A coherent interference suppression method based on Duvall array structure

Abstract

The invention relates to a coherent interference suppression method based on a Duvall array structure, and belongs to the technical field of adaptive beamforming. The method comprises the steps of calculating a signal model received by a main array and a signal model received by an auxiliary array, and a signal difference and a variance matrix between the signal model received by the main array and the signal model received by the auxiliary array in the Duvall array structure, calculating an MVDR weight vector according to the covariance matrix obtained in the third step; beamforming is performed on the main array of the Duvall array structure according to the MVDR weight vector, a receiving null is formed in an interference direction, and coherent interference suppression is completed.

Description

Technical Field

[0001] The invention relates to a coherent interference suppression method based on a Duvall array structure, belonging to the technical field of adaptive beamforming, and specifically to a coherent interference suppression algorithm based on a Duvall array structure that uses a main array and an auxiliary array of the Duvall array structure to perform difference and then solve a weight vector. Background Art

[0002] Adaptive beamforming is a technique that weights the element signal to pass the desired signal while suppressing the interference signal. This technology is widely used in radar, communication, and sonar. Traditional adaptive beamforming, such as MVDR, cannot form a null in the interference direction when there is coherent interference, and may even cause signal cancellation. When the signal propagates in multiple paths or there is "intelligent" interference, it will cause the interference to be coherent with the desired signal.

[0003] Most methods to solve coherent interference use spatial smoothing, which divides the array into several sub-arrays and solves the coherence by weighting the sub-array covariance matrix. However, the improvement of coherent interference by spatial smoothing depends on the spatial separation of the sub-arrays. Therefore, the spatial smoothing method requires a large number of sub-arrays to solve the coherence, which will sacrifice the array aperture. In addition to spatial smoothing methods, there are also methods such as MCMV, SPT, CTMV, and quartic constraints. These methods all add constraints to the direction of coherent interference, so there must be angular information of coherent interference. However, the angular information of coherent interference is often difficult to estimate. In addition, there is a blind beamforming method based on high-order cumulants. This method has a large amount of computation and is not conducive to real-time adaptive beamforming. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a coherent interference suppression method based on a Duvall array structure.

[0005] The technical solution of the present invention is:

[0006] A coherent interference suppression method based on a Duvall array structure, the method comprising the steps of:

[0007] The first step is to calculate the signal model received by the main array and the signal model received by the auxiliary array in the Duvall array structure;

[0008] The second step is to calculate the signal difference between the signal model received by the main array and the signal model received by the auxiliary array obtained in the first step;

[0009] The third step is to calculate the covariance matrix of the signal difference obtained in the second step;

[0010] Step 4: Calculate the MVDR weight vector based on the covariance matrix obtained in step 3;

[0011] The fifth step is to perform beamforming on the main array of the Duvall array structure according to the MVDR weight vector obtained in the fourth step, forming a receiving null in the interference direction, thereby completing the coherent interference suppression;

[0012] In the first step, the Duvall array structure is a uniform linear array, the spacing of the uniform linear array is d, the number of array elements is N, the working wavelength is λ, the signal received by the uniform linear array includes a desired signal, a coherent interference signal and a plurality of incoherent interference signals, the sum of the number of coherent interference signals and the number of incoherent interference signals is I, that is, the number of coherent interference signals is 1, the number of incoherent interference signals is I-1, the angle of the desired signal is θ0, and the interference angle of the coherent interference signal is θ I , the interference angles of the incoherent interference signals are θ1, θ2, …, θ I-1 , the angle of the desired signal is known, the interference angle of the coherent interference signal and the interference angle of the incoherent interference signal are unknown, the first N-1 elements of the uniform linear array constitute the main array, the last N-1 elements constitute the auxiliary array, the main array and the auxiliary array have N-2 elements overlapping, the signal received by the uniform linear array is x(t), the signal models received by the main array and the auxiliary array are x1(t) and x2(t) respectively;

[0013] x1(t),x2(t) are expressed as:

[0014]

[0015]

[0016] in, s0(t) is the expected signal, s I (t) is the coherent interference signal, s i (t), (i=1,…,I-1) are incoherent interference signals, n1(t), n2(t) are additive white Gaussian noise, because s I (t) is related to s0(t), so we have

[0017] s I (t) = k I s0(t)

[0018] where k I is a complex constant, so x1(t) and x2(t) are respectively expressed as

[0019]

[0020]

[0021] In the second step, the signal difference between the signal model received by the main array and the signal model received by the auxiliary array is calculated as:

[0022]

[0023] Substitute the expressions of x1(t) and x2(t) in step 1 to obtain

[0024]

[0025] in

[0026] In the third step, the covariance matrix of the signal difference is:

[0027]

[0028] span{a(θ1),a(θ2),…,a(θ I )}=span{u1,u2,…,u I} is the signal subspace, span{u I+1 ,…,u N-1} is the noise subspace, R′ x The inverse of

[0029]

[0030] (because as well as )

[0031] In the fourth step, the MVDR weight vector is:

[0032]

[0033] In the fifth step, the beam forming is:

[0034] x out (t) = ω′ H x1(t)

[0035] because So there is

[0036]

[0037] That is, the output of beamforming x out (t) Only the desired signal and noise.

