Channel characteristic simulation method for wireless channel model of unmanned ship based on unmanned aerial vehicle and implementation system

Abstract

The invention relates to a method and a system for realizing channel characteristic simulation of a wireless channel model of an unmanned ship based on an unmanned aerial vehicle. The method comprises the following steps: constructing the wireless channel model of the unmanned ship by the unmanned aerial vehicle; the unmanned ship wireless channel model comprises the steps of generating a channel impulse response, generating an unmanned aerial vehicle random track, generating an unmanned ship random track, generating an evaporation waveguide model, generating an evaporation waveguide model, generating small-scale parameters and updating the small-scale parameters; calculating channel statistical characteristics; the channel statistical characteristics comprise time delay power spectrum density, a space-time correlation function, a stationary interval and root-mean-square time delay extension. The method can better correspond to a track in a real environment, and the randomness of the track is realized.

Description

Technical Field

[0001] The invention relates to a channel characteristic simulation method and an implementation system based on a wireless channel model of a drone to an unmanned ship, belonging to the technical field of channel characteristic simulation. Background Art

[0002] With the development of 6G, new requirements are put forward for maritime communications. In recent years, countries around the world have attached increasing importance to the development of unmanned systems, which play an important role in marine exploration, maritime rescue and maritime operations. Compared with land scenarios, the distinguishing factors of marine scenarios include the impact of evaporation ducts and waves on the communication environment. Due to the action of waves, unmanned ships will move irregularly, causing irregular motion of scatterers in the channel. In low-altitude maritime communications, the main influencing factor is the evaporation duct. Changes in temperature, humidity and pressure in the lower atmosphere above the sea surface cause the environment to be humid, which is conducive to multiple reflections and long-distance transmission of signals. Effective theoretical modeling is a necessary condition for in-depth research on maritime communication scenarios.

[0003] Unmanned maritime communication systems are crucial to the development of the maritime economy. However, marine communications still face many problems, such as the complex and changeable marine environment and long transmission distances, which make it difficult to carry out marine communication-related work. Therefore, solving the problem of maritime communications is crucial. In 2010, Wang Yang's team conducted ship-to-shore channel measurements and analyzed the characteristics of the ship-shore channel, including path loss, channel correlation, Doppler shift, and sea surface reflection coefficient, revealing that the path loss in marine scenarios exhibits comb-like characteristics. In 2022, the team studied the characteristics of millimeter-wave band ocean channels, combined meteorological data to analyze the impact of humidity on path loss, and established an ITU-R path loss model. José Rodrí The team conducted channel measurements on drones and unmanned ships and established FE2R and CE2R models for RSL. The autocorrelation characteristics of shadows were analyzed, the numerical fitting of multipath fading distributions was evaluated, and future research directions were proposed on multipath component delay, Doppler, time-varying channel characteristics, and the impact of Kelvin wakes. These studies help to fully understand the behavior of marine communication channels and provide crucial insights for improving the design of communication systems in marine environments. For the construction of channel models, Liu Yu's team introduced a novel three-dimensional non-stationary drone-to-ship geometric random model. It not only considers the direct component, but also considers single reflections caused by rough sea surfaces and multiple reflections due to waveguide effects. He Yubei's team proposed a ship-to-ship random channel model that considers the impact of waves on ships, and introduced the impact of evaporative wind ducts on cluster angles. In addition, Wang Chengxiang's team proposed an innovative massive MIMO ship-to-ship beam domain channel model (BDCM) considering the position-dependent characteristics of marine channels and the sparse user and scattering distribution characteristics. The impact of sea surface and evaporative waveguides on angles was analyzed.

[0004] However, there is little mention of channel models that combine environmental information and random trajectories of unmanned systems. Random trajectories are an important part of unmanned systems. Whether it is maritime combat or maritime transportation, the trajectories of drones and unmanned ships will change at any time, so it is very important to establish a random trajectory model. In addition, environmental information such as temperature, humidity, and wind speed in the ocean will affect the evaporation waveguide, thereby changing the state of the channel, so its impact on the channel needs to be taken into account during the modeling process. Marine communications are an indispensable part of the development of 6G today, so it is very important to build a complete channel model and analyze channel characteristics more accurately. Summary of the invention

[0005] In view of the deficiency that the existing communication channel model of unmanned systems at sea does not incorporate marine environment information and random trajectories of unmanned systems, the present invention aims to propose a method for simulating channel characteristics based on the wireless channel model of unmanned aerial vehicles to unmanned ships, in which the impact of the environment on the channel is integrated, thereby realizing the modeling and simulation of the marine communication channel. The present invention also proposes a system for simulating channel characteristics based on the wireless channel model of unmanned aerial vehicles to unmanned ships.

[0006] The main problems and secondary problems to be solved by the present invention are as follows:

[0007] 1. A communication channel model between a UAV and an unmanned ship at sea is established, and random trajectories of the UAV and the unmanned ship are introduced. The UAV adopts a random trajectory based on a Markov chain, and the unmanned ship adopts a smooth steering maneuverability model.

[0008] 2. The impact of waves on unmanned ships and clusters is introduced. Combined with the three-dimensional wave spectrum model, the three-dimensional movement of waves can be characterized, thereby better representing the changes of ships and wave clusters.

[0009] 3. The PJ evaporation waveguide model is introduced. Adding environmental factors such as temperature, humidity and wind speed to the waveguide model can more accurately represent the evaporation waveguide, thereby improving the accuracy of the waveguide cluster evolution.

[0010] 4. Model the virtual link propagating between the first-hop cluster and the last-hop cluster in the model, abstract the propagation path into a two-dimensional plane, and use the straight-line distance and angle between the first-hop and last-hop clusters to calculate the length of the intermediate transmission process, thereby calculating the delay of the virtual link of the evaporation waveguide cluster.

[0011] Terminology explanation:

[0012] CIR, channel impulse response, describes the response of the communication channel to a unit pulse signal, and reflects the delay, attenuation and multipath effect of the signal during channel propagation.

[0013] The technical solution of the present invention is:

[0014] A method for simulating channel characteristics based on a wireless channel model of an unmanned ship using a drone, comprising:

[0015] Construct a wireless channel model between drone and unmanned ship;

[0016] The unmanned ship wireless channel model includes: generating channel impulse response, generating random trajectory of unmanned aircraft, generating random trajectory of unmanned ship, generating evaporation waveguide model, generating evaporation waveguide model, generating small-scale parameters, and updating small-scale parameters;

[0017] Calculation of channel statistics; channel statistics include delay power spectrum density, space-time correlation function, stationary interval and root mean square delay spread.

[0018] Preferably, according to the present invention, generating a channel impulse response comprises:

[0019] The impulse response matrix H of the maritime communication channel is expressed as formula (1):

[0020] H=[PL·SH] 1 / 2 ·H s (1)

[0021] In formula (1), PL represents path loss, SH represents shadow, and the matrix H s =[h pq (t,τ)] nT×nR , n T 、n R They are respectively expressed as the number of transmitting and receiving antennas and the small-scale CIR of the corresponding antenna;

[0022] h pq (t,τ) represents the antenna With antenna The CIR between is shown in formula (2):

[0023]

[0024] Among them, K R represents Rice K factor, S1 and S2 represent power correlation coefficients, satisfying S1+S2=1, They represent the CIR of the LoS component, sea surface scattering component, and evaporation duct reflection component respectively;

[0025] LoS Component It is obtained from the following formula:

[0026]

[0027] Among them, fc Indicates the carrier frequency, F p / q,H and F p / q,V Represents the horizontal polarization and vertical polarization antenna patterns of the transmitting and receiving ends, is the initial phase that obeys the uniform distribution of (0,2π], is the delay of the time-varying LoS path;

[0028] Sea surface scattering component It is obtained by the following formula:

[0029]

[0030] Evaporative waveguide scattering component It is obtained by the following formula:

[0031]

[0032] in, and is the cross-polarization power ratio of the NLoS component, is the initial phase that obeys the uniform distribution of (0,2π], and is the nth antenna element between the pth Tx antenna element and the qth Rx antenna element w or nth d The time-varying power of the mth ray in a cluster, and is the corresponding delay.

