Beam forming anti-interference method and device in low-orbit satellite communication system

Abstract

The invention discloses a beam forming anti-interference method and device in a low-earth-orbit satellite communication system, and the method comprises the steps: building a low-earth-orbit satellite communication system model based on a planar array antenna, and receiving a signal through a receiving end of the low-earth-orbit satellite communication system; wherein the signals received by the receiving end comprise user signals transmitted by a plurality of ground nodes and interference signals generated by an external interference source; estimating the arrival angles of the user signals and the interference signals by using a multiple signal classification algorithm; by converting a maximized SINR problem into a power minimization problem under an arrival angle gain constraint, inter-user interference and external interference are suppressed so as to solve and obtain a beam forming matrix. According to the invention, the multi-node communication requirement of the satellite communication system can be met, the anti-interference of the satellite receiving end is realized, and the capacity of the satellite communication system is improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite communication technology, and in particular to a beamforming anti-interference method and device in a low-orbit satellite communication system. Background Art

[0002] Array antenna beamforming technology has been widely used in communication systems. In recent years, with the development and application of low-orbit satellite communication systems, satellite uplink anti-interference technology needs to be studied urgently to meet user service quality requirements. The beamforming method based on array antenna can adjust the feed weights of each antenna element according to the channel environment, achieve the required gain in the direction of the desired signal, and form a null in the interference direction to achieve spatial filtering. Applying it to low-orbit satellite communication systems can effectively suppress interference and noise and improve the system communication capacity and service quality.

[0003] The minimum variance distortionless response (MVDR) beamforming algorithm proposed by Capon is widely used. The algorithm performs well when the parameters such as the expected signal arrival angle are accurately known. However, in practical applications, the performance of the algorithm drops sharply due to the estimation error. The linearly constrained minimum variance beamformer (LCMV) proposed by Frost.OJ improves the stability of the algorithm by expanding the main lobe or null width. Reed proposed the sample matrix inverse beamformer (SMI), which uses the sampling covariance matrix inversion method to calculate the weights and reduces the algorithm complexity. However, all of the above algorithms require perfect channel state information. In reality, the channel state information often has errors. At this time, the performance of the above algorithms will drop sharply. Beamforming technology is essentially a spatial filtering of signals from different directions in space. By estimating the signal arrival angle, accurate signal arrival angle information is obtained, and then the array gain in the direction of the signal arrival angle is constrained, which can achieve the reception of user signals and the suppression of interference and noise. The use of beamforming anti-interference methods based on arrival angle estimation can meet the communication needs of more ground node devices within limited spectrum resources, and the utilization of spectrum resources can be greatly improved through multi-node communication system design. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, the present invention proposes a beamforming anti-interference method in a low-orbit satellite communication system to suppress interference between users and external interference and improve the communication quality of satellite uplinks.

[0006] Another object of the present invention is to provide a beamforming anti-interference device in a low-orbit satellite communication system.

[0007] To achieve the above object, the present invention provides a beamforming anti-interference method in a low-orbit satellite communication system, comprising:

[0008] A low-orbit satellite communication system model based on a planar array antenna is established, and signals are received through a receiving end of the low-orbit satellite communication system; wherein the signals received by the receiving end include user signals transmitted by multiple ground nodes and interference signals generated by external interference sources;

[0009] The arrival angles of user and interference signals are estimated using a multiple signal classification algorithm;

[0010] By converting the SINR maximization problem into a power minimization problem under the arrival angle gain constraint, the inter-user interference and external interference are suppressed to obtain the beamforming matrix.

[0011] The beamforming anti-interference method in the low-orbit satellite communication system of the embodiment of the present invention may also have the following additional technical features:

[0012] In one embodiment of the present invention, a low-orbit satellite communication system model based on a planar array antenna is established, and a signal is received through a receiving end of the low-orbit satellite communication system, including:

[0013] Based on the low-orbit satellite communication uplink, the transmitter contains K single antenna nodes, and the receiver uses a uniform linear array antenna with the number of array elements being N = N. x ×N y , where N x and N y Represents the number of antenna array elements in the x-axis and y-axis directions respectively. There are M interference sources. The noise is additive noise independent of each channel. The receiving end estimates the channel state information of the node based on the pilot information sent by K user nodes. The signal received by the receiving end array antenna is:

