CN111859278B - Anti-dynamic interference polarized wave 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 an anti-dynamic interference polarized wave beam forming method, a system and application thereof, wherein a received signal model is built for a uniform linear array formed by dual-polarized array elements; constructing a sampling interference plus noise covariance matrix from received dataDesigning an interference plus noise covariance matrix cone T; sampling covariance matrix tapering processing is carried out by utilizing covariance matrix tapering, and a weight vector W is obtained by utilizing a PI algorithm pi Is a representation of (2); solving an optimal weight vector of the PI-CG-CMT by using a conjugate gradient method; and utilizing the optimal weight value to output the self-adaptive beam y (k). The invention reduces the operation complexity, has higher convergence speed and improves the performance of the polarization sensitive array beam forming algorithm. 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.
Description
Technical Field
The invention belongs to the technical field of array signal processing, and particularly relates to a method, a system and an application for forming a polarized wave beam with dynamic interference resistance.
Background
At present: beamforming, an important branch of array signal processing, has been widely used in radar, sonar, navigation, and other systems. The global navigation satellite system (Global Navigation Satellite System, GNSS) requires a weaker signal and even much lower Power than background noise, however, the interference is strong, so the Power Inversion beamforming algorithm (PI) is better applied, which fully uses the features of the satellite navigation signal, the algorithm does not constrain any direction, and only requires a minimum output Power. The signal power is directly inverted, and the influence on weak expected signals is small while strong interference is restrained.
In the global navigation satellite system, because the platform of the interference receiver vibrates or the flying object carrying the interference source moves at a high speed, the problem that the interference resistance of the algorithm is affected due to the fact that the angle direction of the algorithm for suppressing the interference is not matched with the direction of the actual interference source position is generated. The method of nulling broadening is often employed to suppress interference in a high dynamic environment. Among them, covariance matrix tapering (Covariance Matrix Taper, CMT) is a more classical technique in null widening technique [ j.r. guerci.thory and application of covariance matrix tapers for robust adaptive beamforming [ J ]. IEEE trans.signal process.1999,4 (47): 977-985 ]. The covariance matrix tapering process can broaden the nulling of interference and effectively restrain dynamic interference in the space domain. Xie Ming et al generalized covariance matrix tapering to the polarization domain proposed the PSA-CMT algorithm [ M.Xie, W.Xia, S.Wei, H.Li and P.Li.A Robust GNSS Interference Suppression Method Based on Null Broadening of Dual-polarized Antenna Arrays [ C ].14th IEEE International Conference on Signal Processing (ICSP), beijin, china,2018:197-202]. The method can achieve the effect that the space domain and the polarization domain simultaneously broaden the null, but the method utilizes matrix inversion when solving the optimal weight vector of beam forming, and compared with scalar beam forming, the polarization beam forming has a covariance matrix with higher dimension, and the complexity of the inversion operation is higher. For this reason, the aim is to widen the nulls and reduce the computational complexity at the same time, which is a hot spot of research.
Through the above analysis, the problems and defects existing in the prior art are as follows: the traditional method cannot effectively inhibit dynamic interference and has high operation complexity of matrix inversion.
The difficulty of solving the problems and the defects is as follows: compared with the traditional scalar array, the polarization sensitive array is generally composed of dipoles which are orthogonal to each other, the dimension of a signal in the signal modeling process is twice that of a variable array, and the calculated amount is large when nulling broadening is carried out.
The meaning of solving the problems and the defects is as follows: the meaning of researching the anti-dynamic interference beam forming algorithm is to solve the problem that the angle direction of interference is not matched with the direction of the actual interference source position due to the fact that interference moves too fast, so that updated beam forming weight vectors cannot effectively inhibit current interference. In practical situations, reducing the calculation complexity of the algorithm can enhance the real-time performance of the system and accelerate the update speed of the weight vector, which is important for the anti-dynamic interference algorithm.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a method, a system and an application for forming a polarized wave beam with dynamic interference resistance.
The invention is realized in such a way that a method for forming a polarized beam resistant to 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 dataDesigning an interference plus noise covariance matrix cone T;
sampling covariance matrix tapering processing is carried out by utilizing covariance matrix tapering, and a weight vector W is obtained by utilizing a PI algorithm pi Is a representation of (2);
solving an optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and utilizing the optimal weight value to output the self-adaptive beam y (k).
