CN112543047A - Multi-beam satellite interference suppression method, storage medium and computing device - Google Patents

Multi-beam satellite interference suppression method, storage medium and computing device Download PDF

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CN112543047A
CN112543047A CN202011218980.4A CN202011218980A CN112543047A CN 112543047 A CN112543047 A CN 112543047A CN 202011218980 A CN202011218980 A CN 202011218980A CN 112543047 A CN112543047 A CN 112543047A
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interference
covariance matrix
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vector
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CN112543047B (en
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范建存
罗杰
刘洪吉
刘泽宇
罗新民
张莹
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Xian Jiaotong University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/1851—Systems using a satellite or space-based relay
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0408—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas using two or more beams, i.e. beam diversity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/1851—Systems using a satellite or space-based relay
    • H04B7/18519—Operations control, administration or maintenance

Abstract

The invention discloses a multi-beam satellite interference suppression method, a storage medium and a computing device.A plurality of M receiving antennas of a multi-beam satellite receiving end receive continuous sensing data, and a receiving signal is expressed by a column vector of an M dimension in a kth beat; determining a covariance matrix of the received signal according to the received signal; array signal processing is carried out on the received signals, an optimal beam forming vector is calculated through a maximized signal-to-interference-and-noise ratio criterion, and a steering vector of the expected signal is estimated according to an angle reciprocity principle; reconstructing a covariance matrix of interference and noise by using a Capon spatial spectrum estimator according to spatial spectrum distribution and a covariance matrix of a received signal in all possible directions; and correcting the estimated expected signal guide vector and the reconstructed interference and noise covariance matrix by using a beam forming rule based on worst-case performance optimization, and processing the received data by using the corrected beam forming vector to complete interference suppression. The invention saves the amount of calculation required by DOA estimation.

Description

Multi-beam satellite interference suppression method, storage medium and computing device
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a multi-beam satellite interference suppression method based on angle reciprocity and covariance matrix reconstruction, a storage medium and computing equipment.
Background
With the rapid development of communication technology, terrestrial mobile communication networks can cover most of land areas, but due to the relationship between areas and operation cost, the signal coverage rate of many remote and underdeveloped areas is still very low, and the normal communication requirements of people are far from being met. The satellite communication system has the characteristics of long communication distance, wide coverage range and difficult influence by regions, and can well solve the problem of low signal coverage rate in the regions. As satellite communication becomes one of the indispensable means for global real-time communication, the concept of space-sky integration has also come into play, and satellite communication systems are used as an effective supplement to terrestrial mobile communication systems. The satellite communication is used as an important component of an air-space integrated network, can realize seamless coverage and large coverage range all day, and can be used for solving emergency communication problems such as natural disasters, forestry monitoring, military communication and the like. However, one of the major weaknesses of satellite communication systems is that they are susceptible to interference, either intentional or unintentional, and in severe cases may even result in the unavailability of services. Interference management is considered a key issue in the satellite communications industry, particularly for satellite owners and operators. Therefore, there is a need for effective interference management and cancellation to ensure the quality of satellite communications.
In the existing satellite mobile communication system, a multi-beam coverage technology is mostly adopted for improving the frequency spectrum reuse rate, and the multi-beam coverage can improve the overall frequency spectrum utilization rate while obtaining the space gain. However, in multi-beam satellite communication systems, interference suppression presents a significant challenge due to the diversity and instability of the interference sources. In addition, limited computational resources also limit the interference suppression capabilities of the satellite receiver. The multi-feed technology is adopted at the satellite end, the technology is simplified into the multi-antenna technology, and the arrangement of the multi-feed source mostly follows a certain regular geometric figure. According to the characteristics, the anti-interference capability of the satellite communication system can be improved by utilizing the array signal processing method.
