time, replace the phase term in equation (2) with equation (13) to obtain the processed signal:

[0052] (14)

[0053] Then perform inverse Fourier transform along the distance and Fourier transform along the azimuth for formula (14) to obtain the coarsely focused image under missing data, as shown in the following formula:

[0054] (15)

[0055] Therefore, after the azimuth Fourier transform is performed, the azimuth position of the target focus and The approximate conditions of the distance can be inferred to be the speed of movement.

[0056] (16)

[0057] When the two-dimensional motion parameter information of the non-cooperative moving target is obtained, the velocity information is used to derive the complete echo estimation algorithm of the moving target and construct a new phase compensation function in the Doppler domain:

[0058] (17)

[0059] in Represents the slope distance of the phase compensation point,

[0060] (18)

[0061] Respectively represent the initial positions of the phase compensation point in azimuth and range;

[0062] Multiplying equation (17) by equation (2), we have

[0063] (20)

[0064] The coarsely focused image of the moving target at the corresponding point is obtained, which is sparse in the Doppler domain.

[0065] Furthermore, after obtaining the coarse focus image of the moving target at the corresponding point, the complete signal in the Doppler domain is restored by the compressed sensing recovery method. The process is expressed by the following formula:

[0066] (twenty one)

[0067] in represents the compressed sensing recovery method, is the estimate of the complete signal after phase compensation, S dpc is the missing signal, is the fast time frequency, is the slow time frequency;

[0068] Then, the restored two-dimensional frequency domain echo Return to the range frequency domain-azimuth time domain and perform subsequent phase compensation.

[0069] (twenty two)

[0070] in represents the conjugate symbol, is an estimate of the complete signal after quadratic phase compensation.

[0071] Furthermore, the compressed sensing recovery method includes at least one of a convex optimization algorithm, a non-convex optimization algorithm, a greedy algorithm, and a sparse Bayesian algorithm.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] Under the premise of data missing, the present invention can be used to effectively focus and image a moving target in the data missing situation, and obtain an imaging result that is almost equivalent to an ideal moving target imaging result.

[0074] The present invention utilizes the sparse characteristics of the target, restores the complete aperture through the missing aperture, and then uses the traditional imaging algorithm and the estimated value of the complete aperture to obtain the imaging result of the non-cooperative moving target. Compared with the traditional sparse imaging algorithm, since the imaging principle of the sparse imaging algorithm is to directly restore the strong scattering points in the imaging scene, when the signal-to-noise ratio of the target echo is ideal, the target is a strong scattering point relative to the noise, and the target can be correctly imaged; once the echo signal-to-noise ratio is not ideal, especially when the noise becomes a strong scattering point relative to the target, the target cannot be correctly imaged. Since the algorithm disclosed in the present invention restores the complete echo of the target, the restored echo must undergo two-dimensional matched filtering in the range and azimuth directions to obtain an image. The two-dimensional matched filtering process can greatly increase the signal-to-noise ratio of the original echo, so that the present invention can also obtain the correct moving target focusing result at a lower signal-to-noise ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A comparison chart of complete and missing signals;

[0076] Figure 2 A flow chart of the method for imaging a moving target in the absence of data according to the present invention;

[0077] Figure 3 Ideal imaging result diagram of non-cooperative moving target;

[0078] Figure 4 The result of using the classic stationary target SAR imaging algorithm directly based on the missing signal;

[0079] Figure 5 The result of using the classic moving target SAR imaging algorithm directly based on the missing signal;

[0080] Figure 6 Image result of moving target with missing data using existing methods;

[0081] Figure 7 The present invention provides an imaging result diagram for a moving target with missing data. DETAILED DESCRIPTION

[0082] The present invention is further described below in conjunction with the accompanying drawings, but the present invention is not limited in any way. Any changes or substitutions made based on the teachings of the present invention belong to the protection scope of the present invention.

