CN114415115A - Target signal frequency automatic optimization method for assisting direction of arrival positioning - Google Patents
Target signal frequency automatic optimization method for assisting direction of arrival positioning Download PDFInfo
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
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Abstract
The invention discloses a target signal frequency automatic optimization method for assisting direction of arrival positioning, which comprises the steps of acquiring sound source signals acquired by an array, and constructing a target function based on the sound source signals and a transformation matrix; constructing a first target matrix based on the first position matrix; respectively and randomly orthogonalizing each first target position to obtain a direction vector, and optimizing each corresponding first target position based on each direction vector to obtain a second target matrix; generating a second position matrix based on the first target matrix and the second target matrix; calculating a step length parameter corresponding to the second position matrix, generating a third position matrix based on the step length parameter, and locally replacing the second position matrix based on the third position matrix; and determining an optimal transformation matrix corresponding to the second position matrix, separating the sound source signals based on the optimal transformation matrix to obtain each independent sound source signal, and determining the independent sound source signal with the highest frequency spectrum peak value as a target sound source signal. The method can rapidly and accurately determine the real sound source position.
Description
Technical Field
The application relates to the technical field of sound source positioning, in particular to a target signal frequency automatic optimization method for assisting direction of arrival positioning.
Background
As a passive sound source positioning method, beam forming is easily interfered by noise of other frequencies, so that multiple points are generated on a cloud image, and thus a real sound source position cannot be judged, so that generally, the frequencies are selectively reduced through band-pass filtering to help filter noise interference. However, the method has two disadvantages, one is that the frequency of the target sound source is unknown, and the frequency range can only be changed continuously by an enumeration method to use the cloud picture for focusing, so that the positioning time is greatly increased, and the efficiency is low. Secondly, the target sound source may be a mixture of signals with various frequencies, a band-pass filter cannot accurately contain a signal frequency band, and when the bandwidth is small, signal components may be omitted, so that the cloud picture is not obvious; when the bandwidth is large, other noise frequency components may be included, which may cause a phenomenon of connecting between each point of the cloud image. Therefore, at present, no method for rapidly and accurately judging the position of a real sound source exists.
Disclosure of Invention
In order to solve the above problem, an embodiment of the present application provides an automatic target signal frequency optimizing method for assisting direction of arrival positioning.
In a first aspect, an embodiment of the present application provides an automatic target signal frequency optimizing method for assisting direction of arrival positioning, where the method includes:
acquiring sound source signals acquired by an array, setting a transformation matrix, and constructing an objective function based on the sound source signals and the transformation matrix, wherein the objective function is used for representing non-Gaussian distribution among the sound source signals;
randomly generating a first position matrix based on the dimension of the transformation matrix, and constructing a first target matrix based on corresponding first target values of first target positions in the first position matrix in the target function;
respectively and randomly orthogonalizing the first target positions to obtain direction vectors, and optimizing the corresponding first target positions based on the direction vectors to obtain a second target matrix;
generating a second position matrix based on the first target matrix and the second target matrix, so as to minimize a second target value corresponding to each second target position in the second position matrix;
calculating a step length parameter corresponding to the second position matrix, generating a third position matrix based on the step length parameter, and locally replacing the second position matrix based on the third position matrix so as to minimize each second target value in the replaced second position matrix;
and determining an optimal transformation matrix corresponding to the second position matrix, separating the sound source signals based on the optimal transformation matrix to obtain each independent sound source signal, and determining the independent sound source signal with the highest frequency spectrum peak value as a target sound source signal.
Preferably, the acquiring the sound source signals collected by the array, setting a transformation matrix, and constructing an objective function based on the sound source signals and the transformation matrix, where the objective function is used to represent non-gaussian distribution among the sound source signals, includes:
acquiring each sound source signal acquired by each array element in an array, and generating a first signal matrix based on each sound source signal;
preprocessing the first signal matrix to obtain a second signal matrix;
and setting a transformation matrix, and constructing an objective function based on the transformation matrix and the second signal matrix, wherein the objective function is used for representing non-Gaussian distribution among the sound source signals.
