Digital array pattern nulling optimization control method based on channel amplitude and phase error dynamic compensation

Abstract

The application discloses a digital array directional diagram null optimization control method based on channel amplitude-phase error dynamic compensation, which comprises the following steps: determining an amplitude-phase self-calibration interference noise ratio (INR) threshold ψ according to an expected null depth; performing amplitude-phase compensation on an interference subspace on the basis of interference detection and angle estimation accelerated by two-dimensional Fourier transform (FFT) and an orthogonal projection algorithm based on Schmidt orthogonalization; and dynamically optimizing and controlling the null depth and width of the directional diagram interference resistance. The application can dynamically calibrate amplitude-phase errors, realize optimization control of the null depth and width of interference, avoid matrix inversion in the interference subspace projection matrix through Schmidt orthogonalization, reduce the operation complexity, determine the amplitude-phase self-calibration threshold according to the expected null depth, adopt two-dimensional FFT interference detection and the Schmidt orthogonalization orthogonal projection algorithm, utilize strong interference sources meeting the threshold to perform parallel calculation of amplitude-phase error self-calibration, and achieve the purpose of working on one side and updating the amplitude-phase errors in real time on the other side.

Description

Technical Field

[0001] This invention belongs to the field of spatial anti-interference technology for digital array antennas, and specifically relates to a digital array pattern null optimization control method based on dynamic compensation of channel amplitude and phase error. Background Technology

[0002] Digital array antennas can achieve spatial interference immunity by controlling the radiation pattern to create nulls in the direction of interference. They also offer advantages such as fast beam switching, flexible beam pattern control, and strong spatial interference suppression capabilities. However, each RF channel of a digital array antenna consists of linear or nonlinear active devices such as mixers, amplifiers, and filters. Amplitude and phase errors inevitably exist between RF channels, and these errors change slowly with variations in ambient temperature and temperature differences between channels. These channel amplitude and phase errors affect the accuracy of signal source angle estimation and array gain, and severely impact the depth of interference nulls and sidelobe levels in the array radiation pattern. Therefore, for digital array antennas requiring low sidelobe and deep null interference immunity, dynamic channel amplitude and phase error calibration is essential.

[0003] Existing calibration algorithms are mainly divided into two categories: active calibration methods and self-calibration methods. Active calibration methods require entering a dedicated calibration mode before the system is in normal operation, using an auxiliary signal source with a precisely known spatial location for calibration; therefore, "calibration while operating" is not possible. Self-calibration methods can simultaneously estimate the channel amplitude and phase error parameters and the direction of arrival (DOA). Self-calibration methods mostly obtain more accurate DOA estimates through amplitude and phase calibration. However, in situations where the interference angle changes rapidly, they cannot dynamically optimize the depth and width of the null pattern for anti-interference purposes through amplitude and phase compensation. Summary of the Invention

[0004] The purpose of this invention is to solve the problems mentioned in the background art and to provide a digital array pattern null optimization control method based on dynamic compensation of channel amplitude and phase error. According to the desired pattern null depth and width requirements, the channel amplitude and phase error is dynamically calibrated using a non-cooperative interference source that meets the self-calibration interference-to-noise ratio threshold, thereby effectively controlling the pattern null depth and width.

[0005] To achieve the objective of this invention, a digital array pattern null optimization control method based on dynamic compensation for channel amplitude and phase errors is disclosed, comprising the following steps:

[0006] Step 1: Perform two-dimensional FFT processing on the array received data vector x(k). Based on the relationship between the two-dimensional FFT output and the beamforming output, and the calculated interference detection threshold T, perform fast interference detection and estimate the number of interferences P and the interference angle. and INR p,(p=1,…,P);

[0007] Step 2(a), when P = 1 and INR P When x ≥ ψ, the covariance matrix of x(k) is calculated by exponentiation. Main eigenvectors use With interference directional vector Difference estimation amplitude and phase error

[0008] Step 2(b): When P>0, i.e., there is effective interference, according to P, An interference subspace C with null broadening characteristics is constructed using the null broadening widths Δu and Δv in the u and v directions, and the amplitude and phase errors calculated in step 2(a) are used. After correcting C, we get

[0009] Step 3, for Schmidt orthogonalization is performed, and the orthogonal projection algorithm is used to calculate the spatial null anti-interference weight w for the optimization control of null depth and null width.

