Directional antenna circular array direction finding method for image transmission signals of unmanned aerial vehicle
By using a directional antenna circular array direction finding method, the problem of insufficient gain of omnidirectional antennas in UAV image transmission signal direction finding was solved, realizing high-precision and high-resolution UAV direction finding and enhancing the detection capability of long-distance UAVs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-05
- Publication Date
- 2026-04-10
AI Technical Summary
In existing technologies, the gain of omnidirectional antennas in DOA estimation of UAV image transmission signals is difficult to meet the requirements of long-distance direction finding, and the received noise power and interference power are uniformly amplified, limiting the improvement of signal-to-noise ratio.
A circular array of directional antennas is constructed. By establishing the radiation pattern function of the directional antennas, the steering vector of the circular array is corrected, the covariance matrix is constructed, and eigenvalue decomposition is performed to estimate the direction of incoming waves. The high gain characteristics of the directional antennas are used to suppress noise and interference.
It significantly improved signal power, suppressed interference and noise, enhanced direction finding accuracy and resolution, and enabled high-precision orientation of UAVs at greater distances.
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Figure CN121831672A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of unmanned aerial vehicle (UAV) monitoring and countermeasure, more particularly, to a directional antenna circular array direction finding method for UAV image transmission (image transmission, IMG) signal, which is suitable for high-precision direction of arrival (DOA) estimation of narrowband signal in complex electromagnetic environment. BACKGROUND
[0002] With the rapid increase of consumer and industrial UAVs, their misuse in unauthorized airspace is increasing, which poses a serious threat to public safety, privacy protection and important facility security. In this context, UAV monitoring and countermeasure systems have become a research hotspot. Passive detection and positioning technology based on radio signals is one of the key means due to its good concealment, long action distance, and immunity to weather. UAVs usually continuously emit IMG signals during flight, which are mostly narrowband modulated and have a certain persistence in radiation direction. Therefore, high-precision estimation of the DOA of IMG signals can achieve rapid orientation of UAVs, and further provide guidance for subsequent tracking, identification and even radio frequency countermeasures.
[0003] Among many DOA estimation methods, the multiple signal classification (MUSIC) algorithm is considered the most classic spatial spectrum estimation method due to its high resolution and high accuracy under ideal conditions. This algorithm decomposes the covariance matrix of array received data, and then divides the resulting eigenvectors into two parts: "signal subspace" and "noise subspace". Using the orthogonality of signal steering vectors and noise subspace, the MUSIC algorithm can construct a sharp "spatial spectrum" in space, and by searching for the spectral peak position, it can achieve super-resolution estimation of multiple source directions. The theory proves that under the assumptions of sufficient array elements, sufficient snapshot data, accurate array manifold, and mutually independent sources, the estimation error variance of the MUSIC algorithm can approach the Cramer-Rao lower bound (CRLB), and its angular resolution can be improved by one to two orders of magnitude compared to traditional beamforming based on Fourier transform.
[0004] Traditional MUSIC algorithm research and verification often use omnidirectional antennas to form arrays. Omnidirectional antenna elements have constant gain, and modeling is simple, making them the preferred choice for MUSIC algorithm experimental verification. However, their equal gain characteristics also have drawbacks, as both received noise power and "non-incident wave" direction interference power are uniformly amplified, limiting the improvement of signal-to-noise ratio. SUMMARY
[0005] To overcome the shortcomings of the existing technology, this invention discloses a directional antenna circular array direction finding method for UAV image transmission signals, solving the problem that the gain of omnidirectional antennas in the existing technology is insufficient for long-distance direction finding. This invention uses a directional antenna, which, compared to an omnidirectional antenna, can achieve a gain 6–12 dBi higher in the main lobe beam than an omnidirectional element of the same size, while reducing the sidelobe level to [missing information]. Below 15 dB. Introducing a directional antenna into the array can significantly increase the signal power in the desired direction while suppressing interference and noise from other directions.
[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A direction finding method using a circular array of directional antennas for UAV image transmission signals includes the following steps: I. Establishing the Directional Antenna Pattern Function S1. Establish a directional antenna pattern model and obtain the directional antenna pattern function; Preferably, step S1 includes: obtaining the complex-valued radiation pattern of the nth unit, wherein the complex-valued radiation pattern contains amplitude and phase information.
[0007] Preferably, step S1 includes: establishing the radiation characteristics of the far-field antenna based on simulation or measured data using trigonometric functions with control parameters, and then establishing the radiation pattern model of the directional antenna.
