An anti-interference antenna nulling and widening method assisted by inertial navigation information

Abstract

This application discloses an inertial navigation system (INS)-assisted null widening method for anti-interference antennas, comprising the following steps: S1, constructing a high-dynamic array signal receiving model and calculating the received signal covariance matrix R; S2, calculating the antenna attitude information within the weight update period based on INS measurement information and a quaternion EKF filter attitude estimation algorithm, and obtaining the optimal null spread angle Δθ through elevation and yaw angles; S3, based on the null spread angle Δθ, assuming that the disturbance in the direction of arrival of the interference signal follows a quadratic distribution, generating a spatiotemporal conicification matrix T; S4, calculating the conicated covariance moment based on the received signal covariance matrix R and the spatiotemporal conicification matrix T, and obtaining the optimal weights; S5, weighting the optimal weights with the array received signal to obtain the anti-interference processed output signal. This application utilizes INS-assisted solution to the optimal null spread angle within the weight update time of the anti-interference algorithm, which can effectively improve the robustness of the anti-interference algorithm.

Description

Technical Field

[0001] This application relates to the field of array signal processing, and in particular, to a method, apparatus, and device for null widening of an anti-interference antenna assisted by inertial navigation information. Background Technology

[0002] With the development of electronic warfare and the complex propagation environment, navigation signals are susceptible to interference. To address this issue, array antenna anti-jamming technology has become an important research area in navigation technology. However, in highly dynamic weapon platforms such as airborne and missile-borne systems, the carrier's attitude changes rapidly. Adaptive anti-jamming algorithms use batch processing to calculate weights, making it difficult to track the direction of interference in the next moment even if the direction of interference from the previous moment is calculated. When the weights obtained from the previous moment's data are applied to the data from the next moment, the angle of the interference has shifted, and the generated null trap cannot be aligned with the interference signal, causing traditional anti-jamming algorithms to fail.

[0003] Null widening algorithms address this problem by expanding the null area of ​​the beammap, ensuring that rapidly moving interference does not move out of the null and guaranteeing robustness against interference. Current research on null widening algorithms mainly includes three types: differential constraint methods, Interference-plus-Noise Covariance (INC) matrix reconstruction algorithms, and Covariance Matrix Taper (CMT) algorithms. Differential constraint methods require prior information about the direction of the interference, and increasing constraints reduces spatial degrees of freedom. INC matrix reconstruction algorithms increase computational cost through spatial spectrum search and cannot flexibly control the null width. CMT algorithms do not require determining the direction of the interference signal, have lower computational cost, and are simpler to implement, making them the preferred method for null widening algorithm research.

[0004] While null widening algorithms can effectively widen nulls, research on the null spread angle is based on prior environmental knowledge. An excessively large angle can negatively impact satellite navigation signal reception, while an excessively small angle can hinder interference suppression. In recent years, thanks to the significant improvement in attitude estimation accuracy of Inertial Navigation Systems (INS), their integration with satellite navigation has received widespread attention. For highly dynamic antenna carriers, the antenna attitude change information output by the INS can be combined with weighted calculations to provide data support for the null spread angle in null widening algorithms, ensuring interference suppression. Therefore, further research is needed to improve the performance of null widening algorithms under highly dynamic conditions through the fusion of INS and satellite navigation information. Summary of the Invention

[0005] This application provides an inertial navigation information-assisted null widening method for anti-interference antennas to solve the technical problem that conventional null widening anti-interference algorithms cannot accurately suppress interference under high dynamic conditions because it is difficult to determine the optimal null width.

[0006] The technical solution adopted in this application is as follows:

[0007] A method for null widening an anti-jamming antenna with inertial navigation information assistance, comprising the following steps:

[0008] S1. Construct a high dynamic array signal receiving model. The model assumes that there is a uniform linear array with M array elements and N delay units after each channel. Establish a space-time filtering structure and P far-field broadband signals, including the desired signal and the interference signal. The interference signal is set as dynamic interference. Calculate the covariance matrix R of the received signal.

[0009] S2. Calculate the antenna attitude information within the weight update period based on INS measurement information and quaternion EKF filter attitude estimation algorithm, and obtain the optimal null spread angle Δθ through pitch angle and yaw angle;

[0010] S3. Based on the null extension angle Δθ, assuming that the disturbance in the direction of the incoming interference signal follows a quadratic distribution, generate the spatiotemporal conic matrix T.

[0011] S4. Calculate the tapered covariance moment based on the received signal covariance matrix R and the spatiotemporal tapering matrix T. Find the optimal weights;

[0012] S5. The optimal weight is weighted together with the array received signal to obtain the anti-interference processed output signal.

