CN117527043A - Satellite communication anti-interference method and device based on side lobe suppression - Google Patents

Abstract

The application relates to a satellite communication anti-interference method and device based on side lobe suppression. The method comprises the following steps: calculating the ideal main lobe gain of the wave beam; acquiring the energy of the echo signals of the receiving environment pointed by each wave beam; according to the maximum value and average noise in the received signal energy in each direction, calculating to obtain a dry-to-noise ratio; determining a side lobe level according to the dry-to-noise ratio, and setting constraint conditions according to main lobe gain, the side lobe level and the amplitude of the weight vector value, which are expected to be pointed by the beam; setting an objective function for the main lobe gain to which the beam is expected to be directed; the method comprises the steps of establishing a main lobe gain optimization model, solving the main lobe gain optimization model by using an alternating direction multiplier method, calculating the beam main lobe gain optimization loss, carrying out optimization iteration on the main lobe gain optimization model according to the beam main lobe gain optimization loss to obtain a one-dimensional linear array optimal weight vector, and obtaining a two-dimensional rectangular plane array optimal weight vector by using a Cronecker product. The method can improve the anti-interference capability of satellite communication.

Description

Satellite communication anti-interference method and device based on side lobe suppression

Technical Field

The application relates to the technical field of satellite communication, in particular to a satellite communication anti-interference method and device based on side lobe suppression.

Background

The satellite is located in a complex electromagnetic environment and faces various electromagnetic interferences, meanwhile, the distance between the satellite and a receiver is long, and the level of the satellite signal when reaching the receiver is weak, so that the satellite signal is very important to inhibit interference signals in space and accurately receive the satellite signal. The suppression side lobe can effectively reduce the probability that an interference signal is misjudged as a useful satellite signal, and the conventional side lobe suppression method is to carry out windowing on a signal after time domain or fast Fourier transform processing. Common window functions include rectangular window, triangular window, hanning window, hamming window, chebyshev window and the like, and the window functions can depress side lobes to a certain extent, but also cause main lobe broadening and main lobe passive gain reduction to different extents, so that a receiver cannot accurately receive and identify useful satellite signals with weak level. The side lobe suppression technology realized through windowing ignores the key effect of the passive gain of the main lobe on the identification of weak and small signals, is not suitable for a satellite communication system with complex and various electromagnetic interference and weak satellite signal level, and has weak anti-interference capability.

Disclosure of Invention

Accordingly, in order to solve the above-mentioned problems, it is necessary to provide a satellite communication anti-interference method and device based on side lobe suppression, which can improve the anti-interference capability of satellite communication.

A satellite communication anti-interference method based on side lobe suppression, the method comprising:

step S100: inputting beam related parameters; the beam related parameters comprise beam expected pointing, beam main lobe width, beam discrete angle interval, beam angle lower bound, beam angle upper bound, beam forming parameters, airspace to be covered, number of array elements, array element spacing, beam width factor, wavelength, side lobe level lower bound, side lobe level upper bound, first main lobe gain optimization loss threshold and corresponding first side lobe level optimization step, second main lobe gain optimization loss threshold and corresponding second side lobe level optimization step, first side lobe and main lobe interval and initial weighting vector; calculating according to the beam related parameters to obtain the ideal main lobe gain of the beam, the discrete angle set of the main lobe of the beam and the discrete angle set of the side lobe of the beam;

step S200: scanning a space domain to be covered, and dividing the space domain with a pre-calculated scanning beam width as an interval to acquire the energy of each directional receiving environment echo signal of the beam; taking the average value of the received environment echo signal energy pointed by each wave beam as average noise, and calculating according to the maximum value in the received signal energy in each direction and the average noise to obtain a dry-to-noise ratio; determining the side lobe level according to a certain criterion and a dry-to-noise ratio;

Step S300: setting the main lobe gain to which the beam is expected to be directed as an objective function of main lobe gain optimization;

step S400: setting constraint conditions for main lobe gain optimization according to the main lobe gain, the side lobe level and the amplitude of the weight vector value of the beam expected to point; establishing a main lobe gain optimization model according to constraint conditions and an objective function, and solving the main lobe gain optimization model by using an alternate direction multiplier method to obtain main lobe gain of a beam in an expected direction;

step S500: calculating to obtain the main lobe gain optimization loss of the beam by utilizing the ideal main lobe gain of the beam and the main lobe gain of the beam in the expected direction;

step S600: judging the main lobe gain optimization loss of the beam, if the main lobe gain optimization loss of the beam is larger than a first main lobe gain optimization loss threshold, reducing the side lobe level by a first side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam, if the main lobe gain optimization loss of the beam is not larger than the first main lobe gain optimization loss threshold, judging whether the main lobe gain optimization loss of the beam is larger than a second main lobe gain optimization loss threshold, if the main lobe gain optimization loss of the beam is larger than the second main lobe gain optimization loss threshold, reducing the side lobe level by a second side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam until the main lobe gain optimization loss of the beam is not larger than the second main lobe gain optimization loss threshold, ending the optimization process and obtaining a one-dimensional linear array optimal weight vector;

Step S700: solving the corresponding one-dimensional linear array optimal weight vector by using two different array element numbers input through the step S100 through the step S600, and performing Cronecker product calculation on the two one-dimensional linear array optimal weight vectors to obtain the optimal weight vector of the two-dimensional rectangular planar array; and performing anti-interference processing according to the optimal weight vector.

A satellite communication anti-interference device based on side lobe suppression, the device comprising:

the beam related parameter calculation module is used for inputting beam related parameters; the beam related parameters comprise beam expected pointing, beam main lobe width, beam discrete angle interval, beam angle lower bound, beam angle upper bound, beam forming parameters, airspace to be covered, number of array elements, array element spacing, beam width factor, wavelength, side lobe level lower bound, side lobe level upper bound, first main lobe gain optimization loss threshold and corresponding first side lobe level optimization step, second main lobe gain optimization loss threshold and corresponding second side lobe level optimization step, first side lobe and main lobe interval and initial weighting vector; calculating according to the beam related parameters to obtain the ideal main lobe gain of the beam, the discrete angle set of the main lobe of the beam and the discrete angle set of the side lobe of the beam;