[0008] Preferably, in step S1, the directional antenna pattern function is:
[0009] in, For directional antenna pattern functions; This is the direction angle.
[0010] II. Corrected Circular Array Guiding Vector S2. Correct the circular array steering vector using the directional antenna pattern function, and obtain the directional antenna array manifold matrix using the corrected circular array steering vector; Step S2 includes incorporating the pattern function into the circular array steering vector and obtaining the directional antenna array manifold matrix.
[0011] Preferably, in step S2, the circular array guiding vector is:
[0012] The manifold matrix of the directional antenna array is:
[0013] in, The guiding vector for the circular array; For directional antenna pattern functions; is the Hadamard product; is the omni-directional array steering vector; is the directional antenna array manifold matrix; is the direction of arrival.
[0014] In the S2 step, the directional pattern function and the corresponding element of the omni-directional array steering vector are multiplied, i.e. the Hadamard product, to obtain the steering vector of the directional array corresponding to the direction. The matrix composed of the steering vectors of all directions is the array manifold matrix of the directional array.
[0015] III. Constructing the covariance matrix S3, based on the directional antenna array manifold matrix, obtaining the receiving matrix of the UAV image transmission signal by the receiver, and constructing the covariance matrix through the receiving matrix; Preferably, in the S3 step, the obtaining of the receiving matrix of the UAV image transmission signal by the receiver based on the directional antenna array manifold matrix comprises: Suppose that p far-field narrowband signals are incident from directions, the response of each array element to the signal, i.e. the receiving matrix, is:
[0016] wherein, is the receiving matrix; is the directional antenna array manifold matrix; is the signal matrix; is the omni-directional array steering vector; is the far-field narrowband signal.
[0017] Preferably, in the S3 step, the obtaining of the receiving matrix further comprises: assuming that the noise is Gaussian white noise N, the receiving matrix is:
[0018] wherein, is the receiving matrix; is the directional antenna array manifold matrix; is the signal matrix; is the Gaussian white noise, , is the noise at time t; is the transpose.
[0019] Preferably, in the S3 step, the covariance matrix is:
[0020] wherein, is the covariance matrix; is the expectation; is a received matrix; is a conjugate transpose; is a directional antenna array manifold matrix; is a signal correlation matrix; is a noise correlation matrix, is a noise power, I is an identity matrix; is a signal matrix; is a Gaussian white noise.
[0021] In the S3 step, it can be known that the received matrix is composed of the signal acting on the array manifold matrix and the noise added. The autocorrelation operation is performed on the received matrix, and the expectation is obtained, so that the covariance matrix R can be obtained.
[0022] Four, constructing a signal subspace matrix and a noise subspace matrix S4, performing eigenvalue decomposition on the covariance matrix, and constructing a signal subspace matrix and a noise subspace matrix by using the decomposed eigenvalues; Preferably, in the S4 step, the eigenvalue decomposition on the covariance matrix comprises:
[0023] wherein, R represents the covariance matrix; I represents an identity eigenvector matrix; Λ represents an eigenvalue diagonal matrix; H represents a conjugate transpose; U represents an eigenvector; Λ represents an eigenvalue.
[0024] Preferably, in the S4 step, the signal subspace matrix and the noise subspace matrix are constructed by using the decomposed eigenvalues, which comprises: the eigenvalues in the identity eigenvector matrix obtained after the decomposition are sorted from large to small, wherein the eigenvectors corresponding to the P largest eigenvalues constitute the signal subspace matrix the smallest M-P eigenvalues are equal to the noise power the corresponding eigenvectors constitute the noise subspace matrix .
[0025] In the S4 step, the eigenvalue decomposition is performed on the covariance matrix R, so that a series of eigenvalues and the corresponding eigenvectors are obtained. The eigenvectors corresponding to the large eigenvalues constitute the signal subspace , and the eigenvectors corresponding to the small eigenvalues can constitute the noise subspace .
[0026] Five, spatial spectrum estimation of the direction of arrival S5, estimating the direction of arrival of the UAV image transmission signal by a spatial spectrum function based on the directional antenna array manifold matrix, the signal subspace matrix and the noise subspace matrix.
[0027] Preferably, the S5 step comprises: orthogonal to each other, and the directional antenna array manifold matrix is orthogonal to the signal subspace matrix , estimating the direction of arrival by a spatial spectrum function.
