Unmanned aerial vehicle navigation adaptive anti-interference method based on signal non-circular characteristic

Abstract

The invention relates to the technical field of unmanned aerial vehicle navigation, in particular to an unmanned aerial vehicle navigation adaptive anti-interference method based on a signal non-circular characteristic. Comprising the following steps: feature decomposition of an array covariance matrix is carried out, and a noise subspace is constructed; estimating the direction of an incident signal by using a multiple signal classification method; estimating the power of the incident signal by using a basis tracking noise reduction method; reconstructing an interference and noise covariance matrix; estimating a non-circular coefficient of the non-circular received signal; reconstructing a pseudo interference and noise covariance matrix; reconstructing an amplification interference and noise covariance matrix; estimating an amplification desired signal steering vector; and calculating an amplification weighting vector of the unmanned navigation receiver. According to the design, the non-circular characteristic of a satellite receiving signal is fully utilized, and the second-order information and conjugate receiving signal information of the non-circular receiving signal are utilized, so that an expected satellite navigation signal is reserved, and meanwhile, the interference suppression problem of an unmanned aerial vehicle navigation receiver is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle navigation, in particular to an unmanned aerial vehicle navigation adaptive anti-interference method based on signal non-circular characteristics. BACKGROUND

[0002] Global navigation satellite system has served in communication, power, finance, transportation, national defense and other fields, and has become an indispensable part of daily life and production activities. However, the satellite is 20,000-30,000 kilometers away from the earth's surface, and the satellite signal is very weak when it reaches the ground. In addition, with the increasing complexity of the electromagnetic environment, the form and number of interference are also increasing. Therefore, the navigation receiver is easily disturbed by intentional or unintentional interference, resulting in a decrease in positioning accuracy or failure to work normally. In severe cases, it may cause important infrastructure to collapse. Improving the anti-interference ability of the navigation system has become a research hotspot in the field of navigation system.

[0003] According to the number of receiver terminal antennas, the anti-interference technology can be divided into single antenna and antenna array anti-interference technology. Single antenna anti-interference technology only uses one antenna to suppress the interference spectrum line, which is the best choice for suppressing narrowband interference; antenna array anti-interference technology uses multiple antenna elements to suppress interference. According to the different directions of incident signals, through adaptive weighting vector, the main lobe of the beam is aligned with the expected signal direction, and deep nulls are formed in the interference direction. Not only can it suppress narrowband interference, but also can suppress wideband interference, which is very suitable for strong countermeasures environment. Antenna array anti-interference technology includes spatial adaptive filtering, space-time adaptive processing, space-frequency adaptive processing and other technologies.

[0004] When the interference frequency band is wide, it will cause a serious decline in signal quality. In the electronic countermeasure condition, the enemy will emit strong power interference, and the interference bandwidth is very likely to be large, so the single antenna cannot suppress the interference. In addition, single antenna and antenna array anti-interference technology usually assumes that the received signal is stationary and circularly symmetric, and does not fully utilize the non-circular and non-stationary characteristics of satellite received signals, so it cannot achieve the best anti-interference performance. In view of this, we propose an unmanned aerial vehicle navigation adaptive anti-interference method based on signal non-circular characteristics. SUMMARY

[0005] The purpose of the present application is to provide an unmanned aerial vehicle navigation adaptive anti-interference method based on signal non-circular characteristics, to solve the problem that most array anti-interference technologies usually assume that the received signal is stationary and circularly symmetric, and do not fully utilize the non-circular and non-stationary characteristics of satellite received signals.

[0006] To achieve the above technical problems, one of the purposes of the present application is to provide an unmanned aerial vehicle navigation adaptive anti-interference method based on signal non-circular characteristics. First, the unmanned aerial vehicle navigation receiver receives a signal vector, and then the following steps are performed:

[0007] S1, eigen-decomposition array covariance matrix, construct noise subspace;

[0008] S2, estimate the direction of the incident signal using multiple signal classification method;

[0009] S3, estimate the power of the incident signal using basis pursuit denoising method;

[0010] S4, reconstruct the interference plus noise covariance matrix;

[0011] S5, estimate the non-circular coefficient of the non-circular received signal;

[0012] S6, reconstruct the pseudo-interference plus noise covariance matrix;

[0013] S7, reconstruct the augmented interference plus noise covariance matrix;

[0014] S8, estimate the augmented expected signal steering vector;

[0015] S9, calculate the augmented weight vector of the unmanned navigation receiver.

