CN111342875A - Unmanned aerial vehicle communication robust beam forming method based on DoA estimation - Google Patents

Unmanned aerial vehicle communication robust beam forming method based on DoA estimation Download PDF

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CN111342875A
CN111342875A CN202010143685.0A CN202010143685A CN111342875A CN 111342875 A CN111342875 A CN 111342875A CN 202010143685 A CN202010143685 A CN 202010143685A CN 111342875 A CN111342875 A CN 111342875A
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doa
unmanned aerial
aerial vehicle
coordinate system
drone
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CN111342875B (en
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骆春波
罗杨
许燕
孙文健
吴佳
李智
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0632—Channel quality parameters, e.g. channel quality indicator [CQI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0634—Antenna weights or vector/matrix coefficients
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/0848—Joint weighting
    • H04B7/0857—Joint weighting using maximum ratio combining techniques, e.g. signal-to- interference ratio [SIR], received signal strenght indication [RSS]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming

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Abstract

The invention discloses an unmanned aerial vehicle communication robust beam forming method based on DoA estimation. The algorithm reduces the DoA estimation error based on position prediction, and realizes the characteristics of high precision and low calculation complexity. Second, the algorithm avoids the adverse effects of angular spread effects. The algorithm provides a prediction method to keep accurate DoA information of the fast moving unmanned aerial vehicle, and the link overhead is greatly reduced in the prediction process. The algorithm also realizes timely updating of the steering vector value when the beam forming weight vector is calculated, thereby realizing higher beam directivity.

Description

Unmanned aerial vehicle communication robust beam forming method based on DoA estimation
Technical Field
The invention belongs to the technical field of beam forming methods, and particularly relates to a communication robust beam forming method of an unmanned aerial vehicle based on DoA estimation.
Background
In wireless channel transmission between the drone and the cellular network, line of sight (Los) transmission accounts for about 90% of the total power, and the channel characteristics of line of sight transmission can enhance the received power during communication, but can also cause severe inter-cell interference. Beamforming has received much attention because it has the ability to suppress inter-cell interference and direct power transmission to a desired direction.
Most current beamforming is based on a pilot-assisted Channel State Information (CSI) estimation method. However, the pilot pollution effect poses a serious challenge to this approach. First, in a less obstructed air-to-ground link, the inter-cell uplink pilot interference is typically too strong, which can exacerbate the interference between pilot signals. Secondly, the number of pilot sequences used may increase monotonically with the number of Base Station (BS) antennas and the number of users, which may cause a large overhead for the 5G system. Thus, conventional beamforming presents a significant challenge in current and future cellular networks.
On the other hand, as an alternative to CSI-based solutions, beamforming methods based on signal direction of arrival (DoA) estimation have proven to achieve better performance in several respects. The DoA estimation algorithm can extract accurate DoA information from non-orthogonal signals, so that the serious influence of pilot pollution can be avoided. Despite the great potential of this approach, the DoA estimation-based beamforming approach for drone cellular communication is far from being optimized. One of the challenges it faces is that the mobility of the target affects the accuracy of the DoA estimation. Since the DoA of the signal is estimated over a time interval, there is a certain difference between the true DoA value and the estimated DoA value as the drone moves continuously. This difference will accumulate until the DoA estimation is performed again. Thus, faster drone speeds and longer DoA estimation intervals typically result in larger DoA estimation errors. A common approach to solve this problem is to estimate the DoA information at a higher frequency, but this can significantly increase the overhead of the system. Recent studies of ground vehicle communication propose to counteract the change in angle by predicting the movement of the target. However, these optimization algorithms all assume that a highly predictable ground vehicle travel direction can be achieved by restricting the routes of roads and railways, etc. However, the unmanned aerial vehicle has a flexible heading in a 3-dimensional space and usually has no predefined trajectory, so that the requirement is difficult to achieve.
Another major challenge of DoA-based beamforming methods is the effect of angular spread. In the terrestrial and AG channels of the 3GPP standard, there are multiple paths between the transmitter and receiver, with signals in the paths coming from scattering near the base station, the user, or both. These multiple rays have a slight angular deviation from each other, called intra-cluster angular spread. The beamforming scheme based on spatial information either ignores the effect of angular spread or assumes it has a lower level. The basic solution of the DoA estimation algorithm is eigen decomposition, but the algorithm cannot distinguish between rays in a cluster.
