Method for suppressing interference compatible with fast and slow time-varying suppression interference of high-precision anti-interference navigation

Abstract

The application discloses a high-precision anti-interference navigation fast and slow time-varying suppression interference compatible suppression method, relates to the field of high-precision anti-interference satellite navigation, and comprises the following steps: adopting a space-frequency joint-multiple beam / zero noise subspace projection method on a current data block received by an array antenna; obtaining a projection matrix of an interference subspace and a noise subspace based on multi-stage Wiener filtering forward decomposition to generate a fast response weight vector; obtaining a difference vector length based on the fast response weight vector and a previous applicable weight vector; judging whether the difference vector length exceeds a preset threshold value; obtaining a new applicable weight vector according to a judgment result; performing time alignment and weighted summation on the new applicable weight vector and the current data block; updating the previous applicable weight vector into the new applicable weight vector, and returning to process a next data block; and through deep fusion of the space-frequency joint-multiple beam / zero noise subspace projection method and a method of increasing the number of covariance matrix estimation snapshots, fast and slow time-varying suppression interference can be adaptively and real-timely suppressed.

Description

Technical Field

[0001] This invention relates to the field of high-precision anti-jamming satellite navigation technology, and in particular to a high-precision anti-jamming navigation method for suppressing interference that is compatible with time-varying speed. Background Technology

[0002] The key characteristics of high-precision anti-jamming satellite navigation applications, such as precision approach and landing of aircraft, carrier-based aircraft landing, formation flying and automatic aerial refueling, and rapid direction finding and autonomous north-seeking for missile launchers / aircraft, are complex interference environments and high positioning and orientation requirements. The complexity of the interference environment is mainly manifested in the variety and variability of interference types. From the perspective of interference mechanisms, interference types are classified into suppression interference, deception interference, and a combination of suppression and deception interference. Deception interference is further subdivided into regenerative and repeater types. Interference variations mainly include rapid or slow changes in the number, direction of arrival, modulation, frequency, and intensity of interference over time. Positioning and orientation requirements include providing real-time or near-real-time positioning services at the decimeter / centimeter level and orientation services better than 1 degree / 0.5 degree.

[0003] High-precision positioning and orientation in complex interference environments requires solving three strongly coupled challenges: complex interference detection and real-time suppression, satellite signal distortion tracing and control, and high-precision positioning and orientation under low distortion conditions. One key aspect of complex interference detection and real-time suppression is the compatible suppression of fast and slow time-varying interference. Fast time-varying interference refers to interference whose intensity, direction of attack, number, frequency band, and type change rapidly over time, while slow time-varying interference refers to interference whose intensity, direction of attack, number, frequency band, and type change slowly over time. To suppress fast time-varying interference in real time, the array antenna's weight vector must respond quickly to the rapid changes in interference, meaning the weight vector changes with the interference. To suppress slow time-varying interference in real time, the array antenna's weight vector must respond stably to the slow changes in interference, meaning the weight vector jitter should be minimized. Real-time suppression methods for fast time-varying interference include the space-frequency joint-multibeam / null-noise subspace projection method. Real-time suppression methods for slow time-varying interference mainly include array antenna weight vector smoothing filtering and increasing the covariance matrix to estimate the number of snapshots. In a suppression interference environment where fast time-varying suppression interference and slow time-varying suppression interference relay each other, the above-mentioned real-time suppression methods for fast time-varying suppression interference and slow time-varying suppression interference respectively are ineffective.

[0004] Therefore, a high-precision anti-interference navigation method with time-varying speed suppression and interference compatibility is provided to solve the above problems. Summary of the Invention

[0005] To address the aforementioned challenges, this invention provides a high-precision anti-interference navigation method that is compatible with suppressing interference of varying speeds and times. By deeply integrating the space-frequency joint-multibeam / zeroing-noise subspace projection method with the method of increasing the covariance matrix to estimate the number of snapshots, it can adaptively and in real-time suppress interference of varying speeds and times.

[0006] To achieve the above objectives, this invention provides a high-precision anti-interference navigation method for suppressing interference compatible with time-varying speeds, comprising: S1: Obtain the data blocks received by the array antenna, the pre-application weight vector, the constraint vector, and the covariance matrix; S2: For the data blocks received by the array antenna, the space-frequency joint-multibeam / zeroing-noise subspace projection method is adopted. Based on the forward decomposition of multi-level Wiener filtering, the projection matrices of the interference subspace and noise subspace are obtained, and the fast response weight vector is generated. S3: Based on the fast response weight vector and the pre-application weight vector, obtain the difference vector length; S4: Determine whether the length of the difference vector exceeds the preset threshold, and obtain the judgment result; obtain the new applicable weight vector based on the judgment result; S5: Time-align and weighted summation of the new applicable weight vector with the data block to achieve interference suppression; S6: Update the previously applied weight vector to the current new applied weight vector, and return to step S2 to process the next data block.

