Novel 6G offshore unmanned system environment sensing channel modeling method and system

Abstract

The invention relates to a novel 6G seaborne unmanned system environment sensing channel modeling method and system. The method comprises the steps that 1, a seaborne unmanned system communication framework formed by a seaborne unmanned aerial vehicle to multiple unmanned ships and a seaborne unmanned ship to unmanned ships is built; 2, a channel coefficient generation method is built; comprising the steps of generating a channel impulse response, generating an unmanned aerial vehicle random trajectory, generating an unmanned ship random trajectory, generating an evaporation waveguide model, generating small-scale parameters and updating the small-scale parameters. Step 3, constructing a channel simulator system based on hardware implementation of an FPGA (Field Programmable Gate Array); and step 4, calculating channel statistical characteristics, wherein the channel statistical characteristics comprise time delay power spectrum density, a space-time correlation function, a stationary interval, root-mean-square time delay spread and channel matrix colinearity. According to the invention, the channel can better simulate the real environment over the ocean, so that model construction and characteristic analysis can be more accurately carried out.

Description

Technical Field

[0001] This invention relates to a novel 6G maritime unmanned system environmental perception channel modeling method and system, belonging to the field of channel characteristic simulation technology. Background Technology

[0002] With the development of 6G, new requirements have been placed on maritime communication. In recent years, countries around the world have increasingly emphasized the development of unmanned systems, which play a vital role in marine exploration, maritime rescue, and maritime operations. Compared to land-based scenarios, key differences in maritime scenarios include the impact of evaporative waveguides and ocean waves on the communication environment. Due to wave action, unmanned vessels undergo irregular motion, leading to irregular movement of scatterers within the channel. In low-altitude maritime communication, the primary influencing factor is evaporative waveguides. Changes in temperature, humidity, and pressure in the lower atmosphere above the sea surface create a humid environment, which is conducive to multiple signal reflections and long-distance transmission. Effective theoretical modeling is a necessary condition for in-depth research into maritime communication scenarios.

[0003] Unmanned maritime communication systems are crucial for the development of the maritime economy. However, maritime communication still faces many challenges, such as the complex and variable marine environment, long transmission distances, and interference between multiple users, which hinders maritime communication-related work. Therefore, solving these maritime communication problems is essential. In 2010, Wang Yang's team conducted ship-to-shore channel measurements and analyzed the characteristics of the ship-to-shore channel, including path loss, channel correlation, Doppler shift, and sea surface reflection coefficient, revealing that path loss in maritime scenarios exhibits a comb-like characteristic. In 2022, the team studied the characteristics of millimeter-wave band maritime channels, combined with meteorological data analysis of the impact of humidity on path loss, and established an ITU-R path loss model. The team conducted channel measurements for unmanned aerial vehicles (UAVs) and unmanned aerial vehicles (USVs), establishing FE2R and CE2R models for RSL. They analyzed the autocorrelation characteristics of shadows, evaluated the numerical fitting of multipath fading distributions, and proposed future research directions regarding multipath component delay, Doppler, time-varying channel characteristics, and the impact of Kelvin wakes. These studies contribute to a comprehensive understanding of the behavior of marine communication channels and provide crucial insights for improving the design of communication systems in marine environments. Regarding channel model construction, Liu Yu's team introduced a novel three-dimensional non-stationary UAV-to-ship geometric stochastic model. It considers not only the direct component but also single reflections caused by rough sea surfaces and multiple reflections due to waveguide effects. He Yubei's team proposed a ship-to-ship stochastic channel model considering the impact of waves on ships and introduced the influence of evaporating waveguides on cluster angles. Furthermore, Wang Chengxiang's team, considering the position-dependent characteristics of maritime channels and the sparse user and scattering distribution features, proposed an innovative large-scale MIMO ship-to-ship beamdomain channel model (BDCM). The effects of the sea surface and the evaporation waveguide on the angle were analyzed.

[0004] However, hardware channel models that combine environmental information with multi-user communication in unmanned systems are rarely mentioned. Multi-user cooperation is a crucial component of unmanned systems. Whether in maritime operations or maritime transport, communication between UAVs and multiple unmanned vessels encounters the same scatterers, causing channel correlation. Therefore, establishing a multi-user cooperative channel model is essential. Furthermore, environmental factors such as temperature, humidity, and wind speed in the ocean affect evaporative waveguides, altering channel conditions. Their impact must be considered during modeling. Compared to traditional methods like field measurements and pure software simulation, hardware simulators are increasingly demonstrating their irreplaceable core value and significant advantages, becoming a more effective and practical tool for channel modeling and system evaluation. Maritime communication is an indispensable part of 6G development; therefore, constructing a comprehensive channel model and more accurately analyzing channel characteristics is crucial. Summary of the Invention

[0005] To address the shortcomings of existing communication hardware channel models for maritime unmanned systems, which fail to incorporate marine environmental information and the correlation between multi-user channels, this invention aims to propose a novel communication channel model for maritime unmanned aerial vehicles (UAVs) to multiple unmanned vessels (UAVs) and UAVs to unmanned locations. This model integrates the environmental impact on the channel and considers the interrelationships between multi-user communication channels, thereby enabling the modeling and system design of communication channels for maritime unmanned systems. The main and secondary problems to be solved by this invention are as follows:

[0006] 1. A novel channel modeling method for maritime unmanned systems (UAVs) to unmanned vessels (UVs) and UVs to UVs is constructed, incorporating stochastic trajectories for both UAVs and UVs. The UAVs employ a Markov chain-based stochastic trajectory, while the UVs utilize a smooth steering maneuverability model. The influence of ocean waves on the UVs and wave clusters is introduced. A three-dimensional wave spectrum model is used to characterize the three-dimensional motion of the waves, thus better representing the changes in the vessels and wave clusters. A PJ evaporation waveguide model is introduced. Incorporating environmental factors such as temperature, humidity, and wind speed into the waveguide model allows for a more accurate representation of the evaporation waveguide, thereby improving the accuracy of waveguide cluster evolution.

[0007] 2. A multi-user communication channel model was established, which can better analyze the correlation between multiple users, thereby exploring the channel characteristics in multi-user communication scenarios. This model can cover the entire marine unmanned communication system, including UAV-UAV, UAV-unmanned vessel, and unmanned vessel-unmanned vessel communication systems.

[0008] 3. The channel model was implemented in hardware using a Field Programmable Gate Array (FPGA). This system effectively addresses the real-time and accuracy issues of dynamic multi-user channel modeling in complex maritime environments by considering the cluster's birth and death processes in both the spatial and temporal domains. Furthermore, the programmability of the FPGA allows for the incorporation of realistic environmental information, thereby improving channel simulation.

[0009] The technical solution of this invention is as follows:

[0010] A novel environmental perception channel modeling method for 6G maritime unmanned systems includes:

[0011] Step 1: Establish a communication framework for a maritime unmanned system consisting of UAVs to multiple unmanned vessels and unmanned vessels to each other:

[0012] Step 2: Construct a channel coefficient generation method; including: generating channel impulse response, generating UAV random trajectory, generating unmanned surface vessel random trajectory, generating evaporative waveguide model, generating small-scale parameters, and updating small-scale parameters;

[0013] Step 3: Build a channel simulator system based on FPGA hardware implementation;

[0014] Step 4: Calculation of channel statistical characteristics: Channel statistical characteristics include delay power spectral density, space-time correlation function, stationary interval, root mean square delay spread, and channel matrix collinearity.

[0015] According to a preferred embodiment of the present invention, a communication framework for a maritime unmanned system is established, comprising:

[0016] The framework of a maritime unmanned aerial vehicle (UAV) to multiple unmanned surface vessel (USV) communication system includes a UAV transmitter (Tx), an unmanned surface vessel receiver (Rx), and an unmanned surface vessel (USV). i ;

[0017] The number of antenna elements in Tx and Rx are respectively M T and express;

[0018] The wave scattering path number and the evaporation waveguide scattering path number are respectively represented by n w and n d express; and The numbers represent the total sequence number of the wave scattering path and the total sequence number of the evaporation waveguide scattering path, while the superscript indicates the communication link between the p-th Tx antenna element and the q-th Rx antenna element.

