CN-121995306-A - Black unmanned aerial vehicle DoA estimation method based on cooperative unmanned aerial vehicle auxiliary domain self-adaption

CN121995306ACN 121995306 ACN121995306 ACN 121995306ACN-121995306-A

Abstract

The invention discloses a black unmanned aerial vehicle DoA estimation method based on auxiliary domain self-adaption of a cooperative unmanned aerial vehicle, which comprises the steps of defining an operation domain as an actual environment with multipath interference by utilizing a domain self-adaption thought, defining a reference domain as an ideal multipath free space environment, constructing a linear mapping matrix from the operation domain to the reference domain based on a structural relation of the reference domain covariance matrix and the operation domain covariance matrix so as to map a received signal of the operation domain to the reference domain, constructing the reference domain covariance matrix firstly, determining the operation domain covariance matrix by utilizing multi-snapshot broadcast signals of the cooperative unmanned aerial vehicle, converting the black unmanned aerial vehicle signal acquired by a receiving array into a reference domain signal by utilizing an optimal solution of the linear mapping matrix, determining the covariance matrix of the black unmanned aerial vehicle signal by utilizing the reference domain signal, and finally determining the DoA of the black unmanned aerial vehicle by combining a spatial spectrum function based on characteristic value decomposition of the covariance matrix.

Inventors

CHEN REN
LI YONG
CHENG WEI
LI DAN
DONG LIMENG
ZHAO HONGGANG
CHEN SHAOWEI
LI HUI
LI RUIKE
JI YAXIN

Assignees

西北工业大学

Dates

Publication Date: 20260508
Application Date: 20260119

Claims (10)