Further, the uniform linear array formed by the dual-polarized array elements is built into a received signal model, and the built polarized array received signal model is as follows:
wherein s is 0 (k) Is a GNSS navigation signal s q (k) As an interference signal, n (k) is 0 as the mean and the variance is sigma 2 Gaussian white noise signal vector, a 0 ,a q The desired signal steering vector and the interfering signal steering vector are respectively;
a 0 and a k Is given by the following formula:
a p (θ,γ,η)＝[-cosγ cosθsinγe jη ] H ；
wherein a is s For directing vectors to spatial domain signals, a p To direct the vectors for the polarization domain signals,the Kroncker product, θ, γ, η are spatial angle, polarization angle and polarization phase difference, respectively.
Further, according to the receivingData construction sampling interference plus noise covariance matrixDesigning an interference plus noise covariance matrix cone T;
wherein K is the number of shots, [] H Is a conjugate transpose;
the interference plus noise covariance matrix cone T is expressed as:
wherein T is p Is polarization covariance matrix cone, T s Is a space domain covariance matrix cone, and delta theta and delta gamma are a widening space angle and a polarization angle respectively.
Further, the sampling covariance matrix is subjected to tapering processing by utilizing a covariance matrix taper, and a weight vector W is obtained by utilizing a PI algorithm pi Is a representation of (2);
covariance matrix R after sampling covariance matrix is tapered T The method comprises the following steps:
R T ＝T⊙R；
wherein T is the covariance matrix cone, and the Hadamard product;
according to the principle of power inversion, the weight calculation process can be described as the following extremum function:
power inversion weight vector W pi The expression form is as follows:
wherein the constraint vector s= [1,0, …,0] T The array vector is 2M multiplied by 1, 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 processing PI-CMT The method comprises the following steps:
where μ' is a scalar number that does not affect the algorithm performance may be set as
Further, the PI-CG-CMT optimal vector is solved by using a conjugate gradient method, and the weight vector W is initialized PI-CG-CMT Iteration step alpha, residual vector r and search vector p, and obtain the iterative form of the weight vector;
in particular, for the formulaLeft ride->The method comprises the following steps of:
andObtaining the initial residual vector r by using a conjugate gradient methodThe values are:
the initial value of the search vector p is set as:
p 1 ＝r 0 ；
the expression of the iteration step alpha is:
the expression of the search vector p is:
the iterative expression of the weight vector is:
W i ＝W i-1 +α i p i ；
and ending the iteration to obtain the optimal weight vector when the norm of the residual vector is smaller than 0.1.
Further, the adaptive beam output y (k) using the optimal weights:
another object of the present invention is to provide a radio receiving apparatus, characterized in that the apparatus comprises an array antenna, a multi-channel microwave receiver, a phase shifter, a digital signal processing board, etc., the digital signal processing board storing an executable program, the radio receiving apparatus, when executed by the digital signal processing board, causing the processor to execute the steps of:
constructing a sampling interference plus noise covariance matrix according to the received data and designing an interference plus noise covariance matrix cone;
sampling covariance matrix tapering processing is carried out by utilizing covariance matrix tapering, and a PI algorithm is utilized to obtain a representation form of a weight vector;
solving an optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and outputting the adaptive beam by utilizing the optimal weight.
Another object of the present invention is 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 plus noise covariance matrix according to the received data and designing an interference plus noise covariance matrix cone;
sampling covariance matrix tapering processing is carried out by utilizing covariance matrix tapering, and a PI algorithm is utilized to obtain a representation form of a weight vector;
solving an optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
and outputting the adaptive beam by utilizing the optimal weight.
Another object of the present invention is to provide an anti-dynamic interference polarization beam forming system for operating the anti-dynamic interference polarization beam forming method, the anti-dynamic interference polarization beam forming system comprising:
the receiving signal model building module is used for building a receiving signal model for the uniform linear array formed by the dual-polarized array elements;
the interference plus noise covariance matrix cone design module is used for constructing a sampling interference plus noise covariance matrix according to the received data and designing an interference plus noise covariance matrix cone;
the sampling covariance matrix tapering processing module is used for tapering the sampling covariance matrix by utilizing a covariance matrix taper and obtaining a representation form of a weight vector by utilizing 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 adaptive beam output module is used for obtaining adaptive beam output by utilizing the optimal weight.
It is another object of the present invention to provide a global navigation satellite system, which is equipped with the anti-dynamic interference polarized beam forming system.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention realizes zero notch beam broadening through the tapering processing of the interference plus noise covariance matrix, and can effectively inhibit high dynamic interference; the optimal weight vector of the PI-CG-CMT is solved through a conjugate gradient method, covariance matrix inversion is avoided, and the calculated amount of a beam forming algorithm is reduced.
The invention mainly aims at solving 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 an anti-dynamic interference polarized beam forming method based on conjugate gradient.