[0019] The scattering of signals by the sea surface and evaporation waveguides is abstracted into two types of clusters: sea surface clusters. and and evaporation waveguide clusters and Tx transmits the signal through the antenna array. Part of the signal reaches Rx directly, while the other part first undergoes reflections through the first and last hop clusters before reaching Rx. These multiple reflections are abstracted as virtual links. Setting the latency of the virtual link to 0 indicates a single hop. During signal transmission, the communication link can only be established if the antenna elements of Tx and Rx are simultaneously visible. (Tx antenna to cluster) and The distance of the m-th scatterer is and Rx i Antenna to Cluster and The distance of the m-th scatterer is and

[0020] According to a preferred embodiment of the present invention, generating a channel impulse response includes:

[0021] The impulse response matrix H of the maritime communication channel is expressed as:

[0022] H = [H] 1 H 2 ...H i (1);

[0023] Where H is a five-dimensional matrix, H i For drones to USV i The CIR matrix of the communication links between them is represented as:

[0024]

[0025] In the formula, PL represents path loss, SH represents shading, and the matrix... For the corresponding small-scale CIR of the antenna, n T and This represents the two-dimensional size of the matrix. Wherein, Indicates antenna With antenna The CIR between them, that is:

[0026]

[0027] Among them, K R Let S1 and S2 represent the Rice K-factor, and S1 and S2 represent the power correlation coefficients, satisfying S1 + S2 = 1. CIR values ​​for the LoS component, sea surface scattering component, and evaporation waveguide reflection component, respectively.

[0028] LoS component We obtain it from the following formula:

[0029]

[0030] Where f c F represents the carrier frequency. p / q,H and F p / q,V The antenna patterns representing horizontal and vertical polarization at the transmitting and receiving ends. Assuming an initial phase that follows a uniform distribution within (0, 2π), The time delay of the time-varying LoS path;

[0031] Sea surface scattering component It is given by the following formula:

[0032]

[0033] Evaporated waveguide scattering components It is given by the following formula:

[0034]

[0035] in, and The cross-polarization power ratio of the NLoS component. Assuming an initial phase that follows a uniform distribution within (0, 2π), and For the nth Tx antenna element and the qth Rx antenna element of the i-th receiver, w or the nth d The time-varying power of the m-th ray in a cluster, and This represents the corresponding time delay.

[0036] According to a preferred embodiment of the present invention, generating a random trajectory for a drone includes:

[0037] In single-user communication scenarios, a Markov random process is used to generate the random trajectory of the UAV, setting random azimuth and pitch angles to ultimately simulate the random trajectory of the UAV; where the random variable sequence is set as X = {X0, X1, ..., X...}. n}, X n The state depends only on X n-1 The state, that is:

[0038] P(X i |X0,X1,...,X i-1 )=P(X i |X i-1 ), i = 1, 2, ... (7);

[0039] Wherein, P(X) i |X i-1 ) represents the transition probability distribution;

[0040] According to a preferred embodiment of the present invention, generating a random trajectory for an unmanned surface vessel includes:

[0041] The ship's xy-plane trajectory employs a smooth turning model, which is suitable for situations where sharp turns are infrequent, such as those involving ships or vehicles. Since the ship's height varies with wave height, a three-dimensional wave spectrum is introduced to describe the wave's undulations. The wave height η(x,y,t) at a given point is:

[0042]

[0043] Among them, the three-dimensional sea surface is highly abstracted as the superposition of multiple cosine waves, a i w i ε i and ψ i Let k be the amplitude, angular frequency, random initial phase, and angle between the propagation direction and the x-axis of the i-th cosine wave; i The beam of deep-water waves is represented as g is the acceleration due to gravity; a i Represented as:

[0044]

[0045] Among them, S(w i ,ψ i S(w) represents the wave spectrum, Δw represents the frequency interval between the constituent waves, and Δψ represents the angular interval between the constituent waves. i ,ψ i The wave spectrum S(w) can be expressed as the product of the wave spectrum S(w) and the directional spectrum D(w,ψ), that is:

[0046] S(w,ψ)=S(w)D(w,ψ) (10);

[0047] This invention employs the Pierson-Moskowitz spectrum considering wind speed, with the directional spectrum based on the Stereo Wave Observation Project (SWOP). The formulas for the spectrum S(w) and the directional spectrum D(w,ψ) are as follows:

[0048]

[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 surface; c1 = 0.5 + 0.82Q(w); c2 = 0.32Q(w); Q(w) = exp[-(wU / g)]. 4 / 2].

[0051] According to a preferred embodiment of the present invention, generating an evaporation waveguide model includes:

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

[0053]

[0054] Among them, z observation For the observation altitude, T a and T s To observe air temperature and sea surface temperature at altitude; U observation To observe wind speed at the altitude;

[0055] Next, calculate the Mönning-Obukhoff length L:

[0056]

[0057] In the formula, Γ is the empirical profile coefficient;

[0058] Subsequently, the difference N between the atmospheric refractive index at the ocean surface and the refractive index at the ocean and atmospheric edges was calculated. p ,Right now:

[0059] N p =N a -N s (15);

[0060]

[0061]

[0062] Where, N a N is the air refractive index at the ocean surface. s T represents the refractive index at the ocean and atmospheric edges. ok and T sk For the air humidity and relative humidity of the ocean surface, e and e s The formulas for calculating atmospheric water vapor pressure and seawater water vapor pressure at the ocean surface are as follows:

[0063]

[0064]

[0065] Through R ib Environmental stability is classified when 0 ≤ R ib When R ≤ 1, the environment is in a stable state; when R ib When <0, the environment is in an unstable state; the evaporation waveguide height h d The calculation is as follows:

[0066]

[0067] In the formula, A = -0.125 × B / N p B = ln(z) observation / z0)-Ψ, where z0 is the aerodynamic surface roughness parameter;

[0068] Finally, the atmospheric corrected refractive index is obtained:

[0069]

[0070]

[0071] In the formula, M s To obtain the corrected refractive index of the sea surface, the Newton iteration method is used to solve equations (21) and (22) to obtain...

[0072] According to a preferred embodiment of the present invention, generating small-scale parameters includes:

[0073] A. Cluster Angle Generation

[0074] Upper and lower limits of trap angle The calculation is as follows:

[0075]

[0076] Where n(z) is the refractive index, and n(0)≈1.00035 is the refractive index at sea level.

[0077] The angles of the evaporation waveguide scattering cluster and the sea surface scattering cluster can be distinguished based on the trap angle. It is assumed that the angles of the scattering clusters at the Tx and Rx ends follow a truncated Gaussian distribution, i.e.:

[0078]

[0079] Where θ represents the angle of the cluster, μ θ and σ θ For the corresponding mean and angular expansion, Φ is the standard normal cumulative distribution function;

[0080] B. Cluster location generation;

[0081] Initial time cluster The central location is:

[0082]

[0083] Find the x and y coordinates of the cluster, and then substitute these coordinates into the wave spectrum, i.e.:

[0084]

[0085]

[0086]

[0087] According to a preferred embodiment of the present invention, updating the small-scale parameters includes:

[0088] Let X and Y represent different parameters, where X∈{A,Z} and Y∈{n}. w ,n d}, scattering cluster at time t The location is:

[0089]

[0090] Based on the positions of the transmitting and receiving antennas relative to the cluster, the distance vector between the antennas and the cluster is obtained, i.e.:

[0091]

[0092]

[0093] Determine the incident and exit angles based on the distance vector:

[0094]

[0095]

[0096] Where arctan2 represents the inverse tangent function in the fourth quadrant; Z∈{T,p,R,q}, The sum of the time-varying distances from the transmitter and receiver to the corresponding scattering cluster is expressed as:

[0097] For the calculation of time delay, in the ocean scattering part, the nth... d The formula for calculating the m-th ray in a scatterer is:

[0098]

[0099] in, for and The virtual link latency between them is expressed as: in, for and The direct propagation path between them, τ C,link The virtual link delay follows a unilateral exponential distribution;

[0100] In the evaporation waveguide section, the propagation path can be abstracted as two-dimensional propagation. In the single-link model, the calculation formula is:

[0101]

[0102] Virtual link according to and The pitch angle can be divided into four cases:

[0103] Case 1:

[0104] Case 2:

[0105] Case 3:

[0106] Case 4:

[0107] 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 for N is as follows:

[0108]

[0109]

[0110] Where N is an integer within the interval, and K is a constant. for and The straight-line distance;

[0111] Calculate the values ​​of l1, l2, and l3;

[0112] Finally, the latency of the virtual link is obtained.

[0113] According to a preferred embodiment of the present invention, a channel model is implemented in hardware based on FPGA; comprising:

[0114] ZYNQ includes a processing system module PS and a programmable logic module PL;

[0115] PS runs a dual-core ARM Cortex TM The A9 processor is responsible for scene parameter generation and control tasks, synchronized via a single-ended clock, and directly manages the PS's dedicated DDR3 DRAM memory; the PL implements the FPGA programmable logic to execute channel parameter generation tasks; the PS and PL are connected via an advanced scalable interface bus, with scene parameters generated by the ARM transmitted via UART 0 and channel parameters processed by the FPGA transmitted via RS485.

[0116] Computational tasks for dynamic channel modeling are assigned using ARM and FPGA-based channel simulators.

