Method and apparatus for sparse reconstruction of array signals based on virtual array segment expansion

Abstract

This invention relates to the field of radar signal processing technology, specifically to a method and apparatus for sparse reconstruction of array signals based on virtual array fragment expansion. The method mainly includes: generating an extended coprime physical array based on configuration parameters according to the array element requirements; constructing the far-field signal of the extended coprime physical array; modeling the received signal of the extended coprime array, constructing a virtual array of the extended coprime array and performing continuity judgment; for a continuous virtual array, estimating the target azimuth of a uniform linear array based on the continuous virtual array; for a discontinuous virtual array, selecting the two longest segments from the virtual array fragments, modeling the signal based on the longer segment, and selecting the other segment as a verification array, thereby realizing the reconstruction of discontinuous virtual array signals based on virtual array fragment expansion. This invention, based on the sparse reconstruction of discontinuous virtual array signals based on virtual array fragment expansion, solves the problem of difficult array element damage repair during the deployment of underwater acoustic arrays.

Description

Technical Field

[0001] This invention relates to the field of radar signal processing technology, and more specifically, to a method and apparatus for sparse reconstruction of array signals based on virtual array segment expansion. Background Technology

[0002] Coprime arrays have attracted widespread attention in radar due to their ability to implement highly flexible virtual element domain signal processing algorithms. In recent years, research and discussion on the application of coprime arrays in underwater signal processing have gradually begun in the field of underwater array signal processing. In underwater coprime array signal processing, signal processing methods based on the physical element domain focus on achieving highly robust target detection, while signal processing methods based on the virtual element domain focus on achieving high-resolution target detection.

[0003] When performing signal processing in the virtual domain of coprime arrays, array expansion can be used to achieve a longer virtual array deployment, thereby realizing a large degree of freedom, which is of great significance in the field of radar target detection.

[0004] In underwater array signal processing, the complex underwater environment makes it difficult to guarantee the long-term signal stability of the target, which also necessitates the use of short snapshots in signal processing. Furthermore, repairing damaged array elements during testing involves a huge and complex workload, which is a major problem in practical applications. Summary of the Invention

[0005] To address the need for rapid image processing in complex underwater environments and the significant and complex repair workload when array elements are damaged during underwater acoustic array deployment, this invention provides a method and apparatus for sparse array signal reconstruction based on virtual array segment expansion. This invention also solves the problem of coprime arrays generating discontinuous virtual arrays, thus affecting the target azimuth estimation performance of virtual arrays.

[0006] The technical means employed in this invention are as follows:

[0007] A sparse reconstruction method for array signals based on virtual array segment expansion includes the following steps:

[0008] S1. Obtain the hardware parameters of the initial array, including the total number of array elements and the array aperture; based on the hardware parameters, obtain the nonlinear array-coprime array configuration parameters, including the array expansion cardinality (M, N) and the array expansion factors of the two subarrays. ;

[0009] S2. Obtain the array element requirements, and based on the array element requirements and configuration parameters, expand the coprime array in the physical array element domain to generate an extended coprime physical array.

[0010] S3. Construct the far-field signal of the extended coprime physical array;

[0011] S4. Model the received signal of the extended coprime array, obtain the steering matrix corresponding to each virtual array element in the virtual array, and construct an extended coprime array virtual array based on the steering matrix corresponding to each virtual array element.

[0012] S5. Perform a continuity judgment on the extended coprime virtual array. If the obtained extended coprime virtual array is a continuous virtual array, then execute S6; otherwise, execute S7.

[0013] S6. Target orientation estimation for uniform linear arrays based on continuous virtual arrays;

[0014] S7. Segment the virtual array, select the two longest segments in the virtual array fragments, perform signal modeling based on the longer segment of the selected array, select the other segment of the array signal as the verification array, realize the reconstruction of the non-continuous virtual array signal based on the virtual array fragments, and finally obtain the virtual array information and the high-resolution target azimuth estimation results.

[0015] Furthermore, the far-field signal of the extended coprime physical array is constructed, including:

[0016] The far-field narrowband signal is constructed using the following method:

[0017]

[0018] in, The array receives the target signal vector. The number of far-field narrowband signals. To statistically analyze the independent Gaussian noise components, P is the number of array elements. It is the identity matrix. For noise power,

[0019] For array manifold matrix, , , The array manifold vector of the target in the array. The distance between two adjacent array elements is denoted as . This represents the distance between the first and second array elements. This is the distance between the second and third array elements. Let P be the distance between the (P-1)th array element and the Pth array element. For the signal wavelength, This indicates the direction of signal incidence.