[0028] Preferably, the S5 step comprises:
[0029] wherein, is a spatial spectrum function; is a circular array steering vector; is a conjugate transpose; is a noise subspace matrix.
[0030] In the S5 step, it can be proved that the signal subspace and the noise subspace are orthogonal to each other in theory, and the space formed by the eigenvectors corresponding to the large eigenvalues is the same as the signal subspace, so the space formed by the eigenvectors corresponding to the large eigenvalues is orthogonal to the noise subspace. By performing the above operation on the two, the direction of arrival will be larger, and a sharp peak will be shown in the spatial spectrum curve.
[0031] Advantages of the present application: 1. The present application uses directional antennas for circular array direction finding. Compared with omnidirectional antennas, the gain of directional antennas in the main lobe beam can be 6-12 dBi higher than that of the same size omnidirectional unit, and the sidelobe level can be suppressed to below 15 dB. The introduction of directional units into the array can significantly increase the signal power in the desired direction while suppressing interference and noise in other directions.
[0032] 2. In the present application, due to the improvement of signal-to-noise ratio and the suppression of interference, the directional array can obtain higher estimation accuracy and resolution threshold under the same number of snapshots.
[0033] 3. In the present application, due to the high gain characteristic of the directional antenna, the ability to direction find the arrival of waves from a farther place can be realized.
[0034] 4. In the present application, the directional antenna circular array combines the advantages of omnidirectional coverage of circular array and high gain of directional antenna, realizing unambiguous omnidirectional direction finding and improving the overall direction finding performance. BRIEF DESCRIPTION OF DRAWINGS
[0035] Figure 1 This is a flowchart of the directional antenna circular array direction finding method for UAV image transmission signals according to the present invention; Figure 2 This invention simulates the radiation pattern of a directional antenna using trigonometric functions with control parameters; Figure 3 The simulation results of the 8-element directional circular array of the present invention are as follows (signal-to-noise ratio 0dB, number of snapshots 100, array radius is half wavelength, incoming wave direction 0°, 60°). Figure 4 The second simulation result of the 8-element directional circular array of the present invention (signal-to-noise ratio 10dB, number of snapshots 1000, array radius of half wavelength, incoming wave direction 0°, 10°). Figure 5 The results are Monte Carlo simulations of the omnidirectional circular array and the directional circular array of this invention. Figure 6 These are simulation results of the directional circular array in different directions according to the present invention; Figure 7 This is the array arrangement method of the present invention. Detailed Implementation
[0036] The following will provide a clear and complete description of the concept, specific structure, and technical effects of the present invention in conjunction with the embodiments and accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention.
[0037] Example 1 A directional antenna circular array direction finding method for UAV image transmission signals, such as Figure 1 As shown, it includes the following steps: S1. Establish a directional antenna pattern model and obtain the directional antenna pattern function; S2. Correct the circular array steering vector using the directional antenna pattern function, and obtain the directional antenna array manifold matrix using the corrected circular array steering vector; S3. Based on the directional antenna array manifold matrix, the receiver obtains the UAV image transmission signal reception matrix through the receiver, and constructs the covariance matrix through the reception matrix; S4. Perform eigenvalue decomposition on the covariance matrix, and use the decomposed eigenvalues to construct the signal subspace matrix and the noise subspace matrix. S5. Based on the directional antenna array manifold matrix, signal subspace matrix, and noise subspace matrix, the direction of arrival of the UAV image transmission signal is estimated by the spatial spectrum function.
[0038] In this embodiment, the directional antenna configuration includes: an 8-element uniform circular array, an 8-channel receiver, and a host computer.
[0039] Example 2 This embodiment further elaborates on step S1 based on the above embodiment. Step S1 establishes the directional antenna pattern function g(θ).
[0040] To incorporate the directional gain into the steering vector, the complex numerical radiation pattern of the nth element, including amplitude and phase information, must first be obtained. The radiation pattern information comes from simulation or measured data, and there are various ways to obtain the element antenna radiation pattern model. Here, the radiation characteristics of the far-field antenna are established using trigonometric functions with control parameters.
[0041] In step S1, to facilitate simulation analysis, a special trigonometric function is chosen to simulate the radiation pattern of the directional antenna. The radiation pattern function is:
[0042] in, For directional antenna pattern functions; The direction angle is sin; sin is the sine, and cos is the cosine.
