A method for spatial spectrum direction finding of satellite navigation interference sources based on correlation interferometry correction

Abstract

The present invention discloses a satellite navigation interference source spatial spectrum direction finding method based on correlation interference correction, comprising the following steps: step 1, after the antenna array receives the signals of each co-frequency interference source, the direction finding angle of each interference source is obtained; step 2, the direction finding angle of the interference source with the largest signal power among the interference sources is obtained; step 3, the difference between the co-direction angle and other spatial spectrum direction finding angles is calculated; step 4, adaptive Kalman filtering is performed; step 5, the direction finding angle of the co-direction multiple interference sources after correction is calculated, and the interference sources are checked and eliminated according to the direction finding angle. The method disclosed by the present invention solves the problem that the correlation interferometer cannot perform direction finding for multiple interference sources, and the spatial spectrum algorithm cannot achieve high-precision direction finding for multiple co-frequency interference sources.

Description

Technical Field

[0001] The invention belongs to the field of satellite navigation direction finding, and in particular relates to a satellite navigation interference source space spectrum direction finding method based on correlation interference correction in this field. Background Art

[0002] With the rapid development of satellite navigation interference and anti-interference technology, the electromagnetic environment of satellite navigation is becoming increasingly complex. Electromagnetic interference and radio communication frequency band interference events against GNSS are frequent, posing a serious threat to key industries such as national defense, transportation, civil aviation, and the majority of satellite navigation terminal users. How to achieve high-precision direction finding for one or more interference sources has become the focus of many scholars.

[0003] The correlation interferometer algorithm is one of the most commonly used direction finding methods. Its direction finding principle is to calculate the actual phase difference of the interference signal when receiving the interference source signal, and compare it with the phase difference of the sample library to determine the direction of the incoming wave. The correlation interferometer algorithm has a simple principle, high direction finding accuracy, and strong timeliness. Moreover, since only the phase difference is used for direction finding, it has good direction finding results for both broadband and narrowband interference sources. However, when there are multiple navigation interference sources, the correlation interferometer can only obtain the direction finding result of the interference source with the largest signal power, and cannot perform direction finding for multiple interference sources at the same time.

[0004] The spatial spectrum MUSIC algorithm uses the orthogonal characteristics of the signal subspace and the noise subspace to achieve direction finding of multiple co-frequency interference sources. However, as the number of interference sources increases, the direction finding error of the spatial spectrum algorithm increases significantly. In the actual direction finding and positioning experiment, when there are three satellite navigation interference sources, the direction finding result error of the spatial spectrum is within the range of 10° to 20°, and the direction finding accuracy is low, which greatly hinders the positioning and troubleshooting of the interference source. Summary of the invention

[0005] In order to overcome the problem that the existing direction finding technology cannot achieve high-precision direction finding for multiple co-frequency interference sources, the present invention provides a satellite navigation interference source spatial spectrum direction finding method based on correlation interferometry correction, which can effectively reduce the direction finding error of multiple co-frequency interference sources.

[0006] The present invention adopts the following technical solution:

[0007] A method for spatial spectrum direction finding of satellite navigation interference sources based on correlation interferometry correction is improved in that it comprises the following steps:

[0008] Step 1: After the antenna array receives the signals of each co-frequency interference source, it uses the spatial spectrum MUSIC algorithm to perform direction finding to obtain the direction finding angle of each interference source;

[0009] Step 2, using a correlation interferometer to perform direction finding, and obtain the direction finding angle of the interference source with the largest signal power among the interference sources;

[0010] Step 3, subtract the direction-finding angle of the correlation interferometer from the direction-finding angle of the spatial spectrum, define the spatial spectrum direction-finding angle with the smallest difference as the same-direction angle of the direction-finding angle of the correlation interferometer, and calculate the difference between the same-direction angle and other spatial spectrum direction-finding angles;

[0011] Step 4, using the error value between the correlation interferometer direction finding angle and the spatial spectrum direction finding angle as the state vector, and using the double difference vector between the correlation interferometer and the spatial spectrum direction finding angle as the observation vector, to perform adaptive Kalman filtering;

[0012] Step 5: Subtract the direction-finding angle of the relevant interferometer from the error vector output by the filter, calculate the corrected direction-finding angle of the same-frequency multi-interference source, and then troubleshoot and eliminate the interference source based on the direction-finding angle.

