Unmanned aerial vehicle coherent signal direction finding method and system based on uniform circular array

Abstract

The invention discloses an unmanned aerial vehicle coherent signal direction finding method based on a uniform circular array, and relates to a radio direction finding technology, and the method comprises the steps: defining related parameters, and receiving an unmanned aerial vehicle signal through the uniform circular array; preprocessing the matrix T, and converting a uniform circular array in an original array element space into a virtual uniform linear array in a mode space; defining a vector z, and constructing a response Toeplitz matrix through the vector z; on the basis of the transformed signal, a covariance matrix of a corresponding Hermitian Toeplitz matrix reconstructed from the observation space is calculated; according to the calculated covariance matrix, defining a MUSIC algorithm to search a maximum spectrum peak; and converting the spectral peak estimation of the MUSIC algorithm into a polynomial by using the ROOT-MUSIC algorithm, so as to determine the estimation of the direction of arrival by using the root of the polynomial. According to the method, the defect of direction finding of the unmanned aerial vehicle coherent signals by a traditional direction finding method is overcome, successful separation of the unmanned aerial vehicle coherent signals is ensured, and the direction finding precision of the unmanned aerial vehicle coherent signals is improved.

Description

Technical Field

[0001] The present application relates to the field of radio direction finding technology, and in particular to a direction finding method and system for coherent signals of unmanned aerial vehicles based on a uniform circular array. Background Art

[0002] Traditional UAV radio direction-finding technologies, such as amplitude comparison direction-finding and interferometer direction-finding, cannot correctly estimate the direction of incoming waves or distinguish them when encountering coherent signals from UAVs of the same model and manufacturer. They will treat several coherent signals as one spatial incoming wave signal.

[0003] Traditional drone radio detection is to first find the direction of the drone and then detect it. With the increasing number of drones of the same or similar types on the market, the types of drones with coherent signals have brought serious difficulties to current drone detection. It is impossible to correctly estimate the direction of the waves of these signal-coherent drones, and it is also impossible to distinguish between drones. Summary of the invention

[0004] The embodiment of the present application provides a direction finding method and system for UAV coherent signals based on a uniform circular array, which uses a uniform circular array to receive UAV signals, de-coheres the UAV coherent signals through Toeplitz transformation, and further estimates the transformed signals using a ROOT-MUSIC algorithm to obtain the incoming direction of the UAV signal.

[0005] The embodiment of the present application provides a direction finding method for coherent signals of a drone based on a uniform circular array, comprising:

[0006] Define the uniform circular array single snapshot condition M×1 spatial data vector as X, p×1 signal model as S, M×1 noise space as N, and J as a (2h+1)×(2h+1) diagonal matrix;

[0007] The preprocessing matrix T transforms the uniform circular array in the original array element space into a virtual uniform linear array in the pattern space to obtain the transformed signal;

[0008] Define a vector z, which includes 2(n-1)+1 rows, and construct a response Toeplitz matrix through the vector z;

[0009] Based on the transformed signal, the covariance matrix corresponding to the Hermitian Toeplitz matrix reconstructed from the observation space is calculated;

[0010] Based on the calculated covariance matrix, we search for ||Q from different φ values ​​from (0,2π) H The minimum value of b(φ)|| defines the MUSIC algorithm to find the maximum spectral peak;

[0011] The ROOT-MUSIC algorithm is used to transform the spectrum peak estimate of the MUSIC algorithm into a polynomial, so that the estimate of the direction of arrival can be determined using the roots of the polynomial.

