Method, device and equipment for determining number of signal sources and storage medium

Abstract

The source number determination method, device, equipment and storage medium provided by the present disclosure determine a data matrix according to a received signal, calculate a sample covariance matrix of the data matrix and perform eigenvalue decomposition, calculate a linear spectrum statistic, calculate a generalized Bayesian information quantity corresponding to each source number, sort the generalized Bayesian information quantities from large to small, and determine the source number corresponding to the smallest generalized Bayesian information quantity as the estimation of the source number. By converting the original data matrix into a linear spectrum statistic, the main information contained in the data matrix is extracted, the description framework of the asymptotic behavior of the statistic is changed, and the requirement of accuracy for the signal-to-noise ratio is reduced through the dimension ratio p / n.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of array signal processing, and particularly relates to a signal source number determination method, device, equipment and storage medium. BACKGROUND

[0002] 1. Classical array signal source estimation method

[0003] Commonly used source estimation methods are mainly divided into two categories: one is the hypothesis testing estimation method based on threshold judgment, including spherical test and extreme value test. The hypothesis testing estimation method is to construct a corresponding statistic, derive its asymptotic distribution, and determine the threshold by using the asymptotic distribution to estimate the number of sources. The other is the threshold-free information criterion estimation method, including Akaike information criterion, Bayesian information criterion and minimum description length. The information criterion estimation method is to calculate the likelihood term and the corresponding penalty term two parts, that is, the information amount as the discriminant term, and the maximum value point or the minimum value point is selected as the source number estimation. Since the Bayesian information criterion satisfies the consistency and does not require threshold, the Bayesian information criterion is often used in practical applications.

[0004] In particular, the likelihood term in the information amount is related to the parameters of the population distribution, and these parameters are often unknown. Therefore, in practical applications, the unknown parameters are estimated first, and then the estimated value of the unknown parameters is taken as the true value of the unknown parameters and brought into the likelihood term. However, such substitution method has certain premise assumptions, and whether these assumption conditions are satisfied in actual scenarios is often unknown.

[0005] 2. Large-dimensional random matrix theory

[0006] A matrix whose elements are all random variables is called a random matrix. Generally, large-dimensional random matrix refers to its literal meaning, that is, a random matrix whose row number and column number tend to infinity. This corresponds to two characteristics of the big data era: the data volume (row number) of the sample is increasing rapidly and there are many influencing factors (column number) of the problem being studied, such as a large number of users communicating simultaneously in a communication system. In the face of increasingly large data volume and many influencing factors, the commonly used method is dimension reduction, such as principal component analysis. However, with the reduction of dimension, a large amount of rich information in the original data is inevitably discarded. How to extract as much information as possible from the original data has become a research hotspot, and large-dimensional random matrix is one of the useful tools developed in this process. Large-dimensional random matrix mainly focuses on two parts--the eigenvalues and eigenvectors of the random matrix. In the study of eigenvalues, the convergence of the empirical spectral distribution and the distribution of the linear spectral statistic are included. On the other hand, it is about the study of eigenvectors, but the results of this part are relatively imperfect.

[0007] In particular, the data volume of actual data and the number of observed influence factors are limited, rather than infinite. Therefore, in practical applications, the concept of "large dimension" can be appropriately relaxed. According to practical experience, when the dimension of observed data is not less than 10 dimensions, at this time, the classical statistical method almost fails, and a large-dimensional system analysis method with better performance can be used. In addition, if the large-dimensional analysis method is used for the case where the dimension is less than 10 dimensions, the effect of the large-dimensional analysis will not be too bad compared with the classical statistical method. Therefore, compared with the fixed dimension and the sample size tending to infinity assumed by the classical asymptotic system, the large-dimensional system more accurately describes the statistical characteristics of the observed data.

[0008] In summary, the prior art has the following disadvantages: the traditional method is based on the classical probability statistical framework, and has a large deviation for the case with low signal-to-noise ratio. On the other hand, the data in the real scene is relatively complex. The premise assumption of the classical information criterion cannot be met, so that the information criterion no longer satisfies the consistency. SUMMARY

[0009] The present disclosure provides a signal source number determination method, device, equipment and storage medium, which at least to some extent overcomes the problem of low performance of the traditional signal source estimation method in the case of low signal-to-noise ratio in the phased array radar and multiple-input multiple-output system.

[0010] Other characteristics and advantages of the present disclosure will become apparent from the following detailed description, or will be learned by practice of the present disclosure.

