Unmanned aerial vehicle direction finding method with variable array element spacing
By combining the variable element spacing array model and wavelet transform with the MUSIC algorithm, the problem of insufficient adaptability of traditional uniform linear arrays in high-resolution and dynamic signal environments is solved, achieving high precision and high efficiency in UAV direction finding, which is suitable for scenarios such as positioning of fast-moving UAVs.
Patent Information
- Application Number
- CN202511032181.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-25
- Publication Date
- 2025-11-11
AI Technical Summary
Traditional uniform linear array designs have limitations in terms of high resolution and adaptability to dynamic signal environments. Existing improved solutions, such as sparse arrays and MUSIC algorithms, face problems such as high hardware costs, large computational complexity, difficult calibration, and strict requirements for signal sparsity in practical deployments, making it difficult to achieve a balance between flexibility, high precision, and real-time performance.
By adopting a variable element spacing array model, and through multi-scale analysis and wavelet transform, combined with the MUSIC algorithm and phase difference periodic verification method, the element spacing is dynamically adjusted to achieve high-precision, unambiguous direction finding across the entire angular range.
It achieves high precision and high efficiency in UAV direction finding under complex electromagnetic environments, and is suitable for scenarios such as positioning of fast-moving UAVs. It reduces computational complexity and improves the system's adaptability and real-time performance.
Smart Images
Figure CN120928279A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the intersection of array signal processing and wireless communication technology, and in particular to a direction finding method for unmanned aerial vehicles with variable array element spacing. Background Technology
[0002] Traditional uniform linear array (ULA) designs face numerous inherent limitations in engineering practice. Due to the fixed half-wavelength spacing, according to the Rayleigh criterion, the angular resolution Δλ≈λ / (dcosθ), where d is the array aperture, λ is the wavelength of the incoming signal, and θ is the angle of the incoming signal. Therefore, pursuing high resolution requires a significant increase in the number of array elements, leading to an exponential increase in hardware costs. More importantly, this fixed structure cannot dynamically adjust performance characteristics according to the needs of the actual scenario, often exhibiting insufficient adaptability in complex electromagnetic environments.
[0003] Existing improvement schemes, such as sparse arrays (spacing greater than λ / 2), can expand the aperture and thus improve angular resolution, but they generate periodic grating lobes, leading to spatial blurring. While existing solutions to the angular resolution reduction caused by these grating lobes, such as minimum redundancy arrays (MRA), can partially alleviate the problem, their optimization process is complex and lacks closed-form solutions, presenting challenges such as calibration difficulties in practical deployment. Furthermore, their fixed structure cannot adapt to dynamic signal environments such as UAV positioning.
[0004] On the other hand, traditional high-precision DOA algorithms have a large computational load, such as the MUSIC algorithm which requires O(N) computation. 3 Eigenvalue decomposition is on the order of (N being the number of array elements). When the number of array elements N>32, real-time performance is difficult to guarantee. Although existing compressed sensing methods reduce computational complexity, they have strict requirements on signal sparsity, and their performance drops sharply in dense multipath scenarios. These technical bottlenecks severely restrict the development of modern communication and sensing systems, requiring an innovative solution that combines flexibility, high precision, and real-time performance. Summary of the Invention
[0005] The technical problem to be solved by this invention is to provide a UAV direction finding method with variable array element spacing that applies the multi-scale analysis concept of wavelet transform to array design, uses a method of dynamically maintaining the angle range, integrates the results obtained from different angle resolutions, and obtains spatially unambiguous high angle measurement accuracy results.
[0006] The technical solution adopted by this invention to solve its technical problem is: a UAV direction finding method with variable element spacing, employing a variable element spacing array model; the variable element spacing array model includes multiple subarrays, and each subarray includes multiple elements; the spacing between two adjacent elements in the same subarray is the same, and the spacing between two adjacent elements in different subarrays is different; and adjacent subarrays share some elements; the spacing between two adjacent elements in adjacent subarrays satisfies d k+1 =2d k k = 1, 2; where d k d1 is the distance between two adjacent elements in the k-th subarray; d2 is the distance between two adjacent elements in the 1-th subarray.
