New energy grid-connected scene air-to-air unmanned aerial vehicle channel modeling method

Abstract

The invention provides a new energy grid-connected scene air-to-air unmanned aerial vehicle channel modeling method. The method comprises the steps of obtaining signal propagation characteristics in an environment according to environmental parameters in a new energy grid-connected scene, considering the influence of a transmission tower and introducing scatterer distribution, and constructing a three-dimensional non-stationary air-to-air channel model of an air-to-air unmanned aerial vehicle; introducing a Gaussian Markov model to simulate the motion trail of the unmanned aerial vehicle by considering the motion parameters of the unmanned aerial vehicle, obtaining the motion trail of the unmanned aerial vehicle described by the Gaussian Markov model, and obtaining a time-varying expression of the multipath component parameters; and obtaining a channel characteristic analysis result of the three-dimensional non-stationary air-to-air channel model according to the time-varying expression of the multipath component parameters, the environmental parameters and the motion parameters of the unmanned aerial vehicle. According to the method, the dynamic motion track of the unmanned aerial vehicle is simulated by introducing the Gaussian Markov model, it is ensured that the channel model can reflect the time-varying property of the unmanned aerial vehicle in actual flight, and the reliability and stability of a communication system in a complex environment are improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to an air-to-air unmanned aerial vehicle channel modeling method for a new energy grid-connected scenario. Background Art

[0002] In the scenario of renewable energy grid connection, with the widespread deployment of distributed power sources and transmission towers, the complex layout and scattering characteristics of these facilities have a significant impact on the drone communication channel. Transmission towers, as important equipment supporting transmission lines, play a key role in the power system. In order to ensure the stability of renewable energy grid connection and the safe operation of transmission lines, regular inspections of transmission towers and their surrounding renewable energy facilities (such as wind turbines, photovoltaic panels, etc.) are required.

[0003] In recent years, flexible and efficient UAV inspections have gradually replaced traditional manual inspections and become the main means of inspection of transmission lines and facilities in the renewable energy grid-connected environment. UAVs patrol around transmission towers according to a predetermined flight path to monitor the operating status of transmission lines and renewable energy facilities. This mode is called tower inspection. During the inspection process, drones generate a large amount of images and data, which require the support of efficient and stable wireless communication systems to ensure real-time data transmission and reliability. Compared with traditional communication systems with fixed base stations on the ground, using drones as base stations to establish air-to-air communication links can reduce the impact of multipath fading in complex environments and improve communication performance. Therefore, building an accurate drone channel model for renewable energy grid-connected scenarios is crucial to designing efficient communication systems.

[0004] At present, one solution in the prior art proposes an air-to-air (A2A) channel model for drones that characterizes low-altitude networks based on measured data; another solution in the prior art models the A2A channel at a specific frequency through time and frequency analysis to estimate the path loss index; another solution in the prior art uses ray tracing to calibrate measured data in an urban environment and proposes a channel model suitable for A2A communication. The disadvantages of the above solutions in the prior art include: the limited versatility and high complexity of the deterministic model, which is therefore limited in application in complex scenarios of new energy grid connection. Summary of the invention

[0005] An embodiment of the present invention provides an air-to-air UAV channel modeling method in a renewable energy grid-connected scenario, so as to effectively provide technical support for UAV inspections in renewable energy grid-connected scenarios.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme.

[0007] A new energy grid-connected scenario air-to-air UAV channel modeling method, comprising:

[0008] According to the environmental parameters in the renewable energy grid-connected scenario, the signal propagation characteristics in the environment are obtained, the influence of transmission towers is considered and the scatterer distribution is introduced. Based on the signal propagation characteristics, a three-dimensional non-stationary air-to-air channel model of air-to-air drones in the renewable energy grid-connected scenario based on geometric stochastic modeling is constructed.

[0009] Considering the motion parameters of the UAV, the Gauss-Markov model is introduced to simulate the motion trajectory of the UAV, and the motion trajectory of the UAV described by the Gauss-Markov mobility model is obtained;

[0010] Obtaining a time-varying expression of a multipath component parameter according to the UAV motion trajectory and the signal propagation characteristics;

[0011] The channel characteristic analysis results and the non-stationarity analysis results of the three-dimensional non-stationary air-to-air channel model are obtained according to the time-varying expressions of the multipath component parameters, the environmental parameters and the motion parameters of the UAV.

[0012] Preferably, the signal propagation characteristics in the environment are obtained according to the environmental parameters in the new energy grid-connected scenario, the influence of the transmission tower is considered and the scatterer distribution is introduced, and a three-dimensional non-stationary air-to-air channel model of an air-to-air drone in a new energy grid-connected scenario based on geometric stochastic modeling is constructed based on the signal propagation characteristics, including:

[0013] Considering the local scatterers and far-end scatterers in the scenario of renewable energy grid connection, cylinders and elliptical cylinders are used to represent the distribution of scatterers respectively, and a three-dimensional non-stationary air-to-air channel model of air-to-air UAV in the scenario of renewable energy grid connection based on geometric stochastic modeling is constructed.