[0011] According to one aspect of the present disclosure, a signal source number determination method is provided, comprising:

[0012] determining a data matrix according to the received signal;

[0013] calculating a sample covariance matrix of the data matrix and performing eigenvalue decomposition to calculate a linear spectral statistic;

[0014] calculating a generalized Bayesian information quantity corresponding to each signal source number;

[0015] sorting the generalized Bayesian information quantities from large to small, and determining the signal source number corresponding to the smallest generalized Bayesian information quantity as the estimation of the signal source number.

[0016] Optionally, determining the data matrix according to the received signal comprises:

[0017] receiving signals according to multiple sensors;

[0018] assembling the signals received by the multiple sensors into a data matrix in a preset manner.

[0019] Optionally, assembling the signals received by the multiple sensors into a data matrix in a preset manner comprises:

[0020] Each sensor in the plurality of sensors receives a signal, and the signal is determined as a row in a matrix;

[0021] Each column of data in the matrix is one of a plurality of data received by each sensor.

[0022] Optionally, a sample covariance matrix of the data matrix is calculated and eigenvalue decomposition is performed, and a linear spectrum statistic is calculated, including:

[0023] The absolute value maximum of two linear spectrum statistics of the sample covariance matrix of the data matrix is calculated.

[0024] Optionally, the absolute value maximum of two linear spectrum statistics of the sample covariance matrix of the data matrix includes:

[0025] The absolute value maximum of two linear spectrum statistics of the sample covariance matrix of the data matrix is calculated by a first formula; wherein the first formula is:

[0026] Wherein, k is the current number of sources, and r is the maximum possible number of sources.

[0027] Optionally, the generalized Bayesian information corresponding to each source number is calculated, including:

[0028] The generalized Bayesian information corresponding to each source number is calculated by a second formula; wherein the second formula includes:

[0029]

[0030] Wherein, k is the current number of sources, r is the maximum possible number of sources, and n is the number of sensor sampling points.

[0031] According to one aspect of the present disclosure, a source number determination device is provided, the device comprising:

[0032] A first determination module is configured to determine a data matrix according to the received signal;

[0033] A first calculation module is configured to calculate a sample covariance matrix of the data matrix and perform eigenvalue decomposition, and calculate a linear spectrum statistic.

[0034] A second calculation module is configured to calculate the generalized Bayesian information corresponding to each source number.

[0035] A second determination module is configured to sort the generalized Bayesian information from large to small, and determine the source number corresponding to the smallest generalized Bayesian information as an estimate of the source number.

[0036] According to still another aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the source number determination method described above via execution of the executable instructions.

[0037] According to still another aspect of the present disclosure, a computer readable storage medium is provided, having stored thereon a computer program, which, when executed by a processor, implements the source number determination method described above.

[0038] The source number determination method provided by the embodiments of the present disclosure determines a data matrix according to a received signal, calculates a sample covariance matrix of the data matrix and performs eigenvalue decomposition, calculates a linear spectrum statistic, calculates a generalized Bayesian information quantity corresponding to each source number, sorts the generalized Bayesian information quantities from large to small, determines the source number corresponding to the smallest generalized Bayesian information quantity as an estimation of the source number, and converts the original data matrix into a linear spectrum statistic. The main information contained in the data matrix is extracted, the description framework of the asymptotic behavior of the statistic is changed, and the requirement of the accuracy rate on the signal-to-noise ratio is reduced through the dimension ratio p / n.

[0039] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 A flow chart of a source number determination method in the embodiments of the present disclosure is shown;

[0041] Figure 2 A schematic diagram of determining a source number in the embodiments of the present disclosure is shown;

[0042] Figure 3 A schematic diagram of the accuracy rate corresponding to different signal-to-noise ratios in the embodiments of the present disclosure is shown;

[0043] Figure 4 A schematic diagram of a source number determination device in the embodiments of the present disclosure is shown; and

[0044] Figure 5 A structural block diagram of an electronic device in the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0045] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0046] The present application is illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements, and in which:

[0047] To illustrate, various aspects of embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be implemented in a wide variety of forms and that any specific structure and / or function described herein is merely illustrative. Based on the teachings herein one skilled in the art will appreciate that one or more aspects described herein can be implemented independently of any other aspects described herein and that an aspect can be implemented both in and / or independently of any combination of aspects described herein.

[0048] It is also noted that the illustrative figures can show semicircles and other shapes that are not true to scale. The purpose of these drawings is to illustrate basic concepts and ideas, not to depict actual structures in actual proportions.