1. The method for estimating the DoA of the black unmanned aerial vehicle based on the auxiliary domain self-adaption of the cooperative unmanned aerial vehicle is characterized by comprising the following steps: The method comprises the steps of defining an operation domain as an actual environment with multipath interference, defining a reference domain as an ideal multipath free space environment, constructing a linear mapping matrix from the operation domain to the reference domain based on the structural relation of a covariance matrix of the reference domain and the covariance matrix of the operation domain, and mapping a received signal of the operation domain to the reference domain; constructing an autocorrelation matrix of each reference steering vector based on the reference steering vector matrix, and obtaining a reference domain covariance matrix by utilizing the autocorrelation matrices under different reference angles; The method comprises the steps of deploying a cooperative unmanned aerial vehicle in a region of interest by adopting a remote identification mode of the cooperative unmanned aerial vehicle, continuously broadcasting RID signals, determining an incident angle of the cooperative unmanned aerial vehicle relative to a receiving array based on coordinate information contained in the RID signals, and determining an operation domain covariance matrix by utilizing a plurality of acquired snapshot RID signals; constructing an objective function for solving the linear mapping matrix by using the reference domain covariance matrix and the operation domain covariance matrix, and obtaining an optimal solution of the linear mapping matrix through Cholesky decomposition; For the black unmanned aerial vehicle signal acquired by the receiving array, converting the black unmanned aerial vehicle signal into a reference domain signal by utilizing an optimal solution of a linear mapping matrix, and then determining a covariance matrix of the black unmanned aerial vehicle signal by utilizing the reference domain signal; Performing eigenvalue decomposition on a covariance matrix of the black unmanned aerial vehicle signal, and constructing a spatial spectrum function based on the decomposed signal subspace and the guide vector; the DoA of the black flying drone is determined based on the spectral peak search.
2. The method for estimating DoA of unmanned aerial vehicle based on cooperative unmanned aerial vehicle auxiliary domain adaptation according to claim 1, wherein for constructing the reference structure of the reference domain, a reference steering vector matrix is defined first, and the estimation is performed in an angle space Internal uniform sampling Reference angle of The reference steering vector matrix may be expressed as: In the above Is the minimum value and the maximum value of the angle space, Represent the first The number of reference angles is a number, Each reference steering vector The method comprises the following steps: Wherein, the Represents the array element spacing of the uniform linear array at the receiving end, As a function of the wavelength of the signal, Is the number of array elements; for each reference angle Calculating an autocorrelation matrix of the reference steering vector: Wherein the method comprises the steps of Is a positive constant, is used for numerical stability, Is that Unit matrix, parameter superscript Representing conjugate transpose, taking all reference angles Corresponding autocorrelation matrix Obtaining a reference domain covariance matrix: 。
3. the method for estimating the DoA of the unmanned black plane based on the auxiliary domain adaptation of the cooperative unmanned aerial vehicle according to claim 1, wherein the incident angle of the cooperative unmanned aerial vehicle relative to the receiving array The position coordinates broadcast in the RID signal are calculated according to the known positions of the receiving array: Wherein, the For the horizontal coordinates of the co-operating unmanned aerial vehicle, For receiving the position coordinates of the array reference array elements.
4. The method for estimating the DOA of a black unmanned aerial vehicle based on the auxiliary domain adaptation of the cooperative unmanned aerial vehicle according to claim 1, wherein the receiving array collects RID signals broadcast by the cooperative unmanned aerial vehicle in an environment correction phase, during which period no black unmanned aerial vehicle exists in the actual environment, and collects the RID signals in the region of interest by the cooperative unmanned aerial vehicle Snapshot data, the first The signals received by the receiving array at each snapshot time are as follows: Wherein, the For the complex envelope of the RID signal, For the steering vector corresponding to the direct path, Is the first The steering vector corresponding to the strip multipath, Is a multipath coefficient; Is a noise vector; represents the direct path, coexists in And (3) multipath.
5. The method for estimating the DOA of the unmanned aerial vehicle based on the cooperative unmanned aerial vehicle auxiliary domain adaptation according to claim 1, wherein the method is based on acquisition Data of each snapshot and covariance matrix of operation domain By means of a sample covariance matrix Estimating: Wherein the method comprises the steps of Is the first Each snapshot time receives a signal received by the array.
6. The method for estimating the DOA of the unmanned aerial vehicle based on the cooperative unmanned aerial vehicle auxiliary domain adaptation according to claim 1, wherein the covariance matrix of the reference domain is obtained And an operation domain covariance matrix Then, a linear mapping matrix from the operation domain to the reference domain is defined as So that the mapped signal covariance satisfies: Linear mapping matrix Is achieved by minimizing the following objective function: Wherein, the The derivation of the closed-form solution is as follows: Order the The above can be written as: the optimization problem becomes: For a pair of Cholesky decomposition was performed: Wherein the method comprises the steps of A lower triangular matrix obtained by Cholesky decomposition; Then The optimal solution of (a) is: The linear mapping matrix is thus The optimal solution of (a) is: 。
7. The method for estimating the DOA of the unmanned aerial vehicle based on the cooperative unmanned aerial vehicle auxiliary domain self-adaption of claim 1, wherein the signal of the unmanned aerial vehicle received by the receiving array in the actual application process is that The domain transformed reference domain signal is: The covariance matrix of the transformed unmanned aerial vehicle signal is: Wherein, the Indicating that the desired operation is to be performed, Is the theoretical covariance matrix of the unmanned aerial vehicle signal.
8. The method for estimating the DOA of the unmanned aerial vehicle based on the cooperative unmanned aerial vehicle auxiliary domain self-adaption according to claim 1, wherein the covariance matrix is estimated by adopting the DOA estimation based on the MUSIC algorithm And (3) performing eigenvalue decomposition: Wherein: Is a signal subspace, corresponding to the front Maximum characteristic value ; Is a noise subspace, correspond to the rear Small eigenvalues ; For a diagonal matrix of signal eigenvalues, For the diagonal matrix of noise eigenvalues, In order to receive the number of array elements of the array, For the number of signal sources, Representation of A plurality of dimensions of the space, Is a diagonal matrix; the MUSIC spatial spectral function is defined as: Wherein the method comprises the steps of For unmanned aerial vehicle signal is at A steering vector of the angular direction; by searching within an angular space The DoA estimation of the 'black flying' unmanned plane can be obtained 。
9. The method for estimating the DoA of the black unmanned aerial vehicle based on the auxiliary domain adaptation of the cooperative unmanned aerial vehicle according to claim 1, wherein the signal source number is estimated by adopting an MDL criterion: Wherein, the As covariance matrix A kind of electronic device The value of the characteristic is a value of, Is the first The value of the characteristic is a value of, For the number of the array elements, For the number of signal shots, The number of signal sources to be tested; the number of signal sources is estimated as: 。
10. A terminal device comprising a processor, a memory and a computer program stored in the memory, characterized in that the processor, when executing the computer program, implements the method for estimating the DoA of a black unmanned aerial vehicle based on the auxiliary domain adaptation of a cooperative unmanned aerial vehicle according to any one of claims 1 to 9.