[0014] Set the antenna spacing of the UAV signal transmitter TX and the signal receiver RX to be δ T , δ R L T ×L R Uniform linear antenna arrays form a MIMO system, (x T ,y T ,z T ) and (x R ,y R ,z R ) represent the coordinates of the transmitter and receiver, respectively, where p∈{1,…,L T}, q∈{1,…,L R} represent the pth transmitting antenna and the qth receiving antenna respectively;

[0015] The signal transmission of the UAV is set to include the line-of-sight component LoS component, single reflection components SB1, SB2, SB3 and double reflection component DB. Assume that there are N1 finite scatterers distributed near the transmitting UAV with a radius of R T On the cylindrical surface of The radius around the receiving UAV is R R There are N2 effective scatterers on the surface of the cylinder, and the n2∈{1,....,N2}th effective scatterer is expressed as There are N3 effective scatterers on the surface of an elliptical cylinder with a major axis of 2a, a focal length of D, and an eccentricity of e. The n3∈{1,....,N3}th effective scatterer is The positions of the two UAVs projected on the xoy plane are the two foci of the ellipse. D also represents the horizontal distance between the transmitting UAV and the receiving UAV. The signal reaches the scatterer from the transmitter The departure azimuth and departure inclination, The signal is emitted by the scatterer The arrival azimuth and arrival inclination at the receiving end, Respectively represent AAoD, EAoD, AAoA, and EAoA of the LoS components. T / R ,φ T / R represents the direction angle of the transmitting antenna and the receiving antenna relative to the x-axis and the inclination angle relative to the xoy plane, v T and v R represents the speed of the transmitting UAV and the receiving UAV, γ T and γ R is the azimuth of motion, θ T and θ R Respectively represent the inclination angle of movement.

[0016] Preferably, the method of considering the motion parameters of the UAV and introducing a Gauss-Markov model to simulate the motion trajectory of the UAV to obtain the motion trajectory of the UAV described by the Gauss-Markov mobility model includes:

[0017] The flight speed and direction of the UAV are modeled based on the Gauss-Markov model to simulate different UAV motion trajectories. i The speed and flight direction at time (i=1,2,3....) are calculated based on t i-1 The values ​​of speed and flight direction at the moment are obtained by calculating the random variables:

[0018]

[0019] Where v(t i ),γ(ti ),θ(t i ) respectively represent the UAV at t i The speed, flight direction and flight inclination at the moment, when i = 1, v(t i )=v(t0),γ(t i )=γ(t0),θ(t i )=θ(t0). v(t0), γ(t0), θ(t0) are the velocity, flight direction and flight inclination of the UAV at the initial moment, respectively. v , μ γ , μ θ They are the mean values ​​of UAV speed and flight direction when i approaches infinity;

[0020] The MIMO fading channel between UAVs is represented by a dimension L T ×L R The matrix Description, where h pq (t, Δt) is the time-varying impulse response function between antennas p and q. The CIR is obtained by superimposing the CIRs of the LoS component, SB1, SB2, SB3 components, and DB component:

[0021]

[0022] Preferably, the time-varying expression of the multipath component parameter obtained according to the UAV motion trajectory and the signal propagation characteristics includes:

[0023] According to the coordinates of the transmitting antenna, receiving antenna, and scatterer, the time-varying expressions of the transmission distance, arrival angle, and departure angle of each signal component are obtained in combination with the distance formula;

[0024] The expression of the transmission distance of the LoS component signal is shown in formula (3):

[0025] d pq (t) = [(x p (t)-x q (t)) 2 +(y p (t)-y q (t)) 2 +(z p (t)-z q (t)) 2 ] 1 / 2 (3)

[0026] Among them, (x p (t),y p (t),z p (t))、(x q (t),yq (t),z q (t)) represents the coordinates of the pth transmitting and qth receiving antennas:

[0027]

[0028]

[0029] In formulas (4) and (5), H T and H R They represent the height of the UAV from the xoy plane at the initial moment, and the parameter Δ T represents the distance between the pth transmitting antenna and the center of the antenna array, Δ R represents the distance between the qth receiving antenna and the center of the antenna array, Δ T =(L T -2p+1)δ T / 2,Δ R =(L R -2p+1)δ R / 2, Δx T (t) and Δx R (t), Δy T (t) and Δy R (t), Δz T (t) and Δz R (t) represents the distances of the sending UAV and the receiving UAV in the x-axis, y-axis, and z-axis directions respectively. The time-varying distance of the SB1 component is expressed as:

[0030]

[0031]

[0032] in, Represents a scatterer The coordinates of the scatterer Distributed on the surface of the cylinder, the coordinates are calculated based on the location of the UAV launch and the trigonometric formula.