At the receiving end of the satellite, when the Direction of Arrival (DOA) of the desired signal and the interfering signal are different, the adaptive beamforming technique is often used to process the received signal, and its main advantage is to enhance the desired signal while suppressing the interference and noise output by the antenna array. Minimum Variance Distortionless Response (MVDR) is used as a standard Capon beam forming algorithm, and can adaptively change weight vectors, obtain undistorted expected signal output and inhibit interference signals. If the processed data contains desired signal components, the MVDR beamformer becomes a Minimum Power distortion free Response (MPDR) beamformer, and the performance of the MPDR beamformer is severely degraded when there is a mismatch in the models. In order to improve the stability of the MPDR beamformer in case of model mismatch, a number of robust techniques have been proposed, which can be mainly classified into two categories. The first category is to eliminate or reduce the components of the desired signal before estimating the covariance matrix, such as Diagonal Loading (DL) techniques, however, in practical scenarios, the Diagonal Loading factor is difficult to select, and the interference suppression capability of the DL method is drastically reduced at high signal-to-noise ratios. The second method is to process the steering vector of the desired signal, but since the accurate priori knowledge of the steering vector is not easy to obtain, the DOA needs to be estimated before data processing, and the MUSIC algorithm is a commonly used DOA estimation method, but the calculation amount is large. Most robust constraint algorithms do not need actual steering vectors, and generally adopt error norm constraints of the steering vectors, such as a Beamformer based on Worst-case Performance Optimization (WCB), a Beamformer based on covariance fitting, a Beamformer based on multiple uncertainty set constraints, and the like. This is equivalent to allowing a range of errors to exist in estimating the DOA, and the errors can be corrected in an optimized manner, which provides a theoretical basis for estimating the steering vector using angular reciprocity.
In an actual satellite system, because multi-beam coverage is to be achieved, the satellite needs to perform beam forming, and when performing beam forming by itself, the beam intermediate angle can be regarded as known. Since the beam intermediate angle is small, the maximum offset between the beam intermediate angle and the actual angle is only half of the beam intermediate angle. Through calculation, the beam width of most of the existing multi-beam satellite communication systems is within 1 degree, and with the development of satellite technology, the width of satellite beam coverage will be smaller and smaller, so that a larger frequency reuse rate can be realized.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a multi-beam satellite interference suppression method, a storage medium, and a computing device based on angle reciprocity and covariance matrix reconstruction, so as to greatly improve the satellite-side interference suppression capability.
The invention adopts the following technical scheme:
a multi-beam satellite interference suppression method, comprising the steps of:
s1, M receiving antennas of the multi-beam satellite receiving terminal receive continuous sensing data, and after filtering and sampling, the receiving signals are expressed by M-dimensional column vectors at the kth beat;
s2, determining a covariance matrix of the received signal according to the received signal obtained in the step S1;
s3, array signal processing is carried out on the received signals obtained in the step S1, the optimal beam forming vector is calculated through the maximization of the signal-to-interference-noise ratio criterion, and the guide vector of the expected signal is estimated according to the angle reciprocity principle;
s4, reconstructing a covariance matrix of interference and noise by using a Capon spatial spectrum estimator according to the spatial spectrum distribution in all possible directions and the covariance matrix of the received signals obtained in the step S2;
and S5, correcting the covariance matrix of the expected signal guide vector estimated in the step S3 and the interference plus noise reconstructed in the step S4 by using the beam forming rule based on the worst-case performance optimization, and processing the received data by using the corrected beam forming vector to complete the interference suppression.
Specifically, in step S1, the received signal x (k) is specifically:
Figure BDA0002761405660000031
so(k)、si(k) and n (k) respectively representing the desired signal, interference and noise statistically independent of each other, a0Is the steering vector of the desired signal, P is the number of interfering signals, aiA steering vector for the ith signal or interference is specifically:
Figure BDA0002761405660000041
wherein λ is the electromagnetic wavelength, j is the complex number mark, d is the receiving antenna spacing,
Figure BDA0002761405660000042
is the actual signal angle of arrival.
Specifically, in step S2, the covariance matrix of the samples of K snapshots is used
Figure BDA0002761405660000043
The covariance matrix R instead of the received signal is specifically:
Figure BDA0002761405660000044
where x (k) is the observed value of the kth beat, (. C)HIs a conjugate transpose operation.
Specifically, in step S3, a guide vector of the desired signal
Figure BDA0002761405660000045
Comprises the following steps:
Figure BDA0002761405660000046
wherein λ is the electromagnetic wavelength, j is the complex number mark, d is the receiving antenna spacing, w0Known as the beam intermediate angle.
