An adaptive beamforming method, device, computer equipment and storage medium

Abstract

The application relates to the technical field of communication, in particular to a self-adaptive beam forming method and device, computer equipment and storage medium, the method comprises the following steps: acquiring an input signal, the input signal comprises a desired signal of beam forming, an interference signal and a noise signal; based on the input signal, determining an augmented form of an output signal and the desired signal of beam forming; adopting an M estimation function, based on the augmented form of the output signal and the desired signal of beam forming, constructing an optimization problem, forming an optimization equation; solving the optimization equation to obtain a weight vector of the output signal; since the M estimation function is adopted to construct the optimization problem, the sudden abnormal signal caused by the background noise of the pulse signal can be filtered out, and the performance of the beam forming is improved.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to an adaptive beamforming method, apparatus, computer device, and storage medium. Background Technology

[0002] In the field of signal processing, there are many methods for adaptive beamforming, such as robust adaptive beamforming for direction vector mismatch and phase-controlled adaptive beamforming. However, when the background noise in the scene is impulse noise, it is easy to cause abrupt changes in the beam, which leads to a sharp deterioration in the performance of beamforming, resulting in a large error in the reception of the desired signal and the reception of the interference direction signal, thus affecting the availability of the antenna array.

[0003] Therefore, improving the performance of beamforming in the context of impulse noise is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] In view of the above problems, the present invention provides an adaptive beamforming method, apparatus, computer device and storage medium that overcomes or at least partially solves the above problems.

[0005] In a first aspect, the present invention provides an adaptive beamforming method, comprising:

[0006] Acquire an input signal, which includes the desired beamforming signal, interference signal, and noise signal;

[0007] Based on the input signal, the augmented form of the output signal and the desired signal for beamforming are determined;

[0008] Using the M estimation function, based on the augmented form of the output signal and the desired signal of the beamforming, an optimization problem is constructed, and an optimization equation is formed.

[0009] Solving the optimization equation yields the weight vector of the output signal.

[0010] Preferably, determining the augmented form of the output signal and the desired beamforming signal based on the input signal includes:

[0011] Based on the input signal, the desired signal and interference signal for beamforming are determined;

[0012] An equality constraint is applied to the direction vector of the desired signal and the direction vector of the interference signal to determine the augmented form of the weight vector;

[0013] The augmented form of the output signal is determined based on the augmented form of the weight vector and the augmented form of the input signal.

[0014] Preferably, the step of applying equality constraints to the direction vectors of the desired signal and the interference signal to determine the augmented form of the weight vector includes:

[0015] The direction vectors of the desired signal and the interference signal are subject to the following equality constraints:

[0016]

[0017]

[0018] in, For the weight vector, For the conjugate transpose, the augmented form of the weight vector Apply the following constraints:

[0019]

[0020] in, , This is an augmented form of the direction vector of the interference signal. This is the augmented form of the direction vector of the desired signal. This determines the augmented form of the weight vector.

[0021] Preferably, determining the augmented form of the output signal based on the augmented form of the weight vector and the augmented form of the input signal includes:

[0022] The augmented form of the output signal is determined according to the following formula, based on the augmented form of the weight vector and the augmented form of the input signal:

[0023]

[0024] in, This is an augmented form of the input signal. This is the conjugate transpose of the augmented form of the weight vector. This is the augmented form of the output signal.

[0025] Preferably, the step of employing the M-estimation function, based on the augmented form of the output signal and the desired signal of the beamforming, constructs an optimization problem and forms an optimization equation, including:

[0026] The error signal is determined based on the augmented form of the output signal and the desired signal of the beamforming.

[0027] Using the M estimation function and based on the error signal, an optimization problem is constructed, resulting in the following optimization equation:

[0028]

[0029]

[0030] in, This is an augmented form of the output signal. The desired signal for beamforming, Let M be the estimation function. For mathematical expectation, ,in, This is an augmented form of the direction vector of the interference signal. This is the augmented form of the direction vector of the desired signal. , This is the augmented form of the weight vector.

[0031] Preferably, solving the optimization equation to obtain the weight vector of the output signal includes:

[0032] The cost function is constructed using the Lagrange multiplier method;

[0033] The gradient of the cost function with respect to the weight vector of the output signal is calculated, and the expression for the weight vector of the output signal is obtained by using the stochastic gradient descent method.

[0034] Preferably, after solving the optimization equation to obtain the weight vector of the output signal, the method further includes:

[0035] Based on the mean square error of the error signal between the augmented form of the output signal at each time point and the desired signal for beamforming, the target time when beamforming reaches stability is determined.

