Radio wave propagation simulation system, radio wave propagation simulation device, radio wave propagation simulation method, and radio wave propagation simulation program

Abstract

Provided is a radio wave propagation simulation system comprising: a camera that captures an image; a 3D scanner that outputs 3D data on the basis of image data captured by the camera; and a radio wave propagation simulation device that simulates radio wave propagation. The radio wave propagation simulation device comprises: a 3D model creation unit that creates a 3D model of a target surface in a target environment on the basis of information acquired from the camera or the 3D scanner; a transmission / reception setting unit that sets the position of a transmitter and the position of a receiver in the target environment; a parameter setting unit that sets parameters of the simulation in the target environment; a surface characteristic estimation unit that estimates the surface characteristics of the target surface in the image on the basis of the image captured by the camera and a learning model created by machine learning; and a surface characteristic reflection unit that reflects information about the surface characteristics estimated by the surface characteristic estimation unit in the 3D model created by the 3D model creation unit.

Description

Radio wave propagation simulation system, radio wave propagation simulation device, radio wave propagation simulation method, and radio wave propagation simulation program

[0001] The present invention relates to a radio wave propagation simulation system, a radio wave propagation simulation device, a radio wave propagation simulation method, and a radio wave propagation simulation program.

[0002] As a method for simulating radio wave propagation, a ray tracing method, an electromagnetic field analysis, or the like is generally performed using a computer.

[0003] To obtain accurate simulation results, it is necessary to create a precise three-dimensional (3D) model of the target area. In recent years, the use of camera-based 3D scanners and LiDAR (Light Detection and Ranging) has made it relatively easy to grasp and reproduce the shape.

[0004] To obtain even more accurate simulation results, it is necessary to accurately set not only the shape of the 3D model but also the influence of surface irregularities. In practice, however, it is common to use a method in which the material of all objects is unified to metal (see, for example, Non-Patent Document 1).

[0005] On the other hand, the higher the frequency band, the greater the influence of surface irregularities, and this influence cannot be ignored, especially in the millimeter wave and terahertz bands, because the higher the frequency, the greater the relative irregularities with respect to the wavelength (see, for example, Non-Patent Document 2).

[0006] G. Durgin et. al., "An advanced 3D ray launching method for wireless propagation prediction," 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion. Vittorio Degli-Esposti, et al,. "Measurement and Modelling of Scattering From Buildings," IEEE Trans. Vol 55, Issue 1, Jan. 2007

[0007] FIG. 12 is a diagram showing an example of a target area 110 for which radio wave propagation simulation is performed by the conventional radio wave propagation simulation device 100.

[0008] The radio wave propagation simulator 100 performs, for example, a radio wave propagation simulation in the target area 110. The radio wave propagation simulator 100 performs, for example, a radio wave propagation simulation using a ray tracing method, electromagnetic field analysis, or the like, using a computer (not shown).

[0009] As shown in FIG. 12, a target area 110 may include, for example, a plurality of objects 120, a transmitter 130, and a receiver 140 arranged therein.

[0010] The plurality of objects 120 are, for example, buildings existing within the target area 110, and their positions, shapes, etc. are reproduced using a 3D scanner (not shown) using a camera (not shown), a LiDAR (not shown), etc. On the other hand, the surface characteristics of each of the plurality of objects 120 are not reproduced.

[0011] The transmitter 130 performs wireless communication with the receiver 140 by transmitting radio waves toward the receiver 140. In the example shown in Fig. 12, the radio waves transmitted from the transmitter 130 arrive at the receiver 140 after being reflected by one of the objects 120.

[0012] The receiver 140 performs wireless communication with the transmitter 130 by receiving radio waves transmitted from the transmitter 130. In the example shown in Fig. 12, the radio waves received by the receiver 140 are transmitted from the transmitter 130, reflected by one of the objects 120, and then arrive at the receiver 140.

[0013] Fig. 13 is a flowchart showing an example of processing of a radio wave propagation simulation performed by the conventional radio wave propagation simulation device 100 for the target area 110 shown in Fig. 12. The flowchart shown in Fig. 13 is started, for example, when a radio wave propagation simulation is performed by the radio wave propagation simulation device 100.

[0014] In step S101, the radio wave propagation simulator 100 creates, for example, a 3D model of the target area 110. The radio wave propagation simulator 100 creates the 3D model of the target area 110 using, for example, a 3D scanner using a camera, LiDAR, or the like. As a result, the positions, shapes, and the like of the multiple objects 120 are reproduced. On the other hand, the unevenness and surface condition of the multiple objects 120 are not reproduced.

[0015] In step S102, the radio wave propagation simulator 100 sets the transmitting and receiving positions, for example, the positions of the transmitter 130 and the receiver 140 shown in FIG.

[0016] In step S103, the radio wave propagation simulator 100 sets simulation parameters. The radio wave propagation simulator 100 sets parameters such as frequency band, transmission power, etc. At this time, the unevenness and surface condition of each of the multiple objects 120 are not reproduced and are therefore unknown, so the radio wave propagation simulator 100 sets the parameters assuming that all of the multiple objects 120 are made of metal and have, for example, a mirror-finished surface.