The side lobe level determining module is used for scanning the airspace to be covered and dividing the airspace by taking the pre-calculated scanning beam width as an interval to acquire the energy of the received environment echo signals pointed by each beam; taking the average value of the received environment echo signal energy pointed by each wave beam as average noise, and calculating according to the maximum value in the received signal energy in each direction and the average noise to obtain a dry-to-noise ratio; determining the side lobe level according to a certain criterion and a dry-to-noise ratio;

the main lobe gain calculation module is used for setting the main lobe gain of the beam expected direction as an objective function of main lobe gain optimization; setting constraint conditions for main lobe gain optimization according to the main lobe gain, the side lobe level and the amplitude of the weight vector value of the beam expected to point; establishing a main lobe gain optimization model according to constraint conditions and an objective function, and solving the main lobe gain optimization model by using an alternate direction multiplier method to obtain main lobe gain of a beam in an expected direction;

the one-dimensional linear array optimal weight vector calculation module is used for calculating the main lobe gain of the beam in the desired direction by utilizing the ideal main lobe gain of the beam and the main lobe gain of the beam to obtain the main lobe gain optimization loss of the beam; judging the main lobe gain optimization loss of the beam, if the main lobe gain optimization loss of the beam is larger than a first main lobe gain optimization loss threshold, reducing the side lobe level by a first side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam, if the main lobe gain optimization loss of the beam is not larger than the first main lobe gain optimization loss threshold, judging whether the main lobe gain optimization loss of the beam is larger than a second main lobe gain optimization loss threshold, if the main lobe gain optimization loss of the beam is larger than the second main lobe gain optimization loss threshold, reducing the side lobe level by a second side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam until the main lobe gain optimization loss of the beam is not larger than the second main lobe gain optimization loss threshold, ending the optimization process and obtaining a one-dimensional linear array optimal weight vector;

The anti-interference processing module is used for respectively solving the corresponding one-dimensional linear array optimal weight vectors and carrying out Cronecker product calculation on the two one-dimensional linear array optimal weight vectors to obtain the optimal weight vector of the two-dimensional rectangular planar array; and performing anti-interference processing according to the optimal weight vector.

Compared with the prior art, the application has the remarkable advantages that: 1) The satellite communication anti-interference technology can be realized through side lobe inhibition in a complex electromagnetic environment; 2) The method takes the main lobe gain of the beam expected to point as an objective function; the main lobe gain, the side lobe level and the amplitude of the weight vector value which are expected to be pointed by the wave beam are taken as constraints, the non-convex optimization problem is established, and the side lobe suppression effect is ensured while the maximization of the main lobe gain which is expected to be pointed by the wave beam is realized by adopting an alternate direction multiplication method, so that the accurate receiving and identifying of the microsatellite signals by the receiver are realized; 3) According to the method, the optimal weight vector is solved in an iteration mode through an optimization function and an alternate direction multiplier method, meanwhile, a wave beam main lobe gain optimization loss judgment mark is introduced in the solving process, and the main lobe gain optimization effect is improved; 4) The optimal weight vector solving complexity of the two-dimensional rectangular planar array is too high, the optimal weight vector can not be solved in linear time, and the weight vector solving converted into the linear array can obtain a numerical solution in polynomial time.

Drawings

Fig. 1 is a schematic flow chart of a satellite communication anti-interference method based on side lobe suppression in an embodiment;

fig. 2 is a graph showing the algorithm side lobe suppression effect of a one-dimensional linear array with the number of array elements k=64 in an embodiment;

fig. 3 is a comparison chart of suppression effects of side lobes of a one-dimensional linear array algorithm with the number of array elements k=48 in one embodiment;

FIG. 4 is an ideal beam pattern for a pitch 0 direction of a two-dimensional rectangular planar array with an array element count of 64×48 in one embodiment;

FIG. 5 is a beam pattern of an array element number of 64×48 two-dimensional rectangular planar array optimized by an ADMM algorithm in a pitching 0 ° direction according to an embodiment;

fig. 6 is a block diagram of a satellite communication anti-interference device based on side lobe suppression in an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

In one embodiment, as shown in fig. 1, a satellite communication anti-interference method based on side lobe suppression is provided, which includes the following steps:

Step S100: inputting beam related parameters; the beam related parameters comprise beam expected pointing, beam main lobe width, beam discrete angle interval, beam angle lower bound, beam angle upper bound, beam forming parameters, airspace to be covered, number of array elements, array element spacing, beam width factor, wavelength, side lobe level lower bound, side lobe level upper bound, first main lobe gain optimization loss threshold and corresponding first side lobe level optimization step, second main lobe gain optimization loss threshold and corresponding second side lobe level optimization step, first side lobe and main lobe interval and initial weighting vector; and calculating according to the beam related parameters to obtain the ideal main lobe gain of the beam, the discrete angle set of the main lobe of the beam and the discrete angle set of the side lobe of the beam.

Input beam desired pointingBeam main lobe width +.>Beam discrete angle spacing +.>Lower beam angle boundBeam angle upper bound->Beamforming parameter alpha, airspace theta to be covered, array element number K, array element distance d, beamwidth factor kappa, wavelength lambda and side lobe level lower boundary gamma _l Upper boundary Γ of side lobe level _u First main lobe gain optimization loss threshold +.>Second main lobe gain optimization loss threshold +.>Corresponding flap level optimization step ΔΓ ₁ 、ΔΓ ₂ First side lobe and main lobe spacingInitial weight vector +.>Equal parameters, calculating the ideal main lobe gain M of the wave beam ₀ Discrete angle set of beam main lobe>Beam side lobe discrete angle set +.>

Step S200: scanning a space domain to be covered, and dividing the space domain with a pre-calculated scanning beam width as an interval to acquire the energy of each directional receiving environment echo signal of the beam; taking the average value of the received environment echo signal energy pointed by each wave beam as average noise, and calculating according to the maximum value in the received signal energy in each direction and the average noise to obtain a dry-to-noise ratio; the side lobe level is determined according to certain criteria and the dry-to-noise ratio.

Step S300: the main lobe gain to which the beam is expected to be directed is set as an objective function of the main lobe gain optimization.

Step S400: setting constraint conditions for main lobe gain optimization according to the main lobe gain, the side lobe level and the amplitude of the weight vector value of the beam expected to point; and establishing a main lobe gain optimization model according to the constraint condition and the objective function, and solving the main lobe gain optimization model by using an alternate direction multiplier method to obtain the main lobe gain of the beam in the expected direction.