[0033] According to the preferred embodiment of the present invention, a random trajectory of a drone is generated based on a Markov random process; comprising:

[0034] Set random azimuth and pitch angles to simulate the random trajectory of the drone. n}, X n The state depends only on X n-1 The state is:

[0035] P(X i |X0,X1,...,X i-1 )=P(X i |X i-1 ),i=1,2,... (6)

[0036] Among them, P(X i |X i-1 ) is the transfer probability distribution, the flight angle of the drone is randomly generated by the Markov chain, and the antenna position at time T is:

[0037]

[0038] in, is the initial position, is the velocity vector, expressed as:

[0039]

[0040] Among them, α and γ are the horizontal angle and pitch angle of the velocity vector.

[0041] Preferably, according to the present invention, generating a random trajectory of an unmanned ship comprises:

[0042] The wave height η(x,y,t) at a certain point is:

[0043]

[0044] The three-dimensional sea surface height is abstracted as the superposition of multiple cosine waves, where a i 、w i , ε i and ψ i is the amplitude, angular frequency, random initial phase and the angle between the propagation direction and the x-axis of the i-th cosine wave, k i is the beam of deep water waves, expressed as g is the acceleration due to gravity; a i It is expressed as:

[0045]

[0046] Among them, S(w i ,ψ i ) is the wave spectrum, △w is the frequency interval between the constituent waves, △ψ is the angular interval between the constituent waves, S(w i ,ψ i ) is expressed as the product of the wave spectrum S(w) and the directional spectrum D(w,ψ), that is:

[0047] S(w,ψ)=S(w)D(w,ψ) (11)

[0048] The formulas for the spectrum S(w) and directional spectrum D(w,ψ) are:

[0049]

[0050] Where b1 = 8.1 × 10 -3 , b2 = 0.74, a dimensionless constant, U is the wind speed at 19.5 meters above the sea, c1 = 0.5 + 0.82Q (w), c2 = 0.32Q (w), Q (w) = exp [- (wU / g) 4 / 2].

[0051] Preferably, according to the present invention, generating an evaporation waveguide model comprises:

[0052] First, calculate the overall Richardson number R ib :

[0053]

[0054] Among them, z observation is the observation height, T a and T s is the air temperature and sea surface temperature at the observation height; U observation is the wind speed at the observation height;

[0055] Next, calculate the Monin-Obukhov length L:

[0056]

[0057] Where, Γ is the empirical profile coefficient;

[0058] Then, the difference N between the atmospheric refractive index at the ocean surface and the refractive index at the edge of the ocean and the atmosphere is calculated. p ,Right now:

[0059] N p =N a -N s (16)

[0060]

[0061] Among them, N a is the refractive index of air on the ocean surface, N s is the refractive index at the edge of the ocean and atmosphere, T ok and T sk are the ocean surface air humidity and the ocean surface relative humidity, e and e s is the atmospheric water vapor pressure and the water vapor pressure of seawater on the ocean surface, and the calculation formula is as follows:

[0062]

[0063] By R ib Classify the environmental stability, when 0≤R ib When ≤1, the environment is in a stable state; when R ib <0, the environment is unstable; the evaporation waveguide height h d The calculation is as follows:

[0064]

[0065] Where A = -0.125 × B / N p , B = ln(z observation / z0)-Ψ, z0 is the aerodynamic surface roughness parameter;

[0066] Finally, the atmospheric corrected refractive index is obtained:

[0067]

[0068] Where M s is the corrected refractive index of the sea surface, and solving equations (22) and (23) yields

[0069] Preferably, according to the present invention, small-scale parameters are generated, including: cluster birth and death, cluster angle generation, cluster position generation, and cluster intra-scatterer generation;

[0070] A. The birth and death of clusters

[0071] The probability of cluster birth and death is determined by both the time interval and the antenna spacing. The generation rate and extinction rate of the cluster are defined as λ G and λ R , the survival probability of the transmitter It is expressed as:

[0072]

[0073] Among them, δ d is the channel variation when the scene moves in time, △d T is the antenna spacing, and The joint survival probability P of the clusters is introduced as the correlation distance between the time domain and the array domain. s (δ d ,△d T ,△d R ):

[0074]

[0075] It refers to the survival probability of the transmitting and receiving end cluster;

[0076] The expected number of new clusters E(N new ) is expressed as:

[0077]

[0078] B. Cluster Angle Generation

[0079] The upper and lower limits of the trap angle θ min / max Calculated as:

[0080]

[0081] Where n(z) is the refractive index, n(0)≈1.00035, is the refractive index at sea level, and dn(z) / dz is the vertical gradient of the refractive index, calculated as:

[0082]

[0083] According to the trapping angle, the angles of the evaporation waveguide scattering cluster and the sea surface scattering cluster are divided. It is assumed that the angles of the scattering clusters at the transmitting end and the receiving end Tx end and Rx end obey the truncated Gaussian distribution f(θ), that is:

[0084]

[0085] Where θ represents the angle of the cluster, μ θ and σ θ is the corresponding mean and angle spread, Φ is the standard normal cumulative distribution function; C. Cluster position generation

[0086] Initial time cluster The center position is:

[0087]

[0088] Find the x and y coordinates of the cluster, and then substitute the coordinates into the wave spectrum, that is:

[0089]

[0090] D. Intra-cluster scatterer generation

[0091] The coordinates of the mth ray in the nth cluster are:

[0092]

[0093] in, It obeys the three-dimensional Gaussian distribution.

[0094] Preferably, according to the present invention, updating the small-scale parameters comprises:

[0095] Use X and Y to represent different parameters, X∈{A,Z}, Y∈{n w ,n d}, scattering cluster at time t The location is:

[0096]

[0097] in, is the time-varying velocity vector of the cluster, expressed as:

[0098]

[0099] in, and Indicates the Tx / Rx side cluster Velocity azimuth and pitch angle;

[0100] According to the position of the transmitting and receiving antennas relative to the cluster, the distance vector between the antenna and the cluster is obtained, that is:

[0101]

[0102] In formulas (37) and (38), Represents the position vector of the first antenna at the transceiver end, is the cluster position vector, is the antenna spacing.