[0033]

[0034] The time-varying distance of the SB2 component is expressed as:

[0035]

[0036]

[0037] Among them, the scatterer Coordinates The expression is:

[0038]

[0039] The time-varying distance of the SB3 component is expressed as:

[0040]

[0041]

[0042] Find the scatterer based on the UAV coordinates Coordinates Its expression is as follows:

[0043]

[0044] Where m(t) represents the scatterer The distance between the projection of the launching UAV and the xoy plane is expressed as:

[0045]

[0046]

[0047]

[0048] The time-varying distance of the DB component is expressed as:

[0049]

[0050] Preferably, the channel characteristic analysis result and the non-stationarity analysis result of the three-dimensional non-stationary air-to-air channel model are obtained according to the time-varying expression of the multipath component parameters, the environmental parameters and the motion parameters of the UAV, including:

[0051] Calculate the time delay corresponding to each signal component according to the time-varying distance of each signal component;

[0052] Δt LoS (t) = d pq (t) / c;

[0053]

[0054]

[0055] MMEA is used to discretize the azimuth and inclination angles, and the channel characteristics are simulated and analyzed to obtain the channel characteristics through the scatterer S. (n1) Take the angle as an example:

[0056]

[0057]

[0058] In formula (21), F -1 (·)express The corresponding continuous variable The inverse function of the probability density function is used to obtain the discrete angle set of the three-dimensional non-stationary air-to-air channel model through formulas (21) and (22);

[0059] Different trajectory parameters of the UAV are set in advance, and the trajectory parameters include the speed and angle of the UAV. Different motion trajectories of the UAV are obtained through the Gauss-Markov mobility model, and then the channel characteristic analysis results and non-stationarity analysis results of the three-dimensional non-stationary air-to-air channel model under different motion trajectories of the UAV are obtained.

[0060] It can be seen from the technical solutions provided by the above embodiments of the present invention that the present invention proposes a channel modeling method that adapts to complex environments by considering the distribution of scatterers and the influence of transmission towers on signal transmission. At the same time, by introducing the Gauss-Markov model, the dynamic motion trajectory of the drone is accurately simulated, thereby ensuring that the channel model can reflect the time-varying nature of the drone in actual flight and improve the reliability and stability of the communication system in complex environments.

[0061] Additional aspects and advantages of the present invention will be given in part in the following description, which will become obvious from the following description, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 A schematic diagram of a geometric-based random channel model between drones provided in an embodiment of the present invention;

[0064] Figure 2 A schematic diagram of a horizontal projection of a channel model provided by an embodiment of the present invention.

[0065] Figure 3 A schematic diagram of the change of speed magnitude and motion direction angle of different trajectories over time provided by an embodiment of the present invention;

[0066] Figure 4 A schematic diagram of a spatial correlation function including / excluding LoS components at different times provided by an embodiment of the present invention;

[0067] Figure 5A schematic diagram of a spatial correlation function of different horizontal distances between drones provided by an embodiment of the present invention;

[0068] Figure 6 A schematic diagram of a spatial correlation function with different distances between a local scatterer and a drone provided by an embodiment of the present invention;

[0069] Figure 7 A schematic diagram of a spatial correlation function with different transmitting antenna angles provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

[0071] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or coupling. The term "and / or" used herein includes any unit and all combinations of one or more associated listed items.

[0072] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless defined as herein.

[0073] To facilitate understanding of the embodiments of the present invention, several specific embodiments will be further explained below with reference to the accompanying drawings, and each embodiment does not constitute a limitation on the embodiments of the present invention.

[0074] The A2A (Air-to-Air) channel model proposed in this invention comprehensively considers the impact of near-end and far-end scatterers on signal transmission in the new energy grid-connected environment, and uses the Gauss-Markov model to simulate the dynamic trajectory of the UAV. Based on the Gauss-Markov model, combined with the positional relationship between the scatterer and the UAV, the time-varying parameters of the multipath component are calculated, and the channel characteristics of different transmission paths are derived through the channel impulse response. Finally, the impact of different motion trajectories on channel characteristics and non-stationarity is analyzed, providing theoretical support for the design and evaluation of UAV communication systems in new energy grid-connected scenarios. The good consistency between the simulation results and the theoretical values ​​verifies the effectiveness and applicability of the model.

[0075] In the scenario of new energy grid connection, there are various scatterers around the drone during inspection. Local new energy facilities constitute near-end scatterers, while distant buildings, trees, and mountains constitute far-end scatterers. These near-end and far-end scatterers jointly affect signal transmission, making the channel exhibit more complex propagation characteristics. Wireless signals include line-of-sight (LoS) and non-line-of-sight (NLoS) components. The non-line-of-sight component includes the single bounced (SB) component of the signal passing through the far-end scatterer, as well as the single bounced component and double bounced (DB) component passing through near-end scatterers such as new energy facilities.

[0076] A processing flow chart of a method for modeling an air-to-air drone channel in a new energy grid-connected scenario provided by an embodiment of the present invention is as follows: Figure 2 As shown, the process includes the following steps:

[0077] Step S10: Modeling input data preparation

[0078] According to the environmental parameters in the new energy grid-connected scenario (such as distributed power supply layout, transmission line location, terrain information), the signal propagation characteristics in the environment (such as signal attenuation, scattering and path effect, etc.) are obtained.