[0013] Furthermore, in step S1:

[0014] Assuming the signal bandwidth is B, n m (t) represents the thermal noise of the m-th array element channel. Therefore, the received data of the m-th array element is:

[0015]

[0016] The observation time is divided into K segments, and then the observation data of each segment is subjected to a J-point discrete Fourier transform, X k (f j ), S k (f j ), N k (f j Let F(B) represent the discrete Fourier transforms of the received data, signal, and noise at a given frequency, respectively. Let J represent dividing the signal with bandwidth B into J subbands, resulting in the following broadband signal model:

[0017] X k (f j )=A(f j )S k (f j )+N k (f j k = 1, 2, ..., K, j = 1, 2, ..., J

[0018] A(f j )=[a1(θ1,f j )a2(θ2,f j ...a P (θ P ,f j )]

[0019] The received signal covariance matrix is ​​then expressed as:

[0020]

[0021] In the formula, R S (f j ) is the frequency point f j Upper signal covariance matrix, For frequency point f j The noise power on.

[0022] Furthermore, step S2 specifically includes the following steps:

[0023] S21. Determine the state and observations of the EKF filter;

[0024] S22. Use accelerometers and magnetometers to measure attitude and heading, and initialize the EKF filter state.

[0025] S23. Update the state of the EKF filter according to the state update formula established by the gyroscope output;

[0026] S24. Use the difference between the accelerometer and magnetometer measurements and their respective true values ​​(i.e., the local gravitational field and magnetic field) as observations to update the state and state covariance.

[0027] S25. Based on the relationship between the updated attitude matrix and the quaternion, obtain the pitch angle, yaw angle, and roll angle. From the attitude information, obtain the optimal null spread angle as Δθ.

[0028] Further, step S21 specifically includes the following steps:

[0029] S211. Use the attitude quaternion and gyroscope output error as the EKF filter state:

[0030]

[0031] In the formula, q nb For attitude quaternions; Δx g This refers to the gyroscope output error.

[0032] S212. The difference between the accelerometer and magnetometer measurements and the projections of the local gravitational field and magnetic field intensity onto the carrier coordinate system is taken as the observation. The accelerometer measurement is:

[0033]

[0034] In the formula, For the carrier acceleration; g b Let ε be the projection of gravitational acceleration onto the loaded system. a For accelerometer bias, v a It is Gaussian white noise;

[0035] The S213 and EKF observations Z are:

[0036]

[0037] In the formula, the gravitational field in the navigation system is: G = [0, 0, -g] T The carrier system can be represented as in The coordinate transformation matrix is ​​given by y, where y is the magnetic field measurement value in the navigation system. m In the geography system, the magnetic field strength is M = [0, m]. n ,m u ] T The carrier system can be represented as

[0038] Further, step S22 specifically includes the following steps:

[0039] S221. Acquiring accelerometer data under steady-state conditions. a Magnetometer data y m Calculate the acceleration, average magnetic field strength, and pitch angle θ = arcsinf under steady-state conditions. y , Roll angle γ = atan2(-f y sprt(f y +f z )), heading angle ψ=π-atan2(Mag y Mag x ),in:

[0040] Mag x =m y cosθ+m x sinγsinθ-m z cosγsinθ

[0041] Mag y =m x cosγ+m z sinγ

[0042] S222. Calculate the attitude matrix from the attitude and heading angle:

[0043]

[0044] S223. Calculate the quaternion q = [q0, q1, q2, q3] from the attitude matrix, where:

[0045]

[0046] S224. From the quaternion expression, we know that it is a function of Euler angles. Calculate the Jacobian matrix from Euler angles to quaternions, with an initial covariance value P(0), and its dimension is 7×7:

[0047]

[0048] In the formula, These represent the variances of the accelerometer, magnetometer, and gyroscope, respectively; T is the filtering period of the EKF filter.

[0049] Furthermore, step S23 specifically includes the following steps:

[0050] S231. The state update formula established based on the gyroscope output:

[0051]

[0052]

[0053] In the formula, These are the updated state and the state variance, respectively. These are the gyroscope measurements, where... X represents the measured value of the gyroscope in the system. g This is the gyroscope output deviation. For X g Variance is Gaussian white noise; v g The random offset error is represented by Gaussian white noise; the process noise matrix U is as follows:

[0054]

[0055] S232. Taking the partial derivative with respect to the 7-dimensional state vector, we obtain a 7×7 matrix J(f,x):

[0056]

[0057] S233. Taking the partial derivative with respect to the gyroscope process noise, we obtain the 7×6 matrix J(f,U):

[0058]

[0059] Furthermore, step S24 specifically includes the following steps:

[0060] S241, Calculate the measurement gain K k :

[0061]

[0062]

[0063] In the formula, R k For the measurement variance matrix; H k For measurement equations; The state variance after state update; measurement matrix H k Its components are defined as follows:

[0064]

[0065] In the formula, m n m d These represent the northward and geomagnetic field strengths, respectively; g is the gravitational acceleration. The transformation matrix from navigation system to carrier system:

[0066]

[0067] h a with h m Taking partial derivatives with respect to the state variables, the results are as follows:

[0068]

[0069]

[0070] S242. Update the state measurement using the following formula:

[0071]

[0072] In the formula, The measured state is shown below; K is the gain matrix; Z is the observed quantity. The state before the state update;

[0073] S243. Perform state variance update:

[0074]

[0075] In the formula, This is to measure the updated state variance.

