Secondary radar direction finding method and system based on spatial spectrum estimation, terminal and medium

Abstract

The invention discloses a secondary radar direction finding method and system based on spatial spectrum estimation, a terminal and a medium, and relates to the technical field of radar detection, and the technical scheme is characterized in that an information source transmitting signal received by a receiving antenna array is preprocessed to obtain a baseband I / Q signal; correcting and compensating the baseband I / Q signal according to the amplitude and phase difference of the receiving channel corresponding to the transmitting signal of the receiving signal source to obtain a corrected signal; carrying out initial direction finding on the correction signal by adopting a DBF to obtain an initial direction range of the correction signal; determining an information source number according to the correction signal; and carrying out direction finding operation on the signal source number and the initial direction range of the correction signal by adopting a MUSIC algorithm to obtain a secondary radar direction finding result. According to the method, the precision of secondary radar direction finding can be effectively improved, the calculated amount is low, and the operation rate is high.

Description

Technical Field

[0001] The present invention relates to the field of radar detection technology, and more specifically, to a secondary radar direction finding method, system, terminal and medium based on spatial spectrum estimation. Background Art

[0002] Secondary radar is a radar system corresponding to primary radar. It is often used in air traffic control. With the rise of electronic warfare, secondary radar is gradually applied to identification of friend or foe. Whether it is air traffic control or identification of friend or foe, secondary radar needs to detect information such as the direction and distance of the target. The method commonly used by secondary radar to detect target direction is interferometer direction finding. This method is greatly affected by the external environment. When the system environment changes, it may lead to a decrease in direction finding accuracy. Spatial spectrum estimation technology has super-resolution capability and can achieve accurate direction finding with fewer signal sampling points. It is also adaptable to complex environments and can improve the reliability and stability of the radar system.

[0003] The prior art records a radar spatial spectrum estimation method based on a sparse array. This method applies the spatial spectrum estimation technology to sparse array radars, introduces a "virtual array-observer" model, and transforms the spatial spectrum estimation problem of sparse arrays into an array element weight estimation problem of a virtual array. The introduction of this model increases the complexity of the system, resulting in the need for fine debugging and optimization of the model when applied, which increases product development time and cost. Although this method uses FFT instead of matrix multiplication to reduce computational complexity, its computational complexity is still very large when processing large-scale arrays or high sampling rate signals. In addition, the prior art also records a high-frequency ground wave radar single-shot MUSIC direction finding method based on array data rearrangement. This method applies the MUSIC algorithm to high-frequency ground wave radars to improve the azimuth accuracy and resolution of direction finding.

[0004] However, the above-mentioned literatures all apply spatial spectrum estimation technology to primary radar direction finding, although secondary radar direction finding can also use spatial spectrum estimation technology to achieve super-resolution direction finding. However, the above-mentioned spatial spectrum estimation technology uses complex operations when calculating covariance and eigenvalue decomposition. One complex operation is equivalent to two real multiplications and four real additions, which has a large amount of operations and high complexity. In addition, when performing multi-target direction finding, if the target distance is close, the above-mentioned spatial spectrum estimation technology may judge the two targets as one target, resulting in reduced direction finding accuracy. Therefore, how to study and design a secondary radar direction finding method, system, terminal and medium based on spatial spectrum estimation that can overcome the above-mentioned defects is a problem that we urgently need to solve. Summary of the invention

[0005] To address the deficiencies in the prior art, the present invention aims to provide a secondary radar direction finding method, system, terminal and medium based on spatial spectrum estimation, which uses DBF to roughly find the direction of the signal, determines the coarse filtering direction of the signal, and then estimates the number of signal sources; then, based on the rough direction of the signal and the calculated number of signal sources, uses the MUSIC algorithm to perform precise direction finding to obtain the precise direction information of the signal. This method can effectively improve the accuracy of secondary radar direction finding, and has a low amount of calculation and a fast operation rate.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] In a first aspect, a secondary radar direction finding method based on spatial spectrum estimation is provided, comprising the following steps:

[0008] Preprocessing the source transmission signal received by the receiving antenna array to obtain a baseband I / Q signal;

[0009] The baseband I / Q signal is corrected and compensated according to the amplitude and phase difference of the receiving channel corresponding to the transmitted signal of the receiving source to obtain a correction signal;

[0010] Use DBF to perform initial direction finding on the correction signal to obtain the initial direction range of the correction signal;

[0011] Determine the number of signal sources according to the correction signal;

[0012] The MUSIC algorithm is used to perform direction finding calculation on the number of signal sources and the initial direction range of the correction signal to obtain the secondary radar direction finding result.

