An offshore surface environment unmanned ship dynamic channel modeling method

Abstract

The application relates to a kind of offshore surface environment under unmanned ship dynamic channel modeling method, comprising: constructing three-dimensional offshore surface simulation scene, including sea surface, wharf facilities, unmanned ship cluster and communication node;Based on the ray tracing method, a wireless channel propagation model is established, the propagation mechanism of direct, reflection and diffraction path is simulated;Establish the motion characteristic model of unmanned ship under the action of wave, obtain the six-degree-of-freedom pose data of unmanned ship;Intelligent batch processing dynamic reconstruction method is used, and multiple frames of scene copies are generated in batches;According to the pose data, the antenna pattern in each frame of scene copy is dynamically corrected, to couple the influence of ship motion on the antenna radiation characteristic;The corrected antenna pattern is imported into the wireless channel propagation model, and a dynamic channel response is generated;Simulation is performed, and the channel data obtained by simulation is collected and analyzed.The method can accurately couple the motion characteristics of unmanned ship in wave and the wireless channel propagation mechanism, and improve the accuracy of dynamic channel prediction.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of wireless communication, and particularly relates to a dynamic channel modeling method for unmanned ships in a near-sea surface environment. BACKGROUND

[0002] With the deepening of ocean development, unmanned ships are increasingly widely used in near-sea operations due to their small size, intelligence and multi-purpose characteristics. With the increasing complexity of tasks, single-ship operation has been difficult to meet the requirements, and cluster collaborative operation has become a development trend. Stable and reliable communication is a prerequisite for the cluster collaborative operation of unmanned ships, and an accurate channel model is the basis for the design, theoretical analysis and performance evaluation optimization of a communication system.

[0003] The marine environment is dynamic, and factors such as sea waves and wind speed can significantly affect the propagation characteristics of wireless signals. At present, statistical measurement and deterministic model are mainly used for modeling of a water surface wireless channel. The statistical measurement method obtains channel characteristics through a large amount of measured data, which is simple and intuitive but consumes manpower and material resources and has poor model generalization ability. The deterministic model (such as the ray tracing method) is based on electromagnetic wave propagation theory and uses specific information of the environment for prediction, which has high accuracy but usually requires high computing resources.

[0004] Existing marine channel modeling research, such as the modified earth loss model (REL) proposed by Zhang Jing et al. or the sea line-of-sight channel model based on random ray tracing proposed by Rui Ding et al., more considers the reflection and scattering characteristics of seawater medium, but generally ignores or simplifies the motion characteristics of the ship itself. For small unmanned ships used in near-sea operations, the ship body is small, and the antenna is close to the water surface. The six-degree-of-freedom motion (roll, pitch, yaw, heave, surge and sway) of the ship caused by waves can significantly change the attitude and spatial position of the antenna, thereby having an indiscernible influence on key channel parameters such as path loss, delay spread and Doppler shift. Traditional static or quasi-static channel models are difficult to accurately depict this dynamic multipath effect and are not suitable for the design of high-reliability unmanned ship collaborative networking communication systems.

[0005] Therefore, there is an urgent need for an unmanned ship wireless channel modeling method that can couple the motion characteristics of the ship and accurately reflect the dynamic environment in the near sea. SUMMARY

[0006] The purpose of the present application is to overcome the shortcomings of the prior art and provide a dynamic channel modeling method for unmanned ships in a near-sea surface environment, which can accurately couple the motion characteristics of the unmanned ship 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 an unmanned ship cluster communication system.

