Signal source number estimation method and device and train

Abstract

The invention provides a signal source number estimation method and device and a train, and can be applied to the technical field of rail transit. The method comprises the steps that in response to a signal source estimation instruction, an initial channel signal is collected through a signal collection device, and the initial channel signal is generated by data interaction of an unknown number of wireless devices during train operation; based on a variational mode decomposition algorithm, performing channel expansion on the initial channel signal to obtain a multi-channel eigenvalue matrix; preprocessing the multi-channel eigenvalue matrix by using the channel eigenvalue matrix mean value to obtain a transition eigenvalue matrix; processing the transition characteristic value matrix based on a preset diagonal loading amount formula to obtain a target diagonal loading amount; and processing the target diagonal loading amount based on an akaike information criterion algorithm to obtain signal source quantity information of the wireless equipment.

Description

Technical Field

[0001] The present disclosure relates to the field of rail transit technology, and more specifically, to a signal source quantity estimation method, a signal source quantity estimation device, a train, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] In terms of rail transit, due to the rapid operation of trains, trains will be interfered with by other wireless signals. In order to ensure the stability and reliability of rail communications, without knowing any prior information about the interference signal, it is necessary to blindly estimate the monitoring signal to obtain the information and location of the signal for the next step of control and processing. In order to identify the interference signal and control and protect the interference source, it is necessary to blindly separate the monitoring signal. The most critical step of blind source separation is to estimate the number of sources (signal sources).

[0003] In the process of implementing the concept of the present disclosure, it is found that the accuracy of the number of signal sources estimated by the related art is relatively poor. Summary of the invention

[0004] In view of this, the present disclosure provides a signal source quantity estimation method, a signal source quantity estimation device, a train, an electronic device, a computer-readable storage medium, and a computer program product.

[0005] One aspect of the present disclosure provides a method for estimating the number of signal sources, comprising:

[0006] In response to the signal source estimation instruction, using the signal acquisition device to acquire the initial channel signal, wherein the initial channel signal is generated by data interaction between an unknown number of wireless devices when the train is running;

[0007] Based on the variational mode decomposition algorithm, the above initial channel signal is expanded to obtain a multi-channel eigenvalue matrix;

[0008] Preprocessing the multi-channel eigenvalue matrix using the mean of the channel eigenvalue matrix to obtain a transition eigenvalue matrix;

[0009] The above transition eigenvalue matrix is ​​processed based on a preset diagonal loading formula to obtain a target diagonal loading;

[0010] The target diagonal loading is processed based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device.

[0011] According to an embodiment of the present disclosure, based on the variational mode decomposition algorithm, the above initial channel signal is channel expanded to obtain a multi-channel eigenvalue matrix, including:

[0012] Processing the initial channel signal using the variational mode decomposition algorithm to obtain multiple channel component signals;

[0013] The multi-channel eigenvalue matrix is ​​constructed according to the plurality of channel component signals.

[0014] According to an embodiment of the present disclosure, the multi-channel eigenvalue matrix includes a plurality of initial matrix eigenvalues;

[0015] The multi-channel eigenvalue matrix is ​​preprocessed using the mean of the channel eigenvalue matrix to obtain a transition eigenvalue matrix, including:

[0016] Calculate the mean of the channel eigenvalue matrix according to the multiple initial matrix eigenvalues;

[0017] The plurality of initial matrix eigenvalues ​​are screened by using the channel eigenvalue matrix mean to obtain the transition eigenvalue matrix, wherein the transition eigenvalue matrix includes a plurality of transition matrix eigenvalues.

[0018] According to an embodiment of the present disclosure, the above-mentioned initial matrix eigenvalues ​​are screened and processed using the above-mentioned channel eigenvalue matrix mean to obtain the above-mentioned transition eigenvalue matrix, including:

[0019] For each of the initial matrix eigenvalues, calculating the eigendifference between the initial matrix eigenvalue and the mean of the channel eigenvalue matrix;

[0020] In the case where the eigenvalues ​​satisfy the preset over-decomposed signal condition, the eigenvalues ​​of the initial matrix are assigned to obtain the eigenvalues ​​of the transition matrix.

[0021] According to an embodiment of the present disclosure, when the above-mentioned characteristic difference value satisfies a preset over-decomposed signal condition, the above-mentioned initial matrix eigenvalue is assigned to obtain the above-mentioned transition matrix eigenvalue, including:

[0022] Constructing a judgment threshold according to the initial matrix eigenvalue and the first target element, wherein the first target element represents the last initial matrix eigenvalue in the multi-channel eigenvalue matrix;

[0023] When the judgment threshold is smaller than the first preset value and the characteristic difference is smaller than the second preset value, the initial matrix eigenvalues ​​are assigned to obtain the transition matrix eigenvalues.

[0024] According to an embodiment of the present disclosure, the above transition eigenvalue matrix includes a plurality of transition matrix eigenvalues;

[0025] The above transition eigenvalue matrix is ​​processed based on a preset diagonal loading formula to obtain a target diagonal loading, including:

[0026] According to the transition matrix mean and the diagonal loading empirical formula, the above-mentioned preset diagonal loading formula is constructed;

[0027] Substituting a plurality of the above transition matrix eigenvalues ​​into the above preset diagonal loading formula, the above target diagonal loading is obtained.

