WO2021023045A1 - 多个信号的共同周期确定方法、装置和可读存储介质 - Google Patents

多个信号的共同周期确定方法、装置和可读存储介质 Download PDF

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WO2021023045A1
WO2021023045A1 PCT/CN2020/104864 CN2020104864W WO2021023045A1 WO 2021023045 A1 WO2021023045 A1 WO 2021023045A1 CN 2020104864 W CN2020104864 W CN 2020104864W WO 2021023045 A1 WO2021023045 A1 WO 2021023045A1
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signal
matrix
determining
vector
elements
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PCT/CN2020/104864
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English (en)
French (fr)
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林晓明
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华泰证券股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction

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  • This application relates to the field of signal processing technology, and in particular to a method, device and readable storage medium for determining the common period of multiple signals.
  • the above analysis methods are to study each time series data of a thing as a signal separately, but the thing is a system as a whole, so the cycle of the thing determined by the above analysis method is not accurate.
  • the main purpose of this application is to provide a method, device and readable storage medium for determining the common period of multiple signals, which aims to solve the problem of inaccurate determination of the period of things.
  • the present application provides a method for determining the common period of multiple signals.
  • the method for determining the common period of multiple signals is applied to a terminal provided with an antenna array, and the antenna array is provided with M elements, Each of the array elements receives the signal sent by the signal source under test, and the method for determining the common period of the multiple signals includes the following steps:
  • the frequency parameters are searched according to the solution function to obtain the common frequency parameters of the signals emitted by the K signal sources to be measured, where the common frequency parameters are the common periods of multiple signals.
  • the present application also provides a common period determining device for multiple signals.
  • the common period device for multiple signals is provided with an antenna array and a signal acquisition module.
  • the antenna array is provided with M elements, each The array element receives the signal sent by the signal source, and the signal acquisition module is used to collect the signal sent by the signal source received by the array element;
  • the device for determining the common period of the multiple signals further includes a memory, a processor, and The memory can run on the processor to determine the program of the common cycle, the signal acquisition module is connected to the processor, and the common cycle determination program is executed by the processor to execute the above-mentioned multiple signal The steps of the common cycle determination method.
  • the present application also provides a readable storage medium that stores a common cycle determining program, and the common cycle determining program is executed by a processor to realize the multiple signals described above
  • the common cycle determines the steps of the method.
  • the method, device, and readable storage medium for determining the common period of multiple signals provided in the present application.
  • the device for determining the common period of multiple signals is provided with an antenna array.
  • the antenna array includes multiple elements, and the device can receive multiple signals to be measured at the same time.
  • the device performs N snapshot space-time sampling on the output signals of the K signal sources under test received by the M elements of the antenna element to construct a signal matrix of M elements to the K signal sources under test, and then determine The covariance matrix corresponding to the signal matrix is decomposed into the eigenvalues of the covariance matrix to obtain the signal vector and the noise vector.
  • the device determines the solution function of the frequency parameter according to the noise vector and the signal vector, and finally searches the frequency parameter according to the solution function.
  • the common frequency parameter of the signal output by each signal source to be measured is obtained, and the common frequency parameter is the common period of multiple signals. Because the device analyzes each signal at the same time to analyze the data corresponding to multiple signals as a whole, and separates the noise vector and signal vector of each signal, it reduces the interference of noise on the common cycle and accurately determines the cycle of things .
  • FIG. 1 is a schematic diagram of the hardware architecture of an apparatus for determining a common period of multiple signals involved in an embodiment of the application;
  • FIG. 2 is a schematic flowchart of a first embodiment of a method for determining a common period of multiple signals according to this application;
  • FIG. 3 is a schematic diagram of the detailed flow of step S10 in FIG. 2;
  • step S20 in FIG. 2 is a schematic diagram of the detailed flow of step S20 in FIG. 2;
  • FIG. 5 is a schematic diagram of the detailed flow of step S30 in FIG. 2;
  • FIG. 6 is a schematic flowchart of a second embodiment of a method for determining a common period of multiple signals according to this application;
  • FIG. 7 is a detailed flowchart of step S60 in FIG. 6;
  • FIG. 8 is a schematic flowchart of a third embodiment of a method for determining a common period of multiple signals according to this application.
  • the main solution of the embodiment of the present application is to perform N snapshots of space-time sampling on the output signals of the K signal sources to be measured received by the M elements of the antenna array element to construct the M array elements Determine the first signal matrix of the K signal sources to be measured; determine the first covariance matrix corresponding to the signal matrix, and perform eigenvalue decomposition on the first covariance matrix to determine the noise vector and the signal vector; The noise vector and the signal vector determine the solution function of the frequency parameter; the frequency parameter is searched according to the solution function to obtain the common frequency parameters of the signals emitted by the K signal sources to be measured, wherein the The common frequency parameter is the common period of multiple signals.
  • the device analyzes each signal at the same time to analyze the data corresponding to multiple signals as a whole, and separates the noise vector and signal vector of each signal, it reduces the interference of noise on the common cycle and accurately determines the cycle of things .
  • the device for determining the common period of multiple signals may be as shown in FIG. 1.
  • the solution of the embodiment of the present application relates to a device for determining a common period of multiple signals.
  • the device for determining a common period of multiple signals includes a processor 101, such as a CPU, a memory 102, a communication bus 103, an antenna array 104, and a signal acquisition module 105.
  • the communication bus 103 is used to realize the connection and communication between these components.
  • the antenna array 104 includes a plurality of elements, each element is used to receive the signal sent by the signal source, and the signal acquisition module 105 is used to collect the signal output by each element. .
  • the memory 102 may be a high-speed RAM memory or a flying wing memory.
  • the memory 103 as a computer storage medium may include a common cycle determining program; and the processor 101 may be used to call the common cycle determining program stored in the memory 102 and perform the following operations:
  • the frequency parameters are searched according to the solution function to obtain the common frequency parameters of the signals emitted by the K signal sources to be measured, where the common frequency parameters are the common periods of multiple signals.
  • the processor 101 may be used to call the common cycle determination program stored in the memory 102, and perform the following operations:
  • the signal vector and the noise vector are determined according to each of the eigenvalues and the sequence number corresponding to each of the eigenvalues.
  • the processor 101 may be used to call the common cycle determination program stored in the memory 102, and perform the following operations:
  • each of the characteristic values determine K target characteristic values, wherein the K target characteristic values are all greater than each other characteristic value except the target characteristic value;
  • the K feature vectors corresponding to the target feature values are used as signal vectors, and the feature vectors corresponding to each of the other feature values are used as noise vectors.
  • the processor 101 may be used to call the common cycle determination program stored in the memory 102, and perform the following operations:
  • the processor 101 may be used to call the common cycle determination program stored in the memory 102, and perform the following operations:
  • a first signal matrix of the M array elements to the K signal sources to be measured is constructed.
  • the processor 101 may be used to call the common cycle determination program stored in the memory 102, and perform the following operations:
  • the processor 101 may be used to call the common cycle determination program stored in the memory 102, and perform the following operations:
  • the processor 101 may be used to call the common cycle determination program stored in the memory 102, and perform the following operations:
  • the number of target characteristic values is determined as the number of signal sources, where the number of signal sources is K;
  • the device for determining the common period of multiple signals is provided with an antenna array, and the antenna array includes multiple array elements, and the device can simultaneously receive signals output by multiple signal sources under test.
  • the device performs N snapshot space-time sampling on the output signals of the K signal sources under test received by the M elements of the antenna element to construct a signal matrix of M elements to the K signal sources under test, and then determine The covariance matrix corresponding to the signal matrix is decomposed into the eigenvalues of the covariance matrix to obtain the signal vector and the noise vector.
  • the device determines the solution function of the frequency parameter according to the noise vector and the signal vector, and finally searches the frequency parameter according to the solution function.
  • the common frequency parameter of the signal output by each signal source to be measured is obtained, and the common frequency parameter is the common period of multiple signals. Because the device analyzes each signal at the same time to analyze the data corresponding to multiple signals as a whole, and separates the noise vector and signal vector of each signal, it reduces the interference of noise on the common cycle and accurately determines the cycle of things .
  • FIG. 2 is a first embodiment of a method for determining a common period of multiple signals according to this application.
  • the method for determining a common period of multiple signals includes the following steps:
  • Step S10 Perform N snapshot space-time sampling on the output signals of the K signals to be measured received by the M elements of the antenna array element to construct a The first signal matrix;
  • the execution subject is the common period determining device for multiple signals.
  • the device is used as the abbreviation of the common period determining device for multiple signals to describe this embodiment in detail.
  • the device is equipped with an antenna array, the antenna array includes a plurality of array elements, the number of array elements is M, and M is an integer. M array elements form an evenly spaced linear array. The characteristics of each element are the same, and each element is isotropic. The interval between adjacent elements is d, and d is less than or equal to the wavelength of the highest frequency signal received by the element Half, each array element is not correlated with each other, and each array element is not correlated with each signal source under test.