[0017] In step S104, the radio wave propagation simulator 100 performs a radio wave propagation simulation using a computer (not shown). At this time, the radio wave propagation simulator 100 performs the radio wave propagation simulation using, for example, a ray tracing method or electromagnetic field analysis. This completes the processing shown in the flowchart of FIG. 13.

[0018] As described above, according to conventional radio wave propagation simulation techniques, the position and shape of the target object (plural objects 120) are reproduced and reflected in a 3D model. However, the surface irregularities and surface condition of the target object are not reproduced, so parameters are set assuming that the target object is made of metal and has a mirror-like surface, for example. On the other hand, in order to obtain more accurate simulation results using radio wave propagation simulation, it is necessary to accurately reflect not only the position and shape of the target object, but also the effects of surface irregularities.

[0019] However, in order to simulate radio wave propagation more accurately using a computer, it is extremely laborious and unrealistic to grasp all of the reflection characteristics of the surface of an object and reflect them in a 3D model.

[0020] 14A and 14B are diagrams showing examples of an ER model. Fig. 14A is a diagram showing an image of an incoming wave and a reflected wave when the reflecting object has no irregularities. Fig. 14B is a diagram showing an image of an incoming wave and a reflected wave when the reflecting object has irregularities.

[0021] Here, the ER (Effective Roughness) model is a model of how radio waves are reflected as specular reflection or scattering depending on the surface condition of a material, as shown in, for example, Non-Patent Document 2.

[0022] As shown in Fig. 14(a), when an incoming wave is reflected by a reflecting object without any unevenness, the reflected wave is reflected at the same angle (specular reflection). On the other hand, as shown in Fig. 14(b), when an incoming wave is reflected by a reflecting object with unevenness, the reflected wave is reflected (scattered) at various angles depending on the unevenness of the surface of the reflecting object.

[0023] In this way, if the surface of a reflecting object is uneven, the unevenness affects the reflected wave. As mentioned above, the higher the frequency, the greater the unevenness relative to the wavelength, so the higher the frequency band, the greater the effect of surface unevenness, and this effect cannot be ignored, especially in the millimeter wave band and terahertz band.

[0024] Therefore, the present disclosure aims to provide a technology that can easily reflect the surface condition of an object in a 3D model by estimating the surface condition of the object using a camera and machine learning, thereby enabling radio wave propagation simulation results to be obtained with higher accuracy than conventional methods.

[0025] One aspect of the radio wave propagation simulation system includes a camera that captures images, a 3D scanner that outputs 3D data based on image data captured by the camera, and a radio wave propagation simulation device that performs radio wave propagation simulations. The radio wave propagation simulation device is characterized by comprising: a 3D model creation unit that creates a 3D model of a target surface in a target environment based on information acquired from the camera or the 3D scanner; a transmission / reception setting unit that sets the positions of a transmitter and a receiver in the target environment; a parameter setting unit that sets parameters for simulation in the target environment; a surface characteristic estimation unit that estimates surface characteristics of the target surface in the image based on an image captured by the camera and a learning model created by machine learning; and a surface characteristic reflection unit that reflects information on the surface characteristics estimated by the surface characteristic estimation unit in the 3D model created by the 3D model creation unit.

[0026] A radio wave propagation simulation device according to one aspect is a radio wave propagation simulation device in a radio wave propagation simulation system having a camera that captures images, a 3D scanner that outputs 3D data based on image data captured by the camera, and a radio wave propagation simulation device that performs radio wave propagation simulation. The radio wave propagation simulation device is characterized by comprising: a 3D model creation unit that creates a 3D model of a target surface in a target environment based on information acquired from the camera or the 3D scanner; a transmission / reception setting unit that sets the positions of a transmitter and a receiver in the target environment; a parameter setting unit that sets parameters for simulation in the target environment; a surface characteristic estimation unit that estimates surface characteristics of the target surface in the image based on an image captured by the camera and a learning model created by machine learning; and a surface characteristic reflection unit that reflects information on the surface characteristics estimated by the surface characteristic estimation unit in the 3D model created by the 3D model creation unit.

[0027] One aspect of the radio wave propagation simulation method is a radio wave propagation simulation method in a radio wave propagation simulation system having a camera that captures images, a 3D scanner that outputs 3D data based on image data captured by the camera, and a radio wave propagation simulation device that performs radio wave propagation simulation, wherein the radio wave propagation simulation device creates a 3D model of a target surface in a target environment based on information acquired from the camera or the 3D scanner, sets the positions of a transmitter and a receiver in the target environment, sets simulation parameters for the target environment, estimates surface characteristics of the target surface in the image based on the image captured by the camera and a learning model created by machine learning, and reflects information on the estimated surface characteristics in the created 3D model.

[0028] A radio wave propagation simulation program according to one aspect causes a computer to execute the processing of the radio wave propagation simulation method described above.

[0029] According to the present disclosure, by using a camera and machine learning to estimate the surface condition of an object, the surface condition of the object can be easily reflected in a 3D model, resulting in radio wave propagation simulation results with higher accuracy than conventional methods.