[0036] In a second aspect, the present invention also provides an adaptive beamforming apparatus, comprising:

[0037] An acquisition module is used to acquire input signals, which include the desired beamforming signal, interference signals, and noise signals.

[0038] The determination module is used to determine the augmented form of the output signal and the desired signal for beamforming based on the input signal;

[0039] A forming module is used to construct an optimization problem and form an optimization equation by employing an M-estimation function, based on the augmented form of the output signal and the desired signal of the beamforming;

[0040] The module is used to solve the optimization equation and obtain the weight vector of the output signal.

[0041] Thirdly, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described in the first aspect.

[0042] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0043] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0044] This invention provides an adaptive beamforming method, comprising: acquiring an input signal, the input signal including a desired beamforming signal, an interference signal, and a noise signal; determining the augmented form of the output signal and the desired beamforming signal based on the input signal; constructing an optimization problem and forming an optimization equation using an M-estimation function based on the augmented form of the output signal and the desired beamforming signal; and solving the optimization equation to obtain the weight vector of the output signal. Because the M-estimation function is used to construct the optimization problem, abrupt abnormal signals caused by background noise of the pulse signal can be filtered out, thereby improving the performance of beamforming. Attached Figure Description

[0045] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0046] Figure 1 A schematic flowchart of the adaptive beamforming method in an embodiment of the present invention is shown;

[0047] Figure 2 The beam diagram of the CLMS beamforming method in an embodiment of the present invention is shown;

[0048] Figure 3 The beam diagram of the Continuous Hybrid p-range (CMPN) beamforming method in an embodiment of the present invention is shown;

[0049] Figure 4 The beam diagram of the minimum average M-estimation (LMM) beamforming method in an embodiment of the present invention is shown;

[0050] Figure 5 The beam diagram of the generalized linear complex LMS method in an embodiment of the present invention is shown;

[0051] Figure 6The beam diagram of the adaptive beamforming method in an embodiment of the present invention is shown;

[0052] Figure 7 A schematic diagram of the mean square error curve of the CMPN method in an embodiment of the present invention is shown;

[0053] Figure 8 A schematic diagram of the mean square error curve of the minimum average M-estimation (LMM) beamforming method in an embodiment of the present invention is shown.

[0054] Figure 9 A schematic diagram of the mean square error curve of the generalized linear LMM beamforming method in an embodiment of the present invention is shown;

[0055] Figure 10 A schematic diagram of the mean square error curve of the adaptive beamforming method in an embodiment of the present invention is shown;

[0056] Figure 11 A schematic diagram of the adaptive beamforming device in an embodiment of the present invention is shown;

[0057] Figure 12 A schematic diagram of the structure of a computer device implementing the adaptive beamforming method in an embodiment of the present invention is shown. Detailed Implementation

[0058] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0059] Example 1

[0060] Embodiments of the present invention provide an adaptive beamforming method, such as... Figure 1 As shown, it includes:

[0061] S101, acquire the input signal, which includes the desired beamforming signal, interference signal and noise signal;

[0062] S102, based on the input signal, determine the augmented form of the output signal and the desired signal for beamforming;

[0063] S103 uses the M estimation function to construct an optimization problem based on the augmented form of the output signal and the desired signal of beamforming, and forms an optimization equation.

[0064] S104, Solve the optimization equation to obtain the weight vector of the output signal.

[0065] In S101, within the generalized linear beamforming structure, the equation for the input signal is as follows:

[0066]

[0067] in, For input signal, For the desired signal, Let be the direction vector of the desired signal. This is an interference signal. The direction vector of the interference signal. This is a noise signal. In .

[0068] In a generalized linear beamforming structure, the input signal is used augmented forms Utilizing the second-order non-circular property of the input signal, i.e. ,in, , superscript Indicates transpose, superscript This indicates the conjugate transpose.

[0069] Next, S102 is executed to determine the augmented form of the output signal and the desired signal for beamforming based on the input signal.

[0070] First, after determining the input signal, the desired beamforming signal and interference signal can be determined based on the input signal. From this, the desired beamforming signal can be obtained, i.e. .

[0071] The weight vector is determined by applying equality constraints to the direction vectors of the desired signal and the interference signal. Specifically:

[0072]

[0073]

[0074] Therefore, in the generalized linear beamforming structure, the augmented form of the weight vector... Apply the following constraints:

[0075]

[0076] in, , , This is an augmented form of the direction vector of the interference signal. This is the augmented form of the weight vector.

[0077] Based on the above formula, the augmented form of the weight vector can be determined.