[0013] Furthermore, the process of preprocessing the source transmission signal received by the receiving antenna array is specifically as follows:

[0014] Down-converting the received radio frequency signal to an intermediate frequency to obtain an intermediate frequency signal;

[0015] The intermediate frequency signal is filtered and AD sampled before being down-converted from the intermediate frequency to the baseband signal.

[0016] Furthermore, the process of determining the number of information sources is specifically as follows:

[0017] The covariance of the array data obtained by taking a snapshot of the correction signal is converted into a real number to obtain a first matrix;

[0018] Remove the last row and the last column of the first matrix to form the second matrix;

[0019] Perform eigenvalue decomposition on the second matrix to obtain the first eigenvector;

[0020] A unitary matrix is ​​constructed using the first eigenvector, and the covariance of the array data is unitarily transformed using the unitary matrix to obtain a third matrix;

[0021] The absolute values ​​of the first M-1 numbers in the last column of the third matrix are used as the radius of the Gaussian circle of the third matrix to determine the number of information sources.

[0022] Furthermore, the process of performing direction finding operation on the number of signal sources and the initial direction range of the correction signal using the MUSIC algorithm is specifically as follows:

[0023] The covariance of the array data obtained by taking a snapshot of the correction signal is converted into a real number to obtain a first matrix;

[0024] Perform eigenvalue decomposition on the first matrix to obtain a second eigenvector;

[0025] Divide the second eigenvector into a signal subspace and a noise subspace according to the number of information sources;

[0026] Based on the orthogonal characteristics of signal subspace and noise subspace, spatial spectrum estimation is performed within the initial direction range to obtain the secondary radar direction finding result.

[0027] Furthermore, the real-numbered expression for the covariance of the array data obtained by taking a snapshot of the correction signal is specifically:

[0028]

[0029]

[0030] Among them, R′ represents the first matrix after real numbering; Q represents the M×M dimensional matrix composed of I and J; H represents the conjugate transpose; R represents the covariance of the array data; J represents a matrix with all the elements on the secondary diagonal being 1 and all the other elements being 0; I represents the identity matrix with all the elements on the main diagonal being 1 and all the other elements being 0; * represents the conjugate operation.

[0031] Furthermore, the process of dividing the second eigenvector into a signal subspace and a noise subspace according to the number of information sources is specifically as follows:

[0032] Arrange the eigenvalues ​​in the second eigenvector in descending order to obtain an arrangement sequence;

[0033] The eigenvectors corresponding to the first K eigenvalues ​​in the permutation sequence constitute the signal subspace;

[0034] The eigenvectors corresponding to the last MK eigenvalues ​​in the permutation sequence constitute the noise subspace.

[0035] Furthermore, the expression of the spatial spectrum estimation is:

[0036]

[0037] a H (θ)=[a(θ1),a(θ2),…,a(θ K )] H ;

[0038] Among them, P MUSIC (θ) represents the spatial spectrum estimation function; θ represents the target direction, which is within the initial direction range; a(θ) represents the signal steering vector; H represents the conjugate transpose; U N represents the noise subspace; a(θ K ) represents the Kth direction in the signal-directed vector.

