CN111241470B - Beam synthesis method and device based on self-adaptive null widening algorithm - Google Patents

Abstract

The application is applicable to the technical field of navigation, and provides a beam synthesis method and a device based on a self-adaptive null widening algorithm, wherein the method comprises the following steps: constructing an initial covariance matrix for a circular array signal acquired by a circular array; according to the arrangement information and the interference disturbance parameters of the circular array, calculating a zero notch widening algorithm expansion matrix of the circular array signal based on a Laplacian algorithm; obtaining a covariance matrix after the circular array signal correction according to the initial covariance matrix and the null widening algorithm expansion matrix based on the Laplace algorithm; substituting the covariance matrix corrected by the circular array signal into a multi-level wiener filter, and calculating a self-adaptive weight; and carrying out wave beam synthesis on the circular array signal according to the self-adaptive weight. The application can realize the widening of anti-interference nulls without estimating interference, can effectively reduce the calculated amount and improve the beam synthesis efficiency.

Description

Beamforming method and device based on adaptive nulling widening algorithm

technical field

The invention belongs to the technical field of navigation, and in particular relates to a beam forming method and device based on an adaptive zero-slot widening algorithm.

Background technique

The global navigation satellite system (GNSS) has been widely used because of its high navigation accuracy and the advantages of not accumulating errors over time. However, since the power of the GNSS signal reaching the receiver is 20dB lower than the noise floor, it is easily suppressed by strong interference signals.

At present, in engineering, array antennas are often used at the receiver side, and space-time adaptive processing (space-time adaptive processing, STAP) is used to generate adaptive nulls upward in strong interference to achieve anti-interference to GNSS signals. The traditional algorithm based on covariance matrix tapering and nulling broadening is only suitable for linear arrays, and for circular arrays, it is necessary to estimate the interference direction information, and the estimation of interference direction needs to involve high-order matrix space spectrum analysis, and the amount of calculation is too large. Seriously affects the high dynamic performance of the anti-jamming algorithm.

Contents of the invention

In view of this, an embodiment of the present invention provides a beamforming method and device based on an adaptive nulling widening algorithm, so as to solve the problem of excessive calculation of the circular array based adaptive nulling algorithm in the prior art.

The first aspect of the embodiments of the present invention provides a beamforming method based on an adaptive nulling widening algorithm, including:

Construct the initial covariance matrix for the circular array signal collected by the circular array array;

According to the arrangement information of the circular array array and the disturbance disturbance parameter, calculate the expansion matrix of the zero notching widening algorithm based on the Laplacian algorithm of the circular array signal;

Obtaining the corrected covariance matrix of the circular array signal according to the initial covariance matrix and the Laplacian-based zero trap widening algorithm expansion matrix;

Substituting the corrected covariance matrix of the circular array signal into a multi-stage Wiener filter to calculate the adaptive weight;

performing beamforming on the circular array signals according to the adaptive weights.

The second aspect of the embodiments of the present invention provides a beamforming device based on an adaptive nulling widening algorithm, including:

The initial matrix creation module is used to construct the initial covariance matrix for the circular array signal collected by the circular array array;

The expansion matrix creation module is used to calculate the expansion matrix of the zero trap widening algorithm based on the Laplacian algorithm of the circular array signal according to the arrangement information of the circular array array and the disturbance disturbance parameter;

A matrix correction module, configured to obtain the corrected covariance matrix of the circular array signal according to the initial covariance matrix and the extended matrix of the zero-trap widening algorithm based on the Laplacian algorithm;

A weight calculation module, for substituting the corrected covariance matrix of the circular array signal into a multi-stage Wiener filter to calculate an adaptive weight;

A beamforming module, configured to perform beamforming on the circular array signal according to the adaptive weight.

A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, when the processor executes the computer program The steps of implementing the beamforming method based on the adaptive nulling widening algorithm as described above.

The fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned adaptive zero-notch widening algorithm based on The steps of the beamforming method.