Disclosure of Invention
Aiming at the defects in the prior art, the method for forming the communication robust beam of the unmanned aerial vehicle based on DoA estimation solves the problems in the background art.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a UAV communication robust beam forming method based on DoA estimation comprises the following steps:
s1, determining the flight state information of the unmanned aerial vehicle;
s2, converting the flight state information of the unmanned aerial vehicle into a local horizontal coordinate system based on the vector position of the base station;
s3, calculating DoA estimated values for a plurality of times based on the flight state information of the unmanned aerial vehicle in the local horizontal coordinate system;
s4, calculating M between two continuous DoA estimated values0A secondary DoA prediction value;
s5, updating the real-time guide vector based on the DoA estimation value and the DoA prediction value;
and S6, updating the covariance matrix of the self-adaptive beam forming according to the updated guide vector, and further realizing the beam forming.
Further, the flight status information in step S1 includes the position, heading and motion vector of the drone in the 3-dimensional space;
the course is a rotation angle comprising a yaw angle, a pitch angle and a roll angle;
the motion vector includes a velocity and an acceleration.
Further, in step S2, the method for converting the position of the drone into the local coordinate system specifically includes:
a1, converting the positions of the unmanned aerial vehicles and the positions of the base stations in the geodetic coordinate system into an equivalent earth center coordinate system;
unmanned aerial vehicle position in the geodetic coordinate system
Figure BDA0002399975150000031
Its position P in the earth's central coordinate systemueComprises the following steps:
Figure BDA0002399975150000032
base station position in the geodetic coordinate system
Figure BDA0002399975150000033
Its position P in the earth's central coordinate systembeComprises the following steps:
Figure BDA0002399975150000034
in the formula, reIs the long radius of the earth ellipsoid;
e is a constant;
γu，
Figure BDA0002399975150000041
hurespectively the longitude, the latitude and the ground distance height of the unmanned aerial vehicle in the geodetic coordinate system;
γb，
Figure BDA0002399975150000042
hblongitude, latitude and ground-distance in the geodetic coordinate system for the base station positionHeight.
A2, determining the position of the unmanned aerial vehicle in the local horizontal coordinate system by taking the position of the base station as the origin in the local horizontal coordinate system according to the expression of the position of the unmanned aerial vehicle and the position of the base station in the earth coordinate center coordinate;
position P of the unmanned aerial vehicle in a local horizontal coordinate systemulComprises the following steps:
Pul＝(xul，yul，zul)T＝Ren(Pue-Pbe)
in the formula (x)ul，yul，zul) Position coordinates of the unmanned aerial vehicle in a local horizontal coordinate system;
Renis a transformation matrix from the earth center coordinate system to the local horizontal coordinate system;
Figure BDA0002399975150000043
further, in step S2, the speed v and the acceleration μ of the drone in the local coordinate system are respectively:
v＝Rvb
μ＝Rμb
wherein R is a transformation matrix;
vbthe unmanned aerial vehicle speed in the unmanned aerial vehicle body coordinate system;
μbthe acceleration of the unmanned aerial vehicle in the coordinate system of the unmanned aerial vehicle body is obtained;
wherein the transformation matrix R is:
Figure BDA0002399975150000044
in the formula, phi, theta and psi are respectively a yaw angle coordinate, a pitch angle coordinate and a roll angle coordinate in an unmanned aerial vehicle body coordinate system;
rafor transforming the first intermediate parameter in the matrix G, and ra＝sinφsinθcosΨ-cosφsinΨ；
rbFor transforming matricesSecond intermediate parameter in G, rb＝sinφsinθsinΨ+cosφcosΨ；
rcFor the third intermediate parameter in the transformation matrix G, rc＝cosφsinθcosΨ+sinφsinΨ；
rdFor the fourth intermediate parameter in the transformation matrix G, rd＝cosφsinθsinΨ-sinφcosΨ。
Further, in step S3, the calculation formula of the DoA estimation value is as follows:
Figure BDA0002399975150000051
in the formula, DoA(a)Is a DoA estimated value;
xul、yuland zulRespectively, the positions of the drone on the x, y and z axes in the local horizontal coordinate system.