Further, an optimal beamforming vector woptThe following were used:
Figure BDA0002761405660000047
wherein R isinIs the actual interference plus noise covariance matrix, ao∈CMIs a steering vector that the desired signal assumes is known a priori.
Specifically, in step S4, the interference-plus-noise covariance matrix
Figure BDA0002761405660000048
Comprises the following steps:
Figure BDA0002761405660000049
wherein, γiAnd eiRespectively, the eigenvalue and corresponding eigenvector of the covariance matrix after reconstruction, and the eigenvalues are arranged in descending order
Figure BDA0002761405660000051
EI＝[e1,...,eP]And EN＝[eP+1,...,eM]Respectively representing an interference subspace and a noise subspace, and P is the number of interference signals.
Specifically, in step S5, when the angle-of-arrival interval between the desired signal and the interfering signal is greater than the beam width, there is a case where the angle-of-arrival interval is greater than the beam width
Figure BDA0002761405660000052
It is assumed that this condition is always true,
Figure BDA0002761405660000053
assuming that the number of interfering signals is equal to 1, when there is only one interfering source, it is represented in a characteristic decomposition form
Figure BDA0002761405660000054
Simultaneous arrayRepresentation of signal forms
Figure BDA0002761405660000055
Association
Figure BDA0002761405660000056
The two representations of (1) are modified.
Further, in combination with
Figure BDA0002761405660000057
Two representations of (a) are obtained as follows:
Figure BDA0002761405660000058
Figure BDA0002761405660000059
Figure BDA00027614056600000510
wherein i is 1,2, γ1As characteristic values of interfering signals, gamma2Is a characteristic value of the noise signal,
Figure BDA00027614056600000511
is the variance of the noise, gammaiTo be the eigenvalues of the covariance matrix after reconstruction,
Figure BDA00027614056600000512
as a guide vector of the desired signal, eiAnd the feature vectors corresponding to the covariance matrix after reconstruction.
Another aspect of the invention is a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described.
Another aspect of the present invention is a computing device, including:
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention relates to a multi-beam satellite interference suppression method, which obtains a steering vector of an expected signal by using reciprocity between a known beam central angle and an unknown signal arrival angle, obtains an estimated covariance matrix by utilizing interference and noise covariance matrix reconstruction, and finally solves the weight of a beam former by considering an estimation error and adopting a worst-case performance optimization technology. Therefore, the anti-interference capability of the system can be obviously improved, and the computing resource during DOA estimation can be saved. The robustness of satellite-side interference suppression can be greatly improved, and particularly, a good interference suppression effect can be still ensured under a high signal-to-noise ratio.
Furthermore, after M receiving antennas of the multi-beam satellite receiving end receive the continuous sensing data and are subjected to filtering sampling, at the kth beat, the received signal is represented as an M-dimensional column vector x (k).
Further, a sample covariance matrix of K snapshots is used
Figure BDA0002761405660000061
Instead of the covariance matrix R of the received signal.
Further, assuming that all information is known a priori, the optimal beamforming vector w is calculated by maximizing the SINR criterionopt。
Further, according to the principle of angular reciprocity, a steering vector of the expected signal is estimated
Figure BDA0002761405660000062
And the actual guide vector is replaced, so that the calculation amount required in DOA estimation is saved.
Further, the method comprisesReconstructing the interference plus noise covariance matrix using a Capon spatial spectrum estimator from the spatial spectral distribution in all possible directions
Figure BDA0002761405660000063
Instead of the actual interference plus noise covariance matrix.
Further, the estimation error of the steering vector and the covariance matrix is considered, and the estimation error is corrected by using the beam forming rule based on worst-case performance optimization.
Further, solving for worst case performance optimization based beamformer, joint
Figure BDA0002761405660000064
Solves the solution of the parameters in the WCB beamformer.