[0028] According to an embodiment of the present disclosure, the above-mentioned preset diagonal loading amount formula is constructed according to the transition matrix mean and the diagonal loading empirical formula, including:

[0029] Calculate the transition matrix mean value according to the plurality of transition matrix eigenvalues;

[0030] The diagonal loading empirical formula is generated according to the transition matrix mean and the second target element, wherein the second target element represents the last transition matrix eigenvalue in the transition eigenvalue matrix;

[0031] The preset diagonal loading formula is generated according to the element signs corresponding to the eigenvalues ​​of the transition matrix and the diagonal loading empirical formula.

[0032] According to an embodiment of the present disclosure, the target diagonal loading is processed based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device, including:

[0033] Generate a target eigenvalue matrix according to the transition eigenvalue matrix and the target diagonal loading;

[0034] Substitute the above target eigenvalue matrix into the Akaike information criterion function to obtain the above signal source quantity information.

[0035] According to an embodiment of the present disclosure, the above-mentioned signal acquisition device includes an oscilloscope or a receiving antenna, wherein the above-mentioned signal acquisition device is configured to collect the initial channel signal existing in the train space within a preset time period.

[0036] Another aspect of the present disclosure provides a signal source quantity estimation device, comprising:

[0037] A data acquisition module, configured to respond to a signal source estimation instruction and use a signal acquisition device to acquire an initial channel signal, wherein the initial channel signal is generated by data interaction between an unknown number of wireless devices when the train is running;

[0038] A channel expansion module is used to perform channel expansion on the above initial channel signal based on a variational mode decomposition algorithm to obtain a multi-channel eigenvalue matrix;

[0039] A matrix processing module, used for preprocessing the multi-channel eigenvalue matrix using the mean of the channel eigenvalue matrix to obtain a transition eigenvalue matrix;

[0040] A diagonal loading module, used to process the above transition eigenvalue matrix based on a preset diagonal loading formula to obtain a target diagonal loading;

[0041] The quantity estimation module is used to process the target diagonal loading based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device.

[0042] Another aspect of the present disclosure provides a train, comprising:

[0043] The main body of the train;

[0044] A signal acquisition device, installed on the train body, configured to acquire an initial channel signal within a preset time period;

[0045] The signal source quantity estimation device is configured to process the initial channel signal to obtain signal source quantity information representing the number of wireless devices performing data interaction in the train body.

[0046] Another aspect of the present disclosure provides an electronic device, comprising:

[0047] one or more processors;

[0048] a memory for storing one or more programs,

[0049] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0050] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above method when executed.

[0051] Another aspect of the present disclosure provides a computer program product, which includes computer executable instructions, and when the instructions are executed, are used to implement the method as described above.

[0052] According to the embodiment of the present disclosure, the initial channel signal collected by the signal acquisition device is expanded by using the variational mode decomposition algorithm to obtain a multi-channel eigenvalue matrix. Since the variational mode decomposition algorithm processes data in the process of over-decomposition of the signal, the multi-channel eigenvalue matrix is ​​first pre-processed using the mean of the channel eigenvalue matrix, and then the target diagonal loading of the transition eigenvalue matrix is ​​calculated based on the preset diagonal loading formula. Finally, the target diagonal loading is processed based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device. The multi-channel eigenvalue matrix is ​​pre-processed by the mean of the channel eigenvalue matrix, and the sub-signals obtained by over-decomposition of the signal can be eliminated. At the same time, the signal source quantity information calculated by combining the preset diagonal loading formula that can overcome the divergence of the eigenvalues ​​is relatively accurate, thereby improving the accuracy of the signal source quantity estimation, thereby improving the driving safety of the train. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0054] Figure 1 An exemplary system architecture to which the method for estimating the number of signal sources according to an embodiment of the present disclosure can be applied is schematically shown;

[0055] Figure 2 A flowchart of a method for estimating the number of signal sources according to an embodiment of the present disclosure is schematically shown;

[0056] Figure 3 A schematic diagram showing the variation of the estimation accuracy of a single-channel mixed signal source with different modulation modes according to an embodiment of the present disclosure with the signal-to-noise ratio;

[0057] Figure 4 A block diagram of a signal source quantity estimation device according to an embodiment of the present disclosure is schematically shown;

[0058] Figure 5 Schematically illustrates a schematic diagram of a train according to an embodiment of the present disclosure;

[0059] Figure 6 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0060] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0061] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0062] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0063] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0064] In the electromagnetic interference source identification signal, the receiver sometimes collects many signals from different signal source individuals. Generally speaking, the spectrum parameters of these interference signals are different. The original characteristic parameters of different radiation source individuals can be extracted from the data acquisition signal to determine the type of radiation source individual. According to these extracted signal parameters, suppression measures can be formulated in a targeted manner, and then the spectrum characteristics of the electromagnetic interference source of the current vehicle can be mastered to the greatest extent, and the electromagnetic compatibility performance of the vehicle can be optimized.

[0065] At present, one solution to source estimation is to combine the single-channel source estimation algorithm with empirical mode decomposition (EMD) and cross-validation technology to realize the estimation of the number of mixed signal sources of the modulated signal 2ASK and BPSK. However, this method has the following disadvantages: this method can only realize the estimation of two specific modulated signal sources, and the number and type of sources that can be realized are limited. Another method is to expand the channel through the EEMD algorithm and calculate the information entropy at the same time to realize the estimation of the mixed source of the sine-cosine signal and the AM signal. However, this method is only effective for simple signals and has limitations for more complex modulated signals.