[0013] Further, in step 3, the direction finding angle θ of the relevant interferometer is c Respectively with the spatial spectrum direction finding angles θ1, θ2, …, θ n Difference, we get the difference vector Δθ=[Δθ 1c ,Δθ 2c ,…,Δθ nc ] T , if the minimum value in the difference vector Δθ is Δθ ck , then define the spatial spectrum direction finding angle θ k is the direction-finding angle θ of the correlation interferometer c The same direction angle, θ k Respectively with the spatial spectrum direction finding angles θ1, θ2, …, θ n Make a difference and get the difference vector of the spatial spectrum direction finding angle

[0014] Furthermore, step 4 specifically includes the following steps:

[0015] Step 41: use the difference X between the direction finding angle of the correlation interferometer and the direction finding angle of the spatial spectrum i =[δθ i1 ,δθ i2 ,…,δθ in ] T As the state vector at time i, the predicted value of the state vector at time i is obtained by the state vector at time i-1 through the state propagation formula:

[0016]

[0017] In the above formula, is the predicted value of the state vector from time i-1 to time i, Φ is the state transfer matrix, X i-1 is the estimated value of the state vector at time i-1;

[0018] Step 42, the state prediction value of the error covariance matrix at time i is obtained by using the covariance propagation formula from the error covariance matrix at time i-1:

[0019]

[0020] In the above formula, P i-1 is the error covariance matrix at time i-1, is the predicted value of the state vector The error covariance matrix of , Q is the process noise variance matrix;

[0021] Step 43, predict the value based on the state vector and the error covariance matrix Calculate the Kalman gain:

[0022]

[0023] In the above formula, K i is the Kalman gain at time i, H is the observation matrix, and R is the observation noise variance matrix;

[0024] Step 44: The difference vector Δθ between the correlation interferometer and the spatial spectrum direction finding angle is calculated as follows: 1c ,Δθ 2c ,…,Δθ nc ] T The difference vector between the spatial spectrum direction finding angle Make a difference and get the observation vector at time i:

[0025]

[0026] Step 45, update the state vector with the observation vector at time i:

[0027]

[0028] In the above formula, X i is the state estimate after the system measurement is updated, that is, the updated state vector at time i;

[0029] Step 46: transform the state vector X at time i into i With the preset threshold V 阈 Compare, if X i ≤V 阈 , indicating that the direction-finding angle error between the correlation interferometer and the spatial spectrum is within the normal range. i >V 阈 , indicating that the difference Δθ between the spatial spectrum direction-finding angle and the correlation interferometer direction-finding angle ckis greater than the difference between the actual direction-finding angle and the direction-finding angle of the relevant interferometer, and the adaptive adjustment factor α i Reduce the Kalman gain K i :

[0030] K i =α i K i

[0031]

[0032] Return to step 45 and re-update the state vector X i , until X is satisfied i ≤V 阈 ;

[0033] Step 47, update the error covariance matrix:

[0034]

[0035] In the above formula, P i is the error covariance matrix after system measurement update, and I is the unit matrix.

[0036] Further, in step 46, when there are n interference sources in the space, the threshold V 阈 Greater than or equal to the maximum direction finding error of the spatial spectrum.

[0037] The beneficial effects of the present invention are:

[0038] The method disclosed in the present invention solves the problem that the correlation interferometer cannot perform direction finding for multiple interference sources, and the spatial spectrum algorithm cannot achieve high-precision direction finding for multiple interference sources at the same frequency. The method of the present invention uses the difference between the correlation interferometer and the spatial spectrum direction finding angle as the state vector, and uses the difference vector between the correlation interferometer and the spatial spectrum direction finding angle and the double difference vector of the difference vector between the spatial spectrum direction finding angle as the observation quantity to perform adaptive Kalman filtering to correct the direction finding error. When the observation vector deviation is large and an abnormal value appears in the state quantity, the Kalman gain can be adjusted using an adaptive adjustment factor to reduce the weight of the observation quantity and reduce the influence of the abnormal observation quantity on the direction finding error. The corrected direction finding angle is calculated based on the filtered direction finding error, and the error is effectively reduced on the basis of obtaining the direction finding angle of multiple interference sources at the same frequency, the direction finding accuracy is improved, and high-precision direction finding for multiple interference sources at the same frequency is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic flow diagram of the method of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] Embodiment 1, as Figure 1 As shown, this embodiment discloses a satellite navigation interference source spatial spectrum direction finding method based on correlation interferometry correction, which obtains the direction finding results of multiple co-frequency interference sources through a spatial spectrum algorithm, and uses the error value between the correlation interferometer and the spatial spectrum direction finding result as a state vector to perform adaptive Kalman filtering, which can effectively reduce the error and improve the direction finding accuracy on the basis of obtaining the co-frequency multi-interference source direction finding results. Specifically, it includes the following steps:

[0042] Step 1: After the antenna array receives the signals of each co-frequency interference source, the spatial spectrum MUSIC algorithm is used to perform direction finding to obtain the direction finding angle of each interference source; when there are n (n ≥ 1) co-frequency interference sources, the antenna array receives n co-frequency interference source signals, and the n groups of direction finding angles obtained by the MUSIC algorithm are θ1, θ2, …, θ n ;

[0043] Step 2, using a correlation interferometer to perform direction finding, and obtain the direction finding angle of the interference source with the largest signal power among the interference sources;

[0044] Step 3, subtract the direction-finding angle of the correlation interferometer from the direction-finding angle of the spatial spectrum, define the spatial spectrum direction-finding angle with the smallest difference as the same-direction angle of the direction-finding angle of the correlation interferometer, and calculate the difference between the same-direction angle and other spatial spectrum direction-finding angles;

[0045] The direction-finding angle θ of the relevant interferometer c Respectively with the spatial spectrum direction finding angles θ1, θ2, …, θ n Difference, we get the difference vector Δθ=[Δθ 1c ,Δθ 2c ,…,Δθ nc ] T , if the minimum value in the difference vector Δθ is Δθ ck , then define the spatial spectrum direction finding angle θ k is the direction-finding angle θ of the correlation interferometer c The same direction angle, θ k Respectively with the spatial spectrum direction finding angles θ1, θ2, …, θ n Make a difference and get the difference vector of the spatial spectrum direction finding angle

[0046] Step 4, using the error value between the correlation interferometer direction finding angle and the spatial spectrum direction finding angle as the state vector, and using the double difference vector between the correlation interferometer and the spatial spectrum direction finding angle as the observation vector, to perform adaptive Kalman filtering;

[0047] Step 4 specifically includes the following steps:

[0048] Step 41: use the difference X between the direction finding angle of the correlation interferometer and the direction finding angle of the spatial spectrum i =[δθ i1 ,δθ i2 ,…,δθ in ] T As the state vector at time i, the predicted value of the state vector at time i is obtained by the state vector at time i-1 through the state propagation formula:

[0049]

[0050] In the above formula, is the predicted value of the state vector from time i-1 to time i, Φ is the state transfer matrix, X i-1 is the estimated value of the state vector at time i-1;

[0051] Step 42, the state prediction value of the error covariance matrix at time i is obtained by using the covariance propagation formula from the error covariance matrix at time i-1:

[0052]

[0053] In the above formula, P i-1 is the error covariance matrix at time i-1, is the predicted value of the state vector The error covariance matrix of , Q is the process noise variance matrix;

[0054] Step 43, predict the value based on the state vector and the error covariance matrix Calculate the Kalman gain:

[0055]

[0056] In the above formula, K i is the Kalman gain at time i, H is the observation matrix, and R is the observation noise variance matrix;

[0057] Step 44: The difference vector Δθ between the correlation interferometer and the spatial spectrum direction finding angle is calculated as follows: 1c ,Δθ 2c ,…,Δθ nc ] T The difference vector between the spatial spectrum direction finding angle Make a difference and get the observation vector at time i:

[0058]

[0059] Step 45, update the state vector with the observation vector at time i:

[0060]

[0061] In the above formula, X i is the state estimate after the system measurement is updated, that is, the updated state vector at time i;

[0062] Step 46: transform the state vector X at time i into i With the preset threshold V 阈 For comparison, the threshold V 阈 According to the empirical setting, when there are n interference sources in the space, the threshold V 阈 Greater than or equal to the maximum direction finding error of the spatial spectrum.

