An adaptive beam tracking method

Abstract

The present invention relates to an adaptive beam tracking method, comprising the following steps: S1: a base station estimates channel state information based on an uplink detection signal transmitted by a terminal; S2: a shaping vector is obtained based on the channel state information; S3: the shaping vector is corrected to obtain a corrected shaping vector; S4: data symbols of a drone are weighted based on the corrected shaping vector to obtain a transmission signal; S5: the transmission signal is transmitted from the drone's antenna array. The adaptive beam tracking method of the present invention uses the drone's real-time track information to correct the beam, thereby reducing beam direction deviations caused by non-ideal factors and improving beam tracking accuracy; due to the use of a circular antenna array, no matter the target is in any direction on the horizontal plane, there is no need to drive the antenna to mechanically rotate, thereby quickly tracking targets in any direction.

Description

Technical Field

[0001] The present invention relates to the field of beam tracking, and more particularly to an adaptive beam tracking method. Background Art

[0002] In unmanned aerial combat scenarios, two types of drones are essential: command drones and mission drones. Mission drones advance to the confrontation scene, collect various on-site information, and then report it to the command drone. The command drone, located further away from the confrontation scene, comprehensively analyzes on-site information received from the mission drones, generates execution commands, and then issues them to the mission drones. Because the communication range between the command drone and the mission drone is very long—for example, up to 30 kilometers—the command drone typically uses beamforming to communicate with the mission drone. The mission drone and command drone fly at high speeds, for example, the mission drone can reach speeds of up to 300 kilometers per hour relative to the command drone, and its spatial position relative to the command drone changes rapidly. Therefore, the command drone needs to adjust the beam direction directed toward the mission drone in real time to maintain the communication link between the two drones, a process known as beam tracking.

[0003] There are two main beam tracking methods currently available. One is the smart antenna beam tracking method, which adjusts or generates the beam based on the angle of departure (AOD), and sometimes even requires a servo motor to rotate the antenna array to align the beam with the tracking target. The other is the beam tracking method used in commercial communication networks, which generates a real-time tracking beam based on periodically updated channel state information (CSI).

[0004] However, in unmanned aerial vehicle confrontation scenarios, both beam tracking methods have problems such as inaccurate beams and slow beam direction adjustment. Summary of the Invention

[0005] The object of the present invention is to provide an adaptive beam tracking method to achieve fast and accurate beam tracking.

[0006] The present invention provides an adaptive beam tracking method, comprising the following steps:

[0007] S1: The base station estimates the channel state information based on the uplink sounding signal transmitted by the terminal;

[0008] S2: Obtaining a shaping vector according to the channel state information;

[0009] S3: Correcting the shaping vector to obtain a corrected shaping vector;

[0010] S4: Weighting the data symbols of the UAV according to the modified shaping vector to obtain a transmission signal;

[0011] S5: Transmitting the transmission signal from the antenna array of the UAV.

[0012] Step S2 further includes: performing singular value decomposition on the channel state information to obtain eigenvalues ​​and corresponding eigenvectors, wherein the shaping vector is the eigenvector corresponding to the maximum eigenvalue.

[0013] Step S3 further comprises:

[0014] S31: Determine whether the track information of the UAV is reliable. If so, proceed to S32. If not, set the correction vector to zero vector, proceed to S35, and start a timer to wait for a period of time before correcting the shaping vector.

[0015] S32: Obtaining an azimuth angle and a pitch angle according to the track information;

[0016] S33: Generate a first vector and a second vector according to the azimuth angle and the elevation angle respectively;

[0017] S34: Obtaining a correction vector according to the first vector and the second vector;

[0018] S35: Correct the shaping vector using the correction vector to obtain a corrected shaping vector.

[0019] Furthermore, in step S31, if the UAV equipped with the base station receives n consecutive NACK messages, it indicates that the track information is unreliable; otherwise, it indicates that the track information is reliable.