  • the antenna array is in the far field of each signal source to be measured, that is, the antenna array receives signals from each signal source to be measured as plane waves.
  • the characteristics of each receiving branch receiving the signal of the signal source under test in the device are the same.
  • the signal sources to be tested have the same polarity, and the signal sources to be tested are not related to each other.
  • the signal sources to be tested can be narrowband, and the number of signal sources to be tested is K, and K ⁇ M.
  • step S10 includes:
  • Step S11 determining the optical path difference and phase difference of each signal received by adjacent array elements
  • the phase difference is
  • Step S12 using the target element of the antenna array as a reference point to determine the function of each other element corresponding to the induction signal of each signal source to be measured;
  • the first element of the antenna array can be used as the reference point to determine the m-th induction signal to the k-th signal source in the equidistant linear array at time t, and the induction signal is the m-th element
  • the first element is the target element.
  • the target element can also be other elements, and the first element is not limited.
  • Step S13 determining the objective function corresponding to the output signal of the array element sampled N times of space-time according to the optical path difference, the phase difference and the function;
  • the output signal of the m-th element can be determined:
  • the function to get the output signal of the m elements of the Nth snapshot is:
  • This function is the objective function.
  • Step S14 according to the objective function corresponding to the output signal of the array element sampled N times of space-time, to construct a first signal matrix of the M array elements to the K signal sources to be measured.
  • X WA+E, where, That is, W is a matrix with N rows and K columns, and W contains information about the frequency parameters of K signal sources;
  • A is a matrix of K rows and M columns, and A contains information about the initial phase term ⁇ k of the K signal sources, the amplitude term Ak , and the angle v k of the k-th initial signal source relative to the array;
  • E is a matrix of N rows and M columns, and E represents the noise on M channels in N samplings.
  • Step S20 Determine a first covariance matrix corresponding to the first signal matrix, and perform eigenvalue decomposition on the first covariance matrix to determine a noise vector and a signal vector;
  • step S20 includes:
  • Step S21 Transform the first covariance matrix according to a preset noise assumption to obtain a covariance matrix to be decomposed;
  • Step S22 Decompose the eigenvalues of the covariance matrix to be decomposed to obtain multiple eigenvalues sorted from large to small;
  • Step S23 Determine a signal vector and a noise vector according to each of the characteristic values and the ranking sequence number corresponding to each of the characteristic values.
  • the noise assumption is that the Gaussian white noise is not correlated with each other, and the Gaussian white noise is not correlated with each signal source to be measured. Therefore, the first covariance matrix is transformed according to the noise assumption to obtain the covariance to be decomposed.
  • the first item of the covariance matrix to be decomposed represents the covariance matrix generated by the signal vector
  • the second item represents the covariance matrix generated by the noise vector.
  • the first covariance matrix can also be decomposed into the following form:
  • each eigenvalue is sorted from large to small, and a sorting sequence composed of each eigenvalue is obtained.
  • Each eigenvalue has a corresponding sorting sequence number, and the K with the first sorting sequence number is selected.
  • the feature value is used as the target feature value, that is, each target feature value is greater than each other target feature value except the target feature value, the feature vector corresponding to the target feature value is the signal vector, and the feature vector corresponding to other feature values is Noise vector.
  • Step S30 determining a solution function of frequency parameters according to the noise vector and the signal vector;
  • step S30 After determining each noise vector and signal vector, the frequency parameter solution function is determined according to the noise vector and signal vector.
  • step S30 includes:
  • Step S31 determining a first matrix corresponding to each of the noise vectors and a second matrix corresponding to each of the signal vectors;
  • Step S32 according to the orthogonality between the signal subspace and the noise subspace, determine the second matrix and the function corresponding to the first matrix whose multiplication is zero, so as to obtain a solution function of the frequency parameter;
  • the signal vectors corresponding to the previous target eigenvalues ⁇ 1 , ⁇ 2 , ... ⁇ K are s 1 , s 2 ... s K
  • S [s 1 ,s 2 ,...s K ]
  • the noise vector corresponding to each other eigenvalue ⁇ K+1 , ⁇ K+2 ,... ⁇ N is g K+1 ,g K+2 ,...g N
  • G [g K+1 ,g K+2 ,...g N ]
  • Step S40 Search the frequency parameters according to the solution function to obtain the common frequency parameters of the signals emitted by the K signal sources under test, where the common frequency parameters are the common periods of multiple signals.
  • the frequency parameter is searched.
  • the above-mentioned method for determining the common cycle of multiple signals can be applied to the signal corresponding to the economic and financial field, that is, the economic and financial time series data corresponding to each signal, and the device analyzes the time of the entire financial field based on such signals. Period to determine the exact time period.
  • the common cycle in the economic and financial fields is 42 months, 100 months, and 200 months.
  • the device can also use the above method to determine the common cycle of multiple signals in other fields, and is not limited to applications in the economic and financial fields.
  • the device for determining the common period of multiple signals is provided with an antenna array, and the antenna array includes a plurality of array elements, and the device can simultaneously receive signals output by multiple signal sources under test.
  • the device performs N snapshot space-time sampling on the output signals of the K signal sources under test received by the M elements of the antenna element to construct a signal matrix of M elements to the K signal sources under test, and then determine The covariance matrix corresponding to the signal matrix is decomposed into the eigenvalues of the covariance matrix to obtain the signal vector and the noise vector.
  • the device determines the solution function of the frequency parameter according to the noise vector and the signal vector, and finally searches the frequency parameter according to the solution function.
  • the common frequency parameter of the signal output by each signal source to be measured is obtained, and the common frequency parameter is the common period of multiple signals. Because the device analyzes each signal at the same time to analyze the data corresponding to multiple signals as a whole, and separates the noise vector and signal vector of each signal, it reduces the interference of noise on the common cycle and accurately determines the cycle of things .
  • FIG. 6 is a second embodiment of the method for determining the common period of multiple signals according to this application. Based on the first embodiment, the method for determining the common period of multiple signals further includes:
  • Step S50 Perform N snapshot space-time sampling on the output signals of the multiple signal sources received by the M elements of the antenna element to determine a second covariance matrix, and compare the second covariance matrix Perform eigenvalue decomposition to obtain multiple eigenvectors;
  • the accurate determination of the common period of multiple signals is based on an appropriate number of signal sources. If the number of signal sources is not appropriate, the spectrum peak will be missed or false peaks will occur when the direction of arrival is estimated. In this regard, before determining the common period of multiple signals, it is necessary to determine an appropriate number of signal sources.
  • the eigenvalue decomposition of the second covariance matrix is performed to obtain multiple eigenvectors.
  • the specific decomposition process of the eigenvalues of the second covariance matrix refers to the eigenvalue decomposition of the first covariance proof.
  • Step S60 Determine the weight corresponding to each eigenvector, and construct a second signal matrix according to each eigenvector and the weight corresponding to each eigenvector;
  • each feature vector determines the weight corresponding to each feature vector.
  • the greater the weight of the feature vector the greater the degree of amplification of the noise vector and the smaller the degree of reduction of the signal vector.
  • the weight is the reciprocal of the eigenvalue, and the eigenvalue of the noise vector is smaller, so the reciprocal of the noise vector is larger, thus amplifying the noise vector; in the same way, the eigenvalue of the signal vector is larger, so the reciprocal is smaller, reducing Signal vector.
  • the effect of the signal vector can be weakened by the reasonable setting of the weight, which is equivalent to the situation that only the noise vector is used in the spatial spectrum algorithm.
  • the device constructs a second signal matrix through each eigenvector and the weight corresponding to each eigenvector. Specifically, refer to FIG. 7, that is, in step S60, the second signal matrix is constructed according to each eigenvector and the weight corresponding to each eigenvector.
  • Step S61 determining the characteristic value corresponding to each of the characteristic vectors
  • Step S62 Determine the coefficient corresponding to each of the feature values, and use the coefficient as the multiple power of the feature value to obtain the value corresponding to each feature vector;
  • Step S63 Determine the inverse of each of the numerical values, and construct a second signal matrix according to each of the inverses.
  • Each eigenvector has a corresponding eigenvalue, and each eigenvalue is ⁇ 1 , ⁇ 2 ,... ⁇ N , and the eigenvalue has a corresponding coefficient c, which is used to adjust the weight.
  • the device uses c as the multiple power of the eigenvalue to obtain the value corresponding to each eigenvector, and then determines the reciprocal of each value to construct a second signal matrix, and the reciprocal is the eigenvector weight.
  • the second signal matrix is the matrix weighted by all the eigenvectors of the first covariance matrix.
  • the second signal matrix Transform the second signal matrix U to get
  • Step S70 Determine a spatial spectral function according to the second signal matrix, and estimate the direction of arrival of the spatial spectral function to determine the number of spectral peaks, so as to determine the number of spectral peaks as the number of signal sources, wherein, The number of the signal sources is K;
  • the device can determine the spatial spectrum function through the second signal matrix, and the spatial spectrum function In the estimation of the direction of arrival of the spatial spectrum function, the number of spectral peaks can be obtained.
