A real-time simulation method for multi-granularity modeling of a communication system in a confrontation simulation

Abstract

The application discloses a kind of real-time simulation methods of multi-granularity modeling of communication system in confrontation simulation, constructs military communication system engineering level, function level and task level model.For the scene that higher precision is required, the antenna data of communication equipment is solved offline using professional electromagnetic simulation software, and the accurate data is obtained in real time by online interpolation during simulation running.For the scene that precision is not required, typical parameters are used to describe the directivity of antenna.For large-scale military communication network, real-time requirement is higher, so the directivity of antenna is not considered, only the communication coverage is considered.In addition, the problems of battlefield communication interference, atmosphere and terrain visibility should be considered in the modeling process.Finally, the application establishes a set of multi-granularity model from antenna solution, link simulation to military network communication, and introduces the influence of battlefield environment on communication for the communication system modeling device for confrontation simulation.

Description

Technical Field

[0001] This invention relates to the field of combat simulation and multi-granularity modeling technology for battlefield communication systems. Specifically, it relates to a real-time simulation method for multi-granularity modeling of communication systems in adversarial simulation. Background Technology

[0002] The history of warfare is inextricably linked to the development of military communication systems, evolving from primitive communication systems like smoke signals to today's advanced military communication systems. With technological advancements, informatization has become a crucial indicator of military combat effectiveness, and modeling and simulating communication systems for adversarial simulations is an important research direction for evaluating military informatization.

[0003] Communication systems are highly complex systems, and accurately modeling them while meeting real-time requirements is a worthwhile research topic. Establishing models of different granularities to describe and analyze these systems is a highly effective method for handling complex problems, effectively addressing the issue of model granularity and simulation real-time performance. For communication system modeling and simulation, employing a multi-granularity modeling approach can resolve the contradiction between model granularity and simulation real-time performance; therefore, research on multi-granularity modeling of communication systems is essential.

[0004] Multi-granularity modeling has become a research hotspot in the field of modeling and simulation. Although there are still many unresolved issues in both theory and technology, the importance of multi-granularity modeling technology in distributed simulation has become apparent. Conducting research on multi-granularity modeling is of great significance for improving the reliability, usability, reusability, and interoperability of simulations.

[0005] Furthermore, the complex battlefield environment—whether natural or man-made—affects communication systems. For electromagnetic waves, this primarily manifests as transmission effects along the signal propagation path. Typical battlefield environments are mainly divided into natural and artificial environments. Natural environments include factors such as clouds, fog, rainfall, atmosphere, ionosphere, and terrain, which mainly affect electromagnetic signal transmission and thus communication systems through noise and attenuation. Artificial environments primarily involve electronic interference. Ensuring the environment is realistic and effective, while not compromising the real-time performance of the entire simulation system, is a crucial factor in communication system performance analysis.

[0006] Due to its advantages such as safety, economy, and repeatability, modeling and simulation technology is now widely used in the military field for concept demonstration, research and development, training exercises, and military operations. Therefore, research on multi-granularity modeling of communication systems for adversarial simulation, the impact of the battlefield environment on military communication systems, and performance analysis of military communication systems, followed by demonstration and verification in typical battlefield scenarios, can provide an effective simulation process for modern warfare military communication systems and offer technical support for modern informationized warfare.

[0007] For the application of complex military systems in adversarial simulation, it is necessary to balance the real-time nature and the level of detail required for the simulation. For the communication models in system and adversarial simulation, the following issues should be addressed:

[0008] 1) System-level confrontation simulation needs to consider the impact of the electromagnetic environment on the combat of different entities, while ensuring the real-time performance of the simulation.

[0009] 2) There are some professional simulation software for antenna and link simulation. If you want to get a highly realistic solution, you need to use these software. However, professional software takes a long time and does not have real-time performance. In adversarial simulation, the model needs to be able to run independently of these software and be packaged as a component and integrated into the equipment model (such as aircraft and armored vehicles) to run in real time.

[0010] Simulation of communication networks requires not only simulating the entire packet transmission process, but also taking into account the real-time dynamic characteristics of equipment during combat and the dynamic influence of the simulation environment. Summary of the Invention

[0011] The present invention aims to provide a real-time simulation method for multi-granularity modeling of communication systems in adversarial simulation, so as to solve or improve at least one of the above-mentioned technical problems.

[0012] In view of this, the first aspect of the present invention is to provide a real-time simulation method for multi-granular modeling of communication systems in adversarial simulation.