[0103] Determine the angle of incidence based on the distance vector and the exit angle

[0104]

[0105] Wherein, arctan2 represents the inverse tangent function of the fourth quadrant; Z∈{T,p,R,q}; The sum of the time-varying distances from the transmitter to the corresponding scattering cluster is expressed as

[0106] For the calculation of time delay, in the ocean scattering part, the nth d The calculation formula for the mth ray in a scatterer is:

[0107]

[0108] in, for and The virtual link delay between , is expressed as: in, for and The direct propagation path between C,link is the virtual link delay that obeys the unilateral exponential distribution;

[0109] In the evaporation waveguide part, the propagation path is abstracted as two-dimensional propagation, and the calculation formula is:

[0110]

[0111] Virtual link based and The pitch angle can be divided into four cases:

[0112] First case:

[0113] Second case:

[0114] The third case:

[0115] The fourth case:

[0116] and The propagation distance between them is l=l1+Nl2+l3, and the number of reflections of the ray in the waveguide layer is defined as N. The calculation method of N is as follows:

[0117]

[0118] Where N is an integer in the interval, K is a constant, for and Straight-line distance;

[0119] Calculate the values ​​of l1, l2 and l3, and finally get the delay of the virtual link

[0120] No.n w / d The power of the mth ray in a scattering cluster is:

[0121]

[0122] in, is the shadow fading corresponding to each cluster, in dB, r τ is the delay distribution proportional factor, DS is the delay spread, ξ n (p,q) is the array axis attenuation factor, calculated as:

[0123]

[0124] in, is the fading mean, It obeys a two-dimensional Gaussian random distribution and is generated by a spatial filtering algorithm;

[0125] Finally, after normalization, we get:

[0126]

[0127] Preferably, according to the present invention, channel statistical characteristics are calculated; channel statistical characteristics include delay power spectrum density, space-time correlation function, stationary interval and root mean square delay spread; including:

[0128] The delay power spectrum density represents the power distribution of the channel on the time axis. The delay power spectrum density Λ pq (t,τ) is calculated as:

[0129]

[0130] in, and is the multipath power corrected by the power correlation coefficient;

[0131] Channel h pq (t) and h p′q′ The space-time correlation function between (t-△t) is defined as:

[0132]

[0133] The space-time correlation function is written as the sum of the LoS component, the sea surface scattering component, and the evaporation duct scattering component, that is:

[0134]

[0135] Among them, the correlation function of each component is calculated as:

[0136]

[0137] Where, △ξ={△ξ T ,△ξ R}, When △ξ=0, the time autocorrelation function is calculated; when △t=0, the spatial cross-correlation function is calculated;

[0138] The stationary interval is defined as the maximum time interval during which the absolute value of the Doppler spread change is less than a given threshold, that is:

[0139] T η =max{△tε(△t)≥η} (54)

[0140] in,

[0141] D. RMS delay spread

[0142] RMS delay spread refers to the delay dispersion measure caused by the delay difference of the signal propagation path in the wireless communication channel; RMS delay spread The specific formula is:

[0143]

[0144] in,

[0145]

[0146] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, a method for simulating channel characteristics based on a wireless channel model of an unmanned ship to be implemented is implemented.

[0147] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for simulating channel characteristics based on a wireless channel model of an unmanned aerial vehicle to an unmanned ship.

[0148] A system for simulating channel characteristics based on a wireless channel model of an unmanned aerial vehicle to an unmanned ship, comprising:

[0149] The unmanned ship wireless channel model building module is configured to: build a wireless channel model of the unmanned ship to the unmanned ship; the unmanned ship wireless channel model includes: generating channel impulse response, generating random trajectory of the unmanned ship, generating random trajectory of the unmanned ship, generating evaporation waveguide model, generating evaporation waveguide model, generating small-scale parameters, and updating small-scale parameters;

[0150] The channel statistical characteristic calculation module is configured as follows: the channel statistical characteristics include delay power spectrum density, space-time correlation function, stationary interval and root mean square delay spread.

[0151] The beneficial effects of the present invention are:

[0152] 1. The present invention models the random trajectories of drones and unmanned boats. The drone trajectory uses a random trajectory based on a Markov chain, and the unmanned boat uses a smooth steering model. By setting the random trajectory, it can better correspond to the trajectory in the real environment and achieve the randomness of the trajectory.

[0153] 2. The present invention introduces a three-dimensional wave spectrum model, which can truly reflect the state of the sea surface, thereby better simulating the up and down fluctuations of the ship and the evolution of the cluster with the changes in the waves.

[0154] 3. The present invention introduces the PJ evaporation waveguide model, and introduces temperature, humidity, and wind speed into the channel model, so that the channel can better simulate the real environment above the ocean, thereby more accurately constructing the model and analyzing its characteristics.

[0155] 4. The present invention models the virtual links between clusters, thereby making the signal more accurate during transmission. With the exact propagation route, the calculation of the delay is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0156] Figure 1 It is a schematic diagram of the framework of the marine UAV-to-ship communication system of the present invention;

[0157] Figure 2Generate a schematic diagram for the transmitter trajectory;

[0158] Figure 3 Schematic diagram of the wave spectrum under different wind speeds of the present invention;

[0159] Figure 4 Generate a schematic diagram for the receiving end trajectory;

[0160] Figure 5 Schematic diagram of the relationship between waveguide height and atmospheric refractive index;

[0161] Figure 6 Schematic diagram of the starting pitch angle range of different clusters;

[0162] Figure 7 This is a schematic diagram of the propagation of the evaporation waveguide virtual link;

[0163] FIG8( a ) is a schematic diagram of the power delay spectrum when the evaporation waveguide is not considered;

[0164] Fig. 8(b) is a schematic diagram of the power delay spectrum when considering the evaporation waveguide;

[0165] Fig. 9 It is a schematic diagram of the autocorrelation function at different times and carrier frequencies;

[0166] Fig.10 Schematic diagram of the autocorrelation function at different times and wind speeds;

[0167] Figure 11(a) is a schematic diagram of the 3D autocorrelation function when the wind speed is 5 m / s;

[0168] Figure 11(b) is a schematic diagram of the 3D autocorrelation function when the wind speed is 10 m / s;

[0169] Fig.12 Schematic diagram of cross-correlation function under different antenna orientations;

[0170] Fig.13 Schematic diagram of cross-correlation function at different wind speeds;

[0171] Figure 14(a) is a schematic diagram of stable intervals under different humidity conditions;

[0172] Figure 14(b) is a schematic diagram of stable intervals at different wind speeds;

[0173] Figure 14(c) is a schematic diagram of stable intervals under different air-sea temperature differences;

[0174] Figure 15(a) is a schematic diagram of the RMS delay spread under different humidity conditions;

[0175] Figure 15(b) is a schematic diagram of the RMS delay spread under different wind speeds;

[0176] Figure 15(c) is a schematic diagram of the RMS delay spread under different air-sea temperature differences;

[0177] Figure 16(a) is a diagram of the measurement scene;

[0178] Figure 16(b) is a schematic diagram of the transmitting end;

[0179] Fig.17 The figure is a fitting diagram of the measurement and simulation results. DETAILED DESCRIPTION

[0180] The present invention will be further defined below in conjunction with the accompanying drawings and embodiments, but is not limited thereto.

