Constant continuous virtual array construction method and target orientation estimation method and device

Abstract

The invention relates to the technical field of sonar array signal processing, in particular to a constant continuous virtual array construction method and a target orientation estimation method and device. The construction method of the constant continuous virtual array comprises the following steps: obtaining construction demand information of a physical array, wherein the demand information of the physical array comprises the number of physical array elements or array aperture information; the physical array is a co-prime array and is provided with two sub-arrays; the method comprises the following steps: acquiring a special sequence pair (M, N), randomly selecting the special sequence pair by a special sequence space, and acquiring a co-prime array expansion factor according to the special sequence: expanding a mutual proton array based on the expansion factor so as to construct a constant continuous virtual array. According to the method, aiming at the problem of coprime array physical array element domain expansion, special sequence pair expansion parameters are set, a synchronous-length mutual proton array processing factor is stipulated, and finally constant continuous virtual array construction under a special sequence pair is realized.

Description

Technical Field

[0001] The present invention relates to the field of underwater acoustic array and signal processing technology, and in particular to a method for constructing a constant continuous virtual array, and a method and device for estimating a target orientation. Background Art

[0002] In the field of radar technology, the advantage of coprime arrays is that they can obtain higher degrees of freedom by using virtual domain processing, which is of great significance for the detection and positioning of aerial targets and precision strikes. In underwater acoustic applications, the demand for high freedom is not urgent. In underwater array signal processing, more attention is paid to the improvement of signal resolution and the robustness of signal extraction algorithms. At present, in underwater acoustic radar applications, when implementing signal processing algorithms in the virtual array element domain through coprime arrays, the algorithm performance will be reduced due to the discontinuity of the virtual array, and its effect cannot be compared with the method of physical linear arrays, resulting in barriers to algorithm processing domain transplantation. If the discontinuous part is discarded and only some continuous array elements are used for virtual array signal processing, it will lead to waste of array element information, affecting the efficiency of virtual array element domain signal processing and application promotion. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention provides a method for constructing a constant continuous virtual array, a method and a device for estimating the target direction. The present invention expands and constructs a virtual array based on a special sequence pair to generate a constant continuous virtual array without "holes" to improve the efficiency of signal processing in the virtual array element domain and promote the popularization and application of virtual arrays in signal processing technology.

[0004] The technical means adopted by the present invention are as follows:

[0005] A method for constructing a constant continuous virtual array comprises the following steps:

[0006] S101, obtaining construction requirement information of a physical array, wherein the construction requirement information of the physical array includes the number of physical array elements or array aperture information; the physical array is a coprime array having two sub-arrays;

[0007] S102, obtaining a special sequence pair (M, N), wherein the special sequence pair is randomly selected from a special sequence space, wherein the special sequence space is The coprime matrix expansion factor is obtained by the following calculation based on the special sequence:

[0008]

[0009]

[0010] Wherein, m1, m2 are preset integers, α1, α2 are expansion factors of coproton arrays;

[0011] S103, expanding the coproton array based on the expansion factor, thereby constructing a constant continuous virtual array.

[0012] Further, when the coprime subarray is expanded based on the expansion factor, two subarrays of the expanded coprime array are expanded with the same expansion factor.

[0013] The present invention discloses a target orientation estimation method, comprising the following steps:

[0014] S201, constructing a constant continuous virtual array based on the above method;

[0015] S202, receiving signals based on the constant continuous virtual array, modeling the signals received by the constant continuous virtual array, and generating a mutually prime virtual array receiving signal model;

[0016] S203, performing array signal analysis on the coprime virtual array model in the virtual array element domain, thereby achieving high-resolution target azimuth estimation in the coprime array virtual array element domain.

[0017] Furthermore, the signal received by the constant continuous virtual array is modeled, including:

[0018] Vectorizing the constant continuous virtual array signal;

[0019] Arrange and screen the vectorized constant continuous virtual array;

[0020] The signal is extracted to the arranged virtual array element position.

