Super-resolution one-dimensional range imaging method and system for multi-polarization sparse frequency-hopping signals

Abstract

The present invention relates to a method and system for super-resolution one-dimensional range imaging of multi-polarization sparse frequency-hopping signals. By modeling multi-polarization channel signals as a multi-observation vector model, the information of each polarization channel is effectively utilized, and the defect of independent processing of a single polarization channel and then fusion matching of multi-polarization channels is avoided. At the same time, the one-dimensional range imaging of sparse frequency-hopping radar signals is modeled as a weighted atomic norm minimization problem and is quickly solved using an alternating direction multiplier method. There is no need to establish a dictionary matrix, which avoids off-grid errors, has no minimum atomic distance constraints, and has the ability of super-Rayleigh resolution. It should be noted that the above method is highly flexible and is applicable to any single or multiple polarization channel combinations in the four polarization channels of HH, HV, VH and VV. It can effectively obtain super-resolution high-quality one-dimensional range images of radar targets, and has great engineering application value.

Description

Technical Field

[0001] The invention belongs to the technical field of radar imaging, and relates to a method and system for super-resolution one-dimensional range imaging of multi-polarization sparse frequency-hopping signals. Background Art

[0002] Radar can work in extreme conditions such as clouds, rain, fog and at night, and is now widely used in many industries. High-quality one-dimensional range images can reflect the backscatter distribution of targets on the radar line of sight, and play an important role in applications such as target size estimation, structural feature extraction, multi-dimensional imaging and target recognition.

[0003] However, most of the existing one-dimensional range imaging methods lack the joint consideration of multi-polarization channels and sparse frequency hopping, and there are problems with low range image quality due to insufficient utilization of multi-polarization channel information and mismatch of imaging model grid. At the same time, existing super-resolution one-dimensional range imaging methods (such as subspace methods) are usually only applicable to application scenarios where the echo data is uniform and complete, and cannot be directly used to process sparse frequency hopping signals. Although existing methods can obtain high-resolution one-dimensional range images of sparse frequency hopping signals (such as one-dimensional range image methods based on atomic norms), their application is constrained by the minimum atomic distance and does not have super-resolution capabilities. Summary of the invention

[0004] In view of the problems existing in the above-mentioned traditional technologies, the present invention proposes a multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging method and a multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging system, which can obtain a high-quality one-dimensional range image of the target with super-resolution capability.

[0005] In order to achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0006] On the one hand, a method for super-resolution one-dimensional range imaging of multi-polarization sparse frequency hopping signals is provided, comprising the steps of:

[0007] Receive multi-polarization sparse frequency hopping signals of radar targets;

[0008] Construct a mathematical model for multi-polarization sparse frequency-hopping signals;

[0009] According to the mathematical model of multi-polarization sparse frequency hopping signal, the super-resolution one-dimensional range imaging of multi-polarization sparse frequency hopping signal is established as a multi-observation vector weight atomic norm minimization problem.

[0010] The alternating direction multiplier method is used to quickly solve the multi-observation vector weight atomic norm minimization problem to determine the reconstructed Toeplitz matrix and the reconstructed signal.

[0011] According to the reconstructed Toeplitz matrix and the reconstructed signal, the Vandermonde decomposition and SORTE algorithm are used to determine the multi-polarization scattering center parameters of the radar target, and the multi-polarization super-resolution one-dimensional range image of the target is obtained; the scattering center parameters include the number of scattering centers of the radar target, the position of the scattering center and the scattering intensity of the radar target in each polarization channel.

[0012] On the other hand, a multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging system is also provided, comprising:

[0013] A signal receiving module, used for receiving multi-polarization sparse frequency hopping signals of radar targets;

[0014] Signal characterization module, used to construct a mathematical model of multi-polarization sparse frequency-hopping signals;

[0015] A problem establishment module is used to establish the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging as a multi-observation vector weight atomic norm minimization problem according to the mathematical model of the multi-polarization sparse frequency hopping signal;

[0016] A fast solution module is used to quickly solve the multi-observation vector weight atomic norm minimization problem using an alternating direction multiplier method to determine the reconstructed Toeplitz matrix and the reconstructed signal;

[0017] The super-resolution imaging module is used to determine the multi-polarization scattering center parameters of the radar target based on the reconstructed Toeplitz matrix and the reconstructed signal, and adopt Vandermonde decomposition and SORTE algorithm to obtain the multi-polarization super-resolution one-dimensional range image of the target; the scattering center parameters include the number of scattering centers of the radar target, the position of the scattering center and the scattering intensity of the radar target in each polarization channel.