In conclusion, the invention can save the calculation amount required by DOA estimation on the premise of ensuring the enhanced interference suppression capability.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a schematic view of a scenario in which the method of the present invention is applied;
fig. 2 is a schematic diagram of a multi-beam satellite signal receiving end;
FIG. 3 is a flow diagram of a scheme implementation module;
fig. 4 is a graph of the variation of the output signal-to-interference-and-noise ratio (SINR) with the signal-to-noise ratio (SNR) for different interference suppression methods, when the SIR is 5,10 dB;
fig. 5 is a graph of the variation of the output SIR with the snr for different interference suppression methods when the SIR is 10, 20 dB;
fig. 6 is a graph of the variation of the output SIR with the snr for different interference suppression methods when the SIR is 20, 30 dB;
FIG. 7 is a graph showing variation of the SINR output with the SINR output according to the proposed scheme under different angular deviations;
fig. 8 is a graph of the output signal to interference plus noise ratio of the proposed scheme as a function of the number of single antenna samples, compared to other interference suppression schemes;
fig. 9 is an array pattern of the proposed scheme.
Detailed Description
Referring to fig. 1, the present invention provides a multi-beam satellite interference suppression method, a storage medium, and a computing device based on angle reciprocity and covariance matrix reconstruction, where a uniform linear array receiving model is used at a multi-beam satellite receiving end, the receiving model has M receiving antennas, satellite-end signal reception is shown in fig. 2, a beam coverage central angle is known a priori and is ω, an offset between an actual signal arrival angle and a beam intermediate angle is θ, and the actual signal arrival angle can be expressed as
Figure BDA0002761405660000071
Suppose the desired signal angle of arrival is θ0The arrival angle of the ith interference signal is thetaiBoth noise and signal obey independent cyclic complex Gaussian distributions, i.e.
Figure BDA0002761405660000072
Referring to fig. 3, the multi-beam satellite interference suppression method based on angle reciprocity and covariance matrix reconstruction according to the present invention includes the following steps:
s1, after filtering and sampling the continuous sensing data received by M receiving antennas at the multi-beam satellite receiving end, in the kth beat, the column vector x (k) whose received signal is expressed as M-dimension is specifically:
Figure BDA0002761405660000081
wherein s iso(k)、si(k) And n (k) steering vectors a representing statistically independent desired signal, interference and noise, i-th signal or interference, respectivelyiExpressed as:
Figure BDA0002761405660000082
wherein λ is the electromagnetic wavelength, j is the complex number mark, and d is the receiving antenna spacing.
S2, obtaining a covariance matrix of the received signal x (k), specifically:
Figure BDA0002761405660000083
wherein the content of the first and second substances,
Figure BDA0002761405660000084
and
Figure BDA0002761405660000085
respectively representing the power of the desired signal, the ith interfering signal and the noise.
In the actual case, however, R is unknown, typically replaced with a sample covariance matrix of K snapshots,
Figure BDA0002761405660000086
s3, carrying out array signal processing on the received signal, wherein the output of the self-adaptive beam former is as follows;
y(k)＝wHx(k) (10)
wherein w ═ w1,…,wM]T∈CMIs the weight vector of the beamformer, (.)HAnd (·)TConjugate transpose and transpose are indicated, respectively.
Optimal beamforming vector woptThe solution is found by the criterion of maximizing the signal to interference and noise ratio, i.e. the following optimization problem.
Figure BDA0002761405660000087
The solution of equation (11) is:
Figure BDA0002761405660000091
wherein the content of the first and second substances,
Figure BDA0002761405660000092
is the actual interference plus noise covariance matrix, ao∈CMIs a steering vector that the desired signal assumes is known a priori.
Estimating the steering vector of the expected signal according to the angle reciprocity principle
Figure BDA0002761405660000093
Replacing a in beamforming vectorsoThe method specifically comprises the following steps:
Figure BDA0002761405660000094
s4, reconstructing the covariance matrix of the interference and the noise by using a Capon space spectrum estimator according to the space spectrum distribution in all possible directions
Figure BDA0002761405660000095
The method specifically comprises the following steps:
Figure BDA0002761405660000096
wherein the content of the first and second substances,
Figure BDA0002761405660000097
representing the complement of the signal uncertainty field Ψ.