Further, in step S4, the method for calculating the DoA prediction value for one time specifically includes:
b1, when calculating the DoA estimation for the nth time, the position of the unmanned aerial vehicle is P (α)n，βn，γn) The acceleration vector of the unmanned aerial vehicle is a (α)x，ay，az) And the velocity vector of the drone is
Figure BDA0002399975150000052
B2, according to the acceleration vector and the velocity vector of the unmanned aerial vehicle, setting the position state conversion matrix of the unmanned aerial vehicle as follows:
Figure BDA0002399975150000053
in the formula, tau is the time difference of arrival of signals transmitted by different antenna elements;
b3, performing the above calculation on the unmanned aerial vehicle position and the state transition matrix when calculating the DoA estimated value to obtain the predicted value of the unmanned aerial vehicle position, and thus obtaining the corresponding DoA predicted value DoA according to the predicted unmanned aerial vehicle position information(b)。
Further, in step S5, the DoA value is represented by θ, and the steering vector S (θ) is obtained by considering an array of L omnidirectional antennas:
S(θ)＝[exp(j2πr)，...，exp(j2πτ)]
wherein tau is the arrival time difference of the signals transmitted by different antenna elements,
Figure BDA0002399975150000054
j is an imaginary unit, pi is a circumferential rate, r is a distance between antenna elements, and c is a speed of light;
the DoA value comprises a DoA estimation value and a DoA prediction value, and the DoA value sequentially comprises the following steps:
DoA(a1)，DoA(b1)，DoA(b2)，...，
Figure BDA0002399975150000062
DoA(a2)，...，
Figure BDA0002399975150000063
wherein includes N0Each DoA estimated value comprises M between every two adjacent DoA estimated values0And (4) DoA prediction values.
Further, the covariance matrix in step S6 is updated as:
Figure BDA0002399975150000061
in the formula, N is a sampling frequency index, and N is 1, 2, 3.
R (n +1) is an estimation matrix of a real covariance matrix when sampling is carried out for the (n +1) th time;
x (n) is a sampling sequence of the received signal;
superscript H is the transpose conjugate operator.
The invention has the beneficial effects that:
1) the DoA of the drone signal is efficiently estimated and its variation predicted. Compared with the conventional MUSIC algorithm, the algorithm has relatively small error and much lower computational complexity.
2) With telemetry information of the drone, the algorithm does not add any overhead to the cellular communication system.
3) Due to the small DoA error range and the characteristic of non-pilot assistance, the main beam of the antenna array can update the steering vector in real time.
4) Compared with the traditional beam forming method, the invention can bring higher and more stable beam forming gain.
Drawings
Fig. 1 is a flowchart of a method for forming robust beams for unmanned aerial vehicle communication based on DoA estimation according to the present invention.
FIG. 2 is a diagram illustrating a comparison of the method of the present invention and the DoA estimation error of the MUSIC algorithm in accordance with the present invention.
Fig. 3 is a graph comparing the signal-to-noise ratio performance of the method of the present invention and the MUSUC algorithm in combination with LCMV, DL beamforming in accordance with the embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1:
as shown in fig. 1, the method for forming robust beams for unmanned aerial vehicle communication based on DoA estimation provided by the present invention includes the following steps:
s1, determining the flight state information of the unmanned aerial vehicle;
s2, converting the flight state information of the unmanned aerial vehicle into a local horizontal coordinate system based on the vector position of the base station;
s3, calculating DoA estimated values for a plurality of times based on the flight state information of the unmanned aerial vehicle in the local horizontal coordinate system;
s4, two times of continuousBetween the DoA estimated values of (1), M is calculated0A secondary DoA prediction value;
s5, updating the real-time guide vector based on the DoA estimation value and the DoA prediction value;
and S6, updating the covariance matrix of the self-adaptive beam forming according to the updated guide vector, and further realizing the beam forming.