[0016] As a further improvement of the technical solution, in step S1, the eigen-decomposition array covariance matrix is constructed, and the noise subspace is constructed.

[0017] Consider that Q non-circular signals are incident to the unmanned aerial vehicle navigation receiver uniform linear array of M antenna elements, the distance d between the elements is half the wavelength of the signal; at the lth snapshot, the received signal vector is:

[0018]

[0019] Where a(θ q ) and s q (l) represent the steering vector and the complex beam of the qth signal, respectively, and n(t) represents the noise vector; the direction of the qth signal is θ q .

[0020] Define the global interference plus noise vector as For a uniform linear array, the steering vector is defined as a(θ q ) = [1, β(θ q ), β 2 (θ q ), ···, β M-1 (θ q )] T , where β(θq ) = exp(-j2πd sin(θ q ) / λ) and λ represents signal wavelength, j is a constant, and T represents matrix transpose;

[0021] For non-circular signals s(l), the conditions E{s(l)} = 0, E{s(l)s(l)} ≠ 0 and E{s(l)s * (l)} ≠ 0 are satisfied, E represents a norm set, and a non-circular coefficient γ thereof is defined as:

[0022]

[0023] wherein p = E{|s(l)| 2} represents a time-domain average power of s(l), |γ| ∈ [0, 1] represents a non-circular rate, and φ ∈ [-π, π] represents a non-circular phase; a covariance matrix R x and a pseudo-covariance matrix P x of a received signal vector x(l) are respectively represented as:

[0024]

[0025]

[0026] In an actual system, R x and P x are calculated from sampling snapshots, that is, wherein L represents a total number of snapshots, T represents transposition, H represents conjugate transposition, and I M represents port current of an antenna unit; eigenvalue decomposition of an array covariance matrix can be performed to obtain:

[0027]

[0028] wherein α1≥ α2≥...≥ α M represent eigenvalues of , u m represents an eigenvector corresponding to the eigenvalue α m ; E s = [u1, u2,..., u Q ] represents a signal subspace, Λ s = diag{α1, α2,..., α Q} and Λ n = diag{α Q+1 , α Q+2 ,..., α M} represent eigenvalue diagonal matrices, and E n = [u Q+1 , u Q+2 ,..., uM represents the noise subspace.

[0029] As a further improvement of the technical solution, in the step S2, the direction of the incident signal is estimated by using a multiple signal classification method, and the method is specifically as follows:

[0030] According to the orthogonality of the array steering vector a(θ) and the noise subspace E n , a constructor is constructed:

[0031]

[0032] By searching for the peak value of P(θ) in the entire spatial spectrum [-90°, 90°], the peak value corresponds to the incident direction of the signal

[0033] As a further improvement of the technical solution, in the step S3, the power of the incident signal is estimated by using a basis pursuit denoising method, and the method is specifically as follows:

[0034] By using sparse representation technology, the basis pursuit denoising problem is constructed as:

[0035]

[0036] Wherein, μ represents a regularization parameter, the superscript - represents an average, represents the steering matrix of the spatial region sampling angle , diag represents a diagonal matrix; after solving, the power estimation is obtained as The noise power estimation is Wherein, is the signal direction corresponding to the power estimation; the formula (7) is improved as:

[0037]

[0038] Wherein, represents the steering matrix of the estimated direction , is the noise power estimated from formula (7); the solution of the optimization formula (8) is:

[0039]

[0040] Wherein, vec represents vectorization.

[0041] As a further improvement of the technical solution, in the step S4, the interference plus noise covariance matrix is reconstructed, and the method is specifically as follows:

[0042] By using the estimated signal direction corresponding to the power estimation and noise power estimation The reconstructed interference plus noise covariance matrix is:

[0043]

[0044] The superscript ^ indicates an estimated value.