[0083] The detailed technical solution of the present invention is implemented in the flowchart as shown in FIG. Figure 2As shown. The technical solution of the present invention is introduced by taking a uniformly moving target on the ground as an example. First, when an echo with a missing signal of a moving target is obtained, it is necessary to use the missing echo to estimate the two-dimensional motion parameters of the non-cooperative moving target. In this embodiment, the second-order Keystone method and the time-frequency analysis method are used to estimate the two-dimensional motion parameters of the uniformly moving target on the ground. After obtaining the two-dimensional motion parameters of the moving target, we use the estimated values ​​of the two-dimensional motion parameters to construct a phase compensation function, the purpose of which is to ensure the sparsity of the signal after phase compensation, so as to meet the prerequisites required for the subsequent use of the compressed sensing method. Subsequently, using the missing echo signal after phase compensation, combined with the accuracy and timeliness of signal reconstruction, we use a greedy algorithm to reconstruct and estimate the complete echo of the moving target. After the two-dimensional motion parameters of the non-cooperative moving target and the complete echo of the moving target are effectively estimated, we can use the complete echo estimate of the moving target to use the classic SAR imaging algorithm for cooperative imaging, and obtain a good focusing result of the moving target. Next, we will combine the various formulas to explain the specific technical implementation scheme of the present invention in more detail.

[0084] First, we give an expression for the transmitted signal of a single-station strip SAR system,

[0085] (1)

[0086] in represents the distance window function, represents the imaginary unit, is the signal center frequency, and are fast time and slow time respectively. In addition, The frequency is modulated in the distance direction.

[0087] Naturally, the radar received echo reflected from a point target can be written as

[0088] (2)

[0089] In the above formula is the azimuth window function, It represents the time delay of the signal from the radar transmitter to the target and then back to the radar receiver. After removing the carrier frequency, frequency modulation, de-skewing and Fourier transforming the above formula, the signal after pulse compression in the range direction can be obtained.

[0090] (3)

[0091] in Represents the distance frequency value, is the distance slope distance, which can be expressed by the following formula

[0092] (4)

[0093] In the formula Respectively represent the initial position of the moving target in (azimuth, distance) upward, Represents the operating altitude of the sports radar platform. Represents the speed of the radar platform, and They represent the speed of the moving target along the azimuth and range directions respectively.

[0094] When the moving target echo has missing data along the azimuth, the azimuth sampling points we received are no longer complete. We assume that the complete azimuth sampling points are The azimuth sampling point actually received is , whose sampling position is expressed by the following formula

[0095] (5)

[0096] At this time, in the case of missing data, formula (3) can be rewritten as

[0097] (6)

[0098] Formula (4) is also rewritten as

[0099] (7)

[0100] Expand the above equation (7) by Taylor approximation to obtain

[0101] (8)

[0102] in The shortest slant distance to a moving target.

[0103] Because the orientation to the initial position Usually much smaller than the shortest slope distance Therefore, the above formula (8) is simplified to

[0104] (9)

[0105] Substitute equation (9) into equation (6) and extract its exponential term to obtain

[0106] (10)

[0107] To simplify the above formula (10), let

[0108] (11)

[0109] (12)

[0110] Therefore, formula (10) can be rewritten as

[0111] (13)

[0112] At this time, a second-order keystone transformation is performed first. The expression of the second-order keystone transformation is given by the following formula

[0113] (14)

[0114] Substituting equation (14) into equation (13) to eliminate the second-order coupling, we get

[0115] (15)

[0116] Perform time-frequency analysis on the results to obtain the Doppler modulation frequency of the moving target echo , and thus use the following two formulas to get The estimated value of the azimuth velocity An estimated value of

[0117] (16)

[0118] (17)

[0119] In formula (17), since the distance of the low-speed target in the scene has a relatively small impact on the speed, that is, , so ignore it and get

[0120] (18)

[0121] At this time, the phase compensation function is designed to eliminate the distance bending effect caused by the quadratic coupling term between fast time frequency and slow time, which is:

[0122] (19)

[0123] Multiplying equation (19) with equation (15) cancels out the third phase term and we get

[0124] (20)

[0125] At this time, observing equation (20), we find that there is still a coupling between the fast time frequency and the slow time in the second phase term. Therefore, we use the second-order keystone transform again.

[0126] (twenty one)

[0127] Formula (20) thus becomes

[0128] (twenty two)

[0129] At this time, replace the phase term in equation (6) with equation (22) to obtain the processed signal:

[0130] (twenty three)

[0131] At this time, by performing inverse Fourier transform along the distance and Fourier transform along the azimuth of equation (23), we can obtain a coarsely focused image under missing data, as shown in the following equation:

[0132] (twenty four)

[0133] Therefore, after the azimuth Fourier transform is performed, the azimuth position of the target focus and The approximate conditions of the distance can be roughly inferred to be the speed of movement.