[0079] Taking into account the influence of transmission towers and introducing scatterer distribution (cylinders, ellipsoids), a three-dimensional non-stationary air-to-air channel model of air-to-air drones in new energy grid-connected scenarios based on geometric stochastic modeling is constructed based on the above signal propagation characteristics. The three-dimensional non-stationary air-to-air channel model specifies in detail the geometric parameters of scatterers, such as the dimensions (height, diameter, shape ratio) of cylinders and ellipsoids and their electromagnetic properties (scattering coefficients), which determine the scattering path and energy loss of the signal. The signal propagation characteristic parameters accurately describe the multipath effect, including the number of paths, the delay of each path, the amplitude attenuation and the phase change, which are dynamically updated with the relative position and motion state of the drone.

[0080] Step S10 provides a theoretical basis for subsequent steps such as UAV motion trajectory modeling, time-varying derivation of multipath parameters, and channel characteristic analysis.

[0081] Step S20: Modeling the dynamic motion trajectory of the drone

[0082] Based on the above three-dimensional non-stationary air-to-air channel model, the motion parameters of the UAV (initial position, speed, direction, etc.) are considered, and the Gauss-Markov model is introduced to simulate the motion trajectory of the UAV, and the motion trajectory of the UAV described by the Gauss-Markov mobility model is obtained.

[0083] Step S30: Derivation of time-varying expressions of multipath component parameters

[0084] The time-varying expression of the multipath component parameters is obtained according to the UAV motion trajectory (obtained from step S20) and the signal propagation characteristics (obtained from step S10).

[0085] By considering the UAV motion and environmental factors, the time-varying expressions of the multipath component parameters are further derived, especially their applicability in dynamic environments.

[0086] Step S40: Analysis of channel characteristics and non-stationarity

[0087] The channel characteristic analysis results and non-stationarity analysis results of the three-dimensional non-stationary air-to-air channel model are obtained based on the time-varying multipath component parameters, the environment and the motion parameters of the UAV obtained in step S30.

[0088] This step aims to comprehensively analyze the impact of UAV motion and environmental parameters on channel characteristics and non-stationarity, providing a reference for subsequent communication system design.

[0089] The above step S10 specifically includes:

[0090] The embodiment of the present invention proposes a random channel model between drones based on geometry. Figure 1 The present invention adopts a geometric-based random modeling method to propose a three-dimensional random channel model, in which local scatterers and remote scatterers are considered, and cylinders and elliptical cylinders are used to characterize the distribution of scatterers respectively. Figure 1 Schematic diagram of the A2A three-dimensional non-stationary GBSM (Geometry-Based Stochastic Model) proposed in the present invention. The signal transmitter (Transmitter, TX) and receiver (Receiver, RX) are equipped with antennas with spacings of δ T , δ R L T ×L R Uniform linear antenna arrays form a MIMO system, (xT ,y T ,z T ) and (x R ,y R ,z R ) represent the coordinates of the transmitter and receiver respectively. Where p∈{1,...,L T},q∈{1,...,L R} represent the pth transmitting antenna and the qth receiving antenna respectively. Signal transmission includes LoS component, single reflection (SB1, SB2, SB3) component and double reflection (DB) component. Assume that there are N1 finite scatterers distributed near the transmitting UAV with radius R T On the cylindrical surface of The radius around the receiving UAV is R R There are N2 effective scatterers on the surface of the cylinder, and the n2∈{1,...,N2}th effective scatterer is expressed as There are N3 effective scatterers on the surface of an elliptical cylinder with a major axis of 2a, a focal length of D, and an eccentricity of e. The n3∈{1,...,N3}th effective scatterer is The positions of the two UAVs projected on the xoy plane are the two foci of the ellipse, and D also represents the horizontal distance between the transmitting UAV and the receiving UAV. The signal reaches the scatterer from the transmitter The departure azimuth angle (Azimuth Angle of Departure, AAoD) and departure inclination angle (Elevation Angle of Departure, EAoD). The signal is emitted by the scatterer The azimuth angle of arrival (AAoA) and elevation angle of arrival (EAoA) arriving at the receiving end. Respectively represent AAoD, EAoD, AAoA, and EAoA of the LoS components. T / R ,φ T / R It represents the direction angle of the transmitting antenna and the receiving antenna relative to the x-axis and the inclination angle relative to the xoy plane. T and v R represents the speed of the transmitting UAV and the receiving UAV, γ T and γ R is the azimuth of motion, θ T and θ R Respectively represent the inclination angle of movement.

[0091] The above step S20 specifically includes: the motion trajectory of the UAV patrolling around the tower is complex, and the actual measurement results show that the motion of the UAV will have a significant impact on the statistical characteristics of the channel. Therefore, it is necessary to study the impact of the UAV motion on the channel characteristics. Since the Gauss-Markov model has good randomness and scalability, the present invention models the flight speed and flight direction of the UAV based on the Gauss-Markov model to simulate different UAV motion trajectories. Specifically, the UAV is at t i The speed and flight direction at time (i=1,2,3...) are calculated based on t i-1 The values ​​of speed and flight direction at the moment are obtained by calculating the random variables:

[0092]

[0093] Where v(t i ),γ(t i ),θ(t i ) respectively represent the UAV at t i The speed, flight direction and flight inclination at the moment. When i = 1, v(t i )=v(t0),γ(t i )=γ(t0),θ(t i )=θ(t0). v(t0), γ(t0), θ(t0) are the velocity, flight direction and flight inclination of the UAV at the initial moment. v , μ γ , μ θ are the means of UAV speed and flight direction when i approaches infinity.