In the above embodiment, the basic idea of the present invention is to estimate DoA of a signal by using position information of an unmanned aerial vehicle and a base station, and the algorithm reduces DoA estimation error based on position prediction, thereby achieving the characteristics of high precision and low computation complexity. Secondly, the algorithm avoids the adverse effect of the angular spread effect, in the algorithm, a prediction method is provided to keep accurate DoA information of the fast moving unmanned aerial vehicle, the link overhead is greatly reduced in the prediction process, and the algorithm also realizes timely updating of a guide vector value when a beam forming weight vector is calculated, so that higher beam directivity is realized.
Example 2:
the flight status information in step S1 in embodiment 1 above includes the position, heading, and motion vector of the drone in the 3-dimensional space; the course is a rotation angle comprising a yaw angle, a pitch angle and a roll angle; the motion vector includes a velocity and an acceleration.
The flight state information in the embodiment of the invention is obtained by integrating the FSI (Flying StatusInformation) of the unmanned aerial vehicle and navigation data thereof, the records are generated by a vehicle-mounted navigator (also called an autopilot system) of the unmanned aerial vehicle, and the system can read data from a plurality of sensors and calculate the flight path of the unmanned aerial vehicle; FSI is a reliable information resource with a high QoS priority specified in the 3GPP standard. As a standard rule, the drone FSI needs to be fed back to the ground controller periodically for safety. Thus, the first step in calculating the signal DoA is to extract the flight status data from the telemetry information by establishing an interface between the coordination information stack and the beamforming processor.
The location information presented from UAV Flight Status Information (FSI) in embodiments of the present invention has several benefits. First, deriving the position of the drone from both GPS and Initial Measurement Unit (IMU) data has a higher accuracy than using a GPS sensor alone. Secondly, the FSI is specified to be applicable to any type of drone, so the proposed algorithm is applicable to all civilian applications. Third, by using FSI that already exists in command and control in drone and cellular network links, we reduce the use of training sequences, thereby reducing link overhead.
Example 3:
the vectors from the telemetry information need to be first transformed into a common reference frame and then combined with the location vector of the Base Station (BS), for which purpose a common local horizontal coordinate system (NED) is used, with the BS as the origin for simplicity of calculation, and the same vector can be represented in different forms due to differences between the different processors: the GPS position of the drone may be in the geodetic coordinate system, may be in the local NED coordinate system; the motion vector may be in the body coordinate system or in the local NED coordinate system. In addition, the data in the aircraft body coordinate system also provides the rotation angle between the body coordinate system and the NED coordinate system of the aircraft body.
Typically, the geodetic coordinate system needs to be first converted into the form of an equivalent earth-centered coordinate system, the typical expression for the location of the geodetic coordinate system being
Figure BDA0002399975150000091
h represents the height from the earth's surface, γ,
Figure BDA0002399975150000092
Respectively representing longitude and latitude; therefore, in step S2, the method for converting the position of the drone into the local coordinate system specifically includes:
a1, converting the positions of the unmanned aerial vehicles and the positions of the base stations in the geodetic coordinate system into an equivalent earth center coordinate system;
unmanned aerial vehicle position in the geodetic coordinate system
Figure BDA0002399975150000093
Its position P in the earth's central coordinate systemueComprises the following steps:
Figure BDA0002399975150000094
base station position in the geodetic coordinate system
Figure BDA0002399975150000095
Its position P in the earth's central coordinate systembeComprises the following steps:
Figure BDA0002399975150000096
in the formula, reThe long radius of the earth ellipsoid, 6.378137 meters;
e is a constant, 0.08181919;
γu，
Figure BDA0002399975150000101
hurespectively the longitude, the latitude and the ground distance height of the unmanned aerial vehicle in the geodetic coordinate system;
γb，
Figure BDA0002399975150000102
hbrespectively longitude, latitude and ground-level altitude of the base station location in the geodetic coordinate system.