[0045] As a further improvement to this technical solution, in step S5, estimating the non-circular coefficient of the non-circular received signal specifically involves:

[0046] Using the non-circular coefficient estimator, the non-circular coefficient of the q-th non-circular signal is estimated as follows:

[0047]

[0048] in, λ min Yes Minimum eigenvalue.

[0049] As a further improvement to this technical solution, in step S6, the reconstruction of the pseudo-interference plus noise covariance matrix specifically involves:

[0050] Using the estimated signal direction Corresponding power estimation Corresponding non-circular coefficient estimation The reconstructed pseudo-interference plus noise covariance matrix is:

[0051]

[0052] in, This is the estimated value of the non-circular coefficients of the q-th non-circular signal.

[0053] As a further improvement to this technical solution, in step S7, the reconstruction of the amplification interference plus noise covariance matrix specifically involves:

[0054] use and The reconstructed amplification interference plus noise covariance matrix is:

[0055]

[0056] Among them, superscript The symbol indicates amplification, and the superscript * indicates the adjoint matrix.

[0057] As a further improvement to this technical solution, in step S8, estimating the amplified desired signal steering vector specifically involves:

[0058] Using the estimated desired signal direction Non-circular coefficients of the estimated desired signal The amplified desired signal steering vector is calculated as follows:

[0059]

[0060] in, The amplification vector is used to guide the desired signal.

[0061] As a further improvement to this technical solution, in step S9, the calculation of the augmented weighting vector for the unmanned navigation receiver is specifically as follows:

[0062] Using the reconstructed amplified interference plus noise covariance matrix and amplified desired signal steering vector The expanded weighted vector of the UAV navigation receiver is calculated as follows:

[0063]

[0064] in, Add weighted vectors to the UAV navigation receiver.

[0065] The second objective of this invention is to provide an adaptive anti-interference device for UAV navigation, comprising a processor, a memory, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the aforementioned UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals.

[0066] A third objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned adaptive anti-interference method for UAV navigation based on the non-circular characteristics of signals.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] 1. In this UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals, the direction, power, non-circular coefficient and noise power of all received signals are estimated, and then the amplified interference plus noise covariance matrix is ​​reconstructed.

[0069] 2. In this UAV navigation adaptive anti-interference method based on the non-circular characteristics of the signal, the augmented weighting vector of the UAV navigation receiver is calculated by utilizing the reconstructed interference plus noise covariance matrix and the estimated amplified desired signal steering vector;

[0070] 3. In this UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals, the non-circular characteristics of satellite received signals are fully utilized. By using the second-order information and conjugate received signal information of the non-circular received signals, the desired satellite navigation signal is preserved while the interference suppression problem of the UAV navigation receiver is solved. Attached Figure Description

[0071] Figure 1 This is a schematic diagram illustrating the overall technical process of this invention.

[0072] Figure 2 This is a structural diagram of an exemplary electronic computer platform device in this invention. Detailed Implementation

[0073] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0074] Example 1

[0075] like Figure 1 As shown, in order to fully utilize the non-circular and non-stationary characteristics of the received signal, this embodiment provides an adaptive anti-interference method for UAV navigation based on the non-circular characteristics of the signal. First, the UAV navigation receiver receives the signal vector, and then performs the following steps:

[0076] S1. Decompose the eigenvalue array covariance matrix to construct the noise subspace; specifically:

[0077] Consider Q non-circular signals incident on a uniform linear array of M antenna elements in a UAV navigation receiver, where the spacing between the elements is d, which is half the wavelength of the signal. In the l-th snapshot, the received signal vector is:

[0078]

[0079] Where a(θ) q ) and s q (l) represents the steering vector and the complex beam of the q-th signal, respectively, and n(t) represents the noise vector; the direction of the q-th signal is θ. q In this embodiment, the signal with the first direction θ1 is the desired satellite signal, and the remaining signals are interference.