[0018] One of the above technical solutions has the following advantages and beneficial effects:

[0019] The above-mentioned multi-polarization sparse frequency-hopping signal super-resolution one-dimensional range imaging method and system, by modeling the multi-polarization channel signal as a multi-observation vector model, effectively utilizes the information of each polarization channel, and avoids the defects of independent processing of a single polarization channel and then fusing and matching the multi-polarization channels. At the same time, the one-dimensional range imaging of sparse frequency-hopping radar signals is modeled as a weighted atomic norm minimization problem and the alternating direction multiplier method is used for rapid solution. There is no need to establish a dictionary matrix, which avoids grid errors, has no minimum atomic distance constraints, and has the ability of super-Rayleigh resolution. It should be noted that the above-mentioned method is highly flexible and is applicable to any single or multiple polarization channels in the four polarization channels of HH, HV, VH and VV. It can effectively obtain super-resolution high-quality one-dimensional range images of radar targets and has great engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flowchart of a method for super-resolution one-dimensional range imaging of multi-polarization sparse frequency-hopping signals in one embodiment;

[0022] Figure 2 It is a schematic diagram of a specific implementation process of a method for super-resolution one-dimensional range imaging of multi-polarization sparse frequency-hopping signals in one embodiment;

[0023] Figure 3 Partial polarization imaging results of the IFFT method and the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging method under conventional stepped frequency signals and sparse frequency hopping signals in one embodiment; wherein (a) is the HH polarization imaging result, and (b) is the HV polarization imaging result;

[0024] Figure 4 Another part of polarization imaging results of the IFFT method and the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging method in one embodiment under conventional stepped frequency signals and sparse frequency hopping signals; wherein (a) is the VH polarization imaging result, and (b) is the VV polarization imaging result;

[0025] Figure 5 The imaging results of HH and VV polarizations of the IFFT method and the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging method in one embodiment under conventional stepped frequency signals and sparse frequency hopping signals, wherein (a) is the imaging result of HH polarization, and (b) is the imaging result of VV polarization;

[0026] Figure 6 It is a module block diagram of a multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging system in one embodiment. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0028] It should be noted that the reference to "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The presentation of this phrase at various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It will be appreciated by those skilled in the art that the embodiments described herein may be combined with other embodiments. The term "and / or" used in the specification and appended claims of the present invention refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0029] The following will describe the implementation of the present invention in detail with reference to the accompanying drawings in the embodiment diagram of the present invention.

[0030] The concept of multi-polarization sparse frequency hopping provides a way to solve the above problems. It effectively integrates multi-polarization channel signals, accurately constructs a sparse frequency hopping signal imaging model, and then obtains high-quality one-dimensional range images of the target with super-resolution capability, which has important engineering application value.

[0031] In one embodiment, Figure 1 As shown, a multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging method is provided, which may include the following steps S10 to S18:

[0032] S10, receiving a multi-polarization sparse frequency hopping signal of a radar target; it can be understood that the multi-polarization sparse frequency hopping signal is a multi-polarization sparse frequency hopping echo of the radar target.

[0033] S12, construct a mathematical model of multi-polarization sparse frequency hopping signals.

[0034] S14, based on the mathematical model of multi-polarization sparse frequency hopping signals, the super-resolution one-dimensional range imaging of multi-polarization sparse frequency hopping signals is established as a multi-observation vector weight atomic norm minimization problem.

[0035] S16, the alternating direction multiplier method is used to quickly solve the multi-observation vector weight atomic norm minimization problem to determine the reconstructed Toeplitz matrix and the reconstructed signal.