[0007] It also includes the following steps:
[0008] S1, the array receives signal x(t) = As(t) + n(t), where A is the steering matrix, s(t) is the incoming signal, and n(t) is additive noise;
[0009] S2. Use the MUSIC algorithm to obtain the DOA estimation vector within the response angle range. A hierarchical angle search strategy based on wavelet multi-resolution is employed, dividing the future wavelet angle range into three subarrays: the first subarray corresponds to an angle range of 45° to 90°; the second subarray corresponds to an angle range of 23° to 50°; and the third subarray corresponds to an angle range of 0° to 25°. Angle estimation is performed using subarrays with different element spacings. An unambiguous angle range search is then conducted sequentially according to the subarrays to obtain the DOA estimation vector.
[0010] S3. Perform mirror angle exclusion on the spatially ambiguous area to obtain the estimated value.
[0011] S4. Determine the array aperture size based on the angle measurement accuracy and array element overhead requirements. Perform a rollback operation using the subarray with the largest aperture to increase the angle measurement accuracy of the preceding subarrays that did not meet the requirements, obtaining high-precision results θ1, θ2, ... θ k .
[0012] Furthermore, in step S3, the mirror angle exclusion adopts the phase difference periodicity verification method, using the formula sinθ'=sinθ+kλ / d. Search results for the angle range corresponding to different subarrays; where θ' is the interference, θ is the true angle, λ is the wavelength of the incoming signal, and d is the element spacing; satisfying the mirror angle formula sinθ'=sinθ+kλ / d, Where θ' is the mirror angle, which is the range of spatial blur.
[0013] Furthermore, in step S2, a narrow-angle dense grid is selected to perform spectral peak search over a large angle range; the large angle range refers to 45° to 90°.
[0014] The beneficial effects of this invention are as follows: The UAV direction finding method with variable array element spacing described in this invention has the following advantages:
[0015] 1. The UAV direction finding method with variable array element spacing described in this invention first uses an angle-free fuzzy array structure to perform a coarse estimation of the full angle, then eliminates image interference through a phase difference periodic verification mechanism, and finally uses a rollback operation to improve the angle measurement accuracy, making it approach the high-precision estimation of the full angle range.
[0016] 2. The UAV direction finding method with variable array element spacing described in this invention constructs a hierarchical processing system with "global-local" collaboration, achieving a breakthrough performance improvement through a dynamically adjustable array structure. Furthermore, it ensures that the subarray spacing satisfies d... k+1 =2d k At the macro level, the system uses a basic subarray with half-wavelength spacing to ensure unambiguous scanning capability across all angles; at the meso level, it uses a subarray with one wavelength spacing to enhance coverage of key areas; at the micro level, it uses an ultra-sparse subarray with two wavelength spacing to achieve ultra-high resolution measurement within a specific angular range; it is particularly suitable for practical engineering needs that require both high resolution and a large unambiguous range, such as the positioning of fast-moving drones.
[0017] 3. The UAV direction finding method with variable element spacing described in this invention adopts a pipelined parallel architecture throughout the entire processing flow. Searches for different angle ranges can be processed in parallel; the higher the level (i.e., the larger the angle range), the more potential mirror angles there are. Furthermore, when excluding mirror angles, it needs to be done sequentially according to the hierarchical order. It utilizes iterative prior angle information and the mirror angle formula sinθ'=sinθ+kλ / d. Here, θ' is the mirror angle, representing the appearance of spatial ambiguity. Therefore, the computational cost of the subsequent step is much smaller than that of the previous parallelization step, thus reducing the number of computational steps. Therefore, its parallelizable angle search can significantly improve efficiency in practical implementations.