[0014]

[0015] Among them, s k (t) is the original signal sent by the kth (k=1,2,...,K)th node, d m (t) is the mth (m=1,2,...,M) interference signal, h k and g m are the channel vectors of the kth user signal and the mth interference signal, n(t)∈C N×1is the noise vector; for user node k, the receiving end processes the beamforming vector, and its array antenna output signal is expressed as:

[0016]

[0017] in, Indicates the request for w k The conjugate transpose of k ∈C N×1 represents the beamforming vector for receiving the kth node signal, W = [w1, w2, ..., w K ] represents the beamforming matrix for receiving K node signals;

[0018] The channel vector is expressed as:

[0019] h k =α k ·v k ,

[0020] Among them, α k is the channel gain, v k is the steering vector of the uniform planar array, expressed as:

[0021]

[0022] in, represents the Kronecker product, and represent the arrival angles of the kth user signal in the x-axis direction and the y-axis direction respectively; and They are:

[0023]

[0024] in,[·] T represents the transpose operation; the interference signal channel vector is expressed in a similar form:

[0025] g m =α m ·v m .

[0026] In one embodiment of the present invention, the arrival angles of user signals and interference signals are estimated using a multiple signal classification algorithm, including:

[0027] Based on the obtained uniform planar array receiving signal model, the covariance matrix of the received signal is calculated:

[0028]

[0029] Where T is the number of snapshots of received data, X = [x(1), x(2), ..., x(T)] is the sample value of signal x(t);

[0030] The signal AOA is estimated using the two-dimensional MUSIC algorithm. Perform eigenvalue decomposition:

[0031]

[0032] Among them, Λ S and Λ N are the signal power diagonal matrix and the noise power diagonal matrix respectively, U S and U N are the signal subspace and the noise subspace respectively, [·] H represents the conjugate transpose operation; the two-dimensional AOA spectrum function of the signal is expressed as:

[0033]

[0034] Perform a two-dimensional spectrum peak search on the above formula to obtain the steering vector set corresponding to the signal AOA, Using known user signal channel state information Find the vector in the set with The minimum value of the square of the l2 norm of the difference:

[0035]

[0036] The steering vectors of the K user signals are identified in turn, and the remaining M vectors are the steering vectors corresponding to the interference signal AOA. The steering vectors of the user signals and the interference signals are arranged in sequence to obtain the received signal steering vector matrix:

[0037]

[0038] In one embodiment of the present invention, by converting the SINR maximization problem into a power minimization problem under the arrival angle gain constraint, inter-user interference and external interference are suppressed to obtain a beamforming matrix, including:

[0039] The spectrum efficiency of the low-orbit satellite communication system is expressed as:

[0040]

[0041] Among them, SINR k is the signal-to-interference-and-noise ratio (SINR) of the kth user. k It is expressed as:

[0042]

[0043] in, and Represent the power of user signal, interference signal and noise respectively; solving the optimal beamforming vector can be expressed as maximizing SINR problem:

[0044]

[0045] By substituting into the channel formula, the maximum power ratio is transformed into the problem of minimizing the power under the signal AOA constraint:

[0046]

[0047] Among them, a k =[0…ξ k …0] T is the constraint vector, ξ k As the amplitude constraint, the solution of the above optimization problem is obtained by Lagrange multiplier method:

[0048]

[0049] Where W = [w1 w2…w K ],A=[a1 a2…a K ], in order to ensure the distortion-free output of the signal, the amplitude constraint is set to is the statistical channel gain of the kth user signal.

[0050] To achieve the above-mentioned purpose, a second aspect of the present invention provides a beamforming anti-interference device in a low-orbit satellite communication system, comprising:

[0051] A communication system model building module is used to establish a low-orbit satellite communication system model based on a planar array antenna, and receive signals through a receiving end of the low-orbit satellite communication system; wherein the signals received by the receiving end include user signals transmitted by multiple ground nodes and interference signals generated by external interference sources;

[0052] A signal estimation module, used to estimate the arrival angle of user signals and interference signals using a multiple signal classification algorithm;

[0053] The problem solving module is used to suppress the interference between users and external interference by converting the SINR maximization problem into a power minimization problem under the arrival angle gain constraint to solve the beamforming matrix.