Step S401, main lobe gain M to which beam is desired to be directed _r Is a constraint expression of (2):

wherein M is _r The main lobe gain for which the beam is desired to be directed,is the guiding vector of the main lobe direction, K is the number of array elements input in the step S100, < ->Representing transpose, sin (·) being a sine function, λ being the wavelength input in step S100, < ->For the beam main lobe discrete angle set calculated in step S100, m=12, …, M, M is the set +.>Number of medium angles, w= [ w ] ₁ ,w,…,w _K ] ^T Alpha is the main lobe beam forming parameter input in step S100 and is the weighting vector of the antenna array, (. Cndot. ^H ) Representing conjugate transpose, |·| representing modulo the complex number, (·) ² The representation is squared;

step S402, the side lobe level Γ is a constraint expression:

wherein M is _r The main lobe gain for which the beam is desired to be directed,for the guiding vector of the side lobe direction, K is the number of array elements input in the step S100, and ++>(·) ^T Representing taking the transpose, lambda is the wavelength input in step S100,/>For the beam side lobe discrete angle set calculated in step S100, s=1, 2, …, S is the set +.>Number of medium angles, w= [ w ] ₁ ,w,…,w _K ] ^T As a weighting vector for the antenna array Γ is the side lobe level, ( ^H ) Representing conjugate transpose, || represents modulo the complex number, (·) ² The representation is squared;

step S403, weight vector value w _k Is a constraint expression:

|w _k |＝1,k＝1,2,…,K

Wherein w is _k The weight vector value is the value of the kth element in the antenna array weighting vector w, and I/I represents modulo the complex number; k is the number of array elements input in the step S100;

step S404, a non-convex optimization function of the satellite communication anti-interference method based on side lobe suppression is as follows:

|w _k |＝1,k＝1,2,…,K

wherein,as an objective function, M _r The main lobe gain for which the beam is desired to be directed,is the guiding vector of the main lobe direction, +.>Representing transpose, sin (·) being a sine function, λ being the wavelength input in step S100, < ->For the beam main lobe discrete angle set calculated in step S100, m=1, 2, …, M is the set +.>Number of medium angles, w= [ w ] ₁ ,w,…,w _K ] ^T K is the number of array elements input in the step S100, alpha is the main lobe beam forming parameter input in the step S100,is the guiding vector of the side lobe direction, +.>For the beam side lobe discrete angle set calculated in step S100, s=1, 2, …, S is the set +.>The number of medium angles, Γ is the side lobe level, w _k Is the weight vector value, which is the value of the kth element in the antenna array weight vector w, (. Cndot. ^H ) Representing conjugate transpose, |·| representing modulo the complex number, (·) ² The representation is squared;

step S405, solving the optimization problem by adopting ADMM to obtain the current optimal weighting vector w ^m The specific steps to maximize the main lobe gain at which the beam is expected to be directed are as follows:

the introduction of auxiliary variables simplifies the problem; defining a partial augmented lagrangian multiplier function according to the ADMM principle; decomposing the optimization problem into 2 sub-optimization problems, converting the problem into an iteration solution problem through alternate optimization until the iteration stop condition is met, and obtaining a current optimal weighting vector w under the current side lobe level constraint ^m 。

The method takes the main lobe gain of the beam expected to point as an objective function; the main lobe gain, the side lobe level and the amplitude of the weight vector value which are expected to be pointed by the wave beam are taken as constraints, the non-convex optimization problem is established, and the side lobe suppression effect is ensured while the main lobe gain which is expected to be pointed by the wave beam is maximized by adopting an alternate direction multiplication method, so that the accurate receiving and identifying of the microsatellite signals by the receiver are realized.

Step S500: and calculating the main lobe gain optimization loss of the beam by using the ideal main lobe gain of the beam and the main lobe gain of the beam in the expected direction.

Step S600: judging the main lobe gain optimization loss of the beam, if the main lobe gain optimization loss of the beam is larger than a first main lobe gain optimization loss threshold, reducing the side lobe level by a first side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam, if the main lobe gain optimization loss of the beam is not larger than the first main lobe gain optimization loss threshold, judging whether the main lobe gain optimization loss of the beam is larger than a second main lobe gain optimization loss threshold, if the main lobe gain optimization loss of the beam is larger than the second main lobe gain optimization loss threshold, reducing the side lobe level by a second side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam until the main lobe gain optimization loss of the beam is not larger than the second main lobe gain optimization loss threshold, ending the optimization process and obtaining a one-dimensional linear array optimal weight vector.

Step S601, judging whether the calculated beam main lobe gain optimization loss DeltaM is larger than the first main lobe gain optimization loss threshold input in step S100If->Judging main lobe gain M of beam expected to point _r The optimization effect is quite undesirable, and the side lobe level Γ of step S402 is reduced by the first side lobe level optimization step ΔΓ input by step S100 ₁ Main lobe gain M for subsequent desired pointing of the beam _r Optimizing, namely, entering step S404; if->Step S602 is entered to perform the next round of judgment;

step S602, judging whether the calculated main lobe gain optimization loss DeltaM is larger than the second main lobe gain optimization loss threshold input in step S100If->Judging main lobe gain M of beam expected to point _r The optimization effect is less ideal, and the side lobe level Γ in step S402 is reduced by the second side lobe level optimization step ΔΓ input in step S100 ₂ Main lobe gain M for subsequent desired pointing of the beam _r Optimizing, namely, entering step S404;

step S603, repeating steps S402 to S602 untilJudging main lobe gain M of beam expected to point _r The optimization effect of (a) meets the requirement, the optimization process is ended, and the optimal weighting vector w with ideal main lobe gain and side lobe suppression is obtained ^* 。

And calculating to obtain the main lobe gain optimization loss of the beam by utilizing the ideal main lobe gain of the beam and the main lobe gain of the beam in the expected direction, and introducing the main lobe gain optimization loss of the beam in the solving process to continuously optimize the main lobe gain optimization model, thereby improving the main lobe gain optimization effect.

Step S700: solving the corresponding one-dimensional linear array optimal weight vector by using two different array element numbers input through the step S100 through the step S600, and performing Cronecker product calculation on the two one-dimensional linear array optimal weight vectors to obtain the optimal weight vector of the two-dimensional rectangular planar array; and performing anti-interference processing according to the optimal weight vector.

The optimal weight vector solving complexity of the two-dimensional rectangular planar array is too high, the optimal weight vector can not be solved in linear time, and the weight vector solving converted into the linear array can obtain a numerical solution in polynomial time.

Compared with the prior art, the application has the remarkable advantages that: 1) The satellite communication anti-interference technology can be realized through side lobe inhibition in a complex electromagnetic environment; 2) The method takes the main lobe gain of the beam expected to point as an objective function; the main lobe gain, the side lobe level and the amplitude of the weight vector value which are expected to be pointed by the wave beam are taken as constraints, the non-convex optimization problem is established, and the side lobe suppression effect is ensured while the maximization of the main lobe gain which is expected to be pointed by the wave beam is realized by adopting an alternate direction multiplication method, so that the accurate receiving and identifying of the microsatellite signals by the receiver are realized; 3) According to the method, the optimal weight vector is solved in an iteration mode through an optimization function and an alternate direction multiplier method, meanwhile, a wave beam main lobe gain optimization loss judgment mark is introduced in the solving process, and the main lobe gain optimization effect is improved; 4) The optimal weight vector solving complexity of the two-dimensional rectangular planar array is too high, the optimal weight vector can not be solved in linear time, and the weight vector solving converted into the linear array can obtain a numerical solution in polynomial time.