[0020] When processing a broadband signal, a far-field broadband signal is constructed as follows:

[0021] The broadband signal is divided into several narrowband signals, and then a signal is constructed based on the construction method of the narrowband signals. The pre-processing frequency band range of the far-field broadband is [missing information]. .

[0022] Furthermore, modeling the received signal of the extended coprime array includes:

[0023] Construct the covariance matrix of the received signal of the extended coprime array virtual array based on parameter selection:

[0024]

[0025] Where T is the signal length, For array to receive signals, This is the conjugate transpose of the signal.

[0026] Vectorizing the covariance, we get:

[0027] (5)

[0028] in, It is the steering matrix corresponding to each virtual array element in the virtual array. , , The power of the incident signal.

[0029] Furthermore, target azimuth estimation based on a continuous virtual array of uniform linear arrays includes:

[0030] By using traditional robust beamforming methods, a virtual uniform linear array manifold vector is constructed to obtain the virtual array beam output and target azimuth information.

[0031] Furthermore, target azimuth estimation based on a continuous virtual array of uniform linear arrays includes:

[0032] By utilizing a high-resolution subspace target orientation estimation method, a virtual uniform linear array signal covariance is constructed, and target orientation estimation is achieved through signal and noise subspace processing.

[0033] Furthermore, target azimuth estimation based on a continuous virtual array of uniform linear arrays includes:

[0034] By utilizing the principle of compressed sensing, a virtual uniform linear array is constructed for sparse recovery of targets and estimation of target orientation.

[0035] Furthermore, based on signal modeling of the longer segment of the selected array, another segment of the array signal is selected as the verification array, including:

[0036] Construct the signal vector as follows:

[0037]

[0038]

[0039] in, For measurement vectors in the virtual matrix element field, It is a verification vector:

[0040]

[0041]

[0042] Where p and q represent the positions of the virtual array elements, respectively. , Represents the position of an element in the entire continuous virtual array. Represents the elements in the array segment. It is the number of array segments, let's assume The iterative result of the covariance quantization of the received signal, where k is the iteration number, is the sparse reconstruction. The sparse estimation result of the next iteration can be used to obtain the verification residual based on the virtual array fragment of that iteration. As the number of iterations increases, It becomes smaller; however, when the recovered signal is overfitted... Get bigger, when The iteration will stop when the minimum value is reached:

[0043]

[0044] in This indicates the maximum number of iterations.

[0045] The present invention also discloses an array signal sparse reconstruction device based on virtual array segment expansion, used to implement the above method, comprising:

[0046] The parameter acquisition unit is used to acquire the hardware parameters of the initial array, including the total number of array elements and the array aperture; and to acquire the configuration parameters of a special array type—coprime array—based on the hardware parameters, including the array expansion cardinality (M, N) and the array expansion factors of the two subarrays. ;

[0047] An extended coprime physical array construction unit is used to obtain array element requirements and, based on configuration parameters according to the array element requirements, extend the coprime array in the physical array element domain to construct the extended coprime physical array.

[0048] Signal building unit, used to build far-field signals for extended coprime physical arrays;

[0049] An extended coprime array virtual array construction unit is used to model the received signal of the extended coprime array, obtain the steering matrix corresponding to each virtual array element in the virtual array, and construct an extended coprime array virtual array based on parameter configuration based on the steering matrix corresponding to each virtual array element.

[0050] The judgment unit is used to perform continuity judgment on the extended coprime array virtual array;

[0051] The first target estimation unit is used for target orientation estimation based on a continuous virtual array of uniform linear arrays;

[0052] The second target estimation unit is used to segment the virtual array, select the two longest segments in the virtual array fragments, perform signal modeling based on the longer segment of the selected array, select the other segment of the array signal as the verification array, realize the reconstruction of the non-continuous virtual array signal based on the virtual array fragments, and finally obtain the virtual array information and high-resolution target orientation estimation results.

[0053] The present invention also discloses a storage medium comprising a stored program, wherein, when the program is executed, the method described in any of the preceding embodiments is performed.

