阅读说明：本技术 一种相干信号doa估计方法 (Coherent signal DOA estimation method ) 是由 朱圣棋 王祎 房云飞 许京伟 曾操 刘婧 刘永军 兰岚 于 2020-11-26 设计创作，主要内容包括：本发明公开了一种相干信号DOA估计方法,包括建立相干信号模型,根据所述相干信号模型得到接收信号数据；利用所述相干信号模型计算所述接收信号数据的协方差矩阵；根据所述协方差矩阵计算无噪声接收信号数据；根据所述无噪声接收信号数据建立等效源向量；建立权向量,根据所述权向量、所述等效源向量进行DOA估计。本发明考虑到了空域相关高斯噪声和相干信号,并利用设计的权向量和等效源向量进行DOA估计,提高了DOA估计的性能,且计算成本相对较低。(The invention discloses a coherent signal DOA estimation method, which comprises the steps of establishing a coherent signal model, and obtaining received signal data according to the coherent signal model; calculating a covariance matrix of the received signal data using the coherent signal model; calculating noiseless received signal data according to the covariance matrix; establishing an equivalent source vector according to the noiseless received signal data; and establishing a weight vector, and performing DOA estimation according to the weight vector and the equivalent source vector. The invention considers the space domain correlated Gaussian noise and the coherent signal, and carries out DOA estimation by utilizing the designed weight vector and the equivalent source vector, thereby improving the performance of the DOA estimation and having relatively low calculation cost.)

1. A coherent signal DOA estimation method is characterized by comprising the following steps:

step 1, establishing a coherent signal model, and obtaining received signal data according to the coherent signal model;

step 2, calculating a covariance matrix of the received signal data by using the coherent signal model;

step 3, calculating noiseless received signal data according to the covariance matrix;

step 4, establishing an equivalent source vector according to the noiseless received signal data;

and 5, establishing a weight vector, and performing DOA estimation according to the weight vector and the equivalent source vector.

2. A method of estimating a DOA of a coherent signal according to claim 1, wherein said received signal data x (t) in step 1 is represented by:

wherein the content of the first and second substances,representing a flow pattern matrix, wherein the data of the received signals share 2M +1 linear arrays, L represents the number of coherent signals, a (theta)_l)＝[e^jMβ，e^j(M-1)β，…，1，…，e^-j(M-1)β，e^-jMβ]^TDenotes a guide vector of 2M +1 dimensions, β ═ 2 π dsin (θ)_l) λ represents the l-th direction of arrival, θ_lThe first DOA estimation angle is shown, and d and lambda respectively represent the array element spacing and the wavelength，And alpha is₁＝1，Representing a coherent signal waveform vector, n (t) ═ n_-M(t)，…，n₀(t)，…，n_M(t)]^TRepresenting unknown spatial correlated gaussian noise; wherein the content of the first and second substances,

received signal data x of corresponding m-th array element at time t_m(t) is expressed as:

wherein the content of the first and second substances,representing a non-zero complex constant, p_lAnd delta phi_lAmplitude attenuation factor and phase change, respectively, n_mAnd (t) represents the noise signal received by the m array elements.

3. A method of estimating a DOA of a coherent signal according to claim 2, wherein the covariance matrix R of the received signal data in step 2 is represented as:

R＝E[x(t)x^H(t)]＝AR_sA^H+Q；

wherein R is_s＝E[s(t)s^H(t)]A covariance matrix representing the coherent signal, Q represents a covariance matrix of the noise signal, (-)^HRepresents a hermitian matrix;

the (m, k) th term Q (m, k) of the covariance matrix Q is represented as:

q(m，k)＝Q_mk＝σ_nρ^|m-k|e^j((m-k)π/2)；

where M and k represent the indices of the array elements, respectively, where M, k ═ M, …, 0, …, M, σ_nWhich is indicative of a desired signal-to-noise ratio,ρ represents a regression coefficient;

the (m, k) th term R (m, k) of the covariance matrix R is expressed as:

wherein the content of the first and second substances,and is(·)^*Representing a complex conjugate.