The invention firstly uses CMT to broaden the nulls of the interference direction, and then combines the conjugate gradient method based on Krylov subspace to realize that matrix inversion is avoided through iteration when the weight vector is obtained. The method reduces the operation complexity, has higher convergence speed and improves the performance of the polarization sensitive array beam forming algorithm. 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.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the drawings needed in the embodiments of the present application, and it is obvious that the drawings described below are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for forming a polarization beam with dynamic interference resistance according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a polarization beam forming system with dynamic interference resistance 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. sampling covariance matrix tapering processing module; 4. an optimal weight vector solving module; 5. and the adaptive beam output module.
Fig. 3 is a diagram of a dual-polarized linear array structure with uniform arrangement according to an embodiment of the present invention.
Fig. 4 is a diagram of a proposed algorithm provided by an embodiment of the present invention.
Fig. 5 is a diagram of classical polarized beam forming provided by an embodiment of the present invention.
Fig. 6 is a graph of output signal-to-interference-plus-noise ratio variation with mismatch angle for each algorithm under the condition of mismatch of interference angles provided by the embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Aiming at the problems existing in the prior art, the invention provides a method, a system and an application for forming a polarized beam with dynamic interference resistance, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for forming a polarization beam with anti-dynamic interference provided by the invention comprises 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 plus noise covariance matrix according to the received data and designing an interference plus noise covariance matrix cone;
s103: sampling covariance matrix tapering processing is carried out by utilizing covariance matrix tapering, and a PI algorithm is utilized to obtain a representation form of a weight vector;
s104: solving an optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
s105: and outputting the adaptive beam by utilizing the optimal weight.
Those skilled in the art may implement other steps in the method for forming a polarization beam with dynamic interference resistance provided by the present invention, and the method for forming a polarization beam with dynamic interference resistance provided by the present invention in fig. 1 is merely a specific embodiment.
As shown in fig. 2, the polarization beam forming system for resisting dynamic interference provided by the present invention includes:
the receiving signal model building module 1 is used for building a receiving signal model for a uniform linear array formed by dual-polarized array elements;
the interference plus noise covariance matrix cone design module 2 is used for constructing a sampling interference plus noise covariance matrix according to the received data and designing an interference plus noise covariance matrix cone;
the sampling covariance matrix tapering processing module 3 is used for tapering the sampling covariance matrix by utilizing a covariance matrix taper and obtaining a representation form of a weight vector by utilizing 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 adaptive beam output module 5 is used for outputting the adaptive beam by utilizing the optimal weight.
The technical scheme of the invention is further described below with reference to the accompanying drawings.
The invention provides an anti-dynamic interference polarized beam forming method which specifically comprises the following steps:
the first step: establishing a receiving signal model for a uniform linear array formed by dual-polarized array elements;
specifically, considering that the array receives a GNSS signal and Q narrowband interference signals, the constructed polarized array receiving signal model is:
wherein s is 0 (k) In the form of a GNSS navigation signal,s q (k) As an interference signal, n (k) is 0 as the mean and the variance is sigma 2 Gaussian white noise signal vector, a 0 ,a q The desired signal steering vector and the interfering signal steering vector, respectively.
In particular, a 0 And a k Is given by the following formula:
a s (θ)＝[1,e -j2πdsinθ/λ ,…,e -j2π(M-1)dsinθ/λ ] H (3)
a p (θ,γ,η)＝[-cosγ cosθsinγe jη ] H (4)
in the above formula, a s For directing vectors to spatial domain signals, a p To direct the vectors for the polarization domain signals,the Kroncker product, θ, γ, η are spatial angle, polarization angle and polarization phase difference, respectively.
And a second step of: constructing a sampling interference plus noise covariance matrix from received dataDesigning an interference plus noise covariance matrix cone T;
wherein K is the number of shots, [] H Is a conjugate transpose.
The interference plus noise covariance matrix cone T can be expressed as:
wherein T is p Is polarization covariance matrix cone, T s Is a space domain covariance matrix cone, and delta theta and delta gamma are a widening space angle and a polarization angle respectively.
And a third step of: sampling covariance matrix tapering processing is carried out by utilizing covariance matrix tapering, and a weight vector W is obtained by utilizing a PI algorithm pi Is a representation of (2);
specifically, the covariance matrix R after sampling covariance matrix tapering T The method comprises the following steps:
R T ＝T⊙R (9)
wherein T is the covariance matrix cone, and the Hadamard product is as follows.