[0030] 11 is a diagram showing an example of the configuration of a radio wave propagation simulation system according to an embodiment. FIG. 12 is a flowchart showing an example of the operation (processing) of the radio wave propagation simulation device shown in FIG. 1. FIG. 13 is a flowchart showing an example of a method for calculating characteristic information of a target surface from 3D model information. FIG. 14 is a diagram showing an example of a captured image and a created 3D model. FIG. 15 is a flowchart showing an example of a method for measuring the shape of unevenness of a target surface using a light source. FIG. 16 is a diagram showing a specific example of the measurement method shown in the flowchart of FIG. 5. FIG. 17 is a diagram showing an example of a method for estimating unevenness using a light source. FIG. 18 is a diagram showing an example of input information when a learning model creation unit in the radio wave propagation simulation device shown in FIG. 1 creates a learning model. FIG. 19 is a diagram showing an example of input and output information when a surface characteristic estimation unit in the radio wave propagation simulation device shown in FIG. 1 estimates surface characteristics using a learning model. FIG. 19 is a diagram showing an example of reflection characteristics. FIG. 19 is a conceptual diagram showing an example of the hardware configuration of a processing circuit included in the radio wave propagation simulation device in the embodiment shown in FIGS. 1 to 10. FIG. 19 is a diagram showing an example of a target area for which radio wave propagation simulation is performed by a conventional radio wave propagation simulation device. FIG. 19 is a flowchart showing an example of the processing of radio wave propagation simulation performed by a conventional radio wave propagation simulation device for the target area shown in FIG. 12. FIG. 19 is a diagram showing an example of an image of an ER model.

[0031] Hereinafter, embodiments of the radio wave propagation simulation system, radio wave propagation simulation device, radio wave propagation simulation method, and radio wave propagation simulation program disclosed herein will be described with reference to the drawings. Note that in the following embodiments, the radio wave propagation simulation system will be described as an example of the radio wave propagation simulation system, radio wave propagation simulation device, radio wave propagation simulation method, and radio wave propagation simulation program disclosed herein.

[0032] <Configuration of an embodiment> FIG. 1 is a diagram showing an example of the configuration of a radio wave propagation simulation system 1 according to an embodiment.

[0033] 1 , the radio wave propagation simulation system 1 includes a camera 11, a 3D scanner 12, a LiDAR 13, and a radio wave propagation simulation device 30. Note that the radio wave propagation simulation system 1 may include at least one of the camera 11, the 3D scanner 12, and the LiDAR 13.

[0034] The camera 11 captures an image of the target environment in which the radio wave propagation simulation is performed, and acquires image data of the target environment (hereinafter also referred to as "image information" or simply "image"). The camera 11 may be built into the 3D scanner 12 or the LiDAR 13.

[0035] The 3D scanner 12 creates 3D data (3D model) of the target environment based on, for example, image data of the target environment captured by the camera 11. The 3D scanner 12 may have the camera 11 built in. In this case, the 3D scanner 12 creates 3D data (3D model) of the target environment by, for example, projecting light from a laser, LED (Light Emitting Diode), or the like onto the target environment and capturing and calculating the reflection time difference and irradiation angle with the built-in camera 11. The 3D model of the target environment may be created by the radio wave propagation simulation device 30 based on the image data output from the camera 11 or the 3D data output from the 3D scanner 12.

[0036] The LiDAR 13 is, for example, a sensor device that irradiates a target environment with laser light and measures the distance to a target object in the target environment and the shape of the target object based on information about the reflected light. The LiDAR 13 may have a built-in camera 11 and a 3D scanner 12. The LiDAR 13 creates 3D data (a 3D model) of the target environment based on, for example, measurement information measured by the LiDAR 13 and image data acquired by the camera 11. The 3D model of the target environment may be created by the radio wave propagation simulation device 30 based on the measurement information output from the LiDAR 13 and the image data output from the camera 11.

[0037] The radio wave propagation simulator 30 performs a radio wave propagation simulation of a target environment by, for example, using a computer 36 (described later) to perform a ray tracing method, an electromagnetic field analysis, or the like.

[0038] The radio wave propagation simulator 30 has a processor 91 (see FIG. 11 ) described below, such as a CPU (Central Processing Unit), MPU (Micro Processing Unit), or GPU (Graphics Processing Unit), which operates by executing a program. The radio wave propagation simulator 30 also has a memory 92 (see FIG. 11 ), described below, and executes a predetermined program stored in the memory 92 to operate the processor 91 and perform radio wave propagation simulation processing.

[0039] The radio wave propagation simulator 30 functions as the following units, for example, by executing a predetermined program stored in a memory 92 (see FIG. 11 ), which will be described later. The radio wave propagation simulator 30 functions as a 3D model creation unit 31, a transmission / reception setting unit 32, a parameter setting unit 33, a surface characteristic estimation unit 34, a surface characteristic reflection unit 35, a calculator 36, and a learning model creation unit 37. Note that each of the above functions may be realized by a program executed by a processing unit (not shown) included in the radio wave propagation simulator 30, or by hardware.