[0020] Furthermore, the azimuth angle and the pitch angle respectively satisfy the following relationship:

[0021]

[0022] Among them, θ is the azimuth angle, Φ is the pitch angle, Δy = y1-y2, Δx = x1-x2, Δz = z1-z2, (x1, y1, z1) is the command UAV track information, and (x2, y2, z2) is the mission UAV track information.

[0023] Furthermore, the first vector w1 satisfies the following relationship:

[0024] w1=[w0,w1,...w N-1 ],in d1 represents the distance between adjacent antennas in the horizontal plane, N represents the number of array antennas in the horizontal plane, λ is the wavelength of the uplink detection signal, and j is the imaginary unit.

[0025] Furthermore, the second vector w2 satisfies the following relationship:

[0026] w2=[w0,w1,...w M-1 ],in, d2 represents the vertical distance between adjacent antennas, M represents the number of vertical array antennas, and λ is the wavelength of the uplink detection signal.

[0027] Furthermore, the correction vector satisfies the following relationship:

[0028]

[0029] Among them, w3 is the correction vector, represents the Kronecker product.

[0030] Furthermore, the modified shaping vector satisfies the following relationship:

[0031] w4=(1-α)w+αw3

[0032] Among them, w4 is the modified assignment vector, α is the filter coefficient, and its value range is 0 to 1.

[0033] Furthermore, the transmission signal satisfies the following relationship:

[0034] x=w4s, where x is the transmitted signal and s is the data symbol.

[0035] The adaptive beam tracking method of the present invention uses the real-time track information of the UAV to correct the beam, thereby reducing the beam direction deviation caused by non-ideal factors and improving the beam tracking accuracy; the AOD value obtained based on the real-time track information is accurate, the correction weight calculation is simple, and the beam shaping adopts a phase modulation method, so the beam direction adjustment is fast; in addition, due to the use of a circular antenna array, no mechanical rotation of the antenna is required regardless of the target's position in any direction on the horizontal plane, thereby avoiding the problem of slow beam adjustment caused by the need to drive the antenna rotation, thereby quickly tracking targets in any direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flowchart of an adaptive beam tracking method according to an embodiment of the present invention;

[0037] Figure 2 2 is a schematic diagram of an application of an adaptive beam tracking method according to an embodiment of the present invention in a UAV confrontation scenario. DETAILED DESCRIPTION

[0038] The preferred embodiments of the present invention are given below in conjunction with the accompanying drawings and described in detail.

[0039] In order to track the target quickly and accurately, the embodiment of the present invention provides an adaptive beam tracking method. The core idea is: for unmanned aerial vehicle confrontation scenarios, a ring array antenna array is used to generate a real-time tracking beam based on CSI, and then the beam direction is corrected using the track information of the command UAV and the task UAV. Figure 1 As shown, the method specifically includes the following steps:

[0040] S1: The base station estimates the channel state information (CSI) based on the uplink sounding signal transmitted by the terminal;

[0041] CSI can be represented by a matrix H.

[0042] S2: Obtain a shaping vector based on channel state information;

[0043] For the Time Division Duplex system, the channel state information H is subjected to singular value decomposition to obtain the eigenvalue β i and the corresponding eigenvector v i , the shaping vector is the eigenvector corresponding to the maximum eigenvalue. The eigenvector corresponding to the maximum eigenvalue is calculated as follows:

[0044] i max =argmax i {β i}

[0045] subject to

[0046] [β i , v i ]=SVD(H)

[0047] i=1,2,...,N

[0048] Thus we can get the shaping vector

[0049] For a frequency division duplex (FDD) system, the CSI fed back by the terminal is essentially a shaped vector index, so the base station can directly look up the table to obtain the shaped vector according to the index.

[0050] S3: Correcting the shaping vector to obtain a corrected shaping vector;

[0051] The above-mentioned shaping vector w may not be accurate due to factors such as non-ideal RF channels and delays. Existing technologies mainly improve the CSI that generates w, such as filtering the CSI to improve CSI precision, thereby improving the accuracy of w. However, in unmanned aerial confrontation scenarios, the track information of the command drone and the task drone is known in advance. Therefore, this embodiment corrects w based on the known track information. The method is as follows:

[0052] S31: Determine whether the track information of the UAV is reliable. If so, proceed to S32. If not, set the correction vector to zero vector, proceed to S35, and start a timer to wait for a period of time before correcting the shaping vector.

