Dynamic channel modeling method for unmanned ship in offshore water surface environment

Abstract

The invention relates to an unmanned ship dynamic channel modeling method in an offshore water surface environment, and the method comprises the steps: constructing a three-dimensional offshore water surface simulation scene which comprises a sea surface, a wharf facility, an unmanned ship cluster and a communication node; establishing a wireless channel propagation model based on a ray tracing method, and simulating a propagation mechanism of direct, reflection and diffraction paths; establishing a motion characteristic model of the unmanned ship under the action of waves, and obtaining six-degree-of-freedom pose data of the unmanned ship; generating multiple frames of scene copies in batches by adopting an intelligent batch processing dynamic reconstruction method; dynamically correcting an antenna pattern in each frame of scene copy according to the pose data so as to couple the influence of hull motion on antenna radiation characteristics; importing the corrected antenna pattern into the wireless channel propagation model to generate a dynamic channel response; simulation is executed, and channel data obtained through simulation are collected and analyzed. According to the method, the motion characteristics of the unmanned ship in waves and the wireless channel propagation mechanism can be accurately coupled, and the accuracy of dynamic channel prediction is improved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a dynamic channel modeling method for unmanned vessels in near-shore water environments. Background Technology

[0002] With the deepening of marine development, unmanned surface vessels (USVs) are increasingly widely used in near-shore operations due to their small size, intelligence, and multi-purpose capabilities. As the complexity of tasks increases, single-vessel operations are no longer sufficient, and swarm collaborative operations are becoming a development trend. Stable and reliable communication is a prerequisite for USV swarm collaborative operations, and accurate channel models are the foundation for communication system design, theoretical analysis, and performance evaluation and optimization.

[0003] The marine environment is dynamic and ever-changing; factors such as waves and wind speed significantly affect the propagation characteristics of wireless signals. Currently, surface wireless channel modeling mainly employs statistical measurement methods and deterministic models. Statistical measurement methods acquire channel characteristics through a large amount of measured data; while simple and intuitive, they are resource-intensive and have poor model generalization ability. Deterministic models (such as ray tracing) are based on electromagnetic wave propagation theory and utilize specific environmental information for prediction, offering high accuracy but typically requiring significant computational resources.

[0004] Existing ocean channel modeling research, such as the Modified Earth Loss (REL) model proposed by Zhang Jing et al., or the maritime line-of-sight channel model based on random ray tracing proposed by RuiDing et al., considers more the reflection and scattering characteristics of seawater, while generally neglecting or simplifying the motion characteristics of the vessel itself. For small unmanned surface vessels (USVs) operating in nearshore waters, their small size and close proximity of the antenna to the water surface mean that the six degrees of freedom motion of the vessel caused by waves (roll, pitch, bow, heave, sway, and sway) significantly alters the attitude and spatial position of the antenna, thus having a non-negligible impact on key channel parameters such as path loss, delay spread, and Doppler shift. Traditional static or quasi-static channel models are unable to accurately characterize this dynamic multipath effect and are no longer suitable for the design of high-reliability USV cooperative networking communication systems.

[0005] Therefore, there is an urgent need for a wireless channel modeling method for unmanned vessels that can couple the motion characteristics of the vessel and accurately reflect the dynamic environment of the nearshore area. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a dynamic channel modeling method for unmanned vessels in near-shore water environments. This method can accurately couple the motion characteristics of unmanned vessels in waves with the wireless channel propagation mechanism, improve the accuracy of dynamic channel prediction, and provide reliable theoretical support for the design and optimization of unmanned vessel swarm communication systems.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a dynamic channel modeling method for unmanned vessels in near-shore water environments, comprising the following steps:

[0008] S1: Construct a three-dimensional near-shore water surface simulation scene, including the sea surface, dock facilities, unmanned vessel clusters and communication nodes;

[0009] S2: Establish a wireless channel propagation model based on the ray tracing method to simulate the propagation mechanisms of direct, reflected, and diffracted paths;

[0010] S3: Establish a motion characteristic model of the unmanned vessel under wave action and obtain the six-degree-of-freedom pose data of the unmanned vessel;

[0011] S4: Employs an intelligent batch processing dynamic reconstruction method to generate multiple frame scene copies in batches;

[0012] S5: Dynamically correct the antenna pattern in each frame of the scene replica based on the pose data to couple the effect of the ship's motion on the antenna radiation characteristics.