[0066] In view of this, an embodiment of the present disclosure provides a method, device and train for estimating the number of signal sources, the method comprising: responding to a signal source estimation instruction, using a signal acquisition device to collect an initial channel signal, wherein the initial channel signal is generated by data interaction between an unknown number of wireless devices during train operation; based on a variational mode decomposition algorithm, performing channel expansion on the initial channel signal to obtain a multi-channel eigenvalue matrix; using the mean of the channel eigenvalue matrix to preprocess the multi-channel eigenvalue matrix to obtain a transition eigenvalue matrix; processing the transition eigenvalue matrix based on a preset diagonal loading formula to obtain a target diagonal loading; processing the target diagonal loading based on an Akaike information criterion algorithm to obtain signal source quantity information of the wireless device.

[0067] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security, and national security.

[0068] In the embodiments of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0069] Figure 1 The exemplary system architecture 100 to which the method for estimating the number of signal sources according to an embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0070] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, a server 105, and a train 106. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0071] The user may use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only for example).

[0072] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0073] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0074] The train 106 may be any train that needs to obtain electric energy from a high-voltage power grid, such as a high-speed train, a motor train, a subway, etc.

[0075] It should be noted that the signal source quantity estimation method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the signal source quantity estimation device provided in the embodiment of the present disclosure can generally be set in the server 105. The signal source quantity estimation method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Correspondingly, the signal source quantity estimation device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Alternatively, the signal source quantity estimation method provided in the embodiment of the present disclosure can also be executed by the first terminal device 101, the second terminal device 102 or the third terminal device 103, or can also be executed by other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103. Accordingly, the signal source quantity estimation device provided in the embodiment of the present disclosure may also be arranged in the first terminal device 101, the second terminal device 102 or the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102 or the third terminal device 103.

[0076] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0077] Figure 2 The flowchart of the method for estimating the number of signal sources according to an embodiment of the present disclosure is schematically shown.

[0078] like Figure 2 As shown, the signal source quantity estimation method includes operations S201-S205.

[0079] In operation S201, in response to a signal source estimation instruction, an initial channel signal is collected using a signal collection device, wherein the initial channel signal is generated by data interaction between an unknown number of wireless devices when a train is running;

[0080] In operation S202, based on a variational mode decomposition algorithm, channel expansion is performed on the initial channel signal to obtain a multi-channel eigenvalue matrix;

[0081] In operation S203, the multi-channel eigenvalue matrix is ​​preprocessed using the channel eigenvalue matrix mean to obtain a transition eigenvalue matrix;

[0082] In operation S204, the transition eigenvalue matrix is ​​processed based on a preset diagonal loading formula to obtain a target diagonal loading;

[0083] In operation S205, the target diagonal loading is processed based on the Akaike information criterion algorithm to obtain the number information of signal sources of the wireless device.

[0084] According to an embodiment of the present disclosure, during the travel of a train, a large number of users may use mobile phones, computers and other electronic devices (i.e., signal sources) to access the Internet (i.e., data interaction). However, these signal sources may affect the travel safety of the train. Therefore, the signal source quantity estimation method provided in an embodiment of the present disclosure aims to accurately identify the number of signal sources during the travel of the train.

[0085] According to an embodiment of the present disclosure, the signal source estimation instruction may be a corresponding monitoring operation input by a staff in a train control room or a train dispatching room on an electronic device such as a computer, and the electronic device may automatically generate the instruction in response to the operation.

[0086] According to an embodiment of the present disclosure, the signal acquisition device may be an oscilloscope or a receiving antenna or other signal receiving device.

[0087] According to an embodiment of the present disclosure, the variational mode decomposition (VMD) algorithm is a signal processing algorithm that realizes the frequency domain decomposition of the signal and the effective separation of each component by iteratively searching for the optimal solution of the variational model. The basic principle of the VMD algorithm is to decompose the signal into multiple relatively stable subsequences, each subsequence has a different center frequency and bandwidth.

[0088] According to an embodiment of the present disclosure, the Akaike Information Criterion (AIC) algorithm is used to measure the standard of goodness of statistical model fitting. It is based on the concept of information entropy in information theory and can weigh the complexity of the estimated model and the goodness of the model fitting the data.

[0089] According to the embodiments of the present disclosure, during the running of the train, the initial channel signal is collected by the signal acquisition device, and the initial channel signal is expanded by the variational mode decomposition algorithm to obtain a multi-channel eigenvalue matrix. Since the variational mode decomposition algorithm sets a larger decomposition layer number when the number of signal sources is unknown, the obtained signal component is greater than the number of signal sources, which will cause the center frequencies of the two components to be relatively close, resulting in over-decomposition of the signal. Therefore, the multi-channel eigenvalue matrix is ​​preprocessed using the mean of the channel eigenvalue matrix to obtain a transition eigenvalue matrix, and then the transition eigenvalue matrix is ​​processed based on the preset diagonal loading formula to obtain the target diagonal loading, and finally the target diagonal loading is processed using the Akaike information criterion algorithm to obtain the signal source quantity information representing the number of wireless devices (i.e., signal sources).

[0090] According to the embodiment of the present disclosure, the initial channel signal collected by the signal acquisition device is expanded by using the variational mode decomposition algorithm to obtain a multi-channel eigenvalue matrix. Since the variational mode decomposition algorithm processes data in the process of over-decomposition of the signal, the multi-channel eigenvalue matrix is ​​first pre-processed using the mean of the channel eigenvalue matrix, and then the target diagonal loading of the transition eigenvalue matrix is ​​calculated based on the preset diagonal loading formula. Finally, the target diagonal loading is processed based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device. The multi-channel eigenvalue matrix is ​​pre-processed by the mean of the channel eigenvalue matrix, and the sub-signals obtained by over-decomposition of the signal can be eliminated. At the same time, the signal source quantity information calculated by combining the preset diagonal loading formula that can overcome the divergence of the eigenvalues ​​is relatively accurate, thereby improving the accuracy of the signal source quantity estimation, thereby improving the driving safety of the train.