[0013] The first aspect of this invention provides a real-time simulation method for multi-granular modeling of communication systems in adversarial simulation, comprising: S1, obtaining offline power pattern data of the antenna using electromagnetic simulation software according to the performance requirements of the antenna design, processing the power pattern data into a three-dimensional pattern, and performing online interpolation to obtain online continuous data; S2, constructing a link solution simulation model, establishing an engineering-level model and a functional-level model therein; then, using the frequency, bandwidth, transmit power, and antenna gain of the transmitting equipment, wherein the antenna gain is calculated based on the online continuous data through the engineering-level model using the offline solution and online interpolation method in the first step; in the functional-level model, after obtaining the maximum gain of the antenna using empirical formulas, the antenna's main lobe, half-power beamwidth, sidelobe gain, and back lobe gain are calculated based on the antenna pattern using the antenna's inherent parameters, thereby depicting the antenna's directivity; S3, establishing a task-level model in the link solution simulation model to perform military communication network simulation, calculating only the communication coverage area. S4. Establish a model of the impact of typical battlefield environments on communications. The model includes: a battlefield atmospheric environment model, a terrain visibility model, and a jamming machine model. The granularity of the engineering-level model, the functional-level model, and the mission-level model ranges from fine to coarse.

[0014] This invention provides a real-time simulation method for multi-granular modeling of communication systems in adversarial simulation. By establishing a battlefield atmospheric environment model, a terrain visibility model, and a jammer model respectively, and considering atmospheric absorption loss, including oxygen and water vapor absorption loss, terrain height, and active suppression jamming factors that can affect electromagnetic wave propagation, since signal propagation is achieved through electromagnetic wave propagation, this method can more accurately describe the impact of the electromagnetic environment on the communication system in system-level adversarial simulation while ensuring the real-time performance of the simulation.

[0015] The engineering-level model, functional-level model, and mission-level model for military communication network simulation are models with granularity ranging from fine to coarse. The offline power pattern data of the antenna is obtained using electromagnetic simulation software. The offline data is then processed into a three-dimensional pattern and interpolated online to obtain online continuous data. Together, they form a combination of multi-granularity models. Overall, it integrates the advantages of multiple software such as multi-granularity communication models, network simulation, and electromagnetic environment. Compared with professional electromagnetic simulation software, it can perform real-time online calculations. Compared with network simulation software, the environment modeling is more detailed and can better reflect the physical and dynamic characteristics of equipment, supporting the simulation of combat systems.

[0016] In addition, the technical solutions provided by embodiments of the present invention may also have the following additional technical features:

[0017] In any of the above technical solutions, the typical battlefield environment includes: a natural environment and an artificial environment. The natural environment includes an atmospheric environment and a terrain environment. The atmospheric environment includes oxygen and water vapor. The battlefield atmospheric environment model is used to calculate the impact of oxygen and water vapor absorption loss on electromagnetic wave propagation in the battlefield atmospheric environment model. The terrain visibility model is used to calculate the impact of terrain elevation data on electromagnetic wave propagation in the terrain environment. The jammer model is used to calculate the impact of active suppression jamming on communication in the artificial environment.

[0018] In this technical solution, by integrating various environmental factors in the battlefield environment, a more comprehensive simulation of the battlefield environment is ensured. For example, atmospheric absorption loss includes the absorption loss of oxygen and water vapor, the absorption effect of tropospheric oxygen on electromagnetic waves, the influence of terrain elevation data on electromagnetic wave propagation in the terrain environment, and in free space, the environment of the device to be jammed is close to ideal, and the transmitted and received signals will not be interfered with. The coverage range of the device to be jammed is determined by the ratio between the noise generated inside the receiver and the power intensity of the target echo. The following factors are considered in the jamming: target echo power intensity, average power intensity of thermal noise of the receiver of the device to be jammed, signal-to-noise ratio in free space, interference power intensity of the receiver of the device to be jammed under active suppression jamming, gain of the jamming device antenna in the jammer direction, and signal-to-interference ratio calculation at the receiver output under active suppression jamming.

[0019] In any of the above technical solutions, the functional level model is provided with an output port for at least one of the following functions: obtaining the signal frequency of the transmitting device, obtaining the bandwidth of the transmitting device, obtaining the transmission gain of the transmitting device, obtaining the transmission power of the transmitting device, and obtaining the signal frequency of the transmitting device.

[0020] The location of the link, the connection result of the link, and the bit error rate of the link are obtained; the functional level model is provided with an input port for at least one of the following functions: inputting the model position, inputting the interference power, inputting the transmission parameters of the receiving device, and inputting the attitude angle of the carrier carried by the device.