[0013] S6: Import the corrected antenna pattern into the wireless channel propagation model to generate a dynamic channel response;

[0014] S7: Perform simulation, collect and analyze channel data obtained from the simulation, including path loss, delay spread and Doppler characteristics.

[0015] Furthermore, in step S2, the dominant propagation mode of the wireless channel propagation model is represented by a time-varying multipath channel model:

[0016]

[0017] Where h(τ,t) is the channel impulse response at time t, and represents the complex gain at time delay t; K(t) is the time-varying multipath number at time t; α k (t) represents the complex amplitude of the k-th path at time t; f d,k θ represents the Doppler frequency shift of the k-th path; k (t) represents the phase offset of the k-th path at time t; δ(‧) is the Dirac delta function, representing the phase offset of each path at time delay τ. k There is energy at (t);

[0018] The path loss model of the wireless channel propagation model is expressed as:

[0019]

[0020] in, This represents the path loss at a distance d, where d is the distance between the transmitter and receiver, λ is the wavelength, and N is the wavelength. ref N represents the number of reflection paths. difL is the number of diffraction paths. ref,i For the first Reflection loss of each reflection path, L dif,j For the first Diffraction loss of a diffraction path.

[0021] Furthermore, the single reflection loss L ref Calculated using Fresnel coefficients:

[0022]

[0023] Where θ is the angle of incidence relative to the normal to the reflecting surface. The dielectric constant of the sea surface;

[0024] Single diffraction loss L dif Calculated using the UTD correction formula:

[0025]

[0026] Where k is the wave number, k = 2π / λ; The diffraction factor is . The angle of diffraction; The diffraction wedge angle parameter.

[0027] Furthermore, in step S3, the motion characteristic model is represented by Fossen's nonlinear dynamic equations:

[0028]

[0029] Among them, M total The total mass matrix consists of the hull mass matrix and the empirical additional mass matrix. Let η be the acceleration vector; η = [x, y, z, φ, θ, ψ] T The pose vector contains the three-dimensional position (x, y, z) and Euler angles, including roll φ, pitch θ, and yaw ψ; v = [u, v, w, p, q, r] T D is a velocity vector containing linear velocity (u, v, w) and angular velocity (p, q, r). v G is a linear damping matrix that reflects fluid dynamic losses. η The restoring force matrix in still water represents the balance between buoyancy and gravity; τ wave The wave excitation force is modeled as a simple harmonic wave:

[0030]

[0031] Where ω is the wave frequency.

[0032] Further, in step S4, the intelligent batch processing dynamic reconstruction method includes:

[0033] Based on the motion characteristic model of unmanned ships under wave action, multiple sets of pose data of unmanned ships are generated by MATLAB script;

[0034] Batch generate and run multi-frame scene copies in Wireless InSite.

[0035] Furthermore, in step S4, the antenna pattern in each frame of the scene replica is dynamically corrected using the generated pose data of multiple sets of unmanned vessels; the dynamic correction of the antenna pattern is achieved through a rotation matrix:

[0036]

[0037] in, and These are the roll and pitch angles of the current frame, respectively; G0 is the horizontal reference radiation pattern, reflecting the directivity characteristics of the antenna in a stationary horizontal state; R x (‧) and R y (‧) are the roll and pitch rotation matrices, respectively, defined as:

[0038] .

[0039] Furthermore, step S7 also includes multi-band comparative analysis, comparing the path loss and coverage of the 433MHz and 2.4GHz bands in the same scenario.

[0040] Furthermore, step S7 includes analyzing the impact of ambient humidity on the channel and comparing the changes in received power under different humidity conditions through simulation.

[0041] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the above-described method.