[0091] According to an embodiment of the present disclosure, based on the variational mode decomposition algorithm, the initial channel signal is channel expanded to obtain a multi-channel eigenvalue matrix, including:

[0092] The initial channel signal is processed by using a variational mode decomposition algorithm to obtain multiple channel component signals;

[0093] A multi-channel eigenvalue matrix is ​​constructed according to multiple channel component signals.

[0094] According to an embodiment of the present disclosure, the initial channel signal is processed using a variational mode decomposition algorithm to obtain multiple channel component signals x m , according to multiple channel component signals x m Constructing a multi-channel eigenvalue matrix .

[0095] According to an embodiment of the present disclosure, after obtaining the multi-channel eigenvalue matrix Afterwards, the covariance matrix can be calculated , ,in is the power of the i-th component signal (i.e., channel component signal), , N is the number of signal sampling points.

[0096] According to an embodiment of the present disclosure, a multi-channel eigenvalue matrix includes a plurality of initial matrix eigenvalues.

[0097] According to an embodiment of the present disclosure, a multi-channel eigenvalue matrix is ​​preprocessed using a channel eigenvalue matrix mean to obtain a transition eigenvalue matrix, including:

[0098] Calculate the mean of the channel eigenvalue matrix based on multiple initial matrix eigenvalues;

[0099] A plurality of initial matrix eigenvalues ​​are screened using a channel eigenvalue matrix mean to obtain a transition eigenvalue matrix, wherein the transition eigenvalue matrix includes a plurality of transition matrix eigenvalues.

[0100] According to an embodiment of the present disclosure, the multi-channel eigenvalue matrix can also be used Indicates that its initial matrix eigenvalues ​​include express, The size of reflects the power of the channel component signal. When the VMD algorithm is over-decomposed, it may cause the same signal information to be dispersed into two components, then the eigenvalue of the source signal It will also be dispersed, so first adjust the eigenvalues ​​of the multi-channel matrix after the expansion channel, and judge whether the signal is dispersed based on the relationship with the average eigenvalue. Over-decomposition of the signal will cause the center frequencies between components to be very close and mode repetition will occur, and the eigenvalue will also be affected, so it needs to be processed first to eliminate the influence of mode repetition on subsequent source estimation.

[0101] According to an embodiment of the present disclosure, in order to eliminate the above-mentioned influence, the embodiment of the present disclosure calculates a multi-channel eigenvalue matrix according to multiple initial matrix eigenvalues. The mean of the channel eigenvalue matrix , thus using the mean of the channel eigenvalue matrix to screen multiple initial matrix eigenvalues ​​to obtain a transition eigenvalue matrix.

[0102] According to an embodiment of the present disclosure, by using the mean of the channel eigenvalue matrix to screen multiple initial matrix eigenvalues, the accuracy of subsequent signal source quantity estimation can be improved.

[0103] According to an embodiment of the present disclosure, a plurality of initial matrix eigenvalues ​​are screened using the channel eigenvalue matrix mean to obtain a transition eigenvalue matrix, including:

[0104] For each initial matrix eigenvalue, calculate the eigendifference between the initial matrix eigenvalue and the mean of the channel eigenvalue matrix;

[0105] When the eigenvalues ​​satisfy the preset over-decomposed signal condition, the eigenvalues ​​of the initial matrix are assigned to obtain the eigenvalues ​​of the transition matrix.

[0106] According to an embodiment of the present disclosure, for each initial matrix eigenvalue , calculate the characteristic difference between the initial matrix eigenvalue and the channel eigenvalue matrix mean by formula (1) :

[0107] (1)

[0108] According to an embodiment of the present disclosure, the characteristic difference The size of reflects whether the initial matrix eigenvalue belongs to the eigenvalue corresponding to the noise or the eigenvalue corresponding to the over-decomposed signal. Therefore, when the characteristic difference meets the preset over-decomposed signal condition, the initial matrix eigenvalue is assigned to obtain the transition matrix eigenvalue. The assigned value can be set according to actual needs, for example, it can be 0.

[0109] According to an embodiment of the present disclosure, the characteristic difference between each initial matrix eigenvalue and the mean of the channel eigenvalue matrix is ​​calculated, and the initial matrix eigenvalue with over-decomposition is assigned based on the characteristic difference, thereby reducing the impact of over-decomposition on the accuracy of signal source quantity estimation.

[0110] According to an embodiment of the present disclosure, when the characteristic difference value satisfies a preset over-decomposed signal condition, the initial matrix eigenvalue is assigned to obtain the transition matrix eigenvalue, including:

[0111] Constructing a judgment threshold according to the initial matrix eigenvalue and the first target element, wherein the first target element represents the last initial matrix eigenvalue in the multi-channel eigenvalue matrix;

[0112] When the judgment threshold is less than the first preset value and the characteristic difference value is less than the second preset value, the initial matrix eigenvalues ​​are assigned to obtain transition matrix eigenvalues.

[0113] According to an embodiment of the present disclosure, if It may represent the eigenvalue corresponding to the noise or the eigenvalue corresponding to the over-decomposed signal, the eigenvalue of the over-decomposed signal and the mean of the channel eigenvalue matrix The size is relatively close, while the noise eigenvalue is close to the mean of the channel eigenvalue matrix The difference is large. In order to distinguish these two eigenvalues, we can use The ratio distinguishes the two eigenvalues. If the signal is over-decomposed, its eigenvalue is large and If the ratio is close to 1, the ratio will be less than 1; if it is a noise eigenvalue, due to its small eigenvalue and If the difference is large, the ratio will be greater than 1.

