Near-field information source positioning method and system

Abstract

The invention provides a near-field information source positioning method and system. The method comprises the following steps: on the basis of a direction of arrival (DOA) and a receiving signal model in a near-field environment, creating a fourth-order accumulation matrix only containing DOA information; performing eigenvalue decomposition on the fourth-order accumulation matrix to obtain a strong signal subspace; bringing the strong signal subspace into the noise subspace to form a first extended noise subspace; a DOA representing a weak signal source is determined by using a MUSIC algorithm and the first extended noise subspace; creating a covariance matrix of the received signal; carrying out eigenvalue decomposition on the covariance matrix and expanding a noise subspace to construct a two-dimensional spatial spectrum; the DOA of the weak signal source is input into the two-dimensional spatial spectrum for one-dimensional search to obtain the positioning information of the weak signal source, and the positioning information comprises the distance of the weak signal source, so that the accuracy of positioning the near-field signal source in a scene where strong and weak signals coexist is improved.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a near-field signal source positioning method and system. Background Art

[0002] As a key technology in array signal processing, signal source localization has a wide range of applications in sonar, radar, electronic countermeasures, biomedicine and other fields. According to the distance from the signal source to the receiving antenna array, it can be divided into far-field source localization and near-field source localization. Far-field signal sources refer to signal sources located in the far field area of ​​the array, i.e., r>>2D 2 / λ, where r is the distance from the signal source to the reference array element, D is the array aperture, and λ is the signal wavelength. When the distance from the signal source to the antenna array satisfies 0.62(D 3 / λ) 1 / 2 ≤r≤2D 2 / λ, the signal source is called a near-field signal source. The distance of the far-field signal source can be regarded as infinite, and the propagation in space can be regarded as a plane wave, so the signal source can be located by simply estimating its DOA (Direction of Arrival). The near-field signal source wavefront is regarded as a spherical wave, and the location of the signal source requires two parameters, DOA and the distance of the signal source, to determine.

[0003] The coexistence of strong and weak signals often occurs in the near-field signal source positioning. When there is a strong interference signal source near the weak signal source, the spatial spectrum peak of the weak signal is often very low when using classic DOA estimation algorithms, such as two-dimensional MUSIC and MUSIC methods based on high-order statistics. It is easy to be masked by the spectrum peak of the adjacent strong interference signal, resulting in estimation deviation, or replaced by other pseudo peaks, resulting in estimation loss. Therefore, in the scenario where strong and weak signals coexist, the positioning of near-field signal sources has the problem of inaccurate positioning. Summary of the invention

[0004] In view of this, an object of the embodiments of the present application is to provide a near-field signal source positioning method and system, which can improve the problem of inaccurate positioning of near-field signal sources in a scenario where strong and weak signals coexist.

[0005] In order to achieve the above technical objectives, the technical solutions adopted in this application are as follows:

[0006] In a first aspect, an embodiment of the present application provides a near-field information source positioning method, the method comprising:

[0007] S10, creating a received signal model based on a receiving array for receiving signals and a signal source having an interference signal;

[0008] S20, based on the direction of arrival DOA in the near field environment and the received signal model, creating a fourth-order accumulation matrix containing only DOA information;

[0009] S30, performing eigenvalue decomposition on the fourth-order cumulant matrix to obtain a strong signal subspace;

[0010] S40, incorporating the strong signal subspace into the noise subspace to form a first extended noise subspace;

[0011] S50, determining a DOA characterizing a weak signal source by using a MUSIC algorithm and the first extended noise subspace;

[0012] S60, creating a covariance matrix of the received signal;

[0013] S70, performing eigenvalue decomposition on the covariance matrix and expanding the noise subspace to construct a two-dimensional spatial spectrum;

[0014] S80, inputting the DOA of the weak signal source into the two-dimensional spatial spectrum to perform a one-dimensional search to obtain positioning information of the weak signal source, where the positioning information includes the distance of the weak signal source.

