A design method for distributed satellite cluster cooperative ultra-sparse array

Abstract

The present invention discloses a distributed star cluster collaborative ultra-sparse array design method, the method comprising: analyzing a single satellite-mounted array in a distributed star cluster to obtain an antenna pattern of the single satellite-mounted array; processing the antenna pattern of the single satellite-mounted array to obtain a first reconstructed antenna pattern; performing multi-satellite jointing of p′ single satellites in the distributed star cluster to obtain an ultra-sparse array; dividing the ultra-sparse array into blocks to obtain p′ ultra-sparse array blocks; processing the p′ ultra-sparse array blocks to obtain antenna patterns of the p′ ultra-sparse array blocks; reconstructing the antenna patterns of the p′ ultra-sparse array blocks to obtain a second reconstructed antenna pattern, thereby realizing the distributed star cluster collaborative ultra-sparse array design. The present invention uses a block compressed sensing method to design an ultra-sparse array, improve the directivity and gain of a distributed star cluster array, flexibly form beams, and improve the anti-interference capability of a satellite communication system.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a distributed star cluster collaborative ultra-sparse array design method. Background Art

[0002] At present, my country's satellite networking technology is becoming more and more mature. In order to meet the needs of the development of future satellite communication systems, sparse planar arrays have become important devices for transmitting and receiving electromagnetic waves in emerging antenna systems due to their low cost, low system complexity, and strong anti-interference ability, which has a far-reaching impact on the communication industry. However, due to the scarcity of orbital and spectrum resources and the limitations of single-satellite communication capabilities, in order to meet the construction needs of the integrated space-ground information network, the traditional GEO satellite network will transform into a distributed constellation composed of a large number of micro-nano satellites in the future. In order to adapt to the characteristics of multi-satellite collaboration and interconnection of distributed constellations, and to better improve the resource utilization and anti-interference ability of the entire communication system, it is urgent to carry out research on distributed constellation onboard reconfigurable sparse arrays. Therefore, the distributed constellation collaborative ultra-sparse array design method is proposed to solve the "multi-satellite collaborative reconfigurable" array design problem.

[0003] In existing research, a method for cooperative transmission of distributed constellation antenna arrays with multiple satellites in common position is proposed. For the uplink, each antenna subarray unit receives the signal from the ground user terminal respectively, performs delay compensation, and then performs cross-correlation operation to complete inter-satellite synchronization. Finally, the signal is output to the signal processing unit for subsequent demodulation and other processing. The same orbital position forms a spatial "virtual large antenna", thereby effectively increasing the antenna equivalent aperture and improving the antenna gain, proving that the coordination of distributed constellation antenna arrays with multiple satellites in common position has the characteristics of strong agility, high robustness, and low difficulty in development. Based on the theory of compressed sensing, some researchers have proposed a reconfigurable array design method that minimizes the number of array elements, realizes multi-beam reconstruction with appropriate excitation coefficients, and minimizes the number of array elements, but only considers linear arrays, and does not consider the combination of multiple arrays, which cannot meet the design requirements of distributed constellation antenna arrays. In existing research, compressed sensing based on block strategy is mainly aimed at image processing problems and has no application in antenna design.

[0004] The above methods have some limitations when used alone in future space information networks and cannot meet the needs of multi-satellite collaboration in the collaborative design of distributed constellation arrays. Applying block compressed sensing to reconfigurable ultra-sparse array design provides new possibilities for the collaborative design of distributed constellation antenna arrays. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a distributed star cluster collaborative ultra-sparse array design method, first establish a sparse array antenna pattern mathematical model; use the compressed sensing method to solve the most sparse array element number and excitation matrix of a single satellite-mounted array in a distributed star cluster; define the virtual array aperture, combine multiple satellite-mounted arrays in a distributed star cluster based on the characteristics of multi-star collaboration in a distributed star cluster, and use the block idea to design an ultra-sparse array; then, according to user needs, use the antenna pattern superposition principle and the block compressed sensing method to reconstruct the virtual large aperture array antenna pattern, obtain the most sparse array element number and position, and calculate the reconstructed antenna pattern. The advantage of the present invention is that the block compressed sensing method is used to design an ultra-sparse array, improve the directivity and gain of the distributed star cluster array, flexibly form beams, and improve the anti-interference ability of the satellite communication system, which meets the needs of reducing costs and software and hardware complexity in large array design.