[0012] Optionally, define relevant parameters as follows:

[0013]

[0014]

[0015] Where X is the single snapshot conditional M×1 spatial data vector, A is the M×p spatial flow matrix, S is the p×1 signal model, N is the M×1 noise space, w=e j2π / M ;

[0016] J is a (2h+1)×(2h+1) diagonal matrix satisfying:

[0017]

[0018] Optionally, preprocessing the matrix T to convert the uniform circular array in the original array element space into a virtual uniform linear array in the pattern space to obtain a transformed signal includes:

[0019] Based on the diagonal matrix J, the matrix T satisfies:

[0020]

[0021] The preprocessing matrix T transforms the uniform circular array in the original array element space into a virtual uniform linear array in the pattern space, satisfying:

[0022]

[0023] The uniform circular array in the array element space is transformed into a virtual linear array (VULA) to obtain the transformed signal

[0024] Optionally, a vector z is defined, the vector z includes 2(n-1)+1 rows, and the response Toeplitz matrix constructed by the vector z includes:

[0025] The response Toeplitz matrix constructed by vector z is:

[0026]

[0027] Optionally, based on the transformed signal, calculating the covariance matrix corresponding to reconstructing the Hermitian Toeplitz matrix from the observation space includes:

[0028] The row vector of the observation space signal vector (2h+1) is:

[0029]

[0030] The Hermitian Toeplitz matrix is ​​reconstructed from the observation space as:

[0031]

[0032] The Toeplitz matrix constructed by The covariance matrix of the matrix is ​​calculated as

[0033] The eigenvalues ​​of the matrix are:

[0034]

[0035] Where V is a (h+1)×(h+1) matrix containing all eigenvectors {v1,...,v h+1};

[0036] correspond The matrix composed of the eigenvectors of the noise space is (ph)×(h+1), which satisfies:

[0037] Q = {v p+1 ,...,v h+1}

[0038] b(φ k ) is defined as:

[0039]

[0040] Where:

[0041] u k =exp(j(φ k -2π(n-1) / (h+1))).

[0042] Optionally, defining the MUSIC algorithm to find the maximum spectral peak includes:

[0043] The MUSIC algorithm is defined as:

[0044]

[0045] Optionally, using the ROOT-MUSIC algorithm to convert the spectrum peak estimate of the MUSIC algorithm into a polynomial, and using the root of the polynomial to determine the estimate of the direction of arrival includes:

[0046] The ROOT-MUSIC algorithm is used to convert the spectrum peak estimate of the MUSIC algorithm into a polynomial, the root of which contains the estimate of the direction of arrival:

[0047] F-1 =b H (φ)QQ H b(φ)

[0048] The estimate of the direction of arrival is transformed and the polynomial is expressed as:

[0049]

[0050] Where w z =e jφ ;

[0051] According to the polynomial D(w z ) to estimate the direction of arrival of the signal.

[0052] An embodiment of the present application also proposes a direction finding system for UAV coherent signals based on a uniform circular array, comprising a processor and a memory, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the direction finding method for UAV coherent signals based on a uniform circular array are implemented as described above.

[0053] The embodiment of the present application uses a uniform circular array to receive the drone signal, de-coheres the drone coherent signal through Toeplitz transformation, and further estimates the transformed signal using the ROOT-MUSIC algorithm to obtain the incoming direction of the drone signal.

[0054] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0056] Figure 1 The basic process of the direction finding method of the coherent signal of the UAV based on the uniform circular array in the embodiment of the present application is shown;

[0057] Figure 2 This is a schematic diagram of the estimation of the direction of arrival of two coherent drone signals in the application example of this application;

[0058] Figure 3 This is a schematic diagram of the estimation of the direction of arrival of three coherent drone signals in the application example of this application;

[0059] Figure 4 Schematic diagram of the estimation of the arrival direction of four coherent drone signals in the application example of this application. DETAILED DESCRIPTION

[0060] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] The embodiment of the present application provides a method for direction finding of coherent signals of a UAV based on a uniform circular array, such as Figure 1 As shown, the following steps are included:

[0062] In step S101, the uniform circular array single snapshot condition M×1 spatial data vector is defined as X, the p×1 signal model is defined as S, the M×1 noise space is defined as N, and J is a (2h+1)×(2h+1) diagonal matrix. In a specific example, a uniform circular array can be used to receive drone signals, for example, an eight-element uniform circular array can be used in a specific example.

[0063] In step S102, for the received drone signal, the matrix T is preprocessed to transform the uniform circular array in the original array element space into a virtual uniform linear array in the pattern space to obtain a transformed signal.