[0038] Beneficial Effects

[0039] (1) The method of the present invention can form a receiving null in the coherent interference direction and the incoherent interference direction of the directional pattern, completely suppress the coherent interference signal and the incoherent interference signal, and at the same time control the direction of the main beam, and the directional pattern has very low side lobes, and the output signal of the beamforming has a high signal-to-interference-noise ratio.

[0040] (2) The uniform linear array model in the method of the present invention is easy to implement, which is conducive to the promotion of the method.

[0041] (3) The method of the present invention only uses two arrays, the main array and the auxiliary array, to calculate the signal model. Compared with the traditional method of multiple sub-arrays, this method reduces the loss of array aperture, helps to retain the gain of the array, and improves the signal-to-noise ratio of the output signal.

[0042] (4) In the method of the present invention, when calculating the signal difference between the main array and the auxiliary array, the control of the beam pointing is achieved by controlling the gain of the auxiliary array, thereby realizing beam forming in any direction.

[0043] (5) In the method of the present invention, the covariance matrix is ​​calculated. Since the difference signal in step 2 is used, the calculated covariance matrix does not contain the expected signal component.

[0044] (6) The method of the present invention calculates the weight vector. Since the covariance matrix that does not include the desired signal is used, the calculated weight vector will not cause the desired signal to cancel out, thereby avoiding the loss of signal to interference noise ratio.

[0045] (7) The beamforming of the main array in the method of the present invention can form a directional pattern reception null in the coherent interference direction and the incoherent interference direction at the same time. At the same time, the directional pattern has very low side lobes, which improves the signal-to-interference-noise ratio of the output signal and is beneficial to other subsequent signal processing links. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the Duvall array structure of the present invention;

[0047] Figure 2 It is a comparison between the beamforming pattern of the coherent interference suppression algorithm based on the Duvall array structure described in the present invention and the pattern of the traditional MVDR algorithm and the pattern of the spatial smoothing algorithm;

[0048] Figure 3 is the output signal obtained by traditional MVDR beamforming;

[0049] Figure 4 It is the output signal obtained by beamforming using the spatial smoothing method;

[0050] Figure 5It is the output signal obtained by beamforming of the coherent interference suppression algorithm based on the Duvall array structure described in the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] The present invention can be implemented in different ways and is not limited to the specific embodiments described.

[0053] like Figure 1 FIG. 1 is an array model and an overall flow chart of a coherent interference suppression algorithm based on a Duvall array structure proposed by the present invention. The method comprises the following steps:

[0054] Step 1: Modeling the Duvall array structure and coherent interference signal. The array structure is a uniform linear array with a spacing of d and an array element number of N. The operating wavelength is λ. The signals received by the array include the desired signal, coherent interference and multiple incoherent interferences. The total number of interferences is I. The angle of the desired signal is θ0, and the angle of the coherent interference is θ I , the incoherent interference angles are θ1,θ2,…,θ I-1 , the angle of the desired signal is known, and the interference angle is unknown. The first N-1 elements of the array constitute the main array, and the last N-1 elements constitute the auxiliary array. The main array and the auxiliary array have N-2 elements overlapping. The signal received by the array is x(t), and the signals received by the main array and the auxiliary array are x1(t) and x2(t) respectively. Figure 1 The matrix representations of x(t), x1(t), and x2(t) are

[0055] x(t)=[x0(t),x1(t),…,x N-1 (t)] T

[0056] x1(t)=[x0(t),x1(t),…,x N-2 (t)] T

[0057] x2(t)=[x1(t),x2(t),…,x N-1 (t)] T

[0058] in,(·) T represents transpose, x i (t), (i=0,1,…,N-1) represents the signal on a single array element.

[0059] Step 2: Calculate the difference signal between the main and auxiliary arrays. Figure 1 As shown, the difference signal is calculated by subtracting the signal of the auxiliary array from the signal of the main array multiplied by a complex constant.

[0060]

[0061] Step 3: Calculate the covariance matrix of the difference signal. The covariance matrix is ​​estimated based on a finite number of snapshots, i.e.

[0062]

[0063] Where K is the number of snapshots, (·) H represents the conjugate transpose.

