A Non-Circular Signal Localization Method Based on Distributed Processing

Abstract

The present invention discloses a non-circular signal positioning method based on distributed processing. The method includes the following steps: constructing a distributed signal receiving platform to obtain received signals, and obtaining an extended output of the distributed signal receiving platform according to the received signals and the non-circular characteristics of the received signals; based on the extended output of the distributed signal receiving platform, calculating the eigenvectors of the extended covariance matrix of the received signals by using a distributed average consensus iterative solution method; calculating a non-circular signal positioning spatial spectrum function under distributed operations based on the eigenvectors of the extended covariance matrix of the received signals, and obtaining a non-circular signal positioning result according to the non-circular signal positioning spatial spectrum function under distributed operations. The present invention can reduce data transmission, improve calculation efficiency, meet the real-time requirement, and improve the positioning accuracy of non-circular signals.

Description

Technical Field

[0001] The present invention relates to the technical field of array signal processing, and particularly relates to a non-circular signal positioning method based on distributed processing. Background Art

[0002] A non-circular signal refers to a signal whose probability density function is not circularly symmetric in the complex plane. These signals have unique statistical characteristics and rotational moment properties, making them have significant advantages in signal processing.

[0003] In the non-circular signal positioning technology, the existing method 1 estimates the direction of arrival of the signal through the orthogonality between the signal subspace and the noise subspace. This algorithm requires all sensor data to be centralized to a central processor for covariance matrix calculation and eigenvalue decomposition, and then determines the direction of arrival of the signal through spectral peak search. This process requires a large amount of data transmission, resulting in high communication costs and high computational complexity, and is not applicable to large-scale arrays or distributed sensor networks. The existing method 2 also uses centralized processing and utilizes the second-order and fourth-order cumulants of non-circular signals to improve the positioning accuracy. However, when dealing with large-scale arrays, due to the need for centralized data transmission and processing, the communication and computational costs increase significantly, affecting the real-time performance and robustness of the system. Summary of the Invention

[0004] In view of the above deficiencies in the prior art, the present invention provides a non-circular signal positioning method based on distributed processing.

[0005] In order to achieve the above object of the invention, the technical solution adopted by the present invention is as follows:

[0006] A non-circular signal positioning method based on distributed processing, comprising the following steps:

[0007] S1. Construct a distributed signal receiving platform to obtain received signals, and obtain the extended output of the distributed signal receiving platform according to the received signals and the non-circular characteristics of the received signals;

[0008] S2. Based on the extended output of the distributed signal receiving platform, use the distributed average consensus iterative solution method to calculate the eigenvectors of the extended covariance matrix of the received signals;

[0009] S3. Calculate the non-circular signal positioning spatial spectrum function under distributed operation based on the eigenvectors of the extended covariance matrix of the received signals, and obtain the non-circular signal positioning result according to the non-circular signal positioning spatial spectrum function under distributed operation.

[0010] Further, in step S1, a distributed signal receiving platform is constructed. The specific process is as follows: Determine the wavelength of the signal source, and set the spacing of the array elements according to the wavelength of the signal source; Construct multiple array elements into a sub-array containing a local processor, set the sub-array as a node, set the communication link between sub-arrays as an edge, and construct an undirected graph containing nodes and edges to construct a distributed signal receiving platform.

[0011] Further, in step S1, the expression of the received signal is:

[0012]

[0013] x p (t) = [x p1 (t), x p2 (t),..., x ph (t)],

[0014]

[0015] where: x(t) is the received signal of the distributed signal receiving platform at time t, x0(t), x1(t), x P-1 (t) and x p (t) are the received signals of the sub-arrays numbered 0, 1, P - 1, and p in the distributed signal receiving platform at time t respectively, (·) T represents the matrix transpose operation, P is the number of sub-arrays of the distributed signal receiving platform, x p1 (t), x p2 (t), x ph (t) and x pg (t) are the received signals of the 1st, 2nd, hth, and gth array elements in the sub-array numbered p at time t respectively, h is the number of array elements in the sub-array, K is the number of signal sources, s k (t) is the signal emitted by the kth signal source at time t, r pg,k is the distance between the gth array element in the sub-array numbered p and the kth signal source, r 0,k is the distance between the reference point in the distributed signal receiving platform and the kth signal source, j is the imaginary unit, λ is the wavelength of the signal source, n pg (t) is the additive noise of the gth array element in the sub-array numbered p at time t, is the non-circular phase of the kth signal source.