[0010] Among them, the auxiliary amplitude and phase self-calibration threshold calculation module determines the INR threshold ψ of amplitude and phase error self-calibration by analyzing the relationship between the expected null depth and the channel amplitude and phase error, and the channel amplitude and phase error and the calibration source INR.

[0011] Furthermore, the specific process of step 1 is as follows:

[0012] Step 1-1: Two-dimensional FFT interference detection and angle estimation;

[0013] For a two-dimensional rectangular grid array, the received data vector x(k) is processed by a two-dimensional FFT to obtain...

[0014]

[0015] in, Let N be the output signal of the nth array element. x N y These represent the number of array elements along the x-axis and y-axis, respectively.

[0016] Two-dimensional FFT output channel (l x ,l y ) and angle (u) p ,v p The corresponding relationship is

[0017]

[0018] Where, d x ,dy λ represents the element spacing along the x-axis and y-axis, respectively, and λ is the wavelength.

[0019] Step 1-2: Calculate the interference detection threshold T;

[0020] First, calculate the noise power P. n

[0021]

[0022] Where I is the number of rows and columns in the two-dimensional FFT, I P I0 represents the number of larger peaks to be removed, and I0 represents the set of removal point locations.

[0023] Next, the false alarm probability P is calculated. F The constant false alarm rate detection threshold factor α

[0024]

[0025] Finally, based on P above n Calculate the interference detection threshold T with α

[0026] T = αP n

[0027] Steps 1-3: Estimate the number of interferences P and the interference angle. and INR p ,(p=1,…,P);

[0028] When d x ,d y Greater than λ / 2, due to periodic blurring, in the visible region (u 2 +v 2 Within the range ≤1), the number of all possible locations corresponding to a disturbance after 2D FFT processing is:

[0029]

[0030] in, This is the floor symbol;

[0031] Search for peak values ​​in the data after 2D FFT processing that exceed the interference detection threshold T, and determine the channel position (l) corresponding to the peak value. x ,l y Estimate the interference angle The estimation results are then grouped; the estimated interference angles within the same group differ by an integer multiple of the ambiguity interval, requiring only the selection of any one angle from that group. Finally, the number P of irrelevant interferences is determined.

[0032] According to the peak position corresponding to the interference (l) x ,l y), calculate interference power

[0033] P p =|F(l x ,l y ,k)| 2

[0034] The corresponding noise-to-interference ratio is INR. p =10log(P) p / P n ).

[0035] Furthermore, step 2 consists of two parallel computational sub-steps, specifically:

[0036] Step 2(a), when P = 1 and INR P When ≥ψ, calculate and update the amplitude and phase errors.

[0037] Calculate the covariance matrix of x(k) Iterative estimation using the power method Main eigenvectors The interference steering vector is: Where, x n ,y n Let n represent the positions of the array elements, where n = 1, 2, ..., N, and N is the number of array elements.

[0038] and The relationship is

[0039]

[0040] in, The calculated amplitude and phase error estimates are obtained by dividing the other array elements by the reference array element (the first array element).

[0041]

[0042] in,

[0043] Step 2(b): When P>0, calculate the interference subspace for compensating for amplitude and phase errors.

[0044] According to P, And constructing a disturbance subspace C = [C1, C2, ..., Cv] with null broadening widths Δu, Δv in the u and v directions. P ],in

[0045]

[0046] The amplitude and phase errors calculated using step 2(a) After correcting C, we get

[0047] Furthermore, step 3 specifically involves:

[0048] right The column vectors are subjected to Schmitt orthogonalization to obtain...

[0049] Interference orthogonal subspace projection matrix Z ⊥ for

[0050]

[0051] The spatial null anti-interference weights for null depth and width optimization control are calculated using an orthogonal projection algorithm.

[0052]

[0053] Where w0 is the static weight without zero traps.