[0021] In this technical solution, in order to facilitate subsequent adversarial simulation, a functional-level model with an interface is designed, which enables the modeling method to run independently of these software programs, be independently packaged as a component, and integrated into the equipment model (aircraft) for real-time calculation.

[0022] In any of the above technical solutions, step S1 specifically includes: S101, performing antenna physical field calculation to obtain three-dimensional field strength pattern data of the antenna's two-dimensional gain pattern; S102, calculating the field strength data of the referenced ideal isotropic antenna, then calculating the absolute gain value at each point, and finally obtaining the antenna's three-dimensional gain pattern data; S103, based on the antenna's three-dimensional gain pattern data obtained in step S102, performing interpolation using a bidirectional interpolation algorithm to obtain the continuous data required in the simulation process.

[0023] In this technical solution, physical field-level calculations are performed on the antennas of the transmitting and receiving equipment, followed by data processing. Based on the performance requirements of the antenna design, appropriate geometric parameters are selected, and electromagnetic simulation software is used to obtain a relatively accurate and detailed power pattern, thereby obtaining the gain of the antenna in various directions. After obtaining the offline data, the data is processed into a three-dimensional radiation pattern in "direction angle-elevation angle-gain" format, and online interpolation is performed to obtain continuous online data during real-time simulation.

[0024] In any of the above technical solutions, step S2 specifically includes: S201, establishing an engineering-level model, calculating the maximum gain of a typical aperture antenna based on the empirical formula for the maximum gain of the antenna, and obtaining the half-power beamwidth, sidelobe gain, and backlobe gain based on the inherent parameters of the antenna and the antenna pattern of online continuous data, thus depicting the directivity of the antenna; S202, calculating the free space loss during transmission and the noise loss including system thermal noise and variables of the typical battlefield environment; S203, modeling the functional-level model, transmitting the input parameters required by the functional-level model to the functional-level model at once through data input and output parameters, or transmitting the data that the functional-level model needs to output to the engine in the current frame as a signal; S204, the signal is amplified by the transmitting antenna and propagated, then received by the receiving antenna, the bit error rate of the link is calculated through the signal-to-noise ratio, and the azimuth angle and elevation angle of the antenna pointing relative to the ground coordinate system are obtained by coordinate transformation based on the directivity of the antenna.

[0025] In this technical solution, the frequency, bandwidth, transmit power, and antenna gain of the transmitting equipment are considered. For the calculation of antenna gain, the engineering-level model uses offline calculation and online interpolation from the first step; the functional-level model uses empirical formulas to obtain the maximum antenna gain, and then uses the antenna's inherent parameters to calculate the main lobe (i.e., maximum gain), half-power beamwidth (i.e., the angle when the gain is half of the maximum gain), sidelobe gain, and back lobe gain based on a typical antenna pattern, thus depicting the antenna's directivity.

[0026] In any of the above technical solutions, step S3 specifically includes: S301, establishing a task-level model of the communication system; S302, using ns2 to simulate a military communication network; S303, performing socket communication so that ns2 running in the Linux environment can be connected with the model running in the Windows environment.

[0027] In this technical solution, a mission-level model of the communication system is designed to simulate military communication networks. To meet the real-time requirements of military communication network simulation, the mission-level model temporarily disregards directionality and only calculates the communication coverage. The communication network between different units on the battlefield primarily manifests as a tactical internet, which typically employs AdHoc networks. AdHoc network simulation is performed using communication network simulation software to calculate metrics such as packet loss rate, first packet arrival time, and latency.

[0028] In any of the above technical solutions, step S4 specifically includes: S401, establishing a battlefield atmospheric environment model, including variables such as oxygen and water vapor absorption factors of electromagnetic waves; S402, establishing a terrain visibility model, obtaining multiple sampling points of the projection of the line of sight between the viewpoint and the target point onto the xoy plane, determining the slope of the line connecting the viewpoint and the sampling points and the slope of the line of sight, as well as the visibility between the viewpoint and the target point; S403, establishing a jammer model, including variables such as target echo power intensity, average thermal noise power intensity of the receiver of the device to be jammed, signal-to-noise ratio in free space, jamming power intensity of the receiver of the device to be jammed under active suppression jamming, gain of the jamming device antenna in the jammer direction, and signal-to-interference ratio at the receiver output under active suppression jamming.