[0078] Next, based on the augmented form of the weight vector and the augmented form of the input signal, the augmented form of the output signal is determined. Specifically, the augmented form of the output signal... .

[0079] in, This is an augmented form of the output signal. This is an augmented form of the input signal. It is the conjugate transpose of the augmented form of the weight vector.

[0080] Next, S103 is executed, and the M estimation function is used to construct an optimization problem based on the augmented form of the output signal and the desired signal of beamforming, thus forming an optimization equation.

[0081] First, the error signal is determined based on the augmented form of the output signal and the desired signal for beamforming. Specifically:

[0082]

[0083] This is the error signal.

[0084] Using the M estimation function and based on the error signal, an optimization problem is constructed, resulting in the following optimization equation:

[0085]

[0086]

[0087] in, This is an augmented form of the output signal. The desired signal for beamforming, Let M be the estimation function. For mathematical expectation, ,in, This is an augmented form of the direction vector of the interference signal. This is the augmented form of the direction vector of the desired signal. , This is the augmented form of the weight vector.

[0088] The significance of this optimization equation is to minimize the expectation of the error signal under the action of the M estimation function, while maximizing the gain in the direction of the expected signal and suppressing signals in the direction of interference. The M estimation function used effectively reduces the performance impact caused by impulse noise.

[0089] Where, the M estimation function is ,in, The M estimation function is defined as a positive threshold. When an outlier greater than the threshold occurs, the parameters are restricted to reduce the performance impact caused by impulse noise.

[0090] Next, execute S104 to solve the optimization equation and obtain the weight vector of the output signal.

[0091] First, the cost function is constructed using the Lagrange multiplier method; then, the gradient of the cost function with respect to the weight vector of the output signal is solved, and the expression for the weight vector of the output signal is obtained using the stochastic gradient descent method.

[0092] Specifically, the cost function is obtained using the Lagrange multiplier method as follows:

[0093]

[0094] in, For a P×1 dimensional vector, the gradient of the cost function with respect to the weight vector of the output signal is:

[0095]

[0096] According to the formula for the M estimation function, in When the gradient is 0, At that time, gradient The expression is as follows:

[0097]

[0098] in, To find the conjugate of the variables, the error signal formula is... Substituting into the above equation, the gradient is:

[0099]

[0100] and Specifically, through the Obtained by differentiating the function.

[0101] Using stochastic gradient descent, the update formula for the weight vector is obtained as follows:

[0102]

[0103] in, This is the iteration step size. (Constraints) Substituting into the left side of the above equation, we get:

[0104]

[0105] Finally, the update expression for the weight vector at the next time step is obtained:

[0106]

[0107] Among them, matrix sum matrix The corresponding expressions are:

[0108]

[0109]

[0110] in, for An identity matrix of dimension 1.

[0111] By iteratively calculating and setting k=k+1, the above steps are repeated to obtain the expression for the weight vector at the next time step.

[0112] After obtaining the expression for the weight vector at the next time step, to verify the effectiveness of the present invention, the beam patterns of the method used in this invention are simulated and compared with those of other beamforming methods. Specifically, other beamforming methods include: CLMS beamforming method, Continuous Mixed p-range (CMPN) beamforming method, Generalized Linear Complex LMS method, and Minimum Mean M-estimation (LMM) beamforming method. Figures 2-6 As shown. In impulse noise interference scenarios, the solution of this invention aims to achieve a more precise main lobe in the desired signal direction, and also more precisely suppresses the gain in the direction of the interference signal, thus exhibiting stronger robustness.

[0113] After S104, the target time when beamforming reaches stability is determined based on the mean square error of the error signal between the augmented form of the output signal corresponding to each time step and the desired signal for beamforming.

[0114] Specifically, the mean square error curve of beamforming is plotted, where the expression for calculating the mean square error (MSE) is:

[0115] MSE=

[0116] The mean square error curves of the proposed solution are compared with those of the CMPN method, the minimum average M-estimation (LMM) beamforming method, and the generalized linear LMM beamforming method through simulation. Figures 7-10 As shown. In impulse noise scenarios, the solution of this invention can achieve faster convergence speed and lower steady-state mean square error.

[0117] The simulation experiment was conducted under Bernoulli noise conditions, and the parameters were set as follows: ,in, This represents the probability of pulse interference. The antenna array has 16 sensors, the desired signal direction angle is 50°, and the interference signal direction angles are 20° and -10°. The input signal is modulated using BPSK, the input signal-to-noise ratio is 20dB, and the interference-to-noise ratio is 10dB.