  • the number of spectral peaks can be determined as the number of signal sources.
  • the number of signal sources is K, that is, H is determined as K.
  • Step S80 after the signal sources are set according to the determined number of signal sources, perform N snapshots of the output signals of the K signal sources to be measured received by the M elements of the antenna element. Time sampling to construct a first signal matrix of M said array elements to K signal sources.
  • the device After determining the number of signal sources, the device adjusts the number H to the number K, so as to perform the common period of the signals from the K signal sources. That is, the device executes step S10 to step S40.
  • the device performs N snapshots of space-time sampling on the output signals of the H signal elements to be measured received by the M elements of the antenna element, to construct M elements for each signal Source the second covariance matrix, and decompose multiple eigenvalues of the second covariance matrix, and then determine the weight corresponding to each eigenvector to construct the matrix according to each eigenvector and the corresponding weight, so as to obtain
  • the spatial spectrum function and then according to the spatial spectrum function to estimate the direction of arrival to obtain the number of spectral peaks, the number of spectral peaks can be the number of signal sources; in an ideal state, the number of spectral peaks is the same as the number of signal sources, so According to the number of spectral peaks, the number of signal sources is accurately determined, and then the period of things is accurately determined.
  • FIG. 8 is a third embodiment of a method for determining a common period of multiple signals according to this application. Based on the first embodiment, the method for determining a common period of multiple signals further includes:
  • Step S90 Perform N snapshot space-time sampling on the output signals of the multiple signal sources under test received by the M elements of the antenna element to determine a third covariance matrix, and compare the third Covariance matrix performs eigenvalue decomposition to obtain multiple eigenvalues;
  • the device determines the number of signal sources by explaining the strength. You can set up L signal sources first, and then the device performs N snapshot space-time sampling on the output signals of the L signal sources received by the M elements of the antenna array element to determine the third covariance matrix and the third covariance The determination of the matrix is consistent with the determination of the first covariance matrix, and will not be repeated here.
  • the eigenvalue decomposition of the third covariance matrix is performed to obtain multiple eigenvalues.
  • the specific decomposition process of the eigenvalues of the third covariance matrix refers to the eigenvalue decomposition of the first covariance proof.
  • Step S100 Determine the interpretation strength, and determine a target characteristic value in each of the characteristic values, wherein each of the target characteristic values is greater than other characteristic values except the target characteristic value;
  • the device determines the target characteristic value among each characteristic value. Specifically, each feature value is sorted in descending order to obtain a sequence, and the first L feature values in the sequence are taken as the target feature value, that is, each target feature value is greater than each other except the target feature value Eigenvalues.
  • the device determines the strength of explanation.
  • the strength of explanation is the strength of explaining the essence of a thing.
  • the unit of strength of explanation is the percentage.
  • the explanation power can be determined by the ratio of the sum of the first L target eigenvalues to the sum of all eigenvalues, that is, the explanation power is the variance contribution degree of the first L variables, and the variance contribution degree can be determined by the principle component thinking. specific,
  • y 1 is the largest variance in these linear combinations
  • y 2 is the largest variance in the linear combinations that are not related to y 1 , and so on, generally, y j is irrelevant to y 1 , y 2, etc.
  • the linear combination with the largest variance is the linear combination with the largest variance.
  • a i is the eigenvector corresponding to the i-th eigenvalue ⁇ i of the correlation coefficient matrix of x 1 ,...x p
  • the variance contribution of y i is:
  • the explanation strength can be determined by the technician, that is, the technician inputs the explanation strength in the device.
  • Step S110 judging whether the number of target characteristic values meets a preset condition according to the interpretation strength and each target characteristic value
  • the device determines whether the number of target characteristic values meets the preset conditions according to the interpretation strength and each target characteristic value.
  • the interpretation strength can be determined by the variance contribution degree, and the interpretation strength can be passed through The sum of the variance contributions corresponding to the L eigenvalues is determined, that is, According to this formula, calculate the first sum between all characteristic values, that is, the first sum is the sum of all characteristic values, and calculate the second sum between each target characteristic value, and the second sum is the sum of each target characteristic value , And calculate the ratio between the second sum and the first sum, and then judge whether the ratio is greater than or equal to the explanatory strength. If the ratio is greater than the explanatory strength, and the difference between the ratio and the explanatory strength is less than the preset threshold, the target can be determined The number of characteristic values meets the preset condition.
  • Step S120 when the number of target characteristic values meets a preset condition, the number of target characteristic values is determined as the number of signal sources, where the number of signal sources is K;
  • the number of target eigenvalues can be used as the number of signal sources, so that the signal sources are reset according to the number of signal sources, that is, L is changed to K.
  • Step S130 after the signal sources are set according to the determined number of signal sources, perform N snapshots of the output signals of the K signal sources under test received by the M elements of the antenna array element. Time sampling to construct a first signal matrix of M said array elements to K signal sources.
  • the device After determining the number of signal sources, the device adjusts the number L to the number K, so as to perform a common period of the signals from the K signal sources. That is, the device executes step S10 to step S40.
  • the device performs N snapshots of space-time sampling on the output signals of the L signal sources to be measured received by the M elements of the antenna element, to construct M elements for each signal
  • the third covariance matrix of the source and the third eigenvalue decomposition of the covariance matrix to obtain multiple eigenvalues, and then determine the interpretation strength and determine the target eigenvalue in each eigenvalue, and finally according to the interpretation strength and each target eigenvalue It is judged whether the number of target characteristic values meets the preset condition, and if so, the number of target characteristic values is determined as the number of signal sources, and then the cycle of things is accurately determined.
  • the device determines the ratio, if the ratio is less than the interpretation strength, it indicates that the number of target feature values selected by the device is too small, so that the ratio cannot reach the interpretation strength. In this regard, the device re-determines the target characteristic value in each characteristic value, and the number of the new target characteristic value is greater than the number of the target characteristic value determined last time, that is, the current target characteristic value number L'>L.
  • the number of re-determined target characteristic values can be determined according to the difference between the interpretation strength and the ratio. The greater the difference between the interpretation strength and the ratio, the greater the number of re-determined target characteristic values.
  • the ratio is obtained again. If the ratio is lower than the interpretation strength again, the target characteristic value is re-determined until the number of target characteristic values meets the preset condition.
  • the device re-determines the target feature value after determining that the ratio is less than the interpretation strength, and the number of re-determined target feature values is greater than the number of target feature values determined last time to perform the target feature value again
  • the device has a higher degree of intelligence until the determination of whether the number meets the preset condition until the target number of the target feature value that meets the preset condition is determined.
  • the ratio when the ratio is greater than the interpretation strength, it is necessary to further calculate the difference between the ratio and the interpretation strength, and then determine whether the difference is less than or equal to a preset threshold. If the difference is greater than the preset threshold, it is considered The ratio is too large.
  • the number of target feature values corresponding to the re-determined ratio is the target quantity.
  • the target quantity is the set quantity of the signal source.
  • the number of re-determined target characteristic values can be determined according to the difference between the ratio and the explanatory power. The larger the difference between the ratio and the explanatory power, the smaller the number of re-determined target characteristic values.
  • the device determines that the difference between the ratio and the interpretation strength is greater than the preset threshold, and the ratio is too large, and the target feature value needs to be re-determined, and the number of re-determined target feature values is less than the last time determined
  • the device has a higher degree of intelligence until the target number of target characteristic values that meet the preset conditions is determined.
  • the application also provides a device for determining a common period of multiple signals.
  • the device for common period of multiple signals is provided with an antenna array and a signal acquisition module.
  • the antenna array is provided with M array elements, and each of the array elements receives
  • the signal acquisition module is used to collect the signal sent by the signal source received by the array element;
  • the device for determining the common period of the multiple signals further includes a memory, a processor, and stored in the memory and in the A common cycle determination program running on a processor, the signal acquisition module is connected to the processor, and the common cycle determination program executed by the processor executes the common cycle determination method for multiple signals as described in the above embodiment The various steps.
  • the present application also provides a readable storage medium, the readable storage medium stores a common cycle determination program, and when the common cycle determination program is executed by a processor, the common cycle of multiple signals as described in the above embodiment is realized Identify the steps of the method.