A2, determining the position of the unmanned aerial vehicle in the local horizontal coordinate system by taking the position of the base station as the origin in the local horizontal coordinate system according to the expression of the position of the unmanned aerial vehicle and the position of the base station in the earth coordinate center coordinate;
in a local horizontal coordinate system, the position of the base station is set at an origin (0, 0, 0), and the position P of the unmanned aerial vehicle in the local horizontal coordinate system is obtainedulComprises the following steps:
Pul＝(xul，yul，zul)T＝Ren(Pue-Pbe)
in the formula (I), the compound is shown in the specification,(xul，yul，zul) Position coordinates of the unmanned aerial vehicle in a local horizontal coordinate system;
Renis a transformation matrix from the earth center coordinate system to the local horizontal coordinate system;
Figure BDA0002399975150000103
because the axes of the local horizontal coordinate system and the unmanned aerial vehicle body coordinate system are aligned with each other, the conversion of the heading and the motion vector of the unmanned aerial vehicle is equivalent to the conversion from the unmanned aerial vehicle coordinate system to the unmanned aerial vehicle body coordinate system; therefore, in step S2, the velocity v and the acceleration μ of the drone in the local coordinate system are:
v＝Rvb
μ＝Rμb
wherein R is a transformation matrix;
vbthe unmanned aerial vehicle speed in the unmanned aerial vehicle body coordinate system;
μbthe acceleration of the unmanned aerial vehicle in the coordinate system of the unmanned aerial vehicle body is obtained;
Figure BDA0002399975150000104
in the formula, phi, theta and psi are respectively a yaw angle coordinate, a pitch angle coordinate and a roll angle coordinate in an unmanned aerial vehicle body coordinate system;
rafor transforming the first intermediate parameter in the matrix R, and Ra＝sinφsinθcosΨ-cosφsinΨ；
rbFor transforming the second intermediate parameter, R, in the matrix Rb＝sinφsinθsinΨ+cosφcosΨ；
rcFor transforming the third intermediate parameter, R, in the matrix Rc＝cosφsinθcosΨ+sinφsinΨ；
rdFor the fourth intermediate parameter in the transformation matrix R, Rd＝cosφsinθsinΨ-sinφcosΨ。
Example 4:
in step S3, the DoA of the input signal is defined as the angle between the signal propagation direction and the receiver antenna, and assuming that the linear antenna array of the base station BS is disposed along the x-axis, the DoA estimation value is calculated by the following formula:
Figure BDA0002399975150000111
in the formula, xbl、yblAnd zblThe positions of the base station BS on the x axis, the y axis and the z axis under a local horizontal coordinate system are respectively; x is the number oful、yulAnd zulRespectively, the positions of the drone on the x, y and z axes in the local horizontal coordinate system. Since the base station BS is located at the origin, the above equation can be simplified as:
Figure BDA0002399975150000112
in the embodiment of the invention, a low-complexity DoA estimation algorithm of unmanned aerial vehicle and base station 3-dimensional position information is utilized. The algorithm avoids the effects of angular spread by computing the DoA using geometric data instead of processing the training sequence.
For the problem of the influence of the motion of the unmanned aerial vehicle on the accuracy of the DoA estimation value, the embodiment of the present invention predicts the DoA change in two DoA estimation intervals, so in step S4, the method for calculating the DoA prediction value between two adjacent DoA estimation values includes:
b1, when calculating the DoA estimation for the nth time, the position of the unmanned aerial vehicle is P (α)n，βn，γn) The acceleration vector of the unmanned aerial vehicle is a (α)x，ay，az) And the velocity vector of the drone is
Figure BDA0002399975150000121
B2, according to the acceleration vector and the velocity vector of the unmanned aerial vehicle, setting the position state conversion matrix of the unmanned aerial vehicle as follows:
Figure BDA0002399975150000122
in the formula, tau is the time difference of arrival of signals transmitted by different antenna elements;
wherein, P0，x0And a represents the initial values of all vectors that need to be subtracted from the raw data of the telemetry information.
B3, performing the above calculation on the unmanned aerial vehicle position and the state transition matrix when calculating the DoA estimated value to obtain the predicted value of the unmanned aerial vehicle position, and thus obtaining the corresponding DoA predicted value DoA according to the predicted unmanned aerial vehicle position information(b)。
The estimation and prediction process of the DoA reduces the consumption of link overhead by reusing FSI (unmanned aerial vehicle flight state information) messages; furthermore, since it uses sensor data in the coordination message rather than data in the sampled signal, it is known to have O (M) as is well known2N+M2P) complexity compared to the MUSIC algorithm, our algorithm has a computational complexity of O (1). M, N and P are the number of antennas, the number of samples, and the number of potential DoA values for the MUSIC algorithm, respectively.