[0029] This technical solution establishes a model of the impact of typical battlefield environments on communications. Typical battlefield environments include natural and artificial environments. The natural environment includes atmospheric and terrain environments. The atmospheric environment includes oxygen and water vapor absorption models, while the terrain environment calculates the impact of terrain elevation data on electromagnetic wave propagation visibility. The artificial environment is represented by a jamming machine model, which calculates the impact of active suppression jamming on communications.

[0030] The beneficial effects of this invention compared to the prior art are as follows:

[0031] In system-level confrontation simulation, it is necessary to consider the impact of the electromagnetic environment on the combat of different entities. Most existing confrontation simulation platforms use probability to process communication transmission. This invention can more accurately describe the impact of the electromagnetic environment on the communication system in system-level confrontation simulation while ensuring the real-time performance of the simulation.

[0032] For professional electromagnetic simulation software, highly realistic calculation results can be obtained through these software programs. At the same time, in order to be applied to combat simulation scenarios, the modeling method of this invention can run independently of these software programs, be independently packaged as a component and integrated into the equipment model (aircraft) for real-time calculation.

[0033] Existing communication network simulation software is not realistic enough in simulating physical characteristics such as antennas, nor can it simulate the dynamic characteristics of equipment during movement. This invention can effectively integrate multi-granularity models with high realism and dynamic characteristics to support combat system simulation.

[0034] Additional aspects and advantages of embodiments of the invention will become apparent in the following description or may be learned by practice of embodiments of the invention. Attached Figure Description

[0035] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention.

[0036] Figure 1 This is an overall flowchart of the present invention;

[0037] Figure 2 This is a typical antenna radiation pattern;

[0038] Figure 3 This is a flowchart of the link calculation process of the present invention;

[0039] Figure 4 This is the communication network simulation process of the present invention;

[0040] Figure 5 This is a schematic diagram of the bilinear interpolation method of the present invention;

[0041] Figure 6 This is a flowchart of the signal-to-noise ratio calculation process of the present invention;

[0042] Figure 7 This is a schematic diagram of the functional level model interface parameters of the present invention. Detailed Implementation

[0043] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0044] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0045] Example 1

[0046] An embodiment of the first aspect of the present invention, such as Figure 1-7 As shown, a real-time simulation method for multi-granularity modeling of communication systems in adversarial simulation is provided. The example antenna used in this example is an axial-mode helical antenna. The method includes:

[0047] Step 1: Perform physical field level calculations and data processing on the antennas of the transmitting and receiving equipment. Based on the performance requirements of the antenna design, select appropriate geometric parameters and use electromagnetic simulation software to obtain a relatively accurate and detailed power pattern, thereby obtaining the gain of the antenna in each direction. After obtaining the offline data, process it into a three-dimensional radiation pattern in "azimuth angle-elevation angle-gain" format and perform online interpolation to obtain continuous online data during real-time simulation.

[0048] Step 101: First, perform antenna physical field calculations using AntennaMagus software. Taking an axial-mode helical antenna as an example, the proposed design performance requirements are shown in Table 1.

[0049] Table 1 Performance Requirements for Axial Mode Helical Antenna Design

[0050] <![CDATA[operating frequency f0]]> Absolute gain <![CDATA[Material diameter D w > 1GHz 10dBi 3mm

[0051] Then, the geometric parameters of the axial mode helical antenna were designed, and the geometric parameters are shown in Table 2:

[0052] Table 2 Geometric parameters of axial mode spiral antenna

[0053] <![CDATA[D w ]]> 3mm <![CDATA[D g ]]> 299.8mm Dh 95.43mm h right-handed <![CDATA[H f ]]> 11.99mm N 4 φ 13°

[0054] The three-dimensional field strength pattern data of the antenna's two-dimensional gain pattern are obtained, and the data are shown in Tables 3 and 4:

[0055] Table 3. Partial data of the antenna two-dimensional gain pattern.

[0056]

[0057]

[0058] Table 4. Partial data from the antenna's three-dimensional field strength pattern.