[0021] Furthermore, array signal analysis is performed on the coprime virtual array model in the virtual array element domain, including:

[0022] Construct the observation matrix based on the mutually prime virtual array receiving signal model;

[0023] Let the covariance vector of the received signal based on the constant continuous virtual array be There exists a vector x such that The signal v is represented by a basis matrix The linear representation is as follows:

[0024] v=Ψx

[0025] Using the observation matrix P is the length of vector v, Q represents the dimension after mapping, and maps the sparse signal v to a low-dimensional space. In, we get:

[0026] y=Φv

[0027] The system of equations is optimally evaluated and the sparsest solution of the underdetermined system is obtained by using the norm minimization problem.

[0028] The present invention discloses a device for constructing a constant continuous virtual array, comprising:

[0029] A demand acquisition unit, used to acquire construction demand information of a physical array, wherein the demand information of the physical array includes the number of physical array elements or array aperture information; the physical array is a coprime array having two subarrays;

[0030] The special sequence pair acquisition module is used to acquire a special sequence pair (M, N), wherein the special sequence pair is randomly selected from a special sequence space, wherein the special sequence space is

[0031] The coprime matrix expansion factor acquisition module is used to obtain the coprime matrix expansion factor through the following calculation according to the special sequence:

[0032]

[0033]

[0034] Wherein, m1, m2 are preset integers, α1, α2 are expansion factors of coproton arrays;

[0035] The virtual array construction module is used to expand the coproton array based on the expansion factor to construct a constant continuous virtual array.

[0036] The present invention discloses a target orientation estimation device, comprising:

[0037] An array construction module, used to construct a constant continuous virtual array based on the above method;

[0038] A receiving signal model generating module, used for receiving signals based on the constant continuous virtual array, modeling the signals received by the constant continuous virtual array, and generating a mutually prime virtual array receiving signal model;

[0039] The target azimuth estimation module is used to perform array signal analysis on the coprime virtual array model in the virtual array element domain, thereby realizing high-resolution target azimuth estimation in the coprime array virtual array element domain.

[0040] The present invention also discloses a storage medium, which includes a stored program, wherein when the program is run, the method described in any one of the above claims is executed.

[0041] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the method according to any one of the above claims through the operation of the computer program.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] In the array signal processing, the present invention sets the special sequence pair expansion parameters for the expansion problem of the physical array element domain of the coprime array, stipulates the coprime subarray processing factor of the synchronous length, and finally realizes the construction of a constant continuous virtual array under the special sequence pair. The coprime array expansion method based on the special sequence pair proposed by the present invention can complete the construction of a constant continuous virtual array, and ensure that a continuous and uninterrupted virtual array, that is, there is no array "hole" under any random expansion factor expansion scheme setting, can be obtained. In the case of limited array elements or limited array aperture, the present invention can obtain a constant continuous virtual array, can make full use of inherent resources, and avoid the waste of array element information.

[0044] On the premise of realizing a constant continuous virtual array, the present invention proposes to utilize the sparsity of the signal in the constructed constant continuous virtual array to complete the target azimuth estimation through signal sparse recovery, and to realize the optimal solution of the underdetermined equation by setting the observation matrix, thereby finally completing the high-resolution target azimuth estimation based on the constant continuous virtual array. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 The present invention is a flow chart of a method for constructing a constant continuous virtual array.

[0047] Figure 2 The present invention is a flow chart of a target direction estimation method.

[0048] Figure 3 It is a schematic diagram of the geometric structure of the physical array element domain and the virtual array element domain of the coprime array;

[0049] Figure 4 is a schematic diagram of the sparse recovery algorithm;

[0050] Figure 5 It is the result of constructing virtual arrays of coprime arrays based on special sequence pairs under different extended array factors;

[0051] Figure 6 It is the result of constructing virtual arrays of coprime arrays not based on special sequence pairs under different extended array factors;

[0052] Figure 7 It is the simulation result of the algorithm performance in the physical array element domain, with the target orientations of 10° and 16°;

[0053] Figure 8 It is the flowchart of Product algorithm;

[0054] Fig. 9 It is the Min algorithm flow chart;

[0055] Fig.10 It is the simulation result of the signal sparse reconstruction algorithm performance, with the target bearings of 10° and 16°;

[0056] Fig.11 It is the simulation result of the algorithm performance in the physical array element domain, with the target orientations of 10° and 16°;

[0057] Fig.12 It is the simulation result of the signal sparse reconstruction algorithm performance, and the target azimuth is 10° and 16°. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0059] like Figure 1 As shown, the present invention provides a method for constructing a constant continuous virtual array, comprising the following steps:

[0060] S101. Acquire construction requirement information of a physical array, where the requirement information of the physical array includes the number of physical array elements or array aperture information; the physical array is a coprime array having two sub-arrays.