In the embodiment of the invention, a prediction algorithm is designed to track the DoA change during the interval of receiving the update message, the process is particularly important for short-distance high-mobility air users, and the algorithm can generate stable DoA estimation output, so that the FSI transmission frequency is reduced, and the link overhead is further reduced.
Example 5:
in the step S5, the beamformer usually calculates the steering vector with the assistance of the pilot signal, and if the steering vector is updated frequently, the network overhead will inevitably increase, the beamforming method provided by the present invention has the non-assistance characteristic, the DoA prediction complexity is low, and the real-time update of the steering vector can be realized, in the embodiment of the present invention, the DoA value is represented by θ, and the steering vector S (θ) is obtained by considering an array formed by L omnidirectional antennas:
S(θ)＝[exp(j2πτ)，...，exp(j2πτ)]
where tau is the difference in arrival of the signals transmitted by the different antenna elements,
Figure BDA0002399975150000131
j is an imaginary unit, pi is a circumferential rate, r is a distance between antenna elements, and c is a speed of light;
the DoA value comprises a DoA estimation value and a DoA prediction value, and the DoA value sequentially comprises:
DoA(a1)，DoA(b1)，DoA(b2)，...，
Figure BDA0002399975150000134
DoA(a2)，...，
Figure BDA0002399975150000135
wherein includes N0Each DoA estimated value comprises M between every two adjacent DoA estimated values0And (4) DoA prediction values.
The embodiment of the invention provides a direction vector updating method compatible with the traditional adaptive beam forming method, wherein a covariance matrix R formed by adaptive beam forming is updated in real time during each sampling, and theoretically, the calculation formula of the covariance matrix R is as follows:
Figure BDA0002399975150000132
in the formula, ps、pi(k) And pNSignal power, interference signals and noise, respectively, and k 1, 2.
s(θt) Is a steering vector for signal t;
s(θi(k) is a steering vector of the interference signal i at the k-th sampling;
superscript H denotes the transpose conjugate operator;
ILan identity matrix with rank as the number of antennas;
however, in practice, true power and steering vectors are not available, so the covariance matrix R is found by:
Figure BDA0002399975150000133
wherein x (n) is a sampling sequence of the received signal;
r (n) is an estimation matrix of the true covariance matrix;
n is a sampling frequency index, and N is 1, 2, 3, and N is a total sampling frequency;
therefore, the covariance matrix R in the above step S6 is updated as:
Figure BDA0002399975150000141
in the formula, R (n +1) is an estimation matrix of a real covariance matrix when sampling is carried out at the n +1 th time;
wherein x (n) is used as a sampling sequence of the received signal, the received signal is an analog signal, and values are taken at certain intervals to obtain the sampling sequence of the received signal, and the sampling sequence can be used for estimating a steering vector at the current moment.
In the embodiment of the present invention, the steering vector s (θ) as one component of the covariance matrix Rt) Is determined by the actual angle of the correlation signal; in addition, the guide vector s (θ)t) Is the hypothetical turn of the correlation signal determined by the estimated DoA angle. Thus, the prediction method updates the steering vector s (θ)t) It is decoupled from adaptive beamforming, where R is updated by sampling the received signal.
The embodiment of the invention provides a real-time steering vector self-adaption method for improving beam forming performance. The method is suitable for researching the most extensive DOA-based beam forming method and is compatible with the traditional adaptive beam forming method. The direction vector is updated in time in the calculation of the beam forming weight vector, so that the directivity of the beam is improved, and the signal-to-noise ratio (SINR) pointed by the beam and the achievable data rate are enhanced.
Example 6:
the present example provides a comparison of the method of the present invention with the MUSIC algorithm;
fig. 2 (abscissa is resource allocation time, ordinate is DoA error) is a DoA estimation error comparison graph of the method of the present invention and the MUSIC algorithm, (a) is an initial distance between the drone and the Base Station (BS) of 50 m; (b) the initial distance between the unmanned aerial vehicle and a Base Station (BS) is 2000 m;
it can be seen from fig. 2 that under the conditions that the distances between the unmanned aerial vehicle and the base station are 50m and 2000m, the average DOA estimation error of the method of the present invention is significantly lower than that of the MUSUC algorithm.