Preferably, the randomly orthogonalizing each of the first target positions to obtain a direction vector, and optimizing each of the corresponding first target positions based on each of the direction vectors to obtain a second target matrix includes:
randomly orthogonalizing each first target position pairwise to obtain a direction vector;
calculating a vector optimal position of the direction vector in the objective function, wherein the vector optimal position is a position corresponding to the direction vector with the minimum target value in the objective function;
and optimizing the first target position based on the optimal position of the vector in the same direction vector to obtain third target positions, and constructing a second target matrix according to each third target position.
Preferably, the generating a second position matrix based on the first target matrix and the second target matrix to minimize a second target value corresponding to each second target position in the second position matrix includes:
comparing the first target matrix with the second target matrix, and selecting a preset number of second target values, wherein the second target values are smaller than all unselected target values;
and determining fourth target positions corresponding to the second target values, and generating a second position matrix to minimize the second target values corresponding to the second target positions in the second position matrix.
Preferably, the calculating the step size parameter corresponding to the second position matrix includes:
and acquiring a global optimal position corresponding to the second position matrix in the target function, acquiring an iteration pre-estimated position of the second position matrix, and calculating a step length parameter corresponding to the second position matrix based on the global optimal position and the iteration pre-estimated position.
Preferably, the generating a third position matrix based on the step size parameter, and partially replacing the second position matrix based on the third position matrix, so as to minimize each of the second target values in the replaced second position matrix, includes:
randomly generating a third target position around each second target position of the second position matrix based on the step length parameter, and constructing a third position matrix;
and calculating a third target matrix corresponding to the third position matrix, and replacing the third target position with the second target value at the same position to the second position matrix based on the third target value in the third target matrix being smaller than the second target value at the same position so as to minimize each second target value in the second position matrix after replacement.
Preferably, the calculating a step size parameter corresponding to the second position matrix, generating a third position matrix based on the step size parameter, and locally replacing the second position matrix based on the third position matrix, so as to minimize each second target value in the replaced second position matrix, further includes:
and adding one to the iteration times, repeating the step of randomly and respectively orthogonal to the first target positions to obtain direction vectors, and optimizing the corresponding first target positions on the basis of the direction vectors to obtain a second target matrix until the iteration times meet the maximum iteration times.
In a second aspect, an embodiment of the present application provides an apparatus for automatically optimizing a target signal frequency for assisting direction of arrival positioning, where the apparatus includes:
the acquisition module is used for acquiring sound source signals acquired by the array, setting a transformation matrix, and constructing an objective function based on the sound source signals and the transformation matrix, wherein the objective function is used for representing non-Gaussian distribution among the sound source signals;
a first generating module, configured to randomly generate a first location matrix based on the dimension of the transformation matrix, and construct a first target matrix based on a first target value corresponding to each first target location in the first location matrix in the target function;
the orthogonal module is used for respectively and randomly orthogonal aligning each first target position to obtain a direction vector, and optimizing each corresponding first target position based on each direction vector to obtain a second target matrix;
a second generating module, configured to generate a second position matrix based on the first target matrix and the second target matrix, so as to minimize a second target value corresponding to each second target position in the second position matrix;
a calculating module, configured to calculate a step size parameter corresponding to the second location matrix, generate a third location matrix based on the step size parameter, and locally replace the second location matrix based on the third location matrix, so as to minimize each second target value in the replaced second location matrix;
and the separation module is used for determining an optimal transformation matrix corresponding to the second position matrix, separating the sound source signals based on the optimal transformation matrix to obtain each independent sound source signal, and determining the independent sound source signal with the highest frequency spectrum peak value as a target sound source signal.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method as provided in the first aspect or any one of the possible implementation manners of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method as provided in the first aspect or any one of the possible implementations of the first aspect.