According to the principle of power inversion, the weight calculation process can be described as the following extremum function:
power inversion weight vector W pi The expression form is as follows:
wherein the constraint vector s= [1,0, …,0] T The column vector is 2M multiplied by 1, M is the number of antenna array elements, mu is a scalar, and the value of the column 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 processing PI-CMT The method comprises the following steps:
wherein μ' is a labelThe number of the quantities does not affect the algorithm performance and can be set as
Fourth step: solving PI-CG-CMT optimal vector by conjugate gradient method and initializing weight vector W PI-CG-CMT Iteration step alpha, residual vector r and search vector p, and obtain the iterative form of the weight vector;
in particular, for formula (12) the left multiplierThe method comprises the following steps of:
using a conjugate gradient method for equation (13), the initial value of the residual vector r is obtained as follows:
the initial value of the search vector p is set as:
p 1 ＝r 0 (15)
the expression of the iteration step alpha is:
the expression of the search vector p is:
the iterative expression of the weight vector is:
W i ＝W i-1 +α i p i (18)
and ending the iteration to obtain the optimal weight vector when the norm of the residual vector is smaller than 0.1.
Fifth step: and utilizing the optimal weight value to output the self-adaptive beam y (k).
The technical effects of the present invention will be described in detail with reference to simulation.
1. Simulation experiment one
The experiment adopts a dual-polarized sensitive linear array of 16 array elements (as shown in figure 3), the array element intervals are half wavelength, the incoming wave direction angle of the expected signal is 0 degree, the polarization angle is 30 degrees, the incoming wave direction angle of the 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 snapshot number is 100; comparing the algorithm PI-CG-CMT of the invention with the non-stretched algorithm PI-CG-CMT, and obtaining a pattern (as shown in figures 4 and 5) according to the simulation conditions so as to verify the effectiveness of the invention.
2. Simulation experiment II
The experiment adopts a dual-polarized sensitive linear array of 16 array elements (as shown in figure 3), the array element intervals are half wavelength, the incoming wave direction angle of the expected signal is 0 degree, the polarization angle is 30 degrees, the incoming wave direction angle of the 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 snapshot number is 100; the angle deviation of the interference signal obeys the uniform distribution of [ -2 degrees, 2 degrees ], analyzes and compares the change condition of the input signal to noise ratio of the algorithm when the polarized angle and the space angle of the deviation are changed from-2 degrees to 2 degrees simultaneously, and verifies the performance of the algorithm.
3. Simulation experiment III
The experiment adopts dual polarization sensitive linear arrays of 8, 16 and 32 array elements which are uniformly distributed (as shown in figure 3), the array element interval is half wavelength, the incoming wave direction angle of the expected signal is 0 degree, the polarization angle is 30 degrees, the incoming wave direction angle of the 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 snapshot number is 100; 1000 Monte Carlo experiments were performed, and the running time of PI-CG-CMT and PI-CMT on a computer with Intel i5-8250U processor is shown in Table 1, comparing the different array element numbers.
As can be seen by combining simulation experiment I with figures 4 and 5, the method of the invention widens the null and has good anti-interference capability under the high dynamic condition compared with the traditional algorithm. In a simulation experiment II, the algorithm for reducing the operation complexity by using the conjugate gradient method is compared with the PI-CG and the PI-CG-CMT, and the algorithm has better output signal-to-interference-noise ratio under the condition of larger deviation interference angle. Compared with two null widening algorithms of PI-CMT and PI-CG-CMT, the algorithm has the anti-dynamic interference capability which is not weaker than that of the traditional method.
Comparing the operation time of the algorithm of the invention with the operation 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 in 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 special purpose design hardware. Those of ordinary skill 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 as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (8)
1. The method for forming the anti-dynamic interference polarized beam is characterized by comprising the following steps of:
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 dataDesigning an interference plus noise covariance matrix cone T;
sampling covariance matrix tapering processing is carried out by utilizing covariance matrix tapering, and a power inversion weight vector W after tapering is obtained by combining PI algorithm principle PI-CMT Is a representation of (2);
solving an optimal weight vector of the PI-CG-CMT by using a conjugate gradient method;
PI-CG-CMT is defined as solving the weight vector W by the conjugate gradient method CG PI-CMT Is a process of (1);
utilizing the optimal weight to output a self-adaptive beam y (k);
the PI-CG-CMT optimal vector is solved by using a conjugate gradient method, and a weight vector W is initialized PI-CG-CMT Iteration step alpha, residual vector r and search vector p, and obtain the iterative form of the weight vector;
for the purpose ofLeft ride->The method comprises the following steps of:
wherein,for the sample covariance matrix after tapering, +.>Set to +.>s＝[1,0,…,0] T Is a constraint vector; m is the number of antenna elements; solving optimal weight vector W of PI-CG-CMT by conjugate gradient method PI-CG-CMT ；
AndThe initial value of the residual vector r is obtained by using a conjugate gradient method:
the initial value of the search vector p is set as:
p 1 ＝r 0 ；
the expression of the iteration step alpha is:
the expression of the search vector p is:
the iterative expression of the weight vector is:
W i ＝W i-1 +α i p i ；
and ending the iteration to obtain the optimal weight vector when the norm of the residual vector is smaller than 0.1.