Fig. 3 (abscissa is resource allocation time, ordinate is SINR) is a comparison graph of signal-to-noise ratio performance of the method of the present invention in combination with MUSUC algorithm with LCMV, DL beamforming, (a) is the initial distance between the drone and the Base Station (BS) is 50 m; (b) the initial distance between the unmanned aerial vehicle and a Base Station (BS) is 2000 m;
as can be seen from fig. 3, the DL beamforming based on the present invention yields the highest SINR output; LCMV beam forming effect based on the MUSIC algorithm is the worst. Because the DoA error caused by the motion and position error of the drone is small in the case of a longer distance. The four methods achieve more stable performance in fig. 3(b) compared to fig. 3 (a). Comparing fig. 3(a) and fig. 3(b) with the DoA error group in fig. 2, it can be seen that the trend of SINR output is opposite to that of DoA error, verifying that the larger the DoA error, the smaller the gain. Furthermore, it is observed that in fig. 3(b), the non-robust beamforming (lcmv) combined with the proposed algorithm has a higher performance than the robust beamforming (DL) combined with the MUSIC method. Considering the sensitivity of the LCMV method, the phenomenon shows that the method is helpful for maintaining high-precision DoA information, and the real-time steering vector updating method improves the beam directivity.
The invention has the beneficial effects that:
1) the DoA of the drone signal is efficiently estimated and its variation predicted, with relatively small errors and much less computational complexity than the conventional MUSIC algorithm.
2) With telemetry information of the drone, the algorithm does not add any overhead to the cellular communication system.
3) Due to the small DoA error range and the characteristic of non-pilot assistance, the main beam of the antenna array can update the steering vector in real time.
4) Compared with the traditional beam forming method, the invention can bring higher and more stable beam forming gain.

Claims (8)

1. A UAV communication robust beamforming method based on DoA estimation is characterized by comprising the following steps:
s1, determining the flight state information of the unmanned aerial vehicle;
s2, converting the flight state information of the unmanned aerial vehicle into a local horizontal coordinate system based on the vector position of the base station;
s3, calculating DoA estimated values for a plurality of times based on the flight state information of the unmanned aerial vehicle in the local horizontal coordinate system;
s4, calculating M between two continuous DoA estimated values0A secondary DoA prediction value;
s5, updating the real-time guide vector based on the DoA estimation value and the DoA prediction value;
and S6, updating the covariance matrix of the self-adaptive beam forming according to the updated guide vector, and further realizing the beam forming.
2. The DoA estimation-based drone communication robust beamforming method according to claim 1, wherein the flight status information in step S1 includes the drone' S position, heading and motion vector in 3-dimensional space;
the course is a rotation angle comprising a yaw angle, a pitch angle and a roll angle;
the motion vector includes a velocity and an acceleration.
3. The DoA estimation-based drone communication robust beamforming method according to claim 2, wherein in the step S2, the method of transforming the position of the drone into the local coordinate system is specifically:
a1, converting the positions of the unmanned aerial vehicles and the positions of the base stations in the geodetic coordinate system into an equivalent earth center coordinate system;
unmanned aerial vehicle position in the geodetic coordinate system
Figure FDA0002399975140000011
Its position P in the earth's central coordinate systemueComprises the following steps:
Figure FDA0002399975140000021
base station position in the geodetic coordinate system
Figure FDA0002399975140000022
Its position P in the earth's central coordinate systembeComprises the following steps:
Figure FDA0002399975140000023
in the formula, reIs the long radius of the earth ellipsoid;
e is a constant;
γu，
Figure FDA0002399975140000024
hurespectively the longitude, the latitude and the ground distance height of the unmanned aerial vehicle in the geodetic coordinate system;
γb，
Figure FDA0002399975140000025
hbrespectively longitude, latitude and ground-level altitude of the base station location in the geodetic coordinate system.