[0054] The beamforming anti-interference method and system in the low-orbit satellite communication system of the embodiment of the present invention aims at the complex interference environment faced by the uplink of the low-orbit satellite communication system, and designs a beamforming anti-interference scheme based on two-dimensional arrival angle estimation in the low-orbit satellite communication system. First, the arrival angle of the signal is estimated using a two-dimensional MUSIC algorithm, and the obtained accurate signal arrival angle information is used to construct an optimization problem for constraining the receiving amplitude at a specific angle. The optimal beamforming matrix is ​​given in the form of a closed-form solution, which suppresses interference between users and external interference, realizes spatial domain filtering, and improves the communication quality of the satellite uplink.

[0055] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0057] Figure 1 is a flow chart of a beamforming anti-interference method in a low-orbit satellite communication system according to an embodiment of the present invention;

[0058] Figure 2 This is an application scenario diagram of a beamforming anti-interference method based on a planar array antenna in a satellite communication uplink according to an embodiment of the present invention;

[0059] Figure 3 is a schematic diagram of the relationship between the system spectrum efficiency and the input signal-to-noise ratio when the number of receiving antennas is 100 and the number of snapshots is 1000 according to an embodiment of the present invention and the ratio of interference power to signal power is 1;

[0060] Figure 4 4 is a schematic diagram of the structure of a beamforming anti-interference device in a low-orbit satellite communication system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0062] 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.

[0063] The following describes a beamforming anti-interference method in a low-orbit satellite communication system proposed according to an embodiment of the present invention with reference to the accompanying drawings.

[0064] Figure 1 is a flow chart of a beamforming anti-interference method in a low-orbit satellite communication system according to an embodiment of the present invention. Figure 2 This is an application scenario diagram of the present invention, combined with Figure 2 The method of the present invention is as follows Figure 1 As shown, including:

[0065] S1, establish a low-orbit satellite communication system model based on a planar array antenna, and receive signals through a receiving end of the low-orbit satellite communication system; wherein the signals received by the receiving end include user signals transmitted by multiple ground nodes and interference signals generated by external interference sources.

[0066] It is understandable that the present invention establishes a low-orbit satellite communication system model based on a planar array antenna. The signals received by the receiving end of the communication system include user signals transmitted by multiple ground nodes and interference signals generated by external interference sources.

[0067] Specifically, consider the low-orbit satellite communication uplink, the transmitter contains K single-antenna nodes, and the receiver uses a uniform linear array antenna with N = N array elements. x ×N y , where N x and N y Represents the number of antenna array elements in the x-axis and y-axis directions respectively. There are M interference sources. The noise is additive noise independent of each channel. The receiving end estimates the channel state information of the node based on the pilot information sent by K user nodes. The signal received by the receiving end array antenna is:

[0068]

[0069] Among them, s k (t) is the original signal sent by the kth (k=1,2,…,K)th node, d m (t) is the mth (m=1,2,…,M) interference signal, h k and g m are the channel vectors of the kth user signal and the mth interference signal, n(t)∈C N×1 is the noise vector. For user node k, the receiving end processes the beamforming vector, and its array antenna output signal is expressed as:

[0070]

[0071] in, Indicates the request for w kThe conjugate transpose of k ∈C N×1 represents the beamforming vector for receiving the kth node signal, W = [w1, w2, ..., w K ] represents the beamforming matrix for receiving K node signals.

[0072] The user can eliminate inter-symbol interference and inter-carrier interference by selecting appropriate parameters based on channel transmission delay and Doppler characteristics. The channel vector can be expressed as:

[0073] h k =α k ·v k ,

[0074] Among them, α k is the channel gain, v k is the steering vector of the uniform planar array, which can be expressed as:

[0075]

[0076] in, represents the Kronecker product, and represent the arrival angle of the kth user signal in the x-axis direction and the y-axis direction respectively. and They are:

[0077]

[0078] in,[·] T In the present invention, the suppression of co-channel interference signals by beamforming is mainly considered, so the interference signal channel vector can be expressed in a similar form:

[0079] g m =α m ·v m

[0080] S2, using a multiple signal classification algorithm to estimate the arrival angle of the user signal and the interference signal.

