Generalized non-collinear baseline distributed array and method for obtaining target angle thereof

Abstract

This application relates to the field of array signal processing technology, specifically to a generalized non-common baseline distributed array and a method for obtaining target angles. The generalized non-common baseline distributed array includes a first uniform linear subarray and a second uniform linear subarray, where the array elements are located on the x-axis, and the array elements are distributed on a straight line making an angle θ with the x-axis. The method includes: deploying the array and receiving signals; performing coarse DOA estimation on the two subarrays respectively; selecting target value pairs from the coarse estimation results based on the angle and performing phase compensation; combining the signal and the compensated signal to form equivalent array received data; and performing fine DOA estimation based on the equivalent array received data to obtain a high-precision target angle. This application can achieve high-precision direction-of-arrival estimation even with complex layouts.

Description

Technical Field

[0001] This application relates to the field of array signal processing technology, specifically to a generalized non-common baseline distributed array and a method for obtaining target angles. Background Technology

[0002] As core technologies in modern radar, wireless communication, and sonar systems, array signal processing and antenna technology directly impact the accuracy of target detection, localization, and tracking, as well as the overall anti-jamming capability of the system, playing an irreplaceable role in several key areas. With the increasing demands of electronic systems for target azimuth estimation resolution, real-time performance, and environmental adaptability, distributed arrays, with their advantages of flexible aperture expansion, wide spatial coverage, relatively low deployment costs, and strong damage resistance, have become an important research direction and application hotspot in the field of array signal processing.

[0003] In existing technologies, to achieve effective target angle estimation in complex environments, traditional distributed arrays generally adopt a common baseline layout design, where array elements are arranged according to a preset collinear or coplanar baseline rule. The key advantage of this structure lies in its relatively simple signal model; the phase difference between array elements typically exhibits a clear linear mapping relationship with the target azimuth angle. This facilitates the direct application of mature angle estimation algorithms such as beamforming, MUSIC, and ESPRIT to achieve stable and computationally efficient target azimuth estimation.

[0004] However, existing methods for target angle estimation using distributed arrays with common baselines still have significant limitations in practical applications. Because their layout strictly relies on pre-defined collinear or coplanar baselines, the physical arrangement of the array is severely constrained by the actual deployment space, the shape of the carrier platform, and complex terrain, making it difficult to simply extend the baseline to increase the array aperture and thus improve angular resolution. Simultaneously, the azimuth estimation degrees of freedom for such arrays largely depend on the fixed configuration of the number and length of baselines. In detection scenarios involving multiple targets, strong interference, or heterogeneous platforms, problems such as estimation ambiguity, excessively high sidelobe levels, and decreased estimation accuracy easily arise, limiting their reliable application in more complex environments. Furthermore, since all subarrays point in the same or parallel spatial direction, the spatial information dimension of their received signals is relatively simple, meaning that improving target angle estimation performance often relies solely on increasing the number of array elements or the baseline length, which easily encounters bottlenecks in complex scenarios. Summary of the Invention

[0005] To address the technical problems of existing methods for target angle estimation using common baseline distributed arrays, which suffer from the rigid geometric constraints of the common baseline array severely limiting the flexibility of practical deployment, and the single directional nature making it difficult to effectively improve estimation accuracy by utilizing richer spatial angle information, this application provides a generalized non-common baseline distributed array and its method for obtaining target angles. By allowing non-collinear arrangement of subarrays, the environmental adaptability and deployment freedom of the array are improved. Furthermore, by employing a dedicated method based on phase compensation and equivalent array construction, high-precision direction-of-arrival estimation is achieved under complex layouts.

[0006] In a first aspect, this application provides a generalized non-common baseline distributed array for arranging antenna elements according to their coordinate positions. The generalized non-common baseline distributed array includes a first uniform linear subarray. Second uniform linear subarray First uniform linear subarray Second uniform linear subarray Each contains Each of the array elements is distributed in a preset two-dimensional Cartesian coordinate system. Among them, the first uniform linear subarray In All array elements are located on the x-axis. The Middle The position coordinates of each array element are , express The distance between two adjacent array elements; Second uniform linear subarray The array elements are distributed along a straight line, and the angle between the line and the parallel line to the x-axis is . , The range of values ​​is ; The spacing between two adjacent array elements is ,and ; The Middle The position coordinates of each array element are , express The offset distance of the first array element relative to the origin along the x-axis. express The offset distance of the first array element relative to the origin in the y-axis direction; The first array element in the middle and The distance between the first array elements in the array is defined as the baseline. baseline The length satisfies: .