4. A coherent signal DOA estimation method according to claim 3, characterized in that the noiseless received signal data γ (m, k) calculated in step 3 is represented as:

wherein the content of the first and second substances,and q (m, k) is q^*(-m，-k)＝q^*(k，m)。

5. A method of DOA estimation of a coherent signal according to claim 4, characterized in that said equivalent source vector established in step 4 isExpressed as:

wherein the content of the first and second substances,and is In a corresponding manner, the first and second electrodes are,expressed as:

in a corresponding manner, the first and second electrodes are,represents a switching matrix represented as:

wherein e is_jThis represents an (M +1) × 1-dimensional column vector having a j-th position of 1 and the remaining positions of 0.

6. A method of DOA estimation of a coherent signal according to claim 5, characterized in that step 5 comprises:

step 5.1, establishing a weight vector;

step 5.2, constructing a sparse representation function of the equivalent source vector according to the weight vector;

and 5.3, solving the sparse representation function to carry out DOA estimation.

7. A method of estimation of a DOA of a coherent signal according to claim 6, characterized in that the weight vector established in step 5.1 is represented as:

ω＝[ω₁，ω₂，…，ω_K]^T；

wherein, ω is_kIs denoted by ω_k＝(a^H(θ_k)UU^Ha(θ_k))^1/2，a(θ_k) Representing DOA estimation angle theta_kU represents a noise subspace obtained by performing eigen decomposition on the covariance matrix Q.

8. A method of estimation of a DOA of coherent signals according to claim 7, characterized in that the sparse representation function of the equivalent source vectors constructed in step 5.2 is represented as:

wherein the content of the first and second substances,representing an overcomplete dictionary, K representing the number of directions of arrival of the potential signal,representing a vector of equivalent sourcesRepresents the coefficient of sparsity, wherein,the kth steering vector representing an overcomplete dictionary.

9. A method of estimation of a DOA of a coherent signal according to claim 8, characterized in that the solving of the sparse representation function in step 5.3 is represented by:

wherein | · | purple sweet₀Is represented by₀Norm, θ, represents the final DOA estimation angle.

10. A method of estimation of a DOA of a coherent signal according to claim 8, characterized in that the solving of the sparse representation function in step 5.3 is represented by:

wherein xi > 0 and is a constant, | | ·| Erythroc₁Is represented by₁The norm of the number of the first-order-of-arrival,is represented by₂The square of the norm.

Technical Field

The invention belongs to the technical field of radar signal processing, and particularly relates to a coherent signal DOA estimation method.

Background

Direction-Of-Arrival (DOA) estimation Of signals is an important research topic in array signal processing, and has been widely used in the military field (radar, sonar, communication) and the economic field (unmanned). With the rapid development of the unmanned technology, in a complex urban traffic environment (such as scattering effect of large-area trees and buildings), it is an extremely important task to improve the detection performance and the positioning resolution of a stationary target.