[0059] Theta Phi Re(E_Theta Im(E_Theta) Re(E_Phi) Im(E_Phi) 0.00E+00 0.00E+00 5.11E+00 2.03E+00 1.58E-02 9.04E-02 1.00E+00 0.00E+00 5.04E+00 2.33E+00 1.57E-02 9.01E-02 3.00E+00 0.00E+00 4.95E+00 2.63E+00 1.56E-02 8.97E-02 4.00E+00 0.00E+00 4.85E+00 2.92E+00 1.55E-02 8.94E-02 5.00E+00 0.00E+00 4.73E+00 3.20E+00 1.54E-02 8.91E-02 6.00E+00 0.00E+00 4.60E+00 3.47E+00 1.46E-02 8.88E-02

[0060] Step 102: For subsequent data applications, it is desirable to obtain the three-dimensional gain pattern of the antenna. Gain is defined as the ratio of the antenna's radiation intensity to that of an ideal isotropic antenna. Therefore, to calculate the field data of the antenna gain, it is necessary to calculate the field strength data of the reference ideal isotropic antenna, then calculate the absolute gain value at each point, and finally obtain the three-dimensional gain pattern data of the antenna.

[0061] Step 103: The obtained antenna 3D gain pattern data is discrete data, while continuous data is required in the simulation. Therefore, interpolation processing is necessary. A bilinear interpolation algorithm is used for interpolation. A schematic diagram of bilinear interpolation is shown below. Figure 5 As shown, the interpolation calculation results are as follows:

[0062]

[0063] Some of the interpolation results are shown in Table 5:

[0064] Table 5 shows some interpolation results.

[0065]

[0066] Step 2: Construct a link calculation simulation model and establish engineering-level and functional-level models of the communication system. The process is as follows: Figure 2 As shown. This involves the frequency, bandwidth, transmit power, and antenna gain of the transmitting equipment. For the antenna gain calculation, the engineering-level model uses the offline solution from the first step followed by online interpolation; the functional-level model uses an empirical formula to obtain the maximum antenna gain, and then utilizes the antenna's inherent parameters, such as... Figure 3 The typical antenna pattern shown is used to calculate the antenna's main lobe (i.e., maximum gain), half-power beamwidth (i.e., the angle when the gain is half of the maximum gain), sidelobe gain, and back lobe gain, thereby depicting the antenna's directivity.

[0067] Step 201: To calculate the maximum gain of a typical aperture antenna, empirical formulas for the maximum gain of some typical aperture antennas were obtained by consulting relevant materials, as shown in Table 6:

[0068] Table 5 contains empirical formulas for the maximum gain of some typical aperture antennas.

[0069]

[0070] After obtaining the antenna's maximum gain, the antenna's inherent parameters are used, according to... Figure 3 The typical antenna pattern shown is used to calculate the antenna's main lobe (maximum gain), half-power beamwidth (angle when the gain is half of the maximum gain), sidelobe gain, and back lobe gain, thus depicting the antenna's directivity.

[0071] Step 202: Calculate the free space loss during transmission. The signal-to-noise ratio with only free space loss is shown below:

[0072]

[0073] In addition to the free space loss in the above formula, noise loss also needs to be introduced. This noise, besides the system's own thermal noise, is mainly affected by the battlefield environment; the modeling of this part will be introduced in step four. Then, as... Figure 6 The process shown calculates the signal-to-noise ratio of the link.

[0074] Bit error rate (BER) is an important indicator of communication system performance. The BER is defined as:

[0075]

[0076] The bit error rate of communication can be calculated using the signal-to-noise ratio (SNR) according to the following formula:

[0077]

[0078] The bit error rate is used to determine whether communication is possible, and from this, the coverage area of ​​the communication equipment can be calculated, providing a basis for evaluating the performance of the communication system.

[0079] Step 203: Modeling the link functional level model of the communication system. The first step is to describe the data types, as shown in Table 6:

[0080] Table 6. Introduction to Data Types in the Link Functional Level Model of Communication Systems

[0081] Data type name Data type description UDPosition Model location Comm_tran The information that the communication equipment needs to receive. carrier Attitude angle of carrier equipment Fixed_para Fixed parameters of communication equipment Atmosphere Atmospheric absorption factor relative_POS The relative position of the receiver to the transmitter

[0082] To facilitate subsequent adversarial simulations, the aforementioned data types were designed. The model parameters are then introduced, including initialization parameters and input parameters. The primary function of the initialization parameters is to initialize the model and set its initialization parameters. These initialization parameters only need to be set once and do not need to be set during each solution iteration.

[0083] The data input / output interface transmits all or part of the required input parameters to the model entity at once. Similarly, the data output interface, using the same data structure, transmits all the data that the entity in the current frame needs to output to the engine at once. The model interface is as follows: Figure 7 As shown:

[0084] Step 204: The transmitted signal is amplified by the transmitting antenna, introducing noise during propagation, including transmission loss (free space loss, atmospheric transmission loss), system thermal noise, and external interference, which is then received by the receiving antenna. The bit error rate of the link can be calculated using the signal-to-noise ratio.