[0036] S18, based on the reconstructed Toeplitz matrix and the reconstructed signal, the Vandermonde decomposition and SORTE (Second Order Statistic of Eigenvalues) algorithm are used to determine the multi-polarization scattering center parameters of the radar target, and obtain the multi-polarization super-resolution one-dimensional range image of the target; the scattering center parameters include the number of scattering centers of the radar target, the position of the scattering center and the scattering intensity of the radar target in each polarization channel.

[0037] It can be understood that this embodiment is based on multi-polarization sparse frequency hopping signals to obtain a super-resolution high-quality one-dimensional range image of the target. First, a mathematical representation of the multi-polarization sparse frequency hopping (radar) signal is established. Then, the multi-polarization sparse frequency hopping signal is regarded as a multi-observation vector model and converted into a multi-observation vector weight atomic norm minimization problem without grid error and minimum atomic distance constraint; then, the multi-observation vector weight atomic norm minimization problem is quickly solved by the alternating direction multiplier method (ADMM) to obtain the Toeplitz matrix and reconstructed signal of the multi-polarization sparse frequency hopping signal of the target. Finally, the reconstructed Toeplitz matrix is ​​subjected to Vandermonde decomposition to obtain the position of the scattering center, and on this basis, the scattering amplitude of each polarization channel is estimated respectively to obtain the multi-polarization super-resolution one-dimensional range image of the target.

[0038] The above-mentioned multi-polarization sparse frequency-hopping signal super-resolution one-dimensional range imaging method effectively utilizes the information of each polarization channel by modeling the multi-polarization channel signal as a multi-observation vector model, avoiding the defects of fusion matching of multi-polarization channels after independent processing of single polarization channels. At the same time, the one-dimensional range imaging of sparse frequency-hopping radar signals is modeled as a weighted atomic norm minimization problem and quickly solved by the alternating direction multiplier method. There is no need to establish a dictionary matrix, avoid off-grid errors, no minimum atomic distance constraints, and has the ability of super-Rayleigh resolution. It should be noted that the above-mentioned method is highly flexible and is applicable to any single or multiple polarization channels in the four polarization channels of HH, HV, VH and VV. It can effectively obtain super-resolution high-quality one-dimensional range images of radar targets and has great engineering application value. Among them, off-grid errors refer to the errors introduced when the true parameter value does not fall on the preset discrete grid points when estimating continuous parameters using a discretized grid model. The deviation from the grid error will cause a single true scattering center to split into multiple false scattering centers located at adjacent grid points and with scattering amplitudes lower than the true value.

[0039] Specifically, Figure 2 As shown, first, a mathematical model of multi-polarization sparse frequency hopping signal is constructed. Specifically, in the high frequency region, the backscattered echo of the radar target can be equivalently composed of several strong scattering components. Taking the conventional single-polarization stepped frequency radar as an example (for example, the radar transmits a multi-polarization stepped frequency signal as an example for derivation. In fact, the linear frequency modulation signal system radar also has a similar expression after Dechirp processing (i.e., de-chirp processing)), its received signal can be expressed as:

[0040] (1)

[0041] In formula (1), Indicates four polarization channels: HH (horizontal-horizontal polarization), HV (horizontal-vertical polarization), VH (vertical-horizontal polarization) and VV (vertical-vertical polarization). Indicates The scattering centers are The scattering intensity in the polarization channel is an element of the polarization scattering matrix; Indicates The projection of the distance from the scattering center to the radar reference phase zero point along the radar line of sight; c is the electromagnetic propagation speed, Indicates n The frequency of the step frequency pulse, where represents the initial frequency, Indicates the frequency step interval, ; express l Polarization channel n The observation noise of the stepped frequency pulses is assumed to follow a Gaussian distribution.

[0042] For the convenience of description, Substituting into equation (1), the received signal can be further written as:

[0043] (2)

[0044] In formula (2), (This is equivalent to the existence of an equal phase difference between the amplitude of all scattering centers and the corresponding true value). From equation (2), we can see that for conventional stepped frequency radar, the received signal of each polarization channel is related to n The one-dimensional range image of the target can be obtained by performing an inverse Fourier transform (IFFT). , the Rayleigh distance resolution is .