[0006] In order to solve the above technical problems, an embodiment of the present invention discloses a distributed star cluster collaborative ultra-sparse array design method, the method comprising:

[0007] S1, analyzing a single satellite-borne array in a distributed star cluster to obtain an antenna pattern of the single satellite-borne array; the distributed star cluster includes a plurality of single satellites;

[0008] S2, processing the antenna pattern of the array carried by the single satellite to obtain a first reconstructed antenna pattern;

[0009] S3, combining p′ single satellites in the distributed constellation to obtain a super sparse array;

[0010] S4, dividing the ultra-sparse array into blocks to obtain p′ ultra-sparse array blocks;

[0011] S5, processing the p′ ultra-sparse array blocks to obtain antenna radiation patterns of the p′ ultra-sparse array blocks;

[0012] S6, reconstructing the antenna patterns of the p′ ultra-sparse array blocks to obtain a second reconstructed antenna pattern, thereby realizing distributed constellation collaborative ultra-sparse array design.

[0013] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the antenna pattern expression of the array carried by the single satellite is:

[0014]

[0015] In the formula, represents the antenna pattern of a single satellite-mounted array, θ and are the elevation and azimuth angles of any observation direction in the plane relative to the origin, and w n are the unit pattern and complex excitation of the nth array element, respectively. The complex excitation includes amplitude and phase. β=2π / λ is the number of spatial wavelengths. (x n ,y n ) is the two-dimensional coordinate position of the nth candidate array element, N = N x ×N y is the total number of candidate array elements, e is an exponential function, and the array carried by a single satellite in a distributed constellation is a rectangular array located in the xoy plane, and the array aperture size is D x ×D y , i is the imaginary unit, i 2 =-1.

[0016] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the processing of the antenna pattern of the array carried by the single satellite to obtain the first reconstructed antenna pattern includes:

[0017] S21, performing sparse processing on the complex excitation of the array carried by the single satellite to obtain a sparse complex excitation matrix of the array carried by the single satellite;

[0018] S22, processing the sparse complex excitation matrix to obtain an antenna pattern sampling vector;

[0019] S23, reconstructing the antenna pattern sampling vector to obtain a first reconstructed antenna pattern.

[0020] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the antenna pattern sampling vector expression is:

[0021] F=Aw p

[0022] Where F is the reference direction The antenna pattern sampling vector is expressed as F = [f1, f2, ..., f J ] T , T represents transpose, J represents the number of sampling vectors, w p =[w p1 ,w p2 ,...,w pN ] is the sparse complex excitation matrix of the array carried by the pth satellite, A is the J×N dimensional measurement matrix, and the elements of matrix A are j=1,2,…,J,n=1,2,…,N,N=N x ×N y is the total number of candidate array elements, e is an exponential function, (x n ,y n) is the two-dimensional coordinate position of the nth candidate array element, β = 2π / λ is the number of spatial wavelengths, i is the imaginary unit, i 2 = -1;

[0023] As an optional implementation manner, in the first aspect of the embodiment of the present invention, reconstructing the antenna pattern sampling vector to obtain a first reconstructed antenna pattern includes:

[0024] Reconstructing the antenna pattern sampling vector using an antenna pattern sampling vector reconstruction model to obtain a first reconstructed antenna pattern;

[0025] The antenna pattern sampling vector reconstruction model is:

[0026]

[0027] st||ff REF ||2≤ε

[0028] Among them, w p =[w p1 ,w p2 ,...,w pN ] is the sparse complex excitation matrix of the array carried by the pth satellite, f is the antenna pattern of the array carried by a single satellite in the distributed constellation, and f REF is the reference antenna pattern, ||·||1 is the l1 norm, ||·||2 is the l2 norm, and ε is the upper limit of the reconstruction error set by the user.