[0076] Further, step S25 specifically includes the following steps:

[0077] S251. Obtain the pitch angle, yaw angle, and roll angle of the antenna attitude before and after the weight update:

[0078]

[0079]

[0080]

[0081] S252. Assuming the antenna's vertical direction is a unit vector, let the change in elevation angle before and after the weight update be Δθ. k =θ k1 -θ k2 The change in heading angle is Δγ k =γ k1 -γ k2 Then the optimal null expansion angle Δθ is:

[0082]

[0083] Furthermore, step S3 specifically includes the following steps:

[0084] S31. Let the incident angle of the interference be θ. I The maximum change in the incident angle is the null spread angle Δθ. Considering a two-point distribution, the disturbance to the left of the incident angle is pΔθ, and the disturbance to the right is qΔθ, with p+q=1. Since the magnitude of the interference power only affects the depth of the null, we can assume that the power of the two disturbances is equal. Therefore, the steering vectors formed by the two disturbances under the space-time adaptive processing algorithm are as follows:

[0085]

[0086]

[0087] In the formula, The null extension angle Δθ changes little during the convergence time of the anti-interference weights. According to the first-order Taylor expansion of the function, we have:

[0088]

[0089]

[0090] The incident angle of the interference is unknown. Considering the maximum angular diffusion, we take |cosθ here. I |=1, let |=1, but

[0091]

[0092]

[0093] In the formula And there are:

[0094]

[0095]

[0096] Due to BB H =I MN×MN and DD H =I MN×MN The covariance matrix formed by the disturbance is expressed as

[0097]

[0098]

[0099] In the formula X + (t) represents the value from θ I +pΔθ incident interference, while [X-(t) represents the interference from θ I The interference from the incident -qΔθ is calculated by taking the arithmetic mean of the two matrices, resulting in the covariance matrix:

[0100]

[0101] Since matrices B and D do not have incident angle information, the obtained perturbation covariance matrix is ​​the arithmetic operation of the received signal covariance matrix. The probability of perturbation Δθ is p, and the probability of taking -Δθ is q = 1 - p. The perturbation center is selected in the direction of the incident angle, at which point p = 0.5, and B = D.

[0102] Since both B and D are diagonal matrices with exponential functions as eigenvalues, let r be an element in the received signal covariance matrix R. gh After BRB H The operation is as follows:

[0103]

[0104] After D H The elements after the RD operation are:

[0105]

[0106] Where int() is the integer function that rounds to zero;

[0107] S32, after the above The matrix obtained by the operation is the element-wise operation of the received signal covariance matrix. The spatial angular taper matrix T is defined. S The elements therein are:

[0108]

[0109] The space-time coneification matrix T is obtained as follows:

[0110]

[0111] In the formula, 1 N×N It is an N×N dimensional matrix of all 1s.

[0112] Furthermore, step S4 specifically includes the following steps:

[0113] S41. The covariance matrix after tapering Solve the problem:

[0114]

[0115] In the formula, ⊙ represents the Hadamard product;

[0116] S42, will Substituting into the formula for the linearly constrained minimum variance algorithm, we obtain the weighting coefficients of the array. Then, we calculate the weight vector of the zero-trap broadening algorithm based on the space-time structure as the optimal weight:

[0117] w opt =(R⊙T) -1 C(C H ((R⊙T) -1 C) -1 )b.

[0118] This application also provides an inertial navigation information-assisted anti-interference antenna null widening device, comprising:

[0119] The modeling and covariance matrix calculation module is used to construct a high dynamic array signal receiving model. The model assumes that there is a uniform linear array with M elements, and the number of delay units after each channel is N. A space-time filtering structure is established, and there are P far-field broadband signals, including the desired signal and the interference signal. The interference signal is set as dynamic interference. The covariance matrix R of the received signal is calculated.

[0120] The optimal null spread angle calculation module is used to calculate the antenna attitude information within the weight update period based on the attitude estimation algorithm of the quaternion EKF filter according to the INS measurement information, and obtain the optimal null spread angle Δθ by the elevation angle and yaw angle.

[0121] The spatiotemporal conic matrix calculation module is used to generate the spatiotemporal conic matrix T based on the null extension angle Δθ and assuming that the disturbance in the direction of the incoming interference signal follows a quadratic distribution.

[0122] The optimal weight calculation module is used to calculate the tapered covariance moments based on the received signal covariance matrix R and the spatiotemporal tapering matrix T. Find the optimal weights;

[0123] The weighted processing module is used to weight the optimal weight with the array received signal to obtain the anti-interference processed output signal.

[0124] Another preferred embodiment of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the inertial navigation information-assisted anti-interference antenna null widening method.

[0125] Another preferred embodiment of this application also provides a storage medium including a stored program that, when the program is executed, controls the device where the storage medium is located to perform the steps of the inertial navigation information-assisted anti-interference antenna null widening method.