In one embodiment, the calculating the ideal main lobe gain, the discrete angle set of the main lobe and the discrete angle set of the side lobe according to the related parameters of the beam includes:

calculating the ideal main lobe gain of the beam according to the expected direction of the beam to be

M ₀ ＝20lgK

Wherein K is the number of input array elements, lgK =log ₁₀ K represents a base 10 logarithm of K;

calculating according to the expected beam pointing direction and the beam discrete angle interval to obtain a beam main lobe discrete angle set as

Wherein,for the beam main lobe discrete angle set, +.>For beam desired pointing, +.>For the beam to be at discrete angular intervals,for the maximum discrete interval number of the beam main lobe, floor (·) is a downward rounding function, ++>For the beam main lobe width, m=2m' +1 is the number of angles of the beam main lobe discrete angle set;

calculating to obtain a beam side lobe discrete angle set according to the beam angle lower boundary, the beam angle upper boundary, the beam discrete angle interval and the first side lobe and main lobe interval

Wherein,for the beam side lobe discrete angle set, +.>Is the lower boundary of beam angle->For the upper beam angle limit, +.>For beam discrete angle spacing +.>For the first side lobe to be spaced from the main lobe, < > about->For the desired pointing of the beam, S is the number of angles of the discrete angle set of the beam side lobes.

In one embodiment, the pre-computed scanned beamwidth is

Wherein, kappa is a beam width factor, lambda is a wavelength, K is the number of array elements, d is the spacing of the array elements,for the beam to be directed, cos (·) is a cosine function.

In one embodiment, scanning an airspace to be covered and dividing the airspace with a pre-calculated scanning beam width as an interval to obtain energy of receiving environment echo signals pointed by each beam, including:

scanning the airspace to be covered, dividing the airspace by taking the pre-calculated scanning beam width as an interval, and using x _ij (t) represents the environmental echo signals acquired by the beam pointing to the ith horizontal azimuth and the jth pitching azimuth during airspace scanning, i=0, 1, …, I,is the number of horizontal beam pointing intervals, where floor (·) is a downward rounding function, +.>For right boundary angle of airspace to be covered, < +.>Left boundary angle of airspace to be covered; j=0, 1, …, J, < >>For the number of pitch-wise beam pointing intervals, where floor (·) is a downward rounding function,for the upper boundary angle of the airspace that needs to be covered, < +.>The lower boundary angle of the airspace to be covered; sampling T of the environmental echo signals collected in each beam pointing direction _S Time, obtain sampling signal x _ij [n]And samples the signal x _ij [n]Performing N-point fast Fourier transform processing, and calculating the signals subjected to the fast Fourier transform processing according to the Pasteur theorem to obtain the energy of each directional receiving environment echo signal of the wave beam as follows

Wherein N represents the number of points sampled, X _i,j [k]Representing the signal after the fast fourier transform processing, k representing the sampling sequence number, i representing the horizontal azimuth of beam pointing, j representing the elevation azimuth of beam pointing, |·| representing modulo the complex number, (·) ² The representation is squared.

In a specific embodiment, the spatial scanning specifically includes the steps of: dividing the space domain by taking 5 degrees as interval, and x _i,j (t) represents the direction of the beam in the ith horizontal direction (the horizontal direction beam scanning range is-90 DEG to 90 DEG, thereby obtaining the value of i)The range is as follows: i=1, 2, …, 36), the reception signal acquired at the jth pitch azimuth (pitch beam scanning range is 0 ° to 90 °, thereby obtaining the value range of j: j=1, 2, …, 18), samples the signals received at a certain moment and directed to each other to obtain a sampled signal x _i,j [n]And performing N-point fast Fourier transform to obtain a signal X after the fast Fourier transform _i,j [k]The expression is:

Where k=0, 1, …, N-1,

the signal energy pointed to by (i, j) is calculated according to the pasival theorem, i.e. the energy of the signal is equal in the time and frequency domain.

In one embodiment, taking the average value of the energy of the received environmental echo signals pointed by each beam as average noise includes:

the average value of the energy of the echo signals of the receiving environment, which are pointed by the wave beams, is taken as average noise

Wherein I is the number of horizontal beam pointing intervals, J is the number of pitching beam pointing intervals, E _ij The energy of the environment echo signals acquired when the wave beam points to the ith horizontal azimuth and the jth pitching azimuth.

In one embodiment, determining the side lobe level based on certain criteria and the dry-to-noise ratio includes:

determining the side lobe level as according to a certain criterion and the dry-to-noise ratio

Wherein INR is the dry-to-noise ratio of the environmental echo signal, Γ _l Is the lower boundary of the side lobe level Γ _u Is the upper bound of the sidelobe level.

In one embodiment, the main lobe gain optimization model is built according to constraint conditions and an objective function, and the method comprises the following steps:

establishing a main lobe gain optimization model as according to constraint conditions and an objective function

|w _k |＝1,k＝1,2,…,K

Wherein,mr is the main lobe gain at which the beam is expected to be directed, +. >Is the guiding vector of the main lobe direction, +.>(· ^T ) The representation is transposed, sin (·) is a sine function, λ is wavelength, ++>For the beam main lobe discrete angle set, m=1, 2, …, M is the set +.>Number of medium angles, w= [ w ] ₁ ,w,…,w _K ] ^T Is the weighting vector of the antenna array, K is the number of array elements, alpha is the main lobe beam forming parameter,/and/or>Is the guiding vector of the side lobe direction, +.>For the beam side lobe discrete angle set, s=1, 2, …, S is the set +.>The number of medium angles, Γ is the side lobe level, w _k Is the weight vector value, which is the value of the kth element in the antenna array weight vector w, (. Cndot. ^H ) Representing conjugate transpose, |·| representing modulo the complex number, (·) ² The representation is squared.

In a specific embodiment, step S400 further includes step S405 of solving an optimization problem by using ADMM to obtain a current optimal weighting vector w ^m The specific steps to maximize the main lobe gain at which the beam is expected to be directed are as follows:

introducing auxiliary variables The problem is simplified. Definitions-> The non-convex optimization problem of step S404 is equivalently described as:

according to the ADMM principle, a partial augmented lagrangian multiplier function is defined as:

wherein ρ > 0 is a penalty factor, is a dual variable. The problem can be converted into an iteration solution problem through alternate optimization, and the variables w and M are solved respectively _r ,h,g,δ,λ：

Wherein the superscript t represents the t-th iteration;

solving the sub-problem (3 a): definition of the definition And ignoring extraneous terms in equation (3 a), the sub-problem (3 a) can be translated into:

to simplify the problem, we define u= [ u ] _1,1 ,...,u _1,S ,u _2,1 ,...,u _2,M ] ^T ,Problem (4) can be converted into the following form:

the problem is a non-convex optimization problem, and is solved continuously by ADMM.