[0054] Compared with the prior art, the significant advancements of this invention are: 1) It can dynamically estimate and compensate for channel amplitude and phase errors based on the desired pattern null depth and width, thereby achieving optimized control of the pattern anti-interference null depth and width; 2) It avoids matrix inversion operations in the interference subspace projection matrix through Schmitt orthogonalization calculation, reducing computational complexity; 3) It determines the amplitude and phase self-calibration threshold by determining the desired null depth, and adopts an interference detection and angle estimation algorithm based on two-dimensional FFT and an orthogonal projection algorithm based on Schmitt orthogonalization. It utilizes strong interference sources that meet the self-calibration threshold to perform parallel calculation of amplitude and phase error self-calibration, achieving the goal of updating amplitude and phase errors in real time while working.

[0055] To more clearly illustrate the functional characteristics and structural parameters of the present invention, further explanation is provided below in conjunction with the accompanying drawings and specific embodiments. Attached Figure Description

[0056] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0057] Figure 1 This is a flowchart of the algorithm implementation of the present invention;

[0058] Figure 2 This is a schematic diagram of the array surface distribution using a 64-element rectangular grid planar array;

[0059] Figure 3This is a schematic diagram illustrating the relationship between null depth and amplitude / phase error, and between amplitude / phase error and interference source INR, through simulation analysis in one embodiment of the present invention;

[0060] Figure 4 This is a schematic diagram of two-dimensional FFT interference detection and angle estimation results when there is a sidelobe interference at different array element spacings in one embodiment of the present invention.

[0061] Figure 5 This is a comparison chart of interference pattern nulls with and without amplitude and phase compensation, and of the output signal-to-interference-plus-noise ratio as a function of the number of snapshots, in one embodiment of the present invention. Detailed Implementation

[0062] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Combination Figure 1 A digital array pattern null optimization control method based on dynamic compensation of channel amplitude and phase error includes the following main steps:

[0064] Step 1: Perform two-dimensional FFT processing on the array received data vector x(k). Based on the relationship between the two-dimensional FFT output and the beamforming output, and the calculated interference detection threshold T, perform constant false alarm rate (CFAR) detection, peak detection, and angular ambiguity discrimination on the processed data to estimate the number of interferences P and the interference angle. and INR p ,(p=1,…,P);

[0065] Step 2(a), when P = 1 and INR P When x ≥ ψ, the covariance matrix of x(k) is estimated iteratively using the power method. Main eigenvectors use With interference directional vector Difference estimation amplitude and phase error It is a diagonal matrix.

[0066] Step 2(b), when P>0, i.e., there is effective interference, according to P, An interference subspace C with null broadening characteristics is constructed using the null broadening widths Δu and Δv in the u and v directions, and the amplitude and phase errors calculated in step 2(a) are used. After correcting C, we get:

[0067] Step 3, for Schmidt orthogonalization is performed, and the orthogonal projection algorithm is used to calculate the spatial null anti-interference weight w for the optimization control of null depth and null width.

[0068] The detailed processing steps for step 1 are as follows:

[0069] Step 1-1, Two-dimensional FFT interference detection and angle estimation;

[0070] For a two-dimensional rectangular grid planar array, (u p ,v p The beam output in the direction of ) is:

[0071]

[0072] in, Let N be the output signal of the nth array element. x N y d represents the number of array elements along the x-axis and y-axis, respectively. x ,d y λ represents the element spacing along the x-axis and y-axis, respectively, and λ is the wavelength.

[0073] Performing a two-dimensional FFT on x(k) yields:

[0074]

[0075] It can be seen that the beamforming output y(u) of x(k) p ,v p ,k) and the two-dimensional FFT output F(l) x ,l y There is a direct correspondence between k and k, that is:

[0076]

[0077] Therefore, we get:

[0078]

[0079] The two-dimensional FFT output is used for detection and decision-making, and the two-dimensional FFT output channel (l) given in the above formula is used. x ,l y ) and angle (u) p ,v p By establishing the correspondence, interference detection and angle estimation results are obtained.

[0080] Step 1-2: Calculate the interference detection threshold T;

[0081] First, calculate the noise power P. n :

[0082]

[0083] Where I is the number of rows and columns in the two-dimensional FFT, I P I0 represents the number of larger peaks to be removed, and I0 represents the set of removal point locations.