[0043] Example 2 This embodiment further elaborates on step S2 based on the above embodiment. Step S2 corrects the circular array guiding vector, as follows: Assume P far-field narrowband signals Injected from different directions, the direction of the incoming wave is... Traditional omnidirectional circular array steering vectors only consider geometric phase:
[0044] Where k is the wave number, R is the radius of the circular array, n is the nth element, and M is the number of elements. The manifold matrix of the omnidirectional circular array is:
[0045] This invention incorporates pattern gain into the steering vector:
[0046] The manifold matrix of the directional antenna array is: ; in, The guiding vector for the circular array; For directional antenna pattern functions; For Hadamard product; This is the omnidirectional array steering vector; For directional antenna array manifold matrix; The direction of the incoming wave.
[0047] In step S2, the corresponding elements of the pattern function and the omnidirectional array steering vector are multiplied together, i.e., the Hadamard product, to obtain the steering vector of the directional array in the corresponding direction. The matrix formed by combining the steering vectors of all directions is the array manifold matrix of the directional array.
[0048] Example 3 This embodiment further elaborates on steps S3-S5 based on the above embodiment.
[0049] Suppose there are p far-field narrowband signals from Directional incidence.
[0050] The response of each array element to the signal is as follows:
[0051] in, For receiving matrix; For directional antenna array manifold matrix; For signal matrix; The guiding vector for the circular array; It is a far-field narrowband signal.
[0052] Assume the noise is Gaussian white noise N:
[0053] in , Let be the noise at time t.
[0054] Calculate the covariance matrix of the received data X:
[0055] in, It is the covariance matrix; For expectations; For receiving matrix; It is the conjugate transpose; For directional antenna array manifold matrix; The signal correlation matrix; The noise correlation matrix is... Let I be the noise power, and I be the identity matrix. For signal matrix; It is Gaussian white noise.
[0056] The eigenvalue decomposition of R can be expressed as follows:
[0057] in, Represent the covariance matrix; Represents the unit eigenvector matrix; Represents an eigenvalue diagonal matrix; Indicates conjugate transpose; Represents the eigenvector; Represents the eigenvalue.
[0058] Sort the eigenvalues from largest to smallest, where the eigenvectors corresponding to the P largest eigenvalues form a matrix. The smallest MP eigenvalues are equal to the noise power. The corresponding eigenvectors form a matrix. .matrix and The column vectors are mutually orthogonal, while the manifold matrix A and They are also orthogonal to each other, so the direction of the incoming wave can be estimated using the spatial spectrum function:
[0059] in, For spatial spectral functions; The guiding vector for the circular array; It is the conjugate transpose; Let be the noise subspace matrix.
[0060] like Figure 2 As shown, this embodiment simulates the directional antenna pattern using trigonometric functions with control parameters.
[0061] like Figure 3 and Figure 4 As shown, this is the simulation result of the 8-element directional circular array in this embodiment. Among them, Figure 3 The signal-to-noise ratio is 0dB, the number of snapshots is 100, the array radius is half a wavelength, and the direction of arrival is 0° or 60°. Figure 4 The signal-to-noise ratio is 10dB, the number of snapshots is 1000, the array radius is half a wavelength, and the direction of arrival is 0° or 10°. Figure 3 and Figure 4 As can be seen, the directional array still has good direction finding accuracy under low signal-to-noise ratio and small number of snapshots, and the directional array can accurately distinguish two signals when the incoming wave directions are close, demonstrating excellent array resolution performance.
[0062] like Figure 5 As shown, this is the Monte Carlo simulation result of the omnidirectional circular array and the directional circular array in this embodiment. Specifically, it is the result of 100 Monte Carlo simulations with 100 snapshots, an array radius of half a wavelength, and incoming wave directions of 0° and 30°. Figure 5 As can be seen from the data, under the same conditions, the estimation accuracy curve of the directional circular array is lower than that of the omnidirectional circular array, especially under low signal-to-noise ratio conditions, the directional circular array has better direction finding accuracy.
[0063] like Figure 6As shown, this is the simulation result of the directional circular array in different directions in this embodiment. Specifically, it is the result of 100 Monte Carlo simulations with 100 snapshots, an array radius of half a wavelength, a signal-to-noise ratio of 0dB. From Figure 6 It can be seen that for waves arriving from different directions, the estimation accuracy of the circular array composed of directional antennas is less than 1°. The directional circular array does not have obvious selectivity for waves arriving from different directions and has the ability to estimate in all directions.