[0007] In order to achieve the above object, the technical scheme adopted by the present application is as follows: a dynamic channel modeling method for unmanned ship in offshore water surface environment, comprising the following steps:

[0008] S1: constructing a three-dimensional offshore water surface simulation scene, including sea surface, wharf facilities, unmanned ship cluster and communication nodes;

[0009] S2: establishing a wireless channel propagation model based on ray tracing method, simulating the propagation mechanism of direct, reflected and diffracted paths;

[0010] S3: establishing a motion characteristic model of unmanned ship under the action of waves, obtaining six-degree-of-freedom pose data of the unmanned ship;

[0011] S4: using intelligent batch processing dynamic reconstruction method to generate multiple frames of scene copies in batches;

[0012] S5: dynamically correcting the antenna pattern in each frame of scene copy according to the pose data, so as to couple the influence of ship motion on antenna radiation characteristics;

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

[0014] S7: performing simulation, collecting and analyzing the channel data obtained by simulation, including path loss, delay spread and Doppler characteristics.

[0015] Further, 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, representing the complex gain at time delay t; K(t) is the time-varying multipath number at time t; α k (t) is the complex amplitude of the kth path at time t; f d,k is the Doppler shift of the kth path; θ 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 time delay τ k (t);

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

[0019]

[0020] Where, represents the path loss at distance d, d is the distance between the transmitting end and the receiving end, λ is the wavelength, N ref is the number of reflection paths, N difL is the reflection loss of the mth reflection path ref,i L is the reflection loss of the mth reflection path L is the reflection loss of the mth reflection path dif,j L is the reflection loss of the mth reflection path L is the reflection loss of the mth reflection path

[0021] L is the reflection loss of the mth reflection path ref The Fresnel coefficient is calculated as follows:

[0022]

[0023] where θ is the incident angle relative to the normal of the reflecting surface, ε is the dielectric constant of the sea surface;

[0024] L is the single diffraction loss dif The UTD correction formula is calculated as follows:

[0025]

[0026] where k is the wave number, k = 2π / λ; L is the diffraction coefficient, L is the diffraction angle; L is the diffraction wedge angle parameter.

[0027] Further, in step S3, the motion characteristic model is represented by the Fossen nonlinear dynamics equation:

[0028]

[0029] where M total M is the total mass matrix composed of the hull mass matrix and the empirical added mass matrix; a is the acceleration vector; η = [x, y, z, φ, θ, ψ] T η is the pose vector containing 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 v is the velocity vector containing linear velocity (u, v, w) and angular velocity (p, q, r); D v D is the linear damping matrix reflecting fluid dynamic loss; G η G is the static water restoring moment matrix representing the balance between buoyancy and gravity; τ wave τ is the wave excitation force, 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 comprises:

[0033] Based on the motion characteristic model of the unmanned ship under the action of waves, a plurality of unmanned ship pose data are generated through MATLAB scripts;

[0034] Batch generate and run multiple scene copies in Wireless InSite.

[0035] Further, in step S4, the antenna directional pattern in each scene copy is dynamically corrected by the generated plurality of unmanned ship pose data; the dynamic correction of the antenna directional pattern is realized by a rotation matrix:

[0036]

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

[0038] .

[0039] Further, in step S7, multi-band comparative analysis is also included, and the path loss and coverage range of 433MHz and 2.4GHz bands in the same scene are compared.

[0040] Further, in step S7, the influence of environmental humidity on the channel is analyzed, and the change of the received power under different humidity conditions is compared through simulation.

[0041] The application further provides an electronic device, including a memory, a processor and a computer program stored in the memory, and the processor executes the program to realize the above method.

[0042] The application further provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the above method.

[0043] Compared with the prior art, the present application has the following beneficial effects: the present application provides an unmanned ship dynamic channel modeling method in an offshore water surface environment, which dynamically corrects an antenna pattern by coupling an unmanned ship six-degree-of-freedom motion model and a ray tracing method, so that the channel model can accurately reflect the actual influence of ship body rolling on signal propagation, and the deviation of path loss prediction from measured data is better than that of a traditional model; at the same time, an intelligent batch processing dynamic reconstruction method is adopted to realize end-to-end automatic mapping from a kinematics model to a simulation scene, greatly improving the simulation efficiency of large-scale scenes, and being particularly suitable for long-time, multi-condition ocean channel characteristic research. The present application can clearly reveal the internal mechanism that sea wave fluctuation affects the stability of a communication link by changing the attitude of a ship body, and provides technical support for antenna attitude compensation algorithm design, frequency band optimization and anti-multipath protocol optimization of an offshore unmanned ship ad hoc network. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 is a flowchart of the offshore water surface environment unmanned ship dynamic channel modeling method provided by the embodiment of the present application;