Then to
Figure BDA0002761405660000098
Performing characteristic value decomposition, specifically:
Figure BDA0002761405660000099
wherein, γiAnd eiRespectively of post-reconstruction covariance matricesThe eigenvalues and corresponding eigenvectors, with the eigenvalues being arranged in descending order
Figure BDA00027614056600000910
EI＝[e1,...,eP]And EN＝[eP+1,...,eM]Representing the interference subspace and the noise subspace, respectively.
S5, the estimation error in steps S3 and S4 is corrected using the beamforming (WCB) criterion based on worst case performance optimization.
When the angle-of-arrival interval of the desired signal and the interference signal is larger than the beam width, there is
Figure BDA0002761405660000101
Assuming that this condition is always true, the following approximation is made,
Figure BDA0002761405660000102
the beamformer optimized based on worst case performance is represented as:
Figure BDA0002761405660000103
where δ represents the unknown degree of mismatch between the actual steering vector and its assumed value, and ε is a norm constraint known a priori, defining the maximum value of the estimation error. The solution of equation (16) is represented as:
Figure BDA0002761405660000104
wherein τ is
Figure BDA0002761405660000105
The root of (2).
The eigenvectors of the noise subspace are approximately orthogonal to
Figure BDA0002761405660000106
Namely, it is
Figure BDA0002761405660000107
In order to simplify the theoretical analysis, a theoretical result is obtained assuming that the number of interference signals is equal to 1, and simulation results show that the theoretical result is also valid when the interference number is greater than 1.
When there is only one of the sources of interference,
Figure BDA0002761405660000108
expressed in a characteristic decomposition form as:
Figure BDA0002761405660000109
at the same time, the user can select the desired position,
Figure BDA00027614056600001010
represented in the form of an array signal,
Figure BDA00027614056600001011
association
Figure BDA00027614056600001012
Two representations of (a) are obtained as follows:
Figure BDA00027614056600001013
Figure BDA00027614056600001014
wherein the content of the first and second substances,
Figure BDA00027614056600001015
in order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Next, the interference suppression capability of the present invention is discussed in two cases.
Case 1: very high signal to noise ratio.
Suppose that
Figure BDA0002761405660000111
The following approximation is made:
Figure BDA0002761405660000112
take ε very close
Figure BDA0002761405660000113
Then
Figure BDA0002761405660000114
The denominator of (a) satisfies the following:
(ε+τγi)2p (23) is obtained by the formulae (18), (22) and (23) > M, i ═ 1,2
Figure BDA0002761405660000115
The process is further simplified and the process is simplified,
Figure BDA0002761405660000116
the formula (25) represents that the equivalent DL factor in (17) is
Figure BDA0002761405660000117
And is far greater than the maximum eigenvalue gamma1. The effect of the beamforming weight vector (17) on the steering vector of the interfering signal is therefore:
Figure BDA0002761405660000121
when the signal-to-noise ratio is very high, the signal is generated by
Figure BDA0002761405660000122
The resulting attenuation and
Figure BDA0002761405660000123
a1the orthogonality between them works together, so that | wHa1The value of | is very small, which means that interference can be significantly suppressed.
Case 2: very low signal to noise ratio.
Suppose that
Figure BDA0002761405660000124
Approximately consider that
Figure BDA0002761405660000125
When the signal-to-noise ratio is much less than-10 dB,
Figure BDA0002761405660000126
the DL factor in equation (17) is thus about
Figure BDA0002761405660000127
This is associated with a DL factor of
Figure BDA0002761405660000128
The beamformer performance is the same.
The result shows that the interference suppression capability of the invention changes adaptively with the change of the input signal-to-noise ratio, and particularly, the obvious interference suppression effect is still ensured at the time of high signal-to-noise ratio, which is just deficient in the DL method.
The following is a description of the simulation environment, considering two interference sources, where the number of receive antennas M is 30, the array element spacing is half the wavelength, and the number of single antenna samples K is 50. ARIS-RIN in the subsequent figures represents the interference suppression scheme based on angular reciprocity and covariance matrix reconstruction proposed by the invention, INCM represents
Figure BDA0002761405660000129
FDL stands for constant diagonal loading factor
Figure BDA00027614056600001210
In the DL scheme, MVDR and MPDR are minimum variance undistorted response and minimum power undistorted response schemes, respectively, and Method in represents a scheme of reconstructing a covariance matrix by replacing a noise eigenvalue with an average of the noise eigenvalue.