[0007] It should be further noted that the spacing between array elements wavelength of the incident signal 1 / 2 of.

[0008] It should be further explained that the baseline The length satisfies: .

[0009] It should be further explained that the baseline The length satisfies: .

[0010] It should be further explained that, .

[0011] Secondly, this application provides a method for obtaining target angles using a generalized non-common baseline distributed array, comprising the following steps: S1. Antenna elements are arranged according to the coordinate positions of the elements of the generalized non-common baseline distributed array, with each antenna element corresponding to one element in the generalized non-common baseline distributed array, forming an antenna element array, including the corresponding first uniform linear subarray. The first antenna element array and the corresponding second uniform linear subarray The second antenna element array; S2. Receive The echo data generated by a far-field narrowband signal source incident on the antenna element array is used to obtain the received signal vector of the first antenna element array. Received signal vector of the second antenna element array , Indicates the sampling time; The first in The element is the first element in the first antenna element array. The received signal of each antenna element is denoted as . ; The first in The element is the second antenna cell array. The received signal of each antenna element is denoted as . ; S3. For each and DOA estimation is performed to obtain the first coarsely estimated set of incident angles for each signal source corresponding to the first antenna element array. The second coarse estimate of the incident angle set corresponding to each signal source and the second antenna element array ,in ; S4. Based on the included angle The value, from and The middle part is used to select targets for phase compensation from various signal sources and roughly estimate the incident angle. ; S5. Utilize right Phase compensation is performed to obtain the compensated signal vector for each signal source. , The first in The element is the second antenna cell array. The compensated signal of each antenna element is denoted as... The expression is:

[0012] in, The imaginary unit; Indicates the wavelength of the incident signal; S6. Will and Combined, they form an equivalent array to receive data. , is represented as:

[0013] S7. Receive data from the equivalent array. Perform DOA estimation to obtain The precise estimate of the incident angle is retained only when compared with the value of the current incident angle. The precise estimated angle corresponding to each signal source ; against Each signal source executes steps S4-S7 sequentially, ultimately obtaining all... The precise estimated angle of each signal source.

[0014] It should be further explained that, The far-field narrowband signal sources are uncorrelated signal sources.

[0015] It should be further explained that, .

[0016] It should be further noted that in step S3, the ESPRIT algorithm is used to respectively... and Perform DOA estimation.

[0017] It should be further noted that in step S4, the incident angle is roughly estimated for each signal source by selecting the target for phase compensation. The specific rules include: when hour, The value can be:

[0018] when hour, The value can be:

[0019] in, Indicates the first Each signal source corresponds to the first coarsely estimated incident angle of the first antenna element array; Indicates the first Each signal source corresponds to the second coarsely estimated incident angle of the second antenna element array.

[0020] It should be further noted that in step S7, the parametrically constructed dual-scale ESPRIT algorithm is used to analyze the received data of the equivalent array. Perform DOA estimation.

[0021] It should be further noted that the parametrically constructed dual-scale ESPRIT algorithm is used to process the received data from the equivalent array. The specific steps for performing DOA estimation include: Receive data using an equivalent array Construct two subarrays with a fixed scaling relationship, and obtain the first subarray by solving the generalized eigenvalue problem between the signal subspaces of these two subarrays. Precise estimation angle of each signal source .

[0022] It should be further explained that the precise estimation angle The effective estimation range is determined by and Decisions include: when and hour, The effective estimation range is: ; when and hour, The effective estimation range is: ; when and hour, The effective estimation range is: ; when and hour, The effective estimation range is: ; in, .

[0023] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above for obtaining target angles using a generalized non-common baseline distributed array.

[0024] Fourthly, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the method described above for obtaining target angles using a generalized non-common baseline distributed array.

[0025] As can be seen from the above technical solutions, this application has the following advantages: 1. This application provides a generalized non-common baseline distributed array comprising a first uniform linear subarray and a second uniform linear subarray, wherein the array element arrangement line of the second subarray can form any angle with the x-axis, realizing a breakthrough in array layout from "common baseline" to "non-common baseline", breaking the layout restriction of traditional arrays that must share a common baseline, enabling the array to be flexibly arranged according to spatial constraints such as actual terrain and carrier, significantly reducing the demanding requirements on the deployment environment, and enhancing the array's environmental adaptability and deployment freedom.