[0085] Since communication equipment is typically mounted on other carriers such as aircraft or armored vehicles, and the azimuth and elevation angles of the antennas mentioned above are relative to the aircraft's coordinate system, a coordinate transformation is required during link resolution, which must be performed in the ground coordinate system. The transformation equation from the ground coordinate system S_g to the aircraft coordinate system S_b is as follows:

[0086]

[0087] Assuming the antenna is aligned with the body axis, and both its elevation and azimuth angles are 0, and considering the antenna as a rigid body, then the coordinates of a point on the antenna in the body coordinate system are as follows:

[0088]

[0089] Convert it to coordinates in the ground coordinate system:

[0090]

[0091] The azimuth and elevation angles of the antenna relative to the ground coordinate system can then be obtained:

[0092]

[0093] Step 3: Design a task-level model of the communication system and perform military communication network simulation. The process of military communication network simulation is as follows: Figure 4 As shown. To meet the real-time requirements of military communication network simulation, the mission-level model temporarily disregards directionality and only calculates the communication coverage. The communication network between different units on the battlefield primarily manifests as a tactical internet, which generally employs AdHoc networks. AdHoc network simulation is performed using communication network simulation software to calculate metrics such as packet loss rate, first packet arrival time, and latency. The communication network simulation process is as follows: Figure 4 As shown.

[0094] Step 301: Establish a task-level model of the communication system. In order to meet the real-time requirements of communication network simulation during adversarial simulation, the directionality of communication equipment is not considered. Only the antenna gain of the facilities is fixed, and only the coverage range of the equipment is considered.

[0095] Step 302: The process of using ns2 for communication network modeling and simulation is as follows: Figure 4As shown, the simulation scenario is first set up using a TCL script. The TCL script can set up an interface to receive external input data, such as the location information of nodes, as specified in the simulation. Then, the step size is set, specifying the duration of each simulation step. The resulting tr file is then analyzed to assess the effectiveness of the tactical network. The ns2 transmission model is relatively coarse, therefore the ns2 loss model and antenna model can be modified, thus establishing connections with other established models of different granularities. The specific meanings of the parameters of the wireless communication network are shown in Table 7.

[0096] Table 7. Specific meanings of parameters in wireless communication networks

[0097]

[0098]

[0099] When setting node positions in the TCL script, this can be written in a way that allows real-time reception of external input. In addition, the antenna gain can also receive input from an externally built model instead of being manually set. And because the antenna gain can only be set once and cannot be set for each node separately, this can receive an average value of the entire network gain.

[0100] After running the simulation scenario, the results can be saved to a tr file. The meaning of each column in the tr file is shown in Table 8:

[0101]

[0102]

[0103] The packet loss rate, first packet arrival time, and latency of the entire communication network are calculated by analyzing the tr file.

[0104] Step 303: Because ns2 needs to run in a Linux environment, socket communication is required to connect with the model running in a Windows environment. First, set the simulation step size. The start and end times of CBR transmission can be set as the simulation step size via external input, for example, 0.2s. Then, every 0.2s of the simulation time, the external model transmits information such as the node's position to the Linux environment via socket. The tcl file controls ns2 to perform a 0.2s simulation. The combat simulation program running in Windows acts as the client program, and the program running in the Linux environment that calls ns2 for tactical internet simulation acts as the server program. Every simulation step, the client transmits the information that needs to be transmitted to the tcl script to control the ns2 simulation to the server. After receiving the information, the server creates a child process and calls the system function system(ns***.tcl) to start an ns2 simulation for one simulation step. Each simulation stores the simulation results in a tr file.

[0105] Step 4: Establish a model of the impact of typical battlefield environments on communications. Typical battlefield environments include natural and artificial environments. Natural environments include atmospheric and terrain environments. Atmospheric environment models include oxygen and water vapor absorption models, while terrain environment models calculate the impact of terrain elevation data on electromagnetic wave propagation visibility. Artificial environments are represented by jamming models, calculating the impact of active suppression jamming on communications.

[0106] Step 401: First, establish an atmospheric environment model of the battlefield. Atmospheric absorption loss includes the absorption loss of oxygen and water vapor. According to the American Standard Atmospheric Model, the relationship between tropospheric atmospheric pressure P, temperature T, water vapor density ρ, and altitude h is as follows:

[0107]

[0108]

[0109] In the formula, α = 52561222, β = 0.034164794, R_0 is the Earth's radius (m), h is the altitude (m), T is the temperature (K), and the water vapor density in the troposphere is shown below:

[0110]

[0111] In the formula, h is the height, and c i It is a constant, which can be obtained by consulting relevant literature.