Referring to fig. 4, in the case of SIR of 5,10dB, the output signal-to-interference-and-noise ratio (SINR) of different interference suppression methods varies with the signal-to-noise ratio (SNR), and it can be seen that the performance of these algorithms improves with the improvement of the signal-to-noise ratio under the condition of low signal-to-noise ratio. When SNR is 10dB, the MPDR performance is severely degraded because the interference and noise power are comparable and a severe model mismatch occurs. When the signal-to-noise ratio is greater than 25dB, the performance of the FDL begins to degrade because the signal is always present when the diagonal loading technique is used, and its effect becomes more and more significant as the signal-to-noise ratio increases. As the signal-to-noise ratio increases, the performance deviation of INCM and MVDR relative to ARIS-RIN increases because the interference suppression capability of these methods decreases as the signal-to-noise ratio increases.
Referring to fig. 5, in the case of the SIR of 10, 20dB, the output SIR of different interference suppression methods varies with the SNR, and when the SNR is 15dB, the MPDR performance deteriorates rapidly, and when the SNR is greater than 30dB, the FDL performance starts to decrease.
Referring to fig. 6, in the case of the SIR of 20, 30dB, the output SIR of different interference suppression methods varies with the SNR, and when the SNR is 25dB, the MPDR performance deteriorates rapidly, and when the SNR is greater than 35dB, the FDL performance starts to decrease.
Comparing fig. 4-6, the overall performance of the system is improved with the increase of SIR, and when the noise power is equal to the interference power, the performance degradation point occurs in MPDR, and the degradation position moves to the direction of high snr. As the SIR increases, the FDL method also gets closer to ARIS-RIN with the knee also moving toward high signal-to-noise ratios. Through the comparative analysis, the method provided by the invention can keep a good interference suppression effect in a large signal-to-noise ratio range, and has strong robustness.
Referring to fig. 7, the variation curve of the output signal-to-interference-and-noise ratio of the ARIS-RIN method with respect to the signal-to-noise ratio under different angular deviations. The general trend is that as the signal-to-noise ratio increases, the output signal-to-interference-and-noise ratio also increases, tending to stabilize at very high signal-to-noise ratios. When Δ θ is small, such as Δ θ < 1 °, its interference suppression capability approaches the optimum state. As Δ θ increases, the inhibition performance of the ARIS-RIN method is more and more different from the performance without deviation. This shows that under small angular deviations, we can trade small performance loss for the large amount of computation needed for DOA estimation without affecting the interference suppression performance.
Referring to fig. 8, under different interference suppression methods, the SNR is 20dB according to the variation curve of the output sir with the sampling number of single antenna. As can be seen from the figure, when the number of samples is greater than 80, the performance of the FDL tends to be stable. Increasing the number of samples of a single antenna has no influence on the performance of the ARIS-RIN algorithm, which shows that the ARIS-RIN still has good interference suppression effect at a small number of samples, and the ARIS-RIN has another advantage.
Referring to fig. 9, the array patterns of the two beamforming vectors, ARIS-RIN and FDL, have SNR of 25 dB. In the two schemes, deeper nulls are formed in the interference direction, which shows that the schemes have stronger interference suppression capability, the ARIS-RIN method can point the main beam peak to the actual signal direction, but the FDL method deviates from the actual signal direction when pointing errors exist.