At present, classical DOA estimation algorithms of Signal subspace, such as Multiple Signal Classification Algorithm (MUSIC) and estimation of Signal parameter by rotation invariant technique Algorithm (estimation Signal Parameters via Rotational estimation Techniques, ESPRIT), can provide high resolution DOA estimation in case of uncorrelated and partially correlated signals. The classical signal subspace algorithm represented by MUSIC makes the direction-finding positioning technology break through the limitation of resolution, but the algorithm has the defects of large calculation amount, poor performance under the condition of low signal-to-noise ratio and the like, the algorithm cannot directly process coherent signals, and the coherent solution by using a space smoothing method can lose a certain array aperture. Furthermore, one key assumption typically employed by conventional subspace algorithms is the establishment of uncorrelated or incoherent signal models. In practical circumstances, however, coherent signals tend to be generated due to multipath propagation or intentional interference of the transmitted signal. In this case, since the signal covariance matrix is not full rank, the subspace-based DOA estimation method will fail. Compared with the classical spatial spectrum estimation method, the DOA estimation method based on sparse representation has very high estimation precision, does not need any preprocessing, and can be directly applied to coherent signals, thereby gaining wide attention of domestic and foreign scholars. M.M. Han and X.D.Zhang, "An ESPRIT-like algorithm for coherent DOA estimation," IEEE Antennas Wireless Propag.Lett., vol.4, pp.443-446, Dec.2005, propose a DOA estimation method (hereinafter referred to as similar ESPRIT algorithm for short), this method utilizes symmetrical array transducer structure, obtain An equivalent signal Toeplitz matrix that rank and coherence between the input signals are irrelevant, utilize this equivalent signal Toeplitz matrix to realize DOA estimation; s.u. pilai and b.h.kwon, "Forward/backward spatial smoothing techniques for coherent Signal identification," IEEE trans.initial, Speech, Signal process, vol.37, No.1, pp.8-15, jan.1989, proposes a weighted spatial smoothing method (hereinafter referred to as FBSS algorithm) that achieves DOA estimation by creating a smoothed array output covariance matrix in combination with a feature-based technique without considering Signal correlation; qian, L.Huang, Y.Xiao, and H.C.so, "Localization of signals with out source number knowledge in unknown spatial correlated Gaussian noise," Signal Process, vol.111, pp.170-178, Jun.2015, which proposes a Signal Localization method (hereinafter referred to as Qian algorithm) without source number knowledge, which respectively decorrelates coherent signals and suppresses unknown spatial correlated noise by using a symmetric array model and a fourth-order cumulant, and then realizes DOA estimation by using a joint diagonalization algorithm.

However, the above-mentioned similar Esprit algorithm does not effectively utilize all information of the covariance matrix of the received signal, and the DOA estimation performance is relatively poor; the FBSS algorithm works normally only under conditions where the signal is highly correlated, and signal cancellation may occur to affect DOA estimation performance; the Qian algorithm, while suppressing noise, results in a considerable amount of computation and requires a lot of time.

Disclosure of Invention

In order to solve the above problems in the prior art, the present invention provides a coherent signal DOA estimation method.

One embodiment of the present invention provides a coherent signal DOA estimation method, including the following steps:

step 1, establishing a coherent signal model, and obtaining received signal data according to the coherent signal model;

step 2, calculating a covariance matrix of the received signal data by using the coherent signal model;

step 3, calculating noiseless received signal data according to the covariance matrix;

step 4, establishing an equivalent source vector according to the noiseless received signal data;

and 5, establishing a weight vector, and performing DOA estimation according to the weight vector and the equivalent source vector.

In one embodiment of the present invention, the received signal data x (t) in step 1 is represented as:

wherein the content of the first and second substances,representing a flow pattern matrix, M representing the number of linear arrays, 2M +1 linear arrays shared by received signal data, L representing the number of coherent signals, a (theta)_l)＝[e^jMβ，e^j(M-1)β，...，1，...，e^-j(M-1)β，e^-jMβ]^TDenotes a guide vector of 2M +1 dimensions, β ═ 2 π dsin (θ)_l) λ represents the l-th direction of arrival, θ_lThe ith DOA estimation angle is shown, d and lambda respectively represent the array element spacing and wavelength,and alpha is₁＝1，Representing a coherent signal waveform vector, n (t) ═ n_-M(t)，...，n₀(t)，...，n_M(t)]^TRepresenting unknown spatial correlated gaussian noise; wherein the content of the first and second substances,

received signal data x of corresponding m-th array element at time t_m(t) is expressed as:

wherein the content of the first and second substances,representing a non-zero complex constant, p_lAnd delta phi_lAmplitude attenuation factor and phase change, respectively, n_mAnd (t) represents the noise signal received by the m array elements.