[0084] Next, the false alarm probability P is calculated. F The constant false alarm detection threshold factor α:

[0085]

[0086] Finally, based on P above n Interference detection threshold T is calculated with α:

[0087] T = αP n (7)

[0088] Steps 1-3: Estimate the number of interferences P and the interference angle. and INR p ,(p=1,…,P);

[0089] When d x ,d y Since the two-dimensional FFT is periodic, and the result is greater than λ / 2, the following holds true:

[0090] F(l x ,l y ,k)=F(l x ±r1N x ,l y ±r2N y ,k),(r1=0,1,2,…; r2=0,1,2,…) (8)

[0091] So, the two-dimensional FFT output channel (l) x ,l y ) and angle (u) p ,v p The correspondence becomes:

[0092]

[0093] There exists a situation where the same interference corresponds to multiple blur angles, where the row blur angle interval u dim And column fuzzy angle interval v dim They are respectively: In the visible area (u 2 +v 2 Within ≤1), the total number of possible locations corresponding to a disturbance is: in, This is the floor symbol.

[0094] Search for peak values ​​in the data after 2D FFT processing that exceed the interference detection threshold T, and determine the channel position (l) corresponding to the peak value. x ,l y Estimate the interference angle The estimation results are then grouped, with the angle estimates in the same group differing by an integer multiple of the ambiguity interval. Suppressing one angle will suppress all other ambiguity angles. Therefore, any interference estimation angle in each group is selected to finally determine the number of irrelevant interferences, P.

[0095] According to the peak position corresponding to the interference (l) x ,l y ), calculate the interference power:

[0096] P p =|F(l x ,l y ,k)| 2 (10)

[0097] The corresponding noise-to-interference ratio is: INR p =10log(P) p / P n ). Among them, calculate P n At that time, the number of points to be removed is I P Generally, a value slightly larger than the product of the maximum number of interferences and the number of blurred angles is selected.

[0098] Step 2 is divided into two parallel computational sub-steps, and the detailed processing steps are as follows:

[0099] First, assume that the amplitude and phase errors of the nth channel of the digital array are α and α, respectively. n and β n The array amplitude and phase error matrix is ​​expressed as: N is the number of array elements.

[0100] Step 2(a), when P = 1 and INR P When ≥ψ, calculate and update the amplitude and phase errors.

[0101] The covariance matrix of x(k) is estimated iteratively using the power method. Maximum eigenvector The iterative formula for the l-th iteration of the exponentiation method is:

[0102]

[0103] in, It means The largest element in the medium model.

[0104] First, randomly initialize the iteration vector. Set the iteration count l = 1; then, calculate respectively. make If V (l) Less than the selected threshold Terminate the iteration; otherwise, set l = l + 1 and continue iterating. Finally, obtain the principal eigenvalues ​​after L iterations. The corresponding main feature vector is

[0105] The interference steering vector is: Where, x n ,y n Let n be the position of the array element, n = 1, 2, ..., N.

[0106] and The relationship is: Right now:

[0107]

[0108] in, The normalized amplitude and phase error estimates of the other array elements divided by the reference array element (the first array element) are calculated as follows:

[0109]

[0110] Step 2(b): When P>0, calculate the interference subspace for compensating for amplitude and phase errors.

[0111] According to P, And constructing a disturbance subspace C = [C1, C2, ..., Cv] with null broadening widths Δu, Δv in the u and v directions. P ],in:

[0112]

[0113] The amplitude and phase errors calculated using step 2(a) After correcting C, we get:

[0114] Furthermore, step 3 specifically involves:

[0115] right column vector ε i After performing Schmidt orthogonalization, we obtain in, Each column vector γ i The calculation method for (i = 1, 2, ..., 5P) is as follows:

[0116]

[0117] at this time, Interference orthogonal subspace projection matrix Z ⊥ for:

[0118]

[0119] The spatial null anti-interference weights for null depth and width optimization control are calculated using an orthogonal projection algorithm.