[0081] It can be understood that the present invention uses a Multiple Signal Classification (MUSIC) algorithm to estimate the angle-of-arrival (AOA) of the signal.

[0082] Specifically, through step S1, a uniform planar array receiving signal model is obtained, and then the covariance matrix of the received signal is calculated.

[0083]

[0084] Wherein, T is the number of snapshots of received data, and X=[x(1), x(2), …, x(T)] is the sampling value of signal x(t).

[0085] The signal AOA is estimated using the two-dimensional MUSIC algorithm. Perform eigenvalue decomposition,

[0086]

[0087] Among them, Λ S and Λ N are the signal power diagonal matrix and the noise power diagonal matrix respectively, U S and U N are the signal subspace and the noise subspace respectively, [·] H represents the conjugate transpose operation. The two-dimensional AOA spectrum function of the signal can be expressed as:

[0088]

[0089] Perform a two-dimensional spectrum peak search on the above formula to obtain the steering vector set corresponding to the signal AOA, Using known user signal channel state information Find the vector in the set with The minimum of the square of the l2 norm of the difference,

[0090]

[0091] The steering vectors of the K user signals are identified in turn, and the remaining M vectors are the steering vectors corresponding to the interference signal AOA. The steering vectors of the user signals and the interference signals are arranged in sequence to obtain the received signal steering vector matrix:

[0092]

[0093] S3, by converting the SINR maximization problem into a power minimization problem under the arrival angle gain constraint, the inter-user interference and external interference are suppressed to solve the beamforming matrix.

[0094] Specifically, according to the present invention, through the above steps S1 and S2, the spectrum efficiency of the low-orbit satellite communication system can be expressed as:

[0095]

[0096] Among them, SINR kis the signal-to-interference-plus-noise ratio (Signal-To-Interference-Plus-NoiseRatio) of the kth user. Since the user signal, interference signal and noise are independent of each other, SINR k It can be expressed as:

[0097]

[0098] in, and Represent the power of user signal, interference signal and noise respectively. Solving the optimal beamforming vector can be expressed as maximizing SINR problem:

[0099]

[0100] By substituting into the channel formula, the maximum power ratio is transformed into the problem of minimizing the power under the signal AOA constraint:

[0101]

[0102] Among them, a k =[0…ξ k …0] T is the constraint vector, ξ k As the amplitude constraint, the solution of the above optimization problem can be obtained by the Lagrange multiplier method:

[0103]

[0104] Where W = [w1 w2 ... w K ],A=[a1 a2...a K ]In order to ensure the distortion-free output of the signal, the amplitude constraint is set to is the statistical channel gain of the kth user signal, which can be obtained through the channel state information.

[0105] Through the above steps, a beamforming matrix for suppressing external interference is obtained, thereby suppressing interference between users and external interference. Figure 3 The relationship between the system spectrum efficiency and the input signal-to-noise ratio is shown when the number of receiving antennas is 100, the number of snapshots is 1000, and the interference-to-signal ratio (the ratio of interference power to signal power) is 1. The algorithm with known interference and noise covariance matrices is regarded as the optimal solution. The simulation results show that the beamforming method proposed in the present invention can effectively suppress interference. A beamforming anti-interference method based on two-dimensional arrival angle estimation in a low-orbit satellite communication system can have higher spectrum efficiency without knowing the interference and noise covariance matrices.

[0106] The beamforming anti-interference method in a low-orbit satellite communication system according to an embodiment of the present invention can meet the multi-node communication requirements of the satellite communication system, realize anti-interference of the satellite receiving end, and improve the capacity of the satellite communication system.

[0107] In order to implement the above embodiment, Figure 4 As shown, this embodiment also provides a beamforming anti-interference device 10 in a low-orbit satellite communication system, including:

[0108] The communication system model building module 100 is used to establish a low-orbit satellite communication system model based on a planar array antenna, and receive signals through a receiving end of the low-orbit satellite communication system; wherein the signals received by the receiving end include user signals transmitted by multiple ground nodes and interference signals generated by external interference sources;

[0109] The signal estimation module 200 is used to estimate the arrival angle of the user signal and the interference signal using a multiple signal classification algorithm;

[0110] The problem solving module 300 is used to suppress the inter-user interference and external interference by converting the SINR maximization problem into a power minimization problem under the arrival angle gain constraint to solve the beamforming matrix.