[0015] In combination with the first aspect, in some optional implementations, step S10 includes:

[0016] The total number of antennas of the receiving array and the total number of signal sources in the environment are obtained. The total number of antennas of the receiving array is expressed as 2M+1, and the antennas of the receiving array are uniformly linearly distributed and are used to receive narrowband near-field signals. The total number of signal sources is expressed as P+J, where M is an integer greater than or equal to 0, P is the number of weak signal sources, J is the number of interference signal sources, and the output signal x of the mth antenna is m (t) is expressed as:

[0017]

[0018] Among them, s k (t) refers to the kth narrowband near-field signal source, t refers to the time, e refers to the natural constant, j refers to the imaginary unit, τ mk represents the delay of the kth signal on the mth antenna relative to the reference array element, n m (t) represents additive Gaussian noise, and r is defined m is the distance from the signal source to the mth antenna, r k is the distance from the source to the reference array element, then based on the cosine theorem:

[0019]

[0020] In the formula, r k represents the distance from the signal source to the reference array element, d refers to the antenna spacing, θ k represents the DOA corresponding to the kth signal, the transmission model of the near-field source is a spherical wave, and the delay τmk It is expressed as:

[0021]

[0022] In the formula, λ represents the wavelength of the signal source, and r m Substitute τ mk In, we get:

[0023]

[0024] Perform a second-order Taylor expansion on formula (4), τ mk Converts to:

[0025] τ mk ≈w k m+φ k m 2 (5)

[0026] in:

[0027]

[0028] Based on the output signal x m (t) and the τ obtained by the second-order Taylor expansion mk , create the received signal model and express it in matrix form:

[0029] x(t)=A(θ,r)s(t)+n(t) (8)

[0030] in:

[0031]

[0032] In combination with the first aspect, in some optional implementations, step S20 includes:

[0033] The fourth-order accumulation is defined as:

[0034]

[0035] Among them, y m (t) y p (t), y q * (t) represents the output signals of the mth, nth, pth, and qth antennas respectively; * represents conjugation, represents the kth narrowband near-field signal source s k The fourth-order accumulation of (t) is:

[0036]

[0037] Wherein, m,n,p,q∈[-M,M], mn and pq are located in [-2N,2N], let m=-n and p=-q, based on the received signal model, the formula (10) corresponding to the fourth-order accumulation is converted to:

[0038]

[0039] Based on formula (12), a fourth-order cumulant matrix is ​​created by selecting specified elements, and the specified elements of the cumulant matrix are given by the following formula:

[0040]

[0041] The fourth-order accumulation matrix C1 is expressed by the following formula:

[0042]

[0043] The fourth-order accumulation matrix removes the distance information and only contains the DOA information of the signal. In the formula, H represents the matrix conjugate transpose, A1(θ) and C S The formula is as follows:

[0044] A1(θ)=[a1(θ1),…,a1(θ k ),…,a1(θ P+J )] (15)

[0045]

[0046] In the formula,

[0047] In combination with the first aspect, in some optional implementations, step S30 includes:

[0048] Perform eigenvalue decomposition on the fourth-order cumulant matrix:

[0049]

[0050] In the formula, λ i is the eigenvalue, and λ1≥...≥λ J ≥λ J+1 ≥...λ J+P ≥λ 2M+1 , e i is i The corresponding eigenvector, the subspace of the total signal is represented by E JS =span(e1,...,e J ,e J+1 ,...e J+P ), where the eigenvectors (e1,...,e J ) is determined by the strong interference signal, (e J+1,...e J+P ) is determined by the weak signal, and the subspace of the total signal includes the strong signal subspace.

[0051] In combination with the first aspect, in some optional implementations, step S40 includes:

[0052] The strong signal subspace is incorporated into the noise subspace, and the noise subspace E is composed of the eigenvectors corresponding to the 2M+1-PJ eigenvalues. N =span(e J+P+1 ,...,e 2M+1 ), and construct the first extended noise subspace as:

[0053] E JN =span(e1,...,e J ,e J+P+1 ,...,e 2M+1 ) (18)

[0054] The following transformation is performed on the steering vector of the signal:

[0055]

[0056] Where b(θ) refers to the transformed steering vector, I refers to the unit matrix, a1(θ) refers to the steering vector, is the matrix E J The conjugate transpose of E J =span(e1,...,e J ), the strong signal subspace is orthogonal to the noise subspace Note θ i is the weak signal angle, we get:

[0057]

[0058] The steering vector of the transformed signal is orthogonal to the first extended noise subspace.