[0114] According to an embodiment of the present disclosure, for each initial matrix eigenvalue , according to the initial matrix eigenvalues and the first target element , construct the judgment threshold , if the judgment threshold Less than the first preset value and the characteristic difference When the initial matrix eigenvalue is less than the second preset value, the initial matrix eigenvalue is assigned to obtain the transition matrix eigenvalue.

[0115] In a specific embodiment, when the first preset value is 1 and the second preset value is 0, it can be expressed by formula (2):

[0116]

[0117] According to the embodiment of the present disclosure, the formula (2) is satisfied. That is the eigenvalue of the over-decomposed component signal. Assign a value of zero to eliminate the influence of the over-decomposed component signal, thereby obtaining the transition eigenvalue matrix , is the mzth transition matrix eigenvalue, and z is the number of component signals of the modal repetition after it appears and is eliminated (i.e., assigned a value).

[0118] According to an embodiment of the present disclosure, the transition eigenvalue matrix includes a plurality of transition matrix eigenvalues.

[0119] According to an embodiment of the present disclosure, the transition eigenvalue matrix is ​​processed based on a preset diagonal loading formula to obtain a target diagonal loading, including:

[0120] According to the transition matrix mean and the empirical formula of diagonal loading, a preset diagonal loading formula is constructed;

[0121] Substitute multiple transition matrix eigenvalues ​​into the preset diagonal loading formula to obtain the target diagonal loading.

[0122] According to an embodiment of the present disclosure, a preset diagonal loading formula is constructed based on the transition matrix mean and the diagonal loading empirical formula. The preset diagonal loading formula is shown in formula (3):

[0123] (3)

[0124] According to an embodiment of the present disclosure, a plurality of transition matrix eigenvalues ​​are substituted into the preset diagonal loading formula of formula (3) to obtain the target diagonal loading.

[0125] According to an embodiment of the present disclosure, a preset diagonal loading formula is constructed based on the transition matrix mean and the diagonal loading empirical formula, including:

[0126] Calculating a transition matrix mean value according to a plurality of transition matrix eigenvalues;

[0127] Generate a diagonal loading empirical formula according to the transition matrix mean and the second target element, wherein the second target element represents the last transition matrix eigenvalue in the transition eigenvalue matrix;

[0128] A preset diagonal loading formula is generated according to the element signs corresponding to the eigenvalues ​​of the transition matrix and the empirical formula for diagonal loading.

[0129] According to an embodiment of the present disclosure, the transition matrix mean is calculated based on multiple transition matrix eigenvalues , which is the sum of the average power and average noise of multiple initial channel signals. According to the transition matrix mean and the second target element , generating the empirical formula for diagonal loading as shown in formula (4):

[0130] (4)

[0131] According to an embodiment of the present disclosure, a preset diagonal loading formula is generated according to the element sign corresponding to the eigenvalue of the transition matrix and the diagonal loading empirical formula, that is, formula (4) is substituted into formula (3), thereby obtaining the preset diagonal loading formula shown in formula (5):

[0132] (5)

[0133] According to the embodiment of the present disclosure, after obtaining the preset diagonal loading formula, the target diagonal loading can be obtained by substituting the transition eigenvalue matrix into the preset diagonal loading formula of formula (5). .

[0134] According to an embodiment of the present disclosure, the target diagonal loading is processed based on the Akaike information criterion algorithm to obtain the number of signal sources of the wireless device, including:

[0135] Generate a target eigenvalue matrix according to the transition eigenvalue matrix and the target diagonal loading;

[0136] Substitute the target eigenvalue matrix into the Akaike information criterion function to obtain the signal source quantity information.

[0137] According to an embodiment of the present disclosure, the target diagonal loading DL is used to change the transition eigenvalue matrix again to generate a target eigenvalue matrix, which includes multiple target matrix eigenvalues , as shown in formula (6):

[0138] (6)

[0139] in, is the eigenvalue of the transition matrix.

[0140] According to an embodiment of the present disclosure, the obtained eigenvalue matrix is ​​brought into the Akaike Information Criterion (AIC) function, and the obtained result is the information on the number of signal sources that need to be estimated.

[0141] According to an embodiment of the present disclosure, the specific estimation process of the AIC criterion is as follows:

[0142] The AIC criterion is based on the observation signal being an independent and identically distributed narrowband signal and the noise being white noise, and the number of signals is determined by the position corresponding to the minimum value of the criterion. The specific process is as follows:

[0143] Assume that signal, the signal likelihood function is defined as , then the noise likelihood function term is Item,, is It is easy to find that the AIC criterion based on information theory includes two parts: likelihood function part and penalty function part, which can be expressed by formula (7):

[0144] (7)

[0145] in, represents the information theory criterion, is the penalty function term. The likelihood function term is related to the eigenvalue of the covariance matrix, the number of source signals, the number of channels, and the number of snapshots. The penalty function term is only related to the number of snapshots and has nothing to do with the eigenvalue matrix of the covariance. When the eigenvalues ​​are arranged in descending order, It shows a trend of first decreasing and then increasing. The position corresponding to the extreme point is the number of source signals, that is, the number of signal sources. The embodiment of the present disclosure selects a model that can obtain the minimum AIC value, such as formula (8):

[0146]

[0147] (8)

[0148] in, is the number of samples of the observed signal; n is the number of sampling points; is the probability distribution parameter.