[0061] S102, obtaining a special sequence pair (M, N), wherein the special sequence pair is randomly selected from a special sequence space, wherein the special sequence space is The coprime matrix expansion factor is obtained by the following calculation based on the special sequence:

[0062]

[0063]

[0064] Among them, m1, m2 are preset integers, α1, α2 are expansion factors of coproton arrays, and α1 = α2 = α is generally taken here. Here, the value of α is not unique. Under the premise that the values ​​of m1 and m2 are determined, any expansion factor value that satisfies the above two formulas can be used as the actual expansion factor for virtual array construction.

[0065] Specifically, under the premise of setting the number of array elements or array aperture requirements, the coprime array parameters are selected for the array with the preset number of physical array elements or array aperture. For two subarrays of a coprime array, the coprime array extension cardinality is constructed, that is, the special sequence pair belongs to Initially set the parameters for constructing an extended coprime array:

[0066] Special sequence pairs are set:

[0067]

[0068] Ensure that the generated virtual array is always continuous.

[0069] Generally, there are many ways to choose the extended cardinality of coprime arrays, that is, M' and N'. From the perspective of the physical array element domain, when there is a restriction on the total number of elements in an array, for example, when the total number of array elements must be J, in the non-expanded case, using M' and N' to construct a coprime array requires ensuring that M'+N'-1=J. The use of an extended coprime array solution requires obtaining a larger array aperture with a limited number of elements. On the other hand, the reasonable application of coprime physical array elements is that when the array aperture is preset, the coprime array can be used to fully utilize the array aperture space, which can save the array hardware burden and improve the spatial resolution. The above is a non-expanded case considered from the perspective of the physical array element domain.

[0070] The present invention focuses on the analysis of the problem of expanding the virtual array domain generated by coprime arrays. Figure 3 As shown in the figure, a comparison of array structures in signal processing in the physical array element domain and the virtual array element domain. When considering the virtual array element domain problem, one of the unavoidable problems is the continuity of the virtual array. When the virtual array is discontinuous, the array "holes" that appear will interrupt the virtual array, and then generate multiple array fragments, resulting in many array signal processing methods based on uniform linear array expansion cannot be implemented, affecting the performance of the algorithm. If only the continuous part of the virtual array is used for processing, a large amount of virtual array element information will be wasted, and all array information cannot be fully utilized.

[0071] according to Figure 1 The geometric relationship of the virtual array shown in is used to establish a mathematical model, and the array receiving signal model is established. In the physical array element domain, when L far-field non-correlated narrowband signals are incident on a coprime array with a total number of array elements of Q, the signal received by the array at time t can be expressed as:

[0072] X(t) = [x1(t),x2(t),…x Q (t)] T =Az(t)+ N(t) (2)

[0073] The variables are defined as follows:

[0074] z(t)=[z1(t),z2(t),…z L (t)] T receiving a target signal vector for the array;

[0075] is a statistically independent Gaussian noise component; I is the unit matrix, is the noise power;

[0076] A=[a(θ0),a(θ1),...,a(θ L )] is the array flow matrix, a(θ l ) is the array flow vector of the target in the array, d is the array element spacing, λ is the signal wavelength, θ l is the signal incident direction.