[0181] Example 1

[0182] A method for simulating channel characteristics based on a wireless channel model of an unmanned ship using a drone, comprising:

[0183] In a typical UAV to UAV communication scenario, a linear array is used. The communication process is abstracted into a model framework such as Figure 1 As shown. Figure 1 The left side is the transmitter of the UAV (transmitter, Tx), and the right side is the receiver of the UAV (receiver, Rx). The number of antenna units in the Tx and Rx arrays is represented by M. T and M R Indicates. Where n w and n d represents the ocean wave scattering path number and the evaporation waveguide scattering path number, and Represents the total path number, and the superscript represents the communication link between the pth Tx antenna unit and the qth Rx antenna unit. Since the signal will experience direct radiation, reflection, refraction and scattering during the transmission process, one type of scattering is caused by the undulation of the sea surface, and the other is caused by the evaporation waveguide. Therefore, the scattering of the sea surface and the evaporation waveguide for the signal is abstracted into two types of clusters, namely the sea surface cluster and and evaporative waveguide clusters and exist Figure 1 In the process, Tx transmits the signal through the antenna array, part of which reaches the receiving end directly, and the other part first passes through the first hop cluster ( and ) and the last hop cluster ( and ) reaches the receiving end after reflection, and the multiple bounces in the middle are abstracted as a virtual link. Setting the delay of the virtual link to 0 represents a single hop. During signal transmission, the communication link can only be established if the Tx and Rx antenna units are visible at the same time. The ellipsoid with blue dots in the figure represents the sea surface cluster, and the cluster center is located on the sea surface; the ellipsoid with gray dots represents the evaporation waveguide cluster. v T 、v R and Indicates the moving speed of drones, unmanned ships and clusters. The first or The departure azimuth of the mth ray of a cluster is The pitch angle is exist Received and The arrival azimuth of the mth ray of a cluster is The pitch angle is Tx antenna to cluster and The distance of the mth scatterer is Rx antenna to cluster and The distance of the mth scatterer is antenna and The departure and arrival angles of the LoS path between are Distance is The above parameters are all obtained at the initial moment.

[0184] Construct a wireless channel model between drone and unmanned ship;

[0185] The UAV to UAV ship wireless channel model provides the basis for the establishment of the channel model. The addition of all environments and the derivation of characteristics are based on this framework.

[0186] The unmanned ship wireless channel model includes: generating channel impulse response, generating random trajectory of unmanned aircraft, generating random trajectory of unmanned ship, generating evaporation waveguide model, generating evaporation waveguide model, generating small-scale parameters, and updating small-scale parameters;

[0187] Calculation of channel statistics; channel statistics include delay power spectrum density, space-time correlation function, stationary interval and root mean square delay spread.

[0188] Example 2

[0189] The method for simulating channel characteristics of an unmanned ship wireless channel model based on a drone according to Embodiment 1 is different in that:

[0190] Generate channel impulse response; include:

[0191] The impulse response (CIR) matrix H of the maritime communication channel is expressed as formula (1):

[0192] H=[PL·SH] 1 / 2 ·H s (1)

[0193] In formula (1), PL represents path loss, SH represents shadow, and the matrix n T 、n R They are respectively expressed as the number of transmitting and receiving antennas and the small-scale CIR of the corresponding antenna;

[0194] h pq (t,τ) represents the antenna With antenna The CIR between is shown in formula (2):

[0195]

[0196] Among them, K R represents Rice K factor, S1 and S2 represent power correlation coefficients, satisfying S1+S2=1, They represent the CIR of the LoS component, sea surface scattering component, and evaporation duct reflection component respectively;

[0197] LoS Component It is obtained from the following formula:

[0198]

[0199] Among them, f c Indicates the carrier frequency, F p / q,H and F p / q,V Represents the horizontal polarization and vertical polarization antenna patterns of the transmitting and receiving ends, is the initial phase that obeys the uniform distribution of (0,2π], is the delay of the time-varying LoS path;

[0200] Sea surface scattering component It is obtained from the following formula:

[0201]

[0202] Evaporative waveguide scattering component It is obtained from the following formula:

[0203]

[0204] in, and is the cross-polarization power ratio of the NLoS component, is the initial phase that obeys the uniform distribution of (0,2π], and is the nth antenna element between the pth Tx antenna element and the qth Rx antenna element w or nth d The time-varying power of the mth ray in a cluster, and is the corresponding delay.

[0205] The random trajectory of drones is generated based on Markov random process; the randomness of drones is an important part of drone communication scenarios, and effective modeling of the random trajectory of drones can better simulate communication scenarios. Including:

[0206] Set random azimuth and pitch angles to simulate the random trajectory of the drone. n}, X n The state depends only on X n-1 The state is:

[0207] P(X i |X0,X1,...,X i-1 )=P(X i |X i-1 ),i=1,2,... (6)

[0208] Among them, P(X i |X i-1 ) is the transfer probability distribution, the flight angle of the drone is randomly generated by the Markov chain, and the antenna position at time T is:

[0209]

[0210] in, is the initial position, is the velocity vector, expressed as:

[0211]

[0212] Among them, α and γ are the horizontal angle and pitch angle of the velocity vector. The trajectory can be changed by setting the angle. Since the drone needs to fly within the evaporation waveguide, the flight altitude range is specified as (10, h d ], waveguide height h d Find it in part D. Figure 2 This is a schematic diagram of the random trajectory of the drone. The drone is set to fly at a uniform speed of 5m / s. Figure 2The figure shows that the trajectory rises initially and then drops sharply, highlighting the randomness of the trajectory.

[0213] The random trajectory of the drone is obtained by integrating the above formula.

[0214] Generate random trajectories for unmanned ships; in marine communications, unmanned ships also move randomly, but compared to drones, on the one hand, unmanned ships will float up and down with the waves on the sea; on the other hand, unmanned ships cannot change their trajectories as quickly as drones. Therefore, it is very important to build a three-dimensional smooth steering model that can float up and down with the fluctuations of the waves. Including:

[0215] The xy plane trajectory of the ship adopts a smooth turning model, which can be used in situations where there are few sharp turns, such as ships or cars. Since the height of the ship changes with the height of the waves, a three-dimensional wave spectrum is introduced to describe the fluctuation of the waves. The wave height η(x, y, t) at a certain point is:

[0216]

[0217] The three-dimensional sea surface height is abstracted as the superposition of multiple cosine waves, where a i 、w i , ε i and ψ i is the amplitude, angular frequency, random initial phase and the angle between the propagation direction and the x-axis of the i-th cosine wave, k i is the beam of deep water waves, expressed as g is the acceleration due to gravity; a i It is expressed as:

[0218]

[0219] Among them, S(w i ,ψ i ) is the wave spectrum, △w is the frequency interval between the constituent waves, △ψ is the angular interval between the constituent waves, S(w i ,ψ i ) is expressed as the product of the wave spectrum S(w) and the directional spectrum D(w,ψ), that is:

[0220] S(w,ψ)=S(w)D(w,ψ) (11)

[0221] The present invention adopts the Pierson-Moskowitz spectrum taking into account wind speed, and the directional spectrum is the Stereo Wave Observation Project (SWOP) spectrum. The formulas of the spectrum S(w) and the directional spectrum D(w,ψ) are:

[0222]

[0223] Where b1 = 8.1 × 10 -3 , b2 = 0.74, a dimensionless constant, U is the wind speed at 19.5 meters above the sea, c1 = 0.5 + 0.82Q (w), c2 = 0.32Q (w), Q (w) = exp [- (wU / g) 4 / 2]. Figure 3 The wave spectrum under different wind speeds. It can be seen that as the wind speed increases from 5m / s to 10m / s, the amplitude of the waves increases and the complexity of the sea surface also increases. Figure 4 The random trajectory of the ship is obtained by integrating the above formula and the existing smooth steering model.