[0042] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0043] Compared with existing technologies, this invention has the following advantages: It provides a dynamic channel modeling method for unmanned surface vessels (USVs) in nearshore waters. By coupling a six-degree-of-freedom motion model of the USV with ray tracing, the antenna pattern is dynamically corrected, enabling the channel model to accurately reflect the actual impact of hull sway on signal propagation. The deviation between path loss prediction and measured data is better than that of traditional models. Simultaneously, an intelligent batch processing dynamic reconstruction method is adopted to achieve end-to-end automated mapping from the kinematic model to the simulation scenario, significantly improving the simulation efficiency of large-scale scenarios. This method is particularly suitable for long-term, multi-condition research on marine channel characteristics. Furthermore, this invention clearly reveals the intrinsic mechanism by which wave fluctuations affect the stability of communication links by changing the hull attitude, providing technical support for the design of antenna attitude compensation algorithms, frequency band optimization, and anti-multipath protocol optimization for nearshore USV self-organizing networks. Attached Figure Description

[0044] Figure 1 This is a flowchart of the dynamic channel modeling method for unmanned vessels in near-shore waters provided in this embodiment of the invention;

[0045] Figure 2 This is a three-dimensional near-sea surface simulation scene constructed in this embodiment;

[0046] Figure 3 This is a schematic diagram of the wireless channel and ray tracing principle of the unmanned vessel in a near-sea surface environment in this embodiment;

[0047] Figure 4 This is a schematic diagram of the simulated attitude of the unmanned vessel with six degrees of freedom in an embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of the dynamic motion position of the unmanned vessel in different frames in an embodiment of the present invention;

[0049] Figure 6 This is a comparison diagram of the antenna radiation pattern before and after correction in an embodiment of the present invention;

[0050] Figure 7 This is a schematic diagram of the Rx Grid spatial sampling system in an embodiment of the present invention;

[0051] Figure 8 These are the channel route map and heat map at the initial moment of the dynamic ship-static shore nearshore simulation model in this embodiment of the invention. Detailed Implementation

[0052] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0053] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0054] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0055] like Figure 1 As shown in the figure, this embodiment provides a method for dynamic channel modeling of unmanned surface vessels in nearshore water environments, including the following steps:

[0056] S1: Construct a three-dimensional near-shore water surface simulation scene, including the sea surface, dock facilities, unmanned vessel clusters and communication nodes;

[0057] S2: Establish a wireless channel propagation model based on the ray tracing method to simulate the propagation mechanisms of direct, reflected, and diffracted paths;

[0058] S3: Establish a motion characteristic model of the unmanned vessel under wave action and obtain the six-degree-of-freedom pose data of the unmanned vessel;

[0059] S4: Employs an intelligent batch processing dynamic reconstruction method to generate multiple frame scene copies in batches;

[0060] S5: Dynamically correct the antenna pattern in each frame of the scene replica based on the pose data to couple the effect of the ship's motion on the antenna radiation characteristics.

[0061] S6: Import the corrected antenna pattern into the wireless channel propagation model to generate a dynamic channel response;

[0062] S7: Perform simulation, collect and analyze channel data obtained from the simulation, including path loss, delay spread and Doppler characteristics.

[0063] The following section provides further explanation of the relevant aspects of this method.

[0064] 1. Scene Construction and Parameter Setting

[0065] Build a 3D scene of an offshore dock in Wireless InSite that is 1000m long and 800m wide, including a concrete platform, warehouse, control tower and static water surface.

[0066] The system consists of one main vessel (transmitter) and eight auxiliary vessels (environmental obstacles). A vertically polarized omnidirectional antenna is mounted on the top of the main vessel, operating in the 433MHz LoRa band with a transmit power of 20dBm. Receiving antennas are installed on shore-based facilities.

[0067] As an environmental obstacle, the auxiliary vessel is moored according to its actual functional zones, and its hull material and structure will affect the scattering and reflection of electromagnetic waves. Receiving antennas are installed on shore-based facilities for signal reception and analysis. Figure 2 This is a three-dimensional near-sea surface simulation scene constructed in this embodiment. Among them, (a) shows the effect of the ship being magnified. In order to more intuitively show the relative positional relationship of each entity in the scene, the actual ship size in the model is about 3m long and 2m wide. The actual scene effect is shown in Figure (b).