[0077] In the virtual array element domain, the virtual array signal can be obtained by converting the array received signal covariance matrix into a vector. Here, the signal covariance matrix can be expressed as:

[0078]

[0079] where p l Indicates the power of the lth source signal. In practical applications, R x It can be approximately expressed as:

[0080]

[0081] Vectorizing the covariance, we get:

[0082]

[0083] in, u=vec(I), p=[p1,p2,...,p L ] T . A v is the steering matrix corresponding to each virtual array element in the virtual array, which can be based on It is the difference of coprime arrays and can be expressed as:

[0084]

[0085] Here M and N correspond to The special sequence pairs of α are the expansion multiples of the coprime array. In the virtual array domain, by expanding the coprime array from the physical array element domain, a virtual array with more continuous virtual elements can be obtained.

[0086] Based on special sequence Set the expansion multiple of the coprime array to meet the total number of array elements or array aperture requirements. In the example, M and N correspond to For the special sequence pair of the extended coprime array, the present invention sets the coprime subarray to expand the array by the same multiple. Unlike many other coprime pairs, all extended virtual arrays obtained by the same expansion factor based on special sequence pairs will not have array "holes", which can ensure the constant continuity of the virtual array. When the extended array is not expanded based on special sequence pairs, an uneven virtual array may be obtained, that is, an array with "holes".

[0087] S103, expanding the coproton array based on the expansion factor, thereby constructing a constant continuous virtual array.

[0088] Specifically, the coprime subarray is expanded by using the selected expansion factor to realize the construction of an extended coprime physical array based on a special sequence pair; the selection prerequisite of the expansion factor is set as follows: the two subarrays of the extended coprime array are expanded with the same expansion factor. The advantage of this is that on the one hand, the array processing scheme is generalizable in practical applications; on the other hand, when the expansion factor remains consistent, the stability and consistency of the characteristic points of the subarray beam output are indirectly maintained, thereby improving the overall beam output performance of the coprime array. When the (M, N) number pair is taken hour:

[0089]

[0090]

[0091] Where m1 and m2 are integers, α is the expansion factor of the coprotonic matrix, and the coprotonic matrix is ​​expanded according to the obtained expansion factor α to obtain the expanded matrix of αM+αN-1. Here, the value of α is not unique. Under the premise that the values ​​of m1 and m2 are determined, any expansion factor value that satisfies the above two formulas can be used as the actual expansion factor to construct the virtual array.

[0092] The present invention discloses a target orientation estimation method, comprising the following steps:

[0093] S201, constructing a constant continuous virtual array based on the above method.

[0094] Based on the above S101-S103, a constant continuous virtual array is constructed, and the extended coprime array receiving signal based on the special sequence pair is modeled to construct a coprime virtual array model.

[0095] S202: receiving signals based on the constant continuous virtual array, modeling the signals received by the constant continuous virtual array, and generating a mutually prime virtual array receiving signal model.

[0096] When modeling the extended coprime array receiving signal based on the special sequence pair, the extended coprime array receiving signal is modeled based on the special extended sequence pair and the array extension factor, the corresponding virtual array after vectorization is arranged and screened, and after the virtual array element arrangement is completed, the corresponding signal is extracted to the arranged virtual array element position.

[0097] S203, performing array signal analysis on the coprime virtual array model in the virtual array element domain, thereby achieving high-resolution target azimuth estimation in the coprime array virtual array element domain.

[0098] The array signal in the virtual array element domain is analyzed to achieve high-resolution target azimuth estimation in the virtual array element domain of the coprime array. Based on S202, the virtual array receiving signal model established is used to construct an observation matrix. In the physical array element domain signal compressed sensing sparse algorithm, the target signal is regarded as sparse relative to the physical array. Therefore, the signal sparse recovery method is used to achieve signal reconstruction. In the present invention, the constructed extended coprime virtual array is further expanded on the basis of the physical coprime array, and richer virtual array element information is obtained. Therefore, a signal sparse reconstruction method of multipath matching pursuit is established in the virtual array:

[0099] Let the covariance vector of the received signal of the constant continuous virtual array obtained by expanding the special sequence pair be It is represented as a sparse vector of virtual domain signals, and there exists a vector x that satisfies The signal v is represented by a basis matrix The linear representation is as follows:

[0100] v=Ψx (9)

[0101] The observation matrix can be used Map the sparse signal v to a low-dimensional space In, we get:

[0102] y=Φv (10)

