CN-115840188-B - Marginal array element correlation denoising preprocessing method for coherent signal DOA estimation

CN115840188BCN 115840188 BCN115840188 BCN 115840188BCN-115840188-B

Abstract

The invention discloses a marginal array element correlation denoising pretreatment method for coherent signal DOA estimation, belonging to the field of radio direction finding. The invention provides a preprocessing method for cross-correlation between single marginal array element signals and residual continuous subarray signal construction Toeplitz matrixes based on the spatial independence of noise, which filters noise components generated by self-array element correlation and amplified into square power in subsequent covariance matrix operation. Simulation results show that after preprocessing the coherent array element signals, the estimation accuracy of the subsequent DOA algorithm is improved. The signal-to-noise ratio required by the method of the invention for the same RMSE is 3dB different from the prior art, and the RMSE diverges slowly and only 3 degrees at most under the condition that the signal phase difference is close to 180 degrees, which is lower than the prior art.

Inventors

HUANG XIN
ZHONG JIE

Assignees

浙江大学

Dates

Publication Date: 20260512
Application Date: 20221207

Claims (4)

1. The marginal array element related denoising preprocessing method for coherent signal DOA estimation is characterized by comprising the following steps of: extracting marginal array elements from the built uniform linear array receiving signal model, taking the rest continuous array elements as sub-arrays, and carrying out Toeplitz matrix reconstruction on receiving signals of the sub-arrays; Performing cross-correlation operation on a reconstruction matrix of sub-array received signals and marginal array element received signals corresponding to the sub-array, filtering noise related to self-array elements, and summing squares of two groups of results; in the first step, the method for establishing the uniform linear array receiving signal model comprises the following steps of setting the array element number as Array element unit interval , For the dimensions of the matrix, For carrier wave wavelength, there are The incidence directions are different from each other and are respectively Is a narrowband far field signal of (2) Expressed as And then the signal is received by the uniform linear array Is expressed as a model of (a) ; Wherein, the , Is the first Receiving signals by array elements; , Is the first The average value of each array element is 0, and the power is Is white gaussian noise; Is that Dimensional direction matrix, direction vector ; The marginal array elements are extracted by respectively extracting indexes based on the uniform linear array receiving signal model And The remaining consecutive array elements are used as sub-arrays, i.e. receiving signals for the array The method is divided into two modes respectively, wherein the first group extracts the right marginal array element signals Left continuous subarray signal is left I.e. A second group of extraction left marginal array element signals Left right continuous sub-array signal I.e. 。
2. The marginal array element correlation denoising pretreatment method for coherent signal DOA estimation of claim 1, wherein the Toeplitz matrix reconstruction method comprises the steps of respectively carrying out the following steps of And Toeplitz matrix reconstruction, denoted as ; 。
3. The marginal array element correlation denoising preprocessing method for coherent signal DOA estimation according to claim 1, wherein the cross correlation operation in the second step is specifically as follows And Corresponding marginal array element signals And Performing conjugate cross-correlation operation to obtain ; ; Wherein, the From index of ,..., Is used for the matrix elements of the matrix, From index of Due to the independence of the noise elements, No noise component generated by the correlation of array elements exists; From index of ,..., Is used for the matrix elements of the matrix, From index of Due to the independence of the noise elements, There is no noise component generated from the correlation of the array elements.
4. The marginal array element correlation denoising preprocessing method for coherent signal DOA estimation according to claim 3, wherein in the second step, the cross correlation operation result of two groups of subarrays and marginal array element received signals is obtained And Then, the conjugate squares and sums are carried out, i.e ; The noise component of the self array element which is amplified to square power by covariance matrix operation is calculated in Is also filtered out, then to Performing one-time forward and backward space smoothing to improve decoherence capability ; Wherein, the Is a single element with the exception of the minor diagonal element being 1 and the remaining positions being 0 A dimension switching matrix; the equivalent covariance matrix is obtained after the marginal array element correlation denoising pretreatment method, and finally the traditional DOA algorithm is utilized to carry out the method DOA estimation is performed.