[0045] Figure 2 is a three-dimensional offshore water surface simulation scene diagram constructed in the embodiment;

[0046] Figure 3 is a principle diagram of an offshore water surface environment unmanned ship wireless channel and ray tracing in the embodiment of the present application;

[0047] Figure 4 is a six-degree-of-freedom simulation attitude schematic diagram of an unmanned ship in the embodiment of the present application;

[0048] Figure 5 is a schematic diagram of different frame dynamic motion positions of an unmanned ship in the embodiment of the present application;

[0049] Figure 6 is a comparison diagram of antenna patterns before and after correction in the embodiment of the present application;

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

[0051] Figure 8 is a signal path diagram and a heat map of an initial moment of a dynamic ship-static shore offshore simulation model in the embodiment of the present application. DETAILED DESCRIPTION

[0052] The present application will be further described below in combination with the drawings and embodiments.

[0053] It should be pointed out that the following detailed description is exemplary and is intended to provide further description of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0054] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0055] As shown in FIG. 1, the embodiment provides a dynamic channel modeling method for unmanned ships in offshore surface environment, comprising the following steps: Figure 1

[0056] S1: Construct a three-dimensional offshore surface simulation scene, including sea surface, wharf facilities, unmanned ship cluster and communication nodes;

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

[0058] S3: Establish a motion characteristic model of unmanned ships under the action of waves, obtain six-degree-of-freedom pose data of the unmanned ships;

[0059] S4: Use intelligent batch processing dynamic reconstruction method to generate multiple frames of scene copies in batches;

[0060] S5: Dynamically correct the antenna pattern in each frame of scene copy according to the pose data, to couple the influence of ship motion on antenna radiation characteristics;

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

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

[0063] The related contents involved in the method are further described below.

[0064] 1. Scene construction and parameter setting

[0065] A 1000m long and 800m wide offshore wharf three-dimensional scene is constructed in Wireless InSite, including concrete platform, warehouse, control tower and static water surface.

[0066] 1 ship (transmitting end) and 8 auxiliary ships (environmental obstacles) are set. The top of the main ship is deployed with a vertical polarization omnidirectional antenna, which works at 433MHz LoRa frequency band and has a transmitting power of 20dBm. A receiving antenna is set on the shore-based facility.

[0067] ​The auxiliary ship is parked according to the actual function zoning as an environmental obstacle, and the ship body material and structure will affect the scattering and reflection of electromagnetic waves. A receiving antenna is arranged on the shore-based facility for signal reception and analysis. Figure 2 is a three-dimensional offshore water surface simulation scene graph constructed in this embodiment. Among them, (a) shows the enlarged effect of the ship, in order to more intuitively show the relative position relationship of each entity in the scene, the size of the ship body in the actual model is about 3m long and 2m wide, and the actual scene effect is shown in (b).

[0068] According to the characteristics of different areas in the ship-shore simulation scene, differentiated ray tracing precision settings are adopted: for the wharf buildings, including concrete platforms, logistics warehouses and control towers, full path analysis is adopted, and the reflection order is ≤5; for the open sea, a simplified model is adopted, and the reflection order is ≤3. These parameter settings control the single simulation time within 5 minutes under the condition that the path loss calculation error is ≤1.5dB.

[0069] 2. Wireless channel propagation model construction

[0070] When creating a ray tracing model in the offshore water surface environment, it is necessary to effectively reflect the complex propagation effects such as sea surface reflection, island shielding and ship obstacle, so as to provide accurate channel characteristic prediction for unmanned ship cooperative communication. The principle of unmanned ship wireless channel and ray tracing in offshore water surface environment is shown in Figure 3 , wherein TX is the transmitting end and RX is the receiving end.

