CN111859278A - Anti-dynamic interference polarized beam forming method, system, storage medium and application - Google Patents
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Abstract
The invention belongs to the technical field of array signal processing, and discloses a method, a system and application for forming a polarized wave beam with dynamic interference resistance, wherein a receiving signal model is established for a uniform linear array formed by dual-polarized array elements; constructing a sampling interference plus noise covariance matrix from received data
Designing an interference and noise covariance matrix cone T; the sampling covariance matrix is tapered by using a covariance matrix taper, and a weight vector W is obtained by using a PI algorithmpiA representation of (a); solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method; and obtaining the self-adaptive beam output y (k) by using the optimal weight value. The invention reduces the operation complexity, has higher convergence rate and improves the performance of the polarization sensitive array beam forming algorithm. Combining the simulation experiment results, and when the dynamic stem existsWhen the interference or interference signal guide vector is mismatched, the method has higher output signal-to-interference-and-noise ratio than the traditional algorithm, and can be better applied to engineering practice due to low operation complexity.
Description
Technical Field
The invention belongs to the technical field of array signal processing, and particularly relates to a method, a system and application for forming a polarization beam with dynamic interference resistance.
Background
At present: beamforming is widely used in radar, sonar, navigation, and other systems as an important branch of array signal processing. In a Global Navigation Satellite System (GNSS), a desired signal is weak and even the Power of the desired signal is far lower than background noise, but the interference is strong interference, so that a Power Inversion beamforming algorithm (PI) is better applied, the characteristics of a Satellite Navigation signal are fully utilized, the algorithm is not restricted in any direction, and only the minimum output Power is required. The signal power is directly inverted, and the influence on a weak expected signal is small while strong interference is suppressed.
In a global navigation satellite system, because a platform of an interference receiver shakes or a flyer carrying an interference source moves at a high speed, the problem that the anti-interference performance of an algorithm is influenced because the angular direction of the algorithm suppression interference is not matched with the direction of the actual interference source position is generated. In a high dynamic environment, a null broadening method is often adopted to suppress interference. Among them, Covariance Matrix Tapering (CMT) is a more classical technique in the null broadening technique [ J.R. Guerci.Theoryand application of Covariance Matrix for robust adaptive beamforming [ J.IEEE Trans.Signal Process.1999, 4(47):977 + 985 ]. The covariance matrix is subjected to tapering processing, so that the null of interference can be widened, and dynamic interference can be effectively inhibited in a space domain. Schedule et al generalize covariance matrix to the polarimetric domain proposed the PSA-CMT algorithm [ M.Xie, W.Xia, S.Wei, H.Li and P.Li.A Robust GNSS interference suppression on Method Based on Null broadcasting of Dual-polarized Antenna Arrays [ C ].14th IEEE International Conference on Signal Processing (ICSP), Beijing, China, 2018:197-202 ]. The method can simultaneously widen null in an airspace-polarization domain, but matrix inversion is utilized when the optimal weight vector of beam forming is solved, compared with scalar beam forming, the polarization beam forming has a covariance matrix with higher dimensionality, and the complexity is higher when inversion operation is carried out. For this reason, it is a hot point to research to widen the null and reduce the computational complexity.
Through the above analysis, the problems and defects of the prior art are as follows: the traditional method cannot effectively inhibit dynamic interference and has high operation complexity of matrix inversion.
The difficulty in solving the above problems and defects is: compared with the traditional scalar array, the polarization sensitive array is generally formed by mutually orthogonal dipoles, the dimension of a signal is twice that of a variable array in the signal modeling process, and the calculated amount is large when null broadening is carried out.
The significance of solving the problems and the defects is as follows: the significance of researching the anti-dynamic interference beam forming algorithm is to solve the problem that the angular direction of interference is not matched with the direction of the actual interference source position due to the fact that the interference moves too fast, and therefore the updated beam forming weight vector cannot effectively inhibit the current interference. In practical situations, reducing the computational complexity of the algorithm can enhance the real-time performance of the system, and can increase the update speed of the weight vector, which is important for the anti-dynamic interference algorithm.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method, a system and application for forming a polarization beam for resisting dynamic interference.