[0080] Define the global interference plus noise vector as For a uniform linear array, the steering vector is defined as a(θ) q )=[1,β(θ q ), β 2 (θ q ), ..., β M-1 (θ q )] T , where β(θ) q)=exp(-j2πdsin(θ q )λ) where λ represents the signal wavelength, j is a constant, and T represents the matrix transpose;

[0081] For a non-circular signal s(l), the following conditions are satisfied: E{s(l)}=0, E{s(l)s(l)}≠0 and E{s(l)s * (l)}≠0, E represents the canonical set, and its noncircular coefficient γ is defined as:

[0082]

[0083] Where p=E{|s(l)| 2} represents the time-domain average power of s(l), |γ|∈[0,1] represents the non-circularity, and φ∈[-π,π] represents the non-circular phase; the covariance matrix R of the received signal vector x(l) is... x and pseudo-covariance matrix P x They are represented as follows:

[0084]

[0085]

[0086] In practical systems, R x and P x All of these are calculated from the sample snapshots, i.e. Where L represents the total number of snapshots, T represents the transpose, H represents the conjugate transpose, and I M This represents the port current of the antenna element; eigenvalue decomposition of the array covariance matrix yields:

[0087]

[0088] Where, α1≥α2≥...≥α M express eigenvalues, u m Represents the eigenvalue α m The corresponding eigenvector; E s = [u1, u2, ..., u Q ] represents the signal subspace, Λ s = diag{α1, α2, ..., α Q} and Λ n =diag{α Q+1 α Q+3 , ..., α M} represents an eigenvalue diagonal matrix, E n =[u Q+1 u Q+2 , ..., u M] represents the noise subspace.

[0089] S2. Estimate the direction of the incident signal using a multiple signal classification method; specifically:

[0090] Based on the array steering vector a(θ) and the noise subspace E n Orthogonality, constructor:

[0091]

[0092] By searching for the peak value of P(θ) across the entire spatial spectrum [-90°, 90°], the peak value corresponds to the incident direction of the signal.

[0093] It is worth noting that step S2 can use the ESPRIT algorithm to avoid the process of spectral peak search, thereby reducing the computational complexity of the algorithm.

[0094] S3. Estimate the power of the incident signal using the basis tracking noise reduction method; specifically:

[0095] Using sparse representation techniques, the basis tracking denoising problem is constructed as follows:

[0096]

[0097] Where μ represents the regularization parameter, and the superscript... - Indicates average, Indicates the sampling angle of the spatial region The guiding matrix is ​​denoted by diag; where the optimization problem formula (7) is convex and can be solved to obtain the power estimate as follows: Noise power estimate is in, Signal direction The corresponding power estimate; however, the estimated power spectrum In the process, false peaks often exist; therefore, the optimization problem formula (7) needs to be improved to:

[0098]

[0099] in, Indicates the estimated direction The guiding matrix, The noise power is estimated from equation (7); the solution to the optimization problem equation (8) is:

[0100]

[0101] in, vec indicates vectorization.

[0102] S4. Reconstruct the interference plus noise covariance matrix; specifically:

[0103] Using the estimated signal direction Corresponding power estimation and noise power estimation The reconstructed interference plus noise covariance matrix is:

[0104]

[0105] The superscript ^ indicates an estimated value.

[0106] S5. Estimate the non-circular coefficients of the non-circular received signal; specifically:

[0107] Using the non-circular coefficient estimator, the non-circular coefficient of the q-th non-circular signal is estimated as follows:

[0108]

[0109] in, λ min Yes Minimum eigenvalue.

[0110] S6. Reconstruct the pseudo-interference plus noise covariance matrix; specifically:

[0111] Using the estimated signal direction Corresponding power estimation Corresponding non-circular coefficient estimation The reconstructed pseudo-interference plus noise covariance matrix is:

[0112]

[0113] in, This is the estimated value of the non-circular coefficients of the q-th non-circular signal.

[0114] S7. Reconstruct the amplification interference plus noise covariance matrix; specifically:

[0115] use and The reconstructed amplification interference plus noise covariance matrix is:

[0116]

[0117] Among them, superscript The symbol indicates amplification, and the superscript * indicates the adjoint matrix.