[0039] In a second aspect, a secondary radar direction finding system based on spatial spectrum estimation is provided, the system is used to implement the secondary radar direction finding method based on spatial spectrum estimation as described in any one of the first aspects, including:

[0040] The signal processing module is used to pre-process the source transmission signal received by the receiving antenna array to obtain a baseband I / Q signal;

[0041] The signal compensation module is used to correct and compensate the baseband I / Q signal according to the amplitude and phase difference of the receiving channel corresponding to the transmitted signal of the receiving source to obtain a correction signal;

[0042] The initial direction finding module is used to perform initial direction finding on the correction signal using DBF to obtain the initial direction range of the correction signal;

[0043] An information source determination module, used to determine the number of information sources according to the correction signal;

[0044] The direction finding operation module is used to perform direction finding operation on the number of signal sources and the initial direction range of the correction signal using the MUSIC algorithm to obtain the secondary radar direction finding result.

[0045] In a third aspect, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the secondary radar direction finding method based on spatial spectrum estimation as described in any one of the first aspects is implemented.

[0046] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement the secondary radar direction finding method based on spatial spectrum estimation as described in any one of the first aspects.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. The secondary radar direction finding method based on spatial spectrum estimation provided by the present invention uses DBF to roughly find the direction of the signal, determines the coarse filtering direction of the signal, and then estimates the number of signal sources; then, according to the rough direction of the signal and the calculated number of signal sources, the MUSIC algorithm is used to perform precise direction finding to obtain the precise direction information of the signal. This method can effectively improve the accuracy of secondary radar direction finding, and has a low amount of calculation and a fast operation rate;

[0049] 2. The direction vector calculation, covariance calculation, and eigenvalue decomposition units in the present invention can be implemented in a pipeline manner, saving a large amount of resources. The multipliers and adders are time-division multiplexed, further saving a large amount of resources.

[0050] 3. The present invention converts the covariance matrix into real numbers, converts complex number operations into real number operations, saves resources, improves the operation speed, and does not affect the direction finding results. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0052] Figure 1 is a flow chart of Embodiment 1 of the present invention;

[0053] Figure 2 is a flow chart of the direction finding operation in Embodiment 1 of the present invention;

[0054] Figure 3 is a comparison diagram of the results of single target direction finding in Example 1 of the present invention;

[0055] Figure 4 is a comparison diagram of the results of multi-target direction finding in Example 1 of the present invention;

[0056] Figure 5 It is a system block diagram in Example 2 of the present invention. DETAILED DESCRIPTION

[0057] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0058] It should be noted that when a component is referred to as being "fixed to" or "disposed on" another component, it can be directly on the other component or indirectly on the other component. When a component is referred to as being "connected to" another component, it can be directly or indirectly connected to the other component.

[0059] It should be understood that the orientation or position relationship indicated by terms such as "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside" and "outside" are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0060] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0061] Embodiment 1: A secondary radar direction finding method based on spatial spectrum estimation, such as Figure 1 As shown, the following steps are included:

[0062] S1: pre-process the source transmission signal received by the receiving antenna array to obtain a baseband I / Q signal;

[0063] S2: Correct and compensate the baseband I / Q signal according to the amplitude and phase difference of the receiving channel corresponding to the transmitted signal of the receiving source to obtain a correction signal;

[0064] S3: Use DBF to perform initial direction finding on the correction signal to obtain the initial direction range of the correction signal;

[0065] S4: Determine the number of signal sources according to the correction signal;

[0066] S5: Use the MUSIC algorithm to perform direction finding calculations on the number of signal sources and the initial direction range of the correction signal to obtain the secondary radar direction finding result.

[0067] In step S1, the antenna array is composed of M antenna elements. Each antenna element has one receiving channel. It receives the signal transmitted by the source, transmits it through N receiving channels, and performs down-conversion, filtering, AD sampling and other operations in sequence to convert the originally collected RF signal into an orthogonal baseband I / Q signal.

[0068] For example, the AD sampling clock is 190MHz, the carrier frequency of the intermediate frequency signal is 140MHz, and the signal bandwidth is 10MHz. After the intermediate frequency signal is sampled by AD, the center frequency of the original data is transferred from 140MHz to 50MHz through spectrum shifting. Then, it is mixed with the 50MHz fixed frequency sine and cosine wave data generated by DDS, and then the high-frequency image component is filtered out through a low-pass filter to obtain the required baseband data.