[0029] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the antenna pattern expression of the p′ super sparse array blocks is:

[0030]

[0031] In the formula, is the antenna pattern of the pth super-sparse array block, (x pn ,y pn ) is the two-dimensional coordinate position of the nth candidate array element in the pth satellite in the constellation, w' pn is the sparse post-complex excitation of the nth array element in the pth satellite in the constellation, p = 1, 2, ..., p′, n = 1, 2, ..., N, N is the total number of candidate array elements, e is an exponential function, β = 2π / λ is the number of spatial wavelengths, i is an imaginary unit, i 2 = -1, θ and are the elevation and azimuth angles of any observation direction in the plane relative to the origin, respectively.

[0032] As an optional implementation manner, in the first aspect of the embodiment of the present invention, reconstructing the antenna patterns of the p′ ultra-sparse array blocks to obtain a second reconstructed antenna pattern includes:

[0033] S61, superimposing the antenna patterns of the p′ ultra-sparse array blocks to obtain a superimposed antenna pattern;

[0034] The superposition antenna pattern expression is:

[0035]

[0036] Where f is the superimposed antenna pattern, f p is the antenna pattern of the pth super-sparse array block, p = 1, 2, ..., p′;

[0037] S62, reconstructing the superimposed antenna pattern using an ultra-sparse array antenna pattern reconstruction model to obtain a second reconstructed antenna pattern;

[0038] The ultra-sparse array antenna pattern reconstruction model expression is:

[0039]

[0040]

[0041] In the formula, w P is the sparse complex excitation matrix of the array carried by the pth satellite, f' REF The antenna pattern of interest is proposed to the user, ||·||1 is the l1 norm, ||·||2 is the l2 norm, and ε is the upper limit of the reconstruction error set by the user.

[0042] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0043] (1) Aiming at the characteristics of multi-satellite alliance and mutual coordination in distributed star constellations, the present invention applies the concept of sparse array to distributed star constellations, and combines the arrays carried by satellites in the constellation into ultra-sparse arrays. It can reconstruct the antenna pattern of interest according to user needs and effectively reduce the number of array elements, meeting the requirements of low cost, low software and hardware complexity, and strong anti-interference ability in large array design, and at the same time achieves better performance indicators through optimized design.

[0044] (2) The present invention introduces a block compressed sensing method, which uses the block idea to make the satellite array in the constellation a block in the joint virtual array. According to the antenna pattern superposition principle, an ultra-sparse array is formed, which can obtain antenna patterns with high matching accuracy and has the advantages of reducing sampling rate, power consumption and cache. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0046] Figure 1 It is a flow chart of a distributed star cluster collaborative ultra-sparse array design method disclosed in an embodiment of the present invention;

[0047] Figure 2 It is a schematic diagram of the distributed star cluster collaborative ultra-sparse array design disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] 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 are within the scope of protection of the present invention.

[0049] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0050] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places 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 is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0051] The present invention discloses a distributed star cluster collaborative ultra-sparse array design method. The present invention aims at the problem that a satellite-borne phased array requires quite a number of antenna units and array amplitude and phase control components, which results in high antenna system cost, heavy weight of phased array antenna, high hardware cost, and difficulty in heat dissipation, which is not conducive to being carried on distributed star cluster satellites. As the array aperture increases, the signal processing complexity of the satellite-borne phased array is high, and a large number of receiving channels will make the calculation dimension of the signal processing algorithm too high. The distributed star cluster collaborative ultra-sparse array design method is proposed. The method can reconstruct the antenna radiation pattern of interest through multi-satellite collaboration and array joint design, reduce the cost and system complexity of the satellite-borne antenna, improve the array performance, and is conducive to the optimized design of the distributed star cluster satellite array. The method has good anti-interference ability.