[0117] After the path distance calculation is performed within the ARM processor, the parameters are transmitted to the FPGA to calculate the sine parameter, cosine parameter, and delay; the exponential part in equation (4) The exponent part in equation (5) The exponent part in equation (6) The CIR is obtained by simplifying using Euler's formula; the birth and death process of clusters is fully implemented through hardware integration, and Euler's formula is expressed as:

[0118] e iθ =cosθ+isinθ (40);

[0119] Where e represents the base of the natural logarithm, i represents the imaginary unit, and θ is the parameter to be transformed;

[0120] First, configure the parameters, including the number of users, movement trajectory, number of antennas, initial cluster number, and birth / death probability;

[0121] Then, each snapshot is traversed; during this process, initial cluster positions and multipath components are randomly generated;

[0122] Next, the cluster disappearance process is dynamically detected and the number, location, and multipath components of new clusters are generated in real time; at the same time, the status of surviving clusters is updated and the path information is recalculated, thereby realizing the control of key dynamic processes.

[0123] The distance parameters are integrated, including the cluster position, angle, and distance between the cluster and the transceiver. The distance parameters are transmitted to the PC, and the PC transmits the distance parameters to the FPGA via a serial port transmission assistant. The FPGA is responsible for generating channel parameters through differential clock synchronization, and PS and PL are connected via AXI bus. The FPGA uses its parallel architecture to perform hardware calculations on the massive multipath components received synchronously using lookup tables, triggers, and digital signal processing modules. The FPGA generates channel parameters and stores them in registers. The channel parameters are transmitted to the PC via a serial port transmission assistant. MATLAB is used to substitute the channel parameters into equations (4), (5), and (6) to generate LOS components, sea surface scattering components, and evaporation waveguide scattering components, and finally generate H in equation (1).

[0124] According to a preferred embodiment of the present invention, the calculation of channel statistical characteristics includes:

[0125] Delay power spectral density represents the power distribution of the channel along the time axis. pq (t,τ) is calculated as follows:

[0126]

[0127] in, and This refers to the multipath power after correction for power correlation coefficient;

[0128] Channel h pq (t) and h p′q′ The spacetime correlation function between (t-Δt) is defined as:

[0129]

[0130] The space-time correlation function is written as the sum of the LoS component, the sea surface scattering component, and the evaporation waveguide scattering component, i.e.:

[0131]

[0132] The correlation function for each component is calculated as follows:

[0133]

[0134]

[0135]

[0136] Where, Δξ={Δξ T ,Δξ R}, When Δξ = 0, the time autocorrelation function can be calculated; when Δt = 0, the spatial cross-correlation function can be calculated.

[0137] The stationary interval is defined as the maximum time interval in which the absolute value of the Doppler spread variation is less than a given threshold, i.e.:

[0138] T η =max{Δt|ε(Δt)≥η} (47);

[0139] in,

[0140] Root mean square delay spread (RMS delay spread) is a measure of delay dispersion caused by differences in the delay of the signal propagation path in a wireless communication channel. The specific formula is:

[0141]

[0142] It is a measure of the spatial structure change between two matrices of the same size. The channel matrix collinearity (CMC) between two users is calculated as follows:

[0143]

[0144] Where tr{·} denotes a matrix operator, ||·|| F Let Frobenius norm be (·). H This indicates a conjugate operation.

[0145] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described novel 6G maritime unmanned system environmental perception channel modeling method.

[0146] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described novel 6G maritime unmanned system environmental perception channel modeling method.

[0147] A novel 6G maritime unmanned system environmental perception channel modeling system includes:

[0148] The channel model construction module is configured to: build a communication framework for maritime unmanned systems consisting of UAVs to multiple unmanned vessels and unmanned vessels to each other.

[0149] The channel coefficient generation method construction module is configured to: generate channel impulse response, generate UAV random trajectory, generate unmanned surface vessel random trajectory, generate evaporative waveguide model, generate small-scale parameters, and update small-scale parameters;

[0150] The hardware implementation module is configured as follows: FPGA-based hardware implementation to build a channel simulator system;

[0151] The channel statistical characteristics calculation module is configured to include: channel statistical characteristics such as time delay power spectral density, space-time correlation function, stationary interval, root mean square time delay spread, and channel matrix collinearity.

[0152] The beneficial effects of this invention are as follows:

[0153] 1. This invention models the random trajectories of UAVs and unmanned surface vessels (USVs). The UAV trajectory uses a Markov chain-based random trajectory, while the USV uses a smooth steering model. The invention also introduces a three-dimensional wave spectrum model, which realistically reflects the sea surface state, thus better simulating the ship's vertical movement and the evolution of clusters following wave changes. A PJ evaporation waveguide model is introduced, incorporating temperature, humidity, and wind speed into the channel model, enabling the channel to better simulate the real environment over the ocean, thereby allowing for more accurate model construction and characteristic analysis.

[0154] 2. This invention establishes a novel wireless channel model for UAVs to multiple unmanned vessels. This model considers the channel correlation during multi-user communication and explores the special characteristics of multi-user communication.

[0155] 3. This invention implements the channel model in hardware and incorporates environmental information into the hardware implementation, thereby effectively solving the problems of real-time performance and accuracy in dynamic modeling of multi-user channels in complex maritime environments. Attached Figure Description

[0156] Figure 1 This is a schematic diagram of the framework of a multi-unmanned surface vessel system (USV).

[0157] Figure 2 A schematic diagram of the trajectory generation at the transmitting end;

[0158] Figure 3 Wave spectra at different wind speeds;

[0159] Figure 4 A schematic diagram of the unmanned vessel's trajectory;

[0160] Figure 5 This is a schematic diagram showing the relationship between atmospheric refractive index profile and altitude.

[0161] Figure 6 A schematic diagram showing the range of departure pitch angles for different clusters;

[0162] Figure 7 This is a schematic diagram of the propagation of a virtual link in an evaporating waveguide.

[0163] Figure 8 ZYNQ framework diagram;

[0164] Figure 9 This is a schematic diagram of the overall structure of a channel simulator based on ARM and FPGA.

[0165] Figure 10 This is a schematic diagram of the power delay spectrum;

[0166] Figure 11 A schematic diagram of the autocorrelation function at different times and carrier frequencies;

[0167] Figure 12 A schematic diagram of the autocorrelation function at different times and wind speeds;

[0168] Figure 13 This is a schematic diagram of a 3D autocorrelation function;

[0169] Figure 14 A schematic diagram of the cross-correlation function under different antenna orientations;

[0170] Figure 15 A schematic diagram of the cross-correlation function under different wind speeds;

[0171] Figure 16 This is a schematic diagram of a stable interval;

[0172] Figure 17 This is a schematic diagram of the root mean square delay spread;

[0173] Figure 18 This is a scene diagram;

[0174] Figure 19 Fitting plots for measurement and simulation results;

[0175] Figure 20 CMC diagrams at different drone altitudes and unmanned surface vessel distances;

[0176] Figure 21 Schematic diagram of CMC under different wind speeds;

[0177] Figure 22 This is a schematic diagram of CMC at different USV speeds. Detailed Implementation

[0178] The present invention will be further defined below with reference to the accompanying drawings and embodiments, but is not limited thereto.

[0179] Example 1

[0180] A novel environmental perception channel modeling method for 6G maritime unmanned systems includes:

[0181] Step 1: Establish a communication framework for a maritime unmanned system consisting of UAVs to multiple unmanned vessels and unmanned vessels to each other:

[0182] Step 2: Construct a channel coefficient generation method; including: generating channel impulse response, generating UAV random trajectory, generating unmanned surface vessel random trajectory, generating evaporative waveguide model, generating small-scale parameters, and updating small-scale parameters;

[0183] Step 3: Build a channel simulator system based on FPGA hardware implementation;

[0184] Step 4: Calculation of channel statistical characteristics: Channel statistical characteristics include delay power spectral density, space-time correlation function, stationary interval, root mean square delay spread, and channel matrix collinearity.

[0185] Example 2

[0186] The difference between the novel 6G maritime unmanned system environmental perception channel modeling method described in Example 1 and the one in Example 1 is:

[0187] Establish a communication framework for unmanned maritime systems; including:

[0188] In typical UAV-to-unmanned vessel communication scenarios, linear arrays are used. Figure 2 This is a model framework diagram of a drone versus multiple unmanned surface vessels. Figure 1 The left side shows the drone transmitter (Tx), and the right side shows the unmanned surface vessel (Rx). Different unmanned surface vessels are defined as USVs. i .

[0189] The framework of a maritime unmanned aerial vehicle (UAV) to multiple unmanned surface vessel (USV) communication system includes a UAV transmitter (Tx), an unmanned surface vessel receiver (Rx), and an unmanned surface vessel (USV). i ;

[0190] The number of antenna elements in Tx and Rx are respectively M T and express;

[0191] The wave scattering path number and the evaporation waveguide scattering path number are respectively represented by n w and n d express; and The numbers represent the total sequence number of the wave scattering path and the total sequence number of the evaporation waveguide scattering path, while the superscript indicates the communication link between the p-th Tx antenna element and the q-th Rx antenna element.