[0068] To address the characteristics of different areas in the ship-shore simulation scenario, differentiated ray tracing accuracy settings were adopted: for dock structures, including concrete platforms, logistics warehouses, and control towers, full path analysis was used with a reflection order ≤5; for open sea areas, a simplified model was used with a reflection order ≤3. These parameter settings ensured that the path loss calculation error was ≤1.5dB while keeping the single simulation time within 5 minutes.

[0069] 2. Construction of Wireless Channel Propagation Model

[0070] When creating ray-tracing models in nearshore water environments, it is necessary to effectively represent complex propagation effects such as sea surface reflection, island occlusion, and ship obstacles to provide accurate channel characteristic predictions for unmanned surface vessel (USV) cooperative communication. The principles of USV wireless channels and ray-tracing in nearshore water environments are as follows: Figure 3 As shown, TX is the transmitter and RX is the receiver.

[0071] In nearshore environments, due to changes in sea surface waves and the continuous movement of unmanned vessels, traditional channel models struggle to accurately describe their characteristics and impacts. Therefore, coupled models are needed to analyze the dynamic marine environment and vessel motion characteristics. Channel performance is predicted under different sea states, frequencies, and antenna configurations.

[0072] Maritime radio channels are affected by multiple propagation mechanisms, primarily including direct path (LOS) propagation, specular and diffuse reflection from the sea surface, diffraction from shoreline topography, atmospheric waveguide effects, and scattering from ship structures. The dominant propagation mode of the radio channel propagation model is represented by a time-varying multipath channel model:

[0073]

[0074] Where h(τ,t) is the channel impulse response at time t, and represents the complex gain at time delay t; K(t) is the time-varying multipath number at time t; α k (t) represents the complex amplitude of the k-th path at time t; f d,k θ represents the Doppler frequency shift of the k-th path; k (t) represents the phase offset of the k-th path at time t; δ(‧) is the Dirac delta function, representing the phase offset of each path at time delay τ. kThere is energy at (t).

[0075] A composite path loss model for wireless channel propagation is established by combining the free-space loss, reflection loss, and diffraction loss of the near-sea surface wireless channel, and is expressed as follows:

[0076]

[0077] in, This represents the path loss at a distance d, where d is the distance between the transmitter and receiver, λ is the wavelength, and N is the wavelength. ref N represents the number of reflection paths. dif L is the number of diffraction paths. ref,i For the first Reflection loss of each reflection path, L dif,j For the first The diffraction loss along the diffraction path. Among them, the single reflection loss L... ref Calculated using Fresnel coefficients:

[0078]

[0079] Where θ is the angle of incidence relative to the normal to the reflecting surface. is the dielectric constant of the sea surface.

[0080] Single diffraction loss L dif Calculated using the UTD correction formula:

[0081]

[0082] Where k is the wave number, k = 2π / λ; The diffraction factor is . The angle of diffraction; The diffraction wedge angle parameter.

[0083] 3. Motion coupling modeling of unmanned vessels

[0084] The motion characteristics of unmanned surface vessels (USVs) have a significant impact on their dynamic channel. Therefore, this section will investigate the establishment of a mathematical model for ship motion. The motion of an USV in waves is a complex process, involving translation along three coordinate axes and rotation about those axes, such as... Figure 4 As shown.

[0085] Discretized parameters are used to define the unmanned surface vessel's trajectory. The initial position is set as the origin of the three-dimensional coordinate system, P0=(x0,y0, z0). The displacement in each frame is set as Δx in the positive horizontal direction and Δy in the negative vertical direction, with the vertical direction fixed. The total number of frames is N. The trajectory function uses a linear recursive model. The vessel's position coordinates in the nth frame are:

[0086]

[0087] By combining the signs of the parameters Δx and Δy, the motion modes in any quadrant can be extended, which can then be used to describe changes in the ship's position.

[0088] Because unmanned surface vessels (USVs) have relatively slow speeds and low profiles, their attitude and position during near-shore operations are mainly affected by sea waves. Therefore, the effects of sea waves on the vessel are calculated using wave forces, i.e., the Fossen nonlinear dynamic equations are used to describe the vessel's motion, thus establishing a motion characteristic model for the USV.