[0045] Sparse frequency hopping signals can be considered as conventional stepped frequency radar transceiver signals. A random downsampling of . The index of this random downsampling is , whose complement is , the actual number of transmitted and received signals is According to formula (2), The matrix form of the sparse frequency hopping signal of the polarization channel can be written as:

[0046] (3)

[0047] In formula (3) express The actual received signal of the polarized channel, Indicates that the radar target is The real scattered component in the polarization channel; express Observation noise of polarization channels; represents the observation matrix, whose row vector has only one element of 1 and the rest of the elements are 0; A represents a vector with a Vandermonde structure, specifically the vector here: Depend on Q column vectors, with a Vandermonde structure, where and denote the steering vector and the normalized distance sampling frequency (hereafter referred to as distance frequency), respectively; Indicates that the radar target is The scattering intensity in the polarization channel. It should be noted that when the target size is smaller than the maximum unambiguous distance When the distance frequency The scattering center There is a one-to-one correspondence. Usually the radar system parameters Known, therefore obtained That is, equivalent to obtaining .

[0048] Since the position parameters of the same scattering center in different polarization channels are the same, that is, the distance frequency of each polarization channel same.

[0049] In one embodiment, according to formula (3), the matrix form (i.e., mathematical model / representation) of the multi-polarization channel sparse frequency hopping signal can be expressed as:

[0050] (4)

[0051] In formula (4) ( )express L The actual received signal of each polarization channel (i.e., the multi-polarization sparse frequency hopping signal actually received); Indicates that the radar target is L The real scattered component in each polarization channel; express L The observation noise of each polarization channel; Indicates that the radar target is L The scattering intensity in each polarization channel.

[0052] Then, super-resolution one-dimensional range imaging of multi-polarization sparse frequency-hopping signals is formulated as a multi-observation vector weighted atomic norm minimization problem.

[0053] It can be understood from equation (4) that the essence of multi-polarization one-dimensional range imaging is to use multiple polarization channels to jointly estimate the range frequency and . Further investigation of formula (4) L The real scattered component of each polarization channel , which is equivalent to:

[0054] (5)

[0055] In formula (5) Indicates q The scattering centers are L The scattering intensity in each polarization channel; represents the downsampled steering vector; Indicates q The scattering centers are L Normalized scattered intensity in each polarization channel.

[0056] The existing one-dimensional range imaging methods based on grid-based compressed sensing usually use the Fourier dictionary matrix as the complete word matrix and consider the range frequency of the target (Scattering Center ) are all located on the preset grid. There is a mismatch between the imaging model and the actual distribution of target scattering, and there is a grid error problem, which may produce false scattering centers.

[0057] In one embodiment, regarding the above-mentioned step S14, the process of establishing the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging as a multi-observation vector weight atomic norm minimization problem may specifically include the following processing:

[0058] Based on multi-polarization sparse frequency hopping signals, an atomic set with a characteristic position of arbitrary scattering center is defined;

[0059] The mathematical model of multi-polarization sparse frequency-hopping signal is characterized by using the minimum number of scattering centers and the model is convexly relaxed into an atomic norm minimization problem.

[0060] After converting the atomic norm minimization problem into a weighted atomic norm with super-resolution, the radar system observation noise is introduced to transform the weighted atomic norm into a semi-positive definite constrained optimization problem.

[0061] It can be understood that, unlike the above existing methods, this embodiment is based on multi-polarization sparse frequency hopping signals and defines an atomic set that represents an arbitrary scattering center position:

[0062] (6)

[0063] Intuitively, the minimum number of scattering centers can be used to characterize , that is, atoms l Minimize the zero norm:

[0064] (7)

[0065] However, equation (7) is an NP-hard problem. To facilitate the solution, equation (7) is convexly relaxed to an atomic norm minimization problem:

[0066] (8)

[0067] It is worth noting that the robustness of the atomic norm shown in Eq. (8) is constrained by the minimum atomic distance , does not have super-resolution capability. Therefore, the weighted atomic norm with super-resolution is considered as follows:

[0068] (9)

[0069] In formula (9) is the weight function, where the weight matrix The expression is:

[0070] (10)

[0071] In formula (10), It is a constant factor, and its value is usually small (for example, 0.01). It can be set according to actual conditions.