[0094] The MIMO (Multiple-Input Multiple-Output) fading channel between UAVs can be represented by a channel with dimension L T ×L R The matrix Description, where h pq (t, Δt) is the time-varying impulse response function (channel impulse response, CIR) between antennas p and q, which can be obtained by superimposing the CIR of the LoS component, SB (SB1, SB2, SB3) component, and DB component:

[0095]

[0096] The present invention uses the Gauss-Markov mobility model to model the motion trajectory of the UAV. The speed and flight direction of the UAV are physical quantities that vary with time. Therefore, the distance, arrival angle, and departure angle of the signal transmission are all physical quantities that vary with time. In order to express the CIR of the channel, it is necessary to calculate the time-varying expressions of the transmission distance, arrival angle, and departure angle of each signal component based on the coordinates of the transmitting antenna, the receiving antenna, and the scatterer in combination with the distance formula.

[0097] The above step S30 specifically includes: the expression of the transmission distance of the LoS component signal is shown in formula (3):

[0098] d pq (t) = [(x p (t)-x q (t)) 2 +(y p (t)-y q (t)) 2 +(z p (t)-z q (t)) 2 ] 1 / 2 (3)

[0099] Among them, (x p (t),y p (t),z p (t))、(x q (t),y q (t),z q (t)) represents the coordinates of the pth transmitting and qth receiving antennas:

[0100]

[0101]

[0102] In formulas (4) and (5), H T and H R They represent the height of UAV from the xoy plane at the initial moment. T represents the distance between the pth transmitting antenna and the center of the antenna array, Δ R represents the distance between the qth receiving antenna and the center of the antenna array, Δ T =(L T -2p+1)δ T / 2,Δ R =(L R -2p+1)δ R / 2. Δx T (t) and Δx R (t), Δy T (t) and Δy R(t), Δz T (t) and Δz R (t) represents the distances moved by the sending UAV and the receiving UAV in the x-axis, y-axis, and z-axis directions, respectively.

[0103] The time-varying distance of the SB1 component can be expressed as:

[0104]

[0105]

[0106] in, Represents a scatterer The coordinates of the scatterer Distributed on the surface of the cylinder, the coordinates can be calculated based on the location of the launching UAV and the trigonometric formula:

[0107]

[0108] The time-varying distance of the SB2 component can be expressed as:

[0109]

[0110] Among them, the scatterer Coordinates The expression is:

[0111]

[0112] The horizontal projection of a channel model provided by an embodiment of the present invention is as follows: Figure 2 As shown, the time-varying distance of the SB3 component can be expressed as:

[0113]

[0114] According to the UAV coordinates, combined with the cosine theorem and the knowledge of ellipse, the scatterer S can be calculated. (n3) Coordinates Its expression is as follows:

[0115]

[0116] Where m(t) represents the scatterer The distance between the projection of the launching UAV and the xoy plane is expressed as:

[0117]

[0118]

[0119]

[0120] The time-varying distance of the DB component can be expressed as:

[0121]

[0122]

[0123]

[0124] The above step S40 specifically includes:

[0125] The theoretical model is convenient for analyzing channel characteristics. However, the theoretical model assumes that the number of scatterers tends to infinity, and its computational complexity is high, which brings a lot of inconvenience to practical applications. Therefore, in order to facilitate the simulation and performance evaluation of actual wireless communication systems, it is necessary to adopt a reasonable method to establish a corresponding simulation model to reduce the complexity of the simulation. In order to reduce the complexity, the number of scatterers in the simulation model is limited. In the simulation model, the angle of signal transmission is a discrete value, and the rest of the parameters are the same as the theoretical model. Therefore, using a suitable method to obtain discrete azimuth and inclination angles is the key to establishing a simulation model.

[0126] MMEA (modified method of equal areas) is suitable for angle discretization in non-isotropic environments, and has higher calculation accuracy and lower simulation time. Therefore, the present invention uses MMEA to discretize azimuth and inclination. Take the angle as an example:

[0127]

[0128]

[0129] In formula (21), F -1 (·)express The corresponding continuous variable The inverse function of the probability density function. The discrete angle set of the model can be obtained through formulas (21) and (22), and the simulation model characteristics can be further obtained.