Description

Black unmanned aerial vehicle DoA estimation method based on cooperative unmanned aerial vehicle auxiliary domain self-adaption Technical Field The invention relates to the technical field of array signal processing and radio monitoring, in particular to a black flying unmanned aerial vehicle DoA estimation method based on cooperative unmanned aerial vehicle auxiliary domain self-adaption, which can be applied to scenes such as an anti-unmanned aerial vehicle system, airspace safety management, key region protection, moving target radio frequency positioning and the like. Background In recent years, with the rapid popularization and wide application of unmanned aerial vehicle technology, unauthorized flying of a 'black-flying' unmanned aerial vehicle poses an increasingly serious threat to public safety, critical infrastructure and personal privacy. According to statistics, thousands of related events occur every year worldwide, and safety accidents such as flight delay, activity suspension and the like are frequently caused, so that real-time detection and accurate positioning of a 'black flight' unmanned plane are urgent technical requirements. Among many detection technologies, detection methods based on radio frequency signals are receiving passively, do not depend on optical characteristics, and can work around the clock. In such methods, direction of arrival (Direction of Arrival, doA) estimation is a key link for achieving unmanned aerial vehicle spatial localization and tracking. Through carrying out accurate estimation to unmanned aerial vehicle radio frequency signal's direction of arrival, can further calculate its position, realize real-time supervision and early warning, therefore the DOA estimation has core position in "black flight" unmanned aerial vehicle detecting system. The DOA estimation is a classical problem in the field of array signal processing and is widely applied to radar, sonar, wireless communication and other systems. Conventional DoA estimation algorithms include delay-and-sum beamforming, minimum variance distortion-free response (Minimum Variance Distortionless Response, MVDR), multiple signal classification (Multiple Signal Classification, MUSIC), etc., which can achieve higher direction estimation accuracy in an ideal environment. However, in a complex practical environment, the DoA estimation faces serious challenges, especially in urban scenarios, multipath propagation, electromagnetic interference, non-stationary noise and other factors can significantly reduce algorithm performance, resulting in increased estimation bias and increased false alarm rate. In addition, the detection of the 'black flying' unmanned aerial vehicle has various special difficulties that radio frequency signals are usually weak and change rapidly, working frequency bands (such as 2.4GHz and 5.8 GHz) are overlapped with Wi-Fi and other civil equipment, so that signal extraction and resolution become difficult, prior signal characteristics or position information are lacking, the applicability of a traditional DoA algorithm is further weakened, meanwhile, false spectrum peaks can be generated due to multipath effects of urban environments, a subspace algorithm (such as MUSIC) is caused to generate a plurality of false peaks, misjudgment risks are increased, and the unmanned aerial vehicle has high maneuverability and low radar scattering cross section (Radar Cross Section, RCS), so that the detection distance is short, the cost is high, and the deployment is limited based on a radar detection method. Therefore, the high-robustness and high-precision DOA estimation algorithm is researched aiming at the radio frequency signal of the 'black flight' unmanned aerial vehicle under the complex electromagnetic environment, and the method has important theoretical value and practical significance for realizing the rapid detection and accurate positioning of the unmanned aerial vehicle. Disclosure of Invention The invention aims to provide a black unmanned aerial vehicle DoA estimation method based on cooperative unmanned aerial vehicle auxiliary domain self-adaption, which aims to solve the problems of low accuracy, poor environmental adaptability, dependence on a large amount of calibration data and the like when the black unmanned aerial vehicle DoA is estimated under complex electromagnetic environments such as multipath interference and the like in the prior art. In order to realize the tasks, the invention adopts the following technical scheme: A black unmanned aerial vehicle DoA estimation method based on cooperative unmanned aerial vehicle auxiliary domain self-adaption comprises the following steps: The method comprises the steps of defining an operation domain as an actual environment with multipath interference, defining a reference domain as an ideal multipath free space environment, constructing a linear mapping matrix from the operation domain to the reference domain based on the structural r