[0064] In step S103, a vector z is defined, the vector z includes 2(n-1)+1 rows, and a response Toeplitz matrix is ​​constructed by the vector z.

[0065] In step S104, based on the transformed signal, a covariance matrix corresponding to the HermitianToeplitz matrix reconstructed from the observation space is calculated.

[0066] In step S105, based on the calculated covariance matrix, search for ||Q from different φ values ​​(0, 2π). H The minimum value of b(φ)|| defines the MUSIC algorithm to find the largest spectral peak.

[0067] In step S106, the spectrum peak estimation of the MUSIC algorithm is converted into a polynomial using the ROOT-MUSIC algorithm, so as to determine the estimation of the direction of arrival using the roots of the polynomial.

[0068] In some embodiments, the relevant parameters are defined as follows:

[0069]

[0070]

[0071] Where X is the single snapshot conditional M×1 spatial data vector, A is the M×p spatial flow matrix, S is the p×1 signal model, N is the M×1 noise space, w=e j2π / M ;

[0072] J is a (2h+1)×(2h+1) diagonal matrix satisfying:

[0073]

[0074] In some embodiments, preprocessing the matrix T to convert the uniform circular array in the original array element space into a virtual uniform linear array in the pattern space to obtain the transformed signal includes:

[0075] Based on the diagonal matrix J, substituting the following matrix T satisfies:

[0076]

[0077] The preprocessing matrix T transforms the uniform circular array in the original array element space into a virtual uniform linear array in the pattern space, satisfying:

[0078]

[0079] The uniform circular array in the array element space is transformed into a virtual linear array (VULA), which has an array flow pattern similar to that of a uniform linear array, and the transformed signal is obtained.

[0080] In some embodiments, a vector z is defined, the vector z includes 2(n-1)+1 rows, and constructing a response Toeplitz matrix using the vector z includes:

[0081] The response Toeplitz matrix constructed by vector z is:

[0082]

[0083] Optionally, based on the transformed signal, calculating the covariance matrix corresponding to reconstructing the Hermitian Toeplitz matrix from the observation space includes:

[0084] The Hermitian Toeplitz matrix can be reconstructed from the observation signal vector. The row vector of the observation space signal vector (2h+1) is:

[0085]

[0086] The Hermitian Toeplitz matrix is ​​reconstructed from the observation space as:

[0087]

[0088] The Toeplitz matrix constructed by The covariance matrix of the matrix is ​​calculated as

[0089] The eigenvalues ​​of the matrix are:

[0090]

[0091] Where V is a (h+1)×(h+1) matrix containing all eigenvectors {v1,...,v h+1};

[0092] Q is corresponding The matrix composed of the eigenvectors of the noise space is (ph)×(h+1), which satisfies:

[0093] Q = {v p+1 ,...,v h+1}

[0094] b(φ k ) is defined as:

[0095]

[0096] Where:

[0097] u k =exp(j(φ k -2π(n-1) / (h+1))).

[0098] In order to find the direction of arrival, the method of the embodiment of the present application searches for ||Q from different φ values ​​(0, 2π). H b(φ)||, in some embodiments, defining the MUSIC algorithm to find the maximum spectral peak includes:

[0099] The MUSIC algorithm is defined as:

[0100]

[0101] In some embodiments, using the ROOT-MUSIC algorithm to convert the spectrum peak estimate of the MUSIC algorithm into a polynomial, and using the root of the polynomial to determine the estimate of the direction of arrival includes:

[0102] The ROOT-MUSIC algorithm is used to convert the spectrum peak estimate of the MUSIC algorithm into a polynomial, the root of which contains the estimate of the direction of arrival:

[0103] F -1 =b H (φ)QQ Hb(φ)

[0104] Transform the estimate of the direction of arrival, assuming W = QQ H , then:

[0105] F -1 =b H (φ)Wb(φ)

[0106] Then the above formula can be rewritten as:

[0107]

[0108] Where W nm is the element in the nth row and mth column of the matrix W. When l=mn, the double summation symbol can be simplified to a single summation symbol. The range of l is determined by n and m. From the range of n and m, we can get -(h+l)≤l≤(h+l).