Solving a sub-problem (3 b): ignoring the extraneous item in (3 b), the sub-problem is equivalently:

wherein the method comprises the steps of Obviously M _r And g is equal to _s ，h _m There is a coupling relationship between them. But when M _r Optimal g when fixed _s ，h _m The method can be obtained by the following formula:

it is apparent that the solution of formula (7) is:

bringing (8) and (9) into (6) and lettingCan get only->Equivalent form of related (6):

wherein if itThen w _s =0, otherwise ω _s =1; if->W' _m =0, otherwise w' _m =1; if->W', then _m =0, otherwise w _m =1. Definition [ v ₁ ...,v _K ]，[u ₁ ,...,u _L ][ u ]' ₁ ,...,u' _L ]Respectively isAnd->An ascending sort sequence (while removing duplicate entries in the sequence). Let v ₀ ＝u ₀ ＝u' ₀ ＝0,v _K+1 ＝u _L+1 ＝u' _L+1 By pairing array [ v ] ₁ ,...,v _K ,u ₁ ,...,u _L ,u' ₁ ,...,u' _L ]Performing ascending order arrangement and removing repeated items to obtain an array [ r ] ₁ ,r ₂ ,...,r _P ]. Let r ₀ ＝0，r _P+1 ＝∞，/>Can be converted into a piecewise function:

first, the _p The functional expression of the segment is:

k ', l ' and l ' satisfyAnd +.>ObviouslyIs a quadratic function. Ensuring a by selecting the appropriate ρ _p Constant > 0, minimum->Can be inAnd obtaining the product.

Sequencing the minimum values of the P+1 sections to obtain the optimal valueExpressed as:

optimum ofAnd->By putting->Substituting (8) and (9).

The initial weighting vector is input in step S100When the iteration number T is smaller than the preset iteration number T _max Continuously update->And->Until the iteration process is terminated, the current optimal weighting vector w is output ^m ＝w ^t+1 。

In one embodiment, the calculating the beam main lobe gain optimization loss by using the beam ideal main lobe gain and the main lobe gain of the beam expected direction includes:

the main lobe gain optimization loss of the beam is calculated by utilizing the ideal main lobe gain of the beam and the main lobe gain of the beam in the expected direction

ΔM＝M ₀ -M _r

Wherein M is ₀ For the ideal main lobe gain of the beam, M _r The main lobe gain to which the beam is expected to be directed.

In one embodiment, solving the corresponding one-dimensional linear array optimal weight vector by using two different array element numbers input in step S100 through step S100 to step S600, and performing kronecker product calculation on the two one-dimensional linear array optimal weight vectors to obtain an optimal weight vector of the two-dimensional rectangular planar array, where the method comprises the following steps:

performing Cronecker product calculation on one-dimensional linear array optimal weight vectors corresponding to the number of two different array elements to obtain an optimal weight vector of a two-dimensional rectangular planar array as follows

Wherein the symbols areRepresenting Cronecker product, metropolyl>To number array element K ₁ 、K ₂ Optimal weight vector of corresponding one-dimensional linear array, +.>Representation->For K ₁ K ₂ Complex vector of row, 1 column, (·) ^T The representation takes the transpose.

In a specific embodiment, the method is used for solving the array element number K ₁ ×K ₂ The value of the optimal weighting vector of the two-dimensional rectangular planar array can be equivalent to the Cronecker product of the optimal weighting vectors of the two linear arrays, and the number K of the array elements in the step S100 is respectively K ₁ And K is equal to ₂ Solving the corresponding one-dimensional optimal weighting vector through steps S100 to S600For->Cronecker product is produced>Obtaining an optimal weighting vector of the two-dimensional rectangular planar array:

wherein the symbols areRepresenting Cronecker product, metropolyl>To number array element K ₁ 、K ₂ After the number K of array elements respectively input to the step S100, solving the obtained one-dimensional optimal weighting vector through the steps S100 to S600, and performing +.>Representation->For K ₁ K ₂ Complex vector of row, 1 column, (·) ^T The representation takes the transpose.

In one embodiment, the present embodiment optimizes a two-dimensional rectangular planar array of array elements 64×48, i.e. K ₁ ＝64、K ₂ =48. For a uniform linear array with 64 array elements, the input beam is expected to pointBeam main lobe widthBeam discrete angle spacing +.>Beam angle lower bound- >Beam angle upper bound->Beamforming parameter α= 1.0026, spatial domain required to be covered Θ=Θ ₁ The number of array elements K=64, the array element distance d=0.47 m, the beam width factor k=50.7, the wavelength lambda=0.1 m and the side lobe level lower boundary Γ _l =13 dB, upper side lobe level boundary Γ _u =20 dB, main lobe gain optimization loss threshold +.>Corresponding flap level optimization step ΔΓ ₁ ＝0.5dB、ΔΓ ₂ =0.2 dB, first side lobe is spaced +.>Initial weight vector +.>Calculating the ideal main lobe gain M of a wave beam ₀ = lgK = 36.1236dB, beam main lobe discrete angle set +.>m=1, …, M, where m=5, beam lobe discrete angle set +.>s=1, …, S, where s=1148, to scan the beam width +.>Space domain theta to be covered for interval pair ₁ Dividing right boundary angle of airspace to be covered>Left boundary angle->Beam pointing in the i-th horizontal direction +.>Upper boundary angle of airspace to be covered +.>Lower boundary angle->Beam pointing at the jth elevation>And scanning the airspace to obtain environment echo signals of all directions. Calculating the energy of each direction according to the Pasteur theorem to obtain average noiseMaximum interference energy at i=418, j=162, maximum interference energy E when the pointing is taken, i.e. the beam pointing level 10.5263 °, pitch-2.6430 ° _m = 15.3615, then dry-noise ratio +.>Since the dry-to-noise ratio is greater than the flap level lower bound Γ _l =13 dB, less than the upper side lobe level boundary Γ _u ＝20dBThe flap level Γ=16 dB may be determined according to certain criteria. Main lobe gain M with beam desired pointing _r Main lobe gain M for the desired pointing of the beam for the objective function _r Side lobe level Γ, weight vector value w _k The amplitude of (2) is a constraint, an optimization problem is established, iteration solution is carried out by adopting an alternate direction multiplier method, and fig. 2 is a comparison chart of the algorithm side lobe suppression effect of a one-dimensional linear array with the array element number of K=64. Main lobe gain M of desired pointing after optimization _r = 35.3918dB, maximum side lobe gain +.>Meet the requirement of side lobe level and the optimized loss of the main lobe gain of the wave beam>The method has good optimization effect, effectively inhibits side lobes while maximizing the gain of the one-dimensional phased array antenna, and derives the number K of array elements ₁ One-dimensional optimal weighting vector corresponding to =64 ∈>