[0016] Further, in step S1, the expression for obtaining the extended output of the distributed signal receiving platform according to the received signal and the non-circular characteristics of the received signal is:

[0017]

[0018] where: z p (t) is the extended output of the sub - array numbered p in the distributed signal receiving platform at time t, x p (t) is the received signal of the sub - array numbered p in the distributed signal receiving platform at time t, (·) * is the conjugate operation, A p is the array manifold matrix of the sub - array numbered p in the distributed signal receiving platform, Φ p is the non - circular phase shift matrix of the sub - array numbered p in the distributed signal receiving platform, diag is the operator for converting a vector into a diagonal matrix, are the non - circular phases of the received signals of the 1st, 2nd, and Qth array elements in the sub - array numbered p respectively, s R,p (t) is the receiving part of the sub - array numbered p in s R (t), s R (t) is the real part of the signal emitted by the signal source at time t, n p (t) is the additive noise of the sub - array numbered p at time t.

[0019] Furthermore, step S2 includes the following steps:

[0020] S21. Based on the extended output of the distributed signal receiving platform, use the iterative solution method to calculate the first eigenvector of the extended covariance matrix of the received signal;

[0021] S22. Based on the first eigenvector of the extended covariance matrix of the received signal, use the distributed average consensus iterative solution method to calculate the eigenvectors of the extended covariance matrix of the received signal.

[0022] Furthermore, in step S21, based on the extended output of the distributed signal receiving platform, the expression for calculating the first eigenvector of the extended covariance matrix of the received signal using the iterative solution method is:

[0023]

[0024] where: u1(n + 1) is the (n + 1) - th iteration result of the first eigenvector, P is the number of sub - arrays in the distributed signal receiving platform, L is the number of snapshots, z[l] is the l - th snapshot received data of the extended output of the distributed signal receiving platform, b[l] is a shorthand notation, is to find the exact average value of, z p [l] is the l - th snapshot received data of the extended output of the sub - array numbered p in the distributed signal receiving platform, (·) H is the conjugate transpose operation, u 1,p(n) is the n-th iteration result of the first eigenvector on the sub-array labeled p.

[0025] Further, in step S22, based on the first eigenvector of the extended covariance matrix of the received signal, the expression for calculating the eigenvector of the extended covariance matrix of the received signal using the distributed average consensus iterative solution method is:

[0026]

[0027] where: u q (n + 1) is the (n + 1)-th iteration result of the q-th eigenvector, is the n-th iteration result equivalent to the q-th eigenvector, P is the number of sub-arrays in the distributed signal receiving platform, is the i-th eigenvector of the extended covariance matrix of the received signal, is to find the exact average value of, is the l-th snapshot received data of the sub-vector corresponding to the sub-array labeled p in, (·) H is the conjugate transpose operation, is the sub-vector corresponding to the sub-array labeled p in, u q (n) is the n-th iteration result of the q-th eigenvector, is the extended covariance matrix obtained by the sub-array labeled p in the distributed signal receiving platform using L snapshots, L is the number of snapshots, z p [l] is the l-th snapshot received data of the extended output of the sub-array labeled p in the distributed signal receiving platform.

[0028] Further, in step S3, based on the eigenvector of the extended covariance matrix of the received signal, the expression for calculating the non-circular signal localization spatial spectrum function under distributed operation is:

[0029]

[0030] where: is the non-circular signal localization spatial spectrum function, (x, y) is the signal source coordinate, is the non-circular phase of the signal source, N is the number of array elements in the distributed signal receiving platform, P is the number of sub-arrays in the distributed signal receiving platform, K is the number of signal sources, is to find the exact average value of, is the sub-vector corresponding to the sub-array labeled p in, is the i-th eigenvector of the extended covariance matrix of the received signal, (·)H For the conjugate transpose operation, a k,p (x, y) is the spectral peak search vector, and Φ is the non-circular phase shift matrix.