[0054] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method described in any of the above-mentioned embodiments through the computer program.

[0055] Compared with the prior art, the present invention has the following advantages:

[0056] This invention presents a method for sparse reconstruction of array signals based on virtual array fragment expansion. This method combines the characteristics of coprime arrays and addresses the problem of virtual array fragmentation caused by expansion and array element failures in underwater signal processing applications. It designs a method for sparse reconstruction of discontinuous virtual array signals using virtual array fragment expansion. On the one hand, it meets the small snapshot data processing requirements in underwater array signal processing. On the other hand, it can still make full use of array information to achieve high-resolution target azimuth estimation when array elements fail. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1This is a flowchart of the method of the present invention.

[0059] Figure 2 This is a schematic diagram of the geometric structure of the physical and virtual array elements of a coprime array.

[0060] Figure 3 This is a diagram showing the sparse reconstruction result of the non-continuous virtual domain signal obtained through expansion.

[0061] Figure 4 This is a schematic diagram of a discontinuous virtual array caused by damage to array elements.

[0062] Figure 5 This is a diagram showing the sparse reconstruction result of a non-continuous virtual domain signal.

[0063] Figure 6 The graphs show the results of the algorithm's MSE changing with signal-to-noise ratio and number of snapshots. In the graphs, (a) shows the results of the MSE changing with signal-to-noise ratio and (b) shows the results of the MSE changing with number of snapshots.

[0064] Figure 7 This is a schematic diagram showing that there are errors in the position of the array elements.

[0065] Figure 8 This is an algorithm result diagram with array element position errors, where (a) shows the target azimuth. and (b) indicates the target's location. and . Detailed Implementation

[0066] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0067] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0068] like Figure 1 As shown, this invention provides a method for sparse reconstruction of array signals based on virtual array segment expansion, comprising the following steps:

[0069] S1. Obtain the hardware parameters of the initial array, including the total number of array elements and the array aperture; based on the hardware parameters, obtain the configuration parameters for a special array type—coprime array type, including the array expansion cardinality (M, N) and the array expansion factors of the two subarrays. .

[0070] For an initial array, the parameters required for a special array configuration—a coprime array—are set based on its hardware parameters (total number of array elements, array aperture, etc.), including the array expansion cardinality (M, N) and the array expansion factor. Selection constraints:

[0071] ① M and N are a pair of coprime numbers, and their greatest common divisor is guaranteed to be 1;

[0072] ② Traditional coprime arrays consist of two uniform subarrays. Therefore, there are multiple ways to expand the two subarrays. This invention sets the subarray expansion method so that the two subarrays are guaranteed to have the same array expansion factor. To expand, that is:

[0073]

[0074] The total theoretical number of array elements in the expanded coprime array physical array element field is obtained. for:

[0075]

[0076] Due to the existence of the expansion factor constraint, the total number of array elements in the actual expanded array will be limited. This is because the special nature of the expansion factor may lead to overlapping elements between the expanded coproton arrays. The above formula guarantees an upper limit on the total number of elements in the expanded array. After obtaining the theoretical total number of elements using the above formula, it is necessary to further confirm the total number of elements based on the actual array layout.

[0077] S2. Obtain the array element requirements, and based on the array element requirements and configuration parameters, expand the coprime array in the physical array element domain to generate an extended coprime physical array.

[0078] Typically, when the total number of array elements is fixed at O, the coprime array constructed based on (M,N) satisfies O=M+N-1. A coprime array expansion scheme can usually achieve a large array aperture with a finite number of elements. When the array aperture is fixed, utilizing an expanded coprime array can fully utilize the array aperture space, saving array hardware burden and improving spatial resolution. This invention is based on the expansion of a virtual array domain generated by an expanded coprime array. For example... Figure 2 As shown, the figure is a comparison diagram of array structures in signal processing in the physical array element domain and the virtual array element domain.

[0079] S3. Construct the far-field signal of the extended coprime physical array.