[0149] Figure 3 The schematic diagram shows the variation of the estimation accuracy of a single-channel mixed signal source with different modulation modes according to an embodiment of the present disclosure with the signal-to-noise ratio.

[0150] According to an embodiment of the present disclosure, Figure 3 (a) is a curve showing the accuracy rate versus signal-to-noise ratio obtained by using the signal source number estimation method disclosed in the present invention to estimate the number of signal sources for single-channel mixed signals (i.e., initial channel signals) of three modulation modes: QPSK, BPSK, and 2FSK; Figure 3 (b) is a curve showing the accuracy rate versus signal-to-noise ratio obtained by using the signal source number estimation method disclosed in the present invention to estimate the number of signal sources for single-channel mixed signals of three modulation modes: QPSK, BPSK, and 2ASK; Figure 3 (c) is a curve showing the variation of the accuracy with the signal-to-noise ratio obtained by using the signal source quantity estimation method disclosed in the present invention to estimate the number of signal sources for single-channel mixed signals of three modulation modes: QPSK, 2ASK, and AM; Figure 3 (d) is a curve showing the variation of the accuracy with the signal-to-noise ratio obtained by using the signal source number estimation method disclosed in the present invention to estimate the number of signal sources for a single-channel mixed signal of three modulation modes: QPSK, AM, and FM.

[0151] According to the embodiments of the present disclosure, QPSK represents a quadrature phase shift keying digital modulation method. BPSK represents a binary phase shift keying digital modulation method. 2FSK represents a binary digital frequency modulation modulation method, also known as binary frequency shift keying. 2ASK represents a binary amplitude keying modulation method. AM represents an amplitude modulation method. FM represents a frequency modulation method.

[0152] According to the embodiments of the present disclosure, Figure 3 It can be seen that no matter which modulation mode is used, when the signal source quantity estimation method of the embodiment of the present invention is used to estimate the number of signal sources, as the signal-to-noise ratio increases, the accuracy of signal source estimation increases accordingly. It can be seen that the signal source quantity estimation method provided by the embodiment of the present invention has a relatively good accuracy rate.

[0153] According to an embodiment of the present disclosure, the signal acquisition device includes an oscilloscope or a receiving antenna, wherein the signal acquisition device is configured to acquire an initial channel signal existing in the train space within a preset time period.

[0154] According to an embodiment of the present disclosure, the preset time period can be specifically set according to actual needs, for example, it can be 5 minutes or 1 hour.

[0155] It should be noted that the above-mentioned signal acquisition device is only an example and may also be other acquisition devices with the same function.

[0156] Figure 4 The block diagram of a signal source quantity estimation device according to an embodiment of the present disclosure is schematically shown.

[0157] like Figure 4 As shown, the signal source quantity estimation device 400 includes a data acquisition module 410 , a channel expansion module 420 , a matrix processing module 430 , a diagonal loading module 440 , and a quantity estimation module 450 .

[0158] The data acquisition module 410 is used to collect initial channel signals using a signal acquisition device in response to a signal source estimation instruction, wherein the initial channel signals are generated by data interaction between an unknown number of wireless devices when the train is running;

[0159] A channel expansion module 420 is used to perform channel expansion on the initial channel signal based on a variational mode decomposition algorithm to obtain a multi-channel eigenvalue matrix;

[0160] A matrix processing module 430 is used to pre-process the multi-channel eigenvalue matrix using the channel eigenvalue matrix mean to obtain a transition eigenvalue matrix;

[0161] A diagonal loading module 440 is used to process the transition eigenvalue matrix based on a preset diagonal loading formula to obtain a target diagonal loading;

[0162] The quantity estimation module 450 is used to process the target diagonal loading based on the Akaike information criterion algorithm to obtain the quantity information of the signal sources of the wireless device.

[0163] According to the embodiment of the present disclosure, the initial channel signal collected by the signal acquisition device is expanded by using the variational mode decomposition algorithm to obtain a multi-channel eigenvalue matrix. Since the variational mode decomposition algorithm processes data in the process of over-decomposition of the signal, the multi-channel eigenvalue matrix is ​​first pre-processed using the mean of the channel eigenvalue matrix, and then the target diagonal loading of the transition eigenvalue matrix is ​​calculated based on the preset diagonal loading formula. Finally, the target diagonal loading is processed based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device. The multi-channel eigenvalue matrix is ​​pre-processed by the mean of the channel eigenvalue matrix, and the sub-signals obtained by over-decomposition of the signal can be eliminated. At the same time, the signal source quantity information calculated by combining the preset diagonal loading formula that can overcome the divergence of the eigenvalues ​​is relatively accurate, thereby improving the accuracy of the signal source quantity estimation, thereby improving the driving safety of the train.

[0164] According to an embodiment of the present disclosure, the channel expansion module 420 includes:

[0165] A decomposition unit, used for processing an initial channel signal using a variational mode decomposition algorithm to obtain a plurality of channel component signals;

[0166] The first constructing unit is used to construct a multi-channel eigenvalue matrix according to a plurality of channel component signals.

[0167] According to an embodiment of the present disclosure, a multi-channel eigenvalue matrix includes a plurality of initial matrix eigenvalues.