A2, determining the position of the unmanned aerial vehicle in the local horizontal coordinate system by taking the position of the base station as the origin in the local horizontal coordinate system according to the expression of the position of the unmanned aerial vehicle and the position of the base station in the earth coordinate center coordinate;
position P of the unmanned aerial vehicle in a local horizontal coordinate systemulComprises the following steps:
Pul＝(xul，yul，zul)T＝Ren(Pue-Pbe)
in the formula (x)ul，yul，zul) Position coordinates of the unmanned aerial vehicle in a local horizontal coordinate system;
Renis a transformation matrix from the earth center coordinate system to the local horizontal coordinate system;
Figure FDA0002399975140000026
4. the DoA estimation-based drone communication robust beamforming method according to claim 2, wherein in the step S2, the drone velocity v and the drone acceleration μ in the local coordinate system are respectively:
v＝Rvb
μ＝Rμb
wherein R is a transformation matrix;
vbthe unmanned aerial vehicle speed in the unmanned aerial vehicle body coordinate system;
μbthe acceleration of the unmanned aerial vehicle in the coordinate system of the unmanned aerial vehicle body is obtained;
wherein the transformation matrix R is:
Figure FDA0002399975140000031
in the formula, phi, theta and psi are respectively a yaw angle coordinate, a pitch angle coordinate and a roll angle coordinate in an unmanned aerial vehicle body coordinate system;
rafor transforming the first intermediate parameter in the matrix G, and ra＝sinφsinθcosΨ-cosφsinΨ；
rbFor transforming the second intermediate parameter in the matrix G, rb＝sinφsinθsinΨ+cosφcosΨ；
rcFor the third intermediate parameter in the transformation matrix G, rc＝cosφsinθcosΨ+sinφsinΨ；
rdFor the fourth intermediate parameter in the transformation matrix G, rd＝cosφsinθsinΨ-sinφcosΨ。
5. The method for robust beamforming for unmanned aerial vehicle communication based on DoA estimation as claimed in claim 1, wherein in step S3, the DoA estimation value is calculated by the following formula:
Figure FDA0002399975140000032
in the formula, DoA(a)Is a DoA estimated value;
xul、yuland zulRespectively, the positions of the drone on the x, y and z axes in the local horizontal coordinate system.
6. The method for unmanned aerial vehicle communication robust beamforming based on DoA estimation according to claim 5, wherein in step S4, the method for performing the calculation of the DoA prediction value for one time specifically comprises:
b1, when calculating the DoA estimation for the nth time, the position of the unmanned aerial vehicle is P (α)n，βn，γn) The acceleration vector of the unmanned aerial vehicle is a (α)x，ay，az) And the velocity vector of the drone is xn(μx，θy，ωz)；
B2, according to the acceleration vector and the velocity vector of the unmanned aerial vehicle, setting the position state conversion matrix of the unmanned aerial vehicle as follows:
Figure FDA0002399975140000041
in the formula, tau is the time difference of arrival of signals transmitted by different antenna elements;
b3, performing the above calculation on the unmanned aerial vehicle position and the state transition matrix when calculating the DoA estimated value to obtain the predicted value of the unmanned aerial vehicle position, and thus obtaining the corresponding DoA predicted value DoA according to the predicted unmanned aerial vehicle position information(b)。
7. The DoA estimation-based drone communication robust beamforming method according to claim 6, wherein the step S5 is to use the DoA value as θ, and to consider the array of L omnidirectional antennas, and obtain the steering vector S (θ) as:
S(θ)＝[exp(j2πτ)，...，exp(j2πτ)]
wherein tau is the arrival time difference of the signals transmitted by different antenna elements,
Figure FDA0002399975140000042
j is an imaginary unit, pi is a circumferential rate, r is a distance between antenna elements, and c is a speed of light;
the DoA value comprises a DoA estimation value and a DoA prediction value, and the DoA value sequentially comprises the following steps:
Figure FDA0002399975140000043
wherein includes N0Each DoA estimated value comprises M between every two adjacent DoA estimated values0And (4) DoA prediction values.
8. The DoA estimation-based drone communication robust beamforming method according to claim 7, wherein the covariance matrix in step S6 is updated as:
Figure FDA0002399975140000044
in the formula, N is a sampling frequency index, and N is 1, 2, 3.
R (n +1) is an estimation matrix of a real covariance matrix when sampling is carried out for the (n +1) th time;
x (n) is a sampling sequence of the received signal;
superscript H is the transpose conjugate operator.
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