[0089]

[0090] Among them, M total M is the total mass matrix. total =M+0.2M, from the hull mass matrix M=diag(m, m, m, I) x I y I z It consists of an empirical added mass matrix and an additional mass matrix, with the added mass being 20% ​​of the hull mass; Let η be the acceleration vector; η = [x, y, z, φ, θ, ψ] T The pose vector contains the three-dimensional position (x, y, z) and Euler angles, including roll φ, pitch θ, and yaw ψ; v = [u, v, w, p, q, r] T D is a velocity vector containing linear velocity (u, v, w) and angular velocity (p, q, r). v G is a linear damping matrix that reflects fluid dynamic losses. η This is the still water restoring force matrix, representing the balance between buoyancy and gravity.

[0091] In this embodiment, a six-degree-of-freedom motion model of the unmanned surface vessel is established using Fossen's nonlinear dynamic equations in MATLAB. The wave excitation force τ is then used to... wave Set to simple harmonic form:

[0092]

[0093] Where ω is the wave frequency.

[0094] Then, the trajectory of the unmanned vessel is planned. Assuming the initial position is (223, 106, 0), the displacement per frame is set to Δx = +2m and Δy = +5m, and 100 position points are generated through a linear recursive model.

[0095] Next, the motion model is run to generate 100 sets of discretized pose data. This includes position and attitude angles (roll φ, pitch θ).

[0096] 4. Intelligent batch processing and dynamic correction of antenna pattern

[0097] After completing the trajectory setting and motion characteristic modeling of the unmanned vessel, this invention proposes a dynamic reconstruction method based on intelligent batch processing, which uses batch scene generation technology to achieve automated deployment of the dynamic simulation process.

[0098] First, based on the initial scene template built in the Wireless InSite simulation platform, 100 independent scene copies of the initial Wireless InSite scene were generated using MATLAB automated scripts, completing the batch generation of 100 snapshots of dynamic ship-static shore nearshore dock scenes. This process ensures that each frame of the scene has consistent initial electromagnetic characteristics, and the dynamic trajectory changes of the unmanned vessel and the changes in the antenna attitude during Wireless InSite simulation are realized through scene transformation. The dynamic trajectory of the unmanned vessel is coupled with the attitude angle changes of the unmanned vessel and antenna in each frame. The 100 sets of pose data and the corrected antenna pattern generated above are automatically injected into the corresponding scene copies, automatically generating 100 dynamic ship-static shore communication scenes with different dynamic unmanned vessel positions. Figure 5 This is a schematic diagram of the scene when the unmanned boat moves to different positions at different times. In the figure, (1) is the motion state of the first frame, indicating that the boat is located at coordinates (223.18, 106.03, 0) in the first frame; (2) is the motion state of the 50th frame, indicating that the boat moves to (410.12, 397.06, 0.19) in the 50th frame; and (3) is the motion state of the 100th frame, indicating that the boat moves to (609.45, 696.78, 0.03) in the 100th frame.

[0099] The specific implementation method for dynamic correction of antenna pattern is as follows:

[0100] First, obtain the reference radiation pattern G0 of the antenna in a stationary horizontal state.

[0101] After obtaining the motion pose output, solve for the pose parameters for each frame:

[0102]

[0103] Because the antenna is rigidly connected to the hull, the attitude angle is directly transferred to the antenna from the hull. The mapping relationship is as follows:

[0104]

[0105] Where, α roll α is the antenna's roll angle, which is the same as the ship's roll angle; pitchThe antenna's elevation angle is the same as the ship's pitch angle. The calculated antenna attitude angle will be used to correct the antenna radiation pattern to accurately reflect the impact of the ship's motion on the antenna's radiation characteristics. The ship's motion is mapped to the antenna radiation characteristics through a homogeneous transformation matrix.