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Abstract

本申请公开了一种多个信号的共同周期确定方法，终端设有由多个阵元构成的传感器矩阵，各个阵元接收待测信号源发送的信号，包括：对天线阵元的M个阵元接收的K个待测信号源的输出信号，进行N次快拍的空时采样，以构建M个阵元对K个待测信号源的第一信号矩阵；确定第一信号矩阵对应的第一协方差矩阵，并对第一协方差进行特征值的分解以确定噪声向量以及信号向量；根据噪声向量与信号向量确定频率参数的求解函数；根据求解函数对频率参数进行搜索，以获得K个待测信号源发射的信号的共同频率参数，其中，共同频率参数为多个信号的共同周期。本申请还公开一种多个信号的共同周期确定装置和可读存储介质。

Description

多个信号的共同周期确定方法、装置和可读存储介质
本申请要求2019年8月7日申请的，申请号为201910728448.8，名称为“多个信号的共同周期确定方法、装置和可读存储介质”的中国专利申请的优先权，在此将其全文引入作为参考。
技术领域
本申请涉及信号处理技术领域，尤其涉及一种多个信号的共同周期确定方法、装置和可读存储介质。
背景技术
在对于寻找某一事物的时间规律时，也即确定事物的周期，通过采用表征该事物对应的时间序列进行分析，而时间序列可通过信号表示，也即可通过信号处理确定事物的时间规律。
对于时间规律的确定有多种方法。第一，采用傅里叶分析法将同比序列变换到频域上；第二，利用短时傅里叶变换法研究周期分布在时间轴上的时变性与稳定性，研究事物在受到外部因素影响时周期在时间轴上的变化；第三，选择高斯滤波器提取周期信号并合成，以滤除信号中的噪声，找出准确的周期。第四，运用Z域图研究事物的周期所处的位置，能量大小，并据此对当前事务的未来变化做出判断。
以上分析方法均是将事物的每个时间序列数据作为一个信号单独进行研究，但事物是一个系统整体，故以上分析方法所确定的事物的周期并不准确。
发明内容
本申请的主要目的在于提供一种多个信号的共同周期确定方法、装置和可读存储介质，旨在解决事物的周期的确定不准确的问题。
为实现上述目的，本申请提供了一种多个信号的共同周期确定方法，所述多个信号的共同周期确定方法应用于设有天线阵列的终端，所述天线阵列设有M个阵元，各个所述阵元接收待测信号源发送的信号，所述多个信号的共同周期确定方法包括以下步骤：
对所述天线阵元的M个阵元接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵；
确定所述信号矩阵对应的第一协方差矩阵，并对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量；
根据所述噪声向量与所述信号向量确定频率参数的求解函数；
根据所述求解函数对所述频率参数进行搜索，以获得K个所述待测信号源发射的信号的共同频率参数，其中，所述共同频率参数为多个信号的共同周期。
为实现上述目的，本申请还提供一种多个信号的共同周期确定装置，所 述多个信号的共同周期装置设有天线阵列以及信号采集模块，所述天线阵列设有M个阵元，各个所述阵元接收信号源发送的信号，所述信号采集模块用于采集阵元所接收信号源发送的信号；所述多个信号的共同周期确定装置还包括存储器、处理器以及存储在所述存储器并可在所述处理器上运行的共同周期的确定程序，所述信号采集模块与所述处理器连接，所述共同周期的确定程序被处理器执行时执行如上所述的多个信号的共同周期确定方法的各个步骤。
为实现上述目的，本申请还提供一种可读存储介质，所述可读存储介质存储有共同周期的确定程序，所述共同周期的确定程序被处理器执行时实现如上所述的多个信号的共同周期确定方法的各个步骤。
本申请提供的多个信号的共同周期确定方法、装置和可读存储介质，多个信号的共同周期确定装置设有天线阵列，天线阵列包括多个阵元，装置可同时接收多个待测信号源输出的信号。装置对天线阵元的M个阵元接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个阵元对K个待测信号源的信号矩阵，再确定信号矩阵对应的协方差矩阵，并对协方差矩阵进行特征值的分解得到信号向量以及噪声向量，装置根据噪声向量以及信号向量确定频率参数的求解函数，最后根据求解函数对频率参数进行搜索，从而得到各个待测信号源输出的信号的共同频率参数，共同频率参数即为多个信号的共同周期。由于装置同时对各个信号进行分析，以将多个信号对应的数据进行整体分析，且对各个信号进行噪声向量以及信号向量的分离，减少了噪音对共同周期的干扰，准确地确定了事物的周期。
附图说明
图1为本申请实施例涉及的多个信号的共同周期的确定装置的硬件构架示意图；
图2为本申请多个信号的共同周期的确定方法的第一实施例的流程示意图；
图3为图2中步骤S10的细化流程示意图；
图4为图2中步骤S20的细化流程示意图；
图5为图2中步骤S30的细化流程示意图；
图6为本申请多个信号的共同周期的确定方法的第二实施例的流程示意图；
图7为图6中步骤S60的细化流程示意图；
图8为本申请多个信号的共同周期的确定方法的第三实施例的流程示意图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
应当理解，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。
本申请实施例的主要解决方案是：对所述天线阵元的M个阵元接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵；确定所述信号矩阵对应的第一协方差矩阵，并对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量；根据所述噪声向量与所述信号向量确定频率参数的求解函数；根据所述求解函数对所述频率参数进行搜索，以获得K个所述待测信号源发射的信号的共同频率参数，其中，所述共同频率参数为多个信号的共同周期。
由于装置同时对各个信号进行分析，以将多个信号对应的数据进行整体分析，且对各个信号进行噪声向量以及信号向量的分离，减少了噪音对共同周期的干扰，准确地确定了事物的周期。
作为一种实现方案，多个信号的共同周期确定装置可以如图1所示。
本申请实施例方案涉及的是多个信号的共同周期确定装置，多个信号的共同周期确定装置包括：处理器101，例如CPU，存储器102，通信总线103，天线阵列104以及信号采集模块105。其中，通信总线103用于实现这些组件之间的连接通信，天线阵列104包括多个阵元，各个阵元用于接收信号源发送的信号，信号采集模块105用于采集各个阵元输出的信号。
存储器102可以是高速RAM存储器，也可以是飞翼式存储器。如图1所示，作为一种计算机存储介质的存储器103中可以包括共同周期的确定程序；而处理器101可以用于调用存储器102中存储的共同周期的确定程序，并执行以下操作：
对所述天线阵元的M个阵元接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵；
确定所述信号矩阵对应的第一协方差矩阵，并对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量；
根据所述噪声向量与所述信号向量确定频率参数的求解函数；
根据所述求解函数对所述频率参数进行搜索，以获得K个所述待测信号源发射的信号的共同频率参数，其中，所述共同频率参数为多个信号的共同周期。
在一实施例中，处理器101可以用于调用存储器102中存储的共同周期的确定程序，并执行以下操作：
根据预设的噪声假设对所述第一协方差矩阵进行变换得到待分解协方差矩阵；
对所述待分解协方差矩阵进行特征值的分解，得到多个由大到小排序的特征值；
根据各个所述特征值以及各个所述特征值对应的排序序号确定信号向量以及噪声向量。
在一实施例中，处理器101可以用于调用存储器102中存储的共同周期的确定程序，并执行以下操作：
根据各个所述特征值的排序序号，确定K个目标特征值，其中，K个所 述目标特征值均大于除所述目标特征值的各个其他特征值；
将K个所述目标特征值对应的特征向量作为信号向量，并将各个所述其他特征值对应的特征向量作为噪声向量。
在一实施例中，处理器101可以用于调用存储器102中存储的共同周期的确定程序，并执行以下操作：
确定各个所述噪声向量对应的第一矩阵以及各个所述信号向量对应的第二矩阵；
确定所述第二矩阵以及所述第一矩阵相乘为零的函数，以求出频率参数的求解函数。
在一实施例中，处理器101可以用于调用存储器102中存储的共同周期的确定程序，并执行以下操作：
确定相邻阵元接收的每一信号的光程差以及相位差；
以所述天线阵列的目标阵元为参考点确定各个其他阵元对每一个待测信号源的感应信号对应的函数；
根据所述光程差、所述相位差以及所述函数确定N次空时采样的阵元的输出信号对应的目标函数；
根据N次空时采样的阵元的输出信号对应的目标函数，以构建M个所述阵元对K个待测信号源的第一信号矩阵。
在一实施例中，处理器101可以用于调用存储器102中存储的共同周期的确定程序，并执行以下操作：
根据欧拉公式将所述函数重写为复数形式的函数；
将所述光程差以及所述相位差代入复数形式的函数，以确定N次空时采样的输出信号对应的目标函数。