[0080] By extending the coprime array using a selected expansion factor, an extended coprime physical array based on special sequence pairs can be realized. time Construction of a far-field narrowband signal:

[0081] (2)

[0082] The variables are defined as follows:

[0083] The array receives the target signal vector;

[0084] For statistically independent Gaussian noise components; It is the identity matrix. It is noise power;

[0085] For array manifold matrix, . The array manifold vector of the target in the array. The distance between two adjacent array elements is denoted as . This represents the distance between the first and second array elements. This is the distance between the second and third array elements. Let P be the distance between the (P-1)th array element and the Pth array element. For the signal wavelength, This indicates the direction of signal incidence.

[0086] For broadband signals, such as those with a bandwidth of This divides the broadband into multiple narrowbands. Then, the narrowband signal is modeled in the same way as described above.

[0087] S4. Model the received signal of the extended coprime array, obtain the steering matrix corresponding to each virtual array element in the virtual array, and construct an extended coprime array virtual array based on the steering matrix corresponding to each virtual array element.

[0088] Modeling the received signal of the extended coprime array, and constructing the covariance matrix of the received signal of the extended coprime array virtual array based on parameter selection:

[0089]

[0090] Vectorizing the covariance, we get:

[0091] (5)

[0092] in, , , . It is the steering matrix corresponding to each virtual array element in the virtual array.

[0093] S5. Perform a continuity judgment on the extended coprime virtual array. If the obtained extended coprime virtual array is a continuous virtual array, then execute S6; otherwise, execute S7.

[0094] Examine the obtained virtual array and establish the difference set of the coprime array:

[0095]

[0096] S6. Target orientation estimation for uniform linear arrays based on continuous virtual arrays.

[0097] If the obtained result is a continuous virtual array, then the target azimuth is estimated using a uniform linear array based on the requirements:

[0098] Optionally, conventional robust beamforming methods, such as CBF (Conventional Beamforming) and Capon, can be used to construct a virtual uniform linear array manifold vector to obtain the virtual array beam output and target azimuth information.

[0099] Optionally, a high-resolution subspace target orientation estimation method, such as MUSIC (Multiple Signal Classification), can be used to construct the covariance of a virtual uniform linear array signal, and target orientation estimation can be achieved through signal and noise subspace processing.

[0100] Optionally, the principle of compressed sensing can be used to construct a virtual uniform linear array target sparse recovery and target orientation estimation.

[0101] S7. Segment the virtual array, select the two longest segments in the virtual array fragments, perform signal modeling based on the longer segment of the selected array, select the other segment of the array signal as the verification array, realize the reconstruction of the non-continuous virtual array signal based on the virtual array fragments, and finally obtain the virtual array information and the high-resolution target azimuth estimation results.

[0102] When the resulting virtual array is a non-contiguous virtual array, it is segmented, and the two longest segments are selected. The following are possible reasons for the occurrence of a non-contiguous virtual array:

[0103] ① After expansion, the resulting virtual array has array "holes";

[0104] ② When physical array elements are damaged or malfunction, "holes" are generated in the virtual array.

[0105] Both of these scenarios result in "holes" in the array, breaking the originally continuous virtual array and causing incompleteness. This renders signal processing methods based on uniform and continuous arrays inapplicable, and also wastes the resources of the virtual array elements within the broken array segments. To address this, this invention designs an algorithm that first divides a continuous virtual array into multiple parts through these "holes." The positions of the virtual array elements can then be represented as:

[0106]

[0107] in, Represents the position of an element in the entire continuous virtual array. This represents the elements in the array segment obtained by dividing it by "holes". Here, It represents the number of array segments.

[0108] Signal modeling is performed on a longer segment of the selected array, and another segment of the array signal is selected as a verification array to achieve the reconstruction of discontinuous virtual array signals based on virtual array segments. Finally, virtual array information and high-resolution target orientation estimation results are output. This invention employs sparse signal reconstruction technology for sparse DOA (Direction of Arrival) estimation. The signal corresponding to the discontinuous virtual array is divided into two parts, and compressed sensing technology based on virtual array segment verification is used.

[0109]

[0110]

[0111] in, For measurement vectors in the virtual matrix element field, This is the validation vector in MMP (Multipath Matching Pursuit).

[0112]

[0113]

[0114] set up For sparse reconstruction of the first The sparse estimation result of the next iteration can be used to obtain the verification residual based on the virtual array fragment of that iteration. As the number of iterations increases, It becomes smaller; however, when the recovered signal is overfitted... It gets bigger. When The iteration will stop when the minimum value is reached:

[0115]

[0116] This indicates the maximum number of iterations.