[0053] When not subject to hostile interference, the drone will fly according to the pre-set track. In this case, the track is accurate and can be used to calculate the azimuth angle θ and the pitch angle Φ. However, if subject to hostile interference, the drone may not fly according to the pre-planned track. In this case, the track information cannot be used to calculate the azimuth angle θ and the pitch angle Φ. Therefore, before using the track information, it is necessary to determine whether it is reliable. The specific method for determining whether the drone's track information is reliable is as follows:

[0054] If the drone carrying the base station receives n consecutive NACK messages, it indicates that the track information is not necessarily reliable. Otherwise, it indicates that the track information is reliable and proceeds to the next step S32. Here, n is an empirical value and can be around 10.

[0055] S32: Calculate the azimuth angle θ and the pitch angle Φ according to the track information;

[0056] The calculation formulas for azimuth and elevation angles are as follows:

[0057]

[0058] Here, Δy = y1 - y2, Δx = x1 - x2, and Δz = z1 - z2. (x1, y1, z1) represents the command UAV's track information, while (x2, y2, z2) represents the mission UAV's track information. Because this track information is used to calculate the azimuth and elevation angles, θ and Φ are highly accurate for both line-of-sight (LOS) and non-line-of-sight (NLOS) channels.

[0059] S33: Generate a first vector and a second vector according to the azimuth angle and the pitch angle, respectively, wherein the first vector represents a horizontal plane weight and the second vector represents a vertical plane weight;

[0060] The first vector w1 can be expressed as w1=[w0, w1, ...w N-1 ],in d1 represents the distance between adjacent antennas in the horizontal plane, N represents the number of array antennas in the horizontal plane, λ is the wavelength of the uplink detection signal, and j is the imaginary unit.

[0061] The second vector w2 can be expressed as w2=[w0, w1, ...w M-1 ],in, k=0, 1, 2, ..., M-1, d2 represents the distance between adjacent antennas in the vertical plane, M represents the number of array antennas in the vertical plane, and λ is the wavelength of the uplink detection signal.

[0062] S34: Obtaining a correction vector according to the first vector and the second vector;

[0063] The correction vector satisfies the following relationship:

[0064]

[0065] in, represents the Kronecker product.

[0066] S35: Correct the shaping vector using the correction vector to obtain a corrected shaping vector w4;

[0067] The shaping vector w is modified according to the following formula:

[0068] w4=(1-α)w+αw3

[0069] Here, α is the filter coefficient, ranging from 0 to 1. The value of α is related to the angular velocity of the task UAV relative to the command UAV and the CSI period: the greater the relative angular velocity, the larger the α value; the longer the CSI period, the larger the α value.

[0070] By modifying the shaping vector using the above formula, the beam direction can be finely adjusted to mitigate or overcome the adverse effects of RF channel errors and CSI delay. For example, CSI is acquired and a beam is generated at time t1, and downlink beamforming is performed at time t2 (where t2>t1, and the time difference between t2 and t1 is the CSI delay). In high-speed flight confrontation scenarios, the beam generated at time t1 may have already deviated from the target aircraft at time t2. However, in this embodiment, the track information at time t2 can be used to finely adjust the beam direction generated at time t1, pulling the beam toward the target aircraft, thereby ensuring that the beam is aligned with the target aircraft as much as possible.

[0071] S4: weighting the data symbols according to the modified shaping vector to obtain a transmission signal;

[0072] The transmitted signal x satisfies the following relationship:

[0073] x=w4s, where s is a data symbol.

[0074] S5: Transmit the transmission signal from the drone's antenna array.

[0075] like Figure 2 As shown in the figure, a circular antenna array is mounted on the belly of a command drone. The mission drone maneuvers at high speed relative to the command drone. The command drone estimates the CSI based on the detection signal transmitted by the mission drone and then generates a real-time tracking beam based on the estimated CSI, as shown in Beam 1 in the figure. Due to factors such as RF non-idealities and latency, this beam does not cover the mission drone. However, the command drone adjusts the beam based on the track information. The corrected beam, shown in Beam 2 in the figure, just covers the mission drone.