[0049] In addition, in the following description, numerous specific details are provided for a thorough understanding of the examples. One skilled in the relevant art will recognize, however, that the examples can be practiced without one or more of these specific details, in some instances, well-known structures and functions have not been described in detail to avoid obscuring the description of the examples.

[0050] As shown in Figure 1 A method for determining the number of sources is provided.

[0051] At S110, a data matrix is determined from the received signals.

[0052] In one embodiment, the sensor can receive signals.

[0053] In one embodiment, the number of sensors for receiving signals can be multiple.

[0054] In one specific example, the data matrix can be obtained according to the signals received by the plurality of sensors as follows:

[0055]

[0056] where p is the number of sensors, and n is the number of sampling points.

[0057] S120, calculating the sample covariance matrix of the data matrix and performing eigenvalue decomposition to calculate the linear spectrum statistics.

[0058] In one embodiment, the method for determining the sample covariance of the data matrix can be a conventional method in the art, which will not be described here.

[0059] For example, the sample covariance matrix can be where the eigenvalues are .

[0060] For example, the linear spectrum statistics can be determined according to the following formula.

[0061]

[0062] where k = 0, 1, r is the current number of sources, r is the maximum possible number of sources, p is the number of sensors, and q = p-k.

[0063] Then, the new linear spectrum statistics is obtained as follows:

[0064] S130, calculating the generalized Bayesian information corresponding to each source number.

[0065] In one embodiment, the disclosure does not limit the method for obtaining the generalized Bayesian information corresponding to the source number.

[0066] In one embodiment, the generalized Bayesian information can be determined based on the generalized Bayesian information criterion.

[0067] S140, sorting the generalized Bayesian information from large to small, and determining the source number corresponding to the smallest generalized Bayesian information as the estimation of the source number.

[0068] For example, Figure 2 A schematic diagram for determining the source number in the embodiment of the disclosure is shown as follows: Figure 2 The generalized Bayesian information is sorted from large to small, and the source number corresponding to the last position is the output.

[0069] In one embodiment, the disclosure does not limit the method for sorting.

[0070] Exemplarily, the generalized Bayesian information quantity can be sorted according to the bubble method.

[0071] Exemplarily, the embodiment estimates the number of sources in the case of small sample size, and the random matrix theory is used to well depict the asymptotic property of the linear spectral statistics of the sample covariance matrix, so that the requirement of the statistics on the sample size and the signal-to-noise ratio is greatly reduced. In combination with the generalized Bayesian information criterion, the estimation accuracy is effectively improved, and a new idea is provided for the estimation of the number of sources in the actual engineering. Figure 3 A schematic diagram of the accuracy corresponding to different signal-to-noise ratios in the embodiment of the disclosure is shown. As shown in Figure 3 The embodiment can achieve a correct rate of more than 80% under the condition that the sample size is 200, the number of array elements is 50, and the signal-to-noise ratio is 2 dB.

[0072] The source number determination method provided by the embodiment of the disclosure determines a data matrix according to the received signal, calculates a sample covariance matrix of the data matrix and performs eigenvalue decomposition, calculates a linear spectral statistic, calculates a generalized Bayesian information quantity corresponding to each source number, sorts the generalized Bayesian information quantities from large to small, determines the smallest generalized Bayesian information quantity as the source number, converts the original data matrix into a linear spectral statistic, presents the asymptotic property of different source numbers, reduces the requirement of the statistics on the sample size and the signal-to-noise ratio, can extract the main information contained in the data matrix, changes the description framework of the asymptotic behavior of the statistics, and reduces the requirement of the accuracy on the signal-to-noise ratio through the dimension ratio p / n.

[0073] Optionally, determining the data matrix according to the received signal comprises:

[0074] Receiving the signal by the plurality of sensors;

[0075] Assembling the signal received by the plurality of sensors into the data matrix in a preset manner.

[0076] Optionally, assembling the signal received by the plurality of sensors into the data matrix in a preset manner comprises:

[0077] Determining the signal received by each sensor in the plurality of sensors as a row in the matrix;

[0078] The data in each column in the matrix is one of the plurality of data received by each sensor.

[0079] Optionally, calculating the sample covariance matrix of the data matrix and performing eigenvalue decomposition to calculate the linear spectral statistic comprises:

[0080] Calculating the absolute value maximum of two linear spectral statistics of the sample covariance matrix of the data matrix.

