A method and system for estimating a steering vector

Abstract

The present invention provides a steering vector estimation method and system, wherein the method comprises: obtaining an array observation signal; obtaining a covariance matrix Φ of a sound source signal according to the array observation signal; x；f ; Obtain beamformer η according to the current sound source of the array observation signal; Through the covariance matrix Φ x；f The beamformer η calculates the steering vector of the array through a formula, and the calculation formula is as follows: #imgabs0# wherein σ is the norm of the modified steering vector, and σ≥0. The present invention solves the technical problem that when estimating and determining the steering vector in the prior art, when the actual situation does not conform to the assumed situation, the performance of the array cannot be reliably guaranteed, and the existing steering vector estimation method has low reliability.

Description

Technical Field

[0001] The present invention relates to the technical field related to array signal processing, and in particular to a steering vector estimation method and system. Background Art

[0002] In the field of array signal processing, in order to realize array signal positioning, signal enhancement, noise reduction and other functions through the array model, it is necessary to determine the steering vector of the array model.

[0003] There are many traditional methods for estimating the steering vector within the array model, such as the method of determining the steering vector based on the sound source signal relative to the array and the method of determining the steering vector based on the array geometry.

[0004] However, the inventors of this application found that the above technology has at least the following technical problems in the process of implementing the technical solution of the invention in the embodiment of this application:

[0005] The methods in the prior art introduce too many assumptions in the process of estimating and determining the steering vector. When the actual situation is consistent with the assumptions, the array can achieve better performance. When the actual situation is inconsistent with the assumptions, the performance of the array cannot be reliably guaranteed, and there is a technical problem that the steering vector estimation method has low reliability. Summary of the invention

[0006] The embodiments of the present application provide a steering vector estimation method and system for solving the technical problem in the prior art that when estimating and determining the steering vector, the performance of the array cannot be reliably guaranteed when the actual situation does not match the assumed situation, and the reliability of the steering vector estimation method is low.

[0007] In view of the above problems, an embodiment of the present application provides a steering vector estimation method and system.

[0008] In a first aspect of an embodiment of the present application, a steering vector estimation method is provided, the method comprising: obtaining an array observation signal; obtaining a covariance matrix Φ of a sound source signal according to the array observation signal x；f ; Obtain beamformer η according to the current sound source of the array observation signal; Through the covariance matrix Φ x；f The steering vector of the array is calculated by the beamformer η through the formula, and the calculation formula is as follows: Wherein, σ is the norm of the corrected steering vector, and σ≥0.

[0009] According to a second aspect of an embodiment of the present application, a steering vector estimation system is provided, the system comprising: a first acquisition unit, the first acquisition unit is used to obtain an array observation signal; a first processing unit, the first processing unit is used to obtain a covariance matrix Φ of a sound source signal according to the array observation signalx；f ; a second obtaining unit, the second obtaining unit is used to obtain a beam former n according to the current sound source of the array observation signal; a second processing unit, the second processing unit is used to obtain the beam former n according to the covariance matrix Φ x；f The steering vector of the array is calculated by the beamformer η through the formula, and the calculation formula is as follows: Wherein, σ is the norm of the corrected steering vector, and σ≥0.

[0010] According to a third aspect of an embodiment of the present application, a steering vector estimation system is provided, comprising: a processor, the processor being coupled to a memory, the memory being used to store a program, and when the program is executed by the processor, the system is enabled to perform the steps of the method described in the first aspect.

[0011] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0012] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0013] In the process of estimating the array steering vector, the technical solution provided in the embodiment of the present application first estimates the covariance matrix Φ of the sound source signal from the array observation signal. x；f , where the covariance matrix of the observed signal and the noise signal can be estimated simultaneously, and then the covariance matrix of the sound source signal Φ can be estimated x；f Then, without considering the noise signal, design the beamformer η related to the current sound source. Finally, according to the covariance matrix Φ of the above sound source signal x；f and the beamformer η to calculate the steering vector In the embodiment of the present application, the covariance matrix Φ of the sound source signal is estimated x；f , and without considering the noise signal, designing a beamformer η related to the sound source can effectively alleviate the cancellation problem of multipath sound source signals, and reduce the assumptions in other methods of estimating steering vectors in the prior art, so that the array steering vector can be estimated more flexibly, achieving the technical effect of improving the reliability of array steering vector estimation calculation and improving the reliability of array performance.