[0040] The radio wave propagation simulation device 30 also has a storage unit 40. The radio wave propagation simulation device 30 may also have other configurations or functions, such as an information acquisition unit, a result output unit, an operation unit, a display unit, a control unit, etc., which are not shown in the figures. Other general configurations or functions of the radio wave propagation simulation device 30 will not be described or illustrated.

[0041] The 3D model creation unit 31, the transmission / reception setting unit 32, the parameter setting unit 33, the surface characteristic estimation unit 34, the surface characteristic reflection unit 35, the calculator 36, and the learning model creation unit 37 execute predetermined programs to perform the following processing.

[0042] The 3D model creation unit 31 creates a 3D model of a predetermined target environment (simulation environment) based on information acquired from the camera 11, the 3D scanner 12, and the LiDAR 13. If a 3D model has already been created by the above-mentioned 3D scanner 12 or LiDAR 13, the 3D model creation unit 31 acquires the created 3D model and creates the 3D model using the acquired 3D model. The 3D model creation unit may have the functions of the above-mentioned camera 11, the 3D scanner 12, and the LiDAR 13.

[0043] The transmission / reception setting unit 32 sets the positions of the transmitter and the receiver in the target environment. Note that the set positions of the transmitter and the receiver may be based on, for example, information stored in a storage unit 40 (described later) or information received from a user via an operation unit (not shown).

[0044] The parameter setting unit 33 sets simulation parameters for the target environment. The simulation parameters include, for example, a frequency band, a transmission power, the number of times radio waves are reflected, whether or not diffraction occurs, the gain of a transmitting / receiving antenna, the radiation pattern of a transmitting / receiving antenna, and temperature. The simulation parameters may be based on information stored in a storage unit 40 (described later) or information received from a user via an operation unit (not shown). The simulation parameters may also be calculated by a calculation unit (not shown) based on the information stored in the storage unit 40 (described later) or information received from a user via an operation unit (not shown).

[0045] The surface characteristic estimation unit 34 estimates the surface characteristics (surface characteristic information) of objects in the target environment based on images captured by the camera 11. The surface characteristic estimation unit 34 may also cause the camera 11 to capture a new image and estimate the surface characteristics of the objects in the target environment based on the newly captured image. It is assumed that the surface characteristics of the objects in the target environment are learned by machine learning performed by the learning model creation unit 37 based on a database in which images and surface characteristics (surface characteristic information) are previously linked, and a model capable of estimating (determining) the surface characteristics is created. Therefore, the surface characteristic estimation unit 34 estimates the surface characteristics of the objects in the target environment based on the images and the created model. The surface characteristics (surface characteristic information) of the objects include, for example, reflection characteristics, reflectance, scattering characteristics, etc., for a target frequency.

[0046] The surface characteristic reflecting unit 35 reflects the information on the surface characteristics estimated by the surface characteristic estimating unit 34 in the 3D model created by the 3D model creating unit 31 .

[0047] The computer 36 performs a radio wave propagation simulation of the target environment based on the 3D model created by the 3D model creation unit 31 and on which the surface characteristic information is reflected by the surface characteristic reflection unit 35, and on the set parameters. As described above, the computer 36 performs the radio wave propagation simulation of the target environment by, for example, implementing a known ray tracing method or electromagnetic field analysis. Note that the computer 36 may be provided outside the radio wave propagation simulation device 30.

[0048] The learning model creation unit 37 creates, for example, a learning model (hereinafter also simply referred to as a "model") to be used by the surface characteristic estimation unit 34. The learning model creation unit 37 performs machine learning using a database in which images and surface characteristics (surface characteristic information) are linked in advance, and creates a learning model that enables the surface characteristic estimation unit 34 to estimate surface characteristics based on the image. The created learning model is used by the surface characteristic estimation unit 34. The database used for machine learning and the created learning model may be stored in the storage unit 40.

[0049] The storage unit 40 is a volatile or non-volatile storage medium, such as a hard disk drive (HDD), a solid state drive (SSD), a dynamic random access memory (DRAM), or other semiconductor memory. The storage unit 40 is connected to each unit of the radio wave propagation simulation device 30 via a bus (system bus, not shown), for example, so that various types of information can be input and output, and each unit of the radio wave propagation simulation device 30 writes and reads various types of information to and from the storage unit 40.

[0050] The storage unit 40 also stores, for example, information (image information and measurement information) acquired from the camera 11, the 3D scanner 12, the LiDAR 13, etc., information on the created 3D model, and information on various parameters received from the user via an operation unit (not shown). The storage unit 40 also stores information on surface characteristics estimated by the surface characteristic estimation unit 34, 3D data (3D models) in which the surface characteristic information is reflected by the surface characteristic reflection unit 35, and learning data created by the learning model creation unit 37. The storage unit 40 also stores, for example, various arithmetic expressions, coefficients, thresholds, calculation results, etc. used in calculations by the calculator 36 and other units.

[0051] The storage unit 40 may be provided outside the radio wave propagation simulation device 30 and may be connected to the radio wave propagation simulation device 30 by wire or wirelessly. The storage unit 40 may be an external storage medium such as a memory card or a DVD (Digital Versatile Disc), or may be online storage, or may be shared with a memory 92 (see FIG. 11 ) described later.