[0052] The present invention discloses a distributed star cluster collaborative ultra-sparse array design method, the method comprising: analyzing a single satellite-mounted array in a distributed star cluster to obtain an antenna pattern of the single satellite-mounted array; processing the antenna pattern of the single satellite-mounted array to obtain a first reconstructed antenna pattern; performing multi-satellite jointing of p′ single satellites in a distributed star cluster to obtain an ultra-sparse array; dividing the ultra-sparse array into blocks to obtain p′ ultra-sparse array blocks; processing the p′ ultra-sparse array blocks to obtain the antenna patterns of the p′ ultra-sparse array blocks; reconstructing the antenna patterns of the p′ ultra-sparse array blocks to obtain a second reconstructed antenna pattern, thereby realizing the distributed star cluster collaborative ultra-sparse array design. The present invention utilizes a block compressed sensing method to design an ultra-sparse array, improve the directivity and gain of a distributed star cluster array, flexibly form beams, and improve the anti-interference capability of a satellite communication system, which meets the requirements of reducing costs and software and hardware complexity in large array design. The following are detailed descriptions.

[0053] Embodiment 1

[0054] See also Figure 1 , Figure 1 1 is a flow chart of a distributed star cluster collaborative ultra-sparse array design method disclosed in an embodiment of the present invention. Figure 1 The distributed constellation collaborative ultra-sparse array design method described is applied to the field of satellite communication technology, and block compressed sensing is applied to the reconfigurable ultra-sparse array design to achieve the distributed constellation antenna array collaborative design, which is not limited in the embodiments of the present invention. Figure 1 As shown, the distributed star cluster collaborative ultra-sparse array design method may include the following operations:

[0055] S1, analyzing a single satellite-borne array in a distributed star cluster to obtain an antenna pattern of the single satellite-borne array; the distributed star cluster includes a plurality of single satellites;

[0056] S2, processing the antenna pattern of the array carried by the single satellite to obtain a first reconstructed antenna pattern;

[0057] S3, combining p′ single satellites in the distributed constellation to obtain a super sparse array;

[0058] S4, dividing the ultra-sparse array into blocks to obtain p′ ultra-sparse array blocks;

[0059] S5, processing the p′ ultra-sparse array blocks to obtain antenna radiation patterns of the p′ ultra-sparse array blocks;

[0060] S6, reconstructing the antenna patterns of the p′ ultra-sparse array blocks to obtain a second reconstructed antenna pattern, thereby realizing distributed constellation collaborative ultra-sparse array design.

[0061] Optionally, the antenna pattern expression of the array carried by a single satellite is:

[0062]

[0063] In the formula, represents the antenna pattern of a single satellite-mounted array, θ and are the elevation and azimuth angles of any observation direction in the plane relative to the origin, and w n are the unit pattern and complex excitation of the nth array element, respectively. The complex excitation includes amplitude and phase. β=2π / λ is the number of spatial wavelengths, λ is the signal carrier wavelength, (x n ,y n ) is the two-dimensional coordinate position of the nth candidate array element, N = N x ×N y is the total number of candidate array elements, N x Indicates the number of candidate array elements in the X-axis direction of the rectangular array; N y represents the number of candidate array elements in the Y-axis direction of the rectangular array; e is an exponential function. The array carried by a single satellite in the distributed constellation is a rectangular array located in the xoy plane, and the array aperture size is D x ×D y , i is the imaginary unit, i 2 =-1.

[0064] Optionally, the processing the antenna pattern of the array carried by the single satellite to obtain a first reconstructed antenna pattern includes:

[0065] S21, performing sparse processing on the complex excitation of the array carried by the single satellite to obtain a sparse complex excitation matrix of the array carried by the single satellite;

[0066] S22, processing the sparse complex excitation matrix to obtain an antenna pattern sampling vector;

[0067] S23, reconstructing the antenna pattern sampling vector to obtain a first reconstructed antenna pattern.