The invention is realized in such a way that a polarization beam forming method for resisting dynamic interference comprises the following steps:
establishing a receiving signal model for a uniform linear array formed by dual-polarized array elements;
constructing a sampling interference plus noise covariance matrix from received data
Designing an interference and noise covariance matrix cone T;
the sampling covariance matrix is tapered by using a covariance matrix taper, and a weight vector W is obtained by using a PI algorithmpiA representation of (a);
solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and obtaining the self-adaptive beam output y (k) by using the optimal weight value.
Further, the receiving signal model is established for the uniform linear array formed by the dual-polarized array elements, and the established receiving signal model of the polarized array is as follows:
wherein s is0(k) For GNSS navigation signals, sq(k) For interfering signals, n (k) is mean 0 and variance is σ2Gaussian white noise signal vector, a0,aqRespectively a desired signal steering vector and an interference signal steering vector;
a0and akGiven by the following equation:
ap(θ,γ,η)＝[-cosγ cosθsinγejη]H；
in the formula, asFor space-domain signal steering vector, apFor the polarization domain signal steering vector to be,
is the Kroncker product, and theta, gamma and eta are the airspace angle, the polarization angle and the polarization phase difference respectively.
Further, the interference-plus-noise covariance matrix is constructed according to the received data
Designing an interference and noise covariance matrix cone T;
wherein K is a fast beat number [ ·]HIs a conjugate transpose;
the interference plus noise covariance matrix T is expressed as:
wherein, TpFor polarizing covariance matrix, TsThe space-domain covariance matrix is a space-domain covariance matrix cone, and the delta theta and the delta gamma are respectively a widening space angle and a polarization angle.
Further, the sampling covariance matrix is subjected to tapering processing by using the covariance matrix taper, and a weight vector W is obtained by using a PI algorithmpiA representation of (a);
covariance matrix R after sampling covariance matrix taperingTComprises the following steps:
RT＝T⊙R；
wherein T is a covariance matrix cone, which is a Hadamard product;
according to the power inversion principle, the weight calculation process can be described as the following extremum function:
power inversion weight vector WpiThe representation form is:
wherein, the constraint vector s is [1,0, …,0 ]]TThe array vector is a 2 Mx 1 column vector, M is the number of antenna array elements, mu is a scalar, and the value of the array vector does not influence the filtering characteristic and only influences the output signal power;
obtaining a PI-CMT weight vector W by using the covariance matrix after the tapering treatmentPI-CMTComprises the following steps:
wherein, the number of the mu' standard does not influence the performance of the algorithm and is set as
Further, the optimal vector of PI-CG-CMT is solved by using a conjugate gradient method, and a weight vector W is initializedPI-CG-CMTIteration step length alpha, residual vector r and search vector p are calculated, and an iteration form of the weight vector is obtained;
in particular, for formula (II)
Left ride
Conversion to:
formula (II)
By using a conjugate gradient method, the initial value of the residual vector r is obtained as follows:
the initial value of the search vector p is set to:
p1＝r0；
the expression for the iteration step α is:
the expression of the search vector p is:
the iterative expression of the weight vector is:
Wi＝Wi-1+αipi；
and when the norm of the residual vector is less than 0.1, ending the iteration to obtain the optimal weight vector.
Further, the obtaining of the adaptive beam output y (k) by using the optimal weight value:
another object of the present invention is to provide a radio receiving apparatus characterized in that the apparatus includes an array antenna, a multi-channel microwave receiver, a phase shifter, a digital signal processing board storing an executable program, and the like, and the radio receiving apparatus, when executed by the digital signal processing board, causes the processor to execute the steps of:
constructing a sampling interference and noise covariance matrix according to the received data and designing an interference and noise covariance matrix cone;
the sampling covariance matrix is subjected to tapering processing by using a covariance matrix cone, and a weight vector representation form is obtained by using a PI algorithm;
solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and obtaining the self-adaptive beam output by using the optimal weight.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
constructing a sampling interference and noise covariance matrix according to the received data and designing an interference and noise covariance matrix cone;
the sampling covariance matrix is subjected to tapering processing by using a covariance matrix cone, and a weight vector representation form is obtained by using a PI algorithm;
solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and obtaining the self-adaptive beam output by using the optimal weight.