[0026] 2. By adopting a non-common baseline design, this application allows two sub-arrays to be flexibly configured at a specific angle on the plane, thereby effectively expanding the equivalent aperture of the array without increasing the number of physical array elements. A larger aperture means higher spatial resolution, enabling the system to achieve better performance in target angle estimation, especially in scenarios with dense targets or small angle intervals.

[0027] 3. Since the two subarrays in this application have a non-common baseline spatial geometric relationship, their received signals contain richer spatial information. This specific structure provides more dimensions and constraints for signal processing, which helps to better separate multiple signal sources in complex signal environments and improve the robustness of angle estimation.

[0028] 4. This application uses a method to obtain the target angle by using a generalized non-collinear distributed array. It utilizes two non-collinear subarrays to perform independent coarse estimations, then selects a coarse estimate for phase compensation based on their geometric relationship, and performs precise phase compensation on the received signal vector to construct equivalent array received data. Finally, it performs fine estimation, making full use of the additional angle resolution information brought by the non-collinear structure. The phase compensation operation effectively eliminates the model complexity introduced by the angle difference between subarrays, thereby enabling high-precision direction-of-arrival estimation under complex layouts. This solves the problem of accuracy degradation or failure when traditional algorithms are directly applied to non-collinear arrays. Attached Figure Description

[0029] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of a generalized non-common baseline distributed array in one embodiment of this application.

[0031] Figure 2 This is a flowchart of a method for obtaining target angles using a generalized non-common baseline distributed array in one embodiment of this application.

[0032] Figure 3 This is a schematic diagram illustrating the variation of the root mean square error (RMSE) of the target angle with SNR using different methods in one embodiment of this application.

[0033] Figure 4 This is a schematic diagram illustrating the variation of the root mean square error of the target angle obtained using different methods with the number of snapshots in one embodiment of this application.

[0034] Figure 5 This is a schematic diagram illustrating how the probability of successfully estimating the target angle using different methods varies with SNR in one embodiment of this application.

[0035] Figure 6 This is a schematic diagram of the hardware structure of an electronic device in one embodiment of this application. Detailed Implementation

[0036] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0037] The method for obtaining target angles using a generalized non-common baseline distributed array, as described in this application, will be described in detail below. Specific details, such as particular system architectures and techniques, are presented for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0038] In the method for acquiring target angles using a generalized non-common baseline distributed array disclosed in this application, the term "comprising" indicates the presence of the described feature, whole, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or sets thereof. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0039] To facilitate a clear description of the technical solutions of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0040] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0041] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0042] The method for obtaining target angles using a generalized non-common baseline distributed array, as provided in this application embodiment, is executed by a computer device.

[0043] Figure 1 This is a schematic diagram of a generalized non-common baseline distributed array in one embodiment of this application, which is used to arrange antenna elements according to the coordinate positions of its array elements. Figure 1 As shown, the generalized non-common baseline distributed array includes a first uniform linear subarray. Second uniform linear subarray First uniform linear subarray Second uniform linear subarray Each contains Each of the array elements is distributed in a preset two-dimensional Cartesian coordinate system. Among them, the first uniform linear subarray In All array elements are located on the x-axis. The Middle The position coordinates of each array element are , express The distance between two adjacent array elements; Second uniform linear subarray The array elements are distributed along a straight line, and the angle between the line and the parallel line to the x-axis is . , The range of values ​​is ; The spacing between two adjacent array elements is ,and ; The Middle The position coordinates of each array element are , express The offset distance of the first array element relative to the origin along the x-axis. express The offset distance of the first array element relative to the origin in the y-axis direction; The first array element in the middle and The distance between the first array elements in the array is defined as the baseline. baseline The length satisfies: .

[0044] By constructing a distributed array structure consisting of two uniform linear subarrays with specific non-collinear geometric relationships, where the second subarray can be flexibly arranged at a preset angle relative to the axis of the first subarray, and the element spacing and baseline distance both satisfy explicit mathematical constraints, the array used for antenna element deployment is freed from the strict linear constraints of traditional common baseline arrays in terms of physical layout. This significantly improves the array's environmental adaptability and deployment freedom in complex terrain or confined spaces.

[0045] In some specific embodiments, the element spacing wavelength of the incident signal 1 / 2 of.