[0031] Further, in step S3, the non-circular signal localization result is obtained according to the non-circular signal localization spatial spectrum function under distributed operation. The specific process is as follows: The horizontal and vertical coordinates of the non-circular signal localization spatial spectrum function under distributed operation are traversed and changed respectively, and then two-dimensional spectral peak search is performed on the angle and distance to obtain the estimated spatial spectrum, and the coordinate values corresponding to the peak in the estimated spatial spectrum are determined as the non-circular signal localization result.

[0032] The present invention has the following beneficial effects:

[0033] (1) Aiming at the problems of high data transmission and calculation costs in large-scale arrays, the present invention constructs a distributed signal receiving platform to obtain the received signal, and obtains the extended output of the distributed signal receiving platform according to the received signal and the non-circular characteristics of the received signal, and then uses the distributed average consensus iterative solution method to calculate the eigenvector of the extended covariance matrix of the received signal. This process can reduce data transmission, improve calculation efficiency, and meet the real-time requirements;

[0034] (2) The present invention makes full use of the statistical characteristics of non-circular signals, and can improve the localization accuracy of non-circular signals by constructing a distributed signal receiving platform to perform distributed processing on the received signal. Description of the Drawings

[0035] Figure 1 It is a schematic flowchart of a non-circular signal localization method based on distributed processing;

[0036] Figure 2 It is a schematic diagram of a distributed signal receiving platform;

[0037] Figure 3 It is the spatial spectrum diagram obtained by the traditional centralized MUSIC algorithm;

[0038] Figure 4 It is the enlarged spatial spectrum diagram obtained by the traditional centralized MUSIC algorithm;

[0039] Figure 5 It is the spatial spectrum diagram obtained by the method proposed by the present invention;

[0040] Figure 6 It is the enlarged spatial spectrum diagram obtained by the method proposed by the present invention. Detailed Embodiments

[0041] The specific embodiments of the present invention will be described below to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are within the scope of protection.

[0042] As Figure 1 shown, a non-circular signal positioning method based on distributed processing includes steps S1 - S3, specifically as follows:

[0043] S1. Construct a distributed signal receiving platform to obtain the received signal, and obtain the extended output of the distributed signal receiving platform according to the received signal and the non-circular characteristics of the received signal.

[0044] In an alternative embodiment of the present invention, the process of constructing the distributed signal receiving platform of the present invention is as follows: Determine the wavelength of the signal source, and set the spacing of the array elements according to the wavelength of the signal source; Construct multiple array elements into a sub-array including a local processor, set the sub-array as a node, set the communication link between sub-arrays as an edge, and construct an undirected graph including nodes and edges to construct the distributed signal receiving platform, as Figure 2 shown.

[0045] The expression of the received signal in the present invention is:

[0046]

[0047] x p (t)=[x p1 (t),x p2 (t),...,x ph (t)],

[0048]

[0049] where: x(t) is the received signal of the distributed signal receiving platform at time t, x0(t), x1(t), x P-1 (t) and x p (t) are respectively the received signals of the sub-arrays numbered 0, 1, P - 1, and p in the distributed signal receiving platform at time t, (·) T is the matrix transpose operation, P is the number of sub-arrays of the distributed signal receiving platform, x p1 (t), x p2 (t), x ph (t) and x pg (t) are respectively the received signals of the 1st, 2nd, hth, and gth array elements in the sub-array numbered p at time t, h is the number of array elements in the sub-array, K is the number of signal sources, sk The signal emitted by the k-th signal source at time t is \(s_{k}(t)\), and \(r_{pgk}\) pg,k is the distance between the g-th element in the sub-array labeled p and the k-th signal source, and \(r_{0k}\) 0,k is the distance between the reference point in the distributed signal reception platform and the k-th signal source. j is the imaginary unit, \(\lambda\) is the wavelength of the signal source, and \(n_{gk}(t)\) pg is the additive noise of the g-th element in the sub-array labeled p at time t. \(\gamma_{k}\) is the non-circular phase of the k-th signal source.