[0120]

[0121] Where w0 is a static weight without nulls, such as the guiding vector a(u0,v0) of the beam pointing direction (u0,v0) or an amplitude-weighted low sidelobe weight.

[0122] Based on the above description, and in conjunction with Figure 1 The algorithm implementation flowchart of this invention, and the main implementation method of this invention, includes the following two parts:

[0123] 1. Interference detection and estimation:

[0124] A two-dimensional FFT is performed on the array received data vector x(k). Based on the relationship between the two-dimensional FFT output and the beamforming output, as well as the calculated interference detection threshold, fast interference detection is performed, and the number of interferences P and the interference angle are estimated. and INR p ,(p=1,…,P);

[0125] 2. Calculation of anti-interference weight:

[0126] When P = 1 and INR P When x ≥ ψ, the covariance matrix of x(k) is calculated by exponentiation. Main eigenvectors use With interference directional vector Difference estimation amplitude and phase error

[0127] When P > 0, i.e., there is effective interference, according to P, An interference subspace C with null broadening characteristics is constructed using the null broadening widths Δu and Δv in the u and v directions, and the amplitude and phase errors calculated in step 2(a) are used. After correcting C, we get

[0128] right Schmidt orthogonalization is performed, and the orthogonal projection algorithm is used to calculate the spatial null anti-interference weight w for the optimization control of null depth and null width.

[0129] Among them, the auxiliary step is the amplitude and phase self-calibration threshold calculation module: by analyzing the relationship between the expected null depth and the channel amplitude and phase error, and the channel amplitude and phase error and the calibration source INR, the INR threshold ψ for amplitude and phase error self-calibration is determined.

[0130] The present invention will now be described in detail with reference to specific embodiments.

[0131] Example

[0132] This invention proposes a method for effectively controlling the null depth and width of a radiation pattern by dynamically calibrating the channel amplitude and phase errors using a strong interference source that meets a self-calibration threshold, based on the desired null depth and width. See the flowchart for the method. Figure 1 .

[0133] This example uses an N=64 element matrix lattice planar array, with each element antenna being an isotropic omnidirectional antenna. Inter-element coupling is not considered. Figure 2 The rectangular grid array model used is given. Beam pointing. Interference comes from The INR is 10dB, and the noise is an additive white Gaussian noise vector; the noise and signal are independent of each other. There are amplitude and phase errors between channels, with an amplitude error α. n and phase error β n All follow a uniform distribution, α n =1+r n ,β n =s n ,in That is, α n and β n The root mean squares are respectively Chebyshev amplitude weighting of -35dB was used for low sidelobe processing. The null depth expected to be achieved in this example is -45dB, and the null broadening requirements Δu and Δv are both 0.0348, that is, the null broadening is about ±2°. And the analysis and verification of the two-dimensional FFT angle blur problem under different element spacings were carried out, including: (1) the element spacing is equal to the wavelength, that is, the element spacing in the x-axis and y-axis directions are respectively d x =λ,d y =λ; (2) The element spacing is equal to half the wavelength, that is, the element spacing in the x-axis and y-axis directions are d respectively. x =0.5λ,d y =0.5λ.

[0134] For a 64-element matrix grid planar array with two different element spacings, the implementation of a digital array pattern null optimization control method based on dynamic compensation for channel amplitude and phase errors includes the following processing steps:

[0135] First, through auxiliary steps, the relationship between channel amplitude and phase error and INR, as well as the relationship between null depth and amplitude and phase error, were simulated to determine that the amplitude and phase self-calibration INR threshold for satisfying the desired null depth of -45dB is: ψ = 5dB. The simulation results for determining the threshold are as follows: Figure 3 As shown.

[0136] Figure 3 Figure (a) in the figure simulates the root mean square (σ) of the amplitude and phase errors of four channels. α ,σ β The relationship between residual amplitude and phase error and INR under the conditions of (0.25dB, 2.5°), (0.5dB, 5°), (0.75dB, 7.5°), and (1.0dB, 10°) is shown. Figure 3 Figure (b) shows the relationship between null depth and residual amplitude and phase errors. Figure (b) indicates the range of amplitude and phase calibration residuals when the null depth is better than -45dB. For the array used in the simulation, the root mean square of the residual amplitude error cannot exceed 0.18dB, and the root mean square of the residual phase error cannot exceed 8°. Furthermore, combining the simulation results in Figure (a), it is found that when the channel residual amplitude and phase errors meet the above requirements, the self-calibration INR threshold ψ = 5dB.