The invention has the beneficial effects that: and converting the sound source signals acquired by the array into a matrix, solving and representing by using a non-Gaussian distribution objective function, iteratively calculating the optimal solution of the objective function through a randomly generated ant lion position matrix, and optimizing the ant lion position matrix in a random orthogonal objective position mode in the calculation process so as to quickly and accurately calculate the global optimal solution of the objective function. And then, sound source signals are separated through an optimal transformation matrix corresponding to the global optimal solution, the real sound source position is determined, the result accuracy is high, and the calculation efficiency is high.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for automatically optimizing a target signal frequency for assisting direction of arrival positioning according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an automatic target signal frequency optimizing device for assisting direction of arrival positioning according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
In the following description, the terms "first" and "second" are used for descriptive purposes only and are not intended to indicate or imply relative importance. The following description provides embodiments of the present application, where different embodiments may be substituted or combined, and thus the present application is intended to include all possible combinations of the same and/or different embodiments described. Thus, if one embodiment includes feature A, B, C and another embodiment includes feature B, D, then this application should also be considered to include an embodiment that includes one or more of all other possible combinations of A, B, C, D, even though this embodiment may not be explicitly recited in text below.
The following description provides examples, and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made in the function and arrangement of elements described without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For example, the described methods may be performed in an order different than the order described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.
Referring to fig. 1, fig. 1 is a schematic flowchart of a method for automatically optimizing a target signal frequency for assisting direction of arrival positioning according to an embodiment of the present disclosure. In an embodiment of the present application, the method includes:
s101, sound source signals acquired by an array are acquired, a conversion matrix is set, and an objective function is constructed based on the sound source signals and the conversion matrix and used for representing non-Gaussian distribution among the sound source signals.
The execution subject of the present application may be a controller corresponding to the array.
In the embodiment of the application, the controller collects the sound source signals through the array, and because the sound source signals contain noise interference, the required position of the real sound source needs to be separated and determined from the collected sound source signals. Specifically, a transformation matrix is set as a solution to be solved, an objective function for representing non-Gaussian distribution characteristics is constructed according to the sound source signals and the transformation matrix, so that the most appropriate transformation matrix is determined by solving the target value of the objective function to be the minimum, and then the sound source signals are separated through the transformation matrix.
In one possible embodiment, step S101 includes:
acquiring each sound source signal acquired by each array element in an array, and generating a first signal matrix based on each sound source signal;
preprocessing the first signal matrix to obtain a second signal matrix;
and setting a transformation matrix, and constructing an objective function based on the transformation matrix and the second signal matrix, wherein the objective function is used for representing non-Gaussian distribution among the sound source signals.
In the embodiment of the application, it is assumed that there are N array elements in the array, there are M sound sources, and the mth sound source signal is represented as
Then, the signal received by the nth array element can be expressed as:
wherein
For the mth signal source to the nth array attenuation coefficient,
indicating the delay from the mth signal source to the nth array relative to the reference array element.
Representing a noise-additive signal.
The individual sound source signals are then represented as a mixed signal matrix
I.e. the first signal matrix, for subsequent calculations.
After the first signal matrix is determined, the signals need to be preprocessed first. Specifically, for performing the de-centering and the signal whitening on the signal, the corresponding calculation formula is as follows:
wherein
Is a de-centered signal and E () is desired.
Wherein
Is the covariance, U is the feature vector,
is a matrix of eigenvalues.
The second signal matrix obtained after the preprocessing can be represented as:
then, an objective function can be constructed according to the second signal matrix, and the application can adopt negative entropy as the objective function, and the formula is as follows:
wherein W is a transformation matrix. G () is a sigmoid function.
S102, randomly generating a first position matrix based on the dimension of the conversion matrix, and constructing a first target matrix based on corresponding first target values of first target positions in the first position matrix in the target function.
In the embodiment of the present application, in the objective function constructed in the foregoing steps, the transformation matrix W is to be solved, and in order to obtain the transformation matrix with the best separation effect on the sound source signal, the target value calculated by the objective function needs to be the minimum, and the corresponding transformation matrix at this time is the optimal solution.