[0224] Generate an evaporation duct model; in marine communications, the evaporation duct model is an important part that cannot be ignored, so building the evaporation duct part in the marine communication model is an important part of better modeling marine channel characteristics. Including:

[0225] Ocean evaporation duct refers to the temperature and humidity gradient phenomenon of the atmosphere formed during the evaporation process on the ocean surface. When seawater evaporates, water vapor rises and forms a change in water vapor concentration in the higher atmosphere, resulting in a change in the atmospheric refractive index. This change can affect the propagation path of microwaves and radio waves in the atmosphere, for example, changing their reflection and refraction characteristics. The evaporation duct model is usually used to predict the evaporation duct, and the waveguide information is obtained by inputting environmental information. The present invention introduces the PJ waveguide model, which can characterize the evaporation duct under different temperatures, wind speeds, humidity and atmospheric pressure. The PJ model is derived according to the Morning-Obukhov theory. First, the overall Richardson number R is calculated. ib :

[0226]

[0227] Among them, z observation is the observation height, T a and T s is the air temperature and sea surface temperature at the observation height; U observation is the wind speed at the observation height;

[0228] Next, calculate the Monin-Obukhov length L:

[0229]

[0230] Where, Γ is the empirical profile coefficient;

[0231] Then, the difference N between the atmospheric refractive index at the ocean surface and the refractive index at the edge of the ocean and the atmosphere is calculated. p ,Right now:

[0232] Np =N a -N s (16)

[0233]

[0234]

[0235] Among them, N a is the refractive index of air on the ocean surface, N s is the refractive index at the edge of the ocean and atmosphere, T ok and T sk are the ocean surface air humidity and the ocean surface relative humidity, e and e s is the atmospheric water vapor pressure and the water vapor pressure of seawater on the ocean surface, and the calculation formula is as follows:

[0236]

[0237] By R ib Classify the environmental stability, when 0≤R ib When ≤1, the environment is in a stable state; when R ib <0, the environment is unstable; the evaporation waveguide height h d The calculation is as follows:

[0238]

[0239] Where A = -0.125 × B / N p , B = ln(z observation / z0)-Ψ, z0 is the aerodynamic surface roughness parameter; generally taken as 1.5×10 -4 rice.

[0240] Finally, the atmospheric corrected refractive index is obtained:

[0241]

[0242] Where M s is the corrected refractive index of the sea surface, with a typical value of 330. The Newton iteration method is used to solve equations (22) and (23) to obtain Assuming the air temperature is 299.5K, the observation height is 6m, the wind speed is 5m / s, the relative humidity is 70RH, and the atmospheric pressure is 101.325KPa, the relationship between the atmospheric refractive index profile and the height is as follows: Figure 5 The evaporation waveguide model is obtained by integrating the above formulas.

[0243] Generate small-scale parameters; small-scale parameters are the most important part of the communication channel modeling process. Small-scale parameters can reflect the non-stationarity of drones and unmanned ships in the airspace, time domain and frequency domain, which helps to analyze and optimize system performance, especially in mobile environments, to ensure reliable transmission and reception of signals and improve data rate and quality. Including: cluster birth and death, cluster angle generation, cluster position generation, and cluster scatterer generation;

[0244] As the UAV and USV move, the scattering clusters will also change. As time goes by, some clusters will disappear, and new clusters will be generated. At this time, the clusters need to be modeled. Since the existence and evolution of waveguide clusters and sea surface clusters are different, they need to be discussed separately.

[0245] A. The birth and death of clusters

[0246] In order to extend the cluster time domain non-stationarity model to the space-time joint non-stationarity model, the cluster birth and death probability is determined by both the time interval and the antenna interval, and the cluster generation rate and extinction rate are defined as λ G and λ R , the survival probability of the transmitter It is expressed as:

[0247]

[0248] Among them, δ d is the channel variation when the scene moves in time, △d T is the antenna spacing, and is the correlation distance in the time domain and array domain. The communication link can only be established when the clusters at both ends of the transmitter and receiver exist at the same time and are visible to the transmitter and receiver. Therefore, the joint survival probability P of the clusters is introduced. s (δ d ,△d T ,△d R ):

[0249]

[0250] It refers to the survival probability of the transmitting and receiving end cluster;

[0251] The expected number of new clusters E(N new ) is expressed as:

[0252]

[0253] B. Cluster Angle Generation

[0254] Due to the existence of the waveguide effect, the angle of the cluster will be limited to a certain range. The upper and lower limits of this angle range are the trapping angle. The upper and lower limits of the trapping angle are θ min / maxCalculated as:

[0255]

[0256] Where n(z) is the refractive index, n(0)≈1.00035, is the refractive index at sea level, and dn(z) / dz is the vertical gradient of the refractive index, calculated as:

[0257]

[0258] The angles of the evaporation waveguide scattering cluster and the sea surface scattering cluster are divided according to the trapping angle, such as Figure 6 Assume that the angles of the scattering clusters at the transmitter and receiver Tx and Rx obey the truncated Gaussian distribution f(θ), that is:

[0259]

[0260] Where θ represents the angle of the cluster, μ θ and σ θ are the corresponding mean and angle extension, Φ is the standard normal cumulative distribution function; the values ​​of a and b at different angles are shown in Table 1.

[0261] Table 1

[0262]

[0263] C. Cluster location generation

[0264] Initial time cluster The center position is:

[0265]

[0266] Since the sea surface cluster is located on the sea surface, its distribution must conform to the wave spectrum, from which the x and y coordinates of the cluster can be calculated, and then the coordinates can be brought into the wave spectrum, that is:

[0267]

[0268] D. Intra-cluster scatterer generation

[0269] The coordinates of the mth ray in the nth cluster are:

[0270]

[0271] in, It obeys the three-dimensional Gaussian distribution.

[0272] Update small-scale parameters; including:

[0273] In the process of channel simulation, channel parameters will change with the evolution of time, so it is necessary to continuously update the small-scale parameters. Let X and Y represent different parameters, X∈{A,Z}, Y∈{n w ,n d}, scattering cluster at time t The location is:

[0274]

[0275] in, is the time-varying velocity vector of the cluster, expressed as:

[0276]

[0277] in, and Indicates the Tx / Rx side cluster Velocity azimuth and pitch angle;

[0278] According to the position of the transmitting and receiving antennas relative to the cluster, the distance vector between the antenna and the cluster is obtained, that is:

[0279]

[0280] In formulas (37) and (38), Represents the position vector of the first antenna at the transceiver end, is the cluster position vector, is the antenna spacing.

[0281] Determine the angle of incidence based on the distance vector and the exit angle

[0282]

[0283] Wherein, arctan2 represents the inverse tangent function of the fourth quadrant; Z∈{T,p,R,q}; The sum of the time-varying distances from the transmitter to the corresponding scattering cluster is expressed as

[0284] For the calculation of time delay, in the ocean scattering part, the nth d The calculation formula for the mth ray in a scatterer is:

[0285]

[0286] in, for and The virtual link delay between , is expressed as: in, for and The direct propagation path between C,link is the virtual link delay that obeys the unilateral exponential distribution;

[0287] In the evaporation waveguide part, the propagation path is abstracted as two-dimensional propagation, and the calculation formula is:

[0288]

[0289] Virtual link based and The pitch angle can be divided into four cases:

[0290] First case:

[0291] Second case:

[0292] The third case:

[0293] The fourth case:

[0294] Four situations are Figure 7 shown. and The propagation distance between them is l=l1+Nl2+l3, and the number of reflections of the ray in the waveguide layer is defined as N. The calculation method of N is as follows:

[0295]

[0296] Where N is an integer in the interval, K is a constant, for and Straight-line distance;

[0297] Then, according to Table 2 and Table 3, the values ​​of l1, l2 and l3 are calculated, and finally the delay of the virtual link is obtained.