[0018] In summary, the UAV direction finding method with variable element spacing described in this application can obtain spatially unambiguous high-precision UAV direction finding results using a variable element spacing array, and can also improve efficiency. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of a traditional uniform linear array signal receiving model;
[0020] Figure 2 This is a flowchart of the MUSIC algorithm in an embodiment of the present invention;
[0021] Figure 3 This is a schematic diagram of the variable array element spacing array model in an embodiment of the present invention;
[0022] Figure 4 This is a flowchart of the variable array element spacing algorithm in an embodiment of the present invention. Detailed Implementation
[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0024] like Figure 3 , 4 As shown, the UAV direction finding method with variable element spacing according to the present invention adopts a variable element spacing array model; the variable element spacing array model includes three subarrays, and each subarray includes multiple array elements; the spacing between two adjacent array elements in the same subarray is the same, and the spacing between two adjacent array elements in different subarrays is different; and adjacent subarrays share some array elements; specifically, the spacing between two adjacent array elements in adjacent subarrays satisfies d k+1 =2d k k = 1, 2; where d k d1 is the distance between two adjacent elements in the k-th subarray; d2 is the distance between two adjacent elements in the 1-th subarray; by setting multiple subarrays, each subarray can achieve parallel processing in practical applications.
[0025] It also includes the following steps:
[0026] S1, the array receives signal x(t) = As(t) + n(t), where A is the steering matrix, s(t) is the incoming signal, and n(t) is additive noise.
[0027] S2. Use the MUSIC algorithm to obtain the DOA estimation vector within the response angle range. A hierarchical angle search strategy based on wavelet multi-resolution is employed, dividing the future wavelet angle range into three subarrays: the first subarray corresponds to an angle range of 45° to 90°; the second subarray corresponds to an angle range of 23° to 50°; and the third subarray corresponds to an angle range of 0° to 25°. Angle estimation is performed using subarrays with different element spacings. An unambiguous angle range search is then conducted sequentially according to the subarrays to obtain the DOA estimation vector. That is, the search is divided into three ranges: 45° to 90°, 23° to 50°, and 0° to 25°. These three ranges correspond to three "subarrays," and each subarray uses the MUSIC algorithm to search within its respective range. The three subarrays can operate in parallel. Specifically, a narrow-angle dense grid is selected to search for spectral peaks in the large-angle range. That is, in the range of 45° to 90°, a dense grid, such as an interval of 0.1 degrees, is used for the search.
[0028] S3. Perform mirror angle exclusion on the spatially ambiguous area to obtain the estimated value. Specifically, the mirrors are sequentially eliminated according to the subarray hierarchy. The elimination of mirror angles uses the phase difference periodic verification method, employing the formula sinθ'=sinθ+kλ / d. Search results for the angle range corresponding to different subarrays; where θ' is the interference, θ is the true angle, λ is the wavelength of the incoming signal, and d is the spacing between array elements; Represents an integer.
[0029] The mirror angle formula sinθ'=sinθ+kλ / d is satisfied. Where θ' is the mirror angle, which represents the range of spatial ambiguity. That is, theta is the true angle, and theta' is the mirror angle. If they satisfy the relationship sinθ' = sinθ + kλ / d, then this relationship can be used to exclude the mirror angle theta'.
[0030] S4. Determine the array aperture size based on the angle measurement accuracy and array element overhead requirements. Perform a rollback operation using the subarray with the largest aperture to increase the angle measurement accuracy of the preceding subarrays that did not meet the requirements, obtaining high-precision results θ1, θ2, ... θ k .
[0031] In steps S2, S3, and S4, a coarse estimation of the full angle is first performed using an angle-free fuzzy array structure. Then, the mirror interference is eliminated through a phase difference periodic verification mechanism. Finally, a rollback operation is used to improve the angle measurement accuracy, bringing it close to the high-precision estimation of the full angle range.
[0032] Example
[0033] The algorithm proposed in this invention is based on a variable element spacing array model, where the variable element spacing means that the element spacing of each subarray is different, but together they form a linear array structure. This array model is as follows: Figure 3 As shown.
[0034] Figure 3Taking a 3n array as an example, subarray 1, consisting of the first n elements, satisfies the Nyquist sampling theorem, with an element spacing of half a wavelength λ. In this case, spatial ambiguity is avoided across all angle ranges. However, due to the small element spacing, the angle measurement accuracy is low in small angle ranges, making it suitable for DOA (Direction of Arrival) estimation when the arrival angle is large, as shown in the figure. Subarray 2, consisting of the middle n elements separated from subarray 1 by one element, has an element spacing of one wavelength λ, similar to the downsampling operation in time-frequency analysis. This improves the angle measurement accuracy across the entire angle range but introduces some mirror angle interference, making it suitable for DOA estimation with moderate arrival angles. Similarly, subarray 3 consists of elements with an array spacing of twice the wavelength λ. This generates more mirror angles, but the angular resolution is high in small angle ranges, making it suitable for applications such as... Figure 3 The DOA estimate for the corresponding angle is shown.