First, a first position matrix is randomly generated according to the dimension corresponding to the transformation matrix, and each first target position in the first position matrix is regarded as a local optimal solution in each position area in the current calculation process. As can be seen from the above description, in order to obtain an optimal solution, it is necessary to determine the minimum target value, and therefore, after the first position matrix is generated, a first target matrix is generated based on the first target value corresponding to the first target position in the first position matrix in the target function. The first location matrix is represented as follows:
wherein
Indicating the position of the ith locally optimal solution in the j dimension.
The first target matrix is represented as follows:
s103, randomly orthogonalizing the first target positions respectively to obtain direction vectors, and optimizing the corresponding first target positions based on the direction vectors to obtain a second target matrix.
In the embodiment of the present application, since each of the current first target positions is randomly generated, and an error necessarily exists, the first target position needs to be optimized. Specifically, each first target position is regarded as a current local optimal solution, so that each first target position is randomly orthogonal to obtain a plurality of direction vectors, and the orthogonal direction vectors are used for optimizing the first target position to finally construct a second target matrix.
In one possible embodiment, step S103 includes:
randomly orthogonalizing each first target position pairwise to obtain a direction vector;
calculating a vector optimal position of the direction vector in the objective function, wherein the vector optimal position is a position corresponding to the direction vector with the minimum target value in the objective function;
and optimizing the first target position based on the optimal position of the vector in the same direction vector to obtain third target positions, and constructing a second target matrix according to each third target position.
In the embodiment of the application, a plurality of direction vectors can be obtained by randomly orthogonalizing each first target position two by two. The direction vector can be regarded as being composed of a plurality of position points on a straight line, so the passing direction vector is substituted into the objective function for calculation, the vector optimal position with the minimum target value in the direction vector is determined, the first target position is updated based on the vector optimal position, and the updated first target matrix, namely the second target matrix, is finally obtained. The formula for updating and selecting the first target position is as follows:
wherein the content of the first and second substances,
to update the target value of the p-th locally optimal solution for the t-th iteration before the update,
for the target value of the qth locally optimal solution of the updated tth iteration,
to update the position of the p-th locally optimal solution for the t-th iteration before updating,
and the position of the qth local optimal solution is iterated for the updated tth time.
And S104, generating a second position matrix based on the first target matrix and the second target matrix, so as to minimize a second target value corresponding to each second target position in the second position matrix.
In the embodiment of the present application, since the second objective matrix is constructed and generated by random orthogonality, and the target value corresponding to each position in the second objective matrix is not necessarily better than the target value corresponding to the first objective matrix, the first objective matrix and the second objective matrix are further integrated to generate the second position matrix, so that the target value corresponding to each locally optimal solution in the second position matrix is smaller, and the second position matrix is used as the original matrix for next iterative optimization.
In one possible embodiment, step S104 includes:
comparing the first target matrix with the second target matrix, and selecting a preset number of second target values, wherein the second target values are smaller than all unselected target values;
and determining fourth target positions corresponding to the second target values, and generating a second position matrix to minimize the second target values corresponding to the second target positions in the second position matrix.
In the embodiment of the present application, the first target matrix and the second target matrix are compared, so as to select i local optimal solutions, i.e. the second target values, from the two target matrices, and make the selected second target values smaller. Then, fourth target positions corresponding to the second target values are determined, so as to generate a second position matrix.
S105, calculating a step length parameter corresponding to the second position matrix, generating a third position matrix based on the step length parameter, and locally replacing the second position matrix based on the third position matrix so as to minimize each second target value in the replaced second position matrix.
In the embodiment of the present application, the foregoing process is optimized based on the direction of the direction vector, and the finally obtained global optimal solution is not necessarily on the direction vector formed by the selected first target positions. Therefore, the controller also calculates the step size parameter corresponding to the second position matrix, so as to generate a third position matrix according to the step size parameter. Each third target position in the third position matrix may be considered an attempted solution set based on the step size parameter in the vicinity of the locally optimal solution. And calculating whether a more optimal solution with a smaller target value exists near the local optimal solution through each trial solution, and locally replacing the second position matrix according to the more optimal solution, so that each second target value in the final second position matrix is centered on the local optimal solution, and the step length parameter is the minimum value in the surrounding range of the radius.