[0072] Since equation (9) is an infinite-dimensional optimization problem, it is difficult to solve directly. Considering the radar system observation noise, equation (9) is further transformed into a semi-positive definite constrained optimization problem that is easy to solve:

[0073] (11)

[0074] In formula (11), is an auxiliary variable; Indicates the solution accuracy, which can be set according to the noise level; is a Toeplitz matrix with low rank, positive semidefinite and Hermitian symmetry after reconstruction, where is an auxiliary variable, represents the Toeplitz structure operator. The covariance matrix of the scattering components of the visible multi-polarization radar target is:

[0075] (12)

[0076] because is a semi-positive Toeplitz matrix with a unique Vandermonde decomposition, so once The distance frequency of the scattering center is reconstructed can be uniquely determined, that is, the position of the scattering center is determined.

[0077] Then, ADMM is used to quickly solve the weighted atomic norm.

[0078] It can be understood that ADMM is used to quickly solve the semi-positive definite constrained optimization problem (11) of weighted atomic norm transformation, and its augmented Lagrangian function is shown as follows:

[0079] (13)

[0080] In formula (13), is the regularization parameter, represents the inner product operation of the matrix, represents the Lagrange multiplier, is the penalty parameter.

[0081] To facilitate subsequent description, and matrix Re-write as:

[0082] (14)

[0083] (15)

[0084] The components in (14) and (15) , , , , and .

[0085] initialization and , decompose equation (13) into the following five sub-iterative optimization problems:

[0086] (16)

[0087] In formula (16), Denotes the number of iterations. The five sub-iterative optimization problems shown in equation (16) are derived separately, and the solutions with zero gradient are taken as the update results.

[0088] For the sub-iterative optimization problem (a), After iterations, it is updated to:

[0089] (17)

[0090] For the sub-iterative optimization problem (b), i After iterations, it is updated to:

[0091] (18)

[0092] In formula (18), , is the Toeplitz adjoint operator.

[0093] For the sub-iterative optimization problem (c), i After iterations, it is updated to:

[0094] (19)

[0095] In formula (19), The elements are updated according to the following formula:

[0096] (20)

[0097] For the sub-iterative optimization problem (d), i After iterations, it is updated to:

[0098] (twenty one)

[0099] In formula (21), and They are The eigenvectors and eigenvalues ​​of Determined by the following formula:

[0100] (twenty two)

[0101] For the sub-iterative optimization problem (e), i After iterations, it is updated to:

[0102] (twenty three)

[0103] when or When , the iteration ends and the last updated (reconstructed Toeplitz matrix) and (reconstructed signal) as the optimal solution.

[0104] Finally, the target multi-polarization scattering center parameters are determined and the multi-polarization super-resolution one-dimensional range image of the radar target is obtained.

[0105] It is understandable that the optimal solution Perform Vandermonde decomposition and then use the SORTE algorithm to estimate the number of radar target scattering centers And use MPM (Matrix Pencil Method) to estimate Doppler frequency On this basis, the position of the scattering center is determined by the following formula:

[0106] (twenty four)

[0107] In formula (24), .

[0108] Considering that the radar target has the same scattering center position in each polarization channel, based on The matrix formed and reconstruct the signal , the scattering intensity of the radar target in each polarization channel is determined by the least squares method:

[0109] (25)

[0110] In formula (25), At this point, equations (24) and (25) are solved, that is, the multi-polarization super-resolution one-dimensional range image of the radar target is obtained.

[0111] In some embodiments, the following point scattering center simulation target data and cone combination target darkroom measurement data are used to verify the effectiveness of the above multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging method.

[0112] Table 1 gives the scattering parameters of the point simulation target, where the scattering intensity is a relative value with the value "1". Figures 3 to 5 The radar simulation parameters can be set as follows: initial frequency ,bandwidth , step frequency interval , step frequency points N =101, distance resolution , maximum unambiguous imaging distance R max = 15 m. It should be noted that the distance between scattering centers S1 and S2 is 0.075 m, which is 0.5 times the Rayleigh distance resolution.