[0069] In step S2, in actual applications, the inconsistency of channel amplitude and phase will affect the phased array antenna synthesis gain, sidelobe level, etc., so it is necessary to calibrate the received data before the next step of processing. The direction finding system uses a phased array antenna as a receiving antenna. The amplitude and phase of the phased array antenna have a great influence on it. In order to ensure the direction finding effect, the accuracy of the amplitude and phase of the input signal of each receiving channel must be guaranteed. Therefore, the present invention calibrates the received data before the next step of operation to compensate for the inconsistency of the channels in the M channels so that the phases of each channel are consistent.

[0070] In step S3, the calibrated data is processed by beam synthesis, and rough direction finding can be performed on the received data. Because when the signal is within the range of the synthesized beam, the amplitude of the beam is the largest, and rough direction finding can be performed using beam synthesis. For example, DBF is used to perform initial direction finding on the correction signal to obtain the initial direction range of the correction signal, which can be used as a constraint for subsequent spatial spectrum estimation.

[0071] In step S4, the process of determining the number of information sources is specifically as follows: the covariance of the array data obtained by taking a snapshot of the correction signal is converted into real numbers to obtain a first matrix; the last row and the last column in the first matrix are removed to form a second matrix; the second matrix is ​​subjected to eigenvalue decomposition to obtain a first eigenvector; a unitary matrix is ​​constructed using the first eigenvector, and the covariance of the array data is subjected to a unitary transformation using the unitary matrix to obtain a third matrix; the absolute value of the first M-1 numbers in the last column of the third matrix is ​​used as the radius of the Gauss circle of the third matrix to determine the number of information sources.

[0072] After receiving the signal, the number of signal sources must be estimated first, and the number of signal sources must be determined before proceeding to the subsequent steps. Suppose the signal expression is X(t) = AS(t) + Ns(t), S is the snapshot data of the signal, and Ns is the noise; A is a set of direction vectors, and its expression is shown in formula (1), where d is the array element spacing, θ is the target direction, λ is the wavelength, and T represents the matrix transpose.

[0073] A=(1,e -j2πd sinθ / λ ,…,e -j2π7d sinθ / λ ) T (1).

[0074] Since the signal and noise are independent of each other, the covariance of the array data can be expressed as: R = E[XX H ]=AE[SS H ]A H +E[NsNs H ]=AR S A H +σ 2 I; where R S and σ 2 I are the signal covariance matrix and the noise covariance matrix, σ 2 is the spatial ideal white noise power, and H represents the conjugate transpose of the matrix.

[0075] Next, the covariance is converted to real numbers, and the first matrix calculation formula is: The * sign indicates the conjugation operation.

[0076]

[0077] The values ​​of I and J are:

[0078]

[0079] Among them, R′ represents the first matrix after real numbering; Q represents the M×M dimensional matrix composed of I and J; R represents the covariance of the array data; J represents a matrix with all the elements on the secondary diagonal being 1 and all the other elements being 0, and the matrix dimension is (M / 2)×(M / 2); I represents the identity matrix with all the elements on the main diagonal being 1 and all the other elements being 0, and the matrix dimension is (M / 2)×(M / 2).

[0080] Remove the last row and column of R′ to form a (M-1)×(M-1) dimensional first matrix right Perform eigenvalue decomposition and get Its first eigenvector Σ′ is its eigenvalue.

[0081] use Construct the unitary matrix T:

[0082]

[0083] Use the generated unitary matrix to perform a unitary transformation on the covariance of the received signal to obtain the third matrix The first M-1 numbers in the last column of i The absolute value of (i=1,2,…,M-1) is used as the radius of the Gauss circle of the covariance matrix after unitary transformation, i.e. r i =|ρ i |(i=1,2,…,M-1).

[0084] Then use the following formula to determine the number of signal sources:

[0085]

[0086] In the formula, k ranges from 1 to M-2, DL is an adjustment factor related to the number of samples, which is selected between 1 and 0. When M→∞, DL→0. When k increases from small to large, the first time that GDE(k) becomes negative, k minus one is the number of sources. If k=k0 at this time, then K=k0-1.