In one possible embodiment, the calculating the step size parameter corresponding to the second position matrix includes:
and acquiring a global optimal position corresponding to the second position matrix in the target function, acquiring an iteration pre-estimated position of the second position matrix, and calculating a step length parameter corresponding to the second position matrix based on the global optimal position and the iteration pre-estimated position.
In the embodiment of the present application, since there are a plurality of local optimal solutions in the second position matrix, there are local optimal solutionsThere will be a local optimal solution with the minimum target value in the optimal solution, and the local optimal solution is regarded as the current global optimal position
. In addition, the estimated position of the local optimal solution after the t iteration is calculated by the roulette algorithm
And the step length parameter can be calculated and determined through the two position data. The step size parameter will be larger at the start of the iteration and will decrease slowly as the iteration progresses. The calculation formula is as follows:
in one embodiment, the generating a third location matrix based on the step size parameter and partially replacing the second location matrix based on the third location matrix to minimize each of the second target values in the replaced second location matrix includes:
randomly generating a third target position around each second target position of the second position matrix based on the step length parameter, and constructing a third position matrix;
and calculating a third target matrix corresponding to the third position matrix, and replacing the third target position with the second target value at the same position to the second position matrix based on the third target value in the third target matrix being smaller than the second target value at the same position so as to minimize each second target value in the second position matrix after replacement.
In the embodiment of the present application, after the step size parameter is calculated, a plurality of third target positions are randomly generated around each second target position of the second position matrix based on the step size parameter, so as to construct a third position matrix. After the third position matrix is obtained, the corresponding third target value in the objective function can be obtained based on each third target position. Through the comparison between the third target value and the second target value corresponding to the third target value, the controller replaces the corresponding second target position with a third target position with a smaller third target value, and the optimization process is realized.
In an implementation manner, after the calculating a step size parameter corresponding to the second location matrix, generating a third location matrix based on the step size parameter, and partially replacing the second location matrix based on the third location matrix, so as to minimize each of the second target values in the replaced second location matrix, the method further includes:
and adding one to the iteration times, repeating the step of randomly and respectively orthogonal to the first target positions to obtain direction vectors, and optimizing the corresponding first target positions on the basis of the direction vectors to obtain a second target matrix until the iteration times meet the maximum iteration times.
In the embodiment of the application, after the second target position matrix is optimized based on the third target position matrix, an iterative optimization process is completed. In order to ensure that the finally obtained transformation matrix is optimal, the optimization process is repeated until the maximum iteration number is met.
S106, determining an optimal transformation matrix corresponding to the second position matrix, separating the sound source signals based on the optimal transformation matrix to obtain each independent sound source signal, and determining the independent sound source signal with the highest frequency spectrum peak value as a target sound source signal.
In the embodiment of the application, after the second position matrix after final optimization is obtained, the local optimal solution with the minimum target value in the second position matrix at this time is used as the final global optimal solution, and the optimal transformation matrix is calculated by the target value and the second target position. The optimal conversion matrix is multiplied by a matrix formed by the sound source signals, and each row in the obtained result matrix can represent a single signal, so that the sound source signals are separated. After the corresponding frequency spectrum is determined by performing fourier transform on each independent sound source signal, the peak value of the frequency spectrum of the noise is inevitably less obvious than the peak value of the real sound source, that is, the independent sound source signal with the highest frequency spectrum peak value can be determined as the required target sound source signal.