[0111] In one embodiment of the present invention, the communication system model building module 100 is further used for:

[0112] Based on the low-orbit satellite communication uplink, the transmitter contains K single antenna nodes, and the receiver uses a uniform linear array antenna with the number of array elements being N = N. x ×N y , where N x and N y Represents the number of antenna array elements in the x-axis and y-axis directions respectively. There are M interference sources. The noise is additive noise independent of each channel. The receiving end estimates the channel state information of the node based on the pilot information sent by K user nodes. The signal received by the receiving end array antenna is:

[0113]

[0114] Among them, s k (t) is the original signal sent by the kth (k=1,2,…,K)th node, d m (t) is the mth (m=1,2,…,M) interference signal, h k and g m are the channel vectors of the kth user signal and the mth interference signal, n(t)∈C N×1 is the noise vector; for user node k, the receiving end processes the beamforming vector, and its array antenna output signal is expressed as:

[0115]

[0116] in, Indicates the request for w k The conjugate transpose of k ∈C N×1 represents the beamforming vector for receiving the kth node signal, W = [w1, w2, ..., w K ] represents the beamforming matrix for receiving K node signals;

[0117] The channel vector is expressed as:

[0118] h k =α k ·v k ,

[0119] Among them, α k is the channel gain, v k is the steering vector of the uniform planar array, expressed as:

[0120]

[0121] in, represents the Kronecker product, and represent the arrival angles of the kth user signal in the x-axis direction and the y-axis direction respectively; and They are:

[0122]

[0123] in,[·] T represents the transpose operation; the interference signal channel vector is expressed in a similar form:

[0124] g m =α m ·v m .

[0125] In one embodiment of the present invention, the signal estimation module 200 is further configured to:

[0126] Based on the obtained uniform planar array receiving signal model, the covariance matrix of the received signal is calculated:

[0127]

[0128] Where T is the number of snapshots of received data, X = [x(1), x(2), ..., x(T)] is the sample value of signal x(t);

[0129] The signal AOA is estimated using the two-dimensional MUSIC algorithm. Perform eigenvalue decomposition:

[0130]

[0131] Among them, Λ S and Λ N are the signal power diagonal matrix and the noise power diagonal matrix respectively, U S and U N are the signal subspace and the noise subspace respectively, [·] H represents the conjugate transpose operation; the two-dimensional AOA spectrum function of the signal is expressed as:

[0132]

[0133] Perform a two-dimensional spectrum peak search on the above formula to obtain the steering vector set corresponding to the signal AOA, Using known user signal channel state information Find the vector in the set with The minimum value of the square of the l2 norm of the difference:

[0134]

[0135] The steering vectors of the K user signals are identified in turn, and the remaining M vectors are the steering vectors corresponding to the interference signal AOA. The steering vectors of the user signals and the interference signals are arranged in sequence to obtain the received signal steering vector matrix:

[0136]

[0137] In one embodiment of the present invention, the problem solving module 300 is further used to:

[0138] The spectrum efficiency of the low-orbit satellite communication system is expressed as:

[0139]

[0140] Among them, SINR k is the signal-to-interference-and-noise ratio (SINR) of the kth user. k It is expressed as:

[0141]

[0142] in, and Represent the power of user signal, interference signal and noise respectively; solving the optimal beamforming vector can be expressed as maximizing SINR problem:

[0143]

[0144] By substituting into the channel formula, the maximum power ratio is transformed into the problem of minimizing the power under the signal AOA constraint:

[0145]

[0146] Among them, a k =[0...ξ k ...0] T is the constraint vector, ξ k As the amplitude constraint, the solution of the above optimization problem is obtained by Lagrange multiplier method:

[0147]

[0148] Where W = [w1 w2 ... w K ],A=[a1 a2...a K ], in order to ensure the distortion-free output of the signal, the amplitude constraint is set to is the statistical channel gain of the kth user signal.

[0149] The beamforming anti-interference device in the low-orbit satellite communication system according to the embodiment of the present invention can meet the multi-node communication requirements of the satellite communication system, realize anti-interference of the satellite receiving end, and improve the capacity of the satellite communication system.