[0059] In combination with the first aspect, in some optional implementations, step S50 includes:

[0060] The DOA of a weak signal source is determined by the following formula:

[0061]

[0062] Where θ is the DOA of the weak signal source.

[0063] In combination with the first aspect, in some optional implementations, step S60 includes:

[0064] Construct the covariance matrix R of the received signal x :

[0065] R x =E[x(t)x H (t)]=AR s A H +σ 2 I (22)

[0066] A refers to the array flow matrix, R s represents the covariance matrix of the signal, σ 2 is the noise power.

[0067] In combination with the first aspect, in some optional implementations, step S70 includes:

[0068] Perform eigenvalue decomposition on the covariance matrix of the received signal and expand the noise subspace to construct a two-dimensional spatial spectrum as follows:

[0069]

[0070] Among them U JN is the second extended noise subspace, and d(θ,r) is the steering vector of the transformed signal.

[0071] In a second aspect, an embodiment of the present application further provides a near-field information source positioning system, which includes a processor and a memory coupled to each other, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the near-field information source positioning system executes the above-mentioned method.

[0072] The invention adopting the above technical solution has the following advantages:

[0073] In the technical solution provided by the present application, a fourth-order accumulation matrix containing only DOA information is constructed, and then the fourth-order accumulation matrix is ​​subjected to eigenvalue decomposition to obtain a strong signal subspace, and then the strong signal subspace is incorporated into the noise subspace to form an extended noise subspace. Based on the extended noise subspace, the DOA of the weak signal source can be effectively estimated using the MUSIC algorithm. Secondly, the covariance matrix of the received signal is constructed, and another extended noise subspace is constructed to obtain a two-dimensional spatial spectrum. The estimated DOA of the weak signal source is sequentially brought into the two-dimensional spatial spectrum, and a one-dimensional search is performed to obtain the distance of the corresponding signal source. In this way, by incorporating the eigenvector of the strong interference signal into the noise subspace, the spectrum peak of the strong interference signal can be effectively eliminated, and then the weak signal source is located, which is conducive to improving the accuracy of the weak signal source location. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The present application may be further described by the non-limiting embodiments given in the accompanying drawings. It should be understood that the following drawings only illustrate certain embodiments of the present application and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings may be obtained based on these drawings without creative effort.

[0075] Figure 1 A schematic diagram of the process of the near-field signal source localization method provided in an embodiment of the present application.

[0076] Figure 2 It is a simulation diagram comparing the spatial spectra of conventional high-order accumulation and the method provided in this embodiment.

[0077] Figure 3 It is a simulation diagram of distance comparison between the conventional second-order statistics method and the method provided in this embodiment.

[0078] Figure 4 The figure is a comparison diagram of the relationship between the DOA root mean square error and the signal-to-noise ratio of the conventional method and the method provided in this embodiment.

[0079] Figure 5 The figure is a comparison diagram of the relationship between the distance root mean square error and the signal-to-noise ratio of the conventional method and the method provided in this embodiment. DETAILED DESCRIPTION

[0080] The present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that in the drawings or descriptions, similar or identical parts use the same figure numbers, and the implementation methods not shown or described in the drawings are forms known to ordinary technicians in the relevant technical field. In the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0081] Please refer to Figure 1 The embodiment of the present application provides a near-field source positioning method, which can estimate DOA and distance respectively by constructing a fourth-order cumulant matrix and a covariance matrix, avoiding the high-cost two-dimensional spectrum peak search, and then performing eigenvalue decomposition on the fourth-order cumulant matrix and the covariance matrix to construct the first and second extended noise subspaces respectively. On this basis, the traditional MUSIC algorithm can be used to effectively suppress strong interference, and can accurately locate the weak signal source in a near-field environment where strong and weak signals coexist. Among them, the near-field source positioning method can include the following steps:

[0082] S10, creating a received signal model based on a receiving array for receiving a signal and a signal source having an interference signal;

[0083] S20, based on the DOA (Direction of Arrival) in the near field environment and the received signal model, creating a fourth-order accumulation matrix containing only DOA information;

[0084] S30, performing eigenvalue decomposition on the fourth-order cumulant matrix to obtain a strong signal subspace;

[0085] S40, incorporating the strong signal subspace into the noise subspace to form a first extended noise subspace;

[0086] S50, determining a DOA characterizing a weak signal source by using a MUSIC algorithm and the first extended noise subspace;

[0087] S60, creating a covariance matrix of the received signal;

[0088] S70, performing eigenvalue decomposition on the covariance matrix and expanding the noise subspace to construct a two-dimensional spatial spectrum;

[0089] S80, inputting the DOA of the weak signal source into the two-dimensional spatial spectrum to perform a one-dimensional search to obtain positioning information of the weak signal source, where the positioning information includes the distance of the weak signal source.

[0090] The following will describe in detail the steps of the near-field source positioning method, as follows:

[0091] In this embodiment, step S10, creating a received signal model, may include:

[0092] Get the total number of antennas in the receiving array and the total number of signal sources in the environment. The receiving array can usually include multiple antennas, which can be used to receive signals from the signal source. The signal source is the device that sends signals in the environment. The total number of antennas in the receiving array is expressed as 2M+1, and the total number of signal sources is expressed as P+J, where M is an integer greater than or equal to 0, P is the number of weak signal sources, J is the number of interference signal sources, and the output signal x of the mth antenna m (t) is expressed as:

[0093]

[0094] Among them, s k (t) refers to the kth narrowband near-field signal source, t refers to the time, e refers to the natural constant, j refers to the imaginary unit, τ mk represents the delay of the kth signal on the mth antenna relative to the reference array element. The reference array element is determined in the conventional way. m (t) represents additive Gaussian noise, and r is defined m is the distance from the signal source to the mth antenna, r kis the distance from the source to the reference array element, then based on the cosine theorem:

[0095]

[0096] In the formula, r k represents the distance from the signal source to the reference array element, d refers to the antenna spacing, θ k represents the DOA corresponding to the kth signal, the transmission model of the near-field source is a spherical wave, and the delay τ mk It is expressed as:

[0097]

[0098] In the formula, λ represents the wavelength of the signal source, and r m Substitute τ mk In, we get:

[0099]

[0100] Perform a second-order Taylor expansion on formula (4), τ mk It can be approximated as:

[0101] τ mk ≈w k m+φ k m 2 (5)

[0102] in:

[0103]

[0104] From equations (6) and (7), we can see that w k It is only related to the DOA of the near-field source, while φ k It is related to both the DOA of the near-field source and the distance of the near-field source. Therefore, it can be based on the output signal x m (t) and equations (5) to (7) to create a received signal model and express it in matrix form:

[0105] x(t)=A(θ,r)s(t)+n(t) (8)

[0106] in:

[0107]

[0108] In this embodiment, step S20, based on the direction of arrival DOA in the near field environment and the received signal model, creating a fourth-order accumulation matrix containing only DOA information may include:

[0109] The fourth-order accumulation is defined as:

[0110]

[0111] Among them, y m (t) y p (t), y q * (t) represents the output signals of the mth, nth, pth, and qth antennas respectively; * represents conjugation; represents the kth narrowband near-field signal source s k The fourth-order accumulation of (t) is defined as:

[0112]

[0113] Where m,n,p,q∈[-M,M], mn and pq are located in [-2N,2N]. In order to preserve w k And remove φ k Item, need to ensure (m 2 -n 2 )-(p 2 -q 2 )=0 and (mn)-(pq)≠0. For convenience, let m=-n and p=-q, p=-q. Based on the received signal model, the formula (10) corresponding to the fourth-order accumulation amount can be converted to:

[0114]

[0115] Based on formula (12), a fourth-order cumulant matrix is ​​created by selecting specified elements to construct a cumulant matrix, wherein the specified elements of the cumulant matrix are given by the following formula:

[0116]

[0117] The specified elements can be flexibly selected according to the actual situation. By selecting appropriate elements, a fourth-order accumulation matrix is ​​created. The matrix removes the distance information and only contains the DOA information of the signal. The fourth-order accumulation matrix C1 is expressed by the following formula:

[0118]

[0119] Where H represents the matrix conjugate transpose, B(θ) and C S The formula is as follows:

[0120] A1(θ)=[a1(θ1),…,a1(θ k ),...,a1(θ P+J )] (15)

[0121]

[0122] In the formula,

[0123] In this embodiment, S30, performing eigenvalue decomposition on the fourth-order cumulant matrix to obtain a strong signal subspace may include:

[0124] Perform eigenvalue decomposition on the fourth-order cumulant matrix:

[0125]

[0126] In the formula, λ i is the eigenvalue, and λ1≥...≥λ J ≥λ J+1 ≥...λ J+P ≥λ 2M+1 , e i is i According to the subspace theory, the signal’s characteristic vector and the signal-directed vector form the same subspace. The subspace of the total signal (including interference signals and weak signals) is expressed as E JS =span(e1,...,e J ,e J+1 ,...e J+P ), where the eigenvectors (e1,...,e J ) is determined by the strong interference signal, (e J+1 ,...e J+P ) is determined by the weak signal, and the subspace of the total signal includes the strong signal subspace, the noise subspace E N =span(e J+P+1 ,...,e 2M+1 ) is composed of eigenvectors corresponding to 2M+1-PJ eigenvalues.

[0127] In this embodiment, S40, incorporating the strong signal subspace into the noise subspace to form a first extended noise subspace may include:

[0128] The strong signal subspace is incorporated into the noise subspace, and the noise subspace E is composed of the eigenvectors corresponding to the 2M+1-PJ eigenvalues. N =span(e J+P+1 ,...,e 2M+1 ), incorporating the strong interference signal feature vector into the noise subspace can effectively eliminate the strong interference signal spectrum peak, so the first extended noise subspace is constructed as:

[0129] E JN =span(e1,...,e J ,e J+P+1 ,,e 2M+1 ) (18)

[0130] Usually the weak signal steering vector is not orthogonal to the interference signal steering vector, so in order to ensure the correct formation of the weak signal peak, the signal steering vector is transformed as follows:

[0131]

[0132] Where b(θ) refers to the transformed steering vector, I refers to the unit matrix, a1(θ) refers to the steering vector, is the matrix E J The conjugate transpose of E J =span(e1,…,e J ), the strong signal subspace is orthogonal to the noise subspace Note θ i is the weak signal angle, we get:

[0133]

[0134] The steering vector of the transformed signal is orthogonal to the first extended noise subspace.

[0135] In this embodiment, since the steering vector of the transformed signal is orthogonal to the first extended noise subspace, the DOA of the weak signal can be estimated using the MUSIC spectrum, and the two-dimensional spectrum peak search can be avoided. Specifically, S50, using the MUSIC algorithm and the first extended noise subspace to determine the DOA characterizing the weak signal source, may include:

[0136] The DOA of a weak signal source is determined by the following formula:

[0137]

[0138] Where P MUSIC-JN (θ) is the spatial spectrum based on θ and is a function related to θ; P MUSIC-JN The relationship between (θ) and parameter θ can be understood as the relationship between dependent variable and independent variable. The θ corresponding to the peak of the spatial spectrum is the DOA of the incoming wave direction. θ is the DOA of the weak signal source.

[0139] In this embodiment, S60, creating a covariance matrix of the received signal may include:

[0140] Construct the covariance matrix of the received signal:

[0141] R x =E[x(t)x H (t)]=AR s A H +σ 2 I (22)

[0142] A refers to the array flow matrix, R srepresents the covariance matrix of the signal, σ 2 is the noise power.