The following will describe in detail the target signal frequency automatic optimizing device for assisting direction of arrival positioning provided in the embodiment of the present application with reference to fig. 2. It should be noted that the automatic target signal frequency optimizing device for assisting direction of arrival positioning shown in fig. 2 is used for executing the method of the embodiment shown in fig. 1 of the present application, and for convenience of description, only the portion related to the embodiment of the present application is shown, and details of the specific technology are not disclosed, please refer to the embodiment shown in fig. 1 of the present application.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an automatic target signal frequency optimizing device for assisting direction of arrival positioning according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus includes:
an obtaining module 201, configured to obtain sound source signals acquired by an array, set a transformation matrix, and construct an objective function based on the sound source signals and the transformation matrix, where the objective function is used to represent non-gaussian distribution among the sound source signals;
a first generating module 202, configured to randomly generate a first position matrix based on the dimension of the transformation matrix, and construct a first target matrix based on a first target value corresponding to each first target position in the first position matrix in the target function;
an orthogonal module 203, configured to randomly and respectively orthogonal to each of the first target positions to obtain a direction vector, and optimize each of the corresponding first target positions based on each of the direction vectors to obtain a second target matrix;
a second generating module 204, configured to generate a second position matrix based on the first target matrix and the second target matrix, so as to minimize a second target value corresponding to each second target position in the second position matrix;
a calculating module 205, configured to calculate a step size parameter corresponding to the second location matrix, generate a third location matrix based on the step size parameter, and locally replace the second location matrix based on the third location matrix, so as to minimize each second target value in the replaced second location matrix;
a separating module 206, configured to determine an optimal transformation matrix corresponding to the second location matrix, separate the sound source signals based on the optimal transformation matrix, obtain each independent sound source signal, and determine the independent sound source signal with the highest spectral peak as a target sound source signal.
In one implementation, the obtaining module 201 includes:
the acquisition unit is used for acquiring each sound source signal acquired by each array element in the array and generating a first signal matrix based on each sound source signal;
the preprocessing unit is used for preprocessing the first signal matrix to obtain a second signal matrix;
and the first construction unit is used for setting a transformation matrix and constructing an objective function based on the transformation matrix and the second signal matrix, wherein the objective function is used for representing non-Gaussian distribution among the sound source signals.
In one possible embodiment, the orthogonal module 203 includes:
the orthogonal unit is used for randomly orthogonalizing each first target position pairwise to obtain a direction vector;
a first calculating unit, configured to calculate a vector optimal position of the direction vector in the objective function, where the vector optimal position is a position corresponding to a minimum target value of the direction vector in the objective function;
and the second construction unit is used for optimizing the first target position based on the optimal position of the vector in the same direction vector to obtain third target positions, and constructing a second target matrix according to the third target positions.
In one possible implementation, the second generation module 204 includes:
the comparison unit is used for comparing the first target matrix with the second target matrix and selecting a preset number of second target values, wherein the second target values are smaller than all unselected target values;
and a determining unit, configured to determine fourth target positions corresponding to the second target values, and generate a second position matrix, so as to minimize the second target values corresponding to the second target positions in the second position matrix.
In one possible implementation, the calculation module 205 includes:
and the second calculation unit is used for acquiring a global optimal position corresponding to the second position matrix in the target function, acquiring an iteration estimated position of the second position matrix, and calculating a step length parameter corresponding to the second position matrix based on the global optimal position and the iteration estimated position.
In one possible implementation, the calculation module 205 further includes:
a third constructing unit, configured to randomly generate a third target position around each second target position of the second position matrix based on the step size parameter, and construct a third position matrix;
and the third calculating unit is used for calculating a third target matrix corresponding to the third position matrix, and replacing the third target position with the second target value at the same position to the second position matrix based on the third target value in the third target matrix being smaller than the second target value at the same position so as to minimize each second target value in the second position matrix after replacement.
In one embodiment, the method further comprises:
and the repeating module is used for adding one to the iteration times, repeating the steps of randomly orthogonalizing the first target positions respectively to obtain direction vectors, optimizing the corresponding first target positions based on the direction vectors to obtain a second target matrix until the iteration times meet the maximum iteration times.
It is clear to a person skilled in the art that the solution according to the embodiments of the present application can be implemented by means of software and/or hardware. The "unit" and "module" in this specification refer to software and/or hardware that can perform a specific function independently or in cooperation with other components, where the hardware may be, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), or the like.
Each processing unit and/or module in the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.
Referring to fig. 3, a schematic structural diagram of an electronic device according to an embodiment of the present application is shown, where the electronic device may be used to implement the method in the embodiment shown in fig. 1. As shown in fig. 3, the electronic device 300 may include: at least one central processor 301, at least one network interface 304, a user interface 303, a memory 305, at least one communication bus 302.