[0046] Through the spacing between array elements Specifically, it is limited to the wavelength of the incident signal. Half of the array's spacing allows it to meet the basic requirements of the Nyquist sampling theorem in spatial sampling, effectively avoiding spatial spectral aliasing or grating lobe problems caused by excessively large element spacing. This ensures that the array can cover the entire expected angle observation spatial domain without ambiguity when receiving signals, providing a high-quality raw signal data foundation for subsequent accurate angle estimation. In some specific embodiments, the baseline The length satisfies: .

[0047] By requiring the baseline between the two subarrays The length is greater than the physical aperture of the subarray. This ensures that the two subarrays are effectively separated in space rather than simply overlapping or closely adjacent. This separation introduces considerable phase difference information related to the target's orientation into the received signal, thereby significantly enhancing the angular resolution potential of the entire array and creating favorable spatial geometry conditions for solving multi-target resolution and improving estimation stability under low signal-to-noise ratio.

[0048] In some specific embodiments, the baseline The length satisfies: .

[0049] By further extending the baseline The length is explicitly set to be at least twice the physical aperture of the subarray. This more stringent design requirement significantly increases the spatial distance between the two subarrays, thereby greatly expanding the effective physical aperture of the entire distributed array. The larger aperture directly results in a narrower beamwidth and higher spatial resolution, enabling the system to better distinguish subtle differences in target angles and ultimately achieve higher accuracy in direction-of-arrival estimation.

[0050] In some specific embodiments, .

[0051] By specifying the number of array elements contained in each uniform linear subarray The requirement of at least four subarrays ensures that each subarray possesses basic spatial sampling capabilities and a certain degree of freedom, enabling the subarrays to independently perform preliminary discrimination and estimation of the directions of multiple signal sources. This provides the necessary hardware conditions for effective coarse estimation at the two subarray levels.

[0052] Figure 2 This is a flowchart illustrating a method for obtaining a target angle using a generalized non-common baseline distributed array according to one embodiment of this application. This method belongs to the same inventive concept as the generalized non-common baseline distributed arrays described in the above embodiments. Details not fully described in the method for obtaining a target angle using a generalized non-common baseline distributed array can be found in the above embodiments of the generalized non-common baseline distributed array. The order of steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0053] like Figure 2 As shown, the method for obtaining the target angle using a generalized non-common baseline distributed array includes: Step S1: Arrange antenna elements according to the coordinate positions of the generalized non-common baseline distributed array elements. Each antenna element corresponds to one element in the generalized non-common baseline distributed array, forming an antenna element array, including the corresponding first uniform linear subarray. The first antenna element array and the corresponding second uniform linear subarray The second antenna element array.

[0054] By actually deploying the corresponding antenna elements according to the coordinate positions of the generalized non-common baseline distributed array elements to form a physical antenna array, the abstract array mathematical model is concretized into an implementable hardware system, ensuring that the theoretical non-common baseline geometric relationship can be accurately realized at the physical level, and guaranteeing the authenticity and reliability of the spatial phase relationship.

[0055] Step S2, Receive The echo data generated by a far-field narrowband signal source incident on the antenna element array is used to obtain the received signal vector of the first antenna element array. Received signal vector of the second antenna element array , Indicates the sampling time; The first in The element is the first element in the first antenna element array. The received signal of each antenna element is denoted as . ; The first in The element is the second antenna cell array. The received signal of each antenna element is denoted as . .

[0056] By simultaneously receiving and separately acquiring the independent received signal vectors corresponding to the two non-collinear subarrays, all phase information contained in the two sub-dimensions due to the unique geometry of the arrays during the propagation of the signal source in space is completely captured, providing comprehensive and paired raw data inputs for independent coarse estimation and data fusion processing.

[0057] In some specific embodiments, The far-field narrowband signal sources are uncorrelated signal sources.

[0058] By limiting the incident light onto the array The far-field narrowband signal sources are uncorrelated, ensuring that the covariance matrix of each signal source has a full-rank diagonal structure. This enables the DOA estimation algorithm based on subspace decomposition to clearly distinguish between the signal subspace and the noise subspace, thereby significantly improving the numerical stability and estimation success rate of the coarse and fine estimation processes. It also avoids the problems of subspace dimension shrinkage and algorithm performance degradation caused by signal coherence.

[0059] In some specific embodiments, .

[0060] By limiting the number of signal sources Less than the number of elements in a single subarray This ensures that the signal subspace dimension of the received signal covariance matrix of each uniform linear subarray will not exceed the spatial degrees of freedom provided by the array elements, enabling classical estimation algorithms such as ESPRIT based on subspace rotation invariance to be applied correctly and reliably.

