CN-121995305-A - Unmanned aerial vehicle DOA joint estimation method based on dual-polarized RIS assistance

CN121995305ACN 121995305 ACN121995305 ACN 121995305ACN-121995305-A

Abstract

The invention discloses a dual-polarized RIS-assisted unmanned aerial vehicle DOA joint estimation method, which is characterized in that the joint reflection matrix of dual-polarized RIS is designed, the degree of freedom of an array is fully utilized by the polarization diversity characteristic, so that more effective resolution of signal sources is realized under the same physical aperture, the RIS phase is optimized by means of a random optimization subspace mapping algorithm, the high-dimensional problem is subjected to dimension reduction processing by means of a random projection technology, the calculation complexity is obviously reduced while the convergence is ensured, a spatial spectrum function is constructed based on a multiple signal classification algorithm, and the DOA estimation with high precision and high resolution is realized by combining the global spectrum peak search and characteristic subspace orthogonality principle. The invention effectively improves the signal source resolving power and the estimation precision of unmanned aerial vehicle positioning, and simultaneously has better calculation efficiency and system reliability.

Inventors

GONG YANYUN
Wang Zhuanna
SUN YANDONG
CHEN LILI
LIU HAOCHEN
Gou Yuewen
XIE JIAN
WANG LING

Assignees

西北工业大学

Dates

Publication Date: 20260508
Application Date: 20251225

Claims (8)

1. A dual-polarized RIS-assisted unmanned aerial vehicle DOA joint estimation method is characterized by comprising the following steps: Step 1, constructing a dual-polarized RIS array steering vector matrix and EMVS receiving array configuration parameters; step 2, receiving the unmanned aerial vehicle dual-polarized signal based on the guide vector matrix and performing polarization separation pretreatment; step 3, constructing a joint receiving data matrix containing horizontal/vertical polarization by using the signals after polarization separation; Step 4, optimizing and generating an optimal dual-polarized RIS joint reflection matrix through ROSM algorithm; step 5, calculating a polarization-space joint covariance matrix, decomposing eigenvalues, and extracting a signal subspace and a noise subspace; step 6, constructing a dual-polarized MUSIC spatial spectrum function based on the noise subspace and the dual-polarized guide vector, searching spatial spectrum peaks and obtaining a final two-dimensional DOA estimated value; and 7, calculating the RMSE and CRLB theoretical lower bound evaluation estimation performance.
2. The method for co-estimating the DOA of the unmanned aerial vehicle based on the dual-polarized RIS assistance according to claim 1, wherein the step 1 is specifically: step 1-1 define Unmanned aerial vehicle target, definition target Is the signal incidence azimuth angle of (2) Pitch angle of The array of receiving ends is composed of Each array element is composed of RIS Each unit is composed of a half wavelength The reflected signal is divided into horizontally polarized components And a vertical polarization component The expression is as follows: (1) (2) Wherein, the The horizontal initial phase and the vertical initial phase of the analog RIS unit are respectively of length Each element is independently subject to uniform distribution, and Toeplitz matrix simulates the mutual coupling effect among RIS units; Step 1-2 defining array response vectors The expression is as follows, which characterizes the signal from direction Incident, a set of phase delays induced on the individual sensors of the array: (3) Wherein, the Is the position vector of the array element, Representing wavelength.
3. The method for co-estimating the DOA of the unmanned aerial vehicle based on the dual-polarized RIS assistance according to claim 2, wherein the step 2 is specifically: Defining the horizontal/vertical polarization response matrices as respectively 、 The expression is as follows: Defining the horizontal/vertical polarization response matrices as respectively 、 The expression is as follows: (4) (5) (6) (7) Wherein, the , Respectively horizontal/vertical polarization steering vectors.
4. The method for co-estimating the DOA of the unmanned aerial vehicle based on the dual-polarized RIS assistance according to claim 3, wherein the step 3 is specifically as follows: the observation information in the horizontal direction and the vertical direction is spliced through the matrix to form a joint observation matrix G, and the expression is: (8) Wherein, the And Horizontal phase of RIS respectively And vertical phase The polarization component after the phase optimization, Is 1× Is 1 for all elements; the superscript T denotes a transpose; the expression of the reflected signal received by the EMVS array is as follows: (9) Wherein, the The method is characterized in that the method is a complex Gaussian random signal matrix, signals sent by a target are simulated, each element independently obeys complex Gaussian random distribution, For additive white gaussian noise, K represents the number of snapshots.
5. The method for co-estimating the DOA of the unmanned aerial vehicle based on the dual-polarized RIS assistance according to claim 4, wherein the step 4 is specifically: Initializing the phase of RIS And Performing Performing RIS phase mapping and constructing a joint observation matrix Calculating signal power Generating disturbance phase at each iteration, calculating new signal power, if the new signal power is higher than the current optimal signal power, updating RIS phase until the iteration is completed to obtain optimal RIS phase And This process is represented by the following optimization algorithm: (10) (11) (12) Wherein, the Which is indicative of the received signal power, Represents the optimal joint observation matrix obtained after RIS phase optimization, For the joint observation matrix of the RIS, For all possible joint observation matrix sets, the phase is determined by RIS 、 The generation of the product is carried out, Representing the Kronecker product operation.
6. The method for co-estimating the DOA of the unmanned aerial vehicle based on the dual-polarized RIS assistance according to claim 5, wherein the step 5 is specifically: Modeling the covariance matrix of the horizontal/vertical polarization channel in a joint way to obtain a covariance matrix of a received signal, wherein the covariance matrix is as follows: (13) Wherein, the Then, feature value decomposition is performed: (14) Wherein the method comprises the steps of A diagonal matrix composed of feature vectors; diagonal matrix composed of eigenvalues, arranged in ascending order, front The feature vectors corresponding to the large feature values form a signal subspace, which is defined as The residual eigenvectors constitute a noise subspace defined as 。
7. The method for co-estimating the DOA of the unmanned aerial vehicle based on the dual-polarized RIS assistance according to claim 6, wherein the step 6 is specifically: constructing joint steering vectors for each angular grid The expression is as follows: (15) Wherein, the And Discrete values of azimuth and pitch are represented respectively, Representing the horizontal polarization-guiding vector, Representing a vertical polarization steering vector; The spatial spectrum is then calculated, expressed as follows: (16) Wherein, the Representing noise subspace matrices Is a conjugate transpose of (2); Searching on spatial spectrum The highest peak value and the corresponding angle are estimated DOA values.
8. The method for co-estimating the DOA of the unmanned aerial vehicle based on the dual-polarized RIS assistance according to claim 7, wherein the step 7 is specifically: the RMSE expression is as follows: (17) Wherein, the And Is the first Second snapshot Azimuth and pitch angles estimated by the individual unmanned aerial vehicles; CRLB the calculation formula is: (18) (19) (20) Wherein, the Is Fisher information matrix; Is the noise power; A parameter vector consisting of an azimuth angle and a pitch angle to be estimated; the derivative of the parameter for the joint observation matrix; representing a noise subspace for a projection matrix; is the covariance matrix of the signal source signal.