Compared with the prior art, the embodiment of the present invention has the following beneficial effects: first, in this embodiment, an initial covariance matrix is constructed for the circular array signals collected by the circular array array; according to the arrangement information of the circular array array and the disturbance disturbance parameters, Calculating the Laplacian-based zero-trap widening algorithm expansion matrix of the circular array signal; then according to the initial covariance matrix and the Laplacian-based zero-trap widening algorithm expansion matrix, the circle is obtained The covariance matrix after the correction of the array signal; the covariance matrix after the correction of the circular array signal is substituted into a multi-stage Wiener filter to calculate the adaptive weight; finally, the circular array signal is processed according to the adaptive weight Beamforming. In this embodiment, the covariance matrix of the circular array signal is updated by creating an extended matrix, and the anti-interference null can be widened without estimating the direction of interference, which can effectively reduce the amount of calculation, and according to the updated covariance matrix and multiple The level Wiener filter calculates the adaptive weight, which can further reduce the calculation amount and improve the beamforming efficiency.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the descriptions of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only of the present invention. For some embodiments, those of ordinary skill in the art can also obtain other drawings based on these drawings without paying creative efforts.

FIG. 1 is a schematic flow diagram of a beamforming method based on an adaptive nulling widening algorithm provided by an embodiment of the present invention;

FIG. 2 is a schematic diagram of an implementation flow of S102 in FIG. 1 provided by an embodiment of the present invention;

FIG. 3 is a schematic diagram of an implementation flow of S105 in FIG. 1 provided by an embodiment of the present invention;

Fig. 4 is an example diagram of the circular array array arrangement provided by the embodiment of the present invention;

Fig. 5 is a schematic diagram of zero trap widening curves under two algorithms provided by an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of a beamforming device based on an adaptive nulling widening algorithm provided by an embodiment of the present invention;

Fig. 7 is a schematic diagram of a terminal device provided by an embodiment of the present invention.

Detailed ways

In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

In order to illustrate the technical solutions of the present invention, specific examples are used below to illustrate.

In one embodiment, as shown in FIG. 1, FIG. 1 shows a flow of a beamforming method based on an adaptive nulling widening algorithm provided by an embodiment of the present invention, and the process is described in detail as follows:

S101: Construct an initial covariance matrix for the circular array signals collected by the circular array array.

In this embodiment, it is first necessary to obtain the antenna collection data of the circular array array, and then calculate the autocorrelation matrix of the antenna collection data, that is, the initial covariance matrix. The initial covariance matrix may be: R _x =X(n)X(n) ^H , where R _x represents the initial covariance matrix, and X(n) represents the data input matrix corresponding to the data collected by the antenna. The number of snapshots can be set as required. In this embodiment, the number of snapshots can be set to 128.

S102: According to the arrangement information of the circular array array and the interference disturbance parameter, calculate the expansion matrix of the zero trap widening algorithm based on the Laplacian algorithm of the circular array signal.

In this embodiment, as shown in Figure 4, Figure 4 shows the arrangement of a uniform circular array with seven array elements, and the array is arranged as a uniform circular array. The signal parameters of the array determine the arrangement information of the array.

In this embodiment, the interference disturbance parameter is the standard deviation of the change of the interference angle, which can be counted by prior information. In this embodiment, the extended matrix of the Laplace nulling widening algorithm of the circular array is determined according to the disturbance disturbance parameter and the arrangement information of the circular array.

S103: Obtain a corrected covariance matrix of the circular array signal according to the initial covariance matrix and the extended matrix of the Laplacian-based zero-trap widening algorithm.

In this embodiment, according to the initial covariance matrix of the circular array signal and the extended matrix of the zero trap widening algorithm based on the Laplacian algorithm, the matrix after the covariance matrix has been tapered can be obtained.

S104: Substituting the corrected covariance matrix of the circular array signal into a multi-stage Wiener filter to calculate an adaptive weight.

In this embodiment, the multi-stage Wiener filter is a correlation subtraction multi-stage Wiener filter. According to the weight calculation formula of the multi-stage Wiener filter and the covariance matrix of the circular array array, the multi-stage Wiener filter is established The relationship between the filter and the covariance matrix, thereby optimizing the weight calculation formula of the multi-level Wiener filter, and according to the weight calculation formula of the multi-level Wiener filter and the modified covariance matrix, calculate the adaptive weight .

The present application realizes that the Laplace zero trap widening algorithm is directly applied to the multi-stage Wiener filter, which reduces the amount of calculation.