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 One of the flow charts of a steering vector estimation method provided in an embodiment of the present application;

[0016] Figure 2 A second flow chart of a steering vector estimation method provided in an embodiment of the present application;

[0017] Figure 3 A schematic diagram of a process for designing a beamformer in a steering vector estimation method provided in an embodiment of the present application;

[0018] Figure 4 A schematic diagram of the structure of a steering vector estimation system is provided for an embodiment of the present application;

[0019] Figure 5 It is a schematic diagram of the structure of an exemplary electronic device according to an embodiment of the present application.

[0020] Explanation of the reference numerals: first obtaining unit 11 , first processing unit 12 , second obtaining unit 13 , second processing unit 14 , electronic device 300 , memory 301 , processor 302 , communication interface 303 , bus architecture 304 . DETAILED DESCRIPTION

[0021] The embodiments of the present application provide a steering vector estimation method and system for solving the technical problem that when estimating and determining the steering vector in the technology, the performance of the array cannot be reliably guaranteed when the actual situation does not match the assumed situation, and the reliability of the steering vector estimation method is low.

[0022] Application Overview

[0023] In the field of array signal processing, in order to realize array signal positioning, signal enhancement, noise reduction and other functions through the array model, it is necessary to determine the steering vector of the array model, and the accuracy of the steering vector is very important for the performance of array signal processing.

[0024] In the field of sensor array signal processing, the array model is often described by the following formula:

[0025] Y f (t) = X f (t)+V f (t), then:

[0026] Y f (t) = d f S f (t)+V f (t)

[0027] Wherein, subscript f is frequency, t is time; Y f (t), V f (t) and df There are three vectors, and the length of each vector is the same, and the length is related to the number of array elements. f (t) is the observed signal vector, V f (t) is the noise vector, d f is the steering vector, S f (t) is a scalar, which represents the sound source signal. f (t) = d f S f (t) is also a vector. In the field of array signal processing, there is a basic assumption that S f (t) and V f (t) Not relevant.

[0028] There are many traditional methods for estimating the steering vector d in the array model. f However, the prior art method introduces too many assumptions in the process of estimating and determining the steering vector. When the actual situation is consistent with the assumptions, the array can achieve better performance. When the actual situation is inconsistent with the assumptions, the performance of the array cannot be reliably guaranteed, and there is a technical problem that the steering vector estimation method has low reliability.

[0029] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0030] Obtaining an array observation signal; obtaining a covariance matrix Φ of the sound source signal according to the array observation signal x；f ; Obtain beamformer η according to the current sound source of the array observation signal; Through the covariance matrix Φ x；f The steering vector of the array is calculated by the beamformer η through the formula, and the calculation formula is as follows: Wherein, σ is the norm of the corrected steering vector, and σ≥0.

[0031] After introducing the basic principles of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described here. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0032] Embodiment 1

[0033] Specifically, in the traditional array signal processing field, there are mainly three types of estimation of the steering vector d fThe details are as follows:

[0034] Method 1: Assume that the steering vector d f It is determined by the method of the sound source relative to the array and the geometric structure of the array, where S in the array model f (t) is the signal radiated by the sound source, and in estimating the steering vector d f When calculating the sound source, the direction of the sound source is first estimated and then brought into the model for direct calculation.

[0035] The problem with method 1 is that when estimating the steering vector d f An assumption is introduced in the process that for the sound source signals that arrive at the array through multipath, the model will regard them as noise. The multipath signals in the array are obviously correlated with the sound source signals. Such correlation will cause signal cancellation problems in the optimal enhancement and noise reduction algorithms, and will cause interference sources in the positioning algorithm, thereby affecting the performance of the array.