[0168] According to an embodiment of the present disclosure, the matrix processing module 430 includes:

[0169] A calculation submodule, used for calculating a channel eigenvalue matrix mean value according to a plurality of initial matrix eigenvalues;

[0170] The screening submodule is used to screen multiple initial matrix eigenvalues ​​using the channel eigenvalue matrix mean to obtain a transition eigenvalue matrix, wherein the transition eigenvalue matrix includes multiple transition matrix eigenvalues.

[0171] According to an embodiment of the present disclosure, the screening submodule includes:

[0172] A calculation unit, for calculating, for each initial matrix eigenvalue, an eigendifference between the initial matrix eigenvalue and the mean of the channel eigenvalue matrix;

[0173] The assignment unit is used to assign the initial matrix eigenvalues ​​to obtain the transition matrix eigenvalues ​​when the eigenvalue difference satisfies the preset over-decomposed signal condition.

[0174] According to an embodiment of the present disclosure, the assignment unit includes:

[0175] A construction subunit, used to construct a judgment threshold according to the initial matrix eigenvalue and the first target element, wherein the first target element represents the last initial matrix eigenvalue in the multi-channel eigenvalue matrix;

[0176] The assignment subunit is used to assign the initial matrix eigenvalues ​​to obtain the transition matrix eigenvalues ​​when the judgment threshold is less than the first preset value and the characteristic difference is less than the second preset value.

[0177] According to an embodiment of the present disclosure, the transition eigenvalue matrix includes a plurality of transition matrix eigenvalues.

[0178] According to an embodiment of the present disclosure, the diagonal loading module 440 includes:

[0179] The second construction unit is used to construct a preset diagonal loading formula according to the transition matrix mean and the diagonal loading empirical formula;

[0180] The obtaining unit is used to substitute a plurality of transition matrix eigenvalues ​​into a preset diagonal loading formula to obtain a target diagonal loading.

[0181] According to an embodiment of the present disclosure, the second building unit includes:

[0182] A calculation subunit, used for calculating a transition matrix mean value according to a plurality of transition matrix eigenvalues;

[0183] A first generating subunit is used to generate a diagonal loading empirical formula according to a transition matrix mean value and a second target element, wherein the second target element represents a last transition matrix eigenvalue in a transition eigenvalue matrix;

[0184] The second generating subunit is used to generate a preset diagonal loading formula according to the element signs corresponding to the eigenvalues ​​of the transition matrix and the diagonal loading empirical formula.

[0185] According to an embodiment of the present disclosure, the quantity estimation module 450 includes:

[0186] A generating unit, used for generating a target eigenvalue matrix according to the transition eigenvalue matrix and the target diagonal loading;

[0187] The substitution unit is used to substitute the target eigenvalue matrix into the Akaike information criterion function to obtain the signal source quantity information.

[0188] According to an embodiment of the present disclosure, the signal acquisition device includes an oscilloscope or a receiving antenna, wherein the signal acquisition device is configured to acquire an initial channel signal existing in the train space within a preset time period.

[0189] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0190] For example, any multiple of the data acquisition module 410, the channel expansion module 420, the matrix processing module 430, the diagonal loading module 440, and the quantity estimation module 450 can be combined in one module / unit / subunit for implementation, or any one of the modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functions of one or more of the modules / units / subunits can be combined with at least part of the functions of other modules / units / subunits and implemented in one module / unit / subunit. According to an embodiment of the present disclosure, at least one of the data acquisition module 410, the channel expansion module 420, the matrix processing module 430, the diagonal loading module 440, and the quantity estimation module 450 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the data acquisition module 410, the channel expansion module 420, the matrix processing module 430, the diagonal loading module 440, and the quantity estimation module 450 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0191] It should be noted that the signal source quantity estimation device part in the embodiment of the present disclosure corresponds to the signal source quantity estimation method part in the embodiment of the present disclosure. The description of the signal source quantity estimation device part specifically refers to the signal source quantity estimation method part, which will not be repeated here.

[0192] Figure 5 A schematic diagram of a train according to an embodiment of the present disclosure is schematically shown.

[0193] According to an embodiment of the present disclosure, the train 500 includes:

[0194] Train body 510;

[0195] A signal acquisition device 520 is installed on the train body, and the signal acquisition device is configured to acquire an initial channel signal within a preset time period;

[0196] The signal source quantity estimation device 400 is configured to process the initial channel signal to obtain signal source quantity information representing the number of wireless devices performing data interaction in the train body.

[0197] According to the embodiment of the present disclosure, the initial channel signal collected by the signal acquisition device is expanded by using the variational mode decomposition algorithm to obtain a multi-channel eigenvalue matrix. Since the variational mode decomposition algorithm processes data in the process of over-decomposition of the signal, the multi-channel eigenvalue matrix is ​​first pre-processed using the mean of the channel eigenvalue matrix, and then the target diagonal loading of the transition eigenvalue matrix is ​​calculated based on the preset diagonal loading formula. Finally, the target diagonal loading is processed based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device. The multi-channel eigenvalue matrix is ​​pre-processed by the mean of the channel eigenvalue matrix, and the sub-signals obtained by over-decomposition of the signal can be eliminated. At the same time, the signal source quantity information calculated by combining the preset diagonal loading formula that can overcome the divergence of the eigenvalues ​​is relatively accurate, thereby improving the accuracy of the signal source quantity estimation, thereby improving the driving safety of the train.

[0198] Figure 6 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0199] like Figure 6As shown, the electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 to a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0200] In RAM 603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM 602 and RAM 603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in one or more memories.

[0201] According to an embodiment of the present disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0202] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0203] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0204] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0205] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 602 and / or the RAM 603 described above and / or one or more memories other than the ROM 602 and the RAM 603 .