For a uniform linear array with 48 array elements, the input beam is expected to pointBeam main lobe width +.>Beam discrete angle spacing +.>Beamforming parameter α= 1.0026, spatial domain required to be covered Θ=Θ ₁ The number of array elements K=48, the array element distance d=0.47 m, the beam width factor k=50.7, the wavelength lambda=0.1 m and the side lobe level lower boundary Γ _l =13 dB, upper side lobe level boundary Γ _u =20 dB, main lobe gain optimization loss threshold +.> Corresponding flap level optimization step ΔΓ ₁ ＝0.5dB、ΔΓ ₂ =0.2 dB, first side lobe is spaced +.>Initial weight vectorCalculating the ideal main lobe gain M of a wave beam ₀ = lgK = 33.6248dB, beam main lobe discrete angle setm=1, …, M, where m=5, beam lobe discrete angle sets=1, …, S, where s=830 to scan the beamwidthSpace domain theta to be covered for interval pair ₁ Dividing right boundary angle of airspace to be covered>Left boundary angle->Beam pointing in the ith horizontal azimuthUpper boundary angle of airspace to be covered +.>Lower boundary angleBeam pointing at the jth elevation>And scanning the airspace to obtain environment echo signals of all directions. Calculating the respective orientations according to the pasival theoremEnergy of (2) to obtain average noise +.>Maximum interference energy at i=313, j=121, maximum interference energy E when the pointing is taken, i.e. the beam pointing level 10.5263 °, pitch-2.6430 ° _m = 13.9135, then dry-noise ratio +.>Since the dry-to-noise ratio is greater than the flap level lower bound Γ _l =13 dB, less than the upper side lobe level boundary Γ _u =20 dB, the side lobe level Γ=16 dB may be determined according to certain criteria. Main lobe gain M with beam desired pointing _r Main lobe gain M for the desired pointing of the beam for the objective function _r Side lobe level Γ, weight vector value w _k The amplitude of (2) is used as constraint, an optimization problem is established, iteration solution is carried out by adopting an alternate direction multiplier method, and fig. 3 is a comparison graph of the suppression effect of the side lobes of the one-dimensional linear array algorithm with the array element number of K=48. Main lobe gain M of desired pointing after optimization _r = 32.9364dB, maximum side lobe gain +.> Meet the requirement of side lobe level and the optimized loss of the main lobe gain of the wave beam>The method has good optimization effect, can effectively inhibit side lobes while maximizing the gain of the one-dimensional phased array antenna, and can derive the number K of array elements ₂ One-dimensional optimal weighting vector corresponding to =48 ∈>

For a pair ofPerforming Cronecker product to obtain optimal weighting vector of two-dimensional rectangular planar array with array element number of 64 multiplied by 48Fig. 4 is an ideal beam pattern of pitch 0 direction of a two-dimensional rectangular planar array with array elements 64×48 in the embodiment of the present application. Fig. 5 is a beam pattern of the pitch 0 direction after the two-dimensional rectangular planar array with the array element number of 64×48 is optimized by adopting the ADMM algorithm in the embodiment of the present application. The ideal main lobe gain of the two-dimensional rectangular planar array is 69.7484dB, the maximum side lobe gain is 56.3744dB, the first side lobe level is 13.3540dB < 16dB, the main lobe gain of the two-dimensional rectangular planar array is 68.3282dB after optimization is performed by adopting an ADMM algorithm, the beam main lobe gain optimization loss is 1.4202dB < 2dB, and the optimization effect is relatively non-ideal; the maximum side lobe gain of the two-dimensional rectangular planar array after optimization by adopting an ADMM algorithm is 51.9840dB, the first side lobe level is 16.3442dB & gt16 dB, and the requirement of the side lobe level is met; obtaining the optimal weighting vector of the two-dimensional rectangular planar array with ideal main lobe gain and side lobe inhibition >The antenna gain is maximized, and side lobes are effectively restrained, so that the influence of interference signals in space on satellite communication is greatly reduced.

It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or sub-steps of other steps.

In one embodiment, as shown in fig. 6, there is provided a satellite communication anti-interference device based on side lobe suppression, including: the system comprises a beam related parameter calculation module 602, a side lobe level determination module 604, a main lobe gain calculation module 606 of a beam expected direction, a one-dimensional linear array optimal weight vector calculation module 608 and an anti-interference processing module 610, wherein:

A beam related parameter calculation module 602, configured to input a beam related parameter; the beam related parameters comprise beam expected pointing, beam main lobe width, beam discrete angle interval, beam angle lower bound, beam angle upper bound, beam forming parameters, airspace to be covered, number of array elements, array element spacing, beam width factor, wavelength, side lobe level lower bound, side lobe level upper bound, first main lobe gain optimization loss threshold and corresponding first side lobe level optimization step, second main lobe gain optimization loss threshold and corresponding second side lobe level optimization step, first side lobe and main lobe interval and initial weighting vector; calculating according to the beam related parameters to obtain the ideal main lobe gain of the beam, the discrete angle set of the main lobe of the beam and the discrete angle set of the side lobe of the beam;

the side lobe level determining module 604 is configured to scan an airspace to be covered and divide the airspace with a pre-calculated scan beam width as an interval to obtain energy of a received environment echo signal pointed by each beam; taking the average value of the received environment echo signal energy pointed by each wave beam as average noise, and calculating according to the maximum value in the received signal energy in each direction and the average noise to obtain a dry-to-noise ratio; determining the side lobe level according to a certain criterion and a dry-to-noise ratio;

A main lobe gain calculation module 606 for setting a main lobe gain to which the beam is expected to be directed as an objective function of main lobe gain optimization; setting constraint conditions for main lobe gain optimization according to the main lobe gain, the side lobe level and the amplitude of the weight vector value of the beam expected to point; establishing a main lobe gain optimization model according to constraint conditions and an objective function, and solving the main lobe gain optimization model by using an alternate direction multiplier method to obtain main lobe gain of a beam in an expected direction;

the one-dimensional linear array optimal weight vector calculation module 608 is configured to calculate a main lobe gain optimization loss of the beam by using an ideal main lobe gain of the beam and a main lobe gain of a beam in an expected direction; judging the main lobe gain optimization loss of the beam, if the main lobe gain optimization loss of the beam is larger than a first main lobe gain optimization loss threshold, reducing the side lobe level by a first side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam, if the main lobe gain optimization loss of the beam is not larger than the first main lobe gain optimization loss threshold, judging whether the main lobe gain optimization loss of the beam is larger than a second main lobe gain optimization loss threshold, if the main lobe gain optimization loss of the beam is larger than the second main lobe gain optimization loss threshold, reducing the side lobe level by a second side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam until the main lobe gain optimization loss of the beam is not larger than the second main lobe gain optimization loss threshold, ending the optimization process and obtaining a one-dimensional linear array optimal weight vector;

The anti-interference processing module 610 is configured to respectively solve the corresponding one-dimensional linear array optimal weight vectors and perform kronecker product calculation on the two one-dimensional linear array optimal weight vectors to obtain two-dimensional rectangular planar array optimal weight vectors; and performing anti-interference processing according to the optimal weight vector.