[0036] Method 2: Treat the sound source signal on a sensor as S f (t), at this time d f The elements of describe the correlation between the sound source signal in each channel and the sound source signal in the reference channel. f First estimate the covariance matrix Φ of the sound source signal x；f , then take out a column of the covariance matrix and normalize it to get the steering vector d f .

[0037] Among them, the covariance matrix Φ of the sound source signal is introduced x；f , which are specifically:

[0038]

[0039] in, Identify the mathematical expectation, (·) H Represents the conjugate transpose operation.

[0040] The problem with method 2 is that the expected signal in this method is the sound source signal on a certain sensor. It cannot suppress multipath signals, which will change the timbre of the signal in the field of acoustic signal processing. At the same time, multipath signals will also reduce the accuracy of speech recognition and the accuracy of array signal processing.

[0041] Method 3: Assume that the covariance matrix of the sound source signal is a rank-one matrix, ie, Estimated steering vector d f First estimate the covariance matrix Φ of the sound source signal x；f , and then take out the eigenvector corresponding to the maximum eigenvalue of the covariance matrix to get the steering vector.

[0042] Among them, the variance is introduced It is: in(·) * Indicates conjugation.

[0043] The problem with method 3 is that it assumes that the covariance matrix of the sound source signal is a rank-one matrix. However, in practice, the signal is non-stationary and the channel is time-varying. x；f Usually full rank. The assumption of "rank one" is too strong and does not match most practical situations.

[0044] In order to solve the above-mentioned problems in the method of estimating the steering vector in the prior art, the embodiment of the present application adopts the following method.

[0045] like Figure 1 As shown, an embodiment of the present application provides a steering vector estimation method, the method comprising:

[0046] S100: obtaining array observation signals;

[0047] Figure 2 A possible flow chart of the method provided by the embodiment of the present application is shown. Specifically, the array observation signal is any observation signal processed in the field of array signal processing in the prior art, which can be an observation signal that reaches the array through multiple paths.

[0048] For example, the observation signal is a microphone array signal, which generally picks up a speech signal, which is sparse in the time-frequency domain, i.e., the expected signal does not always exist in the observation signal. In this way, this principle can be used to simultaneously estimate the covariance matrix of the observation signal and the noise signal, and then estimate the covariance matrix of the sound source signal.

[0049] S200: Obtaining the covariance matrix Φ of the sound source signal according to the array observation signal x；f ;

[0050] Step S200 in the method provided in the embodiment of the present application includes step S210, and step S210 includes:

[0051] S211: judging whether there is an expected signal in the array observation signal according to the first judgment instruction;

[0052] S212: When the array observation signal does not contain the expected signal, obtain a noise vector v f (t);

[0053] S213: According to the noise vector v f (t) Calculate the noise covariance matrix, the calculation formula is as follows:

[0054]

[0055] Where N is a signal set about time n, and the observed signal in the signal set does not contain the expected signal, |N| represents the number of elements in the set N, α v；f (n) is the weighting coefficient, and α v；f (n)≥0;

[0056] S214: Obtain the covariance matrix Φ of the sound source signal according to the noise covariance matrix x；f .

[0057] Specifically, estimating the covariance matrix of the sound source signal is a basic problem in array signal processing, and the embodiments of the present application do not focus on improving this problem. For example, without limitation, the covariance matrix of the sound source signal is obtained based on the microphone array observation signal. Since the expected signal in the microphone array signal does not always exist in the array observation signal, the following situation occurs:

[0058] When there is no expected signal in the array observation signal, there is only noise in the array observation signal, ie, Y f (t) = v f (t), get the noise vector, and then use the array observation signal without the expected signal to estimate the noise covariance matrix, as shown in the following formula:

[0059]

[0060] Where N is a set about time n, in which the array observation signal does not have the expected signal, |N| represents the number of elements in the set N, and α v；f (n)≥0 is a series of weighting coefficients.