[0071] In the offshore environment, due to the change of sea surface wave and the continuous movement of unmanned ship, the traditional channel model is difficult to accurately describe its characteristics and influence law, therefore, a coupled model is needed to analyze the dynamic marine environment and ship movement characteristics. The channel performance under different sea conditions, frequencies and antenna configurations is predicted.

[0072] The offshore wireless channel is affected by multiple propagation mechanisms, including direct path (LOS) propagation, sea surface specular reflection and diffuse reflection, coastal terrain diffraction, atmospheric waveguide effect and ship structure scattering. The dominant propagation mode of the wireless channel propagation model is represented by a time-varying multipath channel model:

[0073]

[0074] Among them, h(τ,t) is the channel impulse response at t, representing the complex gain at time delay t; K(t) is the time-varying multipath number at t; α k (t) is the complex amplitude of the kth path at t; f d,k is the Doppler shift of the kth path; θ k (t) is the phase offset of the kth path at t; δ(·) is the Dirac delta function, indicating that each path has a complex gain at time delay τ k(t) has energy at.

[0075] The free space loss, reflection loss and diffraction loss of the offshore water surface wireless channel are integrated to establish a composite path loss model of the wireless channel propagation model, which is expressed as:

[0076]

[0077] wherein, represents the path loss at a distance d, d is the distance between the transmitting end and the receiving end, λ is 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 first reflection path, L dif,j is the diffraction loss of the first diffraction path. Wherein, the single reflection loss L ref is calculated by using the Fresnel coefficient:

[0078]

[0079] wherein, θ is the incident angle relative to the normal of the reflecting surface, is the dielectric constant of the sea surface.

[0080] The single diffraction loss L dif is calculated by using the UTD correction formula:

[0081]

[0082] wherein, k is the wave number, k=2π / λ; is the diffraction coefficient, is the diffraction angle; is the diffraction wedge angle parameter.

[0083] 3. Motion coupling modeling of unmanned ship

[0084] The motion characteristics of the unmanned ship cannot be ignored for the dynamic channel of the unmanned ship. Therefore, this paper will study the establishment of the ship motion mathematical model. The motion of the unmanned ship in the wave is a complex motion process, including three coordinate axis directions of translation and rotation around the coordinate axis, as shown in Figure 4 .

[0085] This paper uses discrete parameters to define the motion trajectory of the unmanned ship. The initial position is set as the origin P0=(x0,y0, z0) of the three-dimensional coordinate system, the horizontal positive direction is set as Δx, the vertical negative direction is set as Δy, the vertical direction is fixed, and the total frame number is N. The motion trajectory function adopts a linear recursive model, and the position coordinates of the ship at the nth frame are:

[0086]

[0087] Any quadrant motion mode can be realized by the combination of the signs of Δx and Δy, which can be used to describe the change of the ship's position.

[0088] Because the unmanned ship is slow and small, its pose is mainly affected by the sea waves in offshore operations, so the effect of sea waves on the ship is calculated by wave force, that is, the ship motion is described by Fossen's nonlinear dynamics equation, and the motion characteristic model of the unmanned ship is established:

[0089]

[0090] where M total is the total mass matrix, M total =M+0.2M, which is composed of the ship mass matrix M=diag(m,m,m,I x , I y , I z ) and the empirical added mass matrix, and the added mass is 20% of the ship mass; is the acceleration vector; η=[x,y,z,φ,θ,ψ] T is the pose vector, which includes three-dimensional position (x, y, z) and Euler angle, including roll φ, pitch θ and yaw ψ; v=[u,v,w,p,q,r] T is the velocity vector, which includes linear velocity (u, v, w) and angular velocity (p, q, r); D v is the linear damping matrix, which reflects the fluid power loss; G η is the static water restoring force matrix, which represents the balance of buoyancy and gravity.