[0118] S8. Estimate the steering vector of the amplified desired signal; specifically:

[0119] Using the estimated desired signal direction Non-circular coefficients of the estimated desired signal The amplified desired signal steering vector is calculated as follows:

[0120]

[0121] in, The amplification vector is used to guide the desired signal.

[0122] S9. The specific steps for calculating the augmented weighting vector of the unmanned navigation receiver are as follows:

[0123] Using the reconstructed amplified interference plus noise covariance matrix and amplified desired signal steering vector The expanded weighted vector of the UAV navigation receiver is calculated as follows:

[0124]

[0125] in, Add weighted vectors to the UAV navigation receiver.

[0126] In summary, this technical solution accurately reconstructs the amplified interference plus noise covariance matrix of the UAV navigation receiver and accurately estimates the steering vector of the desired amplified signal of the UAV navigation receiver, thereby suppressing the interference signal while preserving the desired satellite navigation signal.

[0127] Furthermore, most array anti-jamming techniques typically assume that the received signal is stationary and circularly symmetric, failing to fully utilize the non-circular and non-stationary characteristics of satellite received signals. This scheme fully utilizes the non-circular characteristics of satellite received signals to design an anti-jamming technology for UAV navigation, the key points of which are as follows:

[0128] First, this method reconstructs the amplified interference plus noise covariance matrix by estimating the direction, power, non-circularity coefficient, and noise power of all received signals.

[0129] Secondly, by utilizing the reconstructed interference plus noise covariance matrix and the estimated amplified desired signal steering vector, the amplified weighted vector of the UAV navigation receiver is calculated.

[0130] like Figure 2 As shown, this embodiment also provides an adaptive anti-interference device for UAV navigation, which includes a processor, a memory, and a computer program stored in the memory and running on the processor.

[0131] The processor includes one or more processing cores. The processor is connected to the memory via a bus. The memory is used to store program instructions. When the processor executes the program instructions in the memory, it implements the steps of the above-mentioned UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals.

[0132] Optionally, the memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0133] In addition, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described adaptive anti-interference method for UAV navigation based on the non-circular characteristics of signals.

[0134] Optionally, the present invention also provides a computer program product containing instructions that, when run on a computer, causes the computer to perform the steps of the above-described UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals.

[0135] Those skilled in the art will understand that the process of implementing all or part of the steps of the above embodiments can be carried out by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0136] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An adaptive anti-interference method for UAV navigation based on the non-circular characteristics of signals, characterized in that, First, the UAV navigation receiver receives the signal vector, and then performs the following steps: S1. Decompose the eigenvalue array covariance matrix to construct the noise subspace; S2. Estimate the direction of the incident signal using a multiple signal classification method; S3. Estimate the power of the incident signal using the basis tracking noise reduction method; S4. Reconstruct the interference plus noise covariance matrix; S5. Estimate the non-circular coefficients of the non-circular received signal; S6. Reconstruct the pseudo-interference plus noise covariance matrix; S7. Reconstruct the amplification interference plus noise covariance matrix; S8. Estimate the steering vector of the amplified desired signal; S9. Calculate the weighted vector of the unmanned navigation receiver.