S105: Perform beamforming on the circular array signal according to the adaptive weight.

In this embodiment, the anti-interference processing of the circular array signal can be completed according to the adaptive weight, and the beamforming of the circular array signal can be realized.

As can be seen from the above-mentioned embodiment, in this embodiment, firstly, an initial covariance matrix is constructed for the circular array signal collected by the circular array array; The zero trap widening algorithm expansion matrix of the Laplace algorithm; then according to the initial covariance matrix and the zero trap widening algorithm expansion matrix based on the Laplace algorithm, obtain the corrected covariance matrix of the circular array signal; The corrected covariance matrix of the circular array signal is substituted into a multi-stage Wiener filter to calculate an adaptive weight; finally, beamforming is performed on the circular array signal according to the adaptive weight. In this embodiment, the covariance matrix of the circular array signal is updated by creating an extended matrix, and the anti-interference null can be widened without estimating the direction of interference, which can effectively reduce the amount of calculation, and according to the updated covariance matrix and multiple The level Wiener filter calculates the adaptive weight, which can further reduce the calculation amount and improve the beamforming efficiency.

In one embodiment, as shown in FIG. 2, FIG. 2 shows a specific implementation process of S102 in FIG. 1, and the process is described in detail as follows:

S201: Obtain signal parameters of circular array signals collected by the circular array array; and obtain arrangement information of the circular array array according to the signal parameters of the circular array signals.

In this embodiment, the signal parameters include the elevation angle, azimuth angle, wavelength, etc. of the circular array signal. Suppose the pitch angle of the circular array signal received by the circular array array is θ, and the azimuth angle is The wavelength is λ, then the wave number vector of the signal is shown in formula (1):

in, Represents the beam vector.

Let M be the number of array elements, then the array arrangement of a uniform circular array without a center is shown in formula (2):

Among them, m represents the serial number of the array element, and _rm represents the array arrangement of the mth array element.

In this embodiment, the radius of the circular array is set to be d, and the position vector of the mth array element is obtained from formula (2) as shown in formula (3):

P _m ＝d[cosr _m ,sinr _m ] ^T (3)

In formula (3), P _m represents the position vector of the mth array element.

From formula (1) and formula (3), the spatial steering vector of the circular array signal can be obtained as formula (4):

In this embodiment, it is assumed that in a high dynamic environment, the direction of the qth interference signal is as shown in formula (5):

In formula (5), θ _q is the initial pitch angle of the interference signal, is the initial azimuth angle of the interference signal; Δθ _q is the change range of the pitch angle of the interference signal, /> is the change range of the azimuth angle of the interference signal, and all obey the mean value of 0, and the variance is Laplace distribution; /> Indicates the true value of the pitch angle of the interference signal, /> is the true value of the azimuth angle of the interference signal.

S202: Determine a maximum expansion angle according to the disturbance disturbance parameter.

S203: According to the arrangement information of the circular array, the maximum expansion angle, and the signal parameters of the circular array signal, determine an expansion matrix of the circular array signal based on a Laplacian algorithm-based zero-trap widening algorithm.

In one embodiment, the expansion matrix of the zero notch widening algorithm based on the Laplacian algorithm of the circular array signal is:

In formula (6), Represents the element of the mth row and nth column in the expansion matrix of the zero-notching widening algorithm based on the Laplacian algorithm, _ξmax represents the maximum expansion angle, and r _m represents the arrangement of the mth array element in the circular array array information, r _n represents the arrangement information of the nth array element in the circular array, λ represents the wavelength of the circular array signal, and d represents the radius of the circular array.

In this embodiment, since the satellite signal power is much lower than the noise level power, the covariance matrix R _x of the circular array array is mainly dominated by the interference and noise covariance matrix. Assume that the receiver noise is zero-mean and has a variance of Gaussian white noise. At this time, the average covariance matrix of the Laplace algorithm is constructed as shown in formula (7):

In formula (7), is the qth interference power; /> for /> The joint probability density function of ; δ _mn is the Kronecker δ function, /> (m, n) represents the element in the mth row and the nth column in the average covariance matrix of the Laplace algorithm.