[0052] <Operation of One Embodiment> Fig. 2 is a flowchart showing an example of the operation (processing) of the radio wave propagation simulation device 30 shown in Fig. 1. The flowchart shown in Fig. 2 starts when a target environment is photographed or measured by the 3D scanner 12 or LiDAR 13 using the camera 11.

[0053] In step S1, the 3D model creation unit 31 of the radio wave propagation simulation device 30 creates a 3D model. The 3D model creation unit 31 creates a 3D model of the target environment (simulation environment) based on, for example, information (images) acquired from a 3D scanner 12 using a camera 11 or measurement information acquired from a LiDAR 13 instead of a 3D scanner. Note that the 3D model may also be created by the 3D scanner 12 using a camera 11 or the LiDAR 13.

[0054] In step S2, the transmission / reception setting unit 32 of the radio wave propagation simulator 30 performs transmission / reception setting. For example, the transmission / reception setting unit 32 sets the positions of the transmitter and the receiver in the target environment.

[0055] In step S3, the parameter setting unit 33 of the radio wave propagation simulator 30 sets simulation parameters. The simulation parameters include, for example, frequency band, transmission power, the number of times radio waves are reflected, whether diffraction occurs, gain of the transmitting and receiving antennas, radiation patterns of the transmitting and receiving antennas, temperature, etc. The parameter setting unit 33 sets, for example, at least one of these parameters.

[0056] In step S4, the surface characteristic estimation unit 34 of the radio wave propagation simulation device 30 estimates surface characteristics from the image of the target environment. The surface characteristic estimation unit 34 estimates the surface characteristics (surface characteristic information) of objects in the target environment, for example, by causing the camera 11 to take a photograph of the target environment or by using the image acquired in step S1. The surface characteristic estimation unit 34 estimates the surface characteristics (surface characteristic information) of objects in the captured image of the target environment (e.g., reflection characteristics, reflectance, scattering characteristics, etc. for the target frequency) based on the learning model created by the learning model creation unit 37.

[0057] For the surface characteristics of objects in the target environment, for example, machine learning is performed by the learning model creation unit 37 based on a database in which images and surface characteristic information are linked in advance, and a learning model capable of determining (estimating) surface characteristics is created. Therefore, the surface characteristic estimation unit 34 estimates the surface characteristics (surface characteristic information) of objects in the target environment based on the created learning model.

[0058] In step S5, the surface characteristic reflecting unit 35 of the radio wave propagation simulator 30 reflects the information on the surface characteristics in the 3D model. That is, the surface characteristic reflecting unit 35 reflects the information on the surface characteristics estimated in step S4 in the 3D model created in step S1.

[0059] In step S6, the computer 36 of the radio wave propagation simulation device 30 performs a radio wave propagation simulation of the target environment. That is, the computer 36 performs a radio wave propagation simulation of the target environment based on the 3D model created by the 3D model creation unit 31 and on which surface characteristic information has been reflected by the surface characteristic reflection unit 35, and on the set parameters. As described above, the computer 36 performs a radio wave propagation simulation of the target environment by, for example, implementing a known ray tracing method or electromagnetic field analysis. Then, the computer 36 (radio wave propagation simulation device 30) displays or outputs the simulation results, or stores them in the storage unit 40, and this flowchart ends.

[0060] As described above, by performing the processing of the flowchart shown in FIG. 2, it is possible to easily set surface characteristics in a radio wave propagation simulation, and to realize a radio wave propagation simulation with higher accuracy than conventional methods.

[0061] 3 is a flowchart showing an example of a method for calculating characteristic information of a target surface from 3D model information. The processing of the flowchart shown in FIG. 3 may be performed by the radio wave propagation simulation device 30, or may be performed by a simulation device (not shown) separate from the radio wave propagation simulation device 30. The calculated characteristic information of the target surface (reflection characteristics, reflectance, scattering characteristics, etc. for the target frequency) is used in machine learning by the learning model creation unit 37, for example.

[0062] 4A and 4B are diagrams showing an example of a captured image and a created 3D model. Fig. 4A is a diagram showing an example of an image of the surface (target surface) of an object in the target environment captured by the camera 11. Fig. 4B is a diagram showing an example of a 3D model of the surface (target surface) of an object in the target environment created by the 3D model creation unit 31.

[0063] An example of a method for calculating characteristic information of a target surface from 3D model information will be described below based on Fig. 3 and with reference to Fig. 4. The flowchart shown in Fig. 3 starts when characteristic information of a target surface is calculated.

[0064] In step S11, a 3D model of the target surface is created, for example, by a 3D scanner 12 or LiDAR 13 using a camera 11. Note that the 3D scanner 12 or LiDAR 13 using the camera 11 used in creating the 3D model in step S11 of Fig. 3 is expected to have higher resolution than the 3D scanner 12 or LiDAR 13 using the camera 11 used in step S1 of Fig. 2. Fig. 4(a) shows an example of an image of the target surface, and Fig. 4(b) shows an example of the created 3D model of the target surface.