[0076] The adaptive beam tracking method provided in an embodiment of the present invention uses the real-time track information of the UAV to correct the beam, thereby reducing the beam direction deviation caused by non-ideal factors and improving the beam tracking accuracy; the AOD value obtained based on the real-time track information is accurate, the correction weight calculation is simple, and the beam shaping adopts a phase modulation method, so the beam direction adjustment is fast; in addition, due to the use of a circular antenna array, no mechanical rotation of the antenna is required regardless of the target being in any direction on the horizontal plane, thereby avoiding the problem of slow beam adjustment caused by the need to drive the antenna rotation, thereby quickly tracking targets in any direction.

[0077] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Various modifications are possible. In other words, any simple, equivalent changes and modifications made in accordance with the claims and description of the present invention are within the scope of protection of the patent claims. Anything not fully described in this invention constitutes conventional technology.

Claims

1. An adaptive beam tracking method, characterized in that: The following steps are involved: S1: The base station that commands the UAV estimates the channel state information based on the uplink detection signal transmitted by the mission UAV; S2: Obtaining a shaping vector according to the channel state information; S3: Correcting the shaping vector to obtain a corrected shaping vector; S4: Weighting the data symbols of the command UAV according to the modified shaping vector to obtain a transmission signal; S5: transmitting the transmission signal from the antenna array of the command UAV; Step S2 further includes: performing singular value decomposition on the channel state information to obtain eigenvalues ​​and corresponding eigenvectors, wherein the shaping vector is the eigenvector corresponding to the maximum eigenvalue; Step S3 further comprises: S31: Determine whether the UAV's track information is reliable. If so, proceed to S32. If not, set the correction vector to zero, proceed to S35, and start a timer to wait for a period of time before correcting the shaping vector. If the command UAV carrying the base station receives n consecutive NACK messages, it means that the track information is unreliable. Otherwise, it means that the track information is reliable. S32: Obtaining an azimuth angle and a pitch angle according to the track information; S33: Generate a first vector and a second vector according to the azimuth angle and the elevation angle respectively; S34: Obtaining a correction vector according to the first vector and the second vector; S35: Correct the shaping vector using the correction vector to obtain a corrected shaping vector.

2. The adaptive beam tracking method according to claim 1, wherein: The azimuth angle and the pitch angle respectively satisfy the following relationship: Among them, θ is the azimuth angle, Φ is the pitch angle, Δy=y1-y2, Δx=x1-x2, Δz=z1-z2, (x1, y1, z1) is the command UAV track information, and (x2, y2, z2) is the mission UAV track information.

3. The adaptive beam tracking method according to claim 2, wherein: The first vector w1 satisfies the following relationship: w1=[w0,w1,…w N-1 ],in i = 0, 1, 2, ..., N-1, d1 represents the distance between adjacent antennas in the horizontal plane, N represents the number of array antennas in the horizontal plane, λ is the wavelength of the uplink detection signal, and j is an imaginary unit.

4. The adaptive beam tracking method according to claim 3, wherein: The second vector w2 satisfies the following relationship: w2=[w0,w1,…w M-1 ],in, k=0,1,2,…,M-1, d2 represents the distance between adjacent antennas in the vertical plane, M represents the number of array antennas in the vertical plane, and λ represents the wavelength of the uplink detection signal.

5. The adaptive beam tracking method according to claim 4, wherein: The correction vector satisfies the following relationship: Among them, w3 is the correction vector, represents the Kronecker product.

6. The adaptive beam tracking method according to claim 5, wherein: The modified shaping vector satisfies the following relationship: w4=(1-α)w+αw3 Among them, w4 is the modified assignment vector, α is the filter coefficient, and its value range is 0 to 1.

7. The adaptive beam tracking method according to claim 6, wherein: The transmission signal satisfies the following relationship: x=w4s, where x is the transmitted signal and s is the data symbol.

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