[0137] Step 1: Set the false alarm probability of interference detection to P. F =10 -4 The number of rows and columns in the two-dimensional FFT is 128, and the threshold factor α = 9.21 is calculated using equation (6). Based on the two-dimensional FFT interference detection and angle estimation algorithm, the number of fuzzy angles for interference angle estimation under the two array element spacing conditions is calculated respectively.

[0138] (1) Calculate the visible area (u) under the condition of large array element spacing. 2 +v 2 ≤1) Number of internal fuzzy angles Therefore, an interference will be detected at two angles within the visible area, with intervals of: u dim =λ / d x =1,v dim =λ / d y =1. After angular fuzzy discrimination, it can be determined that there is only one interference, P=1.

[0139] (2) When the array element spacing is small, the number of blurred angles in the visible area is 0, so there is no angle blur problem, P=1.

[0140] Figure 4Figures (a) and (b) in the figure simulate the two-dimensional FFT interference detection and angle estimation results under the conditions of large element spacing and normal element spacing, respectively. The area enclosed by the red dashed line is the visible area. The simulation results show that in the first case, when the element spacing is greater than λ / 2, angular blurring occurs within the visible area. Selecting any angle from this group of blurred angles, for example... Right now The interference power is calculated using equation (10) and the interference-to-noise ratio (INR) is obtained. P ≈10dB; In the second case, the element spacing is equal to λ / 2, there is no angular ambiguity in the visible area, and the interference estimation result is... Right now INR P ≈10dB.

[0141] Step 2(a): P = 1, INR P ≥5dB, calculate amplitude and phase error

[0142] Set the number of snapshots to 256, and calculate the covariance matrix of x(k). Select the threshold for terminating the iteration of the power method Estimate Main eigenvectors and interference directional vector The relationship is:

[0143] The simulation sets the initial amplitude and phase errors σα = 1 dB, σβ = 10°, and INR = 10 dB. The amplitude and phase error matrix is ​​estimated according to equation (13). Table 1 shows a comparison between the actual amplitude and phase errors of the set channels and the amplitude and phase errors estimated by the algorithm.

[0144] Table 1 Comparison of Actual Amplitude and Phase Error and Estimated Amplitude and Phase Error of Channels

[0145]

[0146]

[0147] As can be seen from the data analysis in Table 1, the amplitude and phase error estimated by the amplitude and phase calibration algorithm is basically close to the true amplitude and phase error.

[0148] Step 2(b), when P>0, calculate the interference subspace for compensating for amplitude and phase errors.

[0149] The element spacing is set to λ / 2. An interference subspace C with zero-depression broadening characteristics is constructed using equation (14). The two-dimensional FFT angle estimation results are obtained from step one. P = 1. The widths of the zero-trap broadening Δu and Δv are both 0.0348, then C1 in equation (14) is expressed as:

[0150] C1=[a(0.5625,0),a(0.5625,0-0.0348),a(0.5625,0+0.0348),a(0.5625+0.0348,0),a(0.5625-0.0348,0)]

[0151] use After correcting C1, we get:

[0152] Step 3: Use formula (15) to... Schmidt orthogonalization was performed to obtain The interference orthogonal subspace projection matrix is: The spatial null anti-interference weights for null depth and width optimization control are calculated using an orthogonal projection algorithm.

[0153]

[0154] Where w0 is the -35dB Chebyshev amplitude-weighted low sidelobe weight. Table 2 gives the specific values ​​of w.

[0155] Table 2 Spatial Null Anti-interference Weights w Coefficient

[0156]

[0157]

[0158] Figure 5 Figures (a) and (b) simulate the interference null pattern with and without amplitude and phase compensation, and compare the output signal-to-interference-plus-noise ratio (SNR) with the number of snapshots. It can be seen that the null depth of the pattern after amplitude and phase compensation is better than -45dB, a broadened null of approximately ±2° is formed near the interference, and the output SNR is greater than that without amplitude and phase compensation.