[0081] Optionally, the absolute maximum of two linear spectrum statistics of the sample covariance matrix of the data matrix is calculated, including:

[0082] The absolute maximum of two linear spectrum statistics of the sample covariance matrix of the data matrix is calculated according to a first formula; wherein the first formula is:

[0083]

[0084] Wherein, k is the current number of sources, and r is the maximum possible number of sources.

[0085] Optionally, the generalized Bayesian information quantity corresponding to each source number is calculated, including:

[0086] The generalized Bayesian information quantity corresponding to each source number is calculated according to a second formula; wherein the second formula includes:

[0087] Wherein, k is the current number of sources, r is the maximum possible number of sources, and n is the number of sensor sampling points.

[0088] Figure 4 A schematic diagram of a source number determination device in the embodiment of the present disclosure is shown.

[0089] The first determination module 201 is configured to determine a data matrix according to the received signal.

[0090] The first calculation module 202 is configured to calculate the sample covariance matrix of the data matrix and perform eigenvalue decomposition, and calculate linear spectrum statistics.

[0091] The second calculation module 203 is configured to calculate the generalized Bayesian information quantity corresponding to each source number.

[0092] The second determination module 204 is configured to sort the generalized Bayesian information quantity from large to small, and determine the smallest generalized Bayesian information quantity as the source number.

[0093] Those skilled in the art can understand that each aspect of the present disclosure can be implemented as a system, a method or a program product. Therefore, each aspect of the present disclosure can be embodied as a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system".

[0094] The electronic device 300 according to this embodiment of the present disclosure will be described below with reference to Figure 5 Figure 3 The electronic device 300 shown is only an example and should not limit the functions and use range of the embodiments of the present disclosure. ​

[0095] As shown in Figure 3 The electronic device 300 is in the form of a general computing device. Components of the electronic device 300 can include, but are not limited to, the at least one processing unit 310 described above, the at least one storage unit 320 described above, and a bus 330 that connects the different system components including the storage unit 320 and the processing unit 310.

[0096] The storage unit stores program code that can be executed by the processing unit 310 such that the processing unit 310 performs the steps of the various exemplary embodiments according to the present disclosure described in the "Exemplary Methods" section of the present specification. For example, the processing unit 310 can perform the steps of the method embodiments described above.

[0097] The storage unit 320 can include a readable medium in the form of volatile storage such as a random access memory (RAM) 3201 and / or cache memory 3202, and can further include a read-only memory (ROM) 3203.

[0098] The storage unit 320 can also include a program / utility 3203 having a set of program modules 3205 that include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, and each or a combination thereof can include implementation of a network environment.

[0099] The bus 330 can be representative of one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit bus, or a local bus using any of a variety of bus architectures.

[0100] The electronic device 300 can also communicate with one or more external devices 330 such as a keyboard or pointing device, a Bluetooth device, or a database, and / or can communicate with one or more devices that enable a user to interact with the electronic device 300 and / or one or more devices (e.g., a router, a modem, or the like) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 350. Still yet, the electronic device 300 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, through a network adapter 360. As illustrated, the network adapter 360 communicates with the other components of the electronic device 300 via the bus 330. It should be appreciated that the bus 330 can be one of any suitable type and that the components of the electronic device 300 can be implemented using one or more of any suitable type of hardware and / or software.

[0101] Those skilled in the art will readily understand that the example embodiments described herein can be implemented by software and / or by hardware coupled with software, as described above. Thus, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash disk, a mobile hard disk, or the like) or a network, and includes a number of instructions to enable a computing device (such as a personal computer, a server, a terminal device, or a network device, etc.) to perform the methods according to the embodiments of the present disclosure.

[0102] In the example embodiments of the present disclosure, a computer readable storage medium is also provided, which can be a readable signal medium or a readable storage medium. A program product is stored on the computer readable storage medium, and the program product can implement the method described above. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program codes for causing a terminal device to perform the steps described in the above “example method” section according to various example embodiments of the present disclosure when the program product is run on the terminal device.

[0103] More specific examples of the computer readable storage medium in the present disclosure can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0104] In the present disclosure, a computer readable storage medium can include a data signal transporting computer readable program code embodied in or carried by the signal. Such a data signal can take any of a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. A computer readable medium can also be any medium from which a storing of computer readable program code is possible, and from which a program can be read or downloaded by an instruction execution system, apparatus, or device.