2. The method for forming a polarized beam with dynamic interference resistance according to claim 1, wherein the receiving signal model is built for the uniform linear array formed by the dual polarized array elements, and the built receiving signal model of the polarized array is:
wherein x (k) is a uniform linear array formed by dual-polarized array elements, and a receiving signal model is built; s is(s) 0 (k) Is a GNSS navigation signal s q (k) As an interference signal, n (k) is 0 as the mean and the variance is sigma 2 Gaussian white noise signal vector, a 0 ,a q The desired signal steering vector and the interfering signal steering vector are respectively;
a 0 and a k Is given by the following formula:
a s (θ)＝[1,e -j2πdsinθ/λ ,…,e -j2π(M-1)dsinθ/λ ] H ；
a p (θ,γ,η)＝[-cosγcosθsinγe jη ] H ；
wherein a is s For directing vectors to spatial domain signals, a p To direct the vectors for the polarization domain signals,the Kroncker product, θ, γ, η are spatial angle, polarization angle and polarization phase difference, respectively.
3. The method of polarization beam forming for dynamic interference resistance according to claim 1, wherein the constructing a sampling interference plus noise covariance matrix from received dataDesigning an interference plus noise covariance matrix cone T;
wherein K is the number of shots, [] H Is a conjugate transpose; x (k) is a uniform linear array formed by dual-polarized array elements to establish a receiving signal model;
the interference plus noise covariance matrix cone T is expressed as:
wherein T is p Is polarization covariance matrix cone, T s Is a space domain covariance matrix cone, and delta theta and delta gamma are a widening space angle and a polarization angle respectively.
4. The method for forming polarized beam with dynamic interference resistance as claimed in claim 1, wherein the sampling covariance matrix is tapered by using covariance matrix taper, and the power inversion weight vector W after tapering is obtained by combining PI algorithm principle PI-CMT Is a representation of (2);
according to the principle of power inversion, the weight calculation process can be described as the following extremum function:
power inversion weight vector W PI The expression form is as follows:
wherein the constraint vector s= [1,0, …,0] T The array vector is 2M multiplied by 1, 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;
covariance matrix after sampling covariance matrix is taperedThe method comprises the following steps:
wherein T is the covariance matrix cone, and the Hadamard product;
the cone covariance matrix is carried into a power inversion weight vector expression to obtain a PI-CMT weight vector W PI-CMT The method comprises the following steps:
wherein μ' is a scalar number that does not affect algorithm performance is set to
5. The method of polarization beam forming for dynamic interference resistance according to claim 1, wherein the adaptive beam output y (k) is obtained by using an optimal weight:
wherein W is PI-CG-CMT And (3) establishing a receiving signal model for an even linear array formed by dual-polarized array elements for an optimal weight vector of the PI-CG-CMT, wherein y (k) is self-adaptive beam output data.
6. A dynamic interference resistant polarized beam forming system for operating the dynamic interference resistant polarized beam forming method according to any one of claims 1 to 5, characterized in that the dynamic interference resistant polarized beam forming system comprises:
the receiving signal model building module is used for building a receiving signal model for the uniform linear array formed by the dual-polarized array elements;
the interference plus noise covariance matrix cone design module is used for constructing a sampling interference plus noise covariance matrix according to the received data and designing an interference plus noise covariance matrix cone;
the sampling covariance matrix tapering processing module is used for tapering the sampling covariance matrix by utilizing a covariance matrix taper and obtaining a representation form of a weight vector by utilizing 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 adaptive beam output module is used for obtaining adaptive beam output by utilizing the optimal weight.
7. A global navigation satellite anti-interference system, wherein the global navigation satellite system is equipped with the anti-dynamic interference polarized beam forming system of claim 6.
8. A radio receiving apparatus comprising an array antenna, a multi-channel microwave receiver, a phase shifter, a digital signal processing board storing an executable program, the radio receiving apparatus when executed by the digital signal processing board causing the processor to perform the method of polarization beamforming of dynamic interference rejection according to claim 1.
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