[0113] Table 1

[0114]

[0115] Figure 3 and Figure 4 The full polarization imaging results of the IFFT method and the above-mentioned multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging method (referred to as the method of the present invention) under conventional stepped frequency signals and sparse frequency hopping signals (randomly selecting 50 stepped frequency points) are given, where: Figure 3 (a) is the HH polarization imaging result. Figure 3 (b) is the HV polarization imaging result. Figure 4 (a) is the VH polarization imaging result. Figure 4 (b) is the VV polarization imaging result. For the convenience of quantitative analysis, Table 2 gives the extraction results of scattering parameters of the method of the present invention.

[0116] Table 2

[0117]

[0118] Table 1 and Table 2 S HH , S HV , S VH and S VV They are the components on each polarization channel of the scattering center S. Figure 3 and Figure 4 It can be seen that under the conventional stepped frequency signal (101 stepped frequency points), the one-dimensional range images of the four polarization channels obtained by the IFFT method basically reflect the position and scattering intensity of the scattering center, but all have wide main lobes and high side lobes. At the same time, due to the constraints of Rayleigh resolution, this method cannot distinguish the scattering centers S1 and S2. In addition, since the IFFT method processes the four polarization channels separately and does not jointly utilize the information of each polarization channel, the one-dimensional range images of the HV and VH polarization channels obtained by the IFFT method cannot reflect the scattering center S4.

[0119] Under the above-mentioned fully polarized sparse frequency-hopping signal, the one-dimensional range image of the four polarization channels obtained by the IFFT method is subject to strong grating lobe interference and severe defocusing. At the same time, the wide main lobe and high side lobe problems are significant, and S1 and S2 cannot be distinguished. In contrast, the one-dimensional range image of the four polarization channels obtained by the method of the present invention suppresses grating lobe interference, has no main lobe broadening and side lobes, and distinguishes S1 and S2 with adjacent distances of 0.5 times the resolution, accurately reflecting the position and scattering intensity of each scattering center. Comparing Table 1 and Table 2, it can be seen that the scattering parameters extracted by the method of the present invention are consistent with the true value, which verifies the effectiveness of the method of the present invention.

[0120] The height of the cone combination target is 1.2m and the bottom radius is 0.26m. The parameters of the darkroom radar system are as follows: initial frequency , bandwidth 1GHz, step frequency interval , step frequency points N =51, distance resolution , maximum unambiguous imaging distance R max =7.5m. The radar observation azimuth angle is 2°, the elevation angle is 0°, and the polarization modes are HH and VV polarization.

[0121] Figure 5 The imaging results of HH and VV polarizations of the IFFT method and the method of the present invention under conventional stepped frequency signals and sparse frequency hopping signals (25 stepped frequency points are randomly selected), where (a) is the imaging result of HH polarization and (b) is the imaging result of VV polarization. Figure 5 It can be seen that under the conventional stepped frequency signal (51 stepped frequency points), the one-dimensional range image of the two polarization channels obtained by the IFFT method can reflect the radial scattering distribution of the cone-shaped combined target, and its envelope is consistent with the radial size of the target of 1.2m, but there are problems of wide main lobe and high side lobe.

[0122] Under the above HH and VV polarization sparse frequency hopping signals, the one-dimensional range images of the two polarization channels obtained by the IFFT method are interfered by strong grating lobes, have serious defocusing, and are difficult to accurately reflect the radial scattering distribution of the target. In contrast, the one-dimensional range images of the two polarization channels obtained by the method of the present invention have no grating lobe interference, no main lobe broadening and side lobes, and the scattering intensity and position of the five strong scattering centers extracted are consistent with the model structure of the target, further verifying the effectiveness of the method of the present invention.

[0123] In summary, the super-resolution one-dimensional range imaging method for multi-polarization sparse frequency-hopping signals jointly considers the sparse frequency-hopping signals of multi-polarization channels and models them as a multi-observation vector weight atomic norm minimization problem. There is no need to match the extracted multi-polarization scattering center parameters, no need to establish a gridded dictionary matrix, and no minimum atomic distance constraint. The multi-polarization scattering parameters of the target can be obtained under the condition of super-Rayleigh resolution. The obtained super-resolution one-dimensional range image has no mainlobe broadening and sidelobes, and has great engineering application value.