[0087] In step S5, Figure 2 As shown in the figure, the process of using the MUSIC algorithm to perform direction finding operations on the number of signal sources and the initial direction range of the correction signal is specifically as follows: the covariance of the array data obtained by taking a snapshot of the correction signal is converted into a real number to obtain a first matrix; the first matrix is ​​decomposed into an eigenvalue to obtain a second eigenvector; the second eigenvector is divided into a signal subspace and a noise subspace according to the number of signal sources; and spatial spectrum estimation is performed within the initial direction range based on the mutual orthogonality of the signal subspace and the noise subspace to obtain the secondary radar direction finding result.

[0088] The sent data is sampled L times, and the L snapshot data is used for direction finding. The received data can be expressed as X(t) = AS(t) + Ns(t), where A is a set of direction vectors, which is an M×K dimensional matrix; S is the data obtained by taking L snapshots of the signal, then AS(t) is an M×L dimensional matrix; Ns represents the noise of the signal.

[0089] Calculate an M×L matrix and multiply it by its own transpose (L×M matrix) to obtain an M×M symmetric covariance matrix R (R = E[XX H ]). When calculating, only the diagonal elements and upper triangular elements of the covariance matrix need to be calculated.

[0090] Because the data obtained is complex, and one complex multiplication operation is equivalent to four real multiplications and two real additions, FPGA resources are limited, which will increase the use of FPGA resources; therefore, in order to reduce the use of FPGA resources, the covariance is converted to a real number before subsequent operations.

[0091] Perform eigenvalue decomposition on R. Among the eigenvalues ​​and eigenvectors obtained, the space spanned by the eigenvectors corresponding to the large eigenvalues ​​is called the signal subspace, and the space spanned by the eigenvectors corresponding to the small eigenvalues ​​is called the noise subspace.

[0092] Specifically, the real-numbered covariance R′ is subjected to eigenvalue decomposition to obtain R′=UΣU H , U is the second eigenvector, Σ is the diagonal matrix consisting of the eigenvalues ​​corresponding to the eigenvector, which can be expressed as:

[0093]

[0094] In the formula, λ i (i=1,...,M) is the characteristic value of the signal, and its relationship satisfies λ1≥λ2…≥λ K >λ K+1 =…=λ M =σ 2 The eigenvalues ​​are arranged in descending order, and the eigenvectors corresponding to the first K larger eigenvalues ​​constitute the signal subspace U S =[v1,v2,…v K ], and the eigenvectors corresponding to the last MK smaller eigenvalues ​​constitute the noise subspace U N =[v K+1 ,v K+2 ,…v M ].

[0095] Ideally, the signal and noise are uncorrelated, so the signal subspace and the noise subspace are orthogonal to each other, that is, U S ⊥U N , from which we can conclude:

[0096] [a(θ1),a(θ2),…,a(θ K )] H U N =0;

[0097] Therefore, according to this orthogonal property, the spectrum estimation formula can be obtained as follows:

[0098]

[0099] a H (θ)=[a(θ1),a(θ2),…,a(θ K )] H ;

[0100] Among them, P MUSIC (θ) represents the spatial spectrum estimation function; θ represents the target direction, which is within the initial direction range; a(θ) represents the signal steering vector; H represents the conjugate transpose; U N represents the noise subspace; a(θ K ) represents the Kth direction in the signal-directed vector.

[0101] Assume that the number of incident sources K = 2, and the directions are 20° and 25° respectively. Compare the spatial spectrum estimation results with and without source number estimation as shown in the figure. Figure 4 As shown. Figure 4It can be seen that when the distance between two targets is close, the general spatial spectrum estimation method cannot correctly calculate the target direction. Therefore, it is necessary to estimate the number of signal sources first, determine the number of targets, and then perform direction finding.