Because Δθ _q , independent of each other, will /> Carry out the first-order Taylor series expansion to obtain formula (8):

Substitute formula (8) into formula (7) to get formula (9):

make Then the first term of the integral in formula (9) is shown in formula (10):

In the same way, make Then the second term of the integral in formula (8) is shown in formula (11):

Substitute formula (10) and formula (11) into (9) to get formula (12):

Thus, the elements of the mth row and nth column of the zero-sentence expansion matrix of Laplace distribution are obtained as formula (13):

Substituting D _mn and F _mn into (13) to obtain the zero trap expansion matrix satisfying the Laplace distribution is shown in formula (14):

In formula (14)

Since there is no reference to the specific interference direction, the maximum angle of expansion required in the motion is considered, and when , according to the expansion of trigonometric functions, formula (14) can be reduced to formula (15):

In formula (15), the ξ _max corresponding to the maximum expansion angle can be determined by prior information. In this way, the zero-slip broadening expansion matrix that satisfies the Laplace distribution is obtained.

In one embodiment, the specific implementation process of S103 in FIG. 1 includes:

The Hadamard product is calculated for the initial covariance matrix of the circular array signal and the extended matrix of the zero trapping algorithm based on the Laplacian algorithm to obtain the corrected covariance matrix.

In one embodiment, S104 in FIG. 1 specifically includes:

via caculation:

Obtain the adaptive weight;

In formula (16), W _CSA-MWF represents described self-adaptive weight value, h ₀ represents the initial weight value of desired signal, T _D represents the reduced-rank matrix, represents the collection of matched filter weight value; Denotes the modified covariance matrix.

In this embodiment, the structure of the correlation subtraction multistage Wiener filter CSA-MWF is shown in Figure 1, which is consistent with the common multistage Wiener filter, and it is also a generalized sidelobe canceller. For the desired signal branch, the lower branch obtains the interference and noise by orthogonally decomposing the steering vector of the desired signal, and then subtracts the two paths to realize interference cancellation. CSA-MWF does not need to explicitly calculate the blocking matrix. Compared with the multi-stage Wiener filter GRS-MWF, CSA-MWF has a smaller amount of calculation and can wait for better performance under the snapshot. However, the covariance matrix of the input signal is not needed in the whole iterative process of the conventional CSA-MWF, so the covariance matrix cannot be used for reconstruction to achieve the purpose of zero trap widening. Therefore, the CSA-MWF algorithm should be treated equivalently, and the relationship between the adaptive weight obtained by the algorithm and the covariance matrix of the input data should be established, so that the zero trap widening algorithm can be directly applied.

In this embodiment, the conventional CSA-MWF output weight is shown in formula (17):

W _CSA-MWF = h ₀ -T _D w _d (17)

In formula (17), h ₀ represents the initial weight of the desired signal. If it is equal to the steering vector of the desired signal, a multi-stage Wiener filter with no distortion response will be obtained. In order to simplify the calculation, the specific direction will not be constrained here, that is, h ₀ = δ _mk , δ _mk = [1,0,…,0];

In formula (17), w _d represents the equivalent weight of the cancellation branch, and satisfies relation (18)

In formula (18), T _D =[h ₁ ,h ₂ ,…,h _D ]; T _D is a reduced-rank matrix, representing a set of matched filter weights, T _D =[h ₁ ,h ₂ ,…,h _D ], and Among them, D is the total number of iterations of the algorithm; h _i represents the weight of the i-th matched filter.

because but

In formula (20), R _x =E(x(n)x(n) ^H ) is the autocorrelation matrix of the data collected by the antenna, that is, the covariance matrix.

because F:

but

Substitute formula (23) and formula (24) into formula (18):

Substitute Equation (25) into Equation (17) to get another weight expression of the CSA-MWF algorithm:

Substituting the corrected covariance matrix of the circular array signal into Equation (26), the CSA-MWF output weights after Laplace algorithm zero trap widening can be obtained

In one embodiment, FIG. 3 shows a specific implementation process of S105 in FIG. 1, which includes:

S301: Obtain an input data matrix of the circular array signal;

S302: Multiply the adaptive weight by the input data matrix of the circular array signal to complete the beamforming of the circular array signal.

In this embodiment, the input data matrix is the matrix X(n) of data collected by the antenna.