[0130] The present invention comprehensively explores the influence of different scattering environments on the characteristics of wireless channels through simulation analysis, and reveals the changing rules of channel propagation characteristics in complex scenarios from multiple dimensions. First, the influence of complex coupling coefficient (CCF, Cross-Correlation Function) on signal transmission characteristics in line-of-sight (LoS) and non-line-of-sight (NLoS) environments is studied. The results show that signal transmission in LoS environments has higher stability and efficiency, while CCF in NLoS environments is significantly reduced, showing the high randomness and complexity of channel propagation characteristics. Secondly, the change of channel spatial correlation of drones under uniform motion conditions is analyzed, and the relationship between horizontal distance and CCF and the influence of the distance between local scatterers and drones on spatial correlation are investigated. The results show that the distribution of local scatterers has a significant regulating effect on spatial correlation. In addition, the influence of the geometric configuration of the transmitting antenna array on the channel performance is deeply studied. It is found that when the horizontal and vertical angles of the antenna array are both 0°, the antenna spacing has little effect on CCF, while the change of the vertical angle has a much greater influence on the channel spatial correlation than the change of the horizontal angle.

[0131] According to the transmission distance of each path, the corresponding delay can be calculated, Δt LoS (t) = d pq (t) / c,

[0132]

[0133] In order to study the influence of different new energy grid-connected scattering environments and UAV motion on channel characteristics and their non-stationarity, the channel characteristics are simulated and analyzed. The simulation parameters are as follows: k1 = k2 = k3 = 1, R T =20m, R R =20m, D=300m, e=0.75, f c =5.9GHz,ζ T =ζ R =0,φ T =φ R =0, v T (t0) = 10 m / s, v R (t0) = 12 m / s, γ T (t0) = π / 6, θ T (t0) = π / 6, γ R (t0) = π / 4, θ R (t0) = π / 12, L T =L R=2, p=q=1, p'=q'=2, N1=N2=N3=40, the initial coordinates of the transmitting UAV and the receiving UAV are (300m, 0, 20m), (0, 0, 100m). The proposed channel model is for air-to-air communication. Although the signal propagation produces non-line-of-sight components due to the environment, the LoS component still occupies a large amount of power, so the Ricean factor of the channel is K=1.

[0134] η DB =0.1. The standard deviations of X, Y, and Z are all 0.5. The time interval t before and after the drone movement step =0.05s.

[0135] Table 1 Parameters for generating different trajectories

[0136]

[0137] Figure 3 A schematic diagram of the change of speed magnitude and motion direction angle of different trajectories over time provided by an embodiment of the present invention, such as Figure 3 As shown in the figure, trajectories 1, 2, 3, and 4 represent uniform motion, uniformly variable speed motion, random motion, and completely random motion, respectively. Table 1 shows the parameters corresponding to different motion trajectories of the UAV. The speed and direction of motion of trajectory 1 do not change with time, the speed and direction of motion of trajectory 2 gradually approach a linear function with the increase of time, and the speed and direction of motion of trajectory 3 and trajectory 4 fluctuate significantly with time, and the fluctuation of trajectory 4 is significantly stronger than that of trajectory 3. Therefore, different motion trajectories can be simulated by adjusting the parameters of the Gauss-Markov mobility model.

[0138] Figure 4 A schematic diagram of a spatial correlation function including / excluding LoS components at different times provided by an embodiment of the present invention, Figure 4 The CCF with and without LoS components at different times is shown. At this time, the UAV is in uniformly variable motion, and the parameter χ s =0, χ v / γ / θ =0.9999. Figure 5 It can be seen that the value of CCF changes with time, and as time t increases, the value of CCF becomes larger, indicating that the proposed model has the ability to simulate the non-stationarity of the channel. In addition, it can be observed that the LoS component has a greater impact on CCF, while the impact of the NLoS component cannot be ignored.

[0139] Figure 5 A schematic diagram of a spatial correlation function of different horizontal distances between drones provided by an embodiment of the present invention, Figure 5 The relationship between different horizontal distances D and CCF when the UAV is moving at a constant speed is given. Set the receiving antenna spacing δR =2λ. Figure 6 It can be observed that the larger the value of D, the greater the spatial correlation. This is because as D increases, the effect of antenna spacing on spatial correlation gradually decreases, and the spatial correlation of the channel increases.

[0140] Figure 6 A schematic diagram of a spatial correlation function of a local scatterer with different distances from a drone provided by an embodiment of the present invention, Figure 6 It depicts the effect of the distance between the local scatterer and the drone on the spatial correlation of the channel when the drone moves at a uniform speed. R =2λ. In different scattering environments, the distance between the local scatterer and the UAV is different. It can be seen from the figure that the smaller the distance between the scatterer and the UAV, the greater the spatial correlation of the channel. The larger the distance between the scatterer and the UAV, the more susceptible the spatial correlation is to the influence of the antenna spacing. Therefore, when the distance between the scatterer and the UAV is small, the scatterer has a greater impact on the spatial correlation of the channel. In an environment where the distance between the UAV and the scatterer is large, the change in the antenna spacing plays a dominant role in the impact of the spatial correlation.

[0141] Figure 7 A schematic diagram of a spatial correlation function with different transmitting antenna angles provided by an embodiment of the present invention, Figure 7 The figure shows the effect of the transmitting antenna angle on the spatial correlation when the UAV is moving at a constant speed. Figure 7 It can be seen that the CCF value is different when the antenna array angle is different. When the horizontal angle and vertical angle of the antenna array are both 0, the CCF is less affected by the antenna spacing. The vertical angle change of the antenna array has a greater impact on the spatial correlation characteristics than the horizontal angle change. Therefore, in practical applications, the reasonable design of the antenna direction can reduce the spatial correlation of the channel.