[0007] Preferably, the forward decomposition process in S2 is as follows: initialization: , ; Iteration Second-rate: ; ; ; ; ; in, This represents the number of elements in the array antenna. For scalar conjugate symbols, The symbol for the conjugate transpose of vectors and matrices. The length operator for vectors; the interference subspace is composed of middle The large values ​​correspond to Composition, that is , , As the initial value for iteration, Space-frequency joint input vector The first element, For the interference subspace of the first One orthogonal basis vector, , The first , The expected signal for the next iteration. , They are respectively , conjugate, , The first , The input vector for the next iteration. For the first The mean square value of the desired signal.

[0008] Preferably, the fast response weight vector in S2 is represented as: ; ; in, For fast response weight vector, Let be the projection matrix of the noise subspace. For constraint vectors, It is the product of the projection matrix of the noise subspace and the constraint vector.

[0009] Preferably, the difference vector length is expressed as: ; in, The weight vector is the one that is applied before.

[0010] Preferably, a new applicable weight vector is obtained based on the judgment result, specifically including: If the judgment result is yes, it is a fast time-varying suppression interference environment, and the fast response weight vector is used as the new applicable weight vector; if the judgment result is no, it is a slow time-varying suppression interference environment, the new data block is accumulated into the covariance matrix, and the new applicable weight vector is calculated based on the updated covariance matrix.

[0011] Preferably, the new data blocks are accumulated into the covariance matrix, and a new application weight vector is calculated based on the updated covariance matrix. This specifically includes the following steps: S41: Accumulate the new data block into the covariance matrix; S42: Based on the multi-level Wiener filtering of the covariance matrix hierarchy, the covariance matrix is ​​decomposed forward to obtain the projection matrices of the interference subspace and the noise subspace. S43: Based on the projection matrix and constraint vector of the noise subspace, obtain the projected vector; S44: Perform amplitude normalization on the projected vector to obtain a new applicable weight vector.

[0012] Preferably, the forward decomposition process in S42 is represented as follows: initialization: , , ; Iteration Second-rate:

[0013] ; ; ; ; ; ; ; in, Let covariance matrix be the variance matrix. for M An identity matrix of order 1. , As the initial value for iteration, For the space-frequency joint covariance matrix The vector formed by the first column elements, , The first , The cross-correlation vector of the next iteration, Cross-correlation vector Length, For the interference subspace of the first A set of orthogonal basis vectors For the first The expected mean square value of the signal in the next iteration. For the first The next iteration blocking matrix The covariance matrix of the next iteration.

[0014] Preferably, the projected vector representation in S43 is as follows: ; in, Let be the projection matrix of the noise subspace.

[0015] Preferably, the new applicable weight vector in S44 is represented as follows: .

[0016] Preferably, the preset threshold in S4 is ,in This represents the 3dB beamwidth of the array antenna.

[0017] Therefore, this invention employs a high-precision anti-interference navigation method compatible with both fast and slow time-varying interference suppression. To achieve this, the array antenna's weight vector must respond quickly during fast time-varying interference suppression and stably during slow time-varying interference suppression. First, a real-time fast time-varying interference suppression method is used to predict the fast response weight vector for new data blocks. Then, based on the difference between the fast response weight vector and the previously applied weight vector, the speed of interference change over time is determined. If it is fast time-varying interference, the method reverts to the real-time fast time-varying interference suppression method; if it is slow time-varying interference, it seamlessly transitions to the real-time slow time-varying interference suppression method. This compatible suppression method can both quickly suppress fast time-varying interference and stably suppress slow time-varying interference, offering advantages such as strong compatibility, smooth switching, high applicability, and closed-loop iteration.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a high-precision anti-interference navigation method for suppressing interference that is time-varying in speed. Detailed Implementation