[0061] The step S200 in the method provided in the embodiment of the present application further includes S220, and the step S220 includes:

[0062] S221: When the array observation signal contains the expected signal, obtain the observation signal vector y f (t);

[0063] S222: Based on the observed signal vector y f (t) Calculate the observed signal covariance matrix, the calculation formula is as follows:

[0064]

[0065] Where M is a set about time n, and all observed signals in the set contain expected signals, |M| represents the number of elements in the set M, and α y；f (n) is the weighting coefficient, and αy；f (n)≥0;

[0066] S223: By formula Calculate the covariance matrix Φ x；f .

[0067] Specifically, based on the above example of microphone array signals, when there is a desired signal in the array observation signal, the covariance matrix of the array observation signal can be estimated by the following formula:

[0068]

[0069] Where M is a set about time n, in which all array observation signals in the set have the expected signal, |M| represents the number of elements in the set M, and α y；f (n) is a series of weighting coefficients, and α y；f (n)≥0;

[0070] The noise covariance matrix is ​​obtained in the estimation and the observed signal covariance matrix After that, since the expected signal is uncorrelated with the noise signal, the observed signal covariance matrix satisfies:

[0071]

[0072] Furthermore, the desired signal covariance matrix can be estimated:

[0073]

[0074] In this way, the covariance matrix Φ of the sound source signal is completed x；f Estimates.

[0075] S300: Obtaining a beamformer η according to a current sound source of the array observation signal;

[0076] like Figure 3 As shown, step S300 in the method provided in the embodiment of the present application includes:

[0077] S310: Obtaining the direction information of the sound source and the geometric structure information of the array according to the current sound source;

[0078] S320: Obtaining an array steering vector a(θ ) corresponding to the sound source orientation according to the orientation information of the sound source and the geometric structure information of the array. s );

[0079] S330: According to the array steering vector a(θ s ) obtains the beamformer η.

[0080] Specifically, the embodiment of the present application constructs a database for designing a beamformer n according to the geometric structure of the array and based on historical array signal observation data. The historical array signal observation data includes the azimuth data of the historical sound source signal. In this way, after obtaining the azimuth of the sound source signal of the current observation signal and the geometric structure of the array, an optimal beamformer related to the current sound source and unrelated to noise can be designed according to the azimuth and the array geometric structure.

[0081] For example, taking super-gain fixed beamforming as an example, according to the orientation of the sound source and the geometric structure of the array, in an ideal environment without signal reflection, the array steering vector corresponding to the orientation of the sound source can be directly calculated and recorded as a(θ s ), where θ s Indicates the azimuth information of the sound source. The super gain beamformer suppresses the signals in other directions to the maximum extent while ensuring that the signal in the direction of the desired sound source remains unchanged. The corresponding beamformer is as follows:

[0082]

[0083] Among them, Γ dn is the covariance matrix of the isotropic noise, whose elements can be directly calculated from the geometry and frequency of the array. H (θ s ) is a(θ s ) is the conjugate transposed matrix of .

[0084] The above description is only a description of designing the optimal beamformer n by taking the super gain fixed beamforming as an example, and is not intended to be a limitation of the embodiments of the present application. In other cases, the optimal beamformer n may also be designed and obtained by other methods.

[0085] After designing the optimal beamformer η, X can be suppressed f The multipath signal of the desired sound source signal in (n) can be suppressed to a certain extent according to actual business requirements.

[0086] S400: Through the covariance matrix Φ x；f The steering vector of the array is calculated by the beamformer η through the formula, and the calculation formula is as follows: Wherein, σ is the norm of the corrected steering vector, and σ≥0.

[0087] Specifically, in steps S200 and S300, the desired signal covariance matrix Φ is obtained. x；f After finding the optimal beamformer η, the steering vector of the array is calculated according to the following formula:

[0088]

[0089] Wherein, σ is a scalar, which is the norm used to correct the steering vector, and σ≥0. σ can be obtained by the following formula:

[0090]

[0091] in, is a known steering vector in the historical observation record, (·) H is a conjugate device. The role of σ is equivalent to a normalization operation. In this way, the estimation of the steering vector is completed.