[0118] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages:

[0119] This invention provides an adaptive beamforming method, comprising: acquiring an input signal, the input signal including a desired beamforming signal, an interference signal, and a noise signal; determining the augmented form of the output signal and the desired beamforming signal based on the input signal; constructing an optimization problem and forming an optimization equation using an M-estimation function based on the augmented form of the output signal and the desired beamforming signal; and solving the optimization equation to obtain the weight vector of the output signal. Because the M-estimation function is used to construct the optimization problem, abrupt abnormal signals caused by background noise of the pulse signal can be filtered out, thereby improving the performance of beamforming.

[0120] Example 2

[0121] Based on the same inventive concept, embodiments of the present invention also provide an adaptive beamforming apparatus, such as... Figure 11 As shown, it includes:

[0122] Acquisition module 1101 is used to acquire input signals, the input signals including the desired beamforming signal, interference signal and noise signal;

[0123] The determining module 1102 is used to determine the augmented form of the output signal and the desired signal for beamforming based on the input signal;

[0124] The forming module 1103 is used to construct an optimization problem and form an optimization equation by using the M estimation function, based on the augmented form of the output signal and the desired signal of the beamforming;

[0125] Module 1104 is used to solve the optimization equation and obtain the weight vector of the output signal.

[0126] In one alternative implementation, the determining module 1102 includes:

[0127] The first determining unit is used to determine the desired signal and interference signal for beamforming based on the input signal;

[0128] The second determining unit is used to perform equality constraints on the direction vector of the desired signal and the direction vector of the interference signal to determine the augmented form of the weight vector;

[0129] The third determining unit is used to determine the augmented form of the output signal based on the augmented form of the weight vector and the augmented form of the input signal.

[0130] In one alternative implementation, the second determining unit is configured to:

[0131] The direction vectors of the desired signal and the interference signal are subject to the following equality constraints:

[0132]

[0133]

[0134] in, For the weight vector, For the conjugate transpose, the augmented form of the weight vector Apply the following constraints:

[0135]

[0136] in, , This is an augmented form of the direction vector of the interference signal. This is the augmented form of the direction vector of the desired signal. This determines the augmented form of the weight vector.

[0137] In one alternative implementation, the third determining unit is configured to:

[0138] The augmented form of the output signal is determined according to the following formula, based on the augmented form of the weight vector and the augmented form of the input signal:

[0139]

[0140] in, This is an augmented form of the input signal. This is the conjugate transpose of the augmented form of the weight vector. This is the augmented form of the output signal.

[0141] In one alternative implementation, forming module 1103 is used for:

[0142] The error signal is determined based on the augmented form of the output signal and the desired signal of the beamforming.

[0143] Using the M estimation function and based on the error signal, an optimization problem is constructed, resulting in the following optimization equation:

[0144]

[0145]

[0146] in, This is an augmented form of the output signal. The desired signal for beamforming, Let M be the estimation function. For mathematical expectation, ,in, This is an augmented form of the direction vector of the interference signal. This is the augmented form of the direction vector of the desired signal. , This is the augmented form of the weight vector.

[0147] In one alternative implementation, module 1104 is obtained, which is used for:

[0148] The cost function is constructed using the Lagrange multiplier method;

[0149] The gradient of the cost function with respect to the weight vector of the output signal is calculated, and the expression for the weight vector of the output signal is obtained by using the stochastic gradient descent method.

[0150] In one alternative implementation, it further includes:

[0151] The target time determination module is used to determine the target time when beamforming reaches stability based on the mean square error of the error signal between the augmented form of the output signal corresponding to each time and the desired signal for beamforming.

[0152] Example 3

[0153] Based on the same inventive concept, embodiments of the present invention provide a computer device, such as... Figure 12 As shown, it includes a memory 1204, a processor 1202, and a computer program stored in the memory 1204 and executable on the processor 1202. When the processor 1202 executes the program, it implements the steps of the adaptive beamforming method described above.

[0154] Among them, Figure 12In this document, a bus architecture (represented by bus 1200) is used. Bus 1200 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 1202 and memory represented by memory 1204. Bus 1200 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 1206 provides an interface between bus 1200 and receiver 1201 and transmitter 1203. Receiver 1201 and transmitter 1203 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 1202 is responsible for managing bus 1200 and general processing, while memory 1204 can be used to store data used by processor 1202 during operation.

[0155] Example 4

[0156] Based on the same inventive concept, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described adaptive beamforming method.