[0065] In step S12, parameters such as the frequency band, transmission power, the number of times radio waves are reflected, whether or not diffraction occurs, the gain of the transmitting and receiving antennas, the radiation pattern of the transmitting and receiving antennas, and the temperature are set. The above parameters are set in the radio wave propagation simulator 30 or a simulator (not shown) based on, for example, information stored in the storage unit 40 or information received from a user via an operation unit (not shown).

[0066] In step S13, while changing the transmitting and receiving positions, characteristic information of the target surface (reflection characteristics, reflectance, scattering characteristics, etc. for the target frequency) is calculated, for example, by simulation, etc. The simulation is performed, for example, by the radio wave propagation simulation device 30 or a simulation device not shown, etc., by carrying out a known ray tracing method, electromagnetic field analysis, etc. The simulation device not shown may be the computer 36 of the radio wave propagation simulation device 30.

[0067] As described above, by performing the processes (methods) shown in steps S11 to S13 in the flowchart shown in Fig. 3, characteristic information of the target surface (reflection characteristics, reflectance, scattering characteristics, etc. for the target frequency) is simulated, and the characteristic information of the target surface is calculated. The calculated characteristic information of the target surface (reflection characteristics, reflectance, scattering characteristics, etc. for the target frequency) is used, for example, in machine learning by the learning model creation unit 37. As a result, a learning model is created by machine learning by the learning model creation unit 37, and the created learning model can be used by the surface characteristic estimation unit 34 to estimate surface characteristics.

[0068] Fig. 5 is a flowchart showing an example of a method for measuring the shape of projections and recesses on a target surface using a light source. The processing of the flowchart shown in Fig. 5 may be performed by the radio wave propagation simulator 30, or may be performed by a measuring device (not shown) separate from the radio wave propagation simulator 30.

[0069] Information on the measured uneven shape of the target surface is used, for example, to create a 3D model in step S11 of the flowchart shown in Fig. 3. Note that information on the measured uneven shape of the target surface may also be used to create a 3D model in step S1 of the flowchart shown in Fig. 2. Information on the measured uneven shape of the target surface may also be used in machine learning by the learning model creation unit 37 or in radio wave propagation simulation by the computer 36.

[0070] Fig. 6 is a diagram showing a specific example of the measurement technique shown in the flowchart of Fig. 5. Fig. 6 shows the positional relationship between the camera 11, the light source 14, and the wall 15. In the example shown in Fig. 6, the camera 11 captures an image of the wall 15 from the front. Since the wall 15 has irregularities and the light source 14 is located at an angle, the shadow 16 does not appear in the image when the light source is off, but does appear in the image when the light source is on. The wall 15 is an example of a "surface of an object" or a "target surface."

[0071] An example of a method for measuring the shape of the unevenness of a target surface using a light source will be described below based on Fig. 5 and with reference to Fig. 6. The process of the flowchart shown in Fig. 5 starts when measuring the shape of the unevenness of the target surface (wall 15).

[0072] In step S21, the light source 14 is turned off and an image of the wall 15 is taken. That is, an image of the wall 15 when the light source 14 is turned off is taken. As shown in Fig. 6, no shadow 16 appears in the image when the light source 14 is turned off.

[0073] In step S22, the light source 14 is moved. After the light source 14 is moved, the distance from the wall 15 (the distance and angle between the light source 14 and the wall 15) and the like are measured and recorded.

[0074] In step S23, the light source 14 is turned on and an image of the wall 15 is taken. That is, the wall 15 is taken with the light source 14 turned on, and the state of the shadow 16 is recorded. As shown in Fig. 6, the shadow 16 appears in the image when the light source 14 is on. The state of the shadow 16 at this time, etc., are recorded.

[0075] In step S24, it is determined whether or not image capture has been performed from multiple directions. If it is determined that image capture has been performed from multiple directions (Yes side), the process proceeds to step S25. On the other hand, if it is determined that image capture has not been performed from multiple directions (No side), the process returns to step S22, and the processes from step S22 to step S24 are repeated. Here, "multiple" in multiple directions refers to, for example, a number that allows the state of unevenness to be measured in the next step S25, and the number is thought to vary depending on the state of unevenness of the wall 15 (target surface), etc.

[0076] In step S25, the image taken in step S21 when the light source 14 is off is compared with the image taken in step S23 when the light source 14 is on, and the state of unevenness is measured. That is, the image taken in step S21 when the light source 14 is off is compared with the state of multiple images taken in step S23 when the light source 14 is on, the shadows 16 in the multiple images are measured, and the state of unevenness of the wall 15 (target surface) is measured based on the results.

[0077] As described above, the depth, shape, etc. of the unevenness of the target surface are measured by performing the processes (methods) shown in steps S21 to S25 in the flowchart shown in Fig. 5. That is, the depth, shape, etc. of the unevenness of wall 15 are measured based on the size and state of each shadow 16 formed when light is irradiated from multiple directions by light source 14 onto wall 15. Information on the measured unevenness, such as the shape of the target surface, is used, for example, in creating a 3D model, machine learning, simulation, etc.

[0078] 7 is a diagram showing an example of a method for estimating unevenness using a light source. The method for estimating unevenness using a light source shown in FIG. 7 is used, for example, in measuring unevenness in step S25 of FIG.