[0134] (25)

[0135] When the two-dimensional motion parameter information of the non-cooperative moving target is obtained, the velocity information is used to derive the complete echo estimation algorithm of the moving target and construct a new phase compensation function in the Doppler domain.

[0136] (26)

[0137] in Represents the slope distance of the phase compensation point,

[0138] (27)

[0139] Respectively represent the initial position of the phase compensation point in (azimuth, distance) upward. Multiplying equation (26) with equation (6), we have

[0140] (28)

[0141] At this point, the coarse focus image of the moving target at the corresponding point has been obtained, which is sparse in the Doppler domain. Try to restore its complete signal in the Doppler domain through the compressed sensing method. The process is expressed by the following formula:

[0142] (29)

[0143] in The compressed sensing recovery method includes but is not limited to a convex optimization algorithm, a non-convex optimization algorithm, a greedy algorithm, and a sparse Bayesian algorithm.

[0144] Then, the restored two-dimensional frequency domain echo Return to the range frequency domain-azimuth time domain and perform subsequent phase compensation.

[0145] (30)

[0146] in Represents the conjugate sign.

[0147] Finally, the two-dimensional velocity information of the moving target is added to the classical imaging algorithm, and the estimated complete echo of the moving target is used for cooperative imaging to obtain a well-focused moving target imaging result. Figure 4-Figure 7 For the experimental results, Figure 4 This is the result of using the classic stationary target SAR imaging algorithm directly based on the missing signal; Figure 5 This is the result of using the classic moving target SAR imaging algorithm directly based on the missing signal;

[0148] Figure 6 This is the imaging result of the moving target with missing data using the existing method; Figure 7 The present invention performs imaging of a moving target under missing data. It can be seen from the experimental results that the present invention can effectively focus on imaging a moving target under missing data conditions, and obtain an imaging result that is almost equivalent to the ideal moving target imaging result.

[0149] For different SAR system configurations, it is only necessary to use different existing public moving target motion parameter estimation algorithms corresponding to the current SAR system configuration to ensure that the motion parameters of non-cooperative moving targets can be accurately estimated. Specifically, the present invention has nothing to do with the specific speed change of the moving target (uniform motion, uniform acceleration motion, variable speed motion); nothing to do with the specific motion state of the moving target (two-dimensional motion along the ground, three-dimensional motion); nothing to do with the specific type of the SAR system (single-station SAR system, dual-station SAR system, distributed multi-station SAR system); nothing to do with the specific working mode of the SAR system (strip mode, beam mode, etc.); nothing to do with the specific motion mode of the SAR system (straight line motion, curve motion, circular motion, etc.); nothing to do with the type of the SAR system (airborne SAR system, unmanned aerial vehicle SAR system, vehicle-borne SAR system, satellite-borne SAR system, etc.); nothing to do with the signal waveform of the SAR system (step frequency signal, random frequency signal, linear frequency modulation signal, continuous wave signal, etc.), etc. In the part of complete signal recovery and estimation, there is no special limitation on the specific recovery algorithm. As long as it is a related algorithm that conforms to the compressed sensing framework, it can be used, such as various convex optimization algorithms, non-convex optimization algorithms, greedy algorithms, sparse Bayesian algorithms, etc.

[0150] The present invention aims to propose a complete method for imaging moving targets under missing data. The key point is that for the special configuration of missing data, the existing method only considers how to effectively image stationary targets under missing data. However, since moving targets are different from stationary targets, the motion state of moving targets is unknown. Therefore, once the target moves, the phase relationship between the target and the radar platform will no longer be accurate. Directly using the existing method for effectively imaging stationary targets under missing data will naturally fail to effectively image moving targets. The core idea of ​​the technical solution proposed by the present invention is to first estimate the motion parameters of the moving target under missing data, so that the moving target becomes a cooperative target, and then use the motion parameters of the moving target to determine the phase relationship between the target and the moving radar platform, so as to achieve the purpose of improving the existing data missing imaging algorithm for stationary targets. Therefore, how to estimate the motion parameters is not the focus of the present invention; which method is used to restore and estimate the complete signal is not the focus of the present invention. The most important thing about the present invention is to propose a complete set of moving target imaging methods under missing data, and propose a new idea that can accurately image moving targets under missing data.