It can be seen from the above embodiments that in this embodiment, the autocorrelation matrix is firstly calculated using the signals collected by the array antenna, and then the expansion matrix is calculated according to the array arrangement information and the prior information of the interference disturbance parameters. Then the expansion matrix is multiplied by the signal autocorrelation matrix to obtain the matrix after the covariance matrix is tapered. Substitute the new autocorrelation matrix into the optimized CSA-MWF to calculate the adaptive weight. Finally, according to the calculated adaptive weights, the beamforming of the array signal is completed, and the adaptive nulling broadening to the direction of interference is realized.

The algorithm provided in this embodiment can realize the widening of anti-jamming nulls without estimating where the jamming comes from, and broadens the application area on the premise of reducing the calculation amount. At the same time, the weight calculation method of the CSA-MWF algorithm is re-optimized, the relationship between it and the covariance matrix of the input data is established, and the Laplace zero trap widening algorithm is directly applied to the multi-stage Wiener filter, which reduces the amount of calculation again. This embodiment combines the versatility of the Laplace zero-notching widening algorithm and the characteristics of the multi-stage Wiener filter with a low calculation amount and a small number of required snapshots, and is a solution that is very suitable for engineering. It can be used in various fields such as satellite communication, electronic reconnaissance, navigation research applications, electronic countermeasures (jamming, anti-jamming).

In one embodiment of the present invention, the simulation effect evaluation process of the above method is as follows:

Set up the simulation environment, the signal is the Beidou signal in the B3 frequency band, the carrier-to-noise ratio is 44dB, the pitch angle is 50°, and the azimuth angle is 10°; the array is a uniform circular array with 7 elements, and the radius is half a wavelength; the interference is narrow-band interference with an interference-to-noise ratio of 69dB. The pitch angle is 30°, the azimuth angle is 200°; the number of snapshots is 128. The anti-interference adaptive nulling generated by the common CSA-MWF algorithm is compared with the nulling processed by the Laplace algorithm. The simulation results are shown in Figure 5. The curve 41 in FIG. 5 represents the zero trap widening curve under the common WMF algorithm, and the curve 42 in FIG. 5 represents the zero trap widening curve processed by the Laplace algorithm provided in this embodiment. It can be seen that both algorithms accurately generate nulls in the direction of the interference, but the nulls are obviously wider after being processed by the Laplace algorithm, which shows that the nulling widening algorithm is effective.

It should be understood that the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present invention.

In one embodiment, as shown in FIG. 6 , FIG. 6 shows the structure of a beamforming device 100 based on an adaptive nulling widening algorithm provided by an embodiment of the present invention, which includes:

The initial matrix creation module 110 is used to construct the initial covariance matrix for the circular array signal collected by the circular array array;

The expansion matrix creation module 120 is used to calculate the expansion matrix of the zero trap widening algorithm based on the Laplacian algorithm of the circular array signal according to the arrangement information of the circular array array and the disturbance disturbance parameter;

The matrix correction module 130 is used to obtain the corrected covariance matrix of the circular array signal according to the initial covariance matrix and the extended matrix of the zero trap widening algorithm based on the Laplacian algorithm;

The weight calculation module 140 is used for substituting the covariance matrix after the correction of the circular array signal into the multi-stage Wiener filter to calculate the adaptive weight;

The beamforming module 150 is configured to perform beamforming on the circular array signal according to the adaptive weight.

In one embodiment, the extended matrix creation module 120 in FIG. 6 includes:

An arrangement information acquisition unit, configured to acquire signal parameters of the circular array signals collected by the circular array array; and obtain the arrangement information of the circular array array according to the signal parameters of the circular array signals;

An expansion angle calculation unit, configured to determine a maximum expansion angle according to the disturbance disturbance parameter;

An expansion matrix creation unit, configured to determine the zero trap expansion of the circular array signal based on the Laplacian algorithm according to the arrangement information of the circular array array, the maximum expansion angle, and the signal parameters of the circular array signal Algorithm extension matrix.

In one embodiment, the expansion matrix of the zero notch widening algorithm based on the Laplacian algorithm of the circular array signal is:

in, Represents the element of the mth row and nth column in the expansion matrix of the zero-notching widening algorithm based on the Laplacian algorithm, _ξmax represents the maximum expansion angle, and r _m represents the arrangement of the mth array element in the circular array array information, r _n represents the arrangement information of the nth array element in the circular array, λ represents the wavelength of the circular array signal, and d represents the radius of the circular array.