In one embodiment of the present invention, the covariance matrix R of the received signal data in step 2 is represented as:

R＝E[x(t)x^H(t)]＝AR_sA^H+Q；

wherein R is_s＝E[s(t)s^H(t)]A covariance matrix representing the coherent signal, Q represents a covariance matrix of the noise signal, (-)^HRepresents a hermitian matrix;

the (m, k) th term Q (m, k) of the covariance matrix Q is represented as:

q(m，k)＝Q_mk＝σ_nρ^|m-k|e^j((m-k)π/2)；

wherein M and k respectively represent an index of an array element, wherein M, k ═ M_nRepresenting the desired signal-to-noise ratio, p representing the regression coefficient;

the (m, k) th term R (m, k) of the covariance matrix R is expressed as:

wherein the content of the first and second substances,and is(·)^*Representing a complex conjugate.

In one embodiment of the present invention, the noiseless received signal data γ (m, k) calculated in step 3 is represented as:

wherein the content of the first and second substances,and q (m, k) is q^*(-m，-k)＝q^*(k，m)。

In one embodiment of the invention, the equivalent source vector established in step 4Expressed as:

wherein the content of the first and second substances,and is In a corresponding manner, the first and second electrodes are,expressed as:

in a corresponding manner, the first and second electrodes are,represents a switching matrix represented as:

wherein e is_jThis represents an (M +1) × 1-dimensional column vector having a j-th position of 1 and the remaining positions of 0.

In one embodiment of the present invention, step 5 comprises:

step 5.1, establishing a weight vector;

step 5.2, constructing a sparse representation function of the equivalent source vector according to the weight vector;

and 5.3, solving the sparse representation function to carry out DOA estimation.

In one embodiment of the invention, the weight vector established in step 5.1 is represented as:

ω＝[ω₁，ω₂，...，ω_K]^T；

wherein, ω is_kIs denoted by ω_k＝(a^H(θ_k)UU^Ha(θ_k))^1/2，a(θ_k) Representing DOA estimation angle theta_kU represents a noise subspace obtained by performing eigen decomposition on the covariance matrix Q.

In one embodiment of the invention, the sparse representation function of the equivalent source vector constructed in step 5.2 is represented as:

wherein the content of the first and second substances,representing an overcomplete dictionary, K representing the number of directions of arrival of the potential signal,representing a vector of equivalent sourcesIs used to represent the coefficients of the image,representing the intelligent product of elements, wherein,representing overcomplete dictionariesk steering vectors.

In one embodiment of the invention, solving the sparse representation function in step 5.3 is represented as:

wherein | · | purple sweet₀Is represented by₀Norm, θ, represents the final DOA estimation angle.

In one embodiment of the invention, solving the sparse representation function in step 5.3 is represented as:

wherein xi > 0 and is a constant, | | ·| Erythroc₁Is represented by₁The norm of the number of the first-order-of-arrival,is represented by₂The square of the norm.

Compared with the prior art, the invention has the beneficial effects that:

the coherent signal DOA estimation method provided by the invention considers the spatial domain correlated Gaussian noise and the coherent signal, and carries out DOA estimation by utilizing the designed weight vector and the equivalent source vector, thereby improving the performance of DOA estimation and having relatively low calculation cost.

The present invention will be described in further detail with reference to the accompanying drawings and examples.

Drawings

Fig. 1 is a schematic flowchart of a coherent signal DOA estimation method according to an embodiment of the present invention;

FIG. 2 is a schematic diagram illustrating a comparison between DOA estimation results in different directions of arrival between the DOA estimation method provided by the embodiment of the present invention and three conventional DOA estimation methods;

fig. 3 is a schematic diagram showing a comparison of root mean square errors of DOA estimation under different signal-to-noise ratios between the DOA estimation method provided by the embodiment of the present invention and three conventional DOA estimation methods.

Detailed Description

The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.