[0112] In addition to the tropospheric atmospheric model, there is also the absorption effect of tropospheric oxygen on electromagnetic waves; therefore, an oxygen absorption factor model needs to be established. The absorption of electromagnetic waves by tropospheric oxygen is the sum of absorptions at many resonant spectral lines near 60 GHz. The expression for the oxygen absorption factor for electromagnetic waves of any frequency f is as follows:

[0113]

[0114] In the formula, A N This is the summation term for each resonance number N.

[0115] The summation terms for each resonance number N are shown below:

[0116]

[0117] In the formula,

[0118] The absorption of water vapor in the troposphere consists of two parts: the resonance at 22.235 GHz, with an absorption factor of ξ_22(h), and the side effects of some resonance lines above 100 GHz, with an absorption factor of ξ_res(h).

[0119]

[0120] In the formula, F represents the frequency effect.

[0121]

[0122] The atmospheric absorption loss model can be linked to engineering-level and functional-level models in the form of noise loss. In addition, it can be added to the ns2 transmission model and recompiled to make the transmission loss of ns2 more accurate.

[0123] Step 402: Establish a visibility model of the terrain. Treat the earth as a plane. After determining the viewpoint and target point in the planar geodetic coordinate system, project the line of sight onto the xoy plane and obtain several sampling points evenly along the projection line. Query the DEM data to obtain the elevation of each sampling point and determine the visibility between the viewpoint and the target point.

[0124] Take several sampling points between the viewpoint and the target point. Starting from sampling point 1, if the slope of the line connecting the viewpoint and the sampling point is less than the slope of the line of sight, it means that the current sampling point does not affect the line of sight. Proceed to the next sampling point and continue to compare the slopes.

[0125] If the slope of the line connecting the viewpoint and a sampling point is greater than the slope of the line of sight, then there is no line of sight between the viewpoint and the target point, and the calculation ends. If the calculation can continue until the target point is reached, then there is a line of sight between the viewpoint and the target point.

[0126] Step 403: Establish the jammer model. In free space, the environment of the device to be jammed is nearly idealized; the emitted and received signals will not be subject to any interference. The coverage area of ​​the device to be jammed is determined by the ratio between the noise generated inside the receiver and the target echo power intensity. The target echo power intensity is shown below:

[0127]

[0128] In the formula, P t For transmission power, G t For the transmit antenna gain, G r Here, λ represents the receiving antenna gain, λ represents the wavelength, and R represents the distance.

[0129] The average power intensity of the thermal noise of the receiver of the device to be interfered with is shown below:

[0130] N = kT0B r F

[0131] The signal-to-noise ratio in free space is:

[0132]

[0133] In the formula, K is the Boltzmann constant, T0 is the receiver noise temperature, and B... r Where is the receiver bandwidth and F is the receiver noise figure.

[0134] Under active suppression interference, the interference power intensity of the receiver of the device to be interfered with is:

[0135]

[0136] The gain of the jamming device's antenna in the jammer direction is:

[0137]

[0138] In the formula, θ 0. θ is the motherboard gain of the receiver antenna, θ is the angle between the transmitter-to-receiver connection and the jammer-to-receiver connection, and K is a constant determined based on the receiver's performance parameters.

[0139] Under active interference suppression, the signal-to-interference ratio (SIR) at the receiver output is calculated as follows:

[0140]

[0141] In the formula, P x This refers to the signal power received by the receiver.

[0142] The established jamming parameters are shown in Table 9:

[0143] Table 9 Jammer Parameters

[0144]

[0145]

[0146] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this invention, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention.