Specific limitation of a satellite communication anti-interference device based on side lobe suppression can be referred to above as limitation of a satellite communication anti-interference method based on side lobe suppression, and will not be described herein. Each module in the satellite communication anti-interference device based on side lobe suppression can be fully or partially implemented by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.

Claims (10)

1. Satellite communication anti-interference method based on side lobe suppression is characterized by comprising the following steps:

step S100: inputting beam related parameters; the beam related parameters comprise beam expected pointing, beam main lobe width, beam discrete angle interval, beam angle lower bound, beam angle upper bound, beam forming parameters, airspace to be covered, array element number, array element interval, beam width factor, wavelength, side lobe level lower bound, side lobe level upper bound, first main lobe gain optimization loss threshold and corresponding first side lobe level optimization step, second main lobe gain optimization loss threshold and corresponding second side lobe level optimization step, first side lobe and main lobe interval and initial weighting vector; calculating to obtain the ideal main lobe gain of the beam, the discrete angle set of the main lobe of the beam and the discrete angle set of the side lobe of the beam according to the related parameters of the beam;

Step S200: scanning a space domain to be covered, and dividing the space domain with a pre-calculated scanning beam width as an interval to acquire the energy of each directional receiving environment echo signal of the beam; taking the average value of the energy of the received environment echo signals pointed by the wave beams as average noise, and calculating according to the maximum value in the energy of the received signals in all directions and the average noise to obtain a dry-to-noise ratio; determining a side lobe level according to a certain criterion and the dry-to-noise ratio;

step S300: setting the main lobe gain to which the beam is expected to be directed as an objective function of main lobe gain optimization;

step S400: setting constraint conditions for main lobe gain optimization according to the main lobe gain, the side lobe level and the amplitude of the weight vector value of the beam expected to point; establishing a main lobe gain optimization model according to the constraint condition and the objective function, and solving the main lobe gain optimization model by using an alternate direction multiplier method to obtain main lobe gain of a beam in an expected direction;

step S500: calculating to obtain the main lobe gain optimization loss of the beam by using the ideal main lobe gain of the beam and the main lobe gain of the beam in the expected direction;

step S600: judging the main lobe gain optimization loss of the beam, if the main lobe gain optimization loss of the beam is larger than a first main lobe gain optimization loss threshold, reducing the side lobe level by a first side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam, if the main lobe gain optimization loss of the beam is not larger than the first main lobe gain optimization loss threshold, judging whether the main lobe gain optimization loss of the beam is larger than a second main lobe gain optimization loss threshold, if the main lobe gain optimization loss of the beam is larger than the second main lobe gain optimization loss threshold, reducing the side lobe level by a second side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam until the main lobe gain optimization loss of the beam is not larger than the second main lobe gain optimization loss threshold, ending the optimization process and obtaining a one-dimensional linear array optimal weight vector;

Step S700: solving the corresponding one-dimensional linear array optimal weight vector by using two different array element numbers input through the step S100 through the step S600, and performing Cronecker product calculation on the two one-dimensional linear array optimal weight vectors to obtain the optimal weight vector of the two-dimensional rectangular planar array; and performing anti-interference processing according to the optimal weight vector.

2. The method of claim 1, wherein calculating a beam ideal main lobe gain, a beam main lobe discrete angle set, a beam side lobe discrete angle set from the beam related parameters comprises:

calculating the ideal main lobe gain of the beam according to the expected beam direction to be

M ₀ ＝20lg K

Wherein K is the number of input array elements, lgK =log ₁₀ K represents a base 10 logarithm of K;

calculating to obtain a beam main lobe discrete angle set as a beam main lobe discrete angle set according to the beam expected direction and the beam discrete angle interval

Wherein,for the beam main lobe discrete angle set, +.>For beam desired pointing, +.>For the beam to be at discrete angular intervals,floor () is a downward rounding function, which is the maximum number of discrete intervals of the main lobe of the beam,/->For the beam main lobe width, m=2m' +1 is the number of angles of the beam main lobe discrete angle set;

Calculating to obtain a beam side lobe discrete angle set according to the beam angle lower boundary, the beam angle upper boundary, the beam discrete angle interval and the first side lobe and main lobe interval

Wherein,for the beam side lobe discrete angle set, +.>Is the lower boundary of beam angle->For the upper beam angle limit, +.>For beam discrete angle spacing +.>For the first side lobe to be spaced from the main lobe, < > about->For the desired pointing of the beam, S is the number of angles of the discrete angle set of the beam side lobes.

3. The method of claim 1, wherein the pre-computed scanned beamwidth is

Wherein, kappa is a beam width factor, lambda is a wavelength, K is the number of array elements, d is the spacing of the array elements,for the beam to be directed, cos () is a cosine function.

4. The method of claim 1, wherein scanning the airspace to be covered and dividing the airspace at intervals of a pre-calculated scan beam width to obtain the received environment echo signal energy for each direction of the beam, comprises:

scanning the airspace to be covered, dividing the airspace by taking the pre-calculated scanning beam width as an interval, and using x _ij (t) represents the spatial scanning timeThe beam is directed to the environmental echo signals acquired in the I horizontal azimuth and the j elevation azimuth, i=0, 1, …, I, Is the number of horizontal beam pointing intervals, where floor () is a downward rounding function,for right boundary angle of airspace to be covered, < +.>Left boundary angle of airspace to be covered; j=0, 1, …, J,for pitch-wise beam pointing interval number, wherein floor () is a downward rounding function, +.>For the upper boundary angle of the airspace that needs to be covered, < +.>The lower boundary angle of the airspace to be covered; sampling T of the environmental echo signals collected in each beam pointing direction _S Time, obtain sampling signal x _ij [n]And samples the signal x _ij [n]Performing N-point fast Fourier transform processing, and calculating the signals subjected to the fast Fourier transform processing according to the Pasteur theorem to obtain the energy of each directional receiving environment echo signal of the wave beam as follows

Wherein N represents the number of points sampled, X _i,j [k]Represents the signal after the fast fourier transform processing, k represents the sampling number, i represents the water to which the beam is directedPlane azimuth, j represents the elevation azimuth of the beam pointing, |·| represents modulo the complex number, (·) ² The representation is squared.