Description

Unmanned aerial vehicle DOA joint estimation method based on dual-polarized RIS assistance Technical Field The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle DOA joint estimation method based on dual-polarized RIS assistance. Background Along with the rapid development of the 6G communication technology, the unmanned aerial vehicle has become a key node in an air-ground integrated network by virtue of the advantages of high maneuverability, flexible deployment, low cost and the like, and has great potential in the fields of military reconnaissance, emergency relief, intelligent logistics and the like. Meanwhile, a Reconfigurable Intelligent Surface (RIS) is used as an emerging electromagnetic regulation technology, and a revolutionary solution is provided for breaking through the line-of-sight (LoS) limitation of traditional communication and optimizing channel conditions by intelligently reconstructing a wireless propagation environment. The RIS technology is deeply integrated with the unmanned aerial vehicle system, the cooperative advantages of three-dimensional maneuverability and RIS dynamic beam forming of the unmanned aerial vehicle can be fully exerted, the positioning accuracy and communication reliability under a complex environment can be remarkably improved, particularly in the scene of limited performance of the traditional positioning technology such as urban canyons, strong interference and the like, the RIS-assisted unmanned aerial vehicle positioning system can effectively overcome the multipath effect and the signal shielding problem by optimizing the signal reflection path in real time, an innovative technical approach is provided for the high-precision and high-dynamic positioning requirements under a 6G network, and the important direction of integrated development of future intelligent communication and perception is represented. Classical signal arrival Direction (DOA) estimation methods such as multiple signal classification (MUSIC) algorithm based on subspace decomposition, signal parameter Estimation (ESPRIT) algorithm based on rotation invariant technology and minimum variance undistorted response beam forming are excellent in high signal to noise ratio (SNR) environment, but have obvious limitations in practical application, including problems of remarkably reduced performance, difficulty in processing coherent sources and higher computational complexity under low SNR condition, while single polarization RIS-assisted DOA estimation can improve signal propagation environment through intelligent reflection, but due to limited polarization freedom degree, space characteristics of signals are difficult to fully utilize under limited physical aperture condition, resolution of multiple signal sources is insufficient, particularly in complex electromagnetic environment and dynamic scene, single polarization design can further restrict system freedom degree, so that inherent angle estimation performance bottleneck of traditional array and polarization constraint of RIS are overlapped with each other, and application effect of the traditional DOA estimation technology in complex scene such as 6G space-earth integration network is limited. Disclosure of Invention In order to overcome the defects of the prior art, the invention provides an unmanned aerial vehicle DOA joint estimation method based on dual-polarized RIS assistance, which is characterized in that the joint reflection matrix of the dual-polarized RIS is designed, the degree of freedom of an array is fully utilized, so that more effective resolution of signal sources is realized under the same physical aperture, the RIS phase is optimized by means of a random optimization subspace mapping algorithm, the high-dimensional problem is subjected to dimension reduction processing by means of a random projection technology, the convergence is ensured, the calculation complexity is obviously reduced, a spatial spectrum function is constructed based on a multiple signal classification algorithm, and the DOA estimation with high precision and high resolution is realized by combining the global spectrum peak search and characteristic subspace orthogonality principle. The invention effectively improves the signal source resolving power and the estimation precision of unmanned aerial vehicle positioning, and simultaneously has better calculation efficiency and system reliability. The technical scheme adopted for solving the technical problems is as follows: Step 1, constructing a dual-polarized RIS array steering vector matrix and EMVS receiving array configuration parameters; step 2, receiving the unmanned aerial vehicle dual-polarized signal based on the guide vector matrix and performing polarization separation pretreatment; step 3, constructing a joint receiving data matrix containing horizontal/vertical polarization by using the signals after polarization sepa