[0298] Table 2

[0299]

[0300] Table 3

[0301]

[0302]

[0303] No.n w / d The power of the mth ray in a scattering cluster is:

[0304]

[0305] in, is the shadow fading corresponding to each cluster, in dB, r τ is the delay distribution proportional factor, DS is the delay spread, ξ n (p,q) is the array axis attenuation factor, calculated as:

[0306]

[0307] in, is the fading mean, It obeys a two-dimensional Gaussian random distribution and is generated by a spatial filtering algorithm;

[0308] Finally, after normalization, we get:

[0309]

[0310] Calculation of channel statistics; channel statistics include delay power spectrum density, space-time correlation function, stationary interval and root mean square delay spread; including:

[0311] The delay power spectrum density represents the power distribution of the channel on the time axis. The delay power spectrum density Λ pq (t,τ) is calculated as:

[0312]

[0313] in, and is the multipath power corrected by the power correlation coefficient; environmental changes will affect the delay and power changes of the cluster, thereby affecting the changes in the delay power spectrum density.

[0314] In communication systems, the space-time correlation function (ST-CF) is used to describe the correlation of signals in space and time. Specifically, the space-time correlation function describes the relationship between the received signal power of multiple antennas (or receiver locations) in space and time. pq (t) and h p′q′ The space-time correlation function between (t-△t) is defined as:

[0315]

[0316] The space-time correlation function is written as the sum of the LoS component, the sea surface scattering component, and the evaporation duct scattering component, that is:

[0317]

[0318] Among them, the correlation function of each component is calculated as:

[0319]

[0320] Where, △ξ={△ξ T ,△ξ R}, When △ξ=0, the time autocorrelation function is calculated; when △t=0, the spatial cross-correlation function is calculated;

[0321] In a communication system, a stationary interval refers to a time interval in a channel, during which the statistical characteristics of the channel remain unchanged. It represents the maximum time interval for which the channel satisfies the wide stationary assumption. The present invention selects the spectral divergence method to calculate the stationary interval of the channel. The stationary interval is defined as the maximum time interval in which the absolute value of the Doppler spread change is less than a given threshold, that is:

[0322] T η =max{△tε(△t)≥η} (54)

[0323] in,

[0324] D. RMS delay spread

[0325] Root Mean Square Delay Spread (RMSDS) refers to the delay dispersion measure caused by the delay difference of the signal propagation path in the wireless communication channel; it is an indicator to measure the delay variation of the signal in time, and is usually used to describe the non-uniformity of signal transmission delay caused by multipath propagation. The specific formula is:

[0326]

[0327] in,

[0328]

[0329]

[0330] The definitions of the parameters of the present invention are shown in Table 4:

[0331] Table 4

[0332]

[0333] The trajectory of the UAV is randomly set based on the Markov state, with an initial velocity of 5m / s and an acceleration of 0. The unmanned ship is set to a random trajectory based on smooth turns, with an initial velocity of 5m / s and an acceleration of 0. The two trajectories are uniformly sampled in the time domain. The channels without and with the evaporation waveguide effect are simulated, as shown in Figure 8(a) and Figure 8(b). It is observed that the introduction of the evaporation waveguide significantly increases the number of clusters and enriches the birth and death process of the clusters. The power delay spectrum can reflect the trajectory characteristics of the UAV and the unmanned ship, as well as the birth and death changes of the clusters. The experimental results show that the model can effectively simulate the non-steady-state wireless channels under different trajectories of UAVs and unmanned ships in marine scenarios.

[0334] The temporal autocorrelation function and spatial cross-correlation function can be obtained by setting the antenna spacing and time interval to 0, that is, △t = 0 or △d = 0. The simulation parameters are set to the UAV flight speed of 8m / s and the acceleration of 3m / s 2 , the ship's speed is 5m / s and its acceleration is 3m / s 2 , the simulation time is 2.5s. Fig. 9 The time autocorrelation function of the trajectory at different times and frequencies is shown. Fig. 9 It can be observed that the time autocorrelation function at different frequencies is different. The increase in the center frequency will cause the autocorrelation function to decrease faster, which is the same as the change trend of the channel autocorrelation function with frequency in other scenarios. The drone and the unmanned ship are set to accelerate in order to observe the change in the time-varying characteristics caused by the increase in speed. When the speed continues to increase, the channel time correlation gradually decreases. At the same time, it can be seen from the figure that the autocorrelation characteristics at different times are also different, from which it can be concluded that the channel is non-stationary. Fig.10 is the autocorrelation function at different wind speeds. Fig.10 It can be seen that as the wind speed increases, the cross-correlation function decreases faster. This is because when the wind speed increases, the ups and downs of the waves increase, the height change of the ship increases, and the autocorrelation of the channel decreases. From Figure 11 (a) and Figure 11 (b), we can also see the change of the channel autocorrelation function after the wind speed changes. In addition, the theoretical results calculated according to the formula fit well with the simulation results obtained by calculating the channel impulse response at the moment, which can prove the correctness of the simulation results.

[0335] The theoretical and simulated values ​​of the spatial cross-correlation function at different frequencies and antenna directions are shown in Figure 2. Fig.12 As shown in Figure 2, it is shown that the antenna direction has a significant effect on the spatial cross-correlation function. When the antenna is facing the main direction of the scatterer distribution, the spatial correlation decreases. Fig.13is the spatial cross-correlation function under different wind speeds. The unmanned ship approaches the drone at a speed of 20m / s, and the drone is stationary. As the drone approaches the unmanned ship, the effect of the waves on the spatial cross-correlation function becomes more and more significant, and the observed fluctuations become more obvious. This enhanced effect is due to the closer distance between the transmitter and the receiver, which strengthens the impact of the waves on the channel, thereby amplifying the observable spatial cross-correlation function trends and fluctuations. In addition, from Fig.13 It can be seen that the increase in wind speed causes the spatial cross-correlation function to fluctuate more significantly.

[0336] Figure 14(a) shows the stationary intervals at relative humidity of 65RH, 70RH, and 75RH. It can be seen that the stationary interval of the channel shows an increasing trend with the continuous increase of humidity. This is because with the increase of humidity, the evaporation effect is continuously weakened, which weakens the evaporation waveguide, reduces the trapping angle of the cluster, weakens the dispersion of the cluster, and enhances the stationary interval. Figure 14(b) shows the stationary intervals at wind speeds of 5m / s, 10m / s, and 15m / s. As the wind speed increases, the stationary interval decreases. This is because the increase in wind speed leads to greater fluctuations in the waves on the one hand, and on the other hand, it leads to an increase in the evaporation waveguide effect, an increase in the trapping angle, and a more dispersed cluster, which gradually reduces the stationary interval. Figure 14(c) shows the stationary intervals at air-sea temperature differences of 0.1K, 1K, and 2K. It can be seen that with the increase of the air-sea temperature difference, the stationary interval gradually decreases. This is because with the increase of the temperature difference, the evaporation effect is enhanced, the waveguide effect continues to increase, the trapping angle of the cluster increases, the distribution of the cluster becomes more dispersed, and the stationary interval decreases. It can be seen from this that in channel modeling, the dispersion of the cluster will directly affect the stationary interval of the channel. Generally speaking, the more dispersed the clusters are, the smaller the stationary interval of the channel will become. This is because dispersed clusters cause the statistical characteristics of the channel to vary more significantly in space and time, thereby shortening the stationary interval of the channel.