[0092] In summary, the embodiments of the present application adopt one or more of the above technical solutions to produce the following technical effects:

[0093] In the process of estimating the array steering vector, the technical solution provided in the embodiment of the present application first estimates the covariance matrix Φ of the sound source signal from the array observation signal. x；f , where the covariance matrix of the observed signal and the noise signal can be estimated simultaneously, and then the covariance matrix Φ of the expected sound source signal can be estimated x；f Then, without considering the noise signal, design the beamformer η related to the current sound source. Finally, according to the covariance matrix Φ of the above sound source signal x；f The steering vector d is calculated by the beamformer η f The embodiment of the present application estimates the covariance matrix Φ of the sound source signal x；f , and without considering the noise signal, designing a beamformer η related to the sound source can effectively alleviate the cancellation problem of multipath sound source signals compared to the traditional method of estimating the steering vector, and can allow the reference signal S f (t) It has fewer multipath sound source signals, which is more conducive to the setting of optimal filters such as reverberation suppression, and can avoid introducing the assumption that the rank of the sound source signal covariance matrix is ​​one. In addition to the maximum eigenvector, a more flexible steering vector solution estimation method can be obtained, which reduces the assumptions in the steering vector estimation method in the prior art, and can more flexibly estimate the array steering vector, thereby achieving the technical effect of improving the reliability of the array steering vector estimation calculation and improving the reliability of the array performance.

[0094] Embodiment 2

[0095] Based on the same inventive concept as the steering vector estimation method in the above embodiment, Figure 4 As shown, an embodiment of the present application provides a steering vector estimation system, wherein the system includes:

[0096] A first obtaining unit 11, wherein the first obtaining unit 11 is used to obtain an array observation signal;

[0097] The first processing unit 12 is used to obtain the covariance matrix Φ of the sound source signal according to the array observation signal x；f ;

[0098] A second obtaining unit 13, the second obtaining unit 13 is used to obtain a beamformer n according to a current sound source of the array observation signal;

[0099] The second processing unit 14 is used to calculate the covariance matrix Φ x；f The steering vector of the array is calculated by the beamformer η through the formula, and the calculation formula is as follows: Wherein, σ is the norm of the corrected steering vector, and σ≥0.

[0100] Furthermore, the system also includes:

[0101] a third obtaining unit, the third obtaining unit being used to obtain the azimuth information of the sound source and the geometric structure information of the array according to the current sound source;

[0102] A third processing unit, the third processing unit is used to obtain the array steering vector a (θ corresponding to the sound source orientation) according to the orientation information of the sound source and the geometric structure information of the array. s );

[0103] A fourth processing unit, the fourth processing unit is used to generate an array steering vector a(θ s ) obtains the beamformer η.

[0104] Furthermore, the system also includes:

[0105] A first judgment unit, the first judgment unit is used to judge whether the array observation signal has an expected signal according to a first judgment instruction;

[0106] A fourth obtaining unit, the fourth obtaining unit is used to obtain a noise vector v when the array observation signal does not contain the expected signal f (t);

[0107] A fifth processing unit, the fifth processing unit is used to process the noise vector v f (t) Calculate the noise covariance matrix, the calculation formula is as follows:

[0108]

[0109] Where N is a signal set about time n, and the observed signal in the signal set does not contain the expected signal, |N| represents the number of elements in the set N, α v；f (n) is the weighting coefficient, and α v；f(n)≥0;

[0110] A sixth processing unit, the sixth processing unit is used to obtain a covariance matrix Φ of the sound source signal according to the noise covariance matrix x；f .