[0192] Since signals undergo direct transmission, reflection, refraction, and scattering, one type of scattering is caused by the undulations of the sea surface, while another is caused by evaporation waveguides. Therefore, the scattering of signals by the sea surface and evaporation waveguides can be abstracted into two types of clusters: sea surface clusters. and and evaporation waveguide clusters and exist Figure 1 In the process, Tx transmits the signal through the antenna array, part of which directly reaches Rx, and the other part first passes through the first hop cluster ( and ) and the last hop cluster ( and After reflection, the signal reaches Rx. The multiple bounces in between are abstracted as virtual links. Setting the delay of the virtual link to 0 indicates a single hop. During signal transmission, the communication link can only be established if the antenna elements of Tx and Rx are simultaneously visible. The Tx antenna to the cluster... and The distance of the m-th scatterer is and Rx i Antenna to Cluster and The distance of the m-th scatterer is and The parameters mentioned above were obtained at the initial time. Table 1 summarizes the parameters and definitions of the model.

[0193] Table 1

[0194]

[0195] Generate channel impulse response; including:

[0196] The channel impulse response (CIR) matrix H of the maritime communication channel is represented as:

[0197] H = [H] 1 H 2 ...H i (1);

[0198] Where H is a five-dimensional matrix, H i For drones to USV i The CIR matrix of the communication links between them is represented as:

[0199]

[0200] In the formula, PL represents path loss, SH represents shading, and the matrix... For the corresponding small-scale CIR of the antenna, n T and This represents the two-dimensional size of the matrix. Wherein, Indicates antenna With antenna The CIR between them, that is:

[0201]

[0202] Among them, K R Let S1 and S2 represent the Rice K-factor, and S1 and S2 represent the power correlation coefficients, satisfying S1 + S2 = 1. CIR values ​​for the LoS component, sea surface scattering component, and evaporation waveguide reflection component, respectively.

[0203] LoS component We obtain it from the following formula:

[0204]

[0205] Where f c F represents the carrier frequency. p / q,H and F p / q,V The antenna patterns representing horizontal and vertical polarization at the transmitting and receiving ends. Assuming an initial phase that follows a uniform distribution within (0, 2π), The time delay of the time-varying LoS path;

[0206] Sea surface scattering component It is given by the following formula:

[0207]

[0208] Evaporated waveguide scattering components It is given by the following formula:

[0209]

[0210] in, and The cross-polarization power ratio of the NLoS component. Assuming an initial phase that follows a uniform distribution within (0, 2π), and For the nth Tx antenna element and the qth Rx antenna element of the i-th receiver, w or the nth dThe time-varying power of the m-th ray in a cluster, and This represents the corresponding time delay.

[0211] Generate random trajectories for drones; including:

[0212] In single-user communication scenarios, a Markov random process is used to generate the random trajectory of the UAV, setting random azimuth and pitch angles to ultimately simulate the random trajectory of the UAV; where the random variable sequence is set as X = {X0, X1, ..., X...}. n}, X n The state depends only on X n-1 The state, that is:

[0213] P(X i |X0,X1,...,X i-1 )=P(X i |X i-1 ), i = 1, 2, ... (7);

[0214] Wherein, P(X) i |X i-1 ) represents the transition probability distribution; the flight angle of the drone is randomly generated by a Markov chain. Figure 2 This is a schematic diagram of a drone's random trajectory. The drone is set to fly at a uniform speed of 5 m / s. The diagram shows that the trajectory initially rises and then drops sharply, highlighting the randomness of the trajectory.

[0215] Generate random trajectories for unmanned vessels; including:

[0216] The ship's xy-plane trajectory employs a smooth turning model, which is suitable for situations where sharp turns are infrequent, such as those involving ships or vehicles. Since the ship's height varies with wave height, a three-dimensional wave spectrum is introduced to describe the wave's undulations. The wave height η(x,y,t) at a given point is:

[0217]

[0218] Among them, the three-dimensional sea surface is highly abstracted as the superposition of multiple cosine waves, a i w i ε i and ψ i Let k be the amplitude, angular frequency, random initial phase, and angle between the propagation direction and the x-axis of the i-th cosine wave; i The beam of deep-water waves is represented as g is the acceleration due to gravity; a i Represented as:

[0219]

[0220] Among them, S(w i ,ψ i S(w) represents the wave spectrum, Δw represents the frequency interval between the constituent waves, and Δψ represents the angular interval between the constituent waves. i ,ψ i The wave spectrum S(w) can be expressed as the product of the wave spectrum S(w) and the directional spectrum D(w,ψ), that is:

[0221] S(w,ψ)=S(w)D(w,ψ) (10);

[0222] Figure 3 The images show the wave spectrum at different wind speeds; the x, y, and z axes represent the three-dimensional coordinates of the waves. The top image shows the wave spectrum at a wind speed of 5 m / s, and the bottom image shows the wave spectrum at a wind speed of 10 m / s.

[0223] This invention employs the Pierson-Moskowitz spectrum considering wind speed, with the directional spectrum based on the Stereo Wave Observation Project (SWOP). The formulas for the spectrum S(w) and the directional spectrum D(w,ψ) are as follows:

[0224]

[0225]

[0226] Where, b1 = 8.1 × 10 -3 b2 = 0.74, a dimensionless constant; U is the wind speed at 19.5 meters above the sea surface; c1 = 0.5 + 0.82Q(w); c2 = 0.32Q(w); Q(w) = exp[-(wU / g)]. 4 / 2]. Figure 3 The wave spectrum is shown for different wind speeds. It can be seen that as the wind speed increases from 5 m / s to 10 m / s, the wave amplitude increases, and the complexity of the sea surface also increases. The trajectory of the unmanned vessel is shown below. Figure 4 As shown; the x, y, and z axes represent the three-dimensional coordinates of the unmanned vessel.

[0227] Generate an evaporation waveguide model; including:

[0228] Ocean evaporation waveguides refer to the atmospheric temperature and humidity gradient phenomenon formed during ocean surface evaporation. When seawater evaporates, water vapor rises and creates changes in water vapor concentration in the higher atmosphere, leading to changes in atmospheric refractive index. This change can affect the propagation paths of microwaves and radio waves in the atmosphere, such as altering their reflection and refraction characteristics. Evaporation waveguide models are commonly used for predicting evaporation waveguides, obtaining waveguide information by inputting environmental information. This invention introduces the PJ waveguide model, which can characterize evaporation waveguides under different temperatures, wind speeds, humidity levels, and atmospheric pressures. The PJ model is derived based on the Moning-Obuhoff theory.

[0229] First, calculate the overall Richardson number R. ib :

[0230]

[0231] Among them, z observation For the observation altitude, T a and T s To observe air temperature and sea surface temperature at altitude; U observation To observe the wind speed at the altitude;

[0232] Next, calculate the Mönning-Obukhoff length L:

[0233]

[0234] In the formula, Γ is the empirical profile coefficient;

[0235] Subsequently, the difference N between the atmospheric refractive index at the ocean surface and the refractive index at the ocean and atmospheric edges was calculated. p ,Right now:

[0236] N p =N a -N s (15);

[0237]

[0238]

[0239] Where, N a N is the air refractive index at the ocean surface. s T represents the refractive index at the ocean and atmospheric edges. ok and T sk For the air humidity and relative humidity of the ocean surface, e and e s The formulas for calculating atmospheric water vapor pressure and seawater water vapor pressure at the ocean surface are as follows:

[0240]

[0241]

[0242] Through R ib Classify environmental stability when 0 ≤ R ib When R ≤ 1, the environment is in a stable state; when R ib When <0, the environment is in an unstable state; the evaporation waveguide height h d The calculation is as follows:

[0243]

[0244] In the formula, A = -0.125 × B / N p B = ln(z) observation / z0)-Ψ, where z0 is the aerodynamic surface roughness parameter; typically taken as 1.5×10. -4 rice.

[0245] Finally, the atmospheric corrected refractive index is obtained:

[0246]

[0247]

[0248] In the formula, M s The corrected refractive index of the sea surface is typically 330. Equations (21) and (22) are solved using the Newton iteration method. Assuming an air temperature of 299.5 K, an observation altitude of 6 m, a wind speed of 5 m / s, a relative humidity of 70% RH, and an atmospheric pressure of 101.325 kPa, the relationship between the atmospheric refractive index profile and altitude is shown in the figure below. Figure 5 As shown; the horizontal axis represents the atmospheric corrected refractive index, and the vertical axis represents the waveguide height.