[0079] In Figure 7, the wall is shown as a straight line at the bottom, and the unevenness (convex portion) of the wall is shown as a square slightly left of the center. A light source is also shown in the upper left, and a shadow cast by the light from the light source is shown to the right of the convex portion of the wall at the bottom. Here, in Figure 7, X1m is the height of the convex portion of the wall (depth of the unevenness), and X2m is the length of the shadow. Also in Figure 7, Y1m is the vertical distance between the light source and the wall, and Y2m is the horizontal distance between the light source and the tip of the shadow.

[0080] In FIG. 7, the following equation (1) holds.

[0081] Y1:Y2=X1:X2...(1)

[0082] According to equation (1), the length X2 of the shadow is expressed by the following equation (2).

[0083] X2=(Y2*X1) / Y1...(2)

[0084] An example of a method for estimating unevenness using a light source is based on the above formulas (1) and (2). That is, for example, the depth of unevenness in one direction is basically estimated based on a simple method based on the above formulas (1) and (2). The estimated result is then used, for example, in measuring unevenness in step S25 of FIG. 5.

[0085] Fig. 8 is a diagram showing an example of input information when the learning model creation unit 37 in the radio wave propagation simulation device 30 shown in Fig. 1 creates a learning model. As described in Fig. 1, the learning model creation unit 37 creates a learning model (model) to be used in, for example, the surface characteristic estimation unit 34. Fig. 8 shows an example of information (database) input when the learning model creation unit 37 performs machine learning.

[0086] As shown in FIG. 8, when the learning model creation unit 37 performs machine learning, an image of an object, signal information (for example, incident angle, frequency, etc.), reflection characteristics, etc. are input.

[0087] Based on this input information, machine learning is performed by the learning model creation unit 37, and a learning model to be used by the surface characteristic estimation unit 34 is created. Note that the information (database) input in the machine learning and the created learning model may be stored in the storage unit 40.

[0088] FIG. 9 is a diagram showing an example of input and output information when the surface characteristic estimation unit 34 in the radio wave propagation simulation device 30 shown in FIG. 1 estimates surface characteristics using a learning model.

[0089] 9, when a roughness image (an image of a target surface having roughness) is input by the surface characteristic estimation unit 34, the reflection characteristics, scattering rate, reflection intensity, etc. of the object shown in the roughness image are output based on the learning model created by the learning model creation unit 37. In this way, the surface characteristic estimation unit 34 estimates the surface characteristics of the surface of the object (target surface) in the captured image.

[0090] At this time, signal information such as the angle of incidence and frequency is also input. In this case, information corresponding to the input information is output based on the learning model created by the learning model creation unit 37, for example. That is, surface characteristics such as the reflection characteristics, scattering rate, and reflection intensity are output in accordance with the input of the topography image and signal information.

[0091] Fig. 10 is a diagram showing an example of reflection characteristics. In Fig. 10, the vertical axis represents reflected power and the horizontal axis represents reflection direction. Fig. 10 shows that when the reflection direction is different, the reflected power also differs.

[0092] That is, Figure 10 shows that when the reflection direction varies depending on the unevenness (surface characteristics) of the target surface, the reflected power also varies. As mentioned above, the higher the frequency band, the greater the influence of surface unevenness, and this influence cannot be ignored, especially in the millimeter wave band and terahertz band. This is because the higher the frequency, the greater the unevenness relative to the wavelength. Therefore, in order to obtain accurate radio wave propagation simulation results, it is necessary to set the unevenness of the target surface as accurately as possible.

[0093] <Effects of one embodiment> As described above, according to the embodiment shown in FIGS. 1 to 10, the surface condition of the object is estimated using the camera 11 and machine learning, so that the surface condition of the object can be easily reflected in the 3D model, and as a result, it is possible to obtain radio wave propagation simulation results with higher accuracy than conventional methods.

[0094] 1 to 10, the reflection characteristics of an object in an image are learned by machine learning, and surface characteristics such as the reflection characteristics, scattering rate, reflection intensity, etc. of the object surface can be output. This makes it easier than ever to reflect the surface condition of the object in a 3D model, and as a result, it is possible to obtain radio wave propagation simulation results that are more accurate and more suited to actual conditions than ever before.

[0095] <Hardware Configuration Example> Fig. 11 is a conceptual diagram showing an example of the hardware configuration of the processing circuitry 90 included in the radio wave propagation simulation device 30 in the embodiment shown in Figs. 1 to 10. The functions described above are realized by the processing circuitry 90. In one aspect, the processing circuitry 90 includes at least one processor 91 and at least one memory 92. In another aspect, the processing circuitry 90 includes at least one dedicated hardware 93.

[0096] When the processing circuit 90 includes a processor 91 and a memory 92, each function is realized by software, firmware, or a combination of software and firmware. At least one of the software and firmware is written as a program. At least one of the software and firmware is stored in the memory 92. The processor 91 realizes each function by reading and executing the program stored in the memory 92.