Example one

To solve the problem that the existing DOA estimation cannot achieve both DOA estimation performance and operation time, please refer to fig. 1, where fig. 1 is a schematic structural diagram of a coherent signal DOA estimation method according to an embodiment of the present invention. The embodiment provides a coherent signal DOA estimation method, which includes the following steps:

step 1, establishing a coherent signal model, and obtaining received signal data according to the coherent signal model.

Specifically, the coherent signal model is set as: l far-field narrow-band coherent signalsAt an unknown angleTo one-dimensional symmetric equidistant lines, e.g. Sparse Uniform Line Array (SULA), consisting ofThe array elements are arranged in all directions, and the input signal is uncorrelated with noise. Assuming that the central array element of the array is the reference array element, the received signal data x (t) of the array at time t is represented as:

wherein the content of the first and second substances,representing a flow pattern matrix, M representing the number of linear arrays, 2M +1 linear arrays shared by received signal data, L representing the number of coherent signals, a (theta)_l)＝[e^jMβ，e^j(M-1)β，...，1，...，e^-j(M-1)β，e^-jMβ]^TDenotes a guide vector of 2M +1 dimensions, β ═ 2 π dsin (θ)_l) λ represents the l-th direction of arrival, θ_lThe ith DOA estimation angle is shown, d and lambda respectively represent the array element spacing and wavelength,and alpha is₁＝1，Representing a coherent signal waveform vector, n (t) ═ n_-M(t)，...，n₀(t)，...，n_M(t)]^TRepresenting unknown spatial correlated gaussian noise; wherein the content of the first and second substances,

received signal data x of corresponding m-th array element at time t_m(t) is expressed as:

wherein the content of the first and second substances,representing a non-zero complex constant, p_lAnd delta phi_lAmplitude attenuation factor and phase change, respectively, n_mAnd (t) represents the noise signal received by the m array elements.

And 2, calculating a covariance matrix of the received signal data by using a coherent signal model.

Specifically, the covariance matrix R of the received signal data of the present embodiment is expressed as:

R＝E[x(t)x^H(t)]＝AR_sA^H+Q (3)

wherein R is_s＝E[s(t)s^H(t)]A covariance matrix representing the coherent signal, Q represents a covariance matrix of the noise signal, (-)^HRepresenting the hermitian matrix.

Correspondingly, the (m, k) th term Q (m, k) of the covariance matrix Q of the noise signal is expressed as:

q(m，k)＝Q_mk＝σ_nρ^|m-k|e^j((m-k)π/2) (4)

where M and k respectively represent an index of an array element, specifically, a row index and a column index of the noise covariance matrix Q, where M, k ═ M,. 0.. M, σ ·_nRepresenting a desired signal-to-noise ratio, which can be obtained by adjustment, wherein rho represents a regression coefficient and is used for controlling the relative height of a noise space spectrum peak;

correspondingly, the (m, k) th term R (m, k) of the covariance matrix R of the coherent signal is represented as:

where M and k also represent the indices of the array elements, respectively, and here specifically the row index and the column index of the covariance matrix R of the coherent signal, where M, k is-M, 0, M,and is(·)^*Representing a complex conjugate.

And 3, calculating the noiseless received signal data according to the covariance matrix.

Specifically, the noiseless received signal data γ (m, k) calculated by the present embodiment is expressed as:

when q (m, k) is q^*(-m，-k)＝q^*(k, m), gaussian noise can be removed, and the noiseless received signal data γ (m, k) can be updated by substituting equation (6) as:

wherein the content of the first and second substances,

and 4, establishing an equivalent source vector according to the noiseless received signal data.

Specifically, in order to use all rows of the covariance matrix R to the received signal data in the calculation, thereby improving the estimation performance of the direction of arrival, the present embodiment establishes an equivalent source vector, specifically an equivalent source vectorExpressed as:

wherein the content of the first and second substances,and is In a corresponding manner, the first and second electrodes are,expressed as:

in a corresponding manner, the first and second electrodes are,represents a switching matrix represented as:

wherein e is_jThis represents an (M +1) × 1-dimensional column vector having a j-th position of 1 and the remaining positions of 0.