In one embodiment, the matrix correction module 130 in FIG. 6 includes: calculating the Hadamard product for the initial covariance matrix of the circular array signal and the extended matrix of the zero trap widening algorithm based on the Laplacian algorithm to obtain a correction The resulting covariance matrix.

In one embodiment, the weight calculation module 140 in FIG. 6 includes:

via caculation:

Obtain the adaptive weight;

Among them, W _CSA-MWF represents the adaptive weight, h ₀ represents the initial weight of the desired signal, T _D represents the reduced rank matrix, Denotes the modified covariance matrix.

In one embodiment, the beamforming module 150 in FIG. 6 includes:

an input data matrix acquisition unit, configured to acquire the input data matrix of the circular array signal;

The beam forming unit is configured to multiply the adaptive weight by the input data matrix of the circular array signal to complete the beam forming of the circular array signal.

Fig. 7 is a schematic diagram of a terminal device provided by an embodiment of the present invention. As shown in FIG. 7 , a terminal device 700 in this embodiment includes: a processor 70 , a memory 71 , and a computer program 72 stored in the memory 71 and operable on the processor 70 . When the processor 70 executes the computer program 72, it implements the steps in the various method embodiments above, such as steps 101 to 105 shown in FIG. 1 . Alternatively, when the processor 70 executes the computer program 72, it realizes the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 110 to 150 shown in FIG. 6 .

The computer program 72 can be divided into one or more modules/units, and the one or more modules/units are stored in the memory 71 and executed by the processor 70 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program 72 in the terminal device 700 .

The terminal device 700 may be computing devices such as desktop computers, notebooks, palmtop computers, and cloud servers. The terminal device may include, but not limited to, a processor 70 and a memory 71 . Those skilled in the art can understand that FIG. 7 is only an example of a terminal device 700, and does not constitute a limitation to the terminal device 700. It may include more or less components than those shown in the figure, or combine certain components, or different components. , for example, the terminal device may also include an input and output device, a network access device, a bus, and the like.

The so-called processor 70 may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), Off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

The storage 71 may be an internal storage unit of the terminal device 700 , for example, a hard disk or a memory of the terminal device 700 . The memory 71 can also be an external storage device of the terminal device 700, such as a plug-in hard disk equipped on the terminal device 700, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc. Further, the memory 71 may also include both an internal storage unit of the terminal device 700 and an external storage device. The memory 71 is used to store the computer program and other programs and data required by the terminal device. The memory 71 can also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used for illustration. In practical applications, the above-mentioned functions can be assigned to different functional units, Completion of modules means that the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into one processing unit, or each unit can exist separately physically, or two or more units can be integrated into one unit, and the above-mentioned integrated units can either adopt hardware It can also be implemented in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above system, reference may be made to the corresponding processes in the aforementioned method embodiments, and details will not be repeated here.

In the above-mentioned embodiments, the descriptions of each embodiment have their own emphases, and for parts that are not detailed or recorded in a certain embodiment, refer to the relevant descriptions of other embodiments.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal equipment and method may be implemented in other ways. For example, the device/terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units Or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated module/unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention realizes all or part of the processes in the methods of the above embodiments, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer When the program is executed by the processor, the steps in the above-mentioned various method embodiments can be realized. . Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, computer-readable media Excludes electrical carrier signals and telecommunication signals.

The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still carry out the foregoing embodiments Modifications to the technical solutions recorded in the examples, or equivalent replacement of some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should be included in within the protection scope of the present invention.