在一实施例中，处理器101可以用于调用存储器102中存储的共同周期的确定程序，并执行以下操作：
对所述天线阵元的M个阵元接收的多个信号源的输出信号进行N次快拍的空时采样，以确定第二协方差矩阵，并对所述第二协方差矩阵进行特征值分解得到多个特征向量；
确定各个所述特征向量对应的权重，并根据各个所述特征向量以及各个所述特征向量对应的权重构建第二信号矩阵；
根据所述第二信号矩阵确定空间谱函数，并对所述空间谱函数进行波达方向的估计以确定谱峰数量，以将所述谱峰数量确定为信号源的数量，其中，所述信号源的数量为K；
在根据确定的信号源的数量设定信号源后，执行所述对所述天线阵元的M个阵元的接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个信号源的第一信号矩阵的步骤。
在一实施例中，处理器101可以用于调用存储器102中存储的共同周期的确定程序，并执行以下操作：
对所述天线阵元的M个阵元的接收的多个待测信号源的输出信号进行N 次快拍的空时采样，以确定第三协方差矩阵，并对所述第三协方差矩阵进行特征值分解得到多个特征值；
确定解释力度，并在各个所述特征值中确定目标特征值，其中，各个所述目标特征值均大于除所述目标特征值之外的其他特征值；
根据所述解释力度与各个所述目标特征值判断所述目标特征值的数量是否满足预设条件；
在所述目标特征值的数量满足预设条件时，将所述目标特征值的数量确定为信号源的数量，其中，所述信号源的数量为K；
在根据确定的信号源的数量设定信号源后，执行所述对所述天线阵元的M个阵元的接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个信号源的第一信号矩阵的步骤。
本实施例根据上述方案，多个信号的共同周期确定装置设有天线阵列，天线阵列包括多个阵元，装置可同时接收多个待测信号源输出的信号。装置对天线阵元的M个阵元接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个阵元对K个待测信号源的信号矩阵，再确定信号矩阵对应的协方差矩阵，并对协方差矩阵进行特征值的分解得到信号向量以及噪声向量，装置根据噪声向量以及信号向量确定频率参数的求解函数，最后根据求解函数对频率参数进行搜索，从而得到各个待测信号源输出的信号的共同频率参数，共同频率参数即为多个信号的共同周期。由于装置同时对各个信号进行分析，以将多个信号对应的数据进行整体分析，且对各个信号进行噪声向量以及信号向量的分离，减少了噪音对共同周期的干扰，准确地确定了事物的周期。
基于上述多个信号的共同周期确定装置的硬件构架，提出本申请多个信号的共同周期确定方法的实施例。
参照图2，图2为本申请多个信号的共同周期确定方法的第一实施例，所述多个信号的共同周期确定方法包括以下步骤：
步骤S10，对所述天线阵元的M个阵元接收的K个待测信号的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵；
在本实施例中，执行主体为多个信号的共同周期确定装置，为了便于描述，以装置作为多个信号的共同周期确定装置的简称对本实施例进行详细的说明。装置中设有天线阵列，天线阵列包括多个阵元，阵元的数量为M，M为整数。M个阵元组成等间距直线阵，各个阵元特性相同，且各个阵元的各向同性，相邻阵元之间的间隔为d，d小于或等于阵元接收的最高频率信号的波长的一半，各个阵元互不相关，且各个阵元与各个待测信号源也不相关。
天线阵列处于各个待测信号源的远场中，即天线阵列接收从各个待测信号源传来的信号为平面波。装置中各个接收待测信号源的信号的接收支路的特性都相同。各个待测信号源具有相同的极性，且各个待测信号源互不相关，待测信号源可为窄带，且待测信号源的数量为K，且K＜M。
装置还设有信号采集模块，信号采集模块用于间隔时间T进行快拍的空时采样以得到采样的信号，从而根据各个采样的信号构建矩阵，具体的，参照图3，即步骤S10包括：
步骤S11，确定相邻阵元接收的每一信号的光程差以及相位差；
假设第k个信号源s k(t)相对入射角为v k，则相邻阵元接收的每一个信号s k(t)的光程差为x k＝dsinv k，相位差为
Figure PCTCN2020104864-appb-000001
第k个信号源的信号为简谐波，可以表示为s k(t)＝A ksin(ω kt+θ k)。
步骤S12，以所述天线阵列的目标阵元为参考点确定各个其他阵元对每一个待测信号源的感应信号对应的函数；
在本实施例中，可以以天线阵列的第一个阵元作为参考点确定t时间等间距直线阵中的第m个对第k个信号源的感应信号，感应信号即为第m个阵元对应的输出信号，对s k(t)＝A ksin(ω kt+θ k)进行换算，得到第m个阵元输出信号函数表达式，也即
Figure PCTCN2020104864-appb-000002
第一阵元即为目标阵元，当然，目标阵元还可以是其他阵元，并不限定第一个阵元。
步骤S13，根据所述光程差、所述相位差以及所述函数确定N次空时采样的阵元的输出信号对应的目标函数；
为了方便模型的讨论，减少不必要的参数，通过欧拉公式将
Figure PCTCN2020104864-appb-000003
Figure PCTCN2020104864-appb-000004
重写为复数形式，重写为复数形式的函数为：
Figure PCTCN2020104864-appb-000005
Figure PCTCN2020104864-appb-000006
将光程差以及相位差代入函数
Figure PCTCN2020104864-appb-000007
中，得到s k，m(t)＝A(k,m)exp(jω kt)，其中，A(k,m)包含了振幅项和相位项的信息：θ k，
Figure PCTCN2020104864-appb-000008
v k；j为复数单位。
进一步的，考虑所有信号源来波和传输通道上的噪声，根据上述得到的函数，可确定第m个阵元的输出信号：
Figure PCTCN2020104864-appb-000009
由此，在N次快拍的空时采样中，选取第一次采样的时刻为0时刻，进行时间间隔为T的N次的采样，则t＝T(n-1)，n＝1，2，3……N，根据t以及
Figure PCTCN2020104864-appb-000010
得到第N次快拍的m个阵元的输出信号的函数为：
Figure PCTCN2020104864-appb-000011
该函数即为目标函数。
步骤S14，根据N次空时采样的阵元的输出信号对应的目标函数，以构建M个所述阵元对K个待测信号源的第一信号矩阵。
将上述目标函数写成矩阵形式，也即得到第一信号矩阵，第一信号矩阵
X＝WA+E，其中，
Figure PCTCN2020104864-appb-000012
也即W为N行K列的矩阵，W包含关于K个信号源的频率参数的信息；
Figure PCTCN2020104864-appb-000013
也即A为K行M列的矩阵，A包含关于K个信号源的初始相位项θ k，振幅项A k，以及第k个初始信号源相对于阵列的角度v k的信息；
Figure PCTCN2020104864-appb-000014
也即E为N行M列的矩阵，E代表N次采样中M个通道上的噪声。
步骤S20，确定所述第一信号矩阵对应的第一协方差矩阵，并对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量；
在确定第一信号矩阵后，取第一信号矩阵的第一协方差矩阵，第一协方差矩阵定义为：
R x＝E{XX T}＝＝E{(WA+E)(WA+E) T}，再根据预先设置的噪声假设对第一协方差矩阵进行特征值分解得到噪声向量以及信号向量，具体的，请参照图4，也即步骤S20包括：
步骤S21，根据预设的噪声假设对所述第一协方差矩阵进行变换得到待分解协方差矩阵；
步骤S22，对所述待分解协方差矩阵进行特征值的分解，得到多个由大到小排序的特征值；
步骤S23，根据各个所述特征值以及各个所述特征值对应的排序序号确定信号向量以及噪声向量。
在本实施例中，噪声假设即为高斯白噪声互不相关，且高斯白噪声与各个待测信号源也不相关，由此，根据噪声假设对第一协方差矩阵进行变换得到待分解协方差矩阵，待分解协方差矩阵：R x＝E{XX T}＝E{(WA)(WA) T}+E{EE T}；由于噪声假设，则E{EE T}＝σ 2I，因此，R x＝E{(WA)(WA) T}+σ 2I，在对该表达式再次进行变换得到待分解协方差矩阵，R x＝R+σ 2I，其中，R＝E{(WA)(WA) T}，第一协方差矩阵与变换后的待分解协方差矩阵实则相同。
由此可知，待分解协方差矩阵的第一项表示信号向量产生的协方差矩阵，第二项表示由噪声向量产生的协方差矩阵。由此可对变换后的待分解协方差矩阵R x进行特征值的分解，得到R x＝UΛU T，其中，Λ＝diag(λ 1,λ 2,…λ N)，为R的特征值构成的对角矩阵。由变化后的待分解协方差矩阵的分解，可得知第一协方差矩阵也可分解为以下形式：R x＝UΛU T＝U(Σ+σ 2I)U T，其中，Σ＝diag(λ 1-σ 2,λ 2-σ 2,…λ K-σ 2…0,…0)，为R的特征值构成的对角矩阵。