[0126] Compared with the prior art, this application has the following advantages:

[0127] This application provides an inertial navigation information-assisted null widening method for anti-interference antennas. This method uses an INS and satellite navigation fusion approach to calculate the carrier attitude information and derive the optimal expansion angle within the convergence time of the anti-interference algorithm. This improves the anti-interference performance under high dynamic conditions and overcomes the problem of inaccurate interference suppression under high dynamic conditions. By using INS assistance, the optimal expansion angle of the interference null within the weight update period can be accurately obtained. Based on this, a statistical model is established for the interference direction distribution, and a tapered matrix of the covariance matrix is ​​obtained to achieve accurate null widening. Thus, a null with a suitable width can be formed in the direction of interference, adapting to the environment of rapid interference movement, making it more effective for high dynamic scenarios, ensuring that interference under high dynamic conditions can be accurately suppressed, improving the robustness and anti-interference performance of the system, with low computational load and simple engineering implementation.

[0128] In addition to the purposes, features, and advantages described above, this application provides other purposes, features, and advantages. The application will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0129] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0130] Figure 1 This is a schematic flowchart of the inertial navigation information-assisted anti-interference antenna null widening method according to a preferred embodiment of this application.

[0131] Figure 2 This is a block diagram of the attitude estimation algorithm according to a preferred embodiment of this application.

[0132] Figure 3 This is a schematic diagram of the existing high dynamic zero-depression widening effect.

[0133] Figure 4 This is a schematic diagram of the high dynamic range zero-depression widening effect based on INS assistance.

[0134] Figure 5 This is a schematic diagram of the inertial navigation information-assisted anti-interference antenna null widening device module according to a preferred embodiment of this application.

[0135] Figure 6 This is a schematic block diagram of an electronic device according to a preferred embodiment of this application.

[0136] Figure 7 This is an internal structural diagram of a computer device according to a preferred embodiment of this application. Detailed Implementation

[0137] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0138] Reference Figure 1 A preferred embodiment of this application provides a method for null widening of an anti-interference antenna assisted by inertial navigation information, comprising the following steps:

[0139] S1. Construct a high dynamic array signal receiving model. The model assumes that there is a uniform linear array with M array elements and N delay units after each channel. Establish a space-time filtering structure and P far-field broadband signals, including the desired signal and the interference signal. The interference signal is set as dynamic interference. Calculate the covariance matrix R of the received signal.

[0140] S2. Calculate the antenna attitude information within the weight update period based on INS measurement information and quaternion EKF filter attitude estimation algorithm, and obtain the optimal null spread angle Δθ by using pitch angle and yaw angle;

[0141] S3. Based on the null extension angle Δθ, assuming that the disturbance in the direction of the incoming interference signal follows a quadratic distribution, generate the spatiotemporal conic matrix T.

[0142] S4. Calculate the tapered covariance moment based on the received signal covariance matrix R and the spatiotemporal tapering matrix T. Find the optimal weights;

[0143] S5. The optimal weight is weighted together with the array received signal to obtain the anti-interference processed output signal.

[0144] This embodiment provides an inertial navigation information-assisted null widening method for anti-interference antennas. This method employs an INS and satellite navigation fusion approach to calculate carrier attitude information and derive the optimal extension angle within the convergence time of the anti-interference algorithm. This improves anti-interference performance under high dynamic conditions and overcomes the problem of inaccurate interference suppression under such conditions. INS assistance accurately obtains the optimal extension angle of the interference null within the weight update cycle, thereby establishing a statistical model of the interference direction distribution and obtaining a tapered matrix of the covariance matrix to achieve accurate null widening. This allows for the formation of nulls of suitable width in the direction of interference, adapting to environments with rapid interference movement. This makes the method more effective in high-dynamic scenarios, ensuring accurate suppression of interference under high dynamic conditions. The system's robustness and anti-interference performance are improved, with relatively low computational complexity and simple engineering implementation.

[0145] Preferably, in step S1:

[0146] Assuming the signal bandwidth is B, n m (t) represents the thermal noise of the m-th array element channel. Therefore, the received data of the m-th array element is:

[0147]

[0148] The observation time is divided into K segments, and then the observation data of each segment is subjected to a J-point discrete Fourier transform, X k (f j ), S k (f j ), N k (f j Let F(B) represent the discrete Fourier transforms of the received data, signal, and noise at a given frequency, respectively. Let J represent dividing the signal with bandwidth B into J subbands, resulting in the following broadband signal model:

[0149] X k (f j )=A(f j )S k (f j )+N k (f j ) k=1,2,...,K,j=1,2,...,J (2)

[0150] A(f j )=[a1(θ1,f j a2(θ2,f) j ...a P (θ P ,f j (3)

[0151] The received signal covariance matrix is ​​then expressed as:

[0152]

[0153] In the formula, R S (f j ) is the frequency point f j Upper signal covariance matrix, For frequency point f j The noise power on.

[0154] Preferably, step S2 specifically includes the following steps (see the principle block diagram). Figure 2 ):

[0155] S21. Determine the state and observations of the EKF filter;

[0156] S22. Use accelerometers and magnetometers to measure attitude and heading, and initialize the EKF filter state.