[0106] For each frame of pose data, the antenna pattern is dynamically corrected using the following rotation matrix to obtain the corrected antenna pattern:

[0107]

[0108] in, and These are the roll and pitch angles of the current frame, respectively; G0 is the horizontal reference radiation pattern, reflecting the directivity characteristics of the antenna in a stationary horizontal state. This reference radiation pattern is provided by the antenna manufacturer or obtained through measurement; R x (‧) and R y (‧) are the roll and pitch rotation matrices, respectively, defined as:

[0109]

[0110] Figure 6 This is a comparison of the antenna radiation patterns before and after the correction in this embodiment. Figure 6 In the image, (a) shows the normal antenna pattern before correction, and (b) shows the antenna pattern after dynamic correction due to the influence of the ship's attitude.

[0111] The above steps map the ship's roll and pitch angles into changes in antenna pitch and roll angles in real time.

[0112] Then, the corrected antenna pattern is imported into the wireless channel propagation model to generate a dynamic channel response.

[0113] Under the influence of changing ocean waves, the position and attitude of small unmanned surface vessels (USVs) can be significantly altered. A major innovation of this method lies in importing the USV's pose data into channel simulation software for simulation, which more closely reflects the actual situation of small USVs being affected by waves. Based on this novel dynamic channel-antenna attitude coupling model, a series of operations can be performed, such as comparisons of various frequency bands, humidity comparisons, and channel simulations between multiple vessels.

[0114] 5. Dynamic simulation and channel data acquisition and analysis

[0115] Batch simulation is initiated, with each scene replica automatically queued for computation by Wireless InSite. The processing time per frame is kept under 30 seconds, significantly reducing the total simulation time.

[0116] After simulation, channel data for all scenarios is exported. Then, an Rx Grid spatial sampling system is constructed to generate a received power heatmap, visually displaying signal coverage. Based on this, the variation patterns of parameters such as path loss and delay spread with the unmanned surface vessel's position and attitude are analyzed. Furthermore, the communication performance of the 433MHz and 2.4GHz bands can be compared and analyzed to verify the advantages of the low-frequency band in diffraction capability and resistance to attitude effects. Different environmental humidity parameters can also be set to analyze the impact of humidity on path loss.

[0117] In this embodiment, after completing the batch run of 100 frames of dynamic simulation scenarios, the electromagnetic propagation characteristics are fully recorded and analyzed through multi-dimensional channel data acquisition. The ship communication scenarios under 100 dock settings generated in the Wireless InSite simulation platform are queued and simulated using scripts to obtain visual output, including channel route maps and heatmaps. The heatmaps achieve full-area signal coverage analysis by constructing an Rx Grid spatial sampling system, showing the power distribution at different locations at the same time. Taking the dynamic ship-static shoreline nearshore dock simulation model as an example, a three-dimensional monitoring grid covering the dock area and nearshore area of ​​approximately 950m × 700m × 50m is constructed as follows: Figure 7 As shown, the horizontal resolution is Δx = Δy = 10m, and the vertical direction is divided into 5 layers from 0-50m above the sea surface, with each layer having a 10m interval. The total number of sampling points is approximately 95 × 70 × 5 = 33250 voxels.

[0118] The heatmap uses ray tracing to calculate the received power of each voxel, dynamically fusing the contributions from direct, reflected, and diffracted paths, clearly showing the channel strength and total path length from the transmitter to the receiver. The initial channel path diagram and heatmap are shown below. Figure 8 As shown, Tx is the main ship and Rx is the shore-based receiver.

[0119] The Wireless Insite simulation software's heatmap shading scheme is based on OpenGL volume rendering technology and uses an enhanced rainbow color scale: red area P r >-65dBm indicates excellent coverage; yellow and green transition areas -65dBm >P r >-80dBm indicates an area with available coverage; blue area P r ≤-80dBm is considered a communication dead zone. The vertical profile display supports arbitrary height plane cutting; the example shows a typical operating height of 10m above sea level. The initial moment heatmap clearly shows the signal strength changes from transmission to reception.

[0120] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the program to implement the above-described method.