[0143] In this embodiment, S70, performing eigenvalue decomposition on the covariance matrix and expanding the noise subspace to construct a two-dimensional spatial spectrum may include:

[0144] Perform eigenvalue decomposition on the covariance matrix of the received signal and expand the noise subspace to construct a two-dimensional spatial spectrum as follows:

[0145]

[0146] Among them, P MUSIC-JN (θ, r) is the two-dimensional spatial spectrum based on θ and r, U JN is the second extended noise subspace, and d(θ,r) is the steering vector of the transformed signal.

[0147] The DOA estimate of the weak signal source obtained based on formula (21) can be expressed as {θ k ,k=1,...,P}.

[0148] In step S80, the obtained DOA estimation value is substituted into formula (23), and the distance r of the weak signal source can be estimated one by one. Among them, the positioning information of the weak signal source can include the DOA and distance of the weak signal source relative to the receiving array (i.e., the signal collection point), so that the near-field weak signal source can be accurately positioned. Among them, formula (23) can be rewritten as:

[0149]

[0150] In this embodiment, by sequentially bringing the estimated DOA into the constructed distance space spectrum (i.e., the two-dimensional space spectrum), the distance of the corresponding weak signal source can be estimated. In this way, the two-dimensional spectrum peak search can be avoided, and the parameters are automatically paired to achieve accurate positioning of the weak signal source.

[0151] Please refer to Figure 2, which is a comparison diagram of the spatial spectrum of conventional high-order accumulation and the method provided in this application. An array with 15 array elements and uniform linear distribution is selected, the array element spacing d = λ / 4, and the number of signal sampling snapshots is 2000. The positions of the two weak signal sources are [20°, 3λ] and [34°, 5λ], and the signal-to-noise ratio is 36.5dB. The two weak signal sources are located at [14°, 1λ] and [40, 7λ], and the signal-to-noise ratio is 50dB. It can be seen from the experiment that the pseudo peaks caused by the interference source in the spatial spectrum of the conventional method are very obvious, and the spectral peaks of the weak signal source are almost unrecognizable, which easily causes estimation deviation. The algorithm proposed in this paper effectively suppresses strong interference, and the two spectral peaks of the weak signal are very obvious. Therefore, when there is strong interference in the space, the conventional method is difficult to estimate accurately, and this paper incorporates the strong signal guidance vector into the noise subspace, which effectively suppresses the strong interference and can accurately estimate the weak signal source.

[0152] Please refer to Figure 3 , which is a distance comparison diagram between the conventional second-order statistics method and the proposed algorithm. From the simulation experiment, it can be seen that the conventional method produces spectral peaks at the distances of 3λ and 5λ of the strong signal, while there is a weak spectral peak at 7λ of the weak signal source and almost no spectral peak at 1λ. This is because the distance estimation requires the previous DOA estimation value, and the conventional method cannot estimate the weak signal DOA well, so the distance estimation is distorted. The proposed method produces very sharp spectral peaks at the distances of 1λ and 7λ between the two weak signal sources, indicating that the proposed method effectively suppresses strong interference and can estimate the distance of weak signals well.

[0153] Please refer to Figure 4 , which is a comparison chart of the DOA root mean square error and signal-to-noise ratio of the conventional method and the method in this paper. The signal-to-noise ratio of the strong interference source is 30dB, and the other parameters are the same as above. 300 Monte Carlo experiments are performed. It can be seen from the above figure that as the signal-to-noise ratio of the weak signal source increases, the RMSE of the conventional method and the method in this paper gradually decreases. When the power difference between strong and weak signals is large, the RMSE of the method in this paper is much smaller than that of the conventional method. As the signal-to-noise ratio approaches the strong interference source, the method in this paper always maintains better performance. This experiment shows that the method proposed in this paper can effectively improve the DOA estimation accuracy of weak signals.

[0154] Please refer to Figure 5 , which is a comparison chart of the distance root mean square error and signal-to-noise ratio between the conventional method and the proposed method. It can be seen that the RMSE of the two methods decreases as the signal-to-noise ratio increases, and the proposed method always maintains a better performance. This experiment shows that the proposed algorithm effectively improves the accuracy of weak signal distance estimation under strong interference background.