[0151] Compared with the prior art, the present invention has the following beneficial effects:

[0152] Under the premise of data missing, the present invention can be used to effectively focus and image a moving target in the data missing situation, and obtain an imaging result that is almost equivalent to an ideal moving target imaging result.

[0153] The present invention utilizes the sparse characteristics of the target, restores the complete aperture through the missing aperture, and then uses the traditional imaging algorithm and the estimated value of the complete aperture to obtain the imaging result of the non-cooperative moving target. Compared with the traditional sparse imaging algorithm, since the imaging principle of the sparse imaging algorithm is to directly restore the strong scattering points in the imaging scene, when the signal-to-noise ratio of the target echo is ideal, the target is a strong scattering point relative to the noise, and the target can be correctly imaged; once the echo signal-to-noise ratio is not ideal, especially when the noise becomes a strong scattering point relative to the target, the target cannot be correctly imaged. Since the algorithm disclosed in the present invention restores the complete echo of the target, the restored echo must undergo two-dimensional matched filtering in the range and azimuth directions to obtain an image. The two-dimensional matched filtering process can greatly increase the signal-to-noise ratio of the original echo, so that the present invention can also obtain the correct moving target focusing result at a lower signal-to-noise ratio.

[0154] As used herein, the word "preferred" is intended to be used as an example, instance, or illustration. Any aspect or design described herein as "preferred" is not necessarily to be construed as being more advantageous than other aspects or designs. On the contrary, the use of the word "preferred" is intended to present concepts in a specific way. The term "or" as used in this application is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless otherwise specified or clear from the context, "X uses A or B" means any one of the naturally included permutations. That is, if X uses A; X uses B; or X uses both A and B, then "X uses A or B" is satisfied in any of the foregoing examples.

[0155] Moreover, although the present disclosure has been shown and described with respect to one or implementations, those skilled in the art will think of equivalent variations and modifications based on the reading and understanding of this specification and the accompanying drawings. The present disclosure includes all such modifications and variations, and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the above-mentioned components (such as elements, etc.), the terms used to describe such components are intended to correspond to any component (unless otherwise indicated) that performs the specified function of the component (such as it is functionally equivalent), even if the structure is not equivalent to the disclosed structure of the function in the exemplary implementation of the present disclosure shown herein. In addition, although the specific features of the present disclosure have been disclosed with respect to only one of several implementations, such features can be combined with one or other features of other implementations that may be desired and advantageous for a given or specific application. Moreover, insofar as the terms "including", "having", "containing" or their variations are used in specific embodiments or claims, such terms are intended to be included in a manner similar to the term "comprising".

[0156] The functional units in the embodiments of the present invention may be integrated into a processing module, or each unit may exist physically separately, or multiple or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. The above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc. The above-mentioned devices or systems may execute the storage method in the corresponding method embodiment.