[0035] The following describes the specific implementation process of the algorithm, using a flowchart for intuitive illustration and a detailed explanation of each step. The main algorithm flow of this invention is as follows: Figure 4 As shown.
[0036] 1. The array receives the signal x(t) = As(t) + n(t), where A is the steering matrix, s(t) is the incoming signal, and n(t) is the additive noise.
[0037] 2. Use the MUSIC method to obtain the DOA estimation vector within the response angle range. A hierarchical angle search strategy based on wavelet multi-resolution principles divides the future wavelet angle range and uses subarrays with different element spacings for angle estimation. The unambiguous angle range search is performed sequentially by subarray: the first subarray corresponds to an angle range of 45° to 90°; the second subarray to 23° to 50°; and the third subarray to 0° to 25°. Each subarray employs the MUSIC algorithm to search within its respective range and obtain the DOA estimation vector.
[0038] Specifically, such as Figure 2 As shown, the MUSIC method includes the following steps:
[0039] 2.1 The algorithm first performs time-domain sampling on each array element channel to obtain the discretized received signal matrix (X1(t), X2(t), ..., X...). N (t)) T .
[0040] 2.2 Subsequently, the covariance matrix is calculated. Where snap_num represents the number of snapshots.
[0041] 2.3 Next, eigenvalue decomposition is performed on the covariance matrix R. This process decomposes the matrix into a combination of eigenvalues and eigenvectors: By sorting the eigenvalues, the signal subspace and the noise subspace can be clearly distinguished. The eigenvectors corresponding to the largest eigenvalues constitute the signal subspace U. s The eigenvectors corresponding to the smaller eigenvalues form the noise subspace U. n .
[0042] 2.4 Constructing a spatial spectrum function based on the noise subspace. For each angle to be measured θ, calculate its steering vector. Then through the formula The spatial spectrum value is obtained. This step embodies the core idea of the MUSIC algorithm—utilizing the orthogonality between the signal and noise subspaces to achieve super-resolution estimation.
[0043] 2.5 The calculated spatial spectrum is scanned within a set angle range to identify significant peak points, which are the angle estimation results (θ1, θ2, ... θ) of the algorithm. m ).
[0044] 3. Perform mirror angle elimination on spatially blurred areas to obtain estimated values. Specifically, the mirrors are sequentially eliminated according to the subarray hierarchy. The elimination of mirror angles uses the phase difference periodic verification method, employing the formula sinθ'=sinθ+kλ / d. Search results for the angle range corresponding to different subarrays; where θ' is the interference, θ is the true angle, λ is the wavelength of the incoming signal, and d is the spacing between array elements; Represents an integer.
[0045] The mirror angle formula sinθ'=sinθ+kλ / d is satisfied. Where θ' is the mirror angle, which represents the range of spatial ambiguity. That is, theta is the true angle, and theta' is the mirror angle. If they satisfy the relationship sinθ' = sinθ + kλ / d, then this relationship can be used to exclude the mirror angle theta'.
[0046] Specifically, the first subarray searches for mirror angles within a large angle range and finds none, so no mirror angle exclusion is needed. The second subarray searches for mirror angles within the range of 30° to 50° and excludes them. The third subarray searches for mirror angles within the range of 15° to 25° and excludes them.
[0047] 4. Determine the array aperture size based on the angle measurement accuracy and array element overhead requirements. Perform a rollback operation using the subarray with the largest aperture to increase the angle measurement accuracy of the preceding subarrays that did not meet the requirements, thus obtaining high-precision results θ1, θ2, ... θ k .