[0117] The performance of this method is verified through the following simulation experiments.

[0118] Simulation Experiment 1

[0119] like Figure 3 As shown, the simulation analysis demonstrates target azimuth estimation achieved using a sparse reconstruction method based on discontinuous virtual array signals expanded from virtual array segments. The simulation conditions are as follows: , Array expansion factor At this point, the generated virtual array will have array "holes," disrupting the continuity of the virtual array and resulting in array fragments. In the generated discontinuous virtual array, the array data of 93 elements with continuous virtual element positions is taken as the measurement dataset, and the fragment array data of the broken array segment with 4 elements is taken as the verification dataset. The target azimuth is then... and The signal-to-noise ratio is -10dB, and the number of snapshots is 10. As can be seen from the figure, the method proposed in this invention can obtain the correct azimuth estimate of the target when the virtual array becomes discontinuous due to expansion.

[0120] Simulation Experiment 2

[0121] like Figure 4 and Figure 5 As shown, another possible reason for the "holes" in the virtual array and the resulting array fragments is the failure of array elements in the physical array, taking coprime array parameters. , Array expansion factor At this point, the coprime array does not have any virtual array "holes," and the expanded virtual array is continuous. Based on this, let physical elements 2 and 6 be considered abnormal elements. When elements 2 and 6 fail, the resulting virtual array will produce "holes," such as... Figure 4 As shown. The target locations at this time are respectively... and With a signal-to-noise ratio of 10dB and a snapshot count of 10, the orientation result obtained based on this invention is as follows: Figure 5 As shown in the figure, this invention can achieve accurate estimation of the target signal's orientation under these conditions.

[0122] Simulation Experiment 3

[0123] like Figure 6 As shown, this experiment simulates and analyzes the mean square error (MSE) of the sparse reconstruction method for discontinuous virtual array signals based on virtual array segment expansion under different signal-to-noise ratios and signal snapshot numbers:

[0124]

[0125] in Indicates the first In an independent trial The DOA estimation results, and These represent the number of Monte Carlo experiments and the number of signal sources, respectively.

[0126] In the simulation, , Array expansion factor The target directions are respectively and Let elements 3, 4, 22, and 23 be considered failed elements. The verification data is an array segment containing 5 virtual elements obtained after the virtual array is interrupted by a "hole," with a signal snapshot count of 10. When the signal-to-noise ratio changes, the statistical results (MSE) obtained after 1000 independent trials are as follows: Figure 6 As shown in (a), the MSE decreases and tends to level off as the signal-to-noise ratio (SNR) gradually increases. When the SNR is fixed at -10dB and other simulation conditions remain unchanged, the MSE changes with the number of signal snapshots as shown in (a). Figure 6 As shown in (b), it can be seen from the figure that when the number of signal snapshots is greater than 15, the MSE gradually tends to be constant, while the MSE obtained by other methods also slowly decreases as the signal-to-noise ratio and the number of snapshots gradually increase, but overall, the MSE is relatively high.

[0127] Simulation Experiment 4

[0128] like Figure 7 and Figure 8 As shown, this simulation verifies the performance of the present invention when there are errors in the array element positions. Considering the robustness of the algorithm, the method of the present invention is compared with the robust physical array element domain algorithm. It is assumed that there are array elements with a mean spacing of unit. Position error, element position such as Figure 7 As shown. Simulation conditions. , Array expansion factor The target directions are respectively and Let elements 3, 4, 22, and 23 be considered failed elements. The verification data is a segment array containing 5 virtual elements obtained after the virtual array is interrupted by a "hole," with a signal snapshot count of 100 and a signal-to-noise ratio of -5dB. When the target azimuth is respectively... and At times, such as Figure 8 As shown in (a), it can be seen from the figure that the algorithm's robustness to array position errors is the same as that of the algorithm based on the physical array element domain; both can stably obtain the target's azimuth information. When the target's azimuth is close, the azimuths are respectively... and ,like Figure 8 As shown in (b), the method based on the physical array element domain can no longer distinguish the orientations of the two targets, while the method of the present invention can still achieve correct target orientation estimation.