[0150] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0151] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

Claims

1. A beamforming anti-interference method in a low-orbit satellite communication system, characterized in that: include: A low-orbit satellite communication system model based on a planar array antenna is established, and signals are received through a receiving end of the low-orbit satellite communication system; wherein the signals received by the receiving end include user signals transmitted by multiple ground nodes and interference signals generated by external interference sources; The arrival angles of user and interference signals are estimated using a multiple signal classification algorithm; By converting the SINR maximization problem into a power minimization problem under the arrival angle gain constraint, the inter-user interference and external interference are suppressed to obtain the beamforming matrix.

2. The method according to claim 1, characterized in that Establish a low-orbit satellite communication system model based on planar array antennas, and receive signals through the receiving end of the low-orbit satellite communication system, including: Based on the low-orbit satellite communication uplink, the transmitter contains K single antenna nodes, and the receiver uses a uniform linear array antenna with the number of array elements being N = N. x ×N y , where N x and N y Represents the number of antenna array elements in the x-axis and y-axis directions respectively. There are M interference sources. The noise is additive noise independent of each channel. The receiving end estimates the channel state information of the node based on the pilot information sent by K user nodes. The signal received by the receiving end array antenna is: Among them, s k (t) is the original signal sent by the kth (k=1,2,...,K)th node, d m (t) is the mth (m=1,2,...,M) interference signal, h k and g m are the channel vectors of the kth user signal and the mth interference signal, n(t)∈C N×1 is the noise vector; for user node k, the receiving end processes the beamforming vector, and its array antenna output signal is expressed as: in, Indicates the request for w k The conjugate transpose of k ∈C N×1 represents the beamforming vector for receiving the kth node signal, W = [w1, w2, ..., w K ] represents the beamforming matrix for receiving K node signals; The channel vector is expressed as: h k =a k ·v k , Among them, α k is the channel gain, v k is the steering vector of the uniform planar array, expressed as: in, represents the Kronecker product, and represent the arrival angles of the kth user signal in the x-axis direction and the y-axis direction respectively; and They are: in,[·] T represents the transpose operation; the interference signal channel vector is expressed in a similar form: g m =a m ·v m 。 3. The method according to claim 1, characterized in that: The arrival angles of user and interference signals are estimated using a multiple signal classification algorithm, including: Based on the obtained uniform planar array receiving signal model, the covariance matrix of the received signal is calculated: Where T is the number of snapshots of received data, X = [x(1), x(2), ..., x(T)] is the sample value of signal x(t); The signal AOA is estimated using the two-dimensional MUSIC algorithm. Perform eigenvalue decomposition: Among them, Λ S and Λ N are the signal power diagonal matrix and the noise power diagonal matrix respectively, U S and U N are the signal subspace and the noise subspace respectively, [·] H represents the conjugate transpose operation; the two-dimensional AOA spectrum function of the signal is expressed as: Perform a two-dimensional spectrum peak search on the above formula to obtain the steering vector set corresponding to the signal AOA, Using known user signal channel state information Find the vector in the set with The minimum value of the square of the l2 norm of the difference: The steering vectors of the K user signals are identified in turn, and the remaining M vectors are the steering vectors corresponding to the interference signal AOA. The steering vectors of the user signals and the interference signals are arranged in sequence to obtain the received signal steering vector matrix:

4. The method according to claim 1, characterized in that: By converting the SINR maximization problem into a power minimization problem under the arrival angle gain constraint, the inter-user interference and external interference are suppressed to obtain the beamforming matrix, including: The spectrum efficiency of the low-orbit satellite communication system is expressed as: Among them, SINR k is the signal-to-interference-and-noise ratio (SINR) of the kth user. k It is expressed as: in, and Represent the power of user signal, interference signal and noise respectively; solving the optimal beamforming vector can be expressed as maximizing SINR problem: By substituting into the channel formula, the maximum power ratio is transformed into the problem of minimizing the power under the signal AOA constraint: Among them, a k =[0...ξ k ...0] T is the constraint vector, ξ k As the amplitude constraint, the solution of the above optimization problem is obtained by Lagrange multiplier method: Where W = [w1 w2 ... w K ],A=[a1 a2...a K ], in order to ensure the distortion-free output of the signal, the amplitude constraint is set to is the statistical channel gain of the kth user signal.