And 5, establishing a weight vector, and performing DOA estimation according to the weight vector and the equivalent source vector.

Specifically, step 5 of this embodiment includes step 5.1, step 5.2, and step 5.3:

and 5.1, establishing a weight vector.

Specifically, for better sparse reconstruction performance, the present embodiment establishes a weight vector ω, and the real signal direction is assigned with a smaller weight, which can significantly amplify the corresponding signal power, and according to the MUSIC spatial spectrum, the weight vector ω established in the present embodiment is represented as:

ω＝[ω₁，ω₂，...，ω_K]^T (11)

wherein, ω is_kIs denoted by ω_k＝(a^H(θ_k)UU^Ha(θ_k))^1/2，a(θ_k) Representing DOA estimation angle theta_kU represents a noise subspace obtained by performing eigen decomposition on the covariance matrix Q.

And 5.2, constructing a sparse representation function of the equivalent source vector according to the weight vector.

Specifically, a weight vector ω is established, and the embodiment is equivalent to the source vectorThe problem of deterministic DOA (direction of arrival) estimation is seen as a problem of sparse reconstruction, building an equivalent source vector from the weight vector ωBefore the sparse representation function, the embodiment first constructs a sparse representation function of the equivalent source vector, where the constructed sparse representation function of the equivalent source vector is represented as:

wherein the content of the first and second substances,representing the sparse signal power vector, K representing the number of directions of arrival of the potential signal,represents an overcomplete dictionary and satisfies the constraint isometry property, (2M +1) < K, the kth steering vector of the overcomplete dictionary in equation (12) can be expressed as:

further, sparse representation of p is achieved by using the established weight vector omega. According to the signal subspace theory, it can be seen from the formula (11) that ω is the spatial frequency spectrum of the correlation signal, when θ_kAngle close to the true signal, ω_kIs very small. According to this characteristic, the present embodiment implements a sparse representation of p with a weight vector ω as:

wherein the content of the first and second substances,representing a vector of equivalent sourcesIs a non-zero term ratio of pIs much smaller than the corresponding position of (A) andthe sparsity of the correlation is enhanced and,representing the element intelligence product. Substituting equation (13) into equation (12) results in an updated sparse representation function of the equivalent source vector, which is expressed as:

and 5.3, solving the sparse representation function to carry out DOA estimation.

Specifically, the present implementation ultimately converts the DOA estimate into a sparse representation function that solves for the equivalent source vector, and by solving forMinimum values to achieve an estimate of DOA, in particular:

the solving sparse representation function of the present embodiment is expressed as:

wherein | · | purple sweet₀Is represented by₀Norm, θ, represents the final DOA estimation angle.

Similarly, the solving sparse representation function of the present embodiment is expressed as:

wherein xi > 0 and is a constant, | | ·| Erythroc₁Is represented by₁The norm of the number of the first-order-of-arrival,is represented by₂The square of the norm.

The present embodiment is solved by formula (15) or formula (16)And (3) corresponding to the minimum value through a formula (14) to obtain a DOA estimation angle theta, so as to realize final DOA estimation.

To verify the effectiveness of the coherent signal DOA estimation method proposed in this embodiment, the following simulation experiments are used to further prove the effectiveness of the coherent signal DOA estimation method.

The simulation assumes that M is 11, the interval of array elements is half wavelength, and xi is 10 in the one-dimensional symmetrical uniform linear array^-4. To measure the performance of DOA estimation, the Root Mean Square Error (RMSE) of the direction of arrival estimation is defined as:

wherein, N represents the Monte Carlo experiment times, and L represents the number of related signals.