[0206] The embodiments of the present disclosure also include a computer program product, which includes a computer program, and the computer program contains program code for executing the method provided by the embodiments of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the method provided by the embodiments of the present disclosure.

[0207] When the computer program is executed by the processor 601, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0208] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0209] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0210] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0211] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for estimating the number of signal sources, characterized in that: include: In response to the signal source estimation instruction, using a signal acquisition device to acquire an initial channel signal, wherein the initial channel signal is generated by data interaction between an unknown number of wireless devices when the train is running; Based on the variational mode decomposition algorithm, the initial channel signal is channel expanded to obtain a multi-channel eigenvalue matrix; Preprocessing the multi-channel eigenvalue matrix using the channel eigenvalue matrix mean to obtain a transition eigenvalue matrix; Processing the transition eigenvalue matrix based on a preset diagonal loading formula to obtain a target diagonal loading; The target diagonal loading is processed based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device.

2. The method according to claim 1, characterized in that: Based on the variational mode decomposition algorithm, the initial channel signal is channel expanded to obtain a multi-channel eigenvalue matrix, including: Processing the initial channel signal using the variational mode decomposition algorithm to obtain multiple channel component signals; The multi-channel eigenvalue matrix is ​​constructed according to the plurality of channel component signals.

3. The method according to claim 1 or 2, characterized in that: The multi-channel eigenvalue matrix includes a plurality of initial matrix eigenvalues; The multi-channel eigenvalue matrix is ​​preprocessed using the channel eigenvalue matrix mean to obtain a transition eigenvalue matrix, including: Calculate the channel eigenvalue matrix mean value according to the multiple initial matrix eigenvalues; The plurality of initial matrix eigenvalues ​​are screened using the channel eigenvalue matrix mean to obtain the transition eigenvalue matrix, wherein the transition eigenvalue matrix includes a plurality of transition matrix eigenvalues.

4. The method according to claim 3, characterized in that The plurality of initial matrix eigenvalues ​​are screened using the channel eigenvalue matrix mean to obtain the transition eigenvalue matrix, including: For each of the initial matrix eigenvalues, calculating an eigend difference between the initial matrix eigenvalue and the mean of the channel eigenvalue matrix; In the case where the characteristic difference value satisfies a preset over-decomposed signal condition, the initial matrix eigenvalue is assigned to obtain the transition matrix eigenvalue.

5. The method according to claim 4, characterized in that When the characteristic difference value satisfies a preset over-decomposed signal condition, the initial matrix eigenvalue is assigned to obtain the transition matrix eigenvalue, including: Constructing a judgment threshold according to the initial matrix eigenvalue and a first target element, wherein the first target element represents the last initial matrix eigenvalue in the multi-channel eigenvalue matrix; When the judgment threshold is less than a first preset value and the characteristic difference value is less than a second preset value, the initial matrix eigenvalues ​​are assigned to obtain the transition matrix eigenvalues.

6. The method according to claim 1, characterized in that The transition eigenvalue matrix includes a plurality of transition matrix eigenvalues; The transition eigenvalue matrix is ​​processed based on a preset diagonal loading formula to obtain a target diagonal loading, including: Constructing the preset diagonal loading formula according to the transition matrix mean and the diagonal loading empirical formula; Substituting a plurality of the transition matrix eigenvalues ​​into the preset diagonal loading formula, the target diagonal loading is obtained.

7. The method according to claim 6, characterized in that According to the transition matrix mean and the diagonal loading empirical formula, the preset diagonal loading formula is constructed, including: Calculating the transition matrix mean value according to a plurality of transition matrix eigenvalues; Generate the diagonal loading empirical formula according to the transition matrix mean and the second target element, wherein the second target element represents the last transition matrix eigenvalue in the transition eigenvalue matrix; The preset diagonal loading amount formula is generated according to the element sign corresponding to the eigenvalue of the transition matrix and the diagonal loading empirical formula.

8. The method according to claim 1 or 6, characterized in that: Processing the target diagonal loading based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device includes: generating a target eigenvalue matrix according to the transition eigenvalue matrix and the target diagonal loading; Substitute the target eigenvalue matrix into the Akaike information criterion function to obtain the signal source quantity information.

9. The method according to claim 1, characterized in that: The signal acquisition device includes an oscilloscope or a receiving antenna, wherein the signal acquisition device is configured to acquire an initial channel signal existing in the train space within a preset time period.

10. A signal source quantity estimation device, characterized in that: include: A data acquisition module, configured to respond to a signal source estimation instruction and use a signal acquisition device to acquire an initial channel signal, wherein the initial channel signal is generated by data interaction between an unknown number of wireless devices when the train is running; A channel expansion module is used to perform channel expansion on the initial channel signal based on a variational mode decomposition algorithm to obtain a multi-channel eigenvalue matrix; A matrix processing module, used for preprocessing the multi-channel eigenvalue matrix using the channel eigenvalue matrix mean to obtain a transition eigenvalue matrix; A diagonal loading module, used for processing the transition eigenvalue matrix based on a preset diagonal loading formula to obtain a target diagonal loading; The quantity estimation module is used to process the target diagonal loading based on the Akaike information criterion algorithm to obtain the signal source quantity information of the wireless device.

11. A train, characterized in that: include: The main body of the train; A signal acquisition device, installed on the train body, the signal acquisition device is configured to acquire an initial channel signal within a preset time period; The signal source quantity estimation device as described in claim 10 is configured to process the initial channel signal to obtain signal source quantity information representing the number of wireless devices performing data interaction in the train body.

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