CN-121978614-A - Delay mutual mass sparse linear array and DOA estimation method

CN121978614ACN 121978614 ACN121978614 ACN 121978614ACN-121978614-A

Abstract

A DOA estimation method of a delay cross-prime sparse linear array is suitable for signal processing of radar, sonar and wireless communication direction of arrival estimation, and is characterized in that the method comprises the steps of measuring a signal source by adopting the delay cross-prime sparse array to obtain a signal sample, constructing 2 uniform subarray segmentation schemes with different delays and segmenting the signal sample to obtain data, analyzing the segmented data by utilizing a unitary ESPRIT algorithm to obtain winding phases, constructing a congruence equation set aiming at each pair of winding phases, and solving the winding phases by utilizing a robust Chinese remainder theorem algorithm to obtain a DOA estimation value.

Inventors

CAO JIAHUI
YANG ZHIBO
QIAO BAIJIE
WU SHUMING
TIAN SHAOHUA
SUN RUOBIN
CHEN XUEFENG

Assignees

西安交通大学

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (10)

1. The DOA estimation method of the sparse linear array of the delay interstice is characterized by being suitable for signal processing of radar, sonar and wave arrival direction estimation of wireless communication, and comprises the following steps of: step S1, measuring a signal source by adopting a time-delay mutual mass sparse array to obtain a signal sample; S2, constructing 2 uniform subarray segmentation schemes with different delays and segmenting signal samples to obtain data; s3, analyzing the segmented data by using unitary ESPRIT algorithm to obtain winding phases; And S4, constructing a congruence equation set aiming at each pair of winding phases, and solving the congruence equation set by using a robust Chinese remainder theorem algorithm to obtain a DOA estimated value.
2. The method for DOA estimation of a sparse linear array of delay interstitials according to claim 1, wherein preferably step S1 comprises: parameters L, P and Q of the array are determined from the number of sensors 2m+1 and the array aperture H: (1) (2) Wherein d is half wavelength of electromagnetic signal, the sparse array is formed by combining 2 uniform arrays with array element numbers of M+1 and M respectively, the product Ld of L and d is interval of the uniform subarrays in the sparse array, the product Pd of P and d is forward offset distance of 2 uniform subarrays in the sparse array, the product Qd of Q and d is backward offset distance of 2 uniform subarrays in the sparse array, and the product is In order to round the symbol up, The sparse array samples the signal source, and the signal samples acquired by the sparse array with the sampling duration of N sampling periods are expressed as: (3) Wherein the method comprises the steps of The 1 st signal sample in the sparse array is acquired by an M+1 array element uniform array; For the 2 nd signal sample acquired by the uniform array of M array elements in the sparse array, N is an index from 1 traversal to N.
3. The method for estimating DOA of a sparse linear array of delayed interstitials of claim 2, wherein P and Q are positive integer interstitials and satisfy the requirement of the total array aperture D = M (p+q) D.
4. A method for estimating DOA in a sparse linear array of delay interstitials as claimed in claim 2, wherein, 1 St uniform subarray partitioning scheme: (4) Wherein the method comprises the steps of And For a snapshot consisting of the sample vectors measured by the first and second subarrays in partition 1, 2 Nd uniform subarray partitioning scheme: (5) Wherein the method comprises the steps of And Is a snapshot of the sample vectors measured by the first and second sub-arrays in the 2 nd segmentation scheme.
5. The method for DOA estimation in a sparse linear array of delay interstitials of claim 1, wherein step S3 comprises: Step 1 defining 2 unitary matrices And : (6) (7) Wherein the method comprises the steps of , And Respectively is , And Is a matrix of units of (a); , And Respectively is , And An inverse identity matrix of (2) having an inverse value of 1; representing a 0 vector; is the number of imaginary units, , Indicating the operation of the transpose, Step 2, calculating the extended covariance matrix of two delay schemes And : (8) Wherein the method comprises the steps of Is a conjugate transpose operation; And Extended snapshot representing two delay schemes (9) Step3, calculating real-valued covariance matrix And And decomposing the characteristic value to obtain a signal subspace matrix And : (10) Wherein the method comprises the steps of To take the real part operation, it extracts the real part in the complex matrix/vector/scalar to make up the new real value matrix/vector/scalar, For a pair of And Decomposing the characteristic values to obtain a signal subspace matrix And : (11) (12) Wherein the method comprises the steps of And Respectively represent A noise subspace matrix of the signal subspace matrix of (2); And Respectively represent Signal characteristic values and noise characteristic values of (a); And Respectively represent A noise subspace matrix of the signal subspace matrix of (2); And Respectively represent A signal eigenvalue matrix and a noise eigenvalue matrix; Step 4, calculating real value characteristic matrix And : (13) Wherein the method comprises the steps of And Representing two selection matrices, defined as And ; And the matrix is formed by splicing a zero matrix and an identity matrix: , Is that Is used for the zero-matrix of (c), Is that Is a matrix of units of (a); to take the imaginary part operation, it extracts the imaginary part in the complex matrix/vector/scalar to compose a new real matrix/vector/scalar, Representing a generalized inverse operation of the method, Step 5, constructing a combination matrix Decomposing the characteristic value to obtain a signal characteristic value Further calculating the fuzzy phase obtained under two delay schemes : (14) Wherein the method comprises the steps of K is an integer from 1 traversal to K; As an arctangent function.
6. The method for DOA estimation in a sparse linear array of delay interstitials of claim 5, wherein step S4 comprises: Phase for each pair of windings Constructing a congruence equation set: (15) Wherein the method comprises the steps of And As an integer number that is not known, As an unknown number to be solved for, Solving the congruence equation by using a robust Chinese remainder theorem algorithm, and calculating Obtain accurate DOA estimation value : (16) Wherein the method comprises the steps of As a function of the arcsine, Based on Obtaining DOA estimated values of K signal sources according to each K value in the range 。
7. The method of claim 1, wherein the unitary ESPRIT algorithm constructs a real valued extended covariance matrix by receiving data from the sub-array and performs unitary transformation and eigendecomposition to obtain a wrapped phase estimate.
8. A system for performing the method of any one of claims 1-7, comprising: the measurement module is used for measuring the signal source by adopting the time-delay mutual mass sparse array to obtain a signal sample; the construction module constructs 2 uniform subarray segmentation schemes with different delays and segments the signal samples to obtain data; the analysis module is used for analyzing the segmented data by utilizing unitary ESPRIT algorithm to obtain a winding phase; and the calculation module is used for constructing a congruence equation set aiming at each pair of winding phases, and solving the congruence equation set by utilizing a robust Chinese remainder theorem algorithm to obtain a DOA estimated value.
9. A computer storage medium comprising computer instructions which, when run on a computer, cause the computer to perform the method of any of claims 1-7.
10. An electronic device, the electronic device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein, The processor, when executing the program, implements the method of any one of claims 1-7.