Another object of the present invention is to provide a dynamic interference resistant polarized beamforming system for operating the dynamic interference resistant polarized beamforming method, the dynamic interference resistant polarized beamforming system comprising:
the receiving signal model establishing module is used for establishing a receiving signal model for the uniform linear array formed by the dual-polarized array elements;
the interference and noise covariance matrix cone designing module is used for constructing a sampling interference and noise covariance matrix according to the received data and designing an interference and noise covariance matrix cone;
the sampling covariance matrix tapering processing module is used for tapering a sampling covariance matrix by using a covariance matrix cone and obtaining a representation form of a weight vector by using a PI algorithm;
the optimal weight vector solving module is used for solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and the self-adaptive beam output module is used for obtaining self-adaptive beam output by utilizing the optimal weight value.
Another object of the present invention is to provide a global navigation satellite system equipped with the dynamic interference resistant polarized beam forming system.
By combining all the technical schemes, the invention has the advantages and positive effects that: the zero-trap beam broadening is realized by tapering the interference and noise covariance matrix, so that high dynamic interference can be effectively inhibited; the optimal weight vector of the PI-CG-CMT is solved through a conjugate gradient method, so that the inverse of a covariance matrix is avoided, and the calculated amount of a beam forming algorithm is reduced.
The invention mainly aims at the problems that the traditional method can not effectively inhibit interference and the operation complexity of matrix inversion is high under the high dynamic condition in a global navigation satellite system, and provides a polarized wave beam forming method for resisting dynamic interference based on conjugate gradient.
According to the method, the CMT is used for broadening the null in the interference direction, and then the conjugate gradient method based on the Krylov subspace is combined to solve the weight vector, so that matrix inversion is avoided through iteration. By the method, the operation complexity is reduced, the convergence rate is high, and the performance of the polarization sensitive array beam forming algorithm is improved. By combining simulation experiment results, when dynamic interference or interference signal guide vector mismatch exists, the method has higher output signal-to-interference-and-noise ratio than the traditional algorithm, and can be better applied to engineering practice due to low operation complexity.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a flowchart of a method for forming a polarization beam for resisting dynamic interference according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a polarization beam forming system for resisting dynamic interference according to an embodiment of the present invention;
in fig. 2: 1. a received signal model building module; 2. an interference plus noise covariance matrix cone design module; 3. a sampling covariance matrix tapering processing module; 4. an optimal weight vector solving module; 5. and an adaptive beam output module.
Fig. 3 is a structure diagram of the dual-polarized linear arrays which are uniformly arranged according to the embodiment of the present invention.
Fig. 4 is a diagram of a proposed algorithm provided by an embodiment of the present invention.
Figure 5 is a classical polarization beam forming pattern provided by an embodiment of the present invention.
Fig. 6 is a graph of the variation of the output signal-to-interference-and-noise ratio of each algorithm with the mismatch angle under the condition of the existence of the interference angle mismatch according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a method, a system and an application of polarization beam forming for dynamic interference resistance, and the present invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for forming a polarization beam resisting dynamic interference provided by the present invention includes the following steps:
s101: establishing a receiving signal model for a uniform linear array formed by dual-polarized array elements;
s102: constructing a sampling interference and noise covariance matrix according to the received data and designing an interference and noise covariance matrix cone;
s103: the sampling covariance matrix is subjected to tapering processing by using a covariance matrix cone, and a weight vector representation form is obtained by using a PI algorithm;
s104: solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
s105: and obtaining the self-adaptive beam output by using the optimal weight.
Those skilled in the art can also implement the method of forming a polarized beam for resisting dynamic interference according to the present invention by using other steps, and the method of forming a polarized beam for resisting dynamic interference according to the present invention shown in fig. 1 is only one specific example.