[0159] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0160] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital array pattern null optimization control method based on dynamic compensation for channel amplitude and phase errors, characterized in that, Includes the following steps: Step 1: Receive data vector from the array Two-dimensional FFT processing is performed. Based on the relationship between the two-dimensional FFT output and the beamforming output, as well as the calculated interference detection threshold T, fast interference detection is performed, and the number of interferences P and the interference angle are estimated. and INR p ,(p=1,…,P); Step 2(a), when P=1 and INR P When ≥ѱ, calculate by exponentiation. covariance matrix Main eigenvectors ,use With interference directional vector Difference estimation amplitude and phase error ; Step 2(b): When P>0, i.e., there is effective interference, according to P, Null width in the u and v directions Construct an interference subspace C with zero-depression broadening characteristics, and utilize the amplitude and phase errors calculated in step 2(a). After correcting C, we get ; Step 3, for Schmidt orthogonalization is performed, and the orthogonal projection algorithm is used to calculate the spatial null anti-interference weight w for the optimization control of null depth and null width.

2. The digital array pattern null optimization control method based on dynamic compensation of channel amplitude and phase error according to claim 1, characterized in that, The specific process of step 1 is as follows: Step 1-1: Two-dimensional FFT interference detection and angle estimation; For a two-dimensional rectangular grid planar array, the array receives data vectors. Two-dimensional FFT processing is performed to obtain in, This is the output signal of the nth array element. These represent the number of array elements along the x-axis and y-axis, respectively. 2D FFT output channel and angle The correspondence is in, These represent the element spacing along the x-axis and y-axis, respectively. Wavelength; Step 1-2: Calculate the interference detection threshold ; First, calculate the noise power. Where I is the number of rows and columns in the two-dimensional FFT, I P I0 represents the number of larger peaks to be removed, and I0 represents the set of removal point locations. Next, the false alarm probability is calculated as follows: Constant false alarm detection threshold factor Finally, based on the above and Calculate the interference detection threshold Steps 1-3: Estimate the number of interferences P and the interference angle. and INR p ,(p=1,…,P); when Greater than Due to periodic blurring, in the visible area Within, after 2D FFT processing, the number of all possible locations corresponding to a disturbance is: in," " is the floor symbol; Search for data exceeding the interference detection threshold after 2D FFT processing. The peak value, based on the channel position corresponding to the peak value. Estimate the interference angle The estimation results are then grouped; the estimated interference angles within the same group differ by an integer multiple of the fuzzy distance, so only one angle from the group of interference fuzzy angles needs to be selected; finally, the number of irrelevant interferences P is determined. Based on the peak position corresponding to the interference Calculate interference power The corresponding noise-to-interference ratio is .

3. The digital array pattern null optimization control method based on dynamic compensation for channel amplitude and phase error as described in claim 1, characterized in that, Step 2 consists of two parallel computational sub-steps, specifically: Step 2(a), when P=1 and INR P When ≥ѱ, calculate and update the amplitude and phase errors. ; calculate covariance matrix Iterative estimation using the power method Main eigenvectors The interference directional vector is ,in, For the position of the array element, N is the number of array elements. , ; and The relationship is in, , The normalized amplitude and phase error estimates obtained by dividing other array elements by the reference array element (i.e., the first array element) are calculated. in, ; Step 2(b): When P>0, calculate the interference subspace for compensating for amplitude and phase errors. ; According to P, and the width of the null depression in the u and v directions Construct a disturbance subspace with zero-depression broadening properties. ,in The amplitude and phase errors calculated using step 2(a) After correcting C, we get .

4. The digital array pattern null optimization control method based on dynamic compensation for channel amplitude and phase error according to claim 1, characterized in that, Step 3 specifically involves: right The column vectors are subjected to Schmitt orthogonalization to obtain... ; Interference orthogonal subspace projection matrix for The spatial null anti-interference weights for null depth and width optimization control are calculated using an orthogonal projection algorithm. in, These are static weights without zero traps.

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