Description

Marginal array element correlation denoising preprocessing method for coherent signal DOA estimation Technical Field The invention belongs to the field of radio direction finding, and particularly relates to a marginal array element correlation denoising pretreatment method for coherent signal DOA estimation. Background Direction of arrival estimation (Direction Of Arrival, DOA) is an important research direction in array signal processing, and is widely applied to a plurality of fields such as radar, sonar, wireless communication and the like. With the penetration of theoretical research, DOA estimation methods are continuously proposed and improved, and subspace decomposition type algorithms are the most typical super-resolution direction finding methods, such as a multiple signal classification MUSIC method and a rotation invariant subspace ESPRIT method. However, when there is a coherent signal caused by multipath, a problem of rank deficiency of the covariance matrix occurs, so that estimation performance is impaired or even disabled. Based on this, corresponding decorrelation algorithms are proposed successively, mainly in two categories, spatial smoothing and matrix reconstruction. The space smoothing method (FBSS) divides the uniform linear array into a plurality of mutually overlapped uniform continuous subarrays, the receiving covariance matrix of the subarrays is averaged, the obtained equivalent covariance matrix can be proved to be full-rank, but the number of the subarrays after division is less than that of the array elements of the original array, so that the loss of the effective aperture of the array is caused, and the performance loss is more serious especially in the environment with low signal to noise ratio. The matrix reconstruction class method (TOEP) constructs a toeplitz matrix by using a covariance matrix, and the rank is not affected by the correlation of the incident signal and is only related to the direction of arrival. Zhang proposes a multiple data matrix reconstruction Method (MTOEP) to directly construct a toeplitz matrix for a received signal, and fully utilize all covariance information, so as to improve the robustness (ZHANG W,HAN Y,JIN M,et al.Multiple-Toeplitz matrices reconstruction algorithm for DOA estimation of coherent signals[J].IEEE Access,2019,7:49504 -49512.). of the algorithm, but in the equivalent covariance matrix after transformation, noise components are enlarged to be the square of original noise power, so that anti-noise capability is weakened, and direction-finding performance is attenuated. Disclosure of Invention Aiming at the performance problem caused by noise component expansion in a multiple data matrix reconstruction method, the invention provides a marginal array element correlation denoising preprocessing method for coherent signal DOA estimation. The specific technical scheme adopted by the invention is as follows: the invention provides a marginal array element related denoising preprocessing method for coherent signal DOA estimation, which comprises the following steps: extracting marginal array elements from the built uniform linear array receiving signal model, taking the rest continuous array elements as sub-arrays, and carrying out Toeplitz matrix reconstruction on receiving signals of the sub-arrays; and step two, performing cross-correlation operation on a reconstruction matrix of the sub-array received signals and marginal array element received signals corresponding to the sub-array, filtering noise related to the self-array elements, and summing squares of two groups of results. Preferably, in the first step, the method for establishing the uniform linear array receiving signal model is as follows, the number of array elements is set to be m=2m t, the unit interval d=λ/2 of the array elements is set to be M t, λ is set to be a matrix dimension, λ is set to be a carrier wavelength, K narrow-band far-field signals s (t) with different incident directions and respectively being θ 1,…,θK are represented as s (t) = [ s 1(t),…,sK(t)]T, and the model of the uniform linear array receiving signal x (t) is represented as Wherein x (t) = [ x 1(t),…,xm(t),…,xM(t)]T,xm (t) is the m-th array element receiving signal, n (t) = [ n 1(t),…,nm(t),…,nM(t)]T,nm (t) is the m-th array element with average value of 0 and power of 0A= [ a (θ 1),…,a(θk),…,a(θK) ] is M x K dimension direction matrix, direction vector Further, the marginal array elements are extracted by extracting marginal array elements with indexes of m=1 and m=m respectively based on the uniform linear array receiving signal model, and the rest of continuous array elements are used as subarrays, namely, the array receiving signals x (t) are divided according to the following two modes, namely, a first group extracts right marginal array element signals x M (t) to leave left continuous subarray signals x f (t), namely x (t) = [ x f(t);xM (t) ], and a second group extracts left marginal ar