As shown in fig. 2, the polarization beam forming system for resisting dynamic interference provided by the present invention includes:
the receiving signal model establishing module 1 is used for establishing a receiving signal model for a uniform linear array formed by dual-polarized array elements;
the interference and noise covariance matrix cone design module 2 is used for constructing a sampling interference and noise covariance matrix according to the received data and designing an interference and noise covariance matrix cone;
the sampling covariance matrix tapering processing module 3 is used for tapering the sampling covariance matrix by using a covariance matrix cone and obtaining a representation form of a weight vector by using a PI algorithm;
the optimal weight vector solving module 4 is used for solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and the self-adaptive beam output module 5 is used for obtaining self-adaptive beam output by utilizing the optimal weight value.
The technical solution of the present invention is further described below with reference to the accompanying drawings.
The method for forming the polarization beam for resisting dynamic interference specifically comprises the following steps:
the first step is as follows: establishing a receiving signal model for a uniform linear array formed by dual-polarized array elements;
specifically, considering that the array receives one GNSS signal and Q narrowband interference signals, the constructed polarization array received signal model is:
wherein s is0(k) For GNSS navigation signals, sq(k) For interfering signals, n (k) is mean 0 and variance is σ2Gaussian white noise signal vector, a0,aqA desired signal steering vector and an interfering signal steering vector, respectively.
In particular, a0And akGiven by the following equation:
as(θ)＝[1,e-j2πdsinθ/λ,…,e-j2π(M-1)dsinθ/λ]H(3)
ap(θ,γ,η)＝[-cosγ cosθsinγejη]H(4)
in the above formula, asFor space-domain signal steering vector, apFor the polarization domain signal steering vector to be,
is the Kroncker product, and theta, gamma and eta are the airspace angle, the polarization angle and the polarization phase difference respectively.
The second step is that: constructing a sampling interference plus noise covariance matrix from received data
Designing an interference and noise covariance matrix cone T;
wherein K is a fast beat number [ ·]HIs a conjugate transpose.
The interference plus noise covariance matrix T can be expressed as:
wherein, TpFor polarizing covariance matrix, TsThe space-domain covariance matrix is a space-domain covariance matrix cone, and the delta theta and the delta gamma are respectively a widening space angle and a polarization angle.
The third step: the sampling covariance matrix is tapered by using a covariance matrix taper, and a weight vector W is obtained by using a PI algorithmpiA representation of (a);
specifically, the covariance matrix R after the sampling covariance matrix is taperedTComprises the following steps:
RT＝T⊙R (9)
wherein T is a covariance matrix cone, which is a Hadamard product.
According to the power inversion principle, the weight calculation process can be described as the following extremum function:
power inversion weight vector WpiThe representation form is:
wherein, the constraint vector s is [1,0, …,0 ]]TThe antenna array element number is 2 Mx 1 column vector, M is the antenna array element number, mu is a scalar, and the value of the element does not influence the filtering characteristic and only influences the output signal power.
Obtaining a PI-CMT weight vector W by using the covariance matrix after the tapering treatmentPI-CMTComprises the following steps:
wherein, mu' is not a number of standardsAffecting algorithm performance is not set to
The fourth step: solving the optimal vector of PI-CG-CMT by using a conjugate gradient method, and initializing a weight vector WPI-CG-CMTIteration step length alpha, residual vector r and search vector p are calculated, and an iteration form of the weight vector is obtained;
in particular, for formula (12) left multiplication
Conversion to:
using conjugate gradient method for formula (13), the initial value of residual vector r is obtained as:
the initial value of the search vector p is set to:
p1＝r0(15)
the expression for the iteration step α is:
the expression of the search vector p is:
the iterative expression of the weight vector is:
Wi＝Wi-1+αipi(18)
and when the norm of the residual vector is less than 0.1, ending the iteration to obtain the optimal weight vector.
The fifth step: and obtaining the self-adaptive beam output y (k) by using the optimal weight value.
The technical effects of the present invention will be described in detail with reference to simulations.