[0068] Optionally, the antenna pattern sampling vector expression is:

[0069] F=Aw p

[0070] Where F is the reference direction The antenna pattern sampling vector is expressed as F = [f1, f2, ..., f J ] T ,θ j and are the pitch angle and azimuth angle of the jth sampling reference direction in the plane relative to the origin, T represents transposition, J represents the number of sampling vectors, and w p =[w p1 ,w p2 ,...,w pN ] is the sparse complex excitation matrix of the array carried by the pth satellite, A is the J×N dimensional measurement matrix, and the elements of matrix A are j=1,2,…,J,n=1,2,…,N,N=N x ×N y is the total number of candidate array elements, e is an exponential function, (x n ,y n ) is the two-dimensional coordinate position of the nth candidate array element, β = 2π / λ is the number of spatial wavelengths, i is the imaginary unit, i 2 = -1;

[0071] Optionally, reconstructing the antenna pattern sampling vector to obtain a first reconstructed antenna pattern includes:

[0072] Reconstructing the antenna pattern sampling vector using an antenna pattern sampling vector reconstruction model to obtain a first reconstructed antenna pattern;

[0073] The antenna pattern sampling vector reconstruction model is:

[0074]

[0075] st||ff REF ||2≤ε

[0076] Among them, w p =[w p1 ,w p2 ,...,w pN] is the sparse complex excitation matrix of the array carried by the pth satellite, f is the antenna pattern of the array carried by a single satellite in the distributed constellation, and f REF is the reference antenna pattern, ||·||1 is the l1 norm, ||·||2 is the l2 norm, and ε is the upper limit of the reconstruction error set by the user.

[0077] Optionally, the antenna pattern expression of the p′ super sparse array blocks is:

[0078]

[0079] In the formula, is the antenna pattern of the pth super-sparse array block, (x pn ,y pn ) is the two-dimensional coordinate position of the nth candidate array element in the pth satellite in the constellation, w' pn is the sparse post-complex excitation of the nth array element in the pth satellite in the constellation, p = 1, 2, ..., p′, n = 1, 2, ..., N, N is the total number of candidate array elements, e is an exponential function, β = 2π / λ is the number of spatial wavelengths, i is an imaginary unit, i 2 = -1, θ and are the elevation and azimuth angles of any observation direction in the plane relative to the origin, respectively.

[0080] Optionally, reconstructing the antenna patterns of the p′ ultra-sparse array blocks to obtain a second reconstructed antenna pattern includes:

[0081] S61, superimposing the antenna patterns of the p′ ultra-sparse array blocks to obtain a superimposed antenna pattern;

[0082] The superposition antenna pattern expression is:

[0083]

[0084] Where f is the superimposed antenna pattern, f p is the antenna pattern of the pth super-sparse array block, p = 1, 2, ..., p′;

[0085] S62, reconstructing the superimposed antenna pattern using an ultra-sparse array antenna pattern reconstruction model to obtain a second reconstructed antenna pattern;

[0086] The ultra-sparse array antenna pattern reconstruction model expression is:

[0087]

[0088]

[0089] In the formula, wP is the sparse complex excitation matrix of the array carried by the pth satellite, f' REF The antenna pattern of interest is proposed to the user, ||·||1 is the l1 norm, ||·||2 is the l2 norm, and ε is the upper limit of the reconstruction error set by the user.

[0090] It can be seen that the present invention applies the concept of sparse array to distributed star clusters in view of the characteristics of multi-star alliance and mutual coordination in distributed star clusters. The satellite-mounted array in the joint cluster is an ultra-sparse array; it can reconstruct the antenna pattern of interest according to user needs and effectively reduce the number of array elements, which meets the requirements of low cost, low software and hardware complexity, and strong anti-interference ability in large array design, and achieves better performance indicators through optimized design. The present invention introduces a block compressed sensing method, which uses the block idea to make the satellite-mounted array in the star cluster a block in the joint virtual array, and forms an ultra-sparse array according to the antenna pattern superposition principle, which can obtain antenna patterns with high matching accuracy, and has the advantages of reducing sampling rate, power consumption and cache.