[0142] In summary, the embodiment of the present invention proposes a three-dimensional non-stationary air-to-air UAV channel model for the new energy grid-connected scenario to describe the communication system characteristics between the inspection UAV and the base station. In order to meet the communication needs of UAV inspections in the new energy facility environment, the Gauss-Markov motion model is introduced into the model to simulate the flight trajectory of the UAV in a complex environment. Through simulation analysis, the influence of different scattering environments on the channel characteristics is studied. By adjusting the parameters of the Gauss-Markov model and simulating different motion trajectories, the influence of the change of the UAV trajectory on the channel characteristics is explored, which provides theoretical support for the design and optimization of the communication system in the new energy grid-connected scenario. Finally, by comparing the theoretical calculation and simulation results of the channel characteristics, the effectiveness and applicability of the model are verified.

[0143] The present invention proposes a three-dimensional non-stationary air-to-air channel model based on geometric random modeling, combined with the Gauss-Markov mobility model, and innovatively designs the wireless communication problem in the unmanned aerial vehicle tower inspection task in the new energy grid-connected scenario. In this scenario, the layout of distributed power sources brings significant signal attenuation, scattering and multipath effects, which greatly increases the complexity of the communication channel. To this end, the present invention proposes a channel modeling method that adapts to complex environments by considering the distribution of scatterers and the influence of transmission towers on signal transmission. At the same time, by introducing the Gauss-Markov model, the dynamic motion trajectory of the unmanned aerial vehicle is accurately simulated, thereby ensuring that the channel model can reflect the time-varying nature of the unmanned aerial vehicle in actual flight. By deriving the time-varying expression of the multipath component parameters, the present invention can better describe the non-stationary characteristics of the channel, and study the influence of the motion and environmental factors of the unmanned aerial vehicle on the channel characteristics and non-stationarity. This method not only improves the reliability and stability of the communication system in a complex environment, but also provides effective theoretical support for the design of air-to-air communication systems in practical applications. By comparison with the existing technology, the model of the present invention has significant innovations and advantages in the accuracy of channel modeling, the authenticity of UAV dynamic simulation and the depth of channel characteristic analysis, providing a strong technical guarantee for UAV inspection in the scenario of new energy grid connection.

[0144] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0145] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0146] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0147] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A new energy grid-connected scenario air-to-air UAV channel modeling method, characterized in that: include: According to the environmental parameters in the renewable energy grid-connected scenario, the signal propagation characteristics in the environment are obtained, the influence of transmission towers is considered and the scatterer distribution is introduced. Based on the signal propagation characteristics, a three-dimensional non-stationary air-to-air channel model of air-to-air drones in the renewable energy grid-connected scenario based on geometric stochastic modeling is constructed. Considering the motion parameters of the UAV, the Gauss-Markov model is introduced to simulate the motion trajectory of the UAV, and the motion trajectory of the UAV described by the Gauss-Markov mobility model is obtained; Obtaining a time-varying expression of a multipath component parameter according to the UAV motion trajectory and the signal propagation characteristics; The channel characteristic analysis results and the non-stationarity analysis results of the three-dimensional non-stationary air-to-air channel model are obtained according to the time-varying expressions of the multipath component parameters, the environmental parameters and the motion parameters of the UAV.

2. The method according to claim 1, characterized in that The signal propagation characteristics in the environment are obtained according to the environmental parameters in the new energy grid-connected scenario, the influence of the transmission tower is considered and the scatterer distribution is introduced, and a three-dimensional non-stationary air-to-air channel model of an air-to-air drone in a new energy grid-connected scenario based on geometric random modeling is constructed based on the signal propagation characteristics, including: Considering the local scatterers and far-end scatterers in the scenario of renewable energy grid connection, cylinders and elliptical cylinders are used to represent the distribution of scatterers respectively, and a three-dimensional non-stationary air-to-air channel model of air-to-air UAV in the scenario of renewable energy grid connection based on geometric stochastic modeling is constructed. Set the antenna spacing of the UAV signal transmitter TX and the signal receiver RX to be δ T , δ R L T ×L R Uniform linear antenna arrays form a MIMO system, (x T ,y T ,z T ) and (x R ,y R ,z R ) represent the coordinates of the transmitter and receiver, respectively, where p∈{1,...,L T },q∈{1,...,L R } represent the pth transmitting antenna and the qth receiving antenna respectively; The signal transmission of the UAV is set to include the line-of-sight component LoS component, single reflection components SB1, SB2, SB3 and double reflection component DB. Assume that there are N1 finite scatterers distributed near the transmitting UAV with a radius of R T On the cylindrical surface of The radius around the receiving UAV is R R There are N2 effective scatterers on the surface of the cylinder, and the n2∈{1,...,N2}th effective scatterer is expressed as There are N3 effective scatterers on the surface of an elliptical cylinder with a major axis of 2a, a focal length of D, and an eccentricity of e. The n3∈{1,....,N3}th effective scatterer is The positions of the two UAVs projected on the xoy plane are the two foci of the ellipse. D also represents the horizontal distance between the transmitting UAV and the receiving UAV. The signal reaches the scatterer from the transmitter The departure azimuth and departure inclination, The signal is emitted by the scatterer The arrival azimuth and arrival inclination at the receiving end, Respectively represent AAoD, EAoD, AAoA, and EAoA of the LoS components. T / R ,φ T / R represents the direction angle of the transmitting antenna and the receiving antenna relative to the x-axis and the inclination angle relative to the xoy plane, v T and v R represents the speed of the transmitting UAV and the receiving UAV, γ T and γ R is the azimuth of motion, θ T and θ R Respectively represent the inclination angle of movement.