[0109] The above formula can be transformed into:

[0110]

[0111] In the formula

[0112] The polynomial is expressed as:

[0113]

[0114] Where w z =e jφ ;

[0115] According to the polynomial D(w z ) to estimate the direction of arrival of the signal.

[0116] The embodiment of the present application also proposes an implementation case of a direction finding method for coherent signals of drones based on a uniform circular array. In this example, a uniform circular array is used to find the direction of the drone. The number of direction finding antenna elements is 8, and the radius of the element is r=λ. The drones are drones of the same manufacturer and model, and the signals between the drones are coherent signals. The drone signals are simulated, and different incident angles of the drone signals are set respectively to verify the method of the present application. In this example, the spacing between the incident signals of the drones is set relatively close.

[0117] 1. Two coherent drone signals

[0118] Assume that the incident angles of the two UAV simulation signals are 2° and 10° respectively. The direction estimation based on virtual transformation is adopted as follows Figure 2 As shown, the directions of the incoming waves estimated by the method of the embodiment of the present application are: 2.0044°, 10.0011°. Compared with the set value, the error estimation value of the incoming wave of the drone coherent signal is less than 0.1°.

[0119] 2. Three coherent drone signals

[0120] Assume that the incident angles of the three UAV simulation signals are 2°, 10°, and 15° respectively. The direction estimation based on virtual transformation is adopted as follows Figure 3 As shown, the directions of the incoming waves estimated by the method of the present application are: 2.0040°, 9.9827°, 15.0010°. Compared with the set value, the error estimation value of the incoming wave of the drone coherent signal is less than 0.1°.

[0121] 3. Four coherent drone signals

[0122] Assume that the incident angles of the four drone signals are 2°, 10°, 15°, and 20° respectively. The direction estimation based on virtual transformation is adopted as follows Figure 4 As shown, the directions of the incoming waves estimated by the method of the present application are: 1.9874°, 9.9241°, 15.3489°, 20.2114°. Compared with the set value, the error estimation value of the incoming wave of the drone coherent signal is less than 0.5°.

[0123] As the number of drones increases, the estimation error of the direction of arrival of drone coherent signals also increases. By increasing the number of antenna array elements, the accuracy is improved and the direction finding error is reduced. For example, by expanding the 8-element array to a 16-element array, the direction finding accuracy will be greatly improved.

[0124] Based on a uniformly distributed 8-unit antenna array, by selecting the coherent signals of drones of the same model and manufacturer, the coherent signals of 2 drones, 3 drones, and 4 drones were simulated respectively. The experiment showed that the error of the direction of arrival of 2 and 3 coherent drones was less than 0.1°, and the error of the direction of arrival of 4 coherent drones was less than 0.5°. The verification shows that the method proposed in the embodiment of the present application can correctly estimate the direction of arrival of the coherent drone.

[0125] In view of the characteristics of multiple UAV coherent signals, this application adopts a uniform circular array, converts it into a uniform virtual linear array receiving signal through Toeplitz transformation, and further adopts the ROOT-MUSIC algorithm for direction finding. This application transforms the uniform circular array into a virtual linear array, and processes the virtual linear array signal with the ROOT-MUSIC algorithm, which solves the defects of the traditional direction finding method for UAV coherent signal direction finding, ensures the successful separation of UAV coherent signals, and also ensures the direction finding accuracy of UAV coherent signals, providing a basis for accurate countermeasures against UAVs.

[0126] An embodiment of the present application also proposes a direction finding system for UAV coherent signals based on a uniform circular array, comprising a processor and a memory, wherein a computer program is stored on the memory, and when the computer program is executed by the processor, the steps of the direction finding method for UAV coherent signals based on a uniform circular array are implemented as described above.