[0091] In this embodiment, Fossen's nonlinear dynamics equation is used in MATLAB to establish a six-degree-of-freedom motion model of the unmanned ship. And the wave excitation force τ wave is set to a simple harmonic wave form:

[0092]

[0093] where ω is the wave frequency.

[0094] Then, the motion trajectory of the unmanned ship is planned. Assuming that the initial position is (223, 106, 0), set the displacement amount Δx=+2m, Δy=+5m for each frame, and generate 100 position points through the linear recursive model.

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

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

[0097] After completing the unmanned ship trajectory setting and ship motion characteristic modeling, the application proposes a dynamic reconstruction method based on intelligent batch processing, which realizes the automation of dynamic simulation process deployment by using batch scene generation technology.

[0098] First, based on the completion of the initial scene template in the Wireless Insite simulation platform, 100 independent scene copies of the initial scene of Wireless InSite are generated by MATLAB automatic script, and the batch generation of 100 snapshot dynamic ship-static shore near-sea port scenes is completed. This process ensures that each frame of scene has consistent initial electromagnetic characteristics, and realizes the change of unmanned ship dynamic trajectory and the change of antenna posture in Wireless InSite simulation through scene transformation. Refer to the dynamic trajectory of the unmanned ship to couple each frame of the unmanned ship and the change of antenna posture angle. The 100 groups of pose data generated above and the corrected antenna pattern are automatically injected into the corresponding scene copy, and 100 dynamic ship-static shore communication scenes with different dynamic ship positions are automatically generated. Figure 5 is a scene diagram of the unmanned ship moving to different positions at different times. In the figure, (1) is the first frame of motion state, indicating that the first frame of ship position is at coordinates (223.18, 106.03, 0); (2) is the 50th frame of motion state, indicating that the 50th frame of ship motion is (410.12, 397.06, 0.19), (3) is the 100th frame of motion state, indicating that the 100th frame of motion is (609.45, 696.78, 0.03).

[0099] Among them, the specific implementation method of dynamic correction of antenna pattern is:

[0100] First, the reference pattern G0 of the antenna in the static horizontal state is obtained.

[0101] After obtaining the motion posture output, the pose parameters of each frame are solved:

[0102]

[0103] Since the antenna is rigidly connected to the ship body, the attitude angle is directly transmitted from the ship body to the antenna. The mapping relationship is as follows:

[0104]

[0105] Among them, α roll is the roll angle of the antenna, which is the same as the roll angle of the ship body; α pitchThe pitch angle of the antenna is the same as the roll angle of the ship body. The calculated antenna attitude angle will be used to correct the antenna pattern to accurately reflect the influence of ship body movement on the antenna radiation characteristics. The ship body movement is mapped to the antenna radiation characteristics through a homogeneous transformation matrix.

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

[0107]

[0108] where, and are the roll angle and pitch angle of the current frame respectively; G0 is the horizontal reference pattern reflecting the directional characteristics of the antenna in the static horizontal state, which is provided by the antenna manufacturer or obtained by measurement; R x (‧) and R y (‧) are the roll and pitch rotation matrices respectively, which are defined as:

[0109]

[0110] Figure 6 is the comparison chart of the antenna pattern before and after correction in this embodiment. Figure 6 In the comparison chart, (a) is the normal antenna pattern before correction, and (b) is the dynamically corrected antenna pattern affected by the ship pose.

[0111] The above steps map the roll and pitch angles of the ship body in real time to the changes in the antenna pitch and roll angles.

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

[0113] Under the fluctuation of sea waves, the position and pose of a small unmanned ship will change greatly. One of the great innovations of this method is to import the pose data of the unmanned ship into the channel simulation software for simulation, which is more in line with the actual situation of the small unmanned ship affected by waves. Based on the completion of this new dynamic channel and antenna pose coupling model, a series of operations can be performed, such as various frequency band comparisons, humidity comparisons, and channel simulation between multiple ships.