[0111] Furthermore, the system also includes:

[0112] A fifth obtaining unit, the fifth obtaining unit is used to obtain an observation signal vector y when the array observation signal has the expected signal f (t);

[0113] A seventh processing unit, the seventh processing unit is used to f (t) Calculate the observed signal covariance matrix, the calculation formula is as follows:

[0114]

[0115] Where M is a set about time n, and all observed signals in the set contain expected signals, |M| represents the number of elements in the set M, and α y；f (n) is the weighting coefficient, and α y；f (n)≥0;

[0116] An eighth processing unit is used to Calculate the covariance matrix Φ x；f .

[0117] Embodiment 3

[0118] Based on the same inventive concept as a steering vector estimation method in the aforementioned embodiment, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method in the aforementioned embodiment are implemented.

[0119] Exemplary Electronic Devices

[0120] Reference below Figure 5 To describe the electronic device of the embodiment of the present application,

[0121] Based on the same inventive concept as a steering vector estimation method in the aforementioned embodiment, an embodiment of the present application also provides a steering vector estimation system, including: a processor, the processor is coupled to a memory, the memory is used to store a program, when the program is executed by the processor, the system executes the steps of the method described in embodiment one.

[0122] The electronic device 300 includes: a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, the processor 302, and the memory 301 may be interconnected via the bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0123] The processor 302 may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the present application.

[0124] The communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), wired access networks, etc.

[0125] The memory 301 may be a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via the bus architecture 304. The memory may also be integrated with the processor.

[0126] The memory 301 is used to store computer-executable instructions for executing the solution of the present application, and the execution is controlled by the processor 302. The processor 302 is used to execute the computer-executable instructions stored in the memory 301, thereby realizing a steering vector estimation method provided by the above embodiment of the present application.

[0127] Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application code, which is not specifically limited in the embodiments of the present application.

[0128] In the process of estimating the array steering vector, the technical solution provided in the embodiment of the present application first estimates the covariance matrix Φ of the sound source signal from the array observation signal. x；f , where the covariance matrix of the observed signal and the noise signal can be estimated simultaneously, and then the covariance matrix Φ of the expected sound source signal can be estimated x；f Then, without considering the noise signal, design the beamformer η related to the current sound source. Finally, according to the covariance matrix Φ of the above sound source signal x；f The steering vector d is calculated by the beamformer η f The embodiment of the present application estimates the covariance matrix Φ of the sound source signal x；f , and without considering the noise signal, designing a beamformer η related to the sound source can effectively alleviate the cancellation problem of multipath sound source signals compared to the traditional method of estimating the steering vector, and can allow the reference signal S f (t) It has fewer multipath sound source signals, which is more conducive to the setting of optimal filters such as reverberation suppression, and can avoid introducing the assumption that the rank of the sound source signal covariance matrix is ​​one. In addition to the maximum eigenvector, a more flexible steering vector solution estimation method can be obtained, which reduces the assumptions in the steering vector estimation method in the prior art, and can more flexibly estimate the array steering vector, thereby achieving the technical effect of improving the reliability of the array steering vector estimation calculation and improving the reliability of the array performance.

[0129] Those of ordinary skill in the art will appreciate that the various digital numbers such as the first and second involved in the present application are only for the convenience of description, and are not used to limit the scope of the embodiments of the present application, nor do they represent the order of precedence. "And / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one" refers to one or more. At least two refers to two or more. "At least one", "any one" or similar expressions refer to any combination of these items, including any combination of single items (individuals) or plural items (individuals). For example, at least one item (individual, kind) of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0130] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server, data center, etc. that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0131] The various illustrative logic units and circuits described in the embodiments of the present application can be implemented or operated by a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or the design of any combination of the above functions. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any traditional processor, controller, microcontroller or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration to implement.