[0063] If X i ≤V 阈 , indicating that the direction-finding angle error between the correlation interferometer and the spatial spectrum is within the normal range, and the error covariance matrix can be updated; if X i >V 阈 , indicating that the difference Δθ between the spatial spectrum direction-finding angle and the correlation interferometer direction-finding angle ck If the value is greater than the difference between the actual direction-finding angle and the direction-finding angle of the related interferometer, it indicates that the observed vector has a large abnormal value at this moment, the deviation is large, the confidence is low, and the Kalman gain needs to be reduced. i Reduce the Kalman gain K i , reduce the weight of the observations:

[0064] K i =α i K i

[0065]

[0066] Return to step 45 and re-update the state vector X i , until X is satisfied i ≤V 阈 , the next step of error covariance update can be carried out;

[0067] Step 47, update the error covariance matrix:

[0068]

[0069] In the above formula, P i is the error covariance matrix after system measurement update, and I is the unit matrix.

[0070] Step 5: Subtract the direction-finding angle of the relevant interferometer from the error vector output by the filter, calculate the corrected direction-finding angle of the same-frequency multi-interference source, and then troubleshoot and eliminate the interference source based on the direction-finding angle.

[0071] In order to verify the effectiveness of the method of the present invention, three interference sources were set up in the test field with fixed positions (actual azimuths of 0°, 120° and 270°) to transmit signals with the same center frequency. A detection vehicle equipped with satellite navigation interference source direction-finding equipment was used to monitor the spectrum information of the interference signal. After the interference source was found, identification and direction-finding of multiple interference sources with the same frequency were performed.

[0072] The spatial spectrum direction finding method is used to obtain three sets of direction finding angles θ1, θ2, and θ3, and the correlation interferometer direction finding method is used to obtain the direction finding angle θ c . c Differences are made with θ1, θ2, θ respectively to obtain the difference vector Δθ=[Δθ 1c ,Δθ 2c ,Δθ 3c ] T The minimum value is Δθ c1 , then θ1 in the spatial spectrum direction finding angle is the direction finding angle θ of the related interferometer c The difference vector of the spatial spectrum direction finding angle can be obtained by subtracting θ1 from θ1, θ2, and θ3.

[0073] The difference vector Δθ between the correlation interferometer and the spatial spectrum direction finding angle is Δθ = [Δθ 1c ,Δθ 2c ,Δθ 3c ] T The difference vector between the spatial spectrum direction finding angle Difference, get the double difference vector:

[0074]

[0075] The difference between the direction-finding angle of the correlation interferometer and the direction-finding angle of the spatial spectrum is X = [δθ1, δθ2, δθ3] T As the state vector, the double difference vector Z is used as the observation vector for adaptive Kalman filtering.

[0076] The estimated state value at each moment is compared with the preset threshold V 阈 For comparison, the threshold V 阈 Based on experience, this is set to 20.

[0077] If the estimated value is less than or equal to V 阈 , indicating that the direction-finding angle error between the correlation interferometer and the spatial spectrum is within the normal range, and the error covariance matrix can be updated; if the estimated value is greater than V 阈 , indicating the difference Δθ between the spatial spectrum isotropic angle and the correlation interferometer direction angle ckIf the value is greater than the difference between the actual direction-finding angle and the direction-finding angle of the related interferometer, it indicates that the observed vector has a large abnormal value at this moment, the deviation is large, the confidence is low, and the Kalman gain value needs to be reduced. i Changing the Kalman gain adjusts the weights of the prediction and observation.

[0078] K i =α i K i

[0079]

[0080] When the estimated value is greater than V 阈 When Δθ ck Significantly increased, the observed vector deviation is large, the confidence is low, according to the adjustment factor α i You can reduce the Kalman gain and reduce the weight of the observation. Then re-update the state vector X i , until the estimated value is less than or equal to V 阈 , the next step of error covariance update can be carried out.

[0081] The direction-finding angle of the correlation interferometer is subtracted from the error vector output by the filter to calculate the corrected direction-finding angle of multiple interference sources at the same frequency. The test results are shown in the following table.

[0082]

[0083] The test results show that before using the method of the present invention, the average direction finding error of the three groups of interference sources was 10.05°. After using the method of the present invention to improve, the average direction finding error of the three groups of interference sources was 5.11°. The test results show that the method of the present invention can effectively reduce the direction finding error and improve the direction finding accuracy based on the direction finding of multiple interference sources at the same frequency.