[0064] Step 4: Calculate the MVDR weight vector. The calculation method is:

[0065]

[0066] in a1(θ0) corresponds to the steering vector of the main array in the θ0 direction.

[0067] Step 5: Use the obtained weight vector to perform beamforming on the main array.

[0068] x out (t) = ω′ H x1(t)

[0069] x out (t) is the output of beamforming, where interference has been suppressed.

[0070] Example

[0071] The experimental scenario is as follows: the number of uniform linear array elements is 16, the element spacing is half a wavelength, the desired signal is a linear frequency modulation signal, the incident angle is 10°, the signal-to-noise ratio is 0 dB, the incident angle of the coherent interference is 30°, the incident angles of the three incoherent interferences are -10°, -25°, and 20°, and all are noise suppression interferences, the interference-to-noise ratio of all interferences is 10 dB, and the number of snapshots used is 1024. The beamforming patterns of the method of the present invention, the traditional MVDR method, and the spatial smoothing method obtained in this experimental scenario are as follows: Figure 2 As shown, they correspond to the proposed, MVDR, and spatial smooth in the figure respectively. It can be seen that this method can effectively suppress coherent interference and form deep nulls in the coherent interference direction and the incoherent interference direction. Figure 3 , Figure 4 , Figure 5 They are the output obtained by MVDR beamforming, the output obtained by the spatial smoothing method and the output obtained by the method of the present invention. It can be seen that the method of the present invention can effectively suppress coherent interference, and the output signal only contains the desired signal and noise.

[0072] In summary, the method of the present invention provides a new method for suppressing coherent interference in the field of adaptive beamforming.

[0073] It should be noted that the specific implementation methods described in the present invention are only some implementation methods of the present invention, and the description thereof cannot be regarded as a limitation of the present invention. It should be pointed out that for ordinary researchers in the technical field, several simple deductions can be made without departing from the concept of the present invention, and these should all fall within the protection scope of the present invention.

Claims

1. A coherent interference suppression method based on Duvall array structure, characterized in that The steps of the method include: The first step is to calculate the signal model received by the main array and the signal model received by the auxiliary array in the Duvall array structure; The second step is to calculate the signal difference between the signal model received by the main array and the signal model received by the auxiliary array obtained in the first step; The third step is to calculate the covariance matrix of the signal difference obtained in the second step; Step 4: Calculate the MVDR weight vector based on the covariance matrix obtained in step 3; The fifth step is to perform beamforming on the main array of the Duvall array structure according to the MVDR weight vector obtained in the fourth step, forming a receiving null in the interference direction, thereby completing the coherent interference suppression; In the first step, the Duvall array structure is a uniform linear array, the spacing of the uniform linear array is d, the number of array elements is N, the working wavelength is λ, and the signal received by the uniform linear array includes a desired signal, a coherent interference signal and a plurality of incoherent interference signals, and the sum of the number of coherent interference signals and the number of incoherent interference signals is I, that is, the number of coherent interference signals is 1, and the number of incoherent interference signals is I-1; The angle of the desired signal is θ0, and the interference angle of the coherent interference signal is θ I , the interference angles of the incoherent interference signals are θ1, θ2, …, θ I-1 , the angle of the desired signal is known, the interference angle of the coherent interference signal and the interference angle of the incoherent interference signal are unknown, the first N-1 elements of the uniform linear array constitute the main array, the last N-1 elements constitute the auxiliary array, the main array and the auxiliary array have N-2 elements overlapping, the signal received by the uniform linear array is x(t), the signal models received by the main array and the auxiliary array are x1(t) and x2(t) respectively; x1(t),x2(t) are expressed as: in, s0(t) is the expected signal, s I (t) is the coherent interference signal, s i (t) is an incoherent interference signal, i = 1, ..., I-1, n1(t), n2(t) are both additive white Gaussian noise, s I (t) is related to s0(t), we have s I (t)=k I s0(t) where k I is a complex constant, x1(t) and x2(t) are respectively expressed as In the second step, the signal difference between the signal model received by the main array and the signal model received by the auxiliary array is calculated as: Substituting the expressions of x1(t) and x2(t) into in 2. The coherent interference suppression method based on the Duvall array structure according to claim 1, characterized in that: In the third step, the covariance matrix of the signal difference is: span{a(θ1),a(θ2),…,a(θ I )}=span{u1,u2,…,u I } is the signal subspace, span{u I+1 ,…,u N-1 } is the noise subspace, R ′ x The inverse of 3. The coherent interference suppression method based on the Duvall array structure according to claim 2, characterized in that: In the fourth step, the MVDR weight vector is:

4. The coherent interference suppression method based on the Duvall array structure according to claim 3 is characterized in that: In the fifth step, the beam forming is: x out (t)=ω ′H x1(t)

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