[0103] The signal vector y is obtained by compressing the sparse signal v. However, since Q<<P, the system of equations shown in the above formula is an underdetermined system of equations, and here we have:

[0104] y=Φv=ΦΨx=Ax (11)

[0105] Although this is also an underdetermined linear equation system, since x is sparse, the number of unknowns is much smaller than the dimension of the vector. That is, since there are many zero elements in x, the signal can be recovered by solving an optimization problem with fewer unknowns, which makes signal recovery possible. Assuming that any 2K columns of the matrix A are linearly independent and the vector x is sparse, the sparsest solution of the underdetermined system can be found by minimizing the l0 norm, expressed as:

[0106]

[0107] The original signal v is obtained by restoring the sparse vector x. Here, K represents the sparsity of x, the matrix A is the perception matrix, and the set of position indices of all non-zero elements in x is called its support set. This problem belongs to the category of convex optimization and can be solved by linear programming or second-order cone programming. Although it has the characteristics of high reconstruction accuracy, it has a large amount of computation. The greedy algorithm is another way to solve this problem. When solving this problem, the greedy algorithm uses iteration to update the candidate path, and it is necessary to test and verify all candidates (i.e., combinations of columns) that meet the equality constraints. When the sparsity is K and the length of the signal is n, it is necessary to verify candidate items. If the values ​​of the two are relatively large, it will bring a very large amount of computation. If the values ​​of the two are relatively large, it will bring a very large amount of computation. The present invention develops a multipath matching pursuit (Multipath Matching Pursuit, MMP) algorithm based on the obtained constant continuous virtual array, searches for multiple candidate paths, and then selects the one that minimizes the residual. It avoids the shortcomings of other greedy algorithms such as exponential selection sensitivity and the presence of only one candidate path in the search process. The MMP algorithm can be regarded as an approximate algorithm, which is used to find a candidate path that satisfies the sparse constraint ||x||0=K and minimizes the cost function J(x)=||y-Ax||2. Since the MMP algorithm verifies multiple candidate paths, the probability of correctly estimating the support set is increased. According to relevant research and simulation results, the performance of the MMP algorithm is better than other existing sparse reconstruction algorithms in the presence or absence of noise. The principle of the MMP algorithm is as follows Figure 4 As shown, where Ω i,j is the j-th candidate subpath in the i-th iteration.

[0108] The present invention verifies the performance of the method through the following simulation experiments.

[0109] Simulation experiment 1

[0110] like Figure 5 and Figure 6As shown in FIG6 , the virtual array continuity of the coprime array obtained by the random expansion factor based on the special sequence pair is compared with the virtual array continuity of the extended coprime array obtained by the random expansion factor without the special sequence pair. Figure 5 As shown, the virtual array results obtained by randomly generating expansion factors from 2 to 20 show that the virtual array obtained by expanding the coprime array based on the special sequence proposed in the present invention is always continuous; Figure 6 As shown in (a)-(c), when the extended array is not based on a special sequence pair, the coprime extended sequence pair is (3,4), and the array expansion factor is a random integer; Figure 6 As shown in (d)-(f), the coprime extended sequence pair is (5,7) and the array expansion factor is a random integer. In addition, when the expansion factors of the two subarrays are different, the discontinuity of the array becomes more serious. In this case, if only the continuous part of the virtual array is extracted for signal processing, it will greatly waste the amount of calculation that has been generated and the additional resources of the virtual elements.

[0111] Simulation Experiment 2

[0112] like Figure 7 As shown, it shows the single result of target azimuth estimation based on signal sparse reconstruction in the physical element domain and virtual element domain of coprime extended array, as well as the comparison between the conventional beamforming (CBF) algorithm based on coprime sparse array and the typical algorithms Product and Min methods in the current coprime physical element domain. In the simulation, M=2, N=3, the array expansion coefficient α=4, and the array has a total of 16 elements. The signal-to-noise ratio is -10dB. There are two unrelated narrowband target signals from azimuths 10° and 16° incident on the coprime array, and the number of signal snapshots is 10. It can be seen from the results that the CBF algorithm based on the physical element domain of the coprime array cannot distinguish the two targets. In comparison, Product (the flowchart is shown in Figure 8 As shown) and Min (flow chart as Fig. 9 The method shown in Figure 2 can achieve higher resolution performance and can distinguish targets in two directions, and the Min method has better results. Fig.10 As shown, both the signal sparse reconstruction algorithms based on the virtual array element domain and the physical array element domain can accurately distinguish the directions of the two targets.