[0061] Step S3, respectively for and DOA estimation is performed to obtain the first coarsely estimated set of incident angles for each signal source corresponding to the first antenna element array. The second coarse estimate of the incident angle set corresponding to each signal source and the second antenna element array ,in .

[0062] By performing independent preliminary estimations of the direction of arrival (DOA) of the received signal vectors of the two subarrays, two sets of rough estimates of the incident angle of the signal source are obtained quickly and in parallel, providing preliminary, low-precision prior information about the range of the signal source angle.

[0063] In some specific embodiments, the ESPRIT algorithm is used to respectively... and Perform DOA estimation.

[0064] By explicitly using the ESPRIT algorithm to perform coarse DOA estimation on the received signal vectors of the two subarrays, the rotation invariance of the uniform linear array is utilized to directly and quickly obtain the preliminary angle estimate of the closed-form solution with low computational complexity.

[0065] Step S4, based on the included angle The value, from and The middle part is used to select targets for phase compensation from various signal sources and roughly estimate the incident angle. .

[0066] By according to the included angle The value is determined by selecting a target coarse estimation angle for each signal source from two sets of coarse estimation results based on clear judgment rules. This fully utilizes the preset geometric constraints to guide data selection and ensures that the selected coarse estimation value has higher applicability and reliability in the compensation operation.

[0067] In some specific embodiments, a target for phase compensation is selected for each signal source to coarsely estimate the incident angle. The specific rules include: when hour, The value can be:

[0068] when hour, The value can be:

[0069] in, Indicates the first Each signal source corresponds to the first coarsely estimated incident angle of the first antenna element array; Indicates the first Each signal source corresponds to the second coarsely estimated incident angle of the second antenna element array.

[0070] By providing angle-based The clear and complete selection rules, which use the arithmetic mean of two coarsely estimated angles as the basis for judgment, can adaptively and deterministically select the target angle more suitable for phase compensation operation from the two sets of coarse estimation results for each signal source according to the specific geometric configuration of the array. This ensures the correctness of the phase compensation direction and avoids the introduction of additional phase errors due to improper selection.

[0071] Step S5, using right Phase compensation is performed to obtain the compensated signal vector for each signal source. , The first in The element is the second antenna cell array. The compensated signal of each antenna element is denoted as... The expression is:

[0072] in, The imaginary unit; This indicates the wavelength of the incident signal.

[0073] By performing targeted phase compensation calculations on the received signal vector of the second subarray through a rough estimate of the incident angle, the redundant phase components (mainly related to the cosine of the incident angle) introduced by the non-collinear arrangement of the second subarray and the first subarray can be effectively canceled out, thereby calibrating the received data of the second subarray to an equivalent state that is easier to fuse with the data of the first subarray.

[0074] Step S6, will and Combined, they form an equivalent array to receive data. , is represented as:

[0075] By vertically splicing the signal vector of the second subarray after phase compensation processing with the original signal vector of the first subarray, a new and longer equivalent array received data vector is formed. This constructs a virtual equivalent array received data with a better structure at the signal level, combining the information of the two subarrays while avoiding the direct geometric nonlinearity problem.

[0076] Step S7, receive data from the equivalent array. Perform DOA estimation to obtain The precise estimate of the incident angle is retained only when compared with the value of the current incident angle. The precise estimated angle corresponding to each signal source ; against Each signal source executes steps S4-S7 sequentially, ultimately obtaining all... The precise estimated angle of each signal source.

[0077] By performing a final precise estimation of the direction of arrival (DOA) on the data received by the equivalent array and selecting the accurate angle value corresponding to the current signal source, the larger aperture and superior structure of the data received by the equivalent array are fully utilized. This yields results far exceeding the coarse estimation accuracy of a single subarray, thereby achieving a high-precision, unambiguous estimation of the incident angle of each signal source.

[0078] In some specific embodiments, the parametrically constructed dual-scale ESPRIT algorithm is used to evaluate the received data of the equivalent array. Perform DOA estimation.

[0079] By specifying the use of the parametrically constructed dual-scale ESPRIT algorithm to perform precise DOA estimation on the equivalent array received data, high-precision parameter estimation can be performed for the unique structure of the equivalent array received data, thereby effectively solving the phase ambiguity problem caused by the non-common baseline layout, and ultimately achieving high-resolution and high-precision estimation of the target incident angle.