Wherein a communication bus 302 is used to enable the connection communication between these components.
The user interface 303 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 303 may further include a standard wired interface and a wireless interface.
The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The central processor 301 may include one or more processing cores. The central processor 301 connects various parts within the entire electronic device 300 using various interfaces and lines, and performs various functions of the terminal 300 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305 and calling data stored in the memory 305. Alternatively, the central Processing unit 301 may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The CPU 301 may integrate one or a combination of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the cpu 301, but may be implemented by a single chip.
The Memory 305 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, code sets, or instruction sets. The memory 305 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 305 may alternatively be at least one storage device located remotely from the central processor 301. As shown in fig. 3, memory 305, which is a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and program instructions.
In the electronic device 300 shown in fig. 3, the user interface 303 is mainly used for providing an input interface for a user to obtain data input by the user; the cpu 301 may be configured to invoke a target signal frequency auto-optimizing application program for assisting direction of arrival positioning stored in the memory 305, and specifically perform the following operations:
acquiring sound source signals acquired by an array, setting a transformation matrix, and constructing an objective function based on the sound source signals and the transformation matrix, wherein the objective function is used for representing non-Gaussian distribution among the sound source signals;
randomly generating a first position matrix based on the dimension of the transformation matrix, and constructing a first target matrix based on corresponding first target values of first target positions in the first position matrix in the target function;
respectively and randomly orthogonalizing the first target positions to obtain direction vectors, and optimizing the corresponding first target positions based on the direction vectors to obtain a second target matrix;
generating a second position matrix based on the first target matrix and the second target matrix, so as to minimize a second target value corresponding to each second target position in the second position matrix;
calculating a step length parameter corresponding to the second position matrix, generating a third position matrix based on the step length parameter, and locally replacing the second position matrix based on the third position matrix so as to minimize each second target value in the replaced second position matrix;
and determining an optimal transformation matrix corresponding to the second position matrix, separating the sound source signals based on the optimal transformation matrix to obtain each independent sound source signal, and determining the independent sound source signal with the highest frequency spectrum peak value as a target sound source signal.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk including floppy disks, optical disks, DVD, CD-ROMs, microdrive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or optical cards, nanosystems (including molecular memory ICs), or any type of media or device suitable for storing instructions and/or data.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some service interfaces, devices or units, and may be an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, and the memory may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The above description is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure should not be limited thereby. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure are intended to be included within the scope of the present disclosure. Embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A method for automatically optimizing the frequency of a target signal for assisting the direction of arrival positioning, the method comprising:
acquiring sound source signals acquired by an array, setting a transformation matrix, and constructing an objective function based on the sound source signals and the transformation matrix, wherein the objective function is used for representing non-Gaussian distribution among the sound source signals;
randomly generating a first position matrix based on the dimension of the transformation matrix, and constructing a first target matrix based on corresponding first target values of first target positions in the first position matrix in the target function;
respectively and randomly orthogonalizing the first target positions to obtain direction vectors, and optimizing the corresponding first target positions based on the direction vectors to obtain a second target matrix;
generating a second position matrix based on the first target matrix and the second target matrix, so as to minimize a second target value corresponding to each second target position in the second position matrix;
calculating a step length parameter corresponding to the second position matrix, generating a third position matrix based on the step length parameter, and locally replacing the second position matrix based on the third position matrix so as to minimize each second target value in the replaced second position matrix;
and determining an optimal transformation matrix corresponding to the second position matrix, separating the sound source signals based on the optimal transformation matrix to obtain each independent sound source signal, and determining the independent sound source signal with the highest frequency spectrum peak value as a target sound source signal.