[0113] Simulation experiment three Fig.11 and Fig.12 As shown in the simulation, M = 2, N = 3, array expansion factor α = 4, and the array has 16 elements. The signal-to-noise ratio is -10dB. There are two unrelated narrowband target signals from azimuths 10° and 12° incident on the coprime array. The number of signal snapshots is 10. At this time, Fig.11 As shown in Figure 2, the CBF, Product, and Min methods cannot distinguish the two targets in the simulation. Fig.12 As shown in the figure, the two processing methods based on the physical array element domain and the virtual array element domain of the coprime array still obtain target direction estimation results with relatively high resolution.

[0114] The present invention provides a method for constructing a constant continuous virtual array based on a special sequence pair and realizing the target azimuth estimation thereof. The method combines the characteristics of a coprime array, utilizes the characteristics of a virtual array generated after the coprime subarray is expanded, designs a method for constructing a constant continuous virtual array based on a special sequence pair, and after obtaining the constant continuous virtual array method, uses the coprime array iso-expansion theory to expand the physical coprime array to generate an uninterrupted continuous virtual array without "holes", and uses a sparse signal recovery method based on a virtual array to perform signal processing on the constant continuous virtual array, and finally obtains the output of the target azimuth estimation result of the constant continuous virtual array under the constant continuous virtual array expansion scheme. The method improves the problem that the virtual array obtained by expanding the coprime array has array "holes" that affect the application of the signal processing method and the waste of array element information resources, obtains the virtual array target azimuth estimation with high spatial resolution, and provides the possibility for the utilization of coprime array resources and the realization of high-resolution target azimuth estimation in practical engineering applications.

[0115] Corresponding to the above-mentioned constant continuous virtual array construction method, the present invention also provides a constant continuous virtual array construction device, comprising:

[0116] A demand acquisition unit, used to acquire construction demand information of a physical array, wherein the demand information of the physical array includes the number of physical array elements or array aperture information; the physical array is a coprime array having two subarrays;

[0117] The special sequence pair acquisition module is used to acquire a special sequence pair (M, N), wherein the special sequence pair is randomly selected from a special sequence space, wherein the special sequence space is

[0118] The coprime matrix expansion factor acquisition module is used to obtain the coprime matrix expansion factor through the following calculation according to the special sequence:

[0119]

[0120]

[0121] Among them, m1, m2 are preset integers, α1, α2 are expansion factors of coproton arrays, and α1 = α2 = α is generally taken here. Here, the value of α is not unique. Under the premise that the values ​​of m1 and m2 are determined, any expansion factor value that satisfies the above two formulas can be used as the actual expansion factor for virtual array construction.

[0122] The virtual array construction module is used to expand the coproton array based on the expansion factor to construct a constant continuous virtual array. Specifically, the coproton array is expanded according to the obtained expansion factor α to obtain an expanded array of αM+αN-1.

[0123] As for a constant continuous virtual array construction device of the present invention, since it corresponds to a constant continuous virtual array construction method in the above embodiment, the description is relatively simple. For relevant similarities, please refer to the description of a constant continuous virtual array construction method in the above embodiment, which will not be described in detail here.

[0124] Corresponding to the above-mentioned target orientation estimation method, the present invention further discloses a target orientation estimation device, comprising:

[0125] An array construction module, used to construct a constant continuous virtual array based on the above method;

[0126] A receiving signal model generating module, used for receiving signals based on the constant continuous virtual array, modeling the signals received by the constant continuous virtual array, and generating a mutually prime virtual array receiving signal model;

[0127] The target azimuth estimation module is used to perform array signal analysis on the coprime virtual array model in the virtual array element domain, thereby realizing high-resolution target azimuth estimation in the coprime array virtual array element domain.