[0147] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A real-time simulation method for multi-granularity modeling of communication systems in adversarial simulation, characterized in that, include: S1. Based on the performance requirements of the antenna design, the offline power pattern data of the antenna is obtained using electromagnetic simulation software. The power pattern data is then processed into a three-dimensional pattern and interpolated online to obtain online continuous data. S2, construct a link solution simulation model, and establish an engineering-level model and a functional-level model within it; then, through the frequency, bandwidth, transmit power, and antenna gain of the transmitting equipment, the antenna gain is calculated based on online continuous data and obtained through the engineering-level model using the offline solution in the first step and online interpolation. In the functional-level model, the maximum gain of the antenna is obtained by empirical formula. Then, the antenna's inherent parameters are used to calculate the main lobe, half-power beamwidth, sidelobe gain, and back lobe gain based on the antenna pattern, thereby depicting the antenna's directivity. Step S2 specifically includes: S201, based on the empirical formula for the maximum gain of a typical aperture antenna, calculates the maximum gain of a typical aperture antenna, and obtains the half-power beamwidth, sidelobe gain and backlobe gain according to the antenna's inherent parameters and the antenna pattern of online continuous data, thus depicting the antenna's directivity. S202, calculate the free space loss during transmission and the noise loss including system thermal noise and variables of typical battlefield environment; S203, model the functional level model, and pass the input parameters required by the functional level model to the functional level model at one time through data input and output parameters, or pass the data that the functional level model needs to output to the engine in the current frame in the form of a signal; S204, the signal is amplified by the transmitting antenna and then transmitted, and is received by the receiving antenna. The bit error rate of the link is calculated by the signal-to-noise ratio. Based on the directivity of the antenna, coordinate transformation is used to obtain the azimuth angle and elevation angle of the antenna pointing relative to the ground coordinate system. S3, establish the task-level model in the link solution simulation model, perform military communication network simulation, and only calculate the communication coverage. S4. Establish a model of the impact of typical battlefield environments on communications. The model includes: a battlefield atmospheric environment model, a terrain visibility model, and a jammer model. The granularity of the engineering-level model, the functional-level model, and the task-level model ranges from fine to coarse.

2. The real-time simulation method for multi-granularity modeling of communication systems in adversarial simulation according to claim 1, characterized in that, The typical battlefield environment includes: natural environment and artificial environment. The natural environment includes atmospheric environment and terrain environment. The atmospheric environment includes oxygen and water vapor. The battlefield atmospheric environment model is used to calculate the impact of oxygen and water vapor absorption loss on electromagnetic wave propagation in the battlefield atmospheric environment model. The terrain visibility model is used to calculate the impact of terrain elevation data on electromagnetic wave propagation in the terrain environment. The jamming machine model is used to calculate the impact of active suppression jamming on communication in the artificial environment.

3. The real-time simulation method for multi-granularity modeling of communication systems in adversarial simulation according to claim 1, characterized in that, The functional level model is provided with an output port to implement at least one of the following functions: obtaining the signal frequency of the transmitting device, obtaining the bandwidth of the transmitting device, obtaining the transmission gain of the transmitting device, obtaining the transmission power of the transmitting device, obtaining the location of the transmitting device, obtaining the link connectivity result, and obtaining the link bit error rate. The functional level model is provided with an input port to perform at least one of the following functions: inputting the model position, inputting the interference power, inputting the transmission parameters of the receiving device, and inputting the attitude angle of the carrier carried by the device.

4. The real-time simulation method for multi-granularity modeling of communication systems in adversarial simulation according to claim 1, characterized in that, Step S1 specifically includes: S101, perform antenna physical field calculation to obtain three-dimensional field strength pattern data of antenna two-dimensional gain pattern; S102, calculate the field strength data of the ideal isotropic antenna being referenced, then calculate the absolute gain value at each point, and finally obtain the three-dimensional gain pattern data of the antenna. S103. Based on the antenna three-dimensional gain pattern data obtained in step S102, a two-way interpolation algorithm is used to interpolate and obtain the continuous data required in the simulation process.

5. The real-time simulation method for multi-granularity modeling of communication systems in adversarial simulation according to claim 1, characterized in that, Step S3 specifically includes: S301, Establish a task-level model of the communication system; S302, using ns2 for military communication network simulation; S303 enables socket communication, allowing ns2 running in a Linux environment to connect with the model running in a Windows environment.

6. The real-time simulation method for multi-granularity modeling of communication systems in adversarial simulation according to claim 5, characterized in that, Step S4 specifically includes: S401, Establish a battlefield atmospheric environment model, including variables such as oxygen and water vapor absorption factors of electromagnetic waves. S402, establish a terrain visibility model, obtain multiple sampling points of the projection of the line of sight between the viewpoint and the target point on the xoy plane, and determine the slope of the line connecting the viewpoint and the sampling points, the slope of the line of sight, and the visibility between the viewpoint and the target point. S403. Establish a jammer model, including the target echo power intensity, the average thermal noise power intensity of the receiver of the device to be jammed, the signal-to-noise ratio in free space, the interference power intensity of the receiver of the device to be jammed under active suppression jamming, the gain of the jamming device antenna in the jammer direction, and the signal-to-interference ratio at the receiver output under active suppression jamming.

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