[0091] Embodiment 2

[0092] See also Figure 2 , Figure 2 Schematic diagram of distributed star cluster cooperative ultra-sparse array design disclosed in an embodiment of the present invention. Figure 2 The distributed constellation collaborative ultra-sparse array design method described is applied to the field of satellite communication technology, and block compressed sensing is applied to the reconfigurable ultra-sparse array design to achieve the distributed constellation antenna array collaborative design, which is not limited in the embodiments of the present invention. Figure 2 As shown, the present invention aims at the problem that the satellite-borne phased array requires quite a lot of antenna units and array amplitude and phase control components, which makes the antenna system costly, the phased array antenna heavy, the hardware cost high, and the heat dissipation difficult, which is not conducive to being carried on distributed star cluster satellites. As the array aperture increases, the signal processing complexity of the satellite-borne phased array is high, and a large number of receiving channels will make the calculation dimension of the signal processing algorithm too high. A distributed star cluster collaborative ultra-sparse array design method is proposed, which can reconstruct the antenna radiation pattern of interest through multi-satellite collaboration and array joint design, reduce the cost and system complexity of satellite-borne antennas, improve array performance, and is conducive to the optimized design of distributed star cluster satellite arrays, and has good anti-interference ability. The specific steps include:

[0093] Step 1: The array carried by a single satellite in the distributed constellation is a rectangular array located in the xoy plane, and the array aperture size is D x ×D y ,make To represent the antenna pattern of a single satellite carrying an array, it is specifically:

[0094]

[0095] Among them, θ and are the elevation and azimuth angles of any observation direction in the plane relative to the origin, and w n are the unit radiation pattern and complex excitation (including amplitude and phase) of the nth array element, β=2π / λ is the number of spatial wavelengths, (x n ,y n ) is the two-dimensional coordinate position of the nth candidate array element, N = N x ×N y is the total number of candidate array elements;

[0096] Step 1-1, the antenna pattern is a key indicator for evaluating array performance. It is generally used to indicate the change of the relative field strength of the radiation field with direction. According to the superposition principle, the antenna pattern can be expressed as a unit factor (single element pattern) and array factor product;

[0097] Step 1-2, using isotropic units, the unit pattern of the nth element It is expressed as:

[0098]

[0099] Step 2, using compressed sensing theory, convert the antenna pattern reconstruction problem of the pth array into an optimization problem;

[0100]

[0101] st||ff REF ||2≤ε

[0102] Among them, w p is the excitation matrix of the array, f is the antenna pattern of the array carried by a single satellite in the constellation, and f REF is the antenna pattern of interest, ||·||1 is the l1 norm, ||·||2 is the l2 norm, and ε is the upper limit of the reconstruction error set by the user;

[0103] Step 2-1: According to the theory of compressed sensing, not all candidate elements in the satellite array need to be excited. If an element in the comprehensive excitation matrix is ​​zero, there is no array element at that position. The excitation value matrix of the sparse array carried by the pth satellite is expressed as a sparse matrix w p =[w p1 ,w p2 ,...,w pN ];

[0104] Step 2-2, according to the antenna pattern mathematical model and superposition principle, the antenna pattern can be expressed as a matrix multiplication form to obtain the compressed sensing measurement equation F = Aw p , where A is a J×N dimensional measurement matrix with elements F is the reference direction The antenna pattern sampling vector is expressed as F = [f1, f2, ..., f J ] T ;

[0105] Step 2-3: The signal satisfies the constrained isometry RIP, and a sparse vector is reconstructed based on the measurement vector F, and F=Aw p The only sparse solution of , that is, the number of non-zero elements is the smallest, then the signal can be reconstructed by solving the following equation:

[0106]

[0107] st||ff REF ||2≤ε

[0108] Step 2-4, since solving the problem in the previous step is an NP-hard problem, it is necessary to use the convex l1 norm to approximate the non-convex l0 norm and transform the problem into a convex optimization problem:

[0109]

[0110]

[0111] Step 3: Using the block compressed sensing method, multiple satellites in the distributed constellation jointly form an ultra-sparse array, which is divided into P′ small blocks, each of which represents the array carried by a satellite in the constellation. The pth sparse array excitation matrix is ​​denoted as w′ p , p=1,2,...,P′, then the antenna pattern of each sparse array block is It can be expressed as:

[0112]

[0113] Among them, (x pn ,y pn ) is the two-dimensional coordinate position of the nth candidate array element in the pth satellite in the constellation, w' pn are the complex excitations of the nth array element in the pth satellite in the constellation;

[0114] Step 3-1: Based on the characteristics that each satellite node in the distributed constellation occupies a GEO orbit position less than 1° and multiple satellites are co-located, and because the distance between satellites in the constellation is very small compared to the distance between the ground and the satellites, the ultra-sparse array is equivalent to a virtual ultra-sparse array perpendicular to the observation direction;

[0115] Step 3-2: Since the ultra-sparse array aperture cannot be strictly defined, define the virtual array aperture as a square array, take the distance between the two farthest array elements carried by the satellites in the constellation as the diagonal of the square array, and take the center of the two connected points as the center point of the array;

[0116] Step 3-3 defines the sparse array excitation w' of the pth satellite p , p=1,2,...,P, according to the characteristics of the consistent size of the satellite arrays in the constellation, the same measurement matrix A is used p Observe the antenna pattern of each satellite-borne array;

[0117] Step 3-4 calculates the antenna pattern of the pth satellite based on the array excitation:

[0118]

[0119] Step 4: According to the antenna pattern superposition principle, a block compressed sensing method is used to reconstruct the antenna pattern of interest, and the sparsest number of array elements within the tolerance error range is calculated;

[0120]

[0121]

[0122] Among them, f p is the antenna pattern of the sparse array block, w P is the ultra-sparse array excitation matrix, f' REF Propose antenna patterns of interest to users;

[0123] Step 4-1, under the ultra-sparse array aperture, calculate the superimposed reconstructed antenna pattern, which can be expressed as:

[0124] Step 4-2, based on user needs, propose antenna patterns of interest;

[0125] Step 4-3, according to the block compressed sensing method, the antenna pattern reconstruction problem can be transformed into solving the following equation;

[0126]

[0127]

[0128] It can be seen that the present invention applies the concept of sparse array to distributed star clusters in view of the characteristics of multi-star alliance and mutual coordination in distributed star clusters. The satellite-mounted array in the joint cluster is an ultra-sparse array; it can reconstruct the antenna pattern of interest according to user needs and effectively reduce the number of array elements, which meets the requirements of low cost, low software and hardware complexity, and strong anti-interference ability in large array design, and achieves better performance indicators through optimized design. The present invention introduces a block compressed sensing method, which uses the block idea to make the satellite-mounted array in the star cluster a block in the joint virtual array, and forms an ultra-sparse array according to the antenna pattern superposition principle, which can obtain antenna patterns with high matching accuracy, and has the advantages of reducing sampling rate, power consumption and cache.