5. The method of claim 1, wherein averaging the received environmental echo signal energy at each of the beam orientations as average noise comprises:

The average value of the energy of the echo signals of the receiving environment, which are pointed by the wave beams, is taken as average noise

Wherein I is the number of horizontal beam pointing intervals, J is the number of pitching beam pointing intervals, E _ij The energy of the environment echo signals acquired when the wave beam points to the ith horizontal azimuth and the jth pitching azimuth.

6. The method of claim 1, wherein determining a side lobe level based on certain criteria and the dry-to-noise ratio comprises:

determining the side lobe level as according to a certain criterion and the dry-to-noise ratio

Wherein INR is the dry-to-noise ratio of the environmental echo signal, Γ _l Is the lower boundary of the side lobe level Γ _u Is the upper bound of the sidelobe level.

7. The method of claim 1, wherein building a main lobe gain optimization model from the constraints and the objective function comprises:

establishing a main lobe gain optimization model as according to the constraint condition and the objective function

|w _k |＝1,k＝1,2,…,K

Wherein,as an objective function, M _r Main lobe gain for beam desired pointing, +.>Is the guiding vector of the main lobe direction, +.>( ^T ) The representation takes the transpose, sin () is a sine function, lambda is the wavelength, +.>For the beam main lobe discrete angle set, m=1, 2, …, M is the set +.>Number of medium angles, w= [ w ] ₁ ,w,…,w _K ] ^T Is the weighting vector of the antenna array, K is the number of array elements, alpha is the main lobe beam forming parameter,/and/or>Is the guiding vector of the side lobe direction, +.>For the beam side lobe discrete angle set, s=1, 2, …, S is the set +.>The number of medium angles, Γ is the side lobe level, w _k Is the weight vector value, which is the value of the kth element in the antenna array weight vector w, (. Cndot. ^H ) Represents the conjugate transpose, |·| represents modulo the complex number () ² The representation is squared.

8. The method of claim 1, wherein calculating a beam main lobe gain optimization loss using the beam ideal main lobe gain and a main lobe gain for the beam desired direction comprises:

calculating to obtain the optimal loss of the main lobe gain of the beam by using the ideal main lobe gain of the beam and the main lobe gain of the beam in the expected direction

ΔM＝M ₀ -M _r

Wherein M is ₀ For the ideal main lobe gain of the beam, M _r The main lobe gain to which the beam is expected to be directed.

9. The method according to claim 1, wherein solving the corresponding one-dimensional linear array optimal weight vector and performing kronecker product calculation on the two one-dimensional linear array optimal weight vectors by using the two different array element numbers input through the step S100 through the steps S100 to S600 to obtain the two-dimensional rectangular planar array optimal weight vector comprises:

Performing Cronecker product calculation on one-dimensional linear array optimal weight vectors corresponding to the number of two different array elements to obtain an optimal weight vector of a two-dimensional rectangular planar array as follows

Wherein the symbols areRepresenting Cronecker product, metropolyl>To number array element K ₁ 、K ₂ Optimal weight vector of corresponding one-dimensional linear array, +.>Representation->For K ₁ K ₂ Complex vector of row, 1 column, (·) ^T The representation takes the transpose.

10. Satellite communication anti-interference device based on sidelobe suppression, characterized in that the device comprises:

the beam related parameter calculation module is used for inputting beam related parameters; the beam related parameters comprise beam expected pointing, beam main lobe width, beam discrete angle interval, beam angle lower bound, beam angle upper bound, beam forming parameters, airspace to be covered, array element number, array element interval, beam width factor, wavelength, side lobe level lower bound, side lobe level upper bound, first main lobe gain optimization loss threshold and corresponding first side lobe level optimization step, second main lobe gain optimization loss threshold and corresponding second side lobe level optimization step, first side lobe and main lobe interval and initial weighting vector; calculating to obtain the ideal main lobe gain of the beam, the discrete angle set of the main lobe of the beam and the discrete angle set of the side lobe of the beam according to the related parameters of the beam;

The side lobe level determining module is used for scanning the airspace to be covered and dividing the airspace by taking the pre-calculated scanning beam width as an interval to acquire the energy of the received environment echo signals pointed by each beam; taking the average value of the energy of the received environment echo signals pointed by the wave beams as average noise, and calculating according to the maximum value in the energy of the received signals in all directions and the average noise to obtain a dry-to-noise ratio; determining a side lobe level according to a certain criterion and the dry-to-noise ratio;

the main lobe gain calculation module is used for setting the main lobe gain of the beam expected direction as an objective function of main lobe gain optimization; setting constraint conditions for main lobe gain optimization according to the main lobe gain, the side lobe level and the amplitude of the weight vector value of the beam expected to point; establishing a main lobe gain optimization model according to the constraint condition and the objective function, and solving the main lobe gain optimization model by using an alternate direction multiplier method to obtain main lobe gain of a beam in an expected direction;

the one-dimensional linear array optimal weight vector calculation module is used for calculating the main lobe gain optimization loss of the beam by utilizing the ideal main lobe gain of the beam and the main lobe gain of the beam in the expected direction; judging the main lobe gain optimization loss of the beam, if the main lobe gain optimization loss of the beam is larger than a first main lobe gain optimization loss threshold, reducing the side lobe level by a first side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam, if the main lobe gain optimization loss of the beam is not larger than the first main lobe gain optimization loss threshold, judging whether the main lobe gain optimization loss of the beam is larger than a second main lobe gain optimization loss threshold, if the main lobe gain optimization loss of the beam is larger than the second main lobe gain optimization loss threshold, reducing the side lobe level by a second side lobe level optimization step, continuing to optimize the main lobe gain expected to be pointed by the beam until the main lobe gain optimization loss of the beam is not larger than the second main lobe gain optimization loss threshold, ending the optimization process and obtaining a one-dimensional linear array optimal weight vector;

The anti-interference processing module is used for respectively solving the corresponding one-dimensional linear array optimal weight vectors and carrying out Cronecker product calculation on the two one-dimensional linear array optimal weight vectors to obtain the optimal weight vector of the two-dimensional rectangular planar array; and performing anti-interference processing according to the optimal weight vector.