1. Simulation experiment I
In the experiment, the dual-polarized sensitive linear arrays of 16 array elements are uniformly distributed (as shown in fig. 3), the interval of the array elements is half wavelength, the incoming wave direction angle of an expected signal is 0 degree, the polarization angle is 30 degrees, the incoming wave direction angle of an interference signal is 30 degrees, the polarization angle is 20 degrees, and the polarization phase difference is 90 degrees; the signal-to-noise ratio is-30 dB, the dry-to-noise ratio is 70dB, and the sampling fast beat number is 100; comparing the algorithm PI-CG-CMT with the non-stretched algorithm PI-CG-CMT, the directional diagram (as shown in figures 4 and 5) is obtained according to the simulation conditions so as to verify the effectiveness of the invention.
2. Simulation experiment two
In the experiment, the dual-polarized sensitive linear arrays of 16 array elements are uniformly distributed (as shown in fig. 3), the interval of the array elements is half wavelength, the incoming wave direction angle of an expected signal is 0 degree, the polarization angle is 30 degrees, the incoming wave direction angle of an interference signal is 30 degrees, the polarization angle is 20 degrees, and the polarization phase difference is 90 degrees; the signal-to-noise ratio is-30 dB, the dry-to-noise ratio is 70dB, and the sampling fast beat number is 100; the angular deviation of the interference signals obeys the uniform distribution of [ -2 degrees, 2 degrees ], the variation condition of the input signal-to-noise ratio of the algorithm is analyzed and compared when the polarization angle and the space angle of the deviation of the algorithms PI-CG, PI-CG-CMT and PI-CMT are simultaneously changed from-2 degrees to 2 degrees, and the performance of the algorithm is verified.
3. Simulation experiment III
In the experiment, dual-polarized sensitive linear arrays of 8, 16 and 32 array elements are respectively uniformly arranged (as shown in fig. 3), the interval of the array elements is half wavelength, the incoming wave direction angle of an expected signal is 0 degree, the polarization angle is 30 degrees, the incoming wave direction angle of an interference signal is 30 degrees, the polarization angle is 20 degrees, and the polarization phase difference is 90 degrees; the signal-to-noise ratio is-30 dB, the dry-to-noise ratio is 70dB, and the sampling fast beat number is 100; 1000 Monte Carlo experiments are carried out, and the operation time of the PI-CG-CMT and the PI-CMT on a computer with an Intel i5-8250U processor is shown in the table 1 under the condition of comparing different array element numbers.
As can be seen by combining the first simulation experiment with the graphs in FIGS. 4 and 5, the method of the invention widens the null and has good anti-interference capability under high dynamic conditions compared with the traditional algorithm. In the second simulation experiment, compared with two algorithms of PI-CG and PI-CG-CMT, which also utilize a conjugate gradient method to reduce the operation complexity, the algorithm of the invention has better output signal-to-interference-and-noise ratio under the condition of a larger deviation interference angle. Compared with two null broadening algorithms of PI-CMT and PI-CG-CMT, the algorithm has the capability of dynamic interference resistance which is not weaker than that of the traditional method.
Comparing the running time of the algorithm of the invention with the running time of the traditional algorithm under the condition of different array element numbers in the table 1, the algorithm of the invention has lower operation complexity and is easy for engineering practice.
TABLE 1
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An anti-dynamic interference polarized beam forming method, characterized in that the anti-dynamic interference polarized beam forming method comprises:
establishing a receiving signal model for a uniform linear array formed by dual-polarized array elements;
constructing a sampling interference plus noise covariance matrix from received data
Designing an interference and noise covariance matrix cone T;
the sampling covariance matrix is tapered by using a covariance matrix taper, and a weight vector W is obtained by using a PI algorithmpiA representation of (a);
solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and obtaining the self-adaptive beam output y (k) by using the optimal weight value.
2. The method as claimed in claim 1, wherein the received signal model is built for the uniform linear array formed by dual-polarized array elements, and the built polarized array received signal model is:
wherein s is0(k) For GNSS navigation signals, sq(k) For interfering signals, n (k) is mean 0 and variance is σ2Gaussian white noise signal vector, a0,aqRespectively a desired signal steering vector and an interference signal steering vector;
a0and akGiven by the following equation:
as(θ)＝[1,e-j2πdsinθ/λ,…,e-j2π(M-1)dsinθ/λ]H；
ap(θ,γ,η)＝[-cosγcosθsinγejη]H；
in the formula, asFor space-domain signal steering vector, apFor the polarization domain signal steering vector to be,
is the Kroncker product, and theta, gamma and eta are the airspace angle, the polarization angle and the polarization phase difference respectively.