[0080] In some specific embodiments, the parametrically constructed dual-scale ESPRIT algorithm is used to process the received data from the equivalent array. The specific steps for performing DOA estimation include: Receive data using an equivalent array Construct two subarrays with a fixed scaling relationship, and obtain the first subarray by solving the generalized eigenvalue problem between the signal subspaces of these two subarrays. Precise estimation angle of each signal source .

[0081] By explicitly utilizing the data received by the equivalent array to construct two subarrays with a fixed scaling relationship and solving the generalized eigenvalue problem between their signal subspaces to obtain the precise estimated angle, the phase rotation factor characterizing the target angle can be extracted directly and stably from a mathematical perspective, ensuring the computational efficiency and numerical robustness of the precise estimation process.

[0082] In some specific embodiments, the angle is precisely estimated. The effective estimation range is determined by and Decisions include: when and hour, The effective estimation range is: ; when and hour, The effective estimation range is: ; when and hour, The effective estimation range is: ; when and hour, The effective estimation range is: ; in, .

[0083] By clarifying the precise estimation angle Effective estimation range and array geometry parameters and The specific functional relationship between them clearly defines the angle range that can work without ambiguity and with high precision under different array deployment postures, providing key performance boundary guidance. This allows for pre-evaluation based on the expected target spatial domain and achievable array parameters before actual deployment, ensuring the effectiveness of the entire system's angle estimation performance.

[0084] The method for obtaining target angles using a generalized non-common baseline distributed array, as described in the above embodiments, was used in simulation experiments to evaluate the target angle estimation performance. The simulation experiments provide a comparison of the DOA (Direction of Arrival) estimation performance of a common baseline distributed uniform array with equal array elements and a uniform linear array, and also provide the Cramer-Rao boundary as a reference.

[0085] Wherein, GNCDUA represents the method of obtaining target angles using a generalized non-common baseline distributed array; DUA stands for the method of obtaining the target angle using a common baseline distributed uniform array, which consists of two parallel uniform linear subarrays. ULA stands for the method of obtaining the target angle using a uniform linear array.

[0086] In the simulation experiment, a first uniform linear subarray was set up. Second uniform linear subarray Each contains 14 array elements and an incident signal wavelength. =25m =60 , =10 , =12.5m =10° =2, the incident angle of two equal-power signal sources The angles were 21° and 30° respectively, with a success probability threshold of 0.08°, and 2000 Monte Carlo trials were conducted.

[0087] Simulation results are as follows Figures 3-5 As shown, where: Figure 3 A schematic diagram is given showing how the root mean square error of the target angle obtained by different methods varies with the SNR when the number of snapshots is equal to 500. Figure 4 A schematic diagram is given showing how the root mean square error of the target angle obtained by different methods changes with the number of snapshots when the SNR is equal to 0dB. Figure 5 A schematic diagram is given showing how the probability of successfully estimating the target angle using different methods varies with SNR.

[0088] In the diagram, CRB stands for Cramere-Rao Bound, which is the theoretical lower bound of the variance of an unbiased estimator. Figures 3-5 In Chinese, the formula for calculating CRB is:

[0089] in, This represents the conjugate transpose of a matrix. This represents finding the inverse of a matrix. Indicates matrix transpose. It represents the Hadamah accumulation. Indicates the Department of Seeking Truth. Indicates the noise variance. Indicates the number of snapshots. For the guiding vector matrix, Let A be the matrix obtained by taking the derivative with respect to A. It is the identity matrix. Represents the signal covariance matrix. This represents the received signal vector.

[0090] The GNCDUA coarse estimate represents the preliminary angle estimation result of the generalized non-common baseline distributed array. Specifically, it is the two sets of initial angles obtained by independently estimating the first and second uniform linear subarrays of the generalized non-common baseline distributed array.

[0091] GNCDUA precise estimation represents the final angle estimation result of a generalized non-common baseline distributed array. Specifically, it involves using the included angle parameter to perform phase compensation on the received signal of the second subarray, combining the compensated signal with the signal of the first subarray to form equivalent array data, and then performing DOA estimation on the equivalent data to obtain the angle value.

[0092] DUA coarse estimation represents the preliminary angle estimation result of a common baseline distributed uniform array. Specifically, it involves performing independent DOA estimation on two parallel uniform linear subarrays to obtain two sets of initial angles.

[0093] DUA precise estimation represents the final angle estimation result of a common baseline distributed uniform array. Specifically, it is the angle value obtained by directly merging the data of two parallel subarrays and then performing DOA estimation.