[0157] To sum up, the above embodiment is an implementation mode of the present invention, but the implementation mode of the present invention is not limited by the embodiment. Any other changes, modifications, substitutions, combinations, and simplifications that deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for imaging a moving target in the absence of data, characterized in that: The following steps are involved: Acquire the echo of a moving target with a missing part of the signal, and estimate the two-dimensional motion parameters of the non-cooperative moving target by using the missing echo and the parameter estimation method; After obtaining the two-dimensional motion parameters of the non-cooperative moving target, a phase compensation function is constructed using the estimated values ​​of the two-dimensional motion parameters to ensure the sparsity of the signal after phase compensation and meet the prerequisites required for the subsequent use of the compressed sensing method; The missing echo signal after phase compensation is used to reconstruct and estimate the complete echo of the moving target using the compressed sensing recovery method. After the two-dimensional motion parameters of the non-cooperative moving target and the complete echo of the moving target are effectively estimated, the SAR imaging algorithm is used to perform cooperative imaging on the moving target using the complete echo estimation value of the moving target to obtain the focusing result of the moving target; The phase compensation function is designed to eliminate the distance bending effect caused by the quadratic coupling term between fast time frequency and slow time. （11） in The phase compensation function corresponding to the quadratic coupling term between fast time frequency and slow time is eliminated; Multiplying equation (11) with equation (7) cancels out the third phase term and we get （12） In formula (12), there is a coupling between fast time frequency and slow time in the second phase term. Therefore, using the second-order keystone transform again, formula (12) becomes （13） is the exponential term obtained after the second Keystone transformation; At this time, replace the phase term in equation (2) with equation (13) to obtain the processed signal: （14） Then perform inverse Fourier transform along the distance and Fourier transform along the azimuth for formula (14) to obtain the coarsely focused image under missing data, as shown in the following formula: （15） Therefore, after the azimuth Fourier transform is performed, the azimuth position of the target focus and The approximate conditions of the distance can be inferred to be the speed of movement. （16） When the two-dimensional motion parameter information of the non-cooperative moving target is obtained, the velocity information is used to derive the complete echo estimation algorithm of the moving target and construct a new phase compensation function in the Doppler domain: （17） in Represents the slope distance of the phase compensation point, （18） Respectively represent the initial positions of the phase compensation point in azimuth and range; Multiplying equation (17) by equation (2), we have （20） The coarsely focused image of the moving target at the corresponding point is obtained, which is sparse in the Doppler domain.

2. The method for imaging a moving target in the absence of data according to claim 1, characterized in that: When the moving target echo has missing data along the azimuth, the received azimuth sampling points are no longer complete, and the set of azimuth complete sampling point positions is The set of azimuth sampling point positions actually received is , the specific sampling position of each sampling point is expressed by the following formula: （1） in , , are the m1, m2,…,Mth sampling points; In the case of data loss, the radar received echo is de-carriered, de-FMed, de-skewed and Fourier transformed in the range direction to obtain the signal after pulse compression in the range direction. As shown below: （2） in Represents the distance frequency value, is the signal center frequency, represents the imaginary unit, For slow time, represents the distance window function, is the azimuth window function, c is the speed of light; The distance slope distance is expressed as follows: （3） By performing Taylor approximation expansion on formula (3), we can obtain: （4） in is the shortest slant distance to the moving target, Respectively represent the initial position of the moving target in azimuth and distance upwards, Represents the operating altitude of the motion radar platform, Represents the speed of the radar platform, and Respectively represent the speed of the moving target along the azimuth and distance directions; Because the orientation to the initial position is much smaller than the shortest slope distance Therefore, the slope distance is simplified to （5） Substitute the simplified distance slope distance calculation formula into formula (2) and extract its exponential term to obtain: （6） in, , .

3. The method for imaging a moving target in the absence of data according to claim 1, characterized in that: The parameter estimation method is a second-order keystone transformation method, and the expression of the second-order keystone transformation method is as follows: The method of estimating the two-dimensional motion parameters of the non-cooperative moving target using the parameter estimation method includes: Substituting the second-order keystone transform expression into formula (6) to eliminate the second-order coupling, we obtain （7） in is the exponential term obtained after the second-order Keystone transformation; Perform time-frequency analysis on the results to obtain the Doppler modulation frequency of the moving target echo , and thus use the following two formulas to get The estimated value of and azimuth velocity Estimated value: （8） （9） v x Simplifying gives: （10）。 4. The method for imaging a moving target in the absence of data according to claim 3, characterized in that: After obtaining the coarse focus image of the moving target at the corresponding point, the complete signal in the Doppler domain is restored by the compressed sensing recovery method. The process is expressed by the following formula: （21） in represents the compressed sensing recovery method, is the estimate of the complete signal after phase compensation, S dpc is the missing signal, is the fast time frequency, is the slow time frequency; Then, the restored two-dimensional frequency domain echo Return to the range frequency domain-azimuth time domain and perform subsequent phase compensation. （22） in represents the conjugate symbol, is an estimate of the complete signal after quadratic phase compensation.

5. The method for imaging a moving target in the absence of data according to claim 4, characterized in that: The compressed sensing recovery method includes at least one of a convex optimization algorithm, a non-convex optimization algorithm, a greedy algorithm, and a sparse Bayesian algorithm.

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