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

[0115] Start batch simulation, and each scene copy is automatically queued for calculation by Wireless InSite. The single-frame processing time is controlled within 30 seconds, and the total simulation time is greatly shortened.

[0116] After the simulation is completed, the channel data of all scenes is exported. Then, the Rx Grid spatial sampling system is constructed to generate the received power heat map, which intuitively shows the signal coverage. On this basis, the change law of the path loss, time delay spread and other parameters with the position and attitude of the unmanned ship is analyzed. In addition, the communication performance of 433 MHz and 2.4 GHz frequency bands can be compared and analyzed to verify the advantages of low frequency bands in terms of diffraction ability and attitude influence resistance. Different environmental humidity parameters can also be set to analyze the influence of humidity on path loss.

[0117] In this embodiment, after the batch running of 100 dynamic simulation scenes is completed, the full-element recording and analysis of electromagnetic propagation characteristics are realized through multi-dimensional channel data collection. The 100 wharf scenarios generated in the Wireless InSite simulation platform are simulated through scripts and visual output is obtained, including signal path line graph and heat map. The heat map realizes global signal coverage analysis through the construction of the Rx Grid spatial sampling system, and shows the power distribution at different locations at the same time. Taking the dynamic ship-static shore near-sea wharf simulation model as an example, a three-dimensional monitoring grid covering the wharf area and the near-sea area of about 950m x 700m x 50m is constructed as shown in Figure 7 The horizontal resolution Δx = Δy = 10m, and the vertical direction is divided into 5 layers according to the sea surface above 0-50m, with an interval of 10m each. The total number of sampling points is about 95 x 70 x 5 = 33250 voxels.

[0118] The heat map calculates the received power of each voxel using the ray tracing method, dynamically fuses the contributions of direct, reflected and diffracted paths, and can clearly show the channel strength and total path from the sending end to the receiving end. The signal path line graph and heat map at the initial time are shown in Figure 8 , in which Tx is the main ship and Rx is the shore-based receiving end.

[0119] The heat map coloring scheme of the Wireless Insite simulation software is based on the volume rendering technology of OpenGL, and uses the enhanced version of the rainbow color scale: the red area P r >-65dbm is the high-quality coverage area; the yellow and green transition area -65dbm > P r >-80dbm is the usable coverage area; the blue area P r ≤-80dbm is the communication blind area. The vertical profile supports any height plane cutting display, and the legend is the typical operating height of 10m above sea level. The heat map at the initial time clearly shows the strength change of the signal from the sending end to the receiving end.

[0120] The embodiment also provides an electronic device, including a memory, a processor and a computer program stored in the memory, and the processor implements the method described above when executing the program.

[0121] The embodiment further provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the method.

[0122] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. In addition, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) containing computer-usable program code.

[0123] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0124] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0126] The above merely describes preferred embodiments of the present application, but does not limit the present application in other forms, and any skilled person in the art can modify or change the above disclosed technical content into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change and modification made on the above embodiments without departing from the technical solution content of the present application and according to the technical essence of the present application still belongs to the protection scope of the technical solution of the present application.

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; 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; β 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.

2. The method of claim 1, wherein, Single pass loss L ref Using Fresnel coefficients: wherein is the angle of incidence with respect to the normal to the reflecting surface, is the sea surface dielectric constant; Single diffraction loss L dif Using the UTD correction formula: wherein is the wave number, ; is the diffraction coefficient, is the diffraction angle; is the diffraction wedge angle parameter.

3. 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.

4. 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 a MATLAB script; Multiple frames of scene copies are batch generated and run in Wireless InSite.

5. The method of claim 4, 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: 。 6. 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.

7. 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.

8. 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, causes the electronic device to perform the method of any one of claims 1 to 7. The processor executes the program to implement the method of any one of claims 1-7.

9. 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 1-7.

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