[0129] The present invention also discloses an array signal sparse reconstruction device based on virtual array segment expansion, used to implement the above method, comprising:

[0130] The parameter acquisition unit is used to acquire the hardware parameters of the initial array, including the total number of array elements and the array aperture; and to acquire the configuration parameters of a special array type—coprime array—based on the hardware parameters, including the array expansion cardinality (M, N) and the array expansion factors of the two subarrays. ;

[0131] An extended coprime physical array construction unit is used to obtain array element requirements and, based on configuration parameters according to the array element requirements, extend the coprime array in the physical array element domain to construct the extended coprime physical array.

[0132] Signal building unit, used to build far-field signals for extended coprime physical arrays;

[0133] An extended coprime array virtual array construction unit is used to model the received signal of the extended coprime array, obtain the steering matrix corresponding to each virtual array element in the virtual array, and construct an extended coprime array virtual array based on parameter configuration based on the steering matrix corresponding to each virtual array element.

[0134] The judgment unit is used to perform continuity judgment on the extended coprime array virtual array;

[0135] The first target estimation unit is used for target orientation estimation based on a continuous virtual array of uniform linear arrays;

[0136] The second target estimation unit is used to segment the virtual array, select the two longest segments in the virtual array fragments, perform signal modeling based on the longer segment of the selected array, select the other segment of the array signal as the verification array, realize the reconstruction of the non-continuous virtual array signal based on the virtual array fragments, and finally obtain the virtual array information and high-resolution target orientation estimation results.

[0137] The sparse reconstruction apparatus for array signals based on virtual array segment expansion of the present invention is described simply because it corresponds to the sparse reconstruction method for array signals based on virtual array segment expansion in the above embodiment. For related similarities, please refer to the description of the sparse reconstruction method for array signals based on virtual array segment expansion in the above embodiment, which will not be described in detail here.

[0138] The present invention also discloses a storage medium comprising a stored program, wherein the above-described method is executed when the program is run.

[0139] The present invention also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described method through the computer program.

[0140] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0141] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0142] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0143] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0144] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for sparse reconstruction of array signals based on virtual array segment expansion, characterized in that, Includes the following steps: S1. Obtain the hardware parameters of the initial array, including the total number of array elements and the array aperture; obtain the nonlinear array-coprime array configuration parameters based on the hardware parameters, including the array expansion cardinality (M, N) and the array expansion factor β of the two subarrays; S2. Obtain the array element requirements, and based on the array element requirements and configuration parameters, expand the coprime array in the physical array element domain to generate an extended coprime physical array. S3. Construct the far-field signal of the extended coprime physical array; S4. Model the received signal of the extended coprime array, obtain the steering matrix corresponding to each virtual array element in the virtual array, and construct an extended coprime array virtual array based on the steering matrix corresponding to each virtual array element. S5. Perform a continuity judgment on the extended coprime virtual array. If the obtained extended coprime virtual array is a continuous virtual array, then execute S6; otherwise, execute S7. S6. Target orientation estimation for uniform linear arrays based on continuous virtual arrays; S7. Segment the virtual array, select the two longest segments in the virtual array fragments, perform signal modeling based on the longer segment of the selected array, select the other segment of the array signal as the verification array, realize the reconstruction of the non-continuous virtual array signal based on the virtual array fragments, and finally obtain the virtual array information and the high-resolution target azimuth estimation results.

2. The sparse reconstruction method for array signals based on virtual array segment expansion according to claim 1, characterized in that, Constructing far-field signals for extended coprime physical arrays includes: The far-field narrowband signal is constructed using the following method: X(t)=[x1(t),x2(t),…x P (t)] T =Az(t)+N(t) Where z(t)=[z1(t),z2(t),…z L (t)] T Let L be the target signal vector received by the array, and L be the number of far-field narrowband signals. To statistically analyze independent Gaussian noise components, P is the number of array elements, and I is the identity matrix. For noise power, A = [a(θ0), a(θ1), ..., a(θ)] L ] is an array manifold matrix, 1≤l≤L,a(θ l Let d1, d2, d3, ... d be the array manifold vectors of the target in the array. P d1 represents the distance between two adjacent array elements, where d2 is the distance between the first and second array elements, d3 is the distance between the second and third array elements, and d2 is the distance between the first and second array elements. P Let λ be the distance between the (P-1)th array element and the Pth array element, λ be the signal wavelength, and θ be the distance between the (P-1)th array element and the Pth array element. l The direction of signal incidence; When processing a broadband signal, a far-field broadband signal is constructed as follows: The broadband signal is divided into several narrowband signals, and then a signal is constructed based on the construction method of the narrowband signals. The pre-processing frequency band of the far-field broadband is [f L ,f H ].

