CN-122017734-A - Unmanned aerial vehicle situation awareness method based on radio technology

CN122017734ACN 122017734 ACN122017734 ACN 122017734ACN-122017734-A

Abstract

The invention discloses an unmanned aerial vehicle situation awareness method and system based on radio technology, comprising the following steps that S1, a receiving node receives radio signals transmitted by an unmanned aerial vehicle and processes the received signals into a snapshot matrix The method comprises the steps of S2, inputting a snapshot matrix into a covariance matrix, decomposing the covariance matrix to obtain a noise subspace matrix, inputting array flow vectors into a MUSIC spectrum through the noise subspace matrix and the array flow vectors, searching peaks of P MUSIC (theta) to obtain each incident angle estimation, S3, obtaining an observation vector according to the incident angle, determining an initial coordinate of the unmanned aerial vehicle through the observation vector, an initial coordinate of a receiving node and a least square method, S4, determining an initialization state vector according to the initial coordinate of the unmanned aerial vehicle, S5, carrying out iterative calculation on the current position and speed of the unmanned aerial vehicle at the moment k according to the covariance matrix of the initialization state vector, and outputting the current situation.

Inventors

ZHANG FAN
GE WEI
CHEN RUI
LIN DONGMING
ZHU JIANHAO
LI QINGHANG

Assignees

中国铁塔股份有限公司广东省分公司

Dates

Publication Date: 20260512
Application Date: 20251212

Claims (9)

1. The unmanned aerial vehicle situation awareness method based on the radio technology is characterized by comprising the following steps of: s1, a receiving node receives radio signals transmitted by an unmanned aerial vehicle and processes the received signals into a snapshot matrix ; S2, inputting the snapshot matrix into a covariance matrix, decomposing the covariance matrix to obtain a noise subspace matrix, inputting the vector quantity of the array flow into a MUSIC spectrum through the noise subspace matrix, and searching the peak value of P MUSIC (theta) to obtain each incident angle estimation; s3, obtaining an observation vector according to the incident angle, and determining the initial coordinate of the unmanned aerial vehicle through the observation vector, the initial coordinate of the receiving node and a least square method; S4, determining an initialization state vector according to the initial coordinates of the unmanned aerial vehicle; and S5, carrying out iterative computation on the current position and speed of the unmanned aerial vehicle at the moment k according to the covariance matrix of the initialized state vector, and outputting the current position and speed as the current situation.
2. The unmanned aerial vehicle situation awareness method based on radio technology according to claim 1, wherein the receiving node adopts an N array element uniform linear array, and an array element spacing d=λ/2, where λ is a radio signal wavelength, and N is an integer greater than 2.
3. The unmanned aerial vehicle situation awareness method based on radio technology according to claim 1, wherein the covariance matrix in step S2 is ; Wherein, K is the number of sampling points, Is that Is a conjugate transpose of (2); The noise subspace Un and array flow vector obtained by covariance matrix decomposition are input into a MUSIC spectral function to extract the angle of an incident signal, and the incidence angle theta 1 、θ 2 ...θ M of each node is obtained.
4. The unmanned aerial vehicle situation awareness method based on radio technology according to claim 1, wherein the MUSIC spectral function is: ; The method comprises the steps of (a) determining a noise subspace of an array receiving signal covariance matrix, wherein Un is the noise subspace of the array receiving signal covariance matrix, the superscript H represents conjugate transpose, alpha (theta) is an array flow vector, and the array flow vector is: Alpha (theta) = [1, e −j2πdsinθ/λ ,e −j4πdsinθ/λ ,...,e −j2π(N−1)dsinθ/λ ] T ] where N is the number of array elements, d is the array element spacing and lambda is the signal wavelength.
5. The unmanned aerial vehicle situation awareness method based on radio technology according to claim 1, wherein in step S3, specifically: calculating a global azimuth angle phi i ,φ i =θ i +α i ; Alpha i is the array normal direction deflection angle of the node i; For each node i, a linear equation is constructed from its coordinates (x i ,y i ): ; And converts the above formula into ; Constructing an overdetermined linear equation set and solving the initial position of the unmanned aerial vehicle through a least square method: Wherein: ; ; and then obtaining the initial position (x, y) of the unmanned aerial vehicle.
6. The method for sensing situation of unmanned aerial vehicle based on radio technology according to claim 1, wherein step S5 comprises estimating the state vector based on best of the unmanned aerial vehicle at time k-1 And the best covariance of the last moment Predicting a predicted state at a next time And a prediction covariance matrix ; Based on the observation function and the global direction angle observation and the predicted state And predicting covariance matrix to calculate optimal state estimation at the current time And the current best covariance matrix 。
7. The unmanned aerial vehicle situation awareness method based on the radio technology according to claim 6, wherein, ; ; F is a state transition matrix, Q is a process noise covariance; f is a state transition matrix, Q is a process noise covariance; F= t is the time step.
8. The unmanned aerial vehicle situation awareness method based on radio technology according to claim 6, wherein a jacobian H k of the observation matrix is calculated: ; ; In this embodiment: ; x 1 ,y 1 is the coordinate of the first receiving point, x 2 ,y 2 is the coordinate of the second receiving point, x 3 ,y 3 is the coordinate of the third receiving point, and x and y are the unmanned plane coordinates. Calculate kalman gain K k : ; h k is the observation function in the predicted state The following jacobian matrix, R is the observed noise covariance; The method comprises the following steps: ; The current best covariance matrix is: 。
9. the unmanned aerial vehicle situation awareness system based on the radio technology is characterized by comprising a radio signal transmitter arranged on an unmanned aerial vehicle and a receiving node which is arranged on the ground and is used for receiving signals transmitted by the radio signal transmitter, wherein the receiving node is a linear array with uniform array elements; The system further comprises a signal processing module, wherein the signal received by the receiving node is transmitted to the signal processing module, and the signal processing module realizes the corresponding program module in the steps S1-S5 according to any one of claims 1-7.