Description

Delay mutual mass sparse linear array and DOA estimation method Technical Field The invention relates to the technical fields of signal sampling and signal processing, radar, sonar, wireless communication and the like, in particular to a DOA (DirectionofArrival ) estimation method of a sparse linear array with delayed mutual quality. Background The estimation of the direction of arrival (DirectionofArrival, DOA) is an important problem of array signal processing, and the basic principle is that an array antenna is utilized to receive airspace signals, and the received signals are processed through a statistical signal processing technology to obtain DOA information, which is a key technical means in the fields of radar, sonar, wireless communication and the like. The DOA estimation accuracy is related to the array shape and DOA estimation algorithm. Conventional arrays are typically linear uniform arrays with array element spacing of half the wavelength of the incident signal. Under the condition of higher signal frequency (shorter wavelength), the distance between adjacent array elements in the conventional array is very close, so that the mutual coupling effect among the array elements is obvious, the signals are mutually interfered, and DOA estimation accuracy is verified to be influenced. On the other hand, DOA estimation accuracy typically exhibits a positive correlation with array size. However, the aperture of a linear uniform array with a half wavelength as a pitch is severely limited by the number of array elements, so that it is difficult to realize a large aperture array with a small number of array elements, and further it is difficult to realize high-precision DOA estimation with a small number of array elements. In order to mitigate the cross coupling effect between array elements, the array aperture is expanded under the condition of limited array elements, so as to improve DOA estimation accuracy, a sparse linear array can be adopted, which is generally composed of array elements which are unevenly arranged, and the interval between the array elements is allowed to be larger than half a wavelength of a signal. Conventional sparse arrays require that the array locations form a continuous uniform virtual array by differencing, such as a minimal redundant array, a nested array, a reciprocal array, and variations thereof. Therefore, the existing sparse array cannot realize random sparsity, is essentially constrained by a minimum sparsity rule, and further cannot reduce the number of array elements close to each other and reduce coupling influence among the array elements. The array aperture is also constrained by a minimum sparsity rule and cannot be increased further. On the other hand, the optimal sparse array solution meeting the minimum sparse rule criterion depends on enumeration operation, and the calculated amount increases exponentially with the number of array elements. When the number of array elements is large, calculation is difficult. Therefore, it is necessary to design a linear array which can realize arbitrary sparse arrangement and is simple in design, so as to further increase the aperture of the array and reduce the mutual coupling influence among array elements, and correspondingly develop a DOA estimation method adapted to the array, so that DOA estimation accuracy is improved, and calculation efficiency is improved. The above information disclosed in the background section is only for enhancement of understanding of the background of the invention and therefore may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Disclosure of Invention Aiming at the defects, the DOA estimation method, system, medium and equipment for the delay cross-prime sparse linear array are provided, a large array aperture is realized by using a small number of array elements, the influence of array element coupling is reduced, and DOA estimation accuracy and calculation efficiency are improved. A DOa estimation method of a sparse linear array of delay interstitium, the method being suitable for signal processing of radar, sonar, direction of arrival estimation of wireless communication, the method comprising: step S1, measuring a signal source by adopting a time-delay mutual mass sparse array to obtain a signal sample; S2, constructing 2 uniform subarray segmentation schemes with different delays and segmenting signal samples to obtain data; s3, analyzing the segmented data by using unitary ESPRIT algorithm to obtain winding phases; And S4, constructing a congruence equation set aiming at each pair of winding phases, and solving the congruence equation set by using a robust Chinese remainder theorem algorithm to obtain a DOA estimated value. In the DOA estimation method of the sparse linear array of the delay interstice, the step S1 comprises the following steps: parameters L, P and Q of the array are determined from the number of se