[0121] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A dynamic channel modeling method for unmanned ships in offshore surface environment, characterized in that, The method comprises the following steps: S1: constructing a three-dimensional offshore water surface simulation scene, including a sea surface, a wharf facility, an unmanned ship cluster, and a communication node; S2: establishing a wireless channel propagation model based on a ray tracing method to simulate the propagation mechanism of direct, reflected, and diffracted paths; S3: establishing a motion characteristic model of the unmanned ship under the action of waves to obtain six-degree-of-freedom pose data of the unmanned ship; S4: using an intelligent batch processing dynamic reconstruction method to batch generate multiple frames of scene copies; S5: dynamically correcting the antenna pattern in each frame of scene copy according to the pose data to couple the influence of ship motion on the antenna radiation characteristics; S6: importing the corrected antenna pattern into the wireless channel propagation model to generate a dynamic channel response; S7: performing simulation, collecting, and analyzing the channel data obtained by simulation, including path loss, delay spread, and Doppler characteristics.

2. The method of claim 1, wherein, In step S2, the dominant propagation mode of the wireless channel propagation model is represented by a time-varying multipath channel model: where h(τ, t) is the channel impulse response at time t, representing the complex gain at delay t; K(t) is the time-varying multipath number at time t; a k (k, t) is the complex amplitude of the kth path at time t; f d,k (k) is the Doppler shift of the kth path; θ k (k, t) is the phase offset of the kth path at time t; δ(·) is the Dirac delta function, indicating that each path has energy at delay τ k (k, t). The path loss model of the wireless channel propagation model is represented as: wherein, represents the path loss at a distance d, d being the distance between the transmitting end and the receiving end, λ being the wavelength, N ref is the number of reflection paths, N dif is the number of diffraction paths, L ref,i is the reflection loss of the L th reflection path. L dif,j is the diffraction loss of the L th diffraction path.

3. The method of claim 2, wherein, Single pass loss L ref Using Fresnel coefficients: where θ is the angle of incidence with respect to the normal of the reflecting surface, is the sea surface dielectric constant; Single diffraction loss L dif Using the UTD correction formula: where k is the wave number, k = 2π / λ; is the diffraction coefficient, is the diffraction angle; is the diffraction wedge angle parameter.

4. The method of claim 1, wherein, In step S3, the motion characteristic model is represented by a Fossen nonlinear dynamics equation: where M total is the total mass matrix, composed of the hull mass matrix and the empirical added mass matrix; is the acceleration vector; η = [x, y, z, φ, θ, ψ] T is the pose vector, containing the three-dimensional position (x, y, z) and Euler angles, including roll φ, pitch θ and yaw ψ; v = [u, v, w, p, q, r] T is the velocity vector, containing the linear velocity (u, v, w) and angular velocity (p, q, r); D v is the linear damping matrix, reflecting the fluid dynamic loss; G η is the hydrostatic restoring matrix, representing the balance between buoyancy and gravity; τ wave is the wave excitation force, modeled as a simple harmonic wave: where ω is the wave frequency.

5. The method of claim 1, wherein, In step S4, the intelligent batch processing dynamic reconstruction method comprises: Based on the motion characteristic model of the unmanned ship under the action of waves, multiple sets of pose data of the unmanned ship are generated by MATLAB scripts; Multiple frames of scene copies are batch generated and run in Wireless InSite.

6. The method of claim 5, wherein, In step S4, the antenna pattern in each frame of scene copy is dynamically corrected by the generated multiple sets of pose data of the unmanned ship; dynamic correction of the antenna pattern is realized by a rotation matrix: wherein and are the current frame's roll and pitch angles, respectively; G0is a horizontal reference pattern reflecting the directional characteristics of the antenna in a stationary horizontal state; R x (·) and R y (·) are the roll and pitch rotation matrices, respectively, defined as: 。 7. The method of claim 1, wherein, In step S7, it also includes multi-band comparative analysis to compare the path loss and coverage range of 433 MHz and 2.4 GHz frequency bands under the same scene.

8. The method of claim 1, wherein, In step S7, it includes analyzing the influence of environmental humidity on the channel by simulating and comparing the received power changes under different humidity conditions.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, is arranged to perform the method of any one of claims 1 to 8. The processor executes the program to implement the method of any one of claims 1-8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-8.

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