[0155] In the method provided in the present application, a fourth-order accumulation matrix containing only DOA information is constructed, and then the fourth-order accumulation matrix is ​​eigenvalue decomposed to obtain an approximate strong signal subspace, which is then incorporated into the noise subspace to form an extended noise subspace. Based on the extended noise subspace, the MUSIC algorithm can be used to effectively estimate the DOA of the weak signal source. Secondly, the covariance matrix of the received signal is constructed, and an extended noise subspace is constructed in the same way. The estimated DOA is sequentially brought into the two-dimensional space spectrum of the distance for one-dimensional search to obtain the corresponding distance without parameter matching.

[0156] Based on the above design, the idea of ​​extended noise subspace is applied to the near-field strong interference scenario, which can effectively fill the gap in parameter estimation research when strong and weak signals coexist in this field. Through simulation experiments, it is proved that the method proposed in this application can effectively estimate the positioning information of weak signal sources, and its resolution and accuracy are higher than the conventional mixed order accumulation algorithm, and the amount of calculation is comparable to that of the conventional algorithm.

[0157] The embodiment of the present application also provides a near-field information source positioning system, which may include a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, the near-field information source positioning system can perform the corresponding steps in the near-field information source positioning method.

[0158] In this embodiment, the processor may be an integrated circuit chip having the ability to process signals. For example, the processor may be a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and may implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0159] The memory may be, but is not limited to, a random access memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, etc. In this embodiment, the memory may be used to store preset numbers, etc. Of course, the memory may also be used to store a program, and the processor executes the program after receiving an execution instruction.

[0160] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the near-field source positioning system described above can refer to the corresponding processes of each step in the aforementioned method, and will not be elaborated here.

[0161] The embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed on a computer, the computer executes the near-field information source localization method as described in the above embodiment.

[0162] Through the description of the above implementation methods, technical personnel in this field can clearly understand that the present application can be implemented by hardware, and can also be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a near-field source positioning system, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0163] In the embodiments provided in the present application, it should be understood that the disclosed system and method can also be implemented in other ways. The system and method embodiments described above are merely schematic, for example, the flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or a code, and a part of the module, program segment or code includes one or more executable instructions for implementing the specified logical function. It should also be noted that each box in the block diagram and / or the flow chart, and the combination of the boxes in the block diagram and / or the flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0164] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A near-field source positioning method, characterized in that: The method comprises: S10, creating a received signal model based on a receiving array for receiving a signal and a signal source having an interference signal; S20, based on the direction of arrival DOA in the near field environment and the received signal model, creating a fourth-order accumulation matrix containing only DOA information; S30, performing eigenvalue decomposition on the fourth-order accumulation matrix to obtain a strong signal subspace; S40, incorporating the strong signal subspace into the noise subspace to form a first extended noise subspace; S50, using the MUSIC algorithm and the first extended noise subspace, determining a DOA characterizing a weak signal source; S60, creating a covariance matrix of the received signal; S70, performing eigenvalue decomposition on the covariance matrix and expanding the noise subspace to construct a two-dimensional spatial spectrum; S80, inputting the DOA of the weak signal source into the two-dimensional spatial spectrum to perform a one-dimensional search to obtain positioning information of the weak signal source, where the positioning information includes the distance of the weak signal source.

2. The method according to claim 1, characterized in that Step S10 includes: The total number of antennas of the receiving array and the total number of signal sources in the environment are obtained. The total number of antennas of the receiving array is expressed as 2M+1, and the antennas of the receiving array are uniformly linearly distributed and are used to receive narrowband near-field signals. The total number of signal sources is expressed as P+J, where M is an integer greater than or equal to 0, P is the number of weak signal sources, J is the number of interference signal sources, and the output signal x of the mth antenna is m (t) is expressed as: Among them, s k (t) refers to the kth narrowband near-field signal source, t refers to the time, e refers to the natural constant, j refers to the imaginary unit, τ mk represents the delay of the kth signal on the mth antenna relative to the reference array element, n m (t) represents additive Gaussian noise, and r is defined m is the distance from the signal source to the mth antenna, r k is the distance from the source to the reference array element, then based on the cosine theorem: In the formula, r k represents the distance from the signal source to the reference array element, d refers to the antenna spacing, θ k represents the DOA corresponding to the kth signal, the transmission model of the near-field source is a spherical wave, and the delay τ mk It is expressed as: In the formula, λ represents the wavelength of the signal source, and r m Substitute τ mk In, we get: Perform a second-order Taylor expansion on formula (4), τ mk Converts to: t mk ≈w k m+φ k m 2 (5) in: Based on the output signal x m (t) and the τ obtained by the second-order Taylor expansion mk , create the received signal model and express it in matrix form: x(t)=A(θ,r)s(t)+n(t) (8) in:

3. The method according to claim 2, characterized in that Step S20 includes: The fourth-order cumulant is defined as: Among them, y m (t) y p (t), y q * (t) represents the output signals of the mth, nth, pth, and qth antennas respectively; * represents conjugation, represents the kth narrowband near-field signal source s k The fourth-order accumulation of (t) is: Wherein, m,n,p,q∈[-M,M], mn and pq are located in [-2N,2N], let m=-n and p=-q, based on the received signal model, the formula (10) corresponding to the fourth-order accumulation is converted to: Based on formula (12), a fourth-order cumulant matrix is ​​created by selecting specified elements, and the specified elements of the cumulant matrix are given by the following formula: The fourth-order accumulation matrix C1 is expressed by the following formula: The fourth-order accumulation matrix removes the distance information and only contains the DOA information of the signal. In the formula, H represents the matrix conjugate transpose, A1(θ) and C S The formula is as follows: A1(θ)=[a1(θ1),…,a1(θ k ),…,a1(θ P+J )] (15) In the formula, 4. The method according to claim 3, characterized in that Step S30 includes: Perform eigenvalue decomposition on the fourth-order cumulant matrix: In the formula, λ i is the eigenvalue, and λ1≥...≥λ J ≥λ J+1 ≥...λ J+P ≥λ 2M+1 , e i is i The corresponding eigenvector, the subspace of the total signal is represented by E JS =span(e1,...,e J ,e J+1 ,…,e J+P ), where the eigenvectors (e1,...,e J ) is determined by the strong interference signal, (e J+1 ,...e J+P ) is determined by the weak signal, and the subspace of the total signal includes the strong signal subspace.

5. The method according to claim 4, characterized in that Step S40 includes: The strong signal subspace is incorporated into the noise subspace, and the noise subspace E is composed of the eigenvectors corresponding to the 2M+1-PJ eigenvalues. N =span(e J+P+1 ,...,e 2M+1 ), and construct the first extended noise subspace as: The JN =span(e1,...,e J ,of J+P+1 ,...,of 2M+1 ) (18) The following transformation is performed on the steering vector of the signal: Where b(θ) refers to the transformed steering vector, I refers to the unit matrix, a1(θ) refers to the steering vector, is the matrix E J The conjugate transpose of E J =span(e1,...,e J ), the strong signal subspace is orthogonal to the noise subspace Note θ i is the weak signal angle, we get: The steering vector of the transformed signal is orthogonal to the first extended noise subspace.

6. The method according to claim 5, characterized in that Step S50 includes: The DOA of a weak signal source is determined by the following formula: Where P MUSIC-JN (θ) is the spatial spectrum based on θ, and θ is the DOA of the weak signal source.

7. The method according to claim 6, characterized in that Step S60 includes: Construct the covariance matrix R of the received signal x : R x =E[x(t)x H (t)]=AR s A H +σ 2 I (22) A refers to the array flow matrix, R s represents the covariance matrix of the signal, σ 2 is the noise power.

8. The method according to claim 7, characterized in that Step S70 includes: Perform eigenvalue decomposition on the covariance matrix of the received signal and expand the noise subspace to construct a two-dimensional spatial spectrum as follows: Among them, U JN is the second extended noise subspace, and d(θ,r) is the steering vector of the transformed signal.

9. A near-field source positioning system, characterized in that: The near-field information source positioning system includes a processor and a memory coupled to each other, wherein the memory stores a computer program, and when the computer program is executed by the processor, the near-field information source positioning system executes the method according to any one of claims 1 to 8.

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