In summary, the multi-beam satellite interference suppression method, the storage medium and the computing device based on angle reciprocity and covariance matrix reconstruction of the present invention use reciprocity between a known beam center angle and an unknown signal arrival angle to obtain a steering vector of a desired signal, then use an interference plus noise covariance matrix reconstruction to obtain an estimated covariance matrix, and finally consider an estimation error and use a worst case performance optimization technique to solve a weight of a beam former. Therefore, the anti-interference capability of the system can be obviously improved, and the computing resource during DOA estimation can be saved.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A multi-beam satellite interference suppression method, comprising the steps of:
s1, M receiving antennas of the multi-beam satellite receiving terminal receive continuous sensing data, and after filtering and sampling, the receiving signals are expressed by M-dimensional column vectors at the kth beat;
s2, determining a covariance matrix of the received signal according to the received signal obtained in the step S1;
s3, array signal processing is carried out on the received signals obtained in the step S1, the optimal beam forming vector is calculated through the maximization of the signal-to-interference-noise ratio criterion, and the guide vector of the expected signal is estimated according to the angle reciprocity principle;
s4, reconstructing a covariance matrix of interference and noise by using a Capon spatial spectrum estimator according to the spatial spectrum distribution in all possible directions and the covariance matrix of the received signals obtained in the step S2;
and S5, correcting the covariance matrix of the expected signal guide vector estimated in the step S3 and the interference plus noise reconstructed in the step S4 by using the beam forming rule based on the worst-case performance optimization, and processing the received data by using the corrected beam forming vector to complete the interference suppression.
2. The method of claim 1, wherein in step S1, the received signal x (k) is specifically:
Figure FDA0002761405650000011
so(k)、si(k) and n (k) respectively representing the desired signal, interference and noise statistically independent of each other, a0Is the steering vector of the desired signal, P is the number of interfering signals, aiA steering vector for the ith signal or interference is specifically:
Figure FDA0002761405650000012
wherein λ is the electromagnetic wavelength, j is the complex number mark, d is the receiving antenna spacing,
Figure FDA0002761405650000013
is the actual signal angle of arrival.
3. The method according to claim 1, wherein in step S2, the covariance matrix of samples of K snapshots is used
Figure FDA0002761405650000014
The covariance matrix R instead of the received signal is specifically:
Figure FDA0002761405650000021
where x (k) is the observed value of the kth beat, (. C)HIs a conjugate transpose operation.
4. The method of claim 1, wherein in step S3, a steering vector of the desired signal is obtained
Figure FDA0002761405650000022
Comprises the following steps:
Figure FDA0002761405650000023
wherein λ is the electromagnetic wavelength, j is the complex number mark, d is the receiving antenna spacing, w0Known as the beam intermediate angle.
5. The method of claim 4, wherein the optimal beamforming vector woptThe following were used:
Figure FDA0002761405650000024
wherein R isinIs the actual interference plus noise covariance matrix, ao∈CMIs a steering vector that the desired signal assumes is known a priori.
6. The method of claim 1, wherein in step S4, the covariance matrix of interference plus noise is determined
Figure FDA0002761405650000025
Comprises the following steps:
Figure FDA0002761405650000026
wherein, γiAnd eiRespectively, the eigenvalue and corresponding eigenvector of the covariance matrix after reconstruction, and the eigenvalues are arranged in descending order
Figure FDA0002761405650000027
EI＝[e1,...,eP]And EN＝[eP+1,...,eM]Respectively representing an interference subspace and a noise subspace, and P is the number of interference signals.
7. The method of claim 1, wherein in step S5, when the angle-of-arrival interval between the desired signal and the interference signal is larger than the beam width, there is
Figure FDA0002761405650000028
It is assumed that this condition is always true,
Figure FDA0002761405650000029
assuming that the number of interfering signals is equal to 1, when there is only one interfering source, it is represented in a characteristic decomposition form
Figure FDA00027614056500000210
Simultaneously expressed in array signal form
Figure FDA00027614056500000211
Association
Figure FDA00027614056500000212
The two representations of (1) are modified.
8. The method of claim 7, wherein the combining is performed
Figure FDA0002761405650000031
Two representations of (a) are obtained as follows:
Figure FDA0002761405650000032
Figure FDA0002761405650000033
Figure FDA0002761405650000034
wherein i is 1,2, γ1As characteristic values of interfering signals, gamma2Is a characteristic value of the noise signal,
Figure FDA0002761405650000035
is the variance of the noise, gammaiTo be the eigenvalues of the covariance matrix after reconstruction,
Figure FDA0002761405650000036
as a guide vector of the desired signal, eiAnd the feature vectors corresponding to the covariance matrix after reconstruction.
9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-8.
10. A computing device, comprising:
one or more processors, memory, and one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-8.
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