[0129] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0130] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0131] Finally, it should be noted that the distributed star cluster collaborative ultra-sparse array design method disclosed in the embodiment of the present invention discloses only a preferred embodiment of the present invention, which is only used to illustrate the technical solution 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 the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A distributed star cluster collaborative ultra-sparse array design method, characterized in that: The method comprises: S1, analyzing a single satellite-borne array in a distributed star cluster to obtain an antenna pattern of the single satellite-borne array; the distributed star cluster includes a plurality of single satellites; The antenna pattern expression of the single satellite array is: In the formula, represents the antenna pattern of a single satellite-mounted array, θ and are the elevation and azimuth angles of any observation direction in the plane relative to the origin, and w n are the unit pattern and complex excitation of the nth array element, respectively. The complex excitation includes amplitude and phase. β=2π / λ is the number of spatial wavelengths. (x n ,y n ) is the two-dimensional coordinate position of the nth candidate array element, N = N x ×N y is the total number of candidate array elements, e is an exponential function, and the array carried by a single satellite in a distributed constellation is a rectangular array located in the xoy plane, and the array aperture size is D x ×D y , i is the imaginary unit, i 2 = -1; S2, processing the antenna pattern of the array carried by the single satellite to obtain a first reconstructed antenna pattern, including: S21, performing sparse processing on the complex excitation of the array carried by the single satellite to obtain a sparse complex excitation matrix of the array carried by the single satellite; S22, processing the sparse complex excitation matrix to obtain an antenna pattern sampling vector; The antenna pattern sampling vector expression is: F = Yes p Where F is the reference direction The antenna pattern sampling vector is expressed as F = [f1, f2, ..., f J ] T , T represents transpose, J represents the number of sampling vectors, w p =[w p1 ,w p2 ,...,w pN ] is the sparse complex excitation matrix of the array carried by the pth satellite, A is the J×N dimensional measurement matrix, and the elements of matrix A are j=1,2,…,J,n=1,2,…,N,N=N x ×N y is the total number of candidate array elements, e is an exponential function, (x n ,y n ) is the two-dimensional coordinate position of the nth candidate array element, β = 2π / λ is the number of spatial wavelengths, i is the imaginary unit, i 2 = -1; S23, reconstructing the antenna pattern sampling vector to obtain a first reconstructed antenna pattern, including: Reconstructing the antenna pattern sampling vector using an antenna pattern sampling vector reconstruction model to obtain a first reconstructed antenna pattern; The antenna pattern sampling vector reconstruction model is: Among them, w p =[w p1 ,w p2 ,...,w pN ] is the sparse complex excitation matrix of the array carried by the pth satellite, f is the antenna pattern of the array carried by a single satellite in the distributed constellation, and f REF is the reference antenna pattern, ||·||1 is the l1 norm, ||·||2 is the l2 norm, and ε is the upper limit of the reconstruction error set by the user; S3, combining p′ single satellites in the distributed constellation to obtain a super sparse array; S4, dividing the ultra-sparse array into blocks to obtain p′ ultra-sparse array blocks; S5, processing the p′ ultra-sparse array blocks to obtain antenna radiation patterns of the p′ ultra-sparse array blocks; The antenna pattern expression of the p′ super sparse array blocks is: In the formula, is the antenna pattern of the pth super-sparse array block, (x pn ,y pn ) is the two-dimensional coordinate position of the nth candidate array element in the pth satellite in the constellation, w' pn is the sparse post-complex excitation of the nth array element in the pth satellite in the constellation, p = 1, 2, ..., p′, n = 1, 2, ..., N, N is the total number of candidate array elements, e is an exponential function, β = 2π / λ is the number of spatial wavelengths, i is an imaginary unit, i 2 = -1, θ and are the elevation and azimuth angles of any observation direction in the plane relative to the origin, respectively; S6, reconstructing the antenna patterns of the p′ ultra-sparse array blocks to obtain a second reconstructed antenna pattern, and realizing distributed constellation collaborative ultra-sparse array design, including: S61, superimposing the antenna patterns of the p′ ultra-sparse array blocks to obtain a superimposed antenna pattern; The superposition antenna pattern expression is: Where f is the superimposed antenna pattern, f p is the antenna pattern of the pth super-sparse array block, p = 1, 2, ..., p′; S62, reconstructing the superimposed antenna pattern using an ultra-sparse array antenna pattern reconstruction model to obtain a second reconstructed antenna pattern; The ultra-sparse array antenna pattern reconstruction model expression is: In the formula, w P is the sparse complex excitation matrix of the array carried by the pth satellite, f′ REF The antenna pattern of interest is proposed to the user, ||·||1 is the l1 norm, ||·||2 is the l2 norm, and ε is the upper limit of the reconstruction error set by the user.

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