[0132] The steps of the method or algorithm described in the embodiments of the present application can be directly embedded in hardware, a software unit executed by a processor, or a combination of the two. The software unit can be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC, and the ASIC can be arranged in a terminal. Optionally, the processor and the storage medium can also be arranged in different components in the terminal. These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0133] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. A steering vector estimation method, characterized in that: The method comprises: Obtaining array observation signals; The covariance matrix of the sound source signal is obtained according to the array observation signal ; A beamformer is obtained based on the current sound source of the array observation signal ; Through the covariance matrix and the beamformer The steering vector of the array is obtained by calculation using the formula, which is as follows: = ,in, is the norm of the modified steering vector, and ; The method further comprises: Obtaining the azimuth information of the sound source and the geometric structure information of the array according to the current sound source; The array steering vector corresponding to the sound source orientation is obtained according to the orientation information of the sound source and the geometric structure information of the array. ; According to the array steering vector corresponding to the sound source orientation Get the beamformer ; The method further comprises: The beamformer is obtained by formula calculation , the calculation formula is as follows: in, is the covariance matrix of isotropic noise, for The conjugate transposed matrix of ; The method further comprises: Determine whether there is an expected signal in the array observation signal according to a first judgment instruction; When the array observation signal does not contain the expected signal, the noise vector is obtained. ; According to the noise vector The noise covariance matrix is ​​calculated and the calculation formula is as follows: Wherein, N is a signal set about time n, and the observed signal in the signal set does not contain the expected signal, represents the number of elements in the set N, is the weighting coefficient, and ; Obtain the covariance matrix of the sound source signal according to the noise covariance matrix ; The method further comprises: When the array observation signal contains the expected signal, the observation signal vector is obtained. ; According to the observed signal vector The covariance matrix of the observed signal is calculated and the calculation formula is as follows: Where M is a set about time n, and there is an expected signal in all observed signals in the set, represents the number of elements in the set M, is the weighting coefficient, and ; By formula = Calculate the covariance matrix .

2. A steering vector estimation system, characterized in that: The system comprises: A first obtaining unit, wherein the first obtaining unit is used to obtain an array observation signal; A first processing unit, the first processing unit is used to obtain a covariance matrix of a sound source signal according to the array observation signal ; A second obtaining unit, the second obtaining unit is used to obtain a beamformer according to a current sound source of the array observation signal ; The second processing unit is used to calculate the covariance matrix and the beamformer The steering vector of the array is obtained by calculation using the formula, which is as follows: = ,in, is the norm of the modified steering vector, and ; a third obtaining unit, the third obtaining unit being used to obtain the azimuth information of the sound source and the geometric structure information of the array according to the current sound source; A third processing unit, the third processing unit is used to obtain an array steering vector corresponding to the sound source orientation according to the orientation information of the sound source and the geometric structure information of the array ; A fourth processing unit, the fourth processing unit is used to control the array steering vector corresponding to the sound source orientation Get the beamformer , the beamformer is obtained by formula calculation , the calculation formula is as follows: in, is the covariance matrix of isotropic noise, for The conjugate transposed matrix of ; A first judgment unit, the first judgment unit is used to judge whether the array observation signal has an expected signal according to a first judgment instruction; A fourth obtaining unit, the fourth obtaining unit is used to obtain a noise vector when the array observation signal does not contain the expected signal ; A fifth processing unit, the fifth processing unit is used to process the noise vector The noise covariance matrix is ​​calculated and the calculation formula is as follows: Wherein, N is a signal set about time n, and the observed signal in the signal set does not contain the expected signal, represents the number of elements in the set N, is the weighting coefficient, and ; A sixth processing unit, the sixth processing unit is used to obtain a covariance matrix of a sound source signal according to the noise covariance matrix ; A fifth obtaining unit, the fifth obtaining unit is used to obtain an observation signal vector when the array observation signal has the expected signal ; A seventh processing unit, the seventh processing unit is used to The covariance matrix of the observed signal is calculated and the calculation formula is as follows: Where M is a set about time n, and there is an expected signal in all observed signals in the set, represents the number of elements in the set M, is the weighting coefficient, and ; An eighth processing unit is used to = Calculate the covariance matrix .

3. A steering vector estimation system, characterized in that: include: A processor is coupled to a memory, wherein the memory is used to store a program, and when the program is executed by the processor, the system is enabled to perform the steps of the method as claimed in claim 1.

4. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which implements the steps of the method according to claim 1 when executed by a processor.

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