[0337] Figure 15(a) shows the RMS delay spread under humidity of 65RH, 70RH, and 75RH. It can be seen that the RMS delay spread of the channel shows a decreasing trend as the humidity continues to increase. This is because as the humidity increases, the evaporation effect is continuously weakened, which weakens the evaporation waveguide, reduces the trap angle of the cluster, and makes the cluster distribution more concentrated, which reduces the RMS delay spread. Figure 15(b) shows the RMS delay spread under wind speeds of 5m / s, 10m / s, and 15m / s. As the wind speed increases, the RMS delay spread increases. This is because the increase in wind speed leads to greater fluctuations in the waves on the one hand, and on the other hand, it leads to an enhanced evaporation waveguide effect, an increased trap angle, and a more dispersed cluster, which gradually increases the RMS delay spread. Figure 15(c) shows the RMS delay spread when the air-sea temperature difference is 0.1K, 1K, and 2K. It can be seen that as the air-sea temperature difference increases, the RMS delay spread gradually increases. This is because as the temperature difference increases, the evaporation effect increases, the waveguide effect continues to increase, the cluster trapping angle increases, the cluster distribution becomes more dispersed, and the RMS delay spread increases. It can be seen that humidity, temperature, and wind speed will affect the change of the evaporation waveguide, thereby changing the signal propagation path and thus changing the delay change.

[0338] The UAV maritime channel measurement activity was conducted on the coast near the Qingdao Campus of Shandong University in Qingdao, Shandong Province, China. The Universal Software Radio Peripheral (USRP) X310 was used as a transmitter and receiver with a DJI M600 Pro drone equipped with an omnidirectional antenna. Figure 16(a) shows the measurement scene diagram, and Figure 16(b) shows the schematic diagram of the transmitter; the carrier frequency is set to 3.5GHz, the sampling rate is 200MHz, and the bandwidth is 100MHz. The transmitter runs in a straight line at a uniform speed of 5m / s. The activity simulates a ship docked at the shore, and the receiver is fixed on the shore. The initial distance between the transmitter and the receiver is 20m, and the height of the transmitter is 20m. During the measurement, the ambient temperature was 4.56℃, the wind speed was 5m / s, and the relative humidity was 80RH. The USRP sent a PN sequence and the receiver received it. The transmitter continuously sent the PN sequence, and the receiver sampled it every 0.1s, with each sampling of 2ms. The received signal was directly correlated with the calibration signal directly connected to the transceiver to obtain a useful signal. Then, the CIR is obtained by comparing the Fourier transform of the useful signal with the Fourier transform of the direct signal and then performing an inverse Fourier transform. The RMS delay spread is obtained by equation (55). The simulation is performed using the same parameters as the measurement activity, and the results are as follows: Fig.17 As shown. Fig.17 It can be seen that the measured results are in good agreement with the simulation results, thus verifying the accuracy of the model.

[0339] Example 3

[0340] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for simulating channel characteristics of an unmanned ship wireless channel model based on a drone as described in embodiment 1 or 2 are implemented.

[0341] Example 4

[0342] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of a method for simulating channel characteristics of an unmanned ship wireless channel model based on a drone are implemented as described in Example 1 or 2.

[0343] Example 5

[0344] A system for simulating channel characteristics based on a wireless channel model of an unmanned aerial vehicle to an unmanned ship, comprising:

[0345] The unmanned ship wireless channel model building module is configured to: build a wireless channel model of the unmanned ship to the unmanned ship; the unmanned ship wireless channel model includes: generating channel impulse response, generating random trajectory of the unmanned ship, generating random trajectory of the unmanned ship, generating evaporation waveguide model, generating evaporation waveguide model, generating small-scale parameters, and updating small-scale parameters;

[0346] The channel statistical characteristic calculation module is configured as follows: the channel statistical characteristics include delay power spectrum density, space-time correlation function, stationary interval and root mean square delay spread.

Claims

1. A method for simulating channel characteristics based on a UAV to an unmanned ship wireless channel model, characterized in that: include: Construct a wireless channel model between drone and unmanned ship; The unmanned ship wireless channel model includes: generating channel impulse response, generating random trajectory of unmanned aircraft, generating random trajectory of unmanned ship, generating evaporation waveguide model, generating evaporation waveguide model, generating small-scale parameters, and updating small-scale parameters; Calculation of channel statistics; channel statistics include delay power spectrum density, space-time correlation function, stationary interval and root mean square delay spread.

2. According to claim 1, a method for simulating channel characteristics of an unmanned ship wireless channel model based on a drone is characterized in that: Generate channel impulse response; include: The impulse response matrix H of the maritime communication channel is expressed as formula (1): H=[PL·SH] 1 / 2 ·H s (1) In formula (1), PL represents path loss, SH represents shadow, and the matrix H s =[h pq (t,τ)] nT×nR , n T 、n R They are respectively expressed as the number of transmitting and receiving antennas and the small-scale CIR of the corresponding antenna; h pq (t,τ) represents the antenna With antenna The CIR between is shown in formula (2): Among them, K R represents Rice K factor, S1 and S2 represent power correlation coefficients, satisfying S1+S2=1, They represent the CIR of the LoS component, sea surface scattering component, and evaporation duct reflection component respectively; LoS Component It is obtained from the following formula: Among them, f c Indicates the carrier frequency, F p / q,H and F p / q,V Represents the horizontal polarization and vertical polarization antenna patterns of the transmitting and receiving ends, is the initial phase that obeys the uniform distribution of (0,2π], is the delay of the time-varying LoS path; Sea surface scattering component It is obtained from the following formula: Evaporative waveguide scattering component It is obtained from the following formula: in, and is the cross-polarization power ratio of the NLoS component, is the initial phase that obeys the uniform distribution of (0,2π], and is the nth antenna element between the pth Tx antenna element and the qth Rx antenna element w or nth d The time-varying power of the mth ray in a cluster, and is the corresponding delay.

3. According to claim 1, a method for simulating channel characteristics of an unmanned ship wireless channel model based on a drone, characterized in that: The random trajectory of the UAV is generated based on the Markov random process; including: Set random azimuth and pitch angles to simulate the random trajectory of the drone. n }, X n The state depends only on X n-1 The state is: P(X i |X0,X1,...,X i-1 )=P(X i |X i-1 ),i=1,2,... (6) Among them, P(X i |X i-1 ) is the transfer probability distribution, the flight angle of the drone is randomly generated by the Markov chain, and the antenna position at time T is: in, is the initial position, is the velocity vector, expressed as: Among them, α and γ are the horizontal angle and pitch angle of the velocity vector.

4. According to claim 1, a method for simulating channel characteristics of an unmanned ship wireless channel model based on a drone, characterized in that: Generate random trajectories of unmanned ships; including: The wave height η(x,y,t) at a certain point is: The three-dimensional sea surface height is abstracted as the superposition of multiple cosine waves, where a i 、w i , ε i and ψ i is the amplitude, angular frequency, random initial phase and the angle between the propagation direction and the x-axis of the i-th cosine wave, k i is the beam of deep water waves, expressed as g is the acceleration due to gravity; a i It is expressed as: Among them, S(w i ,ψ i ) is the wave spectrum, △w is the frequency interval between the constituent waves, △ψ is the angular interval between the constituent waves, S(w i ,ψ i ) is expressed as the product of the wave spectrum S(w) and the directional spectrum D(w,ψ), that is: S(w,ψ)=S(w)D(w,ψ) (11) The formulas for the spectrum S(w) and directional spectrum D(w,ψ) are: Where b1 = 8.1 × 10 -3 , b2 = 0.74, a dimensionless constant, U is the wind speed at 19.5 meters above the sea, c1 = 0.5 + 0.82Q (w), c2 = 0.32Q (w), Q (w) = exp [- (wU / g) 4 / 2].