[0102] According to the above steps, the results of using the spatial spectrum estimation algorithm to find the direction in the secondary radar are as follows: Figure 3 and Figure 4 shown. Figure 3 This is the effect of real digitization on the direction finding accuracy for a single target. As can be seen from the figure, real digitization has little effect on the accuracy. Figure 4 The figure is a simulation result of using the method of the present invention and the existing method to perform multi-target direction finding. It can be seen from the figure that the spatial spectrum direction finding method of the existing method may identify multiple targets as a single target when performing multi-target direction finding, causing misjudgment and reducing the direction finding accuracy; while the method of the present invention can identify multiple targets and accurately calculate the target direction. In addition, when the target is close, the method of the present invention can also identify the target. Figure 4 In the above method, the two information sources are separated by 5° in azimuth, and the target is still identified. However, in the existing document, when performing multi-target direction finding, the two information sources are separated by more than 20° in azimuth to measure the target azimuth. Therefore, the method of the present invention has the characteristics of low calculation amount, high accuracy, and can identify multiple targets.

[0103] Embodiment 2: A secondary radar direction finding system based on spatial spectrum estimation, the system is used to implement the secondary radar direction finding method based on spatial spectrum estimation recorded in Embodiment 1, such as Figure 5 As shown, it includes a signal processing module, a signal compensation module, an initial direction finding module, a signal source determination module and a direction finding operation module.

[0104] Among them, the signal processing module is used to pre-process the signal transmitted by the receiving antenna array to obtain the baseband I / Q signal; the signal compensation module is used to correct and compensate the baseband I / Q signal according to the amplitude and phase difference of the receiving channel corresponding to the received signal transmitted by the receiving source to obtain the correction signal; the initial direction finding module is used to perform initial direction finding on the correction signal using DBF to obtain the initial direction range of the correction signal; the signal source determination module is used to determine the number of signal sources according to the correction signal; the direction finding operation module is used to perform direction finding operation on the number of signal sources and the initial direction range of the correction signal using the MUSIC algorithm to obtain the secondary radar direction finding result.

[0105] The present invention also describes a computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the secondary radar direction finding method based on spatial spectrum estimation as described in Example 1 is implemented.

[0106] The present invention also describes a computer-readable medium on which a computer program is stored. The computer program is executed by a processor to implement the secondary radar direction finding method based on spatial spectrum estimation as described in Example 1.

[0107] Working principle: The present invention provides a secondary radar direction finding method based on spatial spectrum estimation, based on the problems of low accuracy of existing secondary radar direction finding technology, inability to correctly identify signal direction due to signal overlap, and large FPGA resource consumption. This method uses DBF to roughly find the direction of the signal, determine the coarse filtering direction of the signal, and then estimate the number of signal sources; then, based on the rough direction of the signal and the calculated number of signal sources, the MUSIC algorithm is used to perform precise direction finding to obtain the precise direction information of the signal. This method can effectively improve the accuracy of secondary radar direction finding, and has a low amount of calculation and a fast operation rate.

[0108] In addition, the direction vector calculation, covariance calculation, and eigenvalue decomposition units in the present invention can be implemented in a pipeline manner, saving a large amount of resources. The multipliers and adders are time-division multiplexed, further saving a large amount of resources.

[0109] In addition, the present invention converts the covariance matrix into real numbers and converts complex number operations into real number operations, which also saves resources and improves the operation speed without affecting the direction finding result.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A secondary radar direction finding method based on spatial spectrum estimation is characterized in that: The following steps are involved: Preprocessing the source transmission signal received by the receiving antenna array to obtain a baseband I / Q signal; The baseband I / Q signal is corrected and compensated according to the amplitude and phase difference of the receiving channel corresponding to the transmitted signal of the receiving source to obtain a correction signal; Use DBF to perform initial direction finding on the correction signal to obtain the initial direction range of the correction signal; Determine the number of signal sources according to the correction signal; The MUSIC algorithm is used to perform direction finding calculation on the number of signal sources and the initial direction range of the correction signal to obtain the secondary radar direction finding result.

2. The secondary radar direction finding method based on spatial spectrum estimation according to claim 1, characterized in that: The process of preprocessing the source transmission signal received by the receiving antenna array is specifically as follows: Down-converting the received radio frequency signal to an intermediate frequency to obtain an intermediate frequency signal; The intermediate frequency signal is filtered and AD sampled before being down-converted from the intermediate frequency to the baseband signal.