通过对协方差矩阵的分解，将特征值中噪声信号的影响剥离出来，特征值的贡献被分为两部分，一部分是由信号向量贡献，另一部分是由噪声向量贡献。在R x的特征值分解得到的N个特征值中，前K个比较大的特征值是由信号向量产生，而后N-K个比较小的特征值由噪声信号产生。故而在分解得到多个 特征值后，对各个特征值进行由大到小的排序，得到有各个特征值构成的排序序列，每一个特征值具有对应的排序序号，取K个排序序号在前的特征值作为目标特征值，也即，各个目标特征值均大于除目标特征值之外的各个其他目特征值，目标特征值对应的特征向量即为信号向量，其他特征值对应的特征向量即为噪声向量。
步骤S30，根据所述噪声向量与所述信号向量确定频率参数的求解函数；
在确定各个噪声向量以及信号向量后，根据噪声向量以及信号向量确定频率参数的求解函数，具体的，请参照图5，即步骤S30包括：
步骤S31，确定各个所述噪声向量对应的第一矩阵以及各个所述信号向量对应的第二矩阵；
步骤S32，根据信号子空间与噪声子空间之间的正交性，确定所述第二矩阵以及所述第一矩阵对应的相乘为零的函数，以求出频率参数的求解函数；
根据信号子空间与噪声子空间之间的正交性，在本实施例中，设前各个目标特征值λ 1,λ 2,…λ K对应的信号向量为s 1，s 2…s K，记S＝[s 1,s 2,…s K]；各个其他特征值λ K+1,λ K+2,…λ N对应的噪声向量为g K+1,g K+2,…g N,记G＝[g K+1,g K+2,…g N]，则有：
Figure PCTCN2020104864-appb-000015
其中，P＝diag(λ 1,λ 2,…λ K)，Q＝diag(λ K+1,λ K+2,…λ N)，S实则为各个信号向量对应的第二矩阵，G为各个噪声向量对应的第一矩阵，
Figure PCTCN2020104864-appb-000016
即为中间函数。
进一步的，考虑到信号子空间与噪声子空间的正交性，由
Figure PCTCN2020104864-appb-000017
Figure PCTCN2020104864-appb-000018
可得到
Figure PCTCN2020104864-appb-000019
Figure PCTCN2020104864-appb-000020
而R xG＝E{(WA)(WA) T}G+σ 2IG，由此，根据二个公式相减，可得到E{(WA)(WA) T}G＝0，也即为WE{(A)(A) T}W TG＝0，等式二边同时乘以G T，得到G TWE{(A)(A) T}W TG＝0。由于E{(A)(A) T}非奇异，可知，W TG＝0，即，包含信号源频率参数信息的矩阵与包含噪声信息的特征向量构成的矩阵是正交的。因此将其，代入W矩阵的各列，得到W(i)＝(1，exp(jω 1T(n-1，exp(jωiTn-1)) T，i＝1,2,3…K；从而得到W(i) TG＝0，由此得到关于第i个信号的频率参数的求解函数。
步骤S40，根据所述求解函数对所述频率参数进行搜索，以获得K个所述待测信号源发射的信号的共同频率参数，其中，所述共同频率参数为多个信号的共同周期。
在确定求解函数后，对频率参数进行搜索，在当满足ω＝ω i时，得到
Figure PCTCN2020104864-appb-000021
再取该函数的峰值，从而根据峰值估算得到K个待测试信号源发射的信号的共同频率参数ω，ω即为多个信号对应的共同周期。
需要说明的是，上述多个信号的共同周期的确定方法可应用与经济金融领域，也即每一个信号对应的经济金融的时间序列数据对应的信号，装置根 据此类信号分析整个金融领域的时间周期，以确定准确的时间周期。通过多个信号的共同周期的确定方法，可确定经济金融领域的共同周期为42个月、100个月以及200个月。当然，装置也可采用上述方法确定其他领域的多个信号的共同周期，并不仅限于在经济金融领域的应用。
在本实施例提供的技术方案中，多个信号的共同周期确定装置设有天线阵列，天线阵列包括多个阵元，装置可同时接收多个待测信号源输出的信号。装置对天线阵元的M个阵元接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个阵元对K个待测信号源的信号矩阵，再确定信号矩阵对应的协方差矩阵，并对协方差矩阵进行特征值的分解得到信号向量以及噪声向量，装置根据噪声向量以及信号向量确定频率参数的求解函数，最后根据求解函数对频率参数进行搜索，从而得到各个待测信号源输出的信号的共同频率参数，共同频率参数即为多个信号的共同周期。由于装置同时对各个信号进行分析，以将多个信号对应的数据进行整体分析，且对各个信号进行噪声向量以及信号向量的分离，减少了噪音对共同周期的干扰，准确的确定了事物的周期。
参照图6，图6为本申请多个信号的共同周期的确定方法的第二实施例，基于第一实施例，多个信号的共同周期的确定方法还包括：
步骤S50，对所述天线阵元的M个阵元接收的多个信号源的输出信号进行N次快拍的空时采样，以确定第二协方差矩阵，并对所述第二协方差矩阵进行特征值分解得到多个特征向量；
在本实施例中，多个信号的共同周期的准确确定是建立在合适数量的信号源的基础上。若是信号源的数量不合适，在进行波达方向估计时，会发生谱峰漏报或者产生伪峰。对此，在确定多个信号的共同周期之前，需要确定合适数量的信号源。
可先设置H个信号源，装置再对天线阵元的M个阵元接收的H个信号源的输出信号进行N次快拍的空时采样，从而确定第二协方差矩阵，第二协方差矩阵的确定与第一协方差矩阵确定一致，在此不再一一赘述。
在确定第二协方差矩阵后，再对第二协方差矩阵进行特征值的分解得到多个特征向量，第二协方差矩阵的特征值的具体分解流程参照第一协方差举证的特征值分解。
步骤S60，确定各个所述特征向量对应的权重，并根据各个所述特征向量以及各个所述特征向量对应的权重构建第二信号矩阵；
在确定各个特征向量后，确定各个特征向量对应的权重，特征向量的权重越大时，噪声向量的放大程度越大，信号向量的缩小程度越小。具体的，权重为特征值的倒数，而噪声向量的特征值较小，因而噪声向量的倒数较大，从而放大了噪声向量；同理，信号向量的特征值较大，故倒数较小，缩小了信号向量。通过权重的合理设置可以使得信号向量的作用变弱，相当于空间谱算法中只利用了噪声向量的情况。而通过对特征向量进行加权计算，自然的放大和保留了噪声向量，并缩小了信号向量，避免了对特征值的排序操作 以及噪声子空间的划分造成特征值界限模糊的问题。装置通过各个特征向量以及各个特征向量对应的权重构建第二信号矩阵，具体的，参照图7，也即步骤S60中根据各个所述特征向量以及各个所述特征向量对应的权重构建第二信号矩阵包括：
步骤S61，确定每个所述特征向量对应的特征值；
步骤S62，确定各个所述特征值对应的系数，并将所述系数作为所述特征值的多次方以得到每个所述特征向量对应的数值；
步骤S63，确定每个所述数值的倒数，并根据各个所述倒数构建第二信号矩阵。
每一个特征向量具有对应的特征值，各个特征值依次为λ 1,λ 2,…λ N，特征值具有对应系数c，系数c用于调节权重。装置将c作为特征值的多次方得到每个特征向量对应的数值，再将确定各个数值的倒数以构建第二信号矩阵，倒数即为特征向量权重。第二信号矩阵即为第一协方差矩阵的所有特征向量加权构成的矩阵，第二信号矩阵
Figure PCTCN2020104864-appb-000022
对第二信号矩阵U进行变换得到
Figure PCTCN2020104864-appb-000023
步骤S70，根据所述第二信号矩阵确定空间谱函数，并对所述空间谱函数进行波达方向的估计以确定谱峰数量，以将所述谱峰数量确定为信号源的数量，其中，所述信号源的数量为K；
装置通过第二信号矩阵可以确定空间谱函数，空间谱函数
Figure PCTCN2020104864-appb-000024
Figure PCTCN2020104864-appb-000025
在对空间谱函数进行波达方向的估计，从而得到谱峰数量，谱峰数量即可确定为信号源的数量，信号源的数量为K，也即将H确定为K。
步骤S80，在根据确定的信号源的数量设定信号源后，执行所述对所述天线阵元的M个阵元的接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个信号源的第一信号矩阵的步骤。
装置在确定信号源的数量后，将数量H调整为数量K，从而进行K个信号源发出的信号的共同周期。也即装置执行步骤S10-步骤S40。
在本实施例提供的技术方案中，装置对天线阵元的M个阵元接收的H个待测信号元的输出信号进行N次快拍的空时采样，以构建M个阵元对各个信号源的第二协方差矩阵，并对第二协方差矩阵进行特征值的分解多个特征向量，再确定各个特征向量对应的权重，以根据各个特征向量以及对应的权重构建矩阵，从而根据矩阵得到空间谱函数，再根据空间谱函数进行波达方向的估计时获得谱峰数量，谱峰数量即可为信号源的数量；由于理想状态下，谱峰数量与信号源的数量相同，由此可以根据谱峰数量准确的确定信号源的设定数量，进而准确的确定了事物的周期。
参照图8，图8为本申请多个信号的共同周期的确定方法的第三实施例，基于第一实施例，所述多个信号的共同周期的确定方法，还包括：
步骤S90，对所述天线阵元的M个阵元的接收的多个待测信号源的输出 信号进行N次快拍的空时采样，以确定第三协方差矩阵，并对所述第三协方差矩阵进行特征值分解得到多个特征值；