5. A beamforming anti-interference device in a low-orbit satellite communication system, characterized in that: include: A communication system model building module is used to establish a low-orbit satellite communication system model based on a planar array antenna, and receive signals through a receiving end of the low-orbit satellite communication system; wherein the signals received by the receiving end include user signals transmitted by multiple ground nodes and interference signals generated by external interference sources; A signal estimation module, used to estimate the arrival angle of user signals and interference signals using a multiple signal classification algorithm; The problem solving module is used to suppress the interference between users and external interference by converting the SINR maximization problem into a power minimization problem under the arrival angle gain constraint to solve the beamforming matrix.

6. The device according to claim 5, characterized in that The communication system model building module is also used for: Based on the low-orbit satellite communication uplink, the transmitter contains K single antenna nodes, and the receiver uses a uniform linear array antenna with the number of array elements being N = N. x ×N y , where N x and N y Represents the number of antenna array elements in the x-axis and y-axis directions respectively. There are M interference sources. The noise is additive noise independent of each channel. The receiving end estimates the channel state information of the node based on the pilot information sent by K user nodes. The signal received by the receiving end array antenna is: where s k (t) is the original signal sent by the kth (k=1,2,...,K)th node, d m (t) is the mth (m=1,2,...,M) interference signal, h k and g m are the channel vectors of the kth user signal and the mth interference signal, n(t)∈C N×1 is the noise vector; for user node k, the receiving end processes the beamforming vector, and its array antenna output signal is expressed as: in, Indicates the request for w k The conjugate transpose of k ∈C N×1 represents the beamforming vector for receiving the kth node signal, W = [w1, w2, ..., w K ] represents the beamforming matrix for receiving K node signals; The channel vector is expressed as: h k =a k ·v k , Among them, α k is the channel gain, v k is the steering vector of the uniform planar array, expressed as: in, represents the Kronecker product, and represent the arrival angles of the kth user signal in the x-axis direction and the y-axis direction respectively; and They are: in,[·] T represents the transpose operation; the interference signal channel vector is expressed in a similar form: g m =a m ·v m 。 7. The device according to claim 5, characterized in that The signal estimation module is further used for: Based on the obtained uniform planar array receiving signal model, the covariance matrix of the received signal is calculated: Where T is the number of snapshots of received data, X = [x(1), x(2), ..., x(T)] is the sample value of signal x(t); The signal AOA is estimated using the two-dimensional MUSIC algorithm. Perform eigenvalue decomposition: Among them, Λ S and Λ N are the signal power diagonal matrix and the noise power diagonal matrix respectively, U S and U N are the signal subspace and the noise subspace respectively, [·] H represents the conjugate transpose operation; the two-dimensional AOA spectrum function of the signal is expressed as: Perform a two-dimensional spectrum peak search on the above formula to obtain the steering vector set corresponding to the signal AOA, Using known user signal channel state information Find the vector in the set with The minimum value of the square of the l2 norm of the difference: The steering vectors of the K user signals are identified in turn, and the remaining M vectors are the steering vectors corresponding to the interference signal AOA. The steering vectors of the user signals and the interference signals are arranged in sequence to obtain the received signal steering vector matrix:

8. The device according to claim 5, characterized in that The problem solving module is also used for: The spectrum efficiency of the low-orbit satellite communication system is expressed as: Among them, SINR k is the signal-to-interference-and-noise ratio (SINR) of the kth user. k It is expressed as: in, and Represent the power of user signal, interference signal and noise respectively; solving the optimal beamforming vector can be expressed as maximizing SINR problem: By substituting into the channel formula, the maximum power ratio is transformed into the problem of minimizing the power under the signal AOA constraint: Among them, a k =[0...ξ k ...0] T is the constraint vector, ξ k As the amplitude constraint, the solution of the above optimization problem is obtained by Lagrange multiplier method: Where W = [w1 w2 ... w K ],A=[a1 a2...a K ], in order to ensure the distortion-free output of the signal, the amplitude constraint is set to is the statistical channel gain of the kth user signal.

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