2. The method of claim 1, wherein the obtaining the acoustic source signals collected by the array and setting a transformation matrix, and constructing an objective function based on the acoustic source signals and the transformation matrix, the objective function being used to characterize a non-gaussian distribution among the acoustic source signals, comprises:
acquiring each sound source signal acquired by each array element in an array, and generating a first signal matrix based on each sound source signal;
preprocessing the first signal matrix to obtain a second signal matrix;
and setting a transformation matrix, and constructing an objective function based on the transformation matrix and the second signal matrix, wherein the objective function is used for representing non-Gaussian distribution among the sound source signals.
3. The method of claim 1, wherein randomly orthogonalizing each of the first target locations to obtain a direction vector, and optimizing each of the corresponding first target locations based on each of the direction vectors to obtain a second target matrix comprises:
randomly orthogonalizing each first target position pairwise to obtain a direction vector;
calculating a vector optimal position of the direction vector in the objective function, wherein the vector optimal position is a position corresponding to the direction vector with the minimum target value in the objective function;
and optimizing the first target position based on the optimal position of the vector in the same direction vector to obtain third target positions, and constructing a second target matrix according to each third target position.
4. The method of claim 1, wherein generating a second location matrix based on the first and second objective matrices to minimize a second target value corresponding to each second objective location in the second location matrix comprises:
comparing the first target matrix with the second target matrix, and selecting a preset number of second target values, wherein the second target values are smaller than all unselected target values;
and determining fourth target positions corresponding to the second target values, and generating a second position matrix to minimize the second target values corresponding to the second target positions in the second position matrix.
5. The method of claim 1, wherein the calculating the step size parameter corresponding to the second location matrix comprises:
and acquiring a global optimal position corresponding to the second position matrix in the target function, acquiring an iteration pre-estimated position of the second position matrix, and calculating a step length parameter corresponding to the second position matrix based on the global optimal position and the iteration pre-estimated position.
6. The method of claim 1, wherein the generating a third location matrix based on the step size parameter and partially replacing the second location matrix based on the third location matrix to minimize each of the second target values in the replaced second location matrix comprises:
randomly generating a third target position around each second target position of the second position matrix based on the step length parameter, and constructing a third position matrix;
and calculating a third target matrix corresponding to the third position matrix, and replacing the third target position with the second target value at the same position to the second position matrix based on the third target value in the third target matrix being smaller than the second target value at the same position so as to minimize each second target value in the second position matrix after replacement.
7. The method according to claim 1, wherein the calculating a step parameter corresponding to the second position matrix, generating a third position matrix based on the step parameter, and locally replacing the second position matrix based on the third position matrix, so as to minimize each second target value in the replaced second position matrix, further comprises:
and adding one to the iteration times, repeating the step of randomly and respectively orthogonal to the first target positions to obtain direction vectors, and optimizing the corresponding first target positions on the basis of the direction vectors to obtain a second target matrix until the iteration times meet the maximum iteration times.
8. An apparatus for automatically optimizing a frequency of a target signal for assisting direction of arrival positioning, the apparatus comprising:
the acquisition module is used for acquiring sound source signals acquired by the array, setting a transformation matrix, and constructing an objective function based on the sound source signals and the transformation matrix, wherein the objective function is used for representing non-Gaussian distribution among the sound source signals;
a first generating module, configured to randomly generate a first location matrix based on the dimension of the transformation matrix, and construct a first target matrix based on a first target value corresponding to each first target location in the first location matrix in the target function;
the orthogonal module is used for respectively and randomly orthogonal aligning each first target position to obtain a direction vector, and optimizing each corresponding first target position based on each direction vector to obtain a second target matrix;
a second generating module, configured to generate a second position matrix based on the first target matrix and the second target matrix, so as to minimize a second target value corresponding to each second target position in the second position matrix;
a calculating module, configured to calculate a step size parameter corresponding to the second location matrix, generate a third location matrix based on the step size parameter, and locally replace the second location matrix based on the third location matrix, so as to minimize each second target value in the replaced second location matrix;
and the separation module is used for determining an optimal transformation matrix corresponding to the second position matrix, separating the sound source signals based on the optimal transformation matrix to obtain each independent sound source signal, and determining the independent sound source signal with the highest frequency spectrum peak value as a target sound source signal.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-7 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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