Claims

1. A method for spatial spectrum direction finding of satellite navigation interference sources based on correlation interferometry correction, characterized in that: The steps include: Step 1: After the antenna array receives the signals of each co-frequency interference source, it uses the spatial spectrum MUSIC algorithm to perform direction finding to obtain the direction finding angle of each interference source; Step 2, using a correlation interferometer to perform direction finding, and obtain the direction finding angle of the interference source with the largest signal power among the interference sources; Step 3, subtract the direction-finding angle of the correlation interferometer from the direction-finding angle of the spatial spectrum, define the spatial spectrum direction-finding angle with the smallest difference as the same-direction angle of the direction-finding angle of the correlation interferometer, and calculate the difference between the same-direction angle and other spatial spectrum direction-finding angles; Step 4, using the error value between the correlation interferometer direction finding angle and the spatial spectrum direction finding angle as the state vector, and using the double difference vector between the correlation interferometer and the spatial spectrum direction finding angle as the observation vector, to perform adaptive Kalman filtering; Step 5: Subtract the direction-finding angle of the relevant interferometer from the error vector output by the filter, calculate the corrected direction-finding angle of the same-frequency multi-interference source, and then troubleshoot and eliminate the interference source based on the direction-finding angle.

2. The method for spatial spectrum direction finding of satellite navigation interference sources based on correlation interferometry correction according to claim 1, characterized in that: In step 3, the direction-finding angle θ of the relevant interferometer is c Respectively with the spatial spectrum direction finding angles θ1, θ2, …, θ n Difference, we get the difference vector Δθ=[Δθ 1c ,Δθ 2c ,…,Δθ nc ] T , if the minimum value in the difference vector Δθ is Δθ ck , then define the spatial spectrum direction finding angle θ k is the direction-finding angle θ of the correlation interferometer c The same direction angle, θ k Respectively with the spatial spectrum direction finding angles θ1, θ2, …, θ n Make a difference and get the difference vector of the spatial spectrum direction finding angle 3. The method for spatial spectrum direction finding of satellite navigation interference sources based on correlation interferometry correction according to claim 1, characterized in that: Step 4 specifically includes the following steps: Step 41: use the difference X between the direction finding angle of the correlation interferometer and the direction finding angle of the spatial spectrum i =[δθ i1 ,δθ i2 ,…,δθ in ] T As the state vector at time i, the predicted value of the state vector at time i is obtained by the state vector at time i-1 through the state propagation formula: In the above formula, is the predicted value of the state vector from time i-1 to time i, Φ is the state transfer matrix, X i-1 is the estimated value of the state vector at time i-1; Step 42, the state prediction value of the error covariance matrix at time i is obtained by using the covariance propagation formula from the error covariance matrix at time i-1: In the above formula, P i-1 is the error covariance matrix at time i-1, is the predicted value of the state vector The error covariance matrix of , Q is the process noise variance matrix; Step 43, predict the value based on the state vector and the error covariance matrix Calculate the Kalman gain: In the above formula, K i is the Kalman gain at time i, H is the observation matrix, and R is the observation noise variance matrix; Step 44: The difference vector Δθ between the correlation interferometer and the spatial spectrum direction finding angle is calculated as follows: 1c ,Δθ 2c ,…,Δθ nc ] T The difference vector between the spatial spectrum direction finding angle Make a difference and get the observation vector at time i: Step 45, update the state vector with the observation vector at time i: In the above formula, X i is the state estimate after the system measurement is updated, that is, the updated state vector at time i; Step 46: transform the state vector X at time i into i With the preset threshold V 阈 For comparison, if X i ≤V 阈 , indicating that the direction-finding angle error between the correlation interferometer and the spatial spectrum is within the normal range. i >V 阈 , indicating that the difference Δθ between the spatial spectrum direction-finding angle and the correlation interferometer direction-finding angle ck is greater than the difference between the actual direction-finding angle and the direction-finding angle of the relevant interferometer, and the adaptive adjustment factor α i Reduce the Kalman gain K i : K i =a i K i Return to step 45 and re-update the state vector X i , until X is satisfied i ≤V 阈 ; Step 47, update the error covariance matrix: In the above formula, P i is the error covariance matrix after system measurement update, and I is the unit matrix.

4. The method for spatial spectrum direction finding of satellite navigation interference sources based on correlation interferometry correction according to claim 3, characterized in that: In step 46, when there are n interference sources in the space, the threshold V 阈 Greater than or equal to the maximum direction finding error of the spatial spectrum.

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