[0020] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0021] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0022] The terms "comprising" or "including" as used in this invention mean that the element preceding the term encompasses the element listed after the term, and do not exclude the possibility of encompassing other elements. Terms such as "inner," "outer," "upper," and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. When the absolute position of the described object changes, the relative positional relationship may also change accordingly. In this invention, unless otherwise explicitly specified and limited, the term "attached" and similar terms should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements or the interaction relationship between two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0023] Example A high-precision anti-interference navigation method for suppressing interference compatible with time-varying speeds, such as... Figure 1 As shown, it includes: S1: Obtain the data blocks received by the array antenna, the pre-application weight vector, the constraint vector, and the covariance matrix; S2: For the data blocks received by the array antenna, the space-frequency joint-multibeam / zeroing-noise subspace projection method is adopted. Based on the forward decomposition of multi-level Wiener filtering, the projection matrices of the interference subspace and noise subspace are obtained, and the fast response weight vector is generated. Specifically, predicting the fast response weight vector for a new data block includes the following steps: First, using the fast time-varying interference suppression real-time method—space-frequency joint-multibeam / zeroing-noise subspace projection method—a multi-level Wiener filtering based on data hierarchy is employed to perform forward decomposition on the new data block, obtaining the projection matrices of the interference subspace and noise subspace. The forward decomposition process is as follows: initialization: , ; Iteration Second-rate: ; ; ; ; ; in This represents the number of elements in the array antenna. For scalar conjugate symbols, The symbol for the conjugate transpose of vectors and matrices. The length operator for vectors; the interference subspace is composed of middle The large values ​​correspond to Composition, that is , , As the initial value for iteration, Space-frequency joint input vector The first element, For the interference subspace of the first One orthogonal basis vector, , The first , The expected signal for the next iteration. , They are respectively , conjugate, , The first , The input vector for the next iteration. For the first The mean square value of the desired signal.

[0024] The interference subspace is composed of middle The large values ​​correspond to Composition, that is Therefore, the projection matrix of the noise subspace is .here, .

[0025] Then, the projection matrix and constraint vector of the noise subspace Multiplying them together gives: ;

[0026] Finally, normalizing its magnitude yields the predicted fast response weight vector, i.e.: ; Where the constraint vector It can be a guiding vector that expects the satellite to come up, or it can be a vector where only the first element is non-zero.

[0027] S3: Based on the fast response weight vector and the pre-application weight vector, obtain the difference vector length; Fast response weight vector With the previously applied weight vector Forming a difference vector The length of the difference vector is: ; The greater the length of the difference vector, the greater the difference between the two vectors; conversely, the smaller the length, the greater the difference.

[0028] S4: Determine whether the length of the difference vector exceeds the preset threshold, and obtain the judgment result; obtain the new applicable weight vector based on the judgment result; Disturbance time-varying speed determination rule: If Exceeding the threshold If the threshold value is 1, it is classified as a fast time-varying suppression interference environment; otherwise, it is classified as a slow time-varying suppression interference environment. Here, the threshold value can be set to... ,in This represents the 3dB beamwidth of the array antenna.

[0029] If the environment is a fast-time-varying suppression interference environment, then the new applicable weight vector is the predicted fast-response weight vector, i.e. .

[0030] In the case of a slow-time-varying environment that suppresses interference, the new data blocks are first accumulated into the covariance matrix. To increase the number of snapshots estimated by the covariance matrix; then, a multi-level Wiener filter based on the covariance matrix hierarchy is used to... Perform forward decomposition to obtain the projection matrices of the interference subspace and the noise subspace. The forward decomposition process is as follows: initialization: , , ; Iteration Second-rate: ; ; ; ; ; ; ; ; The interference subspace is composed of middle The large values ​​correspond to If the structure is such that the projection matrix of the noise subspace is... ,in .

[0031] Then, the projection matrix and constraint vector of the noise subspace Multiplying them together gives: ; Finally, normalizing its amplitude yields the weight vector for suppressing slow time-varying disturbances, namely: ; S5: Time-align and weighted summation of the new applicable weight vector with the data block to achieve interference suppression; Specifically, the new applicable weight vector The new data blocks to be weighted in the memory cache are first time-aligned and then weighted and summed to match the null position and depth of the array antenna pattern with the direction and intensity of the interference, so as to adaptively achieve real-time suppression of fast time-varying and slow time-varying interference.

[0032] S6: Update the previously applied weight vector to the current new applied weight vector, and return to step S2 to process the next data block.

[0033] This new applicable weight vector is used as the pre-applied weight vector in the next data block step, "Obtain the difference vector length based on the fast response weight vector and the pre-applied weight vector," i.e. use renew.