[0124] It should be understood that although the above process Figure 1 and Figure 2 The steps in the flowchart are shown in the order indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps can be executed in other orders. Figure 1 and Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequentially, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0125] In one embodiment, Figure 6As shown, a multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging system 100 is provided, comprising a signal receiving module 11, a signal characterization module 13, a problem establishment module 15, a fast solution module 17 and a super-resolution imaging module 19. Among them, the signal receiving module 11 is used to receive the multi-polarization sparse frequency hopping signal of the radar target. The signal characterization module 13 is used to construct a mathematical model of the multi-polarization sparse frequency hopping signal. The problem establishment module 15 is used to establish the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging as a multi-observation vector weight atomic norm minimization problem based on the mathematical model of the multi-polarization sparse frequency hopping signal. The fast solution module 17 is used to use the alternating direction multiplier method to quickly solve the weight atomic norm of the multi-observation vector weight atomic norm minimization problem, and determine the reconstructed Toeplitz matrix and the reconstructed signal. The super-resolution imaging module 19 is used to determine the multi-polarization scattering center parameters of the radar target based on the reconstructed Toeplitz matrix and the reconstructed signal, using Vandermonde decomposition and SORTE algorithm, and obtain the multi-polarization super-resolution one-dimensional range image of the target; the scattering center parameters include the number of scattering centers of the radar target, the position of the scattering center and the scattering intensity of the radar target in each polarization channel.

[0126] The above-mentioned multi-polarization sparse frequency-hopping signal super-resolution one-dimensional range imaging system 100 effectively utilizes the information of each polarization channel by modeling the multi-polarization channel signal as a multi-observation vector model, avoiding the defect of independent processing of a single polarization channel and then fusing and matching the multi-polarization channels. At the same time, the one-dimensional range imaging of the sparse frequency-hopping radar signal is modeled as a weighted atomic norm minimization problem and the alternating direction multiplier method is used for rapid solution. There is no need to establish a dictionary matrix, which avoids off-grid errors, has no minimum atomic distance constraints, and has the ability of super-Rayleigh resolution. It should be noted that the above method is highly flexible and is applicable to any single or multiple polarization channels in the four polarization channels of HH, HV, VH and VV, and effectively obtains super-resolution high-quality one-dimensional range images of radar targets, which has great engineering application value.

[0127] In one embodiment, the mathematical model of the multi-polarization sparse frequency hopping signal is:

[0128]

[0129] in, ( )express L The actual received signal of each polarization channel; Indicates that the radar target is L The real scattered component in each polarization channel; express L The observation noise of each polarization channel; Indicates that the radar target is L The scattering intensity in each polarization channel.

[0130] In one embodiment, in the process of establishing super-resolution one-dimensional range imaging of multi-polarization sparse frequency hopping signals as a multi-observation vector weighted atomic norm minimization problem, the problem establishment module 15 defines an atomic set that characterizes the position of any scattering center based on the multi-polarization sparse frequency hopping signal, uses a minimum number of scattering centers to characterize the mathematical model of the multi-polarization sparse frequency hopping signal and convexly relaxes it into an atomic norm minimization problem, converts the atomic norm minimization problem into a weighted atomic norm with super-resolution, and then introduces the radar system observation noise to convert the weighted atomic norm into a semi-positive definite constrained optimization problem.

[0131] For specific limitations of the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging system 100, reference may be made to the corresponding limitations of the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging method described above, which will not be repeated here.

[0132] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus dynamic random access memory (RambusDRAM, referred to as RDRAM) and interface dynamic random access memory (DRDRAM).