3. The dynamic interference resistant polarized beamforming method of claim 1 wherein the sampled interference-plus-noise covariance matrix is constructed from received data
Designing an interference and noise covariance matrix cone T;
wherein K is a fast beat number [ ·]HIs a conjugate transpose;
the interference plus noise covariance matrix T is expressed as:
wherein, TpFor polarizing covariance matrix, TsThe space-domain covariance matrix is a space-domain covariance matrix cone, and the delta theta and the delta gamma are respectively a widening space angle and a polarization angle.
4. The dynamic interference resistant polarized beamforming method of claim 1 wherein the sampling covariance matrix is tapered using a covariance matrix taper and the weight vector W is obtained using a PI algorithmpiA representation of (a);
covariance matrix R after sampling covariance matrix taperingTComprises the following steps:
RT＝T⊙R；
wherein T is a covariance matrix cone, which is a Hadamard product;
according to the power inversion principle, the weight calculation process can be described as the following extremum function:
power inversion weight vector WpiThe representation form is:
wherein, the constraint vector s is [1,0, …,0 ]]TThe array vector is a 2 Mx 1 column vector, M is the number of antenna array elements, mu is a scalar, and the value of the array vector does not influence the filtering characteristic and only influences the output signal power;
obtaining a PI-CMT weight vector W by using the covariance matrix after the tapering treatmentPI-CMTComprises the following steps:
wherein, the number of the mu' standard does not influence the performance of the algorithm and is set as
5. The method as claimed in claim 1, wherein the PI-CG-CMT optimal vector is solved by conjugate gradient method, and the weight vector W is initializedPI-CG-CMTIteration step length alpha, residual vector r and search vector p are calculated, and an iteration form of the weight vector is obtained;
in particular, for formula (II)
Left ride
Conversion to:
formula (II)
By using a conjugate gradient method, the initial value of the residual vector r is obtained as follows:
the initial value of the search vector p is set to:
p1＝r0；
the expression for the iteration step α is:
the expression of the search vector p is:
the iterative expression of the weight vector is:
Wi＝Wi-1+αipi；
and when the norm of the residual vector is less than 0.1, ending the iteration to obtain the optimal weight vector.
6. The method according to claim 1, wherein the adaptive beam output y (k) is obtained by using the optimal weight:
7. a radio receiving device comprising an array antenna, a multi-channel microwave receiver, a phase shifter, a digital signal processing board having an executable program stored thereon, the radio receiving device, when executed by the digital signal processing board, causing the processor to perform the steps of:
constructing a sampling interference and noise covariance matrix according to the received data and designing an interference and noise covariance matrix cone;
the sampling covariance matrix is subjected to tapering processing by using a covariance matrix cone, and a weight vector representation form is obtained by using a PI algorithm;
solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and obtaining the self-adaptive beam output by using the optimal weight.
8. An anti-dynamic interference polarized beam forming system for operating the anti-dynamic interference polarized beam forming method according to any one of claims 1 to 6, wherein the anti-dynamic interference polarized beam forming system comprises:
the receiving signal model establishing module is used for establishing a receiving signal model for the uniform linear array formed by the dual-polarized array elements;
the interference and noise covariance matrix cone designing module is used for constructing a sampling interference and noise covariance matrix according to the received data and designing an interference and noise covariance matrix cone;
the sampling covariance matrix tapering processing module is used for tapering a sampling covariance matrix by using a covariance matrix cone and obtaining a representation form of a weight vector by using a PI algorithm;
the optimal weight vector solving module is used for solving the optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and the self-adaptive beam output module is used for obtaining self-adaptive beam output by utilizing the optimal weight value.
9. A global navigation satellite anti-jamming system, characterized in that the global navigation satellite system is equipped with the dynamic interference resistant polarized beam forming system of claim 7.
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