[0105] Optionally, program code embodied on a computer readable storage medium can be transmitted by any data transmission techniques, including but not limited to radio frequency, wireless, cable, wire, optical fiber cable, or any suitable combination thereof.

[0106] In an implementation, the program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, and the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's computing device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider.

[0107] It should be noted that, although several modules or units of the device for action execution are mentioned in the foregoing detailed description, such a division is not mandatory. Indeed, features and functionalities of two or more modules or units described above can be embodied in one module or unit according to embodiments of the present disclosure. Conversely, features and functionalities of one module or unit described above can be further divided into multiple modules or units.

[0108] Moreover, although the various steps of the methods in the present disclosure are described in a particular order in the figures, this is not required or implied in any way as to the order of the steps or that all of the steps are necessarily performed to achieve a desired result. Additionally or alternatively, certain steps can be omitted, multiple steps can be combined into a single step, a single step can be broken into multiple steps, and the like.

[0109] Those skilled in the art can easily understand, through the description of the above embodiments, that the example embodiments described herein can be implemented by software or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the methods according to the embodiments of the present disclosure.

[0110] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure following the general principles thereof and including such modifications and equivalents as come within the scope of the present disclosure. The specification and examples are to be regarded as illustrative only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

[0111] In the specification, the same or similar parts between various embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for the product embodiment described later, since it is corresponding to the method, the description is relatively simple, and the relevant part can be referred to the part of the system embodiment.

[0112] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of the changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for determining the number of sources, characterized in that, The method comprises: determining a data matrix according to the received signals; calculating a sample covariance matrix of the data matrix and performing eigenvalue decomposition to calculate linear spectrum statistics; The sample covariance matrix is where the eigenvalues are respectively; wherein X is a data matrix, and n is the number of sampling points; determining the linear spectrum statistics according to the following formula: where k = 0, 1, r is the current number of sources, rmaxis the maximum possible number of sources, p is the number of sensors, and q = p - k. then, calculating the maximum absolute value of two linear spectrum statistics of the sample covariance matrix of the data matrix, comprising: calculating the maximum absolute value of two linear spectrum statistics of the sample covariance matrix of the data matrix by the first formula; wherein the first formula is: ; where k is the current number of sources, and r is the maximum possible number of sources. calculating the generalized Bayesian information quantity corresponding to each number of sources; sorting the generalized Bayesian information quantities from large to small, and determining the number of sources corresponding to the smallest generalized Bayesian information quantity as the estimation of the number of sources.

2. The method of claim 1, wherein The method comprises: receiving signals by multiple sensors; assembling the data matrix according to the signals received by the multiple sensors in a preset manner.

3. The method of claim 2, wherein The method comprises: determining the signals received by each sensor in the multiple sensors as a row in the matrix; each column of data in the matrix is one of the multiple data received by each sensor.

4. The method of claim 1, wherein The method comprises: calculating the maximum absolute value of two linear spectrum statistics of the sample covariance matrix of the data matrix.

5. The method of claim 1, wherein The method comprises: calculating the generalized Bayesian information quantity corresponding to each number of sources by the second formula; wherein the second formula comprises: wherein k is the current number of sources, r is the maximum possible number of sources, and n is the number of sampling points of the sensor.

6. A device for determining the number of information sources, characterized in that, The device comprises: a first determining module configured to determine a data matrix according to the received signals; The first calculation module is used to calculate the sample covariance matrix of the data matrix and perform eigenvalue decomposition, and calculate the linear spectral statistics; the sample covariance matrix is... The eigenvalues ​​are respectively Where X is the data matrix and n is the number of sampling points; determining the linear spectrum statistics according to the following formula: where k = 0, 1, r is the current number of sources, rmaxis the maximum possible number of sources, p is the number of sensors, and q = p - k. then, calculating the maximum absolute value of two linear spectrum statistics of the sample covariance matrix of the data matrix, comprising: calculating the maximum absolute value of two linear spectrum statistics of the sample covariance matrix of the data matrix by the first formula; wherein the first formula is: ; where k is the current number of sources, and r is the maximum possible number of sources. a second calculating module configured to calculate the generalized Bayesian information quantity corresponding to each number of sources; a second determining module configured to sort the generalized Bayesian information quantities from large to small, and determine the number of sources corresponding to the smallest generalized Bayesian information quantity as the estimation of the number of sources.

7. An electronic device, comprising: The device comprises: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to execute the executable instructions to perform the method for determining the number of sources according to any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method for determining the number of sources according to any one of claims 1-5.

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