3. The secondary radar direction finding method based on spatial spectrum estimation according to claim 1 is characterized in that: The specific process of determining the number of information sources is as follows: The covariance of the array data obtained by taking a snapshot of the correction signal is converted into a real number to obtain a first matrix; Remove the last row and the last column of the first matrix to form the second matrix; Perform eigenvalue decomposition on the second matrix to obtain the first eigenvector; A unitary matrix is ​​constructed using the first eigenvector, and the covariance of the array data is unitarily transformed using the unitary matrix to obtain a third matrix; The absolute values ​​of the first M-1 numbers in the last column of the third matrix are used as the radius of the Gaussian circle of the third matrix to determine the number of information sources.

4. The secondary radar direction finding method based on spatial spectrum estimation according to claim 1, characterized in that: The specific process of using the MUSIC algorithm to perform direction finding calculations on the number of signal sources and the initial direction range of the correction signal is as follows: The covariance of the array data obtained by taking a snapshot of the correction signal is converted into a real number to obtain a first matrix; Perform eigenvalue decomposition on the first matrix to obtain a second eigenvector; Divide the second eigenvector into a signal subspace and a noise subspace according to the number of information sources; Based on the orthogonal characteristics of signal subspace and noise subspace, spatial spectrum estimation is performed within the initial direction range to obtain the secondary radar direction finding result.

5. The secondary radar direction finding method based on spatial spectrum estimation according to claim 3 or 4, characterized in that: The specific expression for converting the covariance of the array data obtained by taking a snapshot of the correction signal into a real number is: Among them, R ′ represents the first matrix after real number conversion; Q represents the M×M dimensional matrix composed of I and J; H represents the conjugate transpose; R represents the covariance of the array data; J represents a matrix with all the elements on the secondary diagonal being 1 and all the other elements being 0; I represents the identity matrix with all the elements on the main diagonal being 1 and all the other elements being 0; * represents the conjugate operation.

6. The secondary radar direction finding method based on spatial spectrum estimation according to claim 4 is characterized in that: The process of dividing the second eigenvector into signal subspace and noise subspace according to the number of information sources is as follows: Arrange the eigenvalues ​​in the second eigenvector in descending order to obtain an arrangement sequence; The eigenvectors corresponding to the first K eigenvalues ​​in the permutation sequence constitute the signal subspace; The eigenvectors corresponding to the last MK eigenvalues ​​in the permutation sequence constitute the noise subspace.

7. The secondary radar direction finding method based on spatial spectrum estimation according to claim 4 is characterized in that: The expression of spectrum estimation is: a H (θ)=[a(θ1),a(θ2),…,a(θ K )] H ; Among them, P MUSIC (θ) represents the spatial spectrum estimation function; θ represents the target direction, which is within the initial direction range; a(θ) represents the signal steering vector; H represents the conjugate transpose; U N represents the noise subspace; a(θ K ) represents the Kth direction in the signal-directed vector.

8. A secondary radar direction finding system based on spatial spectrum estimation, characterized in that: The system is used to implement the secondary radar direction finding method based on spatial spectrum estimation as claimed in any one of claims 1 to 7, comprising: The signal processing module is used to pre-process the source transmission signal received by the receiving antenna array to obtain a baseband I / Q signal; The signal compensation module is used to correct and compensate the baseband I / Q signal according to the amplitude and phase difference of the receiving channel corresponding to the transmitted signal of the receiving source to obtain a correction signal; The initial direction finding module is used to perform initial direction finding on the correction signal using DBF to obtain the initial direction range of the correction signal; An information source determination module, used to determine the number of information sources according to the correction signal; The direction finding operation module is used to perform direction finding operation on the number of signal sources and the initial direction range of the correction signal using the MUSIC algorithm to obtain the secondary radar direction finding result.

9. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the secondary radar direction finding method based on spatial spectrum estimation as claimed in any one of claims 1 to 7 is implemented.

10. A computer readable medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the secondary radar direction finding method based on spatial spectrum estimation as claimed in any one of claims 1 to 7.

Images