3. The sparse reconstruction method for array signals based on virtual array segment expansion according to claim 1, characterized in that, Modeling the received signal of the extended coprime array includes: Construct the covariance matrix of the received signal of the extended coprime array virtual array based on parameter selection: Where T is the signal length, X(t) is the array received signal, and X H (t) is the conjugate transpose of the signal. Vectorizing the covariance, we get: Among them, A v It is the steering matrix corresponding to each virtual array element in the virtual array. u = vec(I), p = [p1, p2, ..., p L ] T The power of the incident signal.

4. The sparse reconstruction method for array signals based on virtual array segment expansion according to claim 1, characterized in that, Target azimuth estimation based on a continuous virtual array of uniform linear arrays includes: By using traditional robust beamforming methods, a virtual uniform linear array manifold vector is constructed to obtain the virtual array beam output and target azimuth information.

5. The sparse reconstruction method for array signals based on virtual array segment expansion according to claim 1, characterized in that, Target azimuth estimation based on a continuous virtual array of uniform linear arrays includes: By utilizing a high-resolution subspace target orientation estimation method, a virtual uniform linear array signal covariance is constructed, and target orientation estimation is achieved through signal and noise subspace processing.

6. The sparse reconstruction method for array signals based on virtual array segment expansion according to claim 1, characterized in that, Target azimuth estimation based on a continuous virtual array of uniform linear arrays includes: By utilizing the principle of compressed sensing, a virtual uniform linear array is constructed for sparse recovery of targets and estimation of target orientation.

7. The sparse reconstruction method for array signals based on virtual array segment expansion according to claim 1, characterized in that, Signal modeling is performed based on a longer segment of the selected array, and another segment of the array signal is selected as the verification array, including: Construct the signal vector as follows: Where V is the measurement vector in the virtual matrix element field, V cv It is a verification vector: Where p and q represent the positions of the virtual array elements, respectively. D represents the element position in the entire continuous virtual array. i Let i represent the elements in the array segment, and i be the number of array segments. To obtain the iterative result of the signal covariance quantization, where k is the iteration number, the sparse estimation result of the k-th iteration of the sparse reconstruction is used. Based on this iteration, the virtual array segment can be used to obtain the verification residual. As the iteration number increases, ε... k ε decreases; and when the recovered signal is overfitted, ε... k Increase, when ε k The iteration will stop when the minimum value is reached: Where E represents the maximum number of iterations.

8. A sparse reconstruction apparatus for array signals based on virtual array segment expansion, used to implement the method as described in claim 1, characterized in that, include: The parameter acquisition unit is used to acquire the hardware parameters of the initial array, including the total number of array elements and the array aperture; and to acquire the nonlinear array coprime array configuration parameters based on the hardware parameters, including the array expansion cardinality (M, N) and the array expansion factor β of the two subarrays. An extended coprime physical array construction unit is used to obtain array element requirements and, based on configuration parameters according to the array element requirements, extend the coprime array in the physical array element domain to construct the extended coprime physical array. Signal building unit, used to build far-field signals for extended coprime physical arrays; An extended coprime array virtual array construction unit is used to model the received signal of the extended coprime array, obtain the steering matrix corresponding to each virtual array element in the virtual array, and construct an extended coprime array virtual array based on parameter configuration based on the steering matrix corresponding to each virtual array element. The judgment unit is used to perform continuity judgment on the extended coprime array virtual array; The first target estimation unit is used for target orientation estimation based on a continuous virtual array of uniform linear arrays; The second target estimation unit is used to segment the virtual array, select the two longest segments in the virtual array fragments, perform signal modeling based on the longer segment of the selected array, select the other segment of the array signal as the verification array, realize the reconstruction of the non-continuous virtual array signal based on the virtual array fragments, and finally obtain the virtual array information and high-resolution target orientation estimation results.

9. A storage medium, characterized in that, The storage medium includes a stored program, wherein when the program is executed, it performs the method described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the method described in any one of claims 1 to 7 through the computer program.

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