[0094] GNCDUA CRB represents the Cramer-Rao boundary of a generalized non-common baseline distributed array. Specifically, it is the theoretical accuracy limit calculated using the CRB calculation formula based on the generalized non-common baseline distributed array. During the calculation process, the corresponding steering vector matrix is ​​obtained by generating the first part of the steering vector based on the coordinates of the first subarray element and the second part of the steering vector based on the coordinates of the second subarray element, and then combining them.

[0095] DUA CRB represents the Cramer-Rao boundary of a common-baseline distributed uniform array. Specifically, it is the theoretical accuracy limit calculated using the CRB calculation formula based on the common-baseline distributed uniform array. During the calculation, the corresponding steering vector matrix is ​​obtained by generating steering vectors for each signal source based on the element coordinates of two parallel uniform linear subarrays, and then combining them.

[0096] ULA CRB represents the Cramer-Rao boundary of a uniform linear array. Specifically, it is the theoretical accuracy limit calculated using the CRB calculation formula based on a uniform linear array. During the calculation, the corresponding steering vector matrix is ​​generated by generating the steering vectors of each signal source based on the coordinates of all array elements arranged at equal intervals on a straight line, and then combining them.

[0097] Depend on Figures 3-4 It can be seen that, compared with uniform linear arrays, when the SNR is greater than -6dB, the DOA estimation accuracy of both the generalized non-common baseline distributed uniform array and the common baseline distributed uniform array is significantly improved, approaching the Cramer-Rao boundary. Using a phase-compensated deblurring algorithm, the DOA estimation accuracy of the designed generalized non-common baseline distributed uniform array is basically the same as that of the common baseline distributed uniform array, demonstrating the effectiveness of the algorithm and array in this application. Similarly, when the SNR is equal to 0dB and the number of snapshots is greater than 100, both the generalized non-common baseline distributed uniform array and the common baseline distributed uniform array can correctly deblur all target angles, and their DOA estimation accuracy is much better than that of the uniform linear array with equal array elements. The DOA estimation accuracy of the generalized non-common baseline distributed uniform array in this application is basically the same as that of the common baseline distributed uniform array. Its advantage lies in the fact that the generalized non-common baseline distributed uniform array has more flexible deployment, is less affected by geographical factors such as terrain and rivers, and has fewer constraints, further expanding the applicability of the array and better adapting to the needs of actual backgrounds.

[0098] Depend on Figure 5It can be seen that when the SNR is greater than -6dB, the generalized non-common baseline distributed uniform array can completely and successfully estimate the incident angle of the signal source with a threshold value of 0.08°, while the signal-to-noise ratio threshold for a uniform linear array with equal array elements to completely and successfully estimate the incident angle of the signal source is 6dB, which is 12dB higher than the signal-to-noise ratio threshold value. Figure 5 The simulation results also demonstrate that the generalized non-common baseline distributed uniform array of this application has excellent DOA estimation performance.

[0099] This application also provides an electronic device for implementing the various embodiments of this application. Figure 6 To illustrate the hardware structure of an electronic device according to various embodiments of this application, as shown in the following diagram... Figure 6 As shown, the electronic device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor.

[0100] Those skilled in the art will understand that the electronic device structure involved in the embodiments of this application does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0101] In embodiments of this application, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0102] In this application embodiment, the processor can be implemented using at least one of an Application-Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such implementations can be implemented within a controller. For software implementations, implementations such as processes or functions can be implemented with separate software modules that allow the performance of at least one function or operation. The software code can be implemented by a software application (or program) written in any suitable programming language, and the software code can be stored in memory and executed by the controller.

[0103] In addition, the electronic device includes some functional modules not shown, which will not be described in detail here.

[0104] Those skilled in the art will understand that the various aspects of the electronic device provided in this application can be implemented as a system, method, or program product. Therefore, the various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0105] This application also provides a storage medium storing a program product capable of implementing a method for acquiring target angles using a generalized non-common baseline distributed array. In some possible implementations, various aspects of this application may also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the foregoing "Exemplary Methods" section of this specification according to various exemplary embodiments of this application.