[0249] Generate small-scale parameters; including:

[0250] As UAVs and USVs move, the scattering clusters also change. Over time, some clusters disappear while new clusters are generated. Therefore, cluster modeling is necessary. Since waveguide clusters and sea surface clusters exist and evolve differently, they need to be discussed separately.

[0251] A. Cluster Angle Generation

[0252] Due to the waveguide effect, the angle of the cluster is limited to a certain range. The upper and lower limits of this angle range are called the trap angle. The calculation is as follows:

[0253]

[0254] Where n(z) is the refractive index, n(0)≈1.00035 is the refractive index of the sea surface, and the angles of the evaporation waveguide scattering cluster and the sea surface scattering cluster can be distinguished according to the trap angle, such as Figure 6 As shown. Among them, For the launch trap angle, Let be the trap angle at the receiver. Assume the angles of the scattering clusters at the Tx and Rx ends follow a truncated Gaussian distribution, i.e.:

[0255]

[0256] Where θ represents the angle of the cluster, μθ and σ θ For the corresponding mean and angular extension, Φ is the standard normal cumulative distribution function; the values ​​of a and b at different angles are shown in Table 2.

[0257] Table 2

[0258]

[0259]

[0260] B. Cluster location generation;

[0261] Initial time cluster The central location is:

[0262]

[0263] Since the sea surface clusters are located on the sea surface, their distribution must conform to the wave spectrum. Therefore, the x and y coordinates of the clusters can be calculated, and then these coordinates can be substituted into the wave spectrum.

[0264]

[0265]

[0266]

[0267] Update small-scale parameters; including:

[0268] During channel simulation, channel parameters evolve over time, necessitating continuous updates to small-scale parameters. Let X and Y represent different parameters, where X∈{A,Z} and Y∈{n}. w ,n d}, scattering cluster at time t The location is:

[0269]

[0270] Based on the positions of the transmitting and receiving antennas relative to the cluster, the distance vector between the antennas and the cluster is obtained, i.e.:

[0271]

[0272]

[0273] Determine the incident and exit angles based on the distance vector:

[0274]

[0275]

[0276] Where arctan2 represents the inverse tangent function in the fourth quadrant; Z∈{T,p,R,q}, The sum of the time-varying distances from the transmitter and receiver to the corresponding scattering cluster is expressed as:

[0277] For the calculation of time delay, in the ocean scattering part, the nth... d The formula for calculating the m-th ray in a scatterer is:

[0278]

[0279] in, for and The virtual link latency between them is expressed as: in, and The direct propagation path between them, τ C,link The virtual link delay follows a unilateral exponential distribution;

[0280] In the evaporation waveguide section, the propagation path can be abstracted as two-dimensional propagation. In the single-link model, the calculation formula is:

[0281]

[0282] Virtual link according to and The pitch angle can be divided into four cases:

[0283] Case 1:

[0284] Case 2:

[0285] Case 3:

[0286] Case 4:

[0287] Four situations by Figure 7 As 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 for N is as follows:

[0288]

[0289]

[0290] Where N is an integer within the interval, and K is a constant. for and The straight-line distance;

[0291] Then, calculate the values ​​of l1, l2, and l3 according to Table 3;

[0292] Table 3

[0293]

[0294]

[0295] First, calculate h using the angle and the drone's altitude. Δ and θ Δ Then calculate the values ​​of d1, d2, and d3, and finally calculate the values ​​of l1, l2, and l3.

[0296] Finally, the latency of the virtual link is obtained.

[0297] The channel model is implemented in hardware based on FPGA; including:

[0298] On the hardware side, RISC machines and FPGA technology are used to implement the channel model in hardware;

[0299] ZYNQ is an embedded development system integrating an ARM processor and an FPGA. This simulation system utilizes the Xilinx ZYNQ-7000 heterogeneous multi-core processor chip to build a high-performance computing platform, specifically implemented on the AX7Z100 development board. Figure 8 As shown, ZYNQ includes a Processing System (PS) module and a Programmable Logic System (PL) module; ZYNQ IP core modules can be used for PS module design. The PS runs a dual-core ARM Cortex processor. TM The A9 processor is responsible for scene parameter generation and control tasks, synchronized via a single-ended clock, and directly manages the PS's dedicated DDR3 DRAM memory. The PL implements the FPGA programmable logic to execute channel parameter generation tasks. This part uses differential clock driving technology and includes basic logic units such as LUTs and flip-flops. The PS and PL are connected via an Advanced Extensible Interface (AXI) bus. Scene parameters generated by the ARM are transmitted via UART 0, and channel parameters processed by the FPGA are transmitted via RS485. This clock-independent design with data interoperability allows the ARM to efficiently handle data generation tasks, while the FPGA focuses on parallel computing. They form a complete collaborative workflow for ARM parameter generation and FPGA channel processing, fully leveraging the performance advantages of the PS-PL heterogeneous architecture.

[0300] Figure 9This is the overall architecture of a channel simulator based on ARM and FPGA. The computational tasks for dynamic channel modeling are allocated through the ARM and FPGA-based channel simulator.

[0301] After the path distance calculation is performed within the ARM processor, the parameters are transmitted to the FPGA to calculate the sine parameter, cosine parameter, and delay; the exponential part in equation (4) The exponent part in equation (5) The exponent part in equation (6) The CIR is obtained by simplifying using Euler's formula; it is worth noting that the cluster birth and death process is implemented entirely through hardware integration, and Euler's formula is expressed as:

[0302] e iθ =cosθ+i sinθ (40);

[0303] Where e represents the base of the natural logarithm, i represents the imaginary unit, and θ is the parameter to be transformed;

[0304] The ARM processor, synchronized via a single-ended clock, belongs to the PS (Processing System) module of the development board and directly manages the dedicated DDR3 DRAM memory of the PS module. The ARM processor is responsible for leading key dynamic processes and generating scene parameters. First, it configures parameters, including the number of users, motion trajectories, number of antennas, initial cluster size, and birth / death probability.

[0305] Then, each snapshot is traversed; during this process, initial cluster positions and multipath components are randomly generated;

[0306] Next, the cluster disappearance process is dynamically detected and the number, location, and multipath components of new clusters are generated in real time; at the same time, the status of surviving clusters is updated and the path information is recalculated, thereby realizing the control of key dynamic processes.

[0307] After completing these dynamic management and calculation tasks, the distance parameters are integrated, including the cluster position, angle, and distance between the cluster and the transceiver. The distance parameters are transmitted to the PC via UART0, and the PC transmits the distance parameters to the FPGA via the RS485 communication module through the serial port transmission assistant. The FPGA is synchronized by a differential clock and belongs to the PL (Programmable Logic) module of the development board. The PL module is responsible for the channel parameter generation task, and PS and PL are connected through the AXI bus. The FPGA uses its parallel architecture to perform hardware calculations on the massive multipath components received synchronously using lookup tables, triggers, and digital signal processing modules. The FPGA generates channel parameters and stores them in registers. The channel parameters are transmitted to the PC via the serial port transmission assistant. MATLAB is used to substitute the channel parameters into equations (4), (5), and (6) to generate LOS components, sea surface scattering components, and evaporation waveguide scattering components, and finally generate H in equation (1).

[0308] The channel parameter generation algorithm in the FPGA employs the CORDIC (Coordinate Rotating Digital Computer) algorithm to efficiently calculate the path phase and directly output sine / cosine values. Furthermore, the FPGA calculates the propagation delay, ultimately generating the channel parameters required for impulse response modeling. This design ensures accurate simulation of non-stationary channel characteristics through ARM-based dynamic cluster management (realizing cluster birth and death); simultaneously, the CORDIC algorithm used in the FPGA effectively saves hardware resources. The proposed ARM-FPGA-based channel simulator further improves accuracy while correspondingly reducing hardware resource consumption.

[0309] Finally, the flexibility and programmability of FPGAs can be leveraged to add environmental sensing modules to the hardware, such as temperature and humidity sensors and wind speed sensors. This allows for the real-time capture of marine environmental information and timely transmission to the FPGA, thereby establishing a more accurate channel model and better analyzing channel characteristics.

[0310] Channel statistical characteristic calculation; including:

[0311] Delay power spectral density represents the power distribution of the channel along the time axis. pq (t,τ) is calculated as follows:

[0312]

[0313] in, and This represents the multipath power after correction for the power correlation coefficient; environmental changes can affect the cluster's time delay and power variation, thereby affecting the change in the time delay power spectral density.

[0314] In communication systems, the Space-Time Correlation Function (ST-CF) describes the spatial and temporal correlation of a signal. Specifically, the ST-CF describes the relationship between the received signal power at multiple antenna (or receiver) locations in space and in time. Channel h pq (t) and h p′q′ The spacetime correlation function between (t-Δt) 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 waveguide scattering component, i.e.:

[0317]

[0318] The correlation function for each component is calculated as follows:

[0319]

[0320]

[0321]

[0322] Where, Δξ={Δξ T ,Δξ R}, When Δξ = 0, the time autocorrelation function can be calculated; when Δt = 0, the spatial cross-correlation function can be calculated.