在本实施例中，装置通过解释力度确定信号源的数量。可先设置L个信号源，装置再对天线阵元的M个阵元接收的L个信号源的输出信号进行N次快拍的空时采样，从而确定第三协方差矩阵，第三协方差矩阵的确定与第一协方差矩阵确定一致，在此不再一一赘述。
在确定第三协方差矩阵后，再对第三协方差矩阵进行特征值的分解得到多个特征值，第三协方差矩阵的特征值的具体分解流程参照第一协方差举证的特征值分解。
步骤S100，确定解释力度，并在各个所述特征值中确定目标特征值，其中，各个所述目标特征值均大于除所述目标特征值之外的其他特征值；
在得到多个特征值后，装置在各个特征值中确定目标特征值。具体的，将各个特征值按照从大到小的顺序进行排序得到序列，在序列中取前L个特征值作为目标特征值，也即各个目标特征值均大于除目标特征值之外的各个其他特征值。装置再确定解释力度，解释力度为解释一个事物本质的力度，解释力度的单位为百分比。解释力度可通过前L个目标特征值的总和占所有特征值总和的比例，也即解释力度为前L个变量的方差贡献度表征，而方差贡献度可通过主成分思想确定。具体的，
假设一组变量x 1,…x p，数学上可以把它们变换成一组新的变量(变量称为成分)y 1,…y p使得：
(1)每一个y是x的线性组合，即：y i＝a i1x 1+a i2x 2+…+a ipx p；
(2)系数a ip的平方和为1，即a i是单位向量；
(3)y 1是这些线性组合中方差最大的，y 2为和y 1不相关的线性组合中方差最大的，如此下去，一般地，y j为与y 1,y 2等都不相关的且方差最大的线性组合。
前几个变量(主成分)由于其方差最大，往往包含了绝大部分信息，可以用来描述原来用p个变量所解释的现象。在实际处理中，a i即为x 1,…x p的相关系数矩阵的第i个特征值λ i对应的特征向量，y i的方差贡献度为：
Figure PCTCN2020104864-appb-000026
需要说明的是，解释力度可由技术人员确定，也即技术人员在装置中输入解释力度。
步骤S110，根据所述解释力度与各个所述目标特征值判断所述目标特征值的数量是否满足预设条件；
在确定解释力度以及各个目标特征值后，装置根据解释力度以及各个目标特征值确定目标特征值的数量是否满足预设条件，具体的，解释力度可通过方差贡献度确定，解释力度可通个前L个特征值对应的方差贡献度之和确定，也即，
Figure PCTCN2020104864-appb-000027
根据此公式，计算所有特征值之间的第一总和，也即第一总和为所有特征值之和，并计算各个目标特征值之间的第二总和，第二总和为各个目标特征值之和，并计算第二总和与第一总和之间的比值， 再判断比值是否大于或等于解释力度，若是比值大于解释力度，且比值与解释力度之间的差值小于预设阈值，则可判定目标特征值的数量满足预设条件。
步骤S120，在所述目标特征值的数量满足预设条件时，将所述目标特征值的数量确定为信号源的数量，其中，所述信号源的数量为K；
在目标特征值的数量满足预设条件，则可将目标特征值的数量作为信号源的数量，从而根据信号源的数量对信号源进行重新设置，也即将L更改为K。
步骤S130，在根据确定的信号源的数量设定信号源后，执行所述对所述天线阵元的M个阵元的接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个信号源的第一信号矩阵的步骤。
装置在确定信号源的数量后，将数量L调整为数量K，从而进行K个信号源发出的信号的共同周期。也即装置执行步骤S10-步骤S40。
在本实施例提供的技术方案中，装置对天线阵元的M个阵元接收的L个待测信号源的输出信号进行N次快拍的空时采样，以构建M个阵元对各个信号源的第三协方差矩阵，并第三对协方差矩阵进行特征值的分解得到多个特征值，再确定解释力度以及在各个特征值中确定目标特征值，最后根据解释力度以及各个目标特征值判断目标特征值的数量是否满足预设条件，若是满足，则将目标特征值的数量确定为信号源的数量，进而准确的确定了事物的周期。
在一实施例中，装置在确定比值后，若是比值小于解释力度时，则表明装置所选择的目标特征值的数量过少，使得比值无法达到解释力度。对此，装置在各个特征值中重新确定目标特征值，重新目标特征值的数量大于上一次确定目标特征值的数量，也即当前目标特征值的数量L’＞L。重新确定的目标特征值的数量可以根据解释力度与比值之间的差值确定，解释力度与比值之间的差值越大，重新确定目标特征值的数量越大。
在重新确定目标特征值后，再重新获得比值，若是比值再次小于解释力度，再重新确定目标特征值，直至目标特征值的数量满足预设条件。
在本实施例提供的技术方案中，装置在判定比值小于解释力度后，重新确定目标特征值，重新确定的目标特征值的数量大于上一次确定的目标特征值的数量，以再次进行目标特征值的数量是否满足预设条件的判断，直至确定满足预设条件的目标特征值的目标数量，装置的智能化程度较高。
在一实施例中，在当比值大于解释力度时，需要进一步计算比值与解释力度之间的差值，再判断该差值是否小于或等于预设阈值，若是差值大于预设阈值，则认为比值过大，此时，需要重新在各个特征值中确定目标特征值的数量，重新确定目标特征值的数量小于上一次确定的目标特征值的数量，进而再次判断重新确定的比值是否大于解释力度，直至重新确定的比值大于解释力度，且重新确定的比值与解释力度之间的差值小于或等于预设阈值，此时，重新确定的比值对应的目标特征值的数量即为目标数量，该目标数量为信号源的设定数量。重新确定的目标特征值的数量可以根据比值与解释力 度之间的差值确定，比值与解释力度之间的差值越大，重新确定目标特征值的数量越小。
在本实施例提供的技术方案中，装置在确定比值与解释力度之间的差值大于预设阈值，比值过大，需要重新确定目标特征值，重新确定的目标特征值的数量小于上一次确定的目标特征值的数量，直至确定满足预设条件的目标特征值的目标数量，装置的智能化程度较高。
本申请还提供一种多个信号的共同周期确定装置，所述多个信号的共同周期装置设有天线阵列以及信号采集模块，所述天线阵列设有M个阵元，各个所述阵元接收信号源发送的信号，所述信号采集模块用于采集阵元接收信号源发送的信号；所述多个信号的共同周期确定装置还包括存储器、处理器以及存储在所述存储器并可在所述处理器上运行的共同周期的确定程序，所述信号采集模块与所述处理器连接，所述共同周期的确定程序被处理器执行时执行如上实施例所述的多个信号的共同周期确定方法的各个步骤。
本申请还提供一种可读存储介质，所述可读存储介质存储有共同周期的确定程序，所述共同周期的确定程序被处理器执行时实现如上实施例所述的多个信号的共同周期确定方法的各个步骤。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (19)

  1. 一种多个信号的共同周期确定方法，其中，所述多个信号的共同周期确定方法被应用于设有天线阵列的终端，所述天线阵列设有M个阵元，各个所述阵元接收待测信号源发送的信号，所述多个信号的共同周期确定方法包括以下步骤：
    对所述天线阵元的M个阵元接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵；
    确定所述信号矩阵对应的第一协方差矩阵，并对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量；
    根据所述噪声向量与所述信号向量确定频率参数的求解函数；以及
    根据所述求解函数对所述频率参数进行搜索，以获得K个所述待测信号源发射的信号的共同频率参数，其中，所述共同频率参数为多个信号的共同周期。
  2. 如权利要求1所述的多个信号的共同周期确定方法，其中，所述对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量的步骤包括：
    根据预设的噪声假设对所述第一协方差矩阵进行变换得到待分解协方差矩阵；
    对所述待分解协方差矩阵进行特征值的分解，得到多个由大到小排序的特征值；以及
    根据各个所述特征值以及各个所述特征值对应的排序序号确定信号向量以及噪声向量。
  3. 如权利要求2所述的多个信号的共同周期确定方法，其中，所述根据各个所述特征值以及各个所述特征值对应的排序序号确定信号向量以及噪声向量的步骤包括：
    根据各个所述特征值的排序序号，确定K个目标特征值，其中，K个所述目标特征值均大于除所述目标特征值的各个其他特征值；以及
    将K个所述目标特征值对应的特征向量作为信号向量，并将各个所述其他特征值对应的特征向量作为噪声向量。
  4. 如权利要求1所述的多个信号的共同周期确定方法，其中，所述根据所述噪声向量与所述信号向量确定频率参数的求解函数的步骤包括：
    确定各个所述噪声向量对应的第一矩阵以及各个所述信号向量对应的第二矩阵；以及
    确定所述第二矩阵以及所述第一矩阵相乘为零的函数，以求出频率参数的求解函数。
  5. 如权利要求1所述的多个信号的共同周期确定方法，其中，所述对所 述天线阵元的M个阵元接收的K个待测信号源进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵的步骤包括：
    确定相邻阵元接收的每一信号的光程差以及相位差；
    以所述天线阵列的目标阵元为参考点确定各个其他阵元对每一个待测信号源的感应信号对应的函数；