[0157] S23. Update the state of the EKF filter according to the state update formula established by the gyroscope output;

[0158] S24. Use the difference between the accelerometer and magnetometer measurements and their respective true values ​​as observations to update the state and state covariance.

[0159] S25. Based on the relationship between the updated attitude matrix and the quaternion, obtain the pitch angle, yaw angle, and roll angle. From the attitude information, obtain the optimal null spread angle as Δθ.

[0160] Preferably, step S21 specifically includes the following steps:

[0161] S211. Use the attitude quaternion and gyroscope output error as the EKF filter state:

[0162]

[0163] In the formula, q nb For attitude quaternions; Δx g This refers to the gyroscope output error.

[0164] S212. The difference between the accelerometer and magnetometer measurements and the projections of the local gravitational field and magnetic field intensity onto the carrier coordinate system is taken as the observation. The accelerometer measurement is:

[0165]

[0166] In the formula, For the carrier acceleration; g b Let ε be the projection of gravitational acceleration onto the loaded system. a For accelerometer bias, v a It is Gaussian white noise;

[0167] The S213 and EKF observations Z are:

[0168]

[0169] In the formula, the gravitational field in the navigation system is: G = [0, 0, -g] T The carrier system can be represented as in The coordinate transformation matrix is ​​given by y, where y is the magnetic field measurement value in the navigation system. m In the geography system, the magnetic field strength is M = [0, m]. n ,m u ] T The carrier system can be represented as

[0170] Preferably, step S22 specifically includes the following steps:

[0171] S221. Acquiring accelerometer data under steady-state conditions. a Magnetometer data y m Calculate the acceleration, average magnetic field strength, and pitch angle θ = arcsinf under steady-state conditions. y Roll angle γ = atan2(-f y sprt(f y +f z )), heading angle ψ=π-atan2(Mag y Mag x ),in:

[0172]

[0173] S222. Calculate the attitude matrix from the attitude and heading angle:

[0174]

[0175] S223. Calculate the quaternion q = [q0, q1, q2, q3] from the attitude matrix, where:

[0176]

[0177] S224. From the quaternion expression, we know that it is a function of Euler angles. Calculate the Jacobian matrix from Euler angles to quaternions, with an initial covariance value P(0), and its dimension is 7×7:

[0178]

[0179] In the formula, These represent the variances of the accelerometer, magnetometer, and gyroscope, respectively; T is the filtering period of the EKF filter.

[0180] Preferably, step S23 specifically includes the following steps:

[0181] S231. The state update formula established based on the gyroscope output:

[0182]

[0183]

[0184] In the formula, These are the updated state and the state variance, respectively. These are the gyroscope measurements, where... X represents the measured value of the gyroscope in the system. g This is the gyroscope output deviation. For X g Variance is Gaussian white noise; v g The random offset error is represented by Gaussian white noise; the process noise matrix U is as follows:

[0185]

[0186] S232. Taking the partial derivative with respect to the 7-dimensional state vector, we obtain a 7×7 matrix J(f,x):

[0187]

[0188] S233. Taking the partial derivative with respect to the gyroscope process noise, we obtain the 7×6 matrix J(f,U):

[0189]

[0190] Preferably, step S24 specifically includes the following steps:

[0191] S241, Calculate the measurement gain K k :

[0192]

[0193] In the formula, R k H is the measurement variance matrix; k The measurement equation; The state variance after state update; measurement matrix H k Its components are defined as follows:

[0194]

[0195] In the formula, m n m d These represent the northward and geomagnetic field strengths, respectively; g is the gravitational acceleration. The transformation matrix from navigation system to carrier system:

[0196]

[0197] h a with h m Taking partial derivatives with respect to the state variables, the results are as follows:

[0198]

[0199]

[0200] S242. Update the state measurement using the following formula:

[0201]

[0202] In the formula, The measured state is shown below; K is the gain matrix; Z is the observed quantity. The state before the state update;

[0203] S243. Perform state variance update:

[0204]

[0205] In the formula, This is to measure the updated state variance.

[0206] Preferably, step S25 specifically includes the following steps:

[0207] S251. Obtain the pitch angle, yaw angle, and roll angle of the antenna attitude before and after the weight update:

[0208]

[0209] S252. Since the weight training time is typically on the order of μs, according to the definition of high dynamics, the incident angle of the interference is small. Therefore, the attitude change angle can be obtained through inertial navigation as the null spread angle, thus obtaining the optimal null width. High dynamic platforms are usually airborne or missile-borne; the attitude change null spread angle during the weight calculation time only needs to consider the pitch and yaw angles. That is, assuming the antenna's vertical direction is a unit vector, let the pitch angle change Δθ before and after the weight update... k =θ k1 -θ k2 The change in heading angle is Δγ k =γ k1 -γ k2 Then the optimal null expansion angle Δθ is:

[0210]

[0211] Preferably, step S3 specifically includes the following steps:

[0212] S31. Let the incident angle of the interference be θ. I The maximum change in the incident angle is the null spread angle Δθ. Considering a two-point distribution, the disturbance to the left of the incident angle is pΔθ, and the disturbance to the right is qΔθ, with p+q=1. Since the magnitude of the interference power only affects the depth of the null, we can assume that the power of the two disturbances is equal. Therefore, the steering vectors formed by the two disturbances under the space-time adaptive processing algorithm are as follows:

[0213]

[0214]

[0215] In the formula, The null extension angle Δθ changes little during the convergence time of the anti-interference weights. According to the first-order Taylor expansion of the function, we have:

[0216]

[0217] The incident angle of the interference is unknown. Considering the maximum angular diffusion, we take |cosθ here. I |=1, let |=1, but

[0218]

[0219]

[0220] In the formula And there are:

[0221]

[0222] Due to BB H =I MN×MN and DD H =I MN×MN The covariance matrix formed by the disturbance is expressed as

[0223]

[0224] In the formula X + (t) represents the value from θ I +pΔθ incident interference, while [X-(t) represents the interference from θ I The interference from the incident -qΔθ is calculated by taking the arithmetic mean of the two matrices, resulting in the covariance matrix:

[0225]

[0226] Since matrices B and D do not have incident angle information, the obtained perturbation covariance matrix is ​​the arithmetic operation of the received signal covariance matrix. The probability of perturbation Δθ is p, and the probability of taking -Δθ is q = 1 - p. The perturbation center is selected in the direction of the incident angle, at which point p = 0.5, and B = D.

[0227] Since both B and D are diagonal matrices with exponential functions as eigenvalues, let r be an element in the received signal covariance matrix R. gh After BRB H The operation is as follows:

[0228]

[0229] After D H The elements after the RD operation are:

[0230]

[0231] Where int() is the integer function that rounds to zero;

[0232] S32, after the above The matrix obtained by the operation is the element-wise operation of the received signal covariance matrix. The spatial angular taper matrix T is defined. S The elements therein are:

[0233]

[0234] The space-time coneification matrix T is obtained as follows:

[0235]

[0236] In the formula, 1 N×N It is an N×N dimensional matrix of all 1s.

[0237] Preferably, step S4 specifically includes the following steps:

[0238] S41. Solve for the tapered covariance matrix R:

[0239]

[0240] In the formula, ⊙ represents the Hadamard product;

[0241] S42, will Substituting into the formula for the linearly constrained minimum variance algorithm, we obtain the weighting coefficients of the array. Then, we calculate the weight vector of the zero-trap broadening algorithm based on the space-time structure as the optimal weight:

[0242] w opt =(R⊙T) -1 C(C H ((R⊙T) -1C) -1 )b (38).

[0243] Because the covariance matrix R is processed by angular expansion, the algorithm can adapt to environments with rapidly moving interference and can form nulls of appropriate width in the direction of the interference.

[0244] Simulation analysis is performed on the zero-dip broadening anti-interference algorithm proposed in this application. For example... Figure 3 and Figure 4 As shown, the interference null widening effect in the interference direction is significant, which improves the matching degree between the weight and the dynamic interference and can meet the interference suppression requirements under high dynamic conditions.

[0245] Because this application uses the received data covariance matrix in its processing and does not exclude the influence of noise, the obtained data is relatively reasonable. Furthermore, in actual engineering hardware implementations, the angle extension matrix only requires calling the multiplier to perform corresponding multiplications, without significantly increasing the computational load. Therefore, this method is suitable for engineering applications.

[0246] This application utilizes INS-assisted solution to find the optimal expansion angle of the zero trap within the weight update time of the anti-interference algorithm, which can effectively improve the robustness of the anti-interference algorithm and can be applied to high-dynamic anti-interference terminal products such as missile-borne and airborne systems.

[0247] like Figure 5 As shown, another preferred embodiment of this application also provides an inertial navigation information-assisted anti-interference antenna null widening device, comprising:

[0248] The modeling and covariance matrix calculation module is used to construct a high dynamic array signal receiving model. The model assumes that there is a uniform linear array with M elements, and the number of delay units after each channel is N. A space-time filtering structure is established, and there are P far-field broadband signals, including the desired signal and the interference signal. The interference signal is set as dynamic interference. The covariance matrix R of the received signal is calculated.

[0249] The optimal null spread angle calculation module is used to calculate the antenna attitude information within the weight update period based on the attitude estimation algorithm of the quaternion EKF filter according to the INS measurement information, and obtain the optimal null spread angle Δθ by the elevation angle and yaw angle.

[0250] The spatiotemporal conic matrix calculation module is used to generate the spatiotemporal conic matrix T based on the null extension angle Δθ and assuming that the disturbance in the direction of the incoming interference signal follows a quadratic distribution.

[0251] The optimal weight calculation module is used to calculate the tapered covariance moments based on the received signal covariance matrix R and the spatiotemporal tapering matrix T. Find the optimal weights;

[0252] The weighted processing module is used to weight the optimal weight with the array received signal to obtain the anti-interference processed output signal.