[0064] like Figure 7 As shown, this is the array arrangement in this embodiment. Specifically, it is an 8-element directional circular array arrangement with an array radius of half a wavelength.
[0065] The embodiments of the present invention have been described in detail above, but the present invention is not limited to the described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalents or substitutions are all included within the scope defined by the claims of the present invention.
Claims
1. A directional antenna circular array direction finding method for UAV image transmission signals, characterized in that, Includes the following steps: S1. Establish a directional antenna pattern model and obtain the directional antenna pattern function; S2. Correct the circular array steering vector using the directional antenna pattern function, and obtain the directional antenna array manifold matrix using the corrected circular array steering vector; S3. Based on the directional antenna array manifold matrix, the receiver obtains the receiving matrix of the UAV image transmission signal through the receiver, and constructs the covariance matrix through the receiving matrix; S4. Perform eigenvalue decomposition on the covariance matrix, and use the decomposed eigenvalues to construct the signal subspace matrix and the noise subspace matrix. S5. Based on the directional antenna array manifold matrix, signal subspace matrix, and noise subspace matrix, the direction of arrival of the UAV image transmission signal is estimated by the spatial spectrum function.
2. The directional antenna circular array direction finding method for UAV image transmission signals as described in claim 1, characterized in that, In step S1, the directional antenna pattern function is: in, For directional antenna pattern functions; This is the direction angle.
3. The directional antenna circular array direction finding method for UAV image transmission signals as described in claim 1, characterized in that, In step S2, the circular array guiding vector is: The manifold matrix of the directional antenna array is: in, The guiding vector for the circular array; For directional antenna pattern functions; For Hadamard product; This is the omnidirectional array steering vector; For directional antenna array manifold matrix; The direction of the incoming wave.
4. The directional antenna circular array direction finding method for UAV image transmission signals as described in claim 1, characterized in that, In step S3, the step of obtaining the UAV image transmission signal reception matrix through the receiver based on the directional antenna array manifold matrix includes: Suppose there are p far-field narrowband signals from With incident light from a specific direction, the response of each array element to the signal, i.e., the receiving matrix, is as follows: in, For receiving matrix; For directional antenna array manifold matrix; For signal matrix; The guiding vector for the circular array; It is a far-field narrowband signal.
5. The directional antenna circular array direction finding method for UAV image transmission signals as described in claim 1, characterized in that, In step S3, obtaining the receiver matrix further includes: assuming the noise is Gaussian white noise N, then the receiver matrix is: in, For receiving matrix; For directional antenna array manifold matrix; For signal matrix; It is Gaussian white noise. , The noise at time t; This is a transpose.
6. The directional antenna circular array direction finding method for UAV image transmission signals as described in claim 1, characterized in that, In step S3, the covariance matrix is: in, It is the covariance matrix; For expectations; For receiving matrix; It is the conjugate transpose; For directional antenna array manifold matrix; The signal correlation matrix; The noise correlation matrix is... Let I be the noise power, and I be the identity matrix. For signal matrix; It is Gaussian white noise.
7. The directional antenna circular array direction finding method for UAV image transmission signals as described in claim 1, characterized in that, In step S4, the eigenvalue decomposition of the covariance matrix includes: in, Represent the covariance matrix; Represents the unit eigenvector matrix; Represents an eigenvalue diagonal matrix; Indicates conjugate transpose; Represents the eigenvector; Represents the eigenvalue.
8. The directional antenna circular array direction finding method for UAV image transmission signals as described in claim 1, characterized in that, In step S4, constructing the signal subspace matrix and noise subspace matrix using the decomposed eigenvalues includes: sorting the eigenvalues in the decomposed unit eigenvector matrix from largest to smallest, wherein the eigenvectors corresponding to the P largest eigenvalues constitute the signal subspace matrix. The smallest MP eigenvalues are equal to the noise power. The corresponding eigenvectors constitute the noise subspace matrix. .
9. The directional antenna circular array direction finding method for UAV image transmission signals as described in claim 1, characterized in that, Step S5 includes: based on the signal subspace matrix and noise subspace matrix The column vectors are mutually orthogonal, and the directional antenna array manifold matrix... With signal subspace matrix They are mutually orthogonal, and the direction of the incoming wave is estimated through the spatial spectrum function.
10. The directional antenna circular array direction finding method for UAV image transmission signals as described in claim 1, characterized in that, The S5 steps include: in, For spatial spectral functions; The guiding vector for the circular array; It is the conjugate transpose; Let be the noise subspace matrix.