[0034] Therefore, this invention employs the aforementioned high-precision anti-interference navigation method for suppressing interference that is compatible with both fast and slow time-varying variations. First, it uses a space-frequency joint-multibeam / zeroing-noise subspace projection method to predict the fast response weight vector for the new data block. Then, based on the length of the difference vector formed by the fast response weight vector and the previously applied weight vector, it determines the rate at which the suppression interference changes over time. Next, it determines the applicable weight vector for the new data block based on whether the suppression interference is fast or slow time-varying. Finally, it uses the new applicable weight vector to perform a weighted summation on the new data block, thereby suppressing the fast and slow time-varying suppression interference in real time.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A high-precision anti-interference navigation method for suppressing interference compatible with time-varying speeds, characterized in that, Includes the following steps: S1: Obtain the current data block received by the array antenna, the previous applicable weight vector, constraint vector, and covariance matrix of the previous data block; S2: For the current data block received by the array antenna, the space-frequency joint-multibeam / zeroing-noise subspace projection method is adopted. Based on the forward decomposition of multi-level Wiener filtering, the projection matrix of the interference subspace and noise subspace is obtained, and the fast response weight vector is generated. S3: Based on the fast response weight vector and the pre-application weight vector, obtain the difference vector length; S4: Determine whether the length of the difference vector exceeds the preset threshold, and obtain the judgment result; obtain the new applicable weight vector based on the judgment result; S5: Align the new applicable weight vector with the current data block in time and perform a weighted summation to suppress interference; S6: Update the previous applicable weight vector to the new applicable weight vector, and return to step S2 to process the next data block.

2. The high-precision anti-interference navigation method for suppressing interference that varies with speed, as described in claim 1, is characterized in that... The forward decomposition process in S2 is as follows: initialization: , ; Iteration Second-rate: ; ； ； ； ； in, This represents the number of elements in the array antenna. For scalar conjugate symbols, The symbol for the conjugate transpose of vectors and matrices. The length operator for vectors; the interference subspace is composed of middle The large values ​​correspond to Composition, that is , , As the initial value for iteration, Space-frequency joint input vector The first element, For the interference subspace of the first One orthogonal basis vector, For mathematical expectation operators, , The first , The expected signal for the next iteration. , They are respectively , conjugate, , The first , The input vector for the next iteration. For the first The mean square value of the desired signal.

3. The high-precision anti-interference navigation method for suppressing interference that varies with speed, as described in claim 2, is characterized in that... The fast response weight vector in S2 is represented as: ； ； in, For fast response weight vector, Let be the projection matrix of the noise subspace. For constraint vectors, It is the product of the projection matrix of the noise subspace and the constraint vector.

4. The high-precision anti-interference navigation method for suppressing interference that varies with speed, as described in claim 3, is characterized in that... The difference vector length is expressed as: ； in, The weight vector is the one that is applied before.

5. The high-precision anti-interference navigation method for suppressing interference that is compatible with time-varying speeds, as described in claim 1, is characterized in that... The new applicable weight vector is obtained based on the judgment result, specifically including: If the judgment result is yes, it is a fast time-varying suppression interference environment, and the fast response weight vector is used as the new applicable weight vector; if the judgment result is no, it is a slow time-varying suppression interference environment, the new data block is accumulated into the covariance matrix, and the new applicable weight vector is calculated based on the updated covariance matrix.

6. The high-precision anti-interference navigation method for suppressing interference that varies with speed, as described in claim 5, is characterized in that... The new data blocks are accumulated into the covariance matrix, and a new application weight vector is calculated based on the updated covariance matrix. This process includes the following steps: S41: Accumulate the new data block into the covariance matrix; S42: Based on the multi-level Wiener filtering of the covariance matrix hierarchy, the covariance matrix is ​​decomposed forward to obtain the projection matrices of the interference subspace and the noise subspace. S43: Based on the projection matrix and constraint vector of the noise subspace, obtain the projected vector; S44: Perform amplitude normalization on the projected vector to obtain a new applicable weight vector.

7. The high-precision anti-interference navigation method for suppressing interference in a time-varying manner according to claim 6, characterized in that: The forward decomposition process in S42 is represented as follows: initialization: , , ; Iteration Second-rate: ; ； ； ； ； ； ； ； in, Let covariance matrix be the variance matrix. for M An identity matrix of order 1. , As the initial value for iteration, For the space-frequency joint covariance matrix The vector formed by the first column elements, , The first , The cross-correlation vector of the next iteration, Cross-correlation vector Length, For the interference subspace of the first A set of orthogonal basis vectors For the first The expected mean square value of the signal in the next iteration. For the first The next iteration blocking matrix For the first The covariance matrix of the next iteration.

8. The high-precision anti-interference navigation method for suppressing interference that varies with speed according to claim 1, characterized in that, The projected vector representation in S43 is as follows: ； in, Let be the projection matrix of the noise subspace.

9. The high-precision anti-interference navigation method for suppressing interference that varies with speed, as described in claim 1, is characterized in that... The new applicable weight vector in S44 is represented as follows: 。 10. The high-precision anti-interference navigation method for suppressing interference that is compatible with time-varying speeds, as described in claim 1, is characterized in that... The preset threshold in S4 is: ,in This represents the 3dB beamwidth of the array antenna.

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