[0253] like Figure 6 As shown, a preferred embodiment of this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the inertial navigation information-assisted anti-interference antenna null widening method in the above embodiments.

[0254] like Figure 7 As shown, a preferred embodiment of this application also provides a computer device, which may be a terminal or a liveness detection server, and its internal structure diagram may be as follows. Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, it implements the steps of the aforementioned inertial navigation information-assisted anti-interference antenna null widening method.

[0255] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0256] A preferred embodiment of this application also provides a storage medium, the storage medium including a stored program, which, when the program is executed, controls the device where the storage medium is located to perform the steps of the inertial navigation information-assisted anti-interference antenna null widening method in the above embodiments.

[0257] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0258] If the functions described in this embodiment are implemented as software functional units and sold or used as independent products, they can be stored in one or more computing device-readable storage media. Based on this understanding, the parts of this application's embodiments that contribute to the prior art or the technical solutions can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computing device (which may be a personal computer, server, mobile computing device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0259] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0260] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0261] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0262] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0263] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0264] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for null widening of an anti-interference antenna assisted by inertial navigation information, characterized in that, Including the following steps: S1. Construct a high dynamic array signal receiving model. The model assumes that there is a uniform linear array with M array elements and N delay units after each channel. Establish a space-time filtering structure and P far-field broadband signals, including the desired signal and the interference signal. The interference signal is set as dynamic interference. Calculate the covariance matrix R of the received signal. S2. Calculate the antenna attitude information within the weight update period based on INS measurement information and quaternion EKF filter attitude estimation algorithm, and obtain the optimal null spread angle Δθ by using pitch angle and yaw angle; S3. Based on the null extension angle Δθ, assuming that the disturbance in the direction of the incoming interference signal follows a quadratic distribution, generate the spatiotemporal conic matrix T. S4. Calculate the tapered covariance moment based on the received signal covariance matrix R and the spatiotemporal tapering matrix T. Find the optimal weights; S5. Weight the optimal weights with the array received signals to obtain the anti-interference processed output signal. Step S2 specifically includes the following steps: S21. Determine the state and observations of the EKF filter; S22. Use accelerometers and magnetometers to measure attitude and heading, and initialize the EKF filter state. S23. Update the state of the EKF filter according to the state update formula established by the gyroscope output; S24. Use the difference between the accelerometer and magnetometer measurements and their respective true values ​​as observations to update the state and state covariance. S25. Based on the relationship between the updated attitude matrix and the quaternion, the pitch angle, yaw angle and roll angle are obtained. From the attitude information, the optimal null spread angle is obtained as Δθ. Step S25 specifically includes the following steps: S251. Obtain the pitch angle, yaw angle, and roll angle of the antenna attitude before and after the weight update: S252. Assuming the antenna's vertical direction is a unit vector, let the change in elevation angle before and after the weight update be Δθ. k =θ k1 -θ k2 The change in heading angle is Δγ k =γ k1 -γ k2 Then the optimal null expansion angle Δθ is:

2. The inertial navigation information-assisted anti-interference antenna null widening method according to claim 1, characterized in that, In step S1: Assuming the signal bandwidth is B, n m (t) represents the thermal noise of the m-th array element channel. Therefore, the received data of the m-th array element is: The observation time is divided into K segments, and then the observation data of each segment is subjected to a J-point discrete Fourier transform, X k (f j ), S k (f j ), N k (f j Let F(B) represent the discrete Fourier transforms of the received data, signal, and noise at a given frequency, respectively. Let J represent dividing the signal with bandwidth B into J subbands, resulting in the following broadband signal model: X k (f j )=A(f j )S k (f j )+N k (f j )k=1,2,...,K,j=1,2,...,J A(f j )=[a1(θ1,f j )a2(θ2,f j )...a P (θ P ,f j )] The received signal covariance matrix is ​​then expressed as: In the formula, R S (f j ) is the frequency point f j Upper signal covariance matrix, For frequency point f j The noise power on.

3. The inertial navigation information-assisted anti-interference antenna null widening method according to claim 1, characterized in that, Step S21 specifically includes the following steps: S211. Use the attitude quaternion and gyroscope output error as the EKF filter state: In the formula, q nb For attitude quaternions; Δx g This refers to the gyroscope output error. S212. The difference between the accelerometer and magnetometer measurements and the projections of the local gravitational field and magnetic field intensity onto the carrier coordinate system is taken as the observation. The accelerometer measurement is: In the formula, For the carrier acceleration; g b Let ε be the projection of gravitational acceleration onto the loaded system. a For accelerometer bias, v a It is Gaussian white noise; The S213 and EKF observations Z are: In the formula, the gravitational field in the navigation system is: G = [0, 0, -g] T The carrier system can be represented as in The coordinate transformation matrix is ​​given by y, where y is the magnetic field measurement value in the navigation system. m In the geography system, the magnetic field strength is M = [0, m]. n ,m u ] T The carrier system can be represented as 4. The inertial navigation information-assisted anti-interference antenna null widening method according to claim 1, characterized in that, Step S22 specifically includes the following steps: S221. Acquiring accelerometer data under steady-state conditions. a Magnetometer data y m Calculate the acceleration, average magnetic field strength, and pitch angle θ = arcsinf under steady-state conditions. y , Roll angle γ = atan2(-f y sprt(f y +f z )), heading angle ψ=π-atan2(Mag y Mag x ),in: Mag x =m y cosθ+m x sinγsinθ-m z cosγsinθ Mag y =m x cosγ+m z sinγ; S222. Calculate the attitude matrix from the attitude and heading angle: S223. Calculate the quaternion q = [q0, q1, q2, q3] from the attitude matrix, where: S224. From the quaternion expression, we know that it is a function of Euler angles. Calculate the Jacobian matrix from Euler angles to quaternions, with an initial covariance value P(0), and its dimension is 7×7: In the formula, These represent the variances of the accelerometer, magnetometer, and gyroscope, respectively; T is the filtering period of the EKF filter.