[0050] The expression for obtaining the extended output of the distributed signal reception platform according to the received signal and the non-circular characteristics of the received signal in the present invention is:

[0051]

[0052] where: \(z_{p}(t)\) p is the extended output of the sub-array labeled p in the distributed signal reception platform at time t, \(x_{p}(t)\) p is the received signal of the sub-array labeled p in the distributed signal reception platform at time t, \((\cdot)^{\mathrm{H}}\) * is the conjugate operation, \(A_{p}\) p is the array manifold matrix of the sub-array labeled p in the distributed signal reception platform, \(\varPhi_{p}\) p is the non-circular phase shift matrix of the sub-array labeled p in the distributed signal reception platform. diag is the operator for converting a vector into a diagonal matrix. \(\gamma_{1p}\), \(\gamma_{2p}\), \(\cdots\), \(\gamma_{Qp}\) are the non-circular phases of the received signals of the 1st, 2nd, \(\cdots\), Q-th elements in the sub-array labeled p respectively, and \(s_{k}(t)\) R,p \(s_{p}(t)\) R is the received part of the sub-array labeled p in \(s_{p}(t)\), \(s_{k}^{\mathrm{R}}(t)\) R is the real part of the signal emitted by the signal source at time t, and \(n_{p}(t)\) p is the additive noise of the sub-array labeled p at time t.

[0053] S2. Based on the extended output of the distributed signal reception platform, use the distributed average consensus iterative solution method to calculate the eigenvector of the extended covariance matrix of the received signal.

[0054] In an optional embodiment of the present invention, step S2 includes the following steps:

[0055] S21. Based on the extended output of the distributed signal reception platform, use the iterative solution method to calculate the first eigenvector of the extended covariance matrix of the received signal, and the expression is:

[0056]

[0057] Where: u1(n + 1) is the (n + 1)-th iteration result of the first eigenvector, P is the number of subarrays in the distributed signal reception platform, L is the number of snapshots, a snapshot refers to a single sampling of the signal, and the number of snapshots represents the number of samplings. z[l] is the l-th snapshot received data of the extended output of the distributed signal reception platform, and b[l] is a shorthand notation. To find the exact average value of, z p [l] is the l-th snapshot received data of the extended output of the subarray numbered p in the distributed signal reception platform, (·) H is the conjugate transpose operation, and u 1,p (n) is the n-th iteration result of the first eigenvector on the subarray numbered p.

[0058] S22. Based on the first eigenvector of the extended covariance matrix of the received signal, use the distributed average consensus iterative solution method to calculate the eigenvectors of the extended covariance matrix of the received signal. The expression is:

[0059]

[0060] Where: u q (n + 1) is the (n + 1)-th iteration result of the q-th eigenvector, is the n-th iteration result equivalent to the q-th eigenvector, P is the number of subarrays in the distributed signal reception platform, is the i-th eigenvector of the extended covariance matrix of the received signal, To find the exact average value of, is the l-th snapshot received data of the subvector corresponding to the subarray numbered p in, (·) H is the conjugate transpose operation, is the subvector corresponding to the subarray numbered p in, u q (n) is the n-th iteration result of the q-th eigenvector, is the extended covariance matrix obtained by the subarray numbered p in the distributed signal reception platform using L snapshots, L is the number of snapshots, and z p [l] is the l-th snapshot received data of the extended output of the subarray numbered p in the distributed signal reception platform.

[0061] S3. Calculate the non-circular signal localization spatial spectrum function under distributed operation based on the eigenvectors of the extended covariance matrix of the received signal, and obtain the non-circular signal localization result according to the non-circular signal localization spatial spectrum function under distributed operation.

[0062] In an alternative embodiment of the present invention, the expression of the non-circular signal localization spatial spectrum function under distributed operation is calculated based on the eigenvector of the extended covariance matrix of the received signal as follows:

[0063]

[0064] Where: is the non-circular signal localization spatial spectrum function, (x, y) is the signal source coordinate, is the non-circular phase of the signal source, N is the number of array elements in the distributed signal receiving platform, P is the number of sub-arrays in the distributed signal receiving platform, K is the number of signal sources, is to find the exact average value of , is the sub-vector corresponding to the sub-array labeled p in the i-th eigenvector of the extended covariance matrix of the received signal, (·) H is the conjugate transpose operation, a k,p (x, y) is the spectral peak search vector, and Φ is the non-circular phase shift matrix.