Description

Unmanned aerial vehicle situation awareness method based on radio technology Technical Field The invention relates to the technical field of unmanned aerial vehicle situation awareness, in particular to an unmanned aerial vehicle situation awareness method based on a radio technology. Background The core of unmanned plane situation awareness is target positioning and state monitoring, and the current mainstream technology comprises GPS positioning, visual positioning and radio positioning. Among them, radio positioning is widely used in complex environments because it is free from shielding and all-weather operating characteristics (quoted: radio positioning principle and application, electronic industry Press, 2020). The angle of arrival (AOA) algorithm is used as one of core methods of radio positioning, and positioning is realized by measuring the incidence angle of radio signals, so that the method has the advantages of low hardware complexity and high response speed. The defects of the prior art are that: The angle measurement error is large, the traditional AOA algorithm depends on a single antenna or a simple array, is influenced by multipath interference, and the incident angle measurement error is usually larger than +/-3 degrees, so that the positioning accuracy is insufficient. In a complex electromagnetic environment, noise is overlapped with multipath signals, and angle estimation is easy to lose efficacy; The positioning model is simplified, the existing algorithm mostly adopts an ideal geometric model, the influence of the unmanned aerial vehicle motion state (speed and acceleration) on the positioning result is not considered, and the dynamic positioning error is obvious. Through retrieval, china patent with the patent application number 202010048409.6 discloses an unmanned plane combination navigation method, and the patent prevents the divergence of a filter and timely adjusts the filter by carrying out positive qualitative detection on a covariance matrix of the filter in real time. Disclosure of Invention In order to solve the technical problems in the background technology, the invention provides an unmanned aerial vehicle situation awareness method based on a radio technology. The invention provides an unmanned aerial vehicle situation awareness method based on a radio technology, which comprises the following steps: s1, a receiving node receives radio signals transmitted by an unmanned aerial vehicle and processes the received signals into a snapshot matrix ; S2, inputting the snapshot matrix into a covariance matrix, decomposing the covariance matrix to obtain a noise subspace matrix, inputting the vector quantity of the array flow into a MUSIC spectrum through the noise subspace matrix, and searching the peak value of P MUSIC (theta) to obtain each incident angle estimation; s3, obtaining an observation vector according to the incident angle, and determining the initial coordinate of the unmanned aerial vehicle through the observation vector, the initial coordinate of the receiving node and a least square method; S4, determining an initialization state vector according to the initial coordinates of the unmanned aerial vehicle; and S5, carrying out iterative computation on the current position and speed of the unmanned aerial vehicle at the moment k according to the covariance matrix of the initialized state vector, and outputting the current position and speed as the current situation. Specifically, the receiving node adopts an N-array element uniform linear array, and the array element spacing d=λ/2, where λ is the radio signal wavelength, and N is an integer greater than 2. Specifically, the covariance matrix in step S2 is Wherein, K is the number of sampling points,Is thatIs a conjugate transpose of (2); The noise subspace Un and array flow vector obtained by covariance matrix decomposition are input into a MUSIC spectral function to extract the angle of an incident signal, and the incidence angle theta 1、θ2...θM of each node is obtained. Specifically, the MUSIC spectral function is: The method comprises the steps of (a) determining a noise subspace of an array receiving signal covariance matrix, wherein Un is the noise subspace of the array receiving signal covariance matrix, the superscript H represents conjugate transpose, alpha (theta) is an array flow vector, and the array flow vector is: Alpha (theta) = [1, e −j2πdsinθ/λ,e−j4πdsinθ/λ,...,e−j2π(N−1)dsinθ/λ]T ] where N is the number of array elements, d is the array element spacing and lambda is the signal wavelength. Step S5 comprises the steps of estimating a state vector according to the best of the unmanned aerial vehicle k-1 momentAnd the best covariance of the last momentPredicting a predicted state at a next timeAnd a prediction covariance matrix; Based on the observation function and the global direction angle observation and the predicted stateAnd predicting covariance matrix to calculate optimal state estimation at