[0323] In communication systems, the stationary interval (SI) refers to a time interval in the channel during which the channel's statistical characteristics remain unchanged. It represents the maximum time interval during which the channel satisfies the wide stationarity assumption. This paper chooses the spectral divergence method to calculate the stationary interval. The stationary interval is defined as the maximum time interval during which the absolute value of the Doppler spread variation is less than a given threshold, i.e.:

[0324] T η =max{Δt|ε(Δt)≥η} (47);

[0325] in,

[0326] Root Mean Square Delay Spread (RMSDS) is a measure of delay dispersion caused by differences in signal propagation path delays in a wireless communication channel. It is an indicator of the variation in signal delay over time and is commonly used to describe the non-uniformity of signal transmission delay caused by multipath propagation. The specific formula is:

[0327]

[0328] In multi-user communication, channel collinearity among different users needs to be considered. Channel matrix collinearity (CMC) is a measure of the spatial structure variation between two matrices of the same size. It compares the subspaces of two complex matrices to determine their similarity. The CMC of channel matrix collinearity between two users is calculated as follows:

[0329]

[0330] Where tr{·} denotes a matrix operator, ||·|| F Let Frobenius norm be (·). H This indicates a conjugate operation.

[0331] The UAV's trajectory is randomly set based on Markov states, with an initial velocity of 5 m / s and zero acceleration. The unmanned surface vessel's trajectory is set based on a smooth turn, with an initial velocity of 5 m / s and zero acceleration. Both trajectories are uniformly sampled in the time domain. Simulations are performed for channels that do not consider evaporation waveguide effects and those that do. Figure 10 This is a schematic diagram of the power time delay spectrum; the horizontal axis represents the time delay axis, and the vertical axis represents the power. Figure 10 (a) shows the power delay spectrum without considering the evaporation waveguide; Figure 10 (b) shows the power delay spectrum considering the evaporation waveguide. It is observed that the introduction of the evaporation waveguide significantly increases the number of clusters and enriches the cluster birth and death processes. The power delay spectrum can reflect the trajectory characteristics of UAVs and unmanned surface vessels, as well as the changes in cluster birth and death. Experimental results show that this model can effectively simulate the unsteady wireless channel under different trajectories of UAVs and unmanned surface vessels in a maritime scenario.

[0332] The time autocorrelation function and spatial cross-correlation function can be obtained by setting the antenna spacing and time interval to 0, respectively, i.e., Δt = 0 or Δd = 0. The simulation parameters are set to a UAV flight speed of 8 m / s² and an acceleration of 3 m / s². 2 The ship's speed is 5 m / s and its acceleration is 3 m / s². 2 The simulation time is 2.5 seconds. Figure 11This displays the time autocorrelation function at different times and frequencies for this trajectory. The horizontal axis represents the time interval Δt, and the vertical axis represents the autocorrelation. The blue line represents the time interval Δt, and the different colored curves represent the autocorrelation function at different times and carrier frequencies, respectively. Figure 11 It can be observed that the time autocorrelation function differs at different frequencies. Increasing the center frequency leads to a faster decrease in the autocorrelation function, which is consistent with the frequency-dependent trend of the channel autocorrelation function in other scenarios. The drone and unmanned surface vessel were set to accelerate to observe the changes in time-varying characteristics as speed increases. As the speed increases, the channel time correlation gradually decreases. Furthermore, the figure shows that the autocorrelation characteristics differ at different times, leading to the conclusion that the channel is non-stationary. Figure 12 This diagram illustrates the autocorrelation function at different times and wind speeds; the horizontal axis represents the time interval Δt, and the vertical axis represents the autocorrelation. Different colored curves represent the autocorrelation function at different times and wind speeds. It can be seen that as wind speed increases, the rate of decrease in the cross-correlation function accelerates. This is because as wind speed increases, the undulation of ocean waves increases, and the change in ship height increases, leading to a decrease in the channel's autocorrelation. Figure 13 This is a schematic diagram of the 3D autocorrelation function; the horizontal axis represents the time interval Δt, and the vertical axis represents the time axis. Figure 13 (a) is a schematic diagram of the 3D autocorrelation function for a wind speed of 5 m / s; Figure 13 Figure (b) shows a schematic diagram of the 3D autocorrelation function at a wind speed of 10 m / s; it can be seen that the autocorrelation function of the channel changes after the wind speed changes. Furthermore, the theoretical results calculated according to the formula fit well with the simulation results obtained by calculating the channel impulse response at time t, which proves the correctness of the simulation results.

[0333] Figure 14 This diagram illustrates the cross-correlation function under different antenna orientations; the horizontal axis represents the time interval Δd, and the vertical axis represents the cross-correlation function. Different colored curves represent the cross-correlation function under different antenna orientations, demonstrating that antenna orientation has a significant impact on the spatial cross-correlation function. When the antenna is oriented towards the main direction of the scatterer distribution, the spatial correlation decreases. Figure 15 This diagram illustrates the cross-correlation function under different wind speeds; the horizontal axis represents the time interval Δd, and the vertical axis represents the cross-correlation function. Different colored curves represent the cross-correlation function under different wind speeds. An unmanned surface vessel (USV) approaches a stationary UAV at a speed of 20 m / s. As the UAV approaches the USV, the influence of ocean waves on the spatial cross-correlation function becomes increasingly significant, and the observed fluctuations become more pronounced. This enhanced effect is due to the closer distance between the transmitter and receiver, which strengthens the influence of ocean waves on the channel, thereby amplifying the observable trend and fluctuations of the spatial cross-correlation function. Furthermore, from... Figure 13 It can be seen that the increase in wind speed leads to more obvious fluctuations in the spatial cross-correlation function.

[0334] Figure 16 This is a schematic diagram of the stationary interval; the horizontal axis represents the stationary interval and the vertical axis represents the probability value. The curves of different colors represent the stationary intervals under different humidity, wind speed, and sea-air temperature differences. Figure 16 (a) represents the stationary interval under different humidity levels; Figure 16 (b) shows the steady intervals at different wind speeds; Figure 16 In the middle (c), the stationary interval is under different air-sea temperature differences. It can be seen that as the humidity increases, the stationary interval of the channel tends to increase. This is because as the humidity increases, the evaporation effect is continuously weakened, thereby weakening the evaporation waveguide, reducing the trapping angle of the cluster, weakening the dispersion of the cluster, and strengthening the stationary interval. Figure 16 (b) shows the stationary intervals at wind speeds of 5 m / s, 10 m / s, and 15 m / s. As the wind speed increases, the stationary intervals decrease. This is because the increase in wind speed leads to larger wave undulations and enhanced evaporation waveguide effect, resulting in a larger trap angle and more dispersed clusters, which in turn causes the stationary intervals to gradually decrease. Figure 16 Figure (c) shows the stationary interval for air-sea temperature differences of 0.1K, 1K, and 2K. It can be seen that the stationary interval gradually decreases as the air-sea temperature difference increases. This is because with increasing temperature difference, evaporation intensifies, waveguide effects increase, the trapping angle of clusters increases, and the cluster distribution becomes more dispersed, thus reducing the stationary interval. Therefore, it can be seen that in channel modeling, the degree of cluster dispersion directly affects the channel's stationary interval. Generally speaking, the more dispersed the clusters, the smaller the channel's stationary interval becomes. This is because dispersed clusters lead to more significant spatial and temporal variations in the channel's statistical characteristics, thereby shortening the channel's stationary interval.

[0335] Figure 17 This is a schematic diagram of the root mean square time delay spread; the horizontal axis represents the root mean square time delay spread, the vertical axis represents the probability value, and the different colored curves represent the root mean square time delay spread under different humidity, wind speed, and sea-air temperature difference. Figure 17 In the middle (a), the root mean square time delay spread is shown under different humidity levels; Figure 17 (b) shows the root mean square time delay spread at different wind speeds; Figure 17 In the middle (c), the root mean square delay spread is shown under different air-sea temperature differences. It can be seen that as the humidity increases, the root mean square delay spread of the channel tends to decrease. This is because as the humidity increases, the evaporation effect is continuously weakened, thereby weakening the evaporation waveguide, reducing the trapping angle of the cluster, making the cluster distribution more concentrated, and thus reducing the root mean square delay spread. Figure 17Figure (b) shows the root mean square time delay spread at wind speeds of 5 m / s, 10 m / s, and 15 m / s. As the wind speed increases, the root mean square time delay spread increases. This is because the increase in wind speed leads to larger wave undulations and enhanced evaporation waveguide effect, resulting in a larger trap angle and more dispersed clusters, which in turn causes the root mean square time delay spread to gradually increase. Figure 17 Image (c) shows the root mean square (RMS) time delay spread for air-sea temperature differences of 0.1 K, 1 K, and 2 K. It can be seen that the RMS time delay spread gradually increases with the increase of the air-sea temperature difference. This is because with the increase of the temperature difference, evaporation intensifies, the waveguide effect increases, the trapping angle of the clusters increases, and the cluster distribution becomes more dispersed, thus increasing the RMS time delay spread. This demonstrates that humidity, temperature, and wind speed all affect the changes in evaporative waveguides, thereby altering the signal propagation path and consequently changing the time delay.