[0065] The present invention obtains the non-circular signal localization result according to the non-circular signal localization spatial spectrum function under distributed operation. The specific process is as follows: The horizontal and vertical coordinates of the non-circular signal localization spatial spectrum function under distributed operation are traversed and changed respectively, and then two-dimensional spectral peak search is performed on the angle and distance to obtain the estimated spatial spectrum, and the coordinate value corresponding to the peak in the estimated spatial spectrum is determined as the non-circular signal localization result.

[0066] Simulation experiment:

[0067] The method proposed in the present invention is simulated and compared with the traditional centralized MUSIC localization algorithm. The simulation scenario is shown in Table 1:

[0068] Table 1 Simulation scenario settings for non-circular signal localization methods

[0069]

[0070]

[0071] When directly constructing the covariance matrix from the array received signals, the simulation results of the traditional centralized MUSIC localization algorithm are as shown in Figure 3 , and the enlarged view at an accuracy of 0.5 m is as shown in Figure 4 . When constructing the extended covariance matrix from the extended matrix of the array received signals, the simulation results of the method proposed in the present invention are as shown in Figure 5 , and the enlarged view at an accuracy of 0.5 m is as shown in Figure 6As shown. By comparing the spectral peak intensities output by the two methods, the peak value of the power spectrum of the traditional method does not exceed 5 dB, while the peak value of the power spectrum of the method proposed in the present invention significantly exceeds 20 dB. This significant increase in the peak value of the power spectrum indicates that the present invention can achieve higher precision under the same environment and is more suitable for the development needs of spacecraft miniaturization (i.e., high-precision positioning and fast signal processing of spaceborne distributed phased arrays). In summary, the method proposed in the present invention is significantly superior to the traditional method in terms of the peak intensity of the positioning estimate, demonstrating more excellent positioning estimate performance.

[0072] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a machine for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0073] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0075] Specific embodiments are applied in the present invention to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, based on the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

[0076] Those of ordinary skill in the art will realize that the embodiments described herein are provided to assist the reader in understanding the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on these technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection of the present invention.

Claims

1. A non-circular signal positioning method based on distributed processing, characterized in that Including the steps: S1. Construct a distributed signal receiving platform to obtain received signals, and obtain the extended output of the distributed signal receiving platform according to the received signals and the non-circular characteristics of the received signals; S2. Based on the extended output of the distributed signal receiving platform, use the distributed average consensus iterative solution method to calculate the eigenvectors of the extended covariance matrix of the received signals; S3. Calculate the non-circular signal localization spatial spectrum function under distributed operation based on the eigenvectors of the extended covariance matrix of the received signals, and obtain the non-circular signal localization result according to the non-circular signal localization spatial spectrum function under distributed operation; The expression for calculating the non-circular signal localization spatial spectrum function under distributed operation based on the eigenvectors of the extended covariance matrix of the received signals is: Wherein: is the non-circular signal localization spatial spectrum function, is the signal source coordinate, is the non-circular phase of the signal source, is the number of array elements in the distributed signal receiving platform, is the number of sub-arrays in the distributed signal receiving platform, is the number of signal sources, is to find the exact average value of, is the sub-vector corresponding to the sub-array with label in, is the th eigenvector of the extended covariance matrix of the received signal, is the conjugate transpose operation, is the spectral peak search vector, is the non-circular phase shift matrix.

2. The non-circular signal positioning method based on distributed processing according to claim 1, wherein In step S1, the specific process of constructing the distributed signal receiving platform is: determine the wavelength of the signal source, and set the element spacing according to the wavelength of the signal source; construct multiple elements into a sub-array including a local processor, set the sub-array as a node, set the communication link between sub-arrays as an edge, and construct an undirected graph including nodes and edges to construct the distributed signal receiving platform.