5. The inertial navigation information-assisted anti-interference antenna null widening method according to claim 1, characterized in that, Step S23 specifically includes the following steps: S231. The state update formula established based on the gyroscope output: In the formula, These are the updated state and the state variance, respectively. These are the gyroscope measurements, where... X represents the measured value of the gyroscope in the system. g This is the gyroscope output deviation. For X g Variance is Gaussian white noise; v g The random offset error is represented by Gaussian white noise; the process noise matrix U is as follows: S232. Taking the partial derivative with respect to the 7-dimensional state vector, we obtain a 7×7 matrix J(f,x): S233. Taking the partial derivative with respect to the gyroscope process noise, we obtain the 7×6 matrix J(f,U):

6. The inertial navigation information-assisted anti-interference antenna null widening method according to claim 1, characterized in that, Step S24 specifically includes the following steps: S241, Calculate the measurement gain K k : In the formula, R k For the measurement variance matrix; H k For measurement equations; The state variance after state update; measurement matrix H k Its components are defined as follows: In the formula, m n m d These represent the northward and geomagnetic field intensities, respectively. g is the acceleration due to gravity. The transformation matrix from navigation system to carrier system: h a With h m Taking partial derivatives with respect to the state variables, the results are as follows: S242. Update the state measurement using the following formula: In the formula, The measured state is shown below; K is the gain matrix; Z is the observed quantity. The state before the state update; S243. Perform state variance update: In the formula, This is to measure the updated state variance.

7. The inertial navigation information-assisted anti-interference antenna null widening method according to claim 1, characterized in that, Step S3 specifically includes the following steps: S31. Let the incident angle of the interference be θ. I The maximum change in the incident angle is the null spread angle Δθ. Considering a two-point distribution, the disturbance to the left of the incident angle is pΔθ, and the disturbance to the right is qΔθ, with p+q=1. Since the magnitude of the interference power only affects the depth of the null, we can assume that the power of the two disturbances is equal. Therefore, the steering vectors formed by the two disturbances under the space-time adaptive processing algorithm are as follows: In the formula, The null extension angle Δθ changes little during the convergence time of the anti-interference weights. According to the first-order Taylor expansion of the function, we have: The incident angle of the interference is unknown. Considering the maximum angular diffusion, we take |cosθ here. I |=1, let |=1, but In the formula And there are: Due to BB H =I MN×MN and DD H =I MN×MN The covariance matrix formed by the disturbance is expressed as In the formula X + (t) represents the value from θ I +pΔθ incident interference, while [X - (t) represents the value from θ I The interference from the incident -qΔθ is calculated by taking the arithmetic mean of the two matrices, resulting in the covariance matrix: Since matrices B and D do not have incident angle information, the obtained perturbation covariance matrix is ​​the arithmetic operation of the received signal covariance matrix. The probability of perturbation Δθ is p, and the probability of taking -Δθ is q = 1 - p. The perturbation center is selected in the direction of the incident angle, at which point p = 0.5, and B = D. Since both B and D are diagonal matrices with exponential functions as eigenvalues, let r be an element in the received signal covariance matrix R. gh After BRB H The operation is as follows: After D H The elements after the RD operation are: Where int() is the integer function that rounds to zero; S32, after the above The matrix obtained by the operation is the element-wise operation of the received signal covariance matrix. The spatial angular taper matrix T is defined. S The elements therein are: The space-time coneification matrix T is obtained as follows: In the formula, 1 N×N It is an N×N dimensional matrix of all 1s.

8. The inertial navigation information-assisted anti-interference antenna null widening method according to claim 1, characterized in that, Step S4 specifically includes the following steps: S41. The covariance matrix after tapering Solve the problem: In the formula, ⊙ represents the Hadamard product; S42, will Substituting into the formula for the linearly constrained minimum variance algorithm, we obtain the weighting coefficients of the array. Then, we calculate the weight vector of the zero-trap broadening algorithm based on the space-time structure as the optimal weight: w opt =(R⊙T) -1 C(C H ((R⊙T) -1 C) -1 )b。

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