[0128] As for a target direction estimation device of the present invention, since it corresponds to a target direction estimation method in the above embodiment, the description is relatively simple. For relevant similarities, please refer to the description of a target direction estimation method in the above embodiment, which will not be described in detail here.

[0129] The present invention also discloses a storage medium, which includes a stored program, wherein when the program is run, the method described in any one of the above claims is executed.

[0130] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the method according to any one of the above claims through the operation of the computer program.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0133] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0134] If the integrated unit is implemented in the form of 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, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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 constructing a constant continuous virtual array, characterized in that: The following steps are involved: S101, obtaining construction requirement information of a physical array, wherein the requirement information of the physical array includes the number of physical array elements or array aperture information; the physical array is a coprime array having two subarrays; S102, obtaining a special sequence pair (M, N), wherein the special sequence pair is randomly selected from a special sequence space, wherein the special sequence space is The coprime matrix expansion factor is obtained by the following calculation based on the special sequence: Wherein, m1, m2 are preset integers, α1, α2 are expansion factors of coproton arrays; S103, expanding the coproton array based on the expansion factor, thereby constructing a constant continuous virtual array.

2. A method for constructing a constant continuous virtual array according to claim 1, characterized in that: When the coprime subarray is expanded based on the expansion factor, two subarrays of the expanded coprime array are expanded with the same expansion factor.

3. A target position estimation method, characterized in that: The following steps are involved: S201, constructing a constant continuous virtual array based on the method of claim 1; S202, receiving signals based on the constant continuous virtual array, modeling the signals received by the constant continuous virtual array, and generating a mutually prime virtual array receiving signal model; S203, performing array signal analysis on the coprime virtual array model in the virtual array element domain, thereby achieving high-resolution target azimuth estimation in the coprime array virtual array element domain.

4. A target orientation estimation method according to claim 3, characterized in that: Modeling of signals received by a constant continuous virtual array, including: Vectorizing the constant continuous virtual array signal; Arrange and screen the vectorized constant continuous virtual array; The signal is extracted to the arranged virtual array element position.

5. A target orientation estimation method according to claim 3, characterized in that: Perform array signal analysis on the coprime virtual array model in the virtual array element domain, including: Construct the observation matrix based on the mutually prime virtual array receiving signal model; Let the covariance vector of the received signal based on the constant continuous virtual array be There exists a vector x such that The signal v is represented by a basis matrix The linear representation is as follows: v=Ψx Using the observation matrix P is the length of vector v, Q represents the dimension after mapping, and maps the sparse signal v to a low-dimensional space. In, we get: y=Φv The equation group is optimized and evaluated, and the sparsest solution of the underdetermined system is obtained by using the norm minimization problem. Finally, the target azimuth angle value is obtained by outputting the signal amplitude at different azimuths.

6. A device for constructing a constant continuous virtual array, characterized in that: include: A requirement acquisition unit, used to acquire construction requirement information of a physical array, wherein the requirement information of the physical array includes the number of physical array elements or array aperture information; The physical array is a coprime array having two sub-arrays; The special sequence pair acquisition module is used to acquire a special sequence pair (M, N), wherein the special sequence pair is randomly selected from a special sequence space, wherein the special sequence space is The coprime matrix expansion factor acquisition module is used to obtain the coprime matrix expansion factor through the following calculation according to the special sequence: Wherein, m1, m2 are preset integers, α1, α2 are expansion factors of coproton arrays; The virtual array construction module is used to expand the coproton array based on the expansion factor to construct a constant continuous virtual array.

7. A target position estimation device, characterized in that: include: An array construction module, used to construct a constant continuous virtual array based on the method described in claim 1; A receiving signal model generating module, used for receiving signals based on the constant continuous virtual array, modeling the signals received by the constant continuous virtual array, and generating a mutually prime virtual array receiving signal model; The target azimuth estimation module is used to perform array signal analysis on the coprime virtual array model in the virtual array element domain, thereby realizing high-resolution target azimuth estimation in the coprime array virtual array element domain.

8. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the method according to any one of claims 1 to 5 is executed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor executes the method according to any one of claims 1 to 5 by running the computer program.

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