3. The non-circular signal positioning method based on distributed processing according to claim 1, characterized in that In step S1, the expression for the received signal is: ， ， Wherein: is the received signal of the distributed signal receiving platform at time ; , , and are respectively the received signals of the sub - arrays numbered 0, 1, and of the distributed signal receiving platform at time ; is the matrix transpose operation; is the number of sub - arrays of the distributed signal receiving platform; , , and are respectively the received signals of the 1st, 2nd, sub - arrays numbered and at time ; is the number of array elements in the sub - array; is the number of signal sources; is the signal emitted by the th signal source at time ; is the distance between the th array element of the sub - array numbered and the th signal source; is the distance between the reference point in the distributed signal receiving platform and the th signal source; is the imaginary unit; is the wavelength of the signal source; is the additive noise of the th array element of the sub - array numbered at time ; is the non - circular phase of the th signal source.

4. The non-circular signal positioning method based on distributed processing according to claim 1, characterized in that In step S1, the expression for obtaining the extended output of the distributed signal receiving platform according to the received signals and the non-circular characteristics of the received signals is: Wherein: is the extended output of the sub-array numbered in the distributed signal reception platform at time ; is the received signal of the sub-array numbered in the distributed signal reception platform at time ; is the conjugate operation; is the array manifold matrix of the sub-array numbered in the distributed signal reception platform; is the non-circular phase shift matrix of the sub-array numbered in the distributed signal reception platform; , is the vector to diagonal matrix operator; , , are respectively the non-circular phases of the received signals of the 1st, 2nd, array elements in the sub-array numbered ; is the receiving part of the sub-array numbered in it; is the real part of the signal emitted by the signal source at time ; is the additive noise of the sub-array numbered in the distributed signal reception platform at time .

5. The non-circular signal positioning method based on distributed processing according to claim 1, characterized in that Step S2 includes the following steps: S21. Based on the extended output of the distributed signal receiving platform, use the iterative solution method to calculate the first eigenvector of the extended covariance matrix of the received signals; S22. Based on the first eigenvector of the extended covariance matrix of the received signals, use the distributed average consensus iterative solution method to calculate the eigenvectors of the extended covariance matrix of the received signals.

6. The non-circular signal positioning method based on distributed processing according to claim 5, wherein In step S21, the expression for calculating the first eigenvector of the extended covariance matrix of the received signals by using the iterative solution method based on the extended output of the distributed signal receiving platform is: ， Wherein: is the th iteration result of the first eigenvector, is the number of sub-arrays in the distributed signal receiving platform, is the number of snapshots, is the th snapshot received data of the extended output of the distributed signal receiving platform, is a shorthand notation, is to find the exact average value of, is the th snapshot received data of the extended output of the sub-array numbered in the distributed signal receiving platform, is the conjugate transpose operation, is the th iteration result of the first eigenvector on the sub-array numbered in the distributed signal receiving platform.

7. The non-circular signal positioning method based on distributed processing according to claim 5, wherein In step S22, the expression for calculating the eigenvectors of the extended covariance matrix of the received signals by using the distributed average consensus iterative solution method based on the first eigenvector of the extended covariance matrix of the received signals is: ， ， Wherein: is the -th iteration result of the -th eigenvector, is the -th iteration result of the -th eigenvector equivalent, is the number of sub-arrays in the distributed signal receiving platform, is the -th eigenvector of the extended covariance matrix of the received signal, is to find the exact average value, is the sub-vector corresponding to the sub-array labeled in the -th snapshot received data, is the conjugate transpose operation, is the sub-vector corresponding to the sub-array labeled is the -th iteration result of the -th eigenvector, is the extended covariance matrix obtained by the sub-array labeled in the distributed signal receiving platform using snapshots, is the number of snapshots, is the -th snapshot received data of the extended output of the sub-array labeled 8. The non-circular signal positioning method based on distributed processing according to claim 1, wherein In step S3, the specific process of obtaining the non-circular signal localization result according to the non-circular signal localization spatial spectrum function under distributed operation is: traverse and change the abscissa and ordinate of the non-circular signal localization spatial spectrum function under distributed operation respectively, then perform two-dimensional spectral peak search on the angle and distance to obtain the estimated spatial spectrum, and determine the coordinate values corresponding to the peak in the estimated spatial spectrum as the non-circular signal localization result.

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