[0336] The unmanned aerial vehicle (UAV) maritime channel measurement activity was conducted on the coast near the Qingdao campus of Shandong University in Qingdao, Shandong Province, China. A Universal Software Radio Peripheral (USRP) X310 was used as both transmitter and receiver, along with a DJI M600 Pro UAV equipped with an omnidirectional antenna. Figure 18 This is a scene diagram; Figure 18 (a) shows the measurement scene. Figure 18 Figure (b) shows a schematic diagram of the transmitter; the carrier frequency is set to 3.5 GHz, the sampling rate to 200 MHz, and the bandwidth to 100 MHz. The transmitter moves in a straight line at a constant speed of 5 m / s. This activity simulates a ship docked at the shore, with the receiver fixed at the shore. The initial distance between the transmitter and receiver is 20 m, and the transmitter's position height is 20 m. During the measurement, the ambient temperature is 4.56℃, the wind speed is 5 m / s, and the relative humidity is 80% RH. The USRP transmits a PN sequence, which is received by the receiver. The transmitter continuously transmits the PN sequence, and the receiver samples once every 0.1 s, with each sample lasting 2 ms. The received signal is directly correlated with the calibration signal directly connected to the transceiver to obtain the useful signal. Then, by comparing the Fourier transform of the useful signal with the Fourier transform of the directly connected signal and performing an inverse Fourier transform, the CIR is obtained. The root mean square delay spread is obtained. The simulation is performed using the same parameters as the measurement activity. Figure 19 This is a fitting graph of measurement and simulation results; the horizontal axis represents the root mean square time delay spread, and the vertical axis represents the probability value. The red line represents the simulated value, and the blue line represents the measured value. Figure 19 As can be seen, the measurement results are in excellent agreement with the simulation results, thus verifying the accuracy of the model. Figure 20 This diagram illustrates the CMC (Combined Control Center) at different UAV altitudes and distances from unmanned surface vessels (USVs). The horizontal axis represents the distance of other USVs from the first USV, and the vertical axis represents the CMC value. Different colored curves represent different UAV altitudes. Figure 21 This is a diagram illustrating CMC at different wind speeds; the horizontal axis represents the distance between other unmanned surface vessels and the first unmanned surface vessel, and the vertical axis represents the CMC value. Different colors represent the CMC at different wind speeds. Figure 22 This diagram illustrates the CMC (Combat Control) at different USV speeds. The horizontal axis represents the distance between other unmanned surface vessels (USVs) and the first USV, while the vertical axis represents the CMC value. Different colors represent the CMC at different USV speeds.

[0337] Example 3

[0338] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the novel 6G maritime unmanned system environmental perception channel modeling method described in Embodiment 1 or 2.

[0339] Example 4

[0340] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the novel 6G maritime unmanned system environmental perception channel modeling method described in Embodiment 1 or 2.

[0341] Example 5

[0342] A novel 6G maritime unmanned system environmental perception channel modeling system includes:

[0343] The channel model construction module is configured to: build a communication framework for maritime unmanned systems consisting of UAVs to multiple unmanned vessels and unmanned vessels to each other.

[0344] The channel coefficient generation method construction module is configured to: generate channel impulse response, generate UAV random trajectory, generate unmanned surface vessel random trajectory, generate evaporative waveguide model, generate small-scale parameters, and update small-scale parameters;

[0345] The hardware implementation module is configured as follows: FPGA-based hardware implementation to build a channel simulator system;

[0346] The channel statistical characteristics calculation module is configured to include: channel statistical characteristics such as time delay power spectral density, space-time correlation function, stationary interval, root mean square time delay spread, and channel matrix collinearity.

Claims

1. A novel 6G offshore unmanned system environment perception channel modeling method, characterized in that, The application relates to a method for constructing a channel simulator for a maritime unmanned system, and belongs to the technical field of channel simulation. Step 1: constructing a maritime unmanned system communication framework composed of a maritime unmanned aerial vehicle and a plurality of unmanned ships or unmanned ship-to-unmanned ship systems; Step 2: constructing a channel coefficient generation method, including generating a channel impulse response, generating a random unmanned aerial vehicle trajectory, generating a random unmanned ship trajectory, generating an evaporation waveguide model, and updating small-scale parameters; Step 3: FPGA-based hardware implementation, constructing a channel simulator system; Step 4: channel statistical property calculation: channel statistical properties include time delay power spectral density, space-time correlation function, stationary interval, root mean square time delay spread and channel matrix collinearity.

2. The novel 6G offshore unmanned system environment perception channel modeling method according to claim 1, characterized in that, The application relates to a method for constructing a channel simulator for a maritime unmanned system, and belongs to the technical field of channel simulation. The marine unmanned aerial vehicle-to-multiple unmanned ship communication system framework comprises an unmanned aerial vehicle transmitting end Tx, an unmanned ship receiving end Rx, and an unmanned ship USV i ; The number of antenna elements in Tx and Rx are M T and denotes; The sea wave scattering path serial number and the evaporation waveguide scattering path serial number are respectively represented by n w and n d ; and represent the total serial number of the sea wave scattering path and the total serial number of the evaporation waveguide scattering path, and the superscript represents the communication link between the pth Tx antenna unit and the qth Rx antenna unit. The scattering of the sea surface and evaporation duct to the signal is abstracted into two types of clusters, respectively, sea surface cluster and and evaporation duct cluster and Tx transmits the signal through the antenna array, a part of which directly reaches Rx, and another part of which reaches Rx after being reflected by the first-hop cluster and the last-hop cluster. The multiple bounces in between are abstracted as virtual links. The time delay of the virtual link is set to 0, which represents single-hop. In the process of signal transmission, the communication link can only be established on the premise that the antenna units of Tx and Rx are visible at the same time. The distance from the Tx antenna to the mth scatterer of the cluster and is and Rx i antenna to the mth scatterer of the cluster and is and 3. The novel 6G offshore unmanned system environment perception channel modeling method according to claim 1, characterized in that, Generating a channel impulse response includes: The impulse response matrix H of the maritime communication channel is expressed as: H = [H 1 H 2 ...H i ] (1); where H is a five-dimensional matrix, H i is the CIR matrix of the communication link between the UAV i and the USV where PL denotes path loss, SH denotes shadowing, and is the small-scale CIR for the corresponding antenna, n T and denotes the two-dimensional size of the matrix. Where, denotes the CIR between antenna and antenna , i.e.: where K R represents the Rician K-factor, S1 and S2 represent the power correlation coefficients, satisfying S1+S2=1, respectively represent the CIRs of the LoS component, the sea surface scattering component and the evaporation duct reflection component; LoS component is obtained from the equation: where f c denotes the carrier frequency, F p / q,H and F p / q,V represent the horizontal and vertical polarized antenna patterns of the transceiver, is an initial phase subject to a uniform distribution over (0, 2π], is the time delay of the time-varying LoS path; sea surface scattering component is given by: Evaporation duct scattering component is given by wherein and is the cross-polarization power ratio of the NLoS component, is an initial phase that obeys a uniform distribution over (0, 2π], and is the time-varying power of the mth ray in the nth cluster between the pth Tx antenna element and the ith receive end qth Rx antenna element, w or the mth ray in the nth cluster, d and is the corresponding time delay;​ Further preferably, the random unmanned aerial vehicle trajectory is generated, and includes: In single-user communication scenarios, a Markov random process is used to generate the random trajectory of the UAV, setting random azimuth and pitch angles to ultimately simulate the random trajectory of the UAV; where the random variable sequence is set as X = {X0, X1, ..., X...}. n }, X n The state depends only on X n-1 The state, that is: P(X i |X i-1 0) = P(X i |X i-1 0), i = 1, 2,... (7); where P(X i |X i-1 ) is the transition probability distribution. Further preferably, the random unmanned ship trajectory is generated, and includes: The x-y plane trajectory of the ship adopts a smooth turning model, which can be used in the case that the ship or a vehicle has less sharp turns. Since the height of the ship changes with the height of the sea wave, a three-dimensional sea wave spectrum is introduced to describe the fluctuation of the sea wave, and the sea wave height at a point is: where a i , w i , ε i and ψ i are 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 wave number of the deep-water wave and is expressed as g is the acceleration of gravity; a i is expressed as: where S(w i , ψ i ) is the sea wave spectrum, Δw is the frequency interval between the component waves, and Δψ is the directional angle interval of the component waves. S(w i , ψ i ) can be expressed as the product of the sea wave spectrum S(w) and the directional spectrum D(w, ψ), i.e.: S(w,psi) = S(w)D(w,psi) (10); The formulas of the spectrum S(w) and the directional spectrum D(w,psi) are: where b1= 8.1 x 10 -3 , b2= 0.74, is a dimensionless constant, U is the wind speed at 19.5 meters above sea level, c1= 0.5 + 0.82 Q(w), c2= 0.32 Q(w), and Q(w) = exp[-(wU / g) 4 / 2] 4. The novel 6G offshore unmanned system environment perception channel modeling method according to claim 1, characterized in that, Generating an evaporation waveguide model includes: First, the overall Richardson number R is calculated ib : where z observation is the height of observation, T a and T s are the air temperature and sea surface temperature, respectively, at the height of observation; and U observation is the wind speed at the height of observation. Next, the Monin-Obukhov length L is calculated: In the formula, Gamma is an empirical profile coefficient; Subsequently, the difference N between the refractive index of the sea surface atmosphere and the refractive index at the edge of the sea and atmosphere is calculated p i.e.: N p = N a - N s (15); where N a is the refractive index of air at the ocean surface, N s is the refractive index at the ocean-atmosphere boundary, T ok and T sk are the air humidity at the ocean surface and the relative humidity at the ocean surface, e and e s are the water vapor pressure in the atmosphere and the water vapor pressure in the ocean surface sea water, and are calculated as follows: By R ib The environment stability is classified, when 0≤R ib ≤1, the environment is stable state; when R ib <0, the environment is unstable state; the evaporation waveguide height h d is calculated as follows: where A = -0.125 x B / N p , B = ln(z observation / z0) - Ψ, z0 is an aerodynamic surface roughness parameter; Finally, the atmospheric correction refractive index is obtained: In the formula, M s is the modified refractive index of the sea surface, and the formula (21) and the formula (22) are solved by using Newton iteration method Further preferably, the small-scale parameter is generated; comprising: A. Angle generation of clusters Upper and lower limits of the entrapping angle The calculation is: Wherein, n(z) is the refractive index, n(0) approximately equal to 1.00035, which is the refractive index of the sea level, According to the trapping angle, the angles of the evaporation waveguide scattering cluster and the sea surface scattering cluster can be divided, and it is assumed that the angles of the Tx section and the Rx end scattering cluster obey a truncated Gaussian distribution, that is: where θ denotes the angle of the cluster, μ θ and σ θ are the respective mean and angular spread, Φ is the standard normal cumulative distribution function; B. Cluster position generation initial time instant cluster the center position of which is: The x and y coordinates of the cluster are obtained, and then the coordinates are brought into the sea wave spectrum, that is:

5. The novel 6G offshore unmanned system environment perception channel modeling method according to claim 1, characterized in that, Updating small-scale parameters includes: Let X, Y denote different parameters, X e {A, Z}, Y e {n w ,n d} and the position of the scattering cluster at time t is given by: According to the positions of the transmitting and receiving antennas relative to the cluster, the distance vector between the antenna and the cluster is obtained, that is: According to the distance vector, the incident angle and the exit angle are determined: where arctan2 denotes the inverse tangent function for the fourth quadrant; Z e {T, p, R, q}, The sum of the time-varying distances from the transceiver to the corresponding scattering clusters is denoted as For the calculation of time delay, in the ocean scattering part, the nth... d The formula for calculating the m-th ray in a scatterer is: wherein, is and the virtual link delay between and, expressed as: wherein, is and the direct propagation path between and, τ C,link is the virtual link delay subject to a one-sided exponential distribution; In the evaporation waveguide part, the propagation path can be abstracted as a two-dimensional propagation, and in the single-link model, the calculation formula is: The virtual link is divided into four cases according to and the pitch angle. Case 1: Case 2: Case 3: Case 4: and The propagation distance between the points and is l = li + Nli2+ l3, where N is the number of reflections of the ray in the waveguide layer, and N is calculated as follows: where N is an integer in the interval, K is a constant, is and the straight-line distance; The values of l1, l2 and l3 are calculated; Finally, the delay of the virtual link is obtained 6. The novel 6G offshore unmanned system environment perception channel modeling method according to claim 1, characterized in that, FPGA-based hardware implementation of the channel model includes: ZYNQ includes a processing system module PS and a programmable logic module PL; PS runs dual-core ARM Cortex TM The -A9 processor is responsible for scene parameter generation control task, synchronized through single-ended clock, and directly manages the special DDR3 DRAM memory of the PS; the PL realizes FPGA programmable logic to execute channel parameter generation task; the PS and the PL are connected through the advanced extensible interface bus, the scene parameters generated by the ARM are transmitted through the UART 0, and the channel parameters processed by the FPGA are transmitted through the RS485; The channel simulator based on ARM and FPGA allocates computing tasks for dynamic channel modeling; After performing path distance calculation within the ARM processor, the parameters are transferred to the FPGA to calculate the sine parameters, cosine parameters, and time delay; the exponential part in equation (4) the exponential part in equation (5) and the exponential part in equation (6) simplified by Euler's formula to obtain the CIR; the birth and death process of the cluster is completely realized by hardware integration, and Euler's formula is expressed as: e iθ = cos θ + isin θ (40); Wherein, e represents the base of natural logarithm, i represents the imaginary unit, and theta is a parameter to be transformed; Firstly, parameters are configured, including the number of users, the motion trajectory, the number of antennas, the initial cluster number, the birth and death probability; Subsequently, each snapshot is traversed; in the process, the initial cluster position and the multipath component are randomly generated; Then, the cluster disappearance process is dynamically detected, and the number, position and multipath component of the new cluster are generated in real time; meanwhile, the state of the surviving cluster is updated and the path information is recalculated, so that the control of the key dynamic process is realized. Integrate distance parameters, distance parameters include the location of the cluster, angle, cluster and transceiver distance; the distance parameters are transmitted to the PC end, and the PC end transmits the distance parameters to the FPGA through a serial transmission assistant; the FPGA is synchronized through differential clock, is responsible for channel parameter generation task, and the PS and the PL are connected through an AXI bus; the FPGA utilizes lookup table, flip-flop and digital signal processing module to perform hardware calculation on the massive multipath components received through synchronization by virtue of its parallel architecture; the FPGA generates channel parameters and stores them in a register, transmits the channel parameters to the PC end through a serial transmission assistant, and uses MATLAB to generate the LOS component, the sea surface scattering component and the evaporation duct scattering component by substituting the channel parameters into formula (4), formula (5) and formula (6) respectively, and finally generates H in formula (1).

7. The novel 6G offshore unmanned system environment perception channel modeling method according to any one of claims 1-6, characterized in that, Channel statistical property calculation; including: The delay power spectral density represents the power distribution of the channel on the time axis, and the delay power spectral density Λ pq (t,τ) is calculated as: wherein, and is the power-dependent coefficient-corrected multipath power; Channel h pq (t) and h p′q′ The space-time correlation function between h(t) and h(t-Δt) 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: Wherein, the correlation function of each component is calculated as: where Δξ = {Δξ T , Δξ R}, The time autocorrelation function is calculated when Δξ = 0 and the spatial cross-correlation function is calculated when Δt = 0. 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: T η = max {At | ε(At) > η} (47); wherein, Root mean square delay spread refers to a measure of the spread of delays caused by differences in the signal propagation paths in a wireless communication channel; root mean square delay spread The specific formula is: Is the measurement of the spatial structure change between two matrices of the same size, and the channel matrix collinearity CMC between two users is calculated as: where tr{•} denotes the matrix operator, ||•||F F denotes the Frobenius norm, (•) H denotes the conjugate operation.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor implements the steps of the novel 6G marine unmanned system environment perception channel modeling method of any one of claims 1-7 when executing the computer program.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the novel 6G marine unmanned system environment perception channel modeling method of any one of claims 1-7.

10. A novel 6G offshore unmanned system environment perception channel modeling system, characterized by, Including: The channel model construction module is configured to build a marine unmanned system communication framework composed of a marine unmanned aerial vehicle and multiple unmanned ships or unmanned ships: The channel coefficient generation method construction module is configured to generate a channel impulse response, generate a random trajectory of the unmanned aerial vehicle, generate a random trajectory of the unmanned ship, generate an evaporation duct model, generate small-scale parameters, and update the small-scale parameters; The hardware implementation module is configured to be based on FPGA hardware implementation and build a channel simulator system; The channel statistical property calculation module is configured to include time delay power spectral density, space-time correlation function, stationary interval, root mean square time delay spread and channel matrix collinearity.

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