    根据所述光程差、所述相位差以及所述函数确定N次空时采样的阵元的输出信号对应的目标函数；以及
    根据N次空时采样的阵元的输出信号对应的目标函数，以构建M个所述阵元对K个待测信号源的第一信号矩阵。
  6. 如权利要求5所述的多个信号的共同周期确定方法，其中，所述根据所述光程差、所述相位差以及所述函数确定N次空时采样的阵元的输出信号对应的目标函数的步骤包括：
    根据欧拉公式将所述函数重写为复数形式的函数；以及
    将所述光程差以及所述相位差代入复数形式的函数，以确定N次空时采样的输出信号对应的目标函数。
  7. 如权利要求1-6任一项所述的多个信号的共同周期确定方法，其中，所述多个信号的共同周期确定方法，还包括：
    对所述天线阵元的M个阵元接收的多个信号源的输出信号进行N次快拍的空时采样，以确定第二协方差矩阵，并对所述第二协方差矩阵进行特征值分解得到多个特征向量；
    确定各个所述特征向量对应的权重，并根据各个所述特征向量以及各个所述特征向量对应的权重构建第二信号矩阵；
    根据所述第二信号矩阵确定空间谱函数，并对所述空间谱函数进行波达方向的估计以确定谱峰数量，以将所述谱峰数量确定为信号源的数量，其中，所述信号源的数量为K；以及
    在根据确定的信号源的数量设定信号源后，执行所述对所述天线阵元的M个阵元的接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个信号源的第一信号矩阵的步骤。
  8. 如权利要求7所述的多个信号的共同周期确定方法，其中，所述根据各个所述特征向量以及各个所述特征向量对应的权重构建第二信号矩阵的步骤包括：
    确定每个所述特征向量对应的特征值；
    确定各个所述特征值对应的系数，并将所述系数作为所述特征值的多次方以得到每个所述特征向量对应的数值；以及
    确定每个所述数值的倒数，并根据各个所述倒数构建第二信号矩阵。
  9. 如权利要求1-6任一项所述的多个信号的共同周期确定方法，其中，所述多个信号的共同周期确定方法，还包括：
    对所述天线阵元的M个阵元的接收的多个待测信号源的输出信号进行N次快拍的空时采样，以确定第三协方差矩阵，并对所述第三协方差矩阵进行 特征值分解得到多个特征值；
    确定解释力度，并在各个所述特征值中确定目标特征值，其中，各个所述目标特征值均大于除所述目标特征值之外的其他特征值；
    根据所述解释力度与各个所述目标特征值判断所述目标特征值的数量是否满足预设条件；
    在所述目标特征值的数量满足预设条件时，将所述目标特征值的数量确定为信号源的数量，其中，所述信号源的数量为K；以及
    在根据确定的信号源的数量设定信号源后，执行所述对所述天线阵元的M个阵元的接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个信号源的第一信号矩阵的步骤。
  10. 一种多个信号的共同周期确定装置，其中，所述多个信号的共同周期装置设有天线阵列以及信号采集模块，所述天线阵列设有M个阵元，各个所述阵元接收信号源发送的信号，所述信号采集模块配置为采集阵元接收信号源发送的信号；所述多个信号的共同周期确定装置还包括存储器、处理器以及存储在所述存储器并可在所述处理器上运行的共同周期的确定程序，所述信号采集模块与所述处理器连接，所述共同周期的确定程序被处理器执行时执行以下步骤：
    对所述天线阵元的M个阵元接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵；
    确定所述信号矩阵对应的第一协方差矩阵，并对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量；
    根据所述噪声向量与所述信号向量确定频率参数的求解函数；以及
    根据所述求解函数对所述频率参数进行搜索，以获得K个所述待测信号源发射的信号的共同频率参数，其中，所述共同频率参数为多个信号的共同周期。
  11. 如权利要求10所述的多个信号的共同周期确定装置，其中，所述共同周期的确定程序被处理器执行时执行的所述对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量的步骤包括：
    根据预设的噪声假设对所述第一协方差矩阵进行变换得到待分解协方差矩阵；
    对所述待分解协方差矩阵进行特征值的分解，得到多个由大到小排序的特征值；以及
    根据各个所述特征值以及各个所述特征值对应的排序序号确定信号向量以及噪声向量。
  12. 如权利要求11所述的多个信号的共同周期确定装置，其中，所述共同周期的确定程序被处理器执行时执行的所述根据各个所述特征值以及各个所述特征值对应的排序序号确定信号向量以及噪声向量的步骤包括：
    根据各个所述特征值的排序序号，确定K个目标特征值，其中，K个所 述目标特征值均大于除所述目标特征值的各个其他特征值；以及
    将K个所述目标特征值对应的特征向量作为信号向量，并将各个所述其他特征值对应的特征向量作为噪声向量。
  13. 如权利要求10所述的多个信号的共同周期确定装置，其中，所述共同周期的确定程序被处理器执行时执行的所述根据所述噪声向量与所述信号向量确定频率参数的求解函数的步骤包括：
    确定各个所述噪声向量对应的第一矩阵以及各个所述信号向量对应的第二矩阵；以及
    确定所述第二矩阵以及所述第一矩阵相乘为零的函数，以求出频率参数的求解函数。
  14. 如权利要求10所述的多个信号的共同周期确定装置，其中，所述共同周期的确定程序被处理器执行时执行的所述对所述天线阵元的M个阵元接收的K个待测信号源进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵的步骤包括：
    确定相邻阵元接收的每一信号的光程差以及相位差；
    以所述天线阵列的目标阵元为参考点确定各个其他阵元对每一个待测信号源的感应信号对应的函数；
    根据所述光程差、所述相位差以及所述函数确定N次空时采样的阵元的输出信号对应的目标函数；以及
    根据N次空时采样的阵元的输出信号对应的目标函数，以构建M个所述阵元对K个待测信号源的第一信号矩阵。
  15. 一种可读存储介质，其中，所述可读存储介质存储有共同周期的确定程序，所述共同周期的确定程序被处理器执行时实现以下步骤：
    对所述天线阵元的M个阵元接收的K个待测信号源的输出信号进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵；
    确定所述信号矩阵对应的第一协方差矩阵，并对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量；
    根据所述噪声向量与所述信号向量确定频率参数的求解函数；以及
    根据所述求解函数对所述频率参数进行搜索，以获得K个所述待测信号源发射的信号的共同频率参数，其中，所述共同频率参数为多个信号的共同周期。
  16. 如权利要求15所述的可读存储介质，其中，所述共同周期的确定程序被处理器执行时实现的所述对所述第一协方差矩阵进行特征值的分解以确定噪声向量以及信号向量的步骤包括：
    根据预设的噪声假设对所述第一协方差矩阵进行变换得到待分解协方差矩阵；
    对所述待分解协方差矩阵进行特征值的分解，得到多个由大到小排序的特征值；以及
    根据各个所述特征值以及各个所述特征值对应的排序序号确定信号向量以及噪声向量。
  17. 如权利要求16所述的可读存储介质，其中，所述共同周期的确定程序被处理器执行时实现的所述根据各个所述特征值以及各个所述特征值对应的排序序号确定信号向量以及噪声向量的步骤包括：
    根据各个所述特征值的排序序号，确定K个目标特征值，其中，K个所述目标特征值均大于除所述目标特征值的各个其他特征值；以及
    将K个所述目标特征值对应的特征向量作为信号向量，并将各个所述其他特征值对应的特征向量作为噪声向量。
  18. 如权利要求15所述的可读存储介质，其中，所述共同周期的确定程序被处理器执行时实现的所述根据所述噪声向量与所述信号向量确定频率参数的求解函数的步骤包括：
    确定各个所述噪声向量对应的第一矩阵以及各个所述信号向量对应的第二矩阵；以及
    确定所述第二矩阵以及所述第一矩阵相乘为零的函数，以求出频率参数的求解函数。
  19. 如权利要求15所述的可读存储介质，其中，所述共同周期的确定程序被处理器执行时实现的所述对所述天线阵元的M个阵元接收的K个待测信号源进行N次快拍的空时采样，以构建M个所述阵元对K个待测信号源的第一信号矩阵的步骤包括：
    确定相邻阵元接收的每一信号的光程差以及相位差；
    以所述天线阵列的目标阵元为参考点确定各个其他阵元对每一个待测信号源的感应信号对应的函数；
    根据所述光程差、所述相位差以及所述函数确定N次空时采样的阵元的输出信号对应的目标函数；以及
    根据N次空时采样的阵元的输出信号对应的目标函数，以构建M个所述阵元对K个待测信号源的第一信号矩阵。
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