Claims (7)

1. The beam forming method based on the adaptive null widening algorithm is characterized by comprising the following steps:

constructing an initial covariance matrix for a circular array signal acquired by a circular array;

according to the arrangement information and the interference disturbance parameters of the circular array, calculating a zero notch widening algorithm expansion matrix of the circular array signal based on a Laplacian algorithm;

obtaining a covariance matrix after the circular array signal correction according to the initial covariance matrix and the null widening algorithm expansion matrix based on the Laplace algorithm;

substituting the covariance matrix corrected by the circular array signal into a multi-level wiener filter, and calculating a self-adaptive weight;

carrying out wave beam synthesis on the circular array signal according to the self-adaptive weight;

the method for calculating the zero notch widening algorithm expansion matrix of the circular array signal based on the Laplacian algorithm according to the arrangement information of the circular array and the interference disturbance parameters comprises the following steps:

acquiring signal parameters of a circular array signal acquired by the circular array, and acquiring arrangement information of the circular array according to the signal parameters of the circular array signal;

determining a maximum expansion angle according to the interference disturbance parameters;

determining a zero notch widening algorithm expansion matrix of the circular array signal based on a Laplacian algorithm according to the arrangement information of the circular array, the maximum expansion angle and the signal parameters of the circular array signal;

the zero notch widening algorithm expansion matrix based on the Laplace algorithm of the circular array signal is as follows:

wherein ,representing the element of the nth row and the nth column in the expansion matrix of the null-dip widening algorithm based on the Laplace algorithm, and xi _max Represents the maximum expansion angle, r _m Representing the arrangement information of the mth array element in the circular array, r _n And representing the arrangement information of the nth array element in the circular array, wherein lambda represents the wavelength of a circular array signal, and d represents the radius of the circular array.

2. The beam forming method based on the adaptive null widening algorithm according to claim 1, wherein the obtaining the covariance matrix after the circular array signal correction according to the initial covariance matrix and the null widening algorithm expansion matrix based on the laplace algorithm comprises:

and solving Hadamard products of the initial covariance matrix of the circular array signal and the null widening algorithm expansion matrix based on the Laplace algorithm to obtain a corrected covariance matrix.

3. The beam forming method based on the adaptive null widening algorithm according to claim 1, wherein substituting the covariance matrix corrected by the circular array signal into a multi-stage wiener filter, and calculating the adaptive weight comprises:

by calculation:

obtaining the self-adaptive weight;

wherein ,W_CSA-MWF Representing the adaptive weight, h ₀ Representing the initial weight of the desired signal, T _D Representing a reduced rank matrix,representing the corrected covariance matrix.

4. The beam forming method based on the adaptive null widening algorithm according to claim 1, wherein the beam forming the circular array signal according to the adaptive weight comprises:

acquiring an input data matrix of the circular array signal;

and multiplying the self-adaptive weight with the input data matrix of the circular array signal to complete the beam forming of the circular array signal.

5. A beam forming device based on an adaptive null widening algorithm, comprising:

the initial matrix creation module is used for constructing an initial covariance matrix for the circular array signals acquired by the circular array;

the expansion matrix creation module is used for calculating a null widening algorithm expansion matrix of the circular array signal based on the Laplace algorithm according to the arrangement information of the circular array and the interference disturbance parameters;

the matrix correction module is used for obtaining the covariance matrix after the circular array signal correction according to the initial covariance matrix and the zero notch widening algorithm expansion matrix based on the Laplace algorithm;

the weight calculation module is used for substituting the covariance matrix corrected by the circular array signal into a multi-level wiener filter to calculate an adaptive weight;

the beam synthesis module is used for carrying out beam synthesis on the circular array signal according to the self-adaptive weight;

the expansion matrix creation module includes:

the arrangement information acquisition unit is used for acquiring signal parameters of the circular array signals acquired by the circular array, and acquiring arrangement information of the circular array according to the signal parameters of the circular array signals;

the expansion angle calculation unit is used for determining a maximum expansion angle according to the interference disturbance parameters;

the expansion matrix creation unit is used for determining a zero-dip widening algorithm expansion matrix of the circular array signal based on the Laplace algorithm according to the arrangement information of the circular array, the maximum expansion angle and the signal parameters of the circular array signal;

the zero notch widening algorithm expansion matrix based on the Laplace algorithm of the circular array signal is as follows:

wherein ,representing the element of the nth row and the nth column in the expansion matrix of the null-dip widening algorithm based on the Laplace algorithm, and xi _max Represents the maximum expansion angle, r _m Representing the arrangement information of the mth array element in the circular array, r _n And representing the arrangement information of the nth array element in the circular array, wherein lambda represents the wavelength of a circular array signal, and d represents the radius of the circular array.

6. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 4 when the computer program is executed.

7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 4.