[0133] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of protection of the invention. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A method for super-resolution one-dimensional range imaging of multi-polarization sparse frequency-hopping signals, characterized in that: Includes steps: Receive multi-polarization sparse frequency hopping signals of radar targets; Constructing a mathematical model of the multi-polarization sparse frequency hopping signal; According to the mathematical model of the multi-polarization sparse frequency hopping signal, the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging is established as a multi-observation vector weight atomic norm minimization problem; The alternating direction multiplier method is used to quickly solve the multi-observation vector weight atomic norm minimization problem to determine the reconstructed Toeplitz matrix and the reconstructed signal; According to the reconstructed Toeplitz matrix and the reconstructed signal, Vandermonde decomposition and SORTE algorithm are used to determine the multi-polarization scattering center parameters of the radar target, and a multi-polarization super-resolution one-dimensional range image of the target is obtained; the scattering center parameters include the number of scattering centers of the radar target, the position of the scattering center and the scattering intensity of the radar target in each polarization channel.

2. The method for super-resolution one-dimensional range imaging of multi-polarization sparse frequency hopping signals according to claim 1, characterized in that: The mathematical model of the multi-polarization sparse frequency hopping signal is: in, express L The actual received signal of each polarization channel; Indicates that the radar target is L The real scattered component in each polarization channel; express L The observation noise of each polarization channel; represents the observation matrix, represents a vector with Vandermonde structure, Indicates that the radar target is L The scattering intensity in each polarization channel.

3. The method for super-resolution one-dimensional range imaging of multi-polarization sparse frequency hopping signals according to claim 1 or 2, characterized in that: The process of establishing the super-resolution one-dimensional range imaging of multi-polarization sparse frequency-hopping signals as a multi-observation vector weight atomic norm minimization problem includes: Based on multi-polarization sparse frequency hopping signals, an atomic set with a characteristic position of arbitrary scattering center is defined; A mathematical model of the multi-polarization sparse frequency hopping signal is characterized by using a minimum number of scattering centers and the model is convexly relaxed into an atomic norm minimization problem; After converting the atomic norm minimization problem into a weighted atomic norm with super-resolution, the radar system observation noise is introduced to transform the weighted atomic norm into a semi-positive definite constrained optimization problem.

4. A multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging system, characterized in that: include: A signal receiving module, used for receiving multi-polarization sparse frequency hopping signals of radar targets; A signal characterization module, used to construct a mathematical model of the multi-polarization sparse frequency hopping signal; A problem establishment module, for establishing the multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging as a multi-observation vector weight atomic norm minimization problem according to the mathematical model of the multi-polarization sparse frequency hopping signal; A fast solution module, used for quickly solving the multi-observation vector weight atomic norm minimization problem by using an alternating direction multiplier method, and determining a reconstructed Toeplitz matrix and a reconstructed signal; The super-resolution imaging module is used to determine the multi-polarization scattering center parameters of the radar target by using Vandermonde decomposition and SORTE algorithm according to the reconstructed Toeplitz matrix and the reconstructed signal, and obtain the multi-polarization super-resolution one-dimensional range image of the target; the scattering center parameters include the number of scattering centers of the radar target, the position of the scattering center and the scattering intensity of the radar target in each polarization channel.

5. The multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging system according to claim 4, characterized in that: The mathematical model of the multi-polarization sparse frequency hopping signal is: in, express L The actual received signal of each polarization channel; Indicates that the radar target is L The real scattered component in each polarization channel; express L The observation noise of each polarization channel; represents the observation matrix, represents a vector with Vandermonde structure, Indicates that the radar target is L The scattering intensity in each polarization channel.

6. The multi-polarization sparse frequency hopping signal super-resolution one-dimensional range imaging system according to claim 4 or 5, characterized in that: In the process of establishing the super-resolution one-dimensional range imaging of multi-polarization sparse frequency hopping signals as a multi-observation vector weighted atomic norm minimization problem, the problem establishment module defines an atom set that characterizes the position of any scattering center based on the multi-polarization sparse frequency hopping signal, uses a minimum number of scattering centers to characterize the mathematical model of the multi-polarization sparse frequency hopping signal and convexly relaxes the model into an atomic norm minimization problem. After converting the atomic norm minimization problem into a weighted atomic norm with super-resolution, the radar system observation noise is introduced to convert the weighted atomic norm into a semi-positive definite constrained optimization problem.

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