[0048] Throughout the process, the various subarrays operate in parallel; specifically as follows:
[0049] Subarray 1 is configured to meet the Nyquist sampling requirements. In this case, smaller element spacing is not necessary; setting the element spacing to half the wavelength λ is sufficient to achieve the requirement of no spatial ambiguity. Based on prior information about the number of incoming wave angles, the MUSIC algorithm is used to obtain the DOA estimation vector within a large angle range of 45° to 90°. Due to the constraints of the array aperture, the angle measurement accuracy of subarray 1 is low for small angle ranges. Therefore, only narrow-angle dense grids can be selected to perform spectral peak search steps for large angle ranges, thereby reducing the computational load.
[0050] Subarray 2, with a larger element spacing, is used for DOA estimation within the corresponding angular range. This provides higher angular resolution, and because the mirror angle and the true DOA satisfy the formula sinθ'=sinθ+kλ / d, Therefore, by applying the unambiguous condition, i.e., requiring a unique phase difference period, it can be calculated that there are no mirror angles within the range (0, 30°). Simultaneously, to avoid angle search errors caused by boundary effects and to continue the wavelet bisection method, the search range of subarray 2 is set to (23°, 50°). Due to the potential interference from mirror angles, the MUSIC algorithm is used to estimate the vector. By eliminating possible ranges based on mirror angles, a series of DOA estimates existing within the angle range (30°, 90°) are obtained and formed into a vector.
[0051] 3. Based on the required angle measurement accuracy, this embodiment takes subarray 3 as an example, where the element spacing is twice the wavelength. At this point, the beam energy is highly concentrated, resulting in high angle measurement accuracy. First, the angle measurement process of subarray 2 is followed to acquire angles within the corresponding range. Then, the unambiguous DOA estimation result after full angle traversal is obtained. At this point, it can be rolled back to a larger angle range where the angle measurement accuracy is not ideal. Finally, subarray 3 is used to... and Improve the accuracy of angle measurement.
Claims
1. A UAV direction finding method with variable element spacing, characterized in that: A variable element spacing array model is adopted; the variable element spacing array model includes three subarrays, and each subarray includes multiple array elements; the spacing between two adjacent array elements in the same subarray is the same, and the spacing between two adjacent array elements in different subarrays is different; and adjacent subarrays share some array elements; the spacing between two adjacent array elements in adjacent subarrays satisfies d k+1 =2d k k = 1, 2; where d k d1 is the distance between two adjacent elements in the k-th subarray; d2 is the distance between two adjacent elements in the 1-th subarray. It also includes the following steps: S1, the array receives signal x(t) = As(t) + n(t), where A is the steering matrix, s(t) is the incoming signal, and n(t) is additive noise; S2. Use the MUSIC algorithm to obtain the DOA estimation vector within the response angle range. A hierarchical angle search strategy based on wavelet multi-resolution is employed, dividing the future wavelet angle range into three subarrays: the first subarray corresponds to an angle range of 45° to 90°; the second subarray corresponds to an angle range of 23° to 50°; and the third subarray corresponds to an angle range of 0° to 25°. Angle estimation is performed using subarrays with different element spacings. An unambiguous angle range search is then conducted sequentially according to the subarrays to obtain the DOA estimation vector. S3. Perform mirror angle exclusion on the spatially ambiguous area to obtain the estimated value. S4. Determine the array aperture size based on the angle measurement accuracy and array element overhead requirements. Perform a rollback operation using the subarray with the largest aperture to increase the angle measurement accuracy of the preceding subarrays that did not meet the requirements, obtaining high-precision results θ1, θ2, ... θ k .
2. The UAV direction finding method with variable element spacing as described in claim 1, characterized in that: In step S3, mirror angle elimination is performed sequentially according to the subarray hierarchy. The mirror angle elimination employs a phase difference periodic verification method, using the formula sinθ′=sinθ+kλ / d. Search results for the angle range corresponding to different subarrays; where θ' is the interference, θ is the true angle, λ is the wavelength of the incoming signal, and d is the spacing between array elements; Represents an integer; The mirror angle formula sinθ'=sinθ+kλ / d is satisfied. Where θ' is the mirror angle, which is the range of spatial blur.
3. The UAV direction finding method with variable element spacing as described in claim 1, characterized in that: In step S2, a narrow-angle dense grid is selected to perform spectral peak search over a large angle range; the large angle range refers to 45° to 90°.