2. The UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals according to claim 1, characterized in that: In step S1, the eigenvalue decomposition array covariance matrix is ​​used to construct the noise subspace as follows: Consider Q non-circular signals incident on a uniform linear array of M antenna elements in a UAV navigation receiver, where the spacing d between the elements is half the wavelength of the signal; in the l-th snapshot, the received signal vector is: Where, a(θ) q ) and s q (l) represents the steering vector and the complex beam of the q-th signal, respectively, and n(t) represents the noise vector; the direction of the q-th signal is θ. q ; Define the global interference plus noise vector as For a uniform linear array, the steering vector is defined as a(θ) q )=[1,β(θ q ), β 2 (θ q ), ..., β M-1 (θ q )] T , where β(θ) q )=exp(-j2πdsin(θ q () / λ), where λ represents the signal wavelength, j is a constant, and T represents the matrix transpose; For a non-circular signal s(l), the following conditions are satisfied: E{s(l)}=0, E{s(l)s(l)}≠0 and E{s(l)s * (l)}≠0, E represents the canonical set, and its noncircular coefficient γ is defined as: Where p=E{|s(l)| 2 } represents the time-domain average power of s(l), |γ|∈[0,1] represents the non-circularity, and φ∈[-π,π] represents the non-circular phase; the covariance matrix R of the received signal vector x(l) is... x and pseudo-covariance matrix P x They are represented as follows: In practical systems, R x and P x All of these are calculated from the sample snapshots, i.e. Where L represents the total number of snapshots, T represents the transpose, H represents the conjugate transpose, and I M This represents the port current of the antenna element; eigenvalue decomposition of the array covariance matrix yields: Where, α1≥α2≥...≥α M express eigenvalues, u m Represents the eigenvalue α m The corresponding eigenvector; E s = [u1, u2, ..., u Q ] represents the signal subspace, Λ s = diag{α1, α2, ..., α Q } and Λ n =diag{α Q+1 α Q+2 , ..., α M E represents an eigenvalue diagonal matrix. n =[u Q+1 u Q+2 , ..., u M ] represents the noise subspace.

3. The UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals according to claim 2, characterized in that: In step S2, estimating the direction of the incident signal using the multiple signal classification method specifically involves: Based on the array steering vector a(θ) and the noise subspace E n Orthogonality, constructor: By searching for the peak value of P(θ) across the entire spatial spectrum [-90°, 90°], the peak value corresponds to the incident direction of the signal.

4. The UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals according to claim 3, characterized in that: In step S3, estimating the power of the incident signal using the basis tracking noise reduction method specifically involves: Using sparse representation techniques, the basis tracking denoising problem is constructed as follows: Where μ represents the regularization parameter, and the superscript - indicates the average. Indicates the sampling angle of the spatial region The guidance matrix is ​​given by , where diag denotes a diagonal matrix; the power estimate obtained after solving is: Noise power estimate is in, Signal direction The corresponding power estimate; the formula (7) is improved as follows: in, Indicates the estimated direction The guiding matrix, The noise power is estimated from equation (7); the solution to the optimization equation (8) is: in, vec indicates vectorization.

5. The UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals according to claim 4, characterized in that: In step S4, the reconstruction of the interference plus noise covariance matrix specifically involves: Using the estimated signal direction Corresponding power estimation and noise power estimation The reconstructed interference plus noise covariance matrix is: The superscript ^ indicates an estimated value.

6. The UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals according to claim 5, characterized in that: In step S5, estimating the non-circular coefficients of the non-circular received signal specifically involves: Using the non-circular coefficient estimator, the non-circular coefficient of the q-th non-circular signal is estimated as follows: in, λ min Yes Minimum eigenvalue.

7. The UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals according to claim 1, characterized in that: In step S6, the reconstruction of the pseudo-interference plus noise covariance matrix specifically involves: Using the estimated signal direction Corresponding power estimation Corresponding non-circular coefficient estimation The reconstructed pseudo-interference plus noise covariance matrix is: in, This is the estimated value of the non-circular coefficients of the q-th non-circular signal.

8. The UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals according to claim 7, characterized in that: In step S7, the reconstruction of the amplification interference plus noise covariance matrix specifically involves: use and The reconstructed amplification interference plus noise covariance matrix is: Among them, superscript The symbol indicates amplification, and the superscript * indicates the adjoint matrix.

9. The UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals according to claim 8, characterized in that: In step S8, estimating the amplified desired signal steering vector specifically involves: Using the estimated desired signal direction Non-circular coefficients of the estimated desired signal The amplified desired signal steering vector is calculated as follows: in, The amplification vector is used to guide the desired signal.

10. The UAV navigation adaptive anti-interference method based on the non-circular characteristics of signals according to claim 9, characterized in that: In step S9, the calculation of the augmented weighting vector for the unmanned navigation receiver is specifically as follows: Using the reconstructed amplified interference plus noise covariance matrix and amplified desired signal steering vector The expanded weighted vector of the UAV navigation receiver is calculated as follows: in, Add weighted vectors to the UAV navigation receiver.

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