[0127] It should be noted that in the various embodiments of the present application, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0128] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0129] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0130] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for direction finding of coherent signals of unmanned aerial vehicles based on uniform circular array, characterized in that: include: Define the uniform circular array single snapshot condition M×1 spatial data vector as X, p×1 signal model as S, M×1 noise space as N, and J as a (2h+1)×(2h+1) diagonal matrix; The preprocessing matrix T transforms the uniform circular array in the original array element space into a virtual uniform linear array in the pattern space to obtain the transformed signal; Define a vector z, which includes 2(n-1)+1 rows, and construct a response Toeplitz matrix through the vector z; Based on the transformed signal, the covariance matrix corresponding to the Hermitian Toeplitz matrix reconstructed from the observation space is calculated; Based on the calculated covariance matrix, we search for ||Q from different φ values ​​from (0,2π) H The minimum value of b(φ)|| defines the MUSIC algorithm to find the maximum spectral peak; The ROOT-MUSIC algorithm is used to transform the spectrum peak estimate of the MUSIC algorithm into a polynomial, so that the estimate of the direction of arrival can be determined using the roots of the polynomial.

2. The method for finding the direction of coherent signals of unmanned aerial vehicles based on uniform circular array according to claim 1, characterized in that: The relevant parameters are defined as follows: Where X is the single snapshot conditional M×1 spatial data vector, A is the M×p spatial flow matrix, S is the p×1 signal model, N is the M×1 noise space, w=e j2π / M ; J is a (2h+1)×(2h+1) diagonal matrix satisfying:

3. The method for finding the direction of coherent signals of unmanned aerial vehicles based on uniform circular array according to claim 2, characterized in that: The preprocessing matrix T converts the uniform circular array in the original array element space into a virtual uniform linear array in the pattern space to obtain the transformed signal including: Based on the diagonal matrix J, the matrix T satisfies: The preprocessing matrix T transforms the uniform circular array in the original array element space into a virtual uniform linear array in the pattern space, satisfying: The uniform circular array in the array element space is transformed into a virtual linear array (VULA) to obtain the transformed signal 4. The method for finding the direction of coherent signals of unmanned aerial vehicles based on uniform circular array according to claim 3, characterized in that: Define vector z, which includes 2(n-1)+1 rows. The response Toeplitz matrix constructed by vector z includes: The response Toeplitz matrix constructed by vector z is:

5. The method for finding the direction of coherent signals of unmanned aerial vehicles based on uniform circular array according to claim 4, characterized in that: Based on the transformed signal, the covariance matrix corresponding to the Hermitian Toeplitz matrix reconstructed from the observation space is calculated, including: The row vector of the observation space signal vector (2h+1) is: The Hermitian Toeplitz matrix is ​​reconstructed from the observation space as: The Toeplitz matrix constructed by The covariance matrix of the matrix is ​​calculated as The eigenvalues ​​of the matrix are: Where V is a (h+1)×(h+1) matrix containing all eigenvectors {v1,...,v h+1 }; correspond The matrix composed of the eigenvectors of the noise space is (ph)×(h+1), which satisfies: Q={v p+1 ,...,v h+1 } b(φ k ) is defined as: Where: u k =exp(j(φ k -2π(n-1) / (h+1)))。 6. The method for finding the direction of coherent signals of unmanned aerial vehicles based on uniform circular array according to claim 5, characterized in that: Defining the MUSIC algorithm to find the largest spectral peak involves: The MUSIC algorithm is defined as:

7. The method for finding the direction of coherent signals of unmanned aerial vehicles based on uniform circular array according to claim 6, characterized in that: The ROOT-MUSIC algorithm is used to convert the spectrum peak estimate of the MUSIC algorithm into a polynomial, and the estimation of the direction of arrival is determined by using the root of the polynomial, including: The ROOT-MUSIC algorithm is used to convert the spectrum peak estimate of the MUSIC algorithm into a polynomial, the root of which contains the estimate of the direction of arrival: F -1 =b H (φ)QQ H b(φ) The estimate of the direction of arrival is transformed and the polynomial is expressed as: Where w z =e jφ ; According to the polynomial D(w z ) to estimate the direction of arrival of the signal.

8. A direction finding system for coherent signals of unmanned aerial vehicles based on uniform circular array, characterized in that: It comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the direction finding method of the coherent signal of the unmanned aerial vehicle based on the uniform circular array are implemented as described in any one of claims 1 to 7.

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