In this embodiment, similar Esprit algorithm, FBSS algorithm, and Qian algorithm are respectively adopted to compare with the method provided by the present invention, where similar Esprit is a similar technical algorithm (An Esprit-like algorithm for coherent DOA estimation, for short) that estimates signal parameters by using a subspace rotation method, and a Forward/Backward Spatial Smoothing algorithm (FBSS, for short), and a Qian algorithm is a signal localization algorithm that does not need to know the source number.

Simulation experiment I:

three coherent signals acting on the one-dimensional symmetrical equidistant linear array are considered, and the arrival directions of the three coherent signals are as follows: -12 °, 2 °, 16 ° }, colored noise ρ ═ 0.8 and SNR ═ 5 dB. Referring to fig. 2, fig. 2 is a schematic diagram illustrating a comparison between DOA estimation results in different directions of arrival of the DOA estimation method provided by the embodiment of the present invention and three traditional DOA estimation methods, as shown in fig. 2: the DOA estimation performance similar to Esprit algorithm and FBSS algorithm is significantly degraded; although colored noise can be eliminated by utilizing the fourth-order cumulant mentioned by the Qian algorithm, the Qian algorithm can only estimate a signal with the arrival direction of 16 degrees under the background of low signal-to-noise ratio; the DOA estimation can be well carried out on the algorithms under the conditions of-12 degrees, 2 degrees and 16 degrees, and the algorithms have good performance.

And (2) simulation experiment II:

three coherent signals acting on a one-dimensional symmetrical equidistant linear array are considered, the arrival directions of the three coherent signals are { -12 °, 2 °, 16 ° }, the signal-to-noise ratio varies from-5 dB to 20dB, and the SNR is 0dB and the ρ is 0.8. The RMSE of the directions of arrival of three coherent signals calculated by 500 monte carlo experiments, please refer to fig. 3, a schematic diagram showing the root mean square error comparison of the DOA estimation method provided in the embodiment of the present invention and the conventional three DOA estimation methods under different signal-to-noise ratios, and the embodiment evaluates the DOA estimation performance under different signal-to-noise ratios. As can be seen from fig. 3: the colored noise is not processed, so that the estimation performance of the FBSS algorithm and the similar Esprit algorithm DOA is particularly poor; the DOA estimation performance of the algorithm provided by the invention is superior to that of other methods in the whole signal-to-noise ratio range, and particularly under the condition of low signal-to-noise ratio, the estimation performance of the algorithm provided by the invention is the best.

Meanwhile, the embodiment also counts the time required by the algorithm to realize DOA estimation under different array element numbers, and concretely refers to Table 1.

TABLE 1 simulation time required for different algorithms under different array element numbers

Number of array elements	11	13	15	17	19	21	23	25
									Operation time(s)	0.8028	0.8031	0.7483	0.7759	0.7787	0.8388	0.8062	0.8467

As can be seen from Table 1, the algorithm provided by the invention is short in time consumption, and the execution time change is increased less with the increase of the number of array elements.

In summary, the coherent signal DOA estimation method provided in this embodiment considers spatial domain correlated gaussian noise and coherent signals, and performs DOA estimation by using the designed weight vector and equivalent source vector, specifically: the embodiment establishes a coherent signal model, eliminates the Gaussian noise related to the space domain based on a one-dimensional symmetrical equidistant linear array, reconstructs the noiseless item in the covariance matrix to calculate the noiseless received signal data, then constructs an equivalent source vector according to the noiseless received signal data, thereby simulating the received signal data in the extended virtual array without considering the coherence among the received signal data, enhancing the sparsity of the signal by using the obtained equivalent source vector and the weight vector designed by using the signal subspace theory, the weight vector can realize more robust DOA estimation of sparse reconstruction performance, improve the performance of the DOA estimation, particularly obviously improve the DOA estimation performance under the condition of low signal-to-noise ratio, and the computing cost is relatively low, the computing efficiency is high, and the method is crucial to a large array/real-time data processing system.

The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.