[0106] The storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0107] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A generalized non-common baseline distributed array, used to arrange antenna elements according to the coordinate positions of its array elements, characterized in that, The generalized non-common-baseline distributed array comprises a first uniform linear sub-array and a second uniform linear sub-array , the first uniform linear sub-array and the second uniform linear sub-array respectively contain array elements, and each array element is distributed in a preset x-y two-dimensional plane rectangular coordinate system. Among them, the first uniform linear subarray In All array elements are located on the x-axis. The Middle The position coordinates of each array element are , express The distance between two adjacent array elements; Second uniform linear subarray The array elements are distributed along a straight line, and the angle between the line and the parallel line to the x-axis is . , The range of values ​​is ; The spacing between two adjacent array elements is ,and ; The Middle The position coordinates of each array element are , express The offset distance of the first array element relative to the origin along the x-axis. express The offset distance of the first array element relative to the origin in the y-axis direction; The first array element in the middle and The distance between the first array elements in the array is defined as the baseline. baseline The length satisfies: .

2. The generalized non-common baseline distributed array as described in claim 1, characterized in that, Baseline The length satisfies: .

3. The generalized non-common baseline distributed array as described in claim 1, characterized in that, 。 4. A method for obtaining a target angle using a generalized non-common baseline distributed array as described in any one of claims 1-3, characterized in that, include: S1. Antenna elements are arranged according to the coordinate positions of the elements of the generalized non-common baseline distributed array, with each antenna element corresponding to one element in the generalized non-common baseline distributed array, forming an antenna element array, including the corresponding first uniform linear subarray. The first antenna element array and the corresponding second uniform linear subarray The second antenna element array; S2. Receive The echo data generated by a far-field narrowband signal source incident on the antenna element array is used to obtain the received signal vector of the first antenna element array. Received signal vector of the second antenna element array , Indicates the sampling time; The first in The element is the first element in the first antenna element array. The received signal of each antenna element is denoted as . ; The first in The element is the second antenna cell array. The received signal of each antenna element is denoted as . ; S3. For each and DOA estimation is performed to obtain the first coarsely estimated set of incident angles for each signal source corresponding to the first antenna element array. The second coarse estimate of the incident angle set corresponding to each signal source and the second antenna element array ,in ; S4. Based on the included angle The value, from and The middle part is used to select targets for phase compensation from various signal sources and roughly estimate the incident angle. ; S5. Utilize right Phase compensation is performed to obtain the compensated signal vector for each signal source. , The first in The element is the second antenna cell array. The compensated signal of each antenna element is denoted as... The expression is: in, The imaginary unit; Indicates the wavelength of the incident signal; S6. Will and Combined, they form an equivalent array to receive data. , is represented as: S7. Receive data from the equivalent array. Perform DOA estimation to obtain The precise estimate of the incident angle is retained only when compared with the value of the current incident angle. The precise estimated angle corresponding to each signal source ; against Each signal source executes steps S4-S7 sequentially, ultimately obtaining all... The precise estimated angle of each signal source.

5. The method for obtaining target angles using a generalized non-common baseline distributed array as described in claim 4, characterized in that, The far-field narrowband signal sources are uncorrelated signal sources.

6. The method for obtaining target angles using a generalized non-common baseline distributed array as described in claim 4, characterized in that, In step S3, the ESPRIT algorithm is used to process the samples respectively. and Perform DOA estimation.

7. The method for obtaining target angles using a generalized non-common baseline distributed array as described in claim 4, characterized in that, In step S4, a target for phase compensation is selected for each signal source to coarsely estimate the incident angle. The specific rules include: when hour, The value can be: when hour, The value can be: in, Indicates the first Each signal source corresponds to the first coarsely estimated incident angle of the first antenna element array; Indicates the first Each signal source corresponds to the second coarsely estimated incident angle of the second antenna element array.

8. The method for obtaining target angles using a generalized non-common baseline distributed array as described in claim 4, characterized in that, In step S7, the parametrically constructed dual-scale ESPRIT algorithm is used to analyze the received data of the equivalent array. Perform DOA estimation.

9. The method for obtaining target angles using a generalized non-common baseline distributed array as described in claim 8, characterized in that, The equivalent array received data was processed using the parametrically constructed two-scale ESPRIT algorithm. The specific steps for performing DOA estimation include: Receive data using an equivalent array Construct two subarrays with a fixed scaling relationship, and obtain the first subarray by solving the generalized eigenvalue problem between the signal subspaces of these two subarrays. Precise estimation angle of each signal source .

10. The method for obtaining target angles using a generalized non-common baseline distributed array as described in claim 4, characterized in that, Precise angle estimation The effective estimation range is determined by and Decisions include: when and hour, The effective estimation range is: ； when and hour, The effective estimation range is: ； when and hour, The effective estimation range is: ； when and hour, The effective estimation range is: ； in, .

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