3. The method according to claim 2, characterized in that The Gauss-Markov model is introduced to simulate the motion trajectory of the UAV by considering the motion parameters of the UAV, and the motion trajectory of the UAV described by the Gauss-Markov mobile model is obtained, including: The flight speed and direction of the UAV are modeled based on the Gauss-Markov model to simulate different UAV motion trajectories. i The speed and flight direction at time (i=1,2,3....) are calculated based on t i-1 The values ​​of speed and flight direction at the moment are obtained by calculating the random variables: Where v(t i ),γ(t i ),θ(t i ) respectively represent the UAV at t i The speed, flight direction and flight inclination at the moment, when i = 1, v(t i )=v(t0),γ(t i )=γ(t0),θ(t i )=θ(t0). v(t0), γ(t0), θ(t0) are the velocity, flight direction and flight inclination of the UAV at the initial moment, respectively. v , μ γ , μ θ They are the mean values ​​of UAV speed and flight direction when i approaches infinity; The MIMO fading channel between UAVs is represented by a dimension L T ×L R The matrix Description, where h pq (t, Δt) is the time-varying impulse response function between antennas p and q. The CIR is obtained by superimposing the CIRs of the LoS component, SB1, SB2, SB3 components, and DB component:

4. The method according to claim 3, characterized in that The time-varying expression of the multipath component parameter obtained according to the UAV motion trajectory and the signal propagation characteristics includes: According to the coordinates of the transmitting antenna, receiving antenna, and scatterer, the time-varying expressions of the transmission distance, arrival angle, and departure angle of each signal component are obtained in combination with the distance formula; The expression of the transmission distance of the LoS component signal is shown in formula (3): d pq (t)=[(x p (t)-x q (t)) 2 +(y p (t)-y q (t)) 2 +(z p (t)-z q (t)) 2 ] 1 / 2 (3) Among them, (x p (t),y p (t),z p (t))、(x q (t),y q (t),z q (t)) represents the coordinates of the pth transmitting and qth receiving antennas: In formulas (4) and (5), H T and H R They represent the height of the UAV from the xoy plane at the initial moment, and the parameter Δ T represents the distance between the pth transmitting antenna and the center of the antenna array, Δ R represents the distance between the qth receiving antenna and the center of the antenna array, Δ T =(L T -2p+1)δ T / 2,Δ R =(L R -2p+1)δ R / 2, Δx T (t) and Δx R (t), Δy T (t) and Δy R (t), Δz T (t) and Δz R (t) represents the distances traveled by the sending UAV and the receiving UAV in the x-axis, y-axis, and z-axis directions, respectively. The time-varying distance of the SB1 component is expressed as: in, Represents a scatterer The coordinates of the scatterer Distributed on the surface of the cylinder, the coordinates are calculated based on the location of the UAV launch and the trigonometric formula. The time-varying distance of the SB2 component is expressed as: Among them, the scatterer Coordinates The expression is: The time-varying distance of the SB3 component is expressed as: Find the scatterer based on the UAV coordinates Coordinates Its expression is as follows: Where m(t) represents the scatterer The distance between the projection of the launching UAV and the xoy plane is expressed as: The time-varying distance of the DB component is expressed as:

5. The method according to claim 4, characterized in that The channel characteristic analysis results and non-stationarity analysis results of the three-dimensional non-stationary air-to-air channel model are obtained according to the time-varying expressions of the multipath component parameters, the environmental parameters and the motion parameters of the UAV, including: Calculate the time delay corresponding to each signal component according to the time-varying distance of each signal component; Δt LoS (t)=d pq (t) / c; MMEA is used to discretize the azimuth and inclination angles, and the channel characteristics are simulated and analyzed to obtain the channel characteristics after passing through the scatterer. Take the angle as an example: In formula (21), F -1 (·)express The corresponding continuous variable The inverse function of the probability density function is used to obtain the discrete angle set of the three-dimensional non-stationary air-to-air channel model through formulas (21) and (22); Different trajectory parameters of the UAV are set in advance, and the trajectory parameters include the speed and angle of the UAV. Different motion trajectories of the UAV are obtained through the Gauss-Markov mobility model, and then the channel characteristic analysis results and non-stationarity analysis results of the three-dimensional non-stationary air-to-air channel model under different motion trajectories of the UAV are obtained.

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