[0157] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0158] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0159] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are explicitly recited in each embodiment. Rather, as reflected in each embodiment, inventive aspects lie in fewer than all features of the single embodiment disclosed above. Therefore, the claims, following the detailed description, are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0160] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0161] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the specific implementation, any of the claimed embodiments can be used in any combination.

[0162] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the adaptive beamforming apparatus or computer device according to embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0163] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

Claims

1. An adaptive beamforming method, characterized in that, include: Acquire an input signal, which includes the desired beamforming signal, interference signal, and noise signal; Based on the input signal, determining the augmented form of the output signal and the desired signal for beamforming includes: Based on the input signal, the desired signal and interference signal for beamforming are determined; An equality constraint is applied to the direction vector of the desired signal and the direction vector of the interference signal to determine the augmented form of the weight vector; Based on the augmented form of the weight vector and the augmented form of the input signal, the augmented form of the output signal is determined; Using the M-estimation function, based on the augmented form of the output signal and the desired signal of the beamforming, an optimization problem is constructed, forming optimization equations, including: The error signal is determined based on the augmented form of the output signal and the desired signal of the beamforming. Using the M estimation function and based on the error signal, an optimization problem is constructed, resulting in the following optimization equation: ； ； in, This is an augmented form of the output signal. The desired signal for beamforming, Let M be the estimation function, and let M be the estimation function. ,in, A positive threshold For error signals, , For mathematical expectation, ,in, This is an augmented form of the direction vector of the interference signal. This is the augmented form of the direction vector of the desired signal. , This is the augmented form of the weight vector; Solving the optimization equation yields the weight vector of the output signal, including: The cost function is constructed using the Lagrange multiplier method; The gradient of the cost function with respect to the weight vector of the output signal is calculated, and the expression for the weight vector of the output signal is obtained by using the stochastic gradient descent method.

2. The method as described in claim 1, characterized in that, The step of applying equality constraints to the direction vectors of the desired signal and the interference signal to determine the augmented form of the weight vector includes: The direction vectors of the desired signal and the interference signal are subject to the following equality constraints: ； ； in, For the weight vector, It is the conjugate transpose. Let be the direction vector of the desired signal. The angle of arrival of the desired signal. The direction vector of the interference signal. The angle of arrival of the interfering signal is given by the augmented form of the weight vector. Apply the following constraints: ； in, , This is an augmented form of the direction vector of the interference signal. This is the augmented form of the direction vector of the desired signal. This determines the augmented form of the weight vector.

3. The method as described in claim 1, characterized in that, Determining the augmented form of the output signal based on the augmented form of the weight vector and the augmented form of the input signal includes: The augmented form of the output signal is determined according to the following formula, based on the augmented form of the weight vector and the augmented form of the input signal: ； in, This is an augmented form of the input signal. This is the conjugate transpose of the augmented form of the weight vector. This is the augmented form of the output signal.

4. The method as described in claim 1, characterized in that, After solving the optimization equation to obtain the weight vector of the output signal, the method further includes: Based on the mean square error of the error signal between the augmented form of the output signal at each time point and the desired signal for beamforming, the target time when beamforming reaches stability is determined.

5. An adaptive beamforming device, characterized in that, include: An acquisition module is used to acquire input signals, which include the desired beamforming signal, interference signals, and noise signals. The determining module is configured to determine, based on the input signal, the augmented form of the output signal and the desired signal for beamforming; the determining module is configured to: Based on the input signal, the desired signal and interference signal for beamforming are determined; An equality constraint is applied to the direction vector of the desired signal and the direction vector of the interference signal to determine the augmented form of the weight vector; Based on the augmented form of the weight vector and the augmented form of the input signal, the augmented form of the output signal is determined; The forming module is used to construct an optimization problem and form an optimization equation based on the augmented form of the output signal and the desired signal of the beamforming, using the M-estimation function. The forming module is used to: The error signal is determined based on the augmented form of the output signal and the desired signal of the beamforming. Using the M estimation function and based on the error signal, an optimization problem is constructed, resulting in the following optimization equation: ； ； in, This is an augmented form of the output signal. The desired signal for beamforming, Let M be the estimation function, and let M be the estimation function. ,in, A positive threshold For error signals, , For mathematical expectation, ,in, This is an augmented form of the direction vector of the interference signal. This is the augmented form of the direction vector of the desired signal. , This is the augmented form of the weight vector; The module is used to solve the optimization equation and obtain the weight vector of the output signal. The module is used to: The cost function is constructed using the Lagrange multiplier method; The gradient of the cost function with respect to the weight vector of the output signal is calculated, and the expression for the weight vector of the output signal is obtained by using the stochastic gradient descent method.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 4.

Images