5. According to claim 1, a method for simulating channel characteristics of an unmanned ship wireless channel model based on a drone, characterized in that: Generates an evaporation waveguide model; includes: First, calculate the overall Richardson number R ib : Among them, z observation is the observation height, T a and T s is the air temperature and sea surface temperature at the observation height; U observation is the wind speed at the observation height; Next, calculate the Monin-Obukhov length L: Where, Γ is the empirical profile coefficient; Then, the difference N between the atmospheric refractive index at the ocean surface and the refractive index at the edge of the ocean and the atmosphere is calculated. p ,Right now: N p =N a -N s (16) Among them, N a is the refractive index of air on the ocean surface, N s is the refractive index at the edge of the ocean and atmosphere, T ok and T sk are the ocean surface air humidity and the ocean surface relative humidity, e and e s is the atmospheric water vapor pressure and the water vapor pressure of seawater on the ocean surface, and the calculation formula is as follows: By R ib Classify the environmental stability, when 0≤R ib When ≤1, the environment is in a stable state; when R ib <0, the environment is unstable; the evaporation waveguide height h d The calculation is as follows: Where A = -0.125 × B / Np, B = ln(z observation / z0)-Ψ, z0 is the aerodynamic surface roughness parameter; Finally, the atmospheric corrected refractive index is obtained: Where M s is the corrected refractive index of the sea surface, and solving equations (22) and (23) yields 6. The method for simulating channel characteristics of an unmanned ship wireless channel model based on a UAV according to claim 1, characterized in that: Generate small-scale parameters, including: cluster birth and death, cluster angle generation, cluster position generation, and cluster scatterer generation; A. The birth and death of clusters The probability of cluster birth and death is determined by both the time interval and the antenna spacing. The generation rate and extinction rate of the cluster are defined as λ G and λ R , the survival probability of the transmitter It is expressed as: Among them, δ d is the channel variation when the scene moves in time, △d T is the antenna spacing, and The joint survival probability P of the clusters is introduced as the correlation distance between the time domain and the array domain. s (δ d ,△d T ,△d R ): It refers to the survival probability of the transmitting and receiving end cluster; The expected number of new clusters E(N new ) is expressed as: B. Cluster Angle Generation The upper and lower limits of the trap angle θ min / max Calculated as: Where n(z) is the refractive index, n(0)≈1.00035, is the refractive index at sea level, and dn(z) / dz is the vertical gradient of the refractive index, calculated as: According to the trapping angle, the angles of the evaporation waveguide scattering cluster and the sea surface scattering cluster are divided. It is assumed that the angles of the scattering clusters at the transmitting end and the receiving end Tx end and Rx end obey the truncated Gaussian distribution f(θ), that is: Where θ represents the angle of the cluster, μ θ and σ θ is the corresponding mean and angle spread, Φ is the standard normal cumulative distribution function; C. Cluster position generation Initial time cluster The center position is: Find the x and y coordinates of the cluster, and then substitute the coordinates into the wave spectrum, that is: D. Intra-cluster scatterer generation The coordinates of the mth ray in the nth cluster are: in, It obeys the three-dimensional Gaussian distribution.

7. The method for simulating channel characteristics of an unmanned ship wireless channel model based on a UAV according to claim 1, characterized in that: Update small-scale parameters; including: Use X and Y to represent different parameters, X∈{A,Z}, Y∈{n w ,n d }, scattering cluster at time t The location is: in, is the time-varying velocity vector of the cluster, expressed as: in, and Indicates the Tx / Rx side cluster Velocity azimuth and pitch angle; According to the position of the transmitting and receiving antennas relative to the cluster, the distance vector between the antenna and the cluster is obtained, that is: In formulas (37) and (38), Represents the position vector of the first antenna at the transceiver end, is the cluster position vector, is the antenna spacing. Determine the angle of incidence based on the distance vector and the exit angle Wherein, arctan2 represents the inverse tangent function of the fourth quadrant; Z∈{T,p,R,q}; The sum of the time-varying distances from the transmitter to the corresponding scattering cluster is expressed as For the calculation of time delay, in the ocean scattering part, the nth d The calculation formula for the mth ray in a scatterer is: in, for and The virtual link delay between , is expressed as: in, for and The direct propagation path between C,link is the virtual link delay that obeys the unilateral exponential distribution; In the evaporation waveguide part, the propagation path is abstracted as two-dimensional propagation, and the calculation formula is: Virtual link based and The pitch angle can be divided into four cases: First case: Second case: The third case: The fourth case: and The propagation distance between them is l=l1+Nl2+l3, and the number of reflections of the ray in the waveguide layer is defined as N. The calculation method of N is as follows: Where N is an integer in the interval, K is a constant, for and Straight-line distance; Calculate the values ​​of l1, l2 and l3, and finally get the delay of the virtual link No.n w / d The power of the mth ray in a scattering cluster is: in, is the shadow fading corresponding to each cluster, in dB, r τ is the delay distribution proportional factor, DS is the delay spread, ξ n (p,q) is the array axis attenuation factor, calculated as: in, is the fading mean, It obeys a two-dimensional Gaussian random distribution and is generated by a spatial filtering algorithm; Finally, after normalization, we get: Further preferably, channel statistical characteristics are calculated; the channel statistical characteristics include delay power spectrum density, space-time correlation function, stationary interval and root mean square delay spread; including: The delay power spectrum density represents the power distribution of the channel on the time axis. The delay power spectrum density Λ pq (t,τ) is calculated as: in, and is the multipath power corrected by the power correlation coefficient; Channel h pq (t) and h p′q′ The space-time correlation function between (t-△t) is defined as: The space-time correlation function is written as the sum of the LoS component, the sea surface scattering component, and the evaporation duct scattering component, that is: Among them, the correlation function of each component is calculated as: Where, △ξ={△ξ T ,△ξ R }, When △ξ=0, the time autocorrelation function is calculated; when △t=0, the spatial cross-correlation function is calculated; The stationary interval is defined as the maximum time interval during which the absolute value of the Doppler spread change is less than a given threshold, that is: T η =max{△tε(△t)≥η} (54) in, D. RMS delay spread RMS delay spread refers to the delay dispersion measure caused by the delay difference of the signal propagation path in the wireless communication channel; RMS delay spread The specific formula is: in, 8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for simulating channel characteristics of an unmanned ship wireless channel model based on a drone are implemented as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of a method for simulating channel characteristics based on a wireless channel model of an unmanned ship for an unmanned ship based on any one of claims 1-7 are implemented.

10. A system for simulating channel characteristics based on a UAV to an unmanned ship wireless channel model, characterized in that: include: The unmanned ship wireless channel model building module is configured to: build a wireless channel model between the unmanned aircraft and the unmanned ship; The unmanned ship wireless channel model includes: generating channel impulse response, generating random trajectory of unmanned aircraft, generating random trajectory of unmanned ship, generating evaporation waveguide model, generating evaporation waveguide model, generating small-scale parameters, and updating small-scale parameters; The channel statistical characteristic calculation module is configured as follows: the channel statistical characteristics include delay power spectrum density, space-time correlation function, stationary interval and root mean square delay spread.

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