[0097] When the processing circuitry 90 includes dedicated hardware 93, the processing circuitry 90 may be, for example, a single circuit, multiple circuits, a programmed processor, or a combination thereof. Each function is implemented by the processing circuitry 90.

[0098] Each function of the radio wave propagation simulator 30 may be partially or entirely configured by hardware, or may be configured as a program executed by a processor. In other words, the radio wave propagation simulator 30 can also be realized by a computer and a program, and the program can be stored in a storage medium or provided via a network.

[0099] 1 to 11 , one aspect of the present disclosure has been described using the radio wave propagation simulation system 1 including the camera 11, the 3D scanner 12, the LiDAR 13, and the radio wave propagation simulation device 30 as an example, but the present disclosure is not limited to this. The form of the system configuration and the like of the present disclosure is not particularly limited.

[0100] 1 to 11, the radio wave propagation simulation system 1 has been described as an example of one aspect of the present disclosure, but the present disclosure is not limited to this. The present disclosure can also be realized as a radio wave propagation simulation method in which processing steps are performed in each part of the radio wave propagation simulation system 1 or the radio wave propagation simulation device 30.

[0101] The present disclosure can also be realized as a radio wave propagation simulation program that causes a computer to execute the processing steps in each part of the radio wave propagation simulation system 1 or the radio wave propagation simulation device 30.

[0102] The present disclosure can also be realized as a storage medium (non-transitory computer-readable storage medium) on which a radio wave propagation simulation program is stored. The radio wave propagation simulation program can be stored and distributed on removable media such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a USB (Universal Serial Bus) memory. The radio wave propagation simulation program may be uploaded to a network via a network interface (not shown) included in the radio wave propagation simulation device 30, or may be downloaded from the network and stored in the memory 92.

[0103] The features and advantages of the embodiments will be apparent from the above detailed description. It is intended that the claims encompass the features and advantages of the above-described embodiments without departing from the spirit and scope of the claims. Furthermore, any improvements and modifications will be readily apparent to those skilled in the art. Therefore, it is not intended that the scope of the inventive embodiments be limited to the above-described embodiments, and appropriate improvements and equivalents within the scope of the disclosed embodiments may be utilized.

[0104] 1...Radio wave propagation simulation system; 11...Camera; 12...3D scanner; 13...LiDAR; 14...Light source; 15...Wall; 16...Shadow; 30...Radio wave propagation simulation device; 31...3D model creation unit; 32...Transmission and reception setting unit; 33...Parameter setting unit; 34...Surface characteristic estimation unit; 35...Surface characteristic reflection unit; 36...Calculator; 37...Learning model creation unit; 40...Memory unit; 90...Processing circuit; 91...Processor; 92...Memory; 93...Hardware; 100...Radio wave propagation simulation device; 110...Target area; 120...Object; 130...Transmitter; 140...Receiver

Claims

1. A radio wave propagation simulation system having a camera for capturing images, a 3D scanner for outputting 3D data based on image data captured by the camera, and a radio wave propagation simulation device for performing radio wave propagation simulation, wherein the radio wave propagation simulation device comprises: a 3D model creation unit for creating a 3D model of a target surface in a target environment based on information acquired from the camera or the 3D scanner; a transmission / reception setting unit for setting the position of a transmitter and the position of a receiver in the target environment; a parameter setting unit for setting parameters for a simulation in the target environment; a surface characteristic estimation unit for estimating surface characteristics of the target surface in the image based on the image captured by the camera and a learning model created by machine learning; and a surface characteristic reflection unit for reflecting information on the surface characteristics estimated by the surface characteristic estimation unit in the 3D model created by the 3D model creation unit.

2. A radio wave propagation simulation system as described in claim 1, characterized in that, instead of the 3D scanner, it has a LiDAR that measures the target surface in the target environment, and the 3D model creation unit creates the 3D model of the target surface based on the image captured by the camera and information obtained from the LiDAR.

3. A radio wave propagation simulation system as described in claim 1, wherein the radio wave propagation simulation device further comprises a learning model creation unit that performs machine learning using a database in which predetermined images and surface characteristics are linked, and creates a learning model that can estimate surface characteristics based on the image captured by the camera, and the surface characteristic estimation unit estimates the surface characteristics of the target surface in the image based on the image captured by the camera and the learning model created by the machine learning by the learning model creation unit.

4. A radio wave propagation simulation system as described in claim 3, wherein the learning model creation unit performs machine learning using a database in which a predetermined image is linked to surface characteristics based on the uneven state of the target surface measured based on a method of measuring the uneven shape of the target surface using a light source, and creates a learning model capable of estimating surface characteristics based on the image captured by the camera, and the surface characteristic estimation unit estimates the surface characteristics of the target surface in the image based on the image captured by the camera and the learning model created by the machine learning by the learning model creation unit.

5. A radio wave propagation simulation device in a radio wave propagation simulation system having a camera that captures images, a 3D scanner that outputs 3D data based on image data captured by the camera, and a radio wave propagation simulation device that performs radio wave propagation simulation, wherein the radio wave propagation simulation device comprises: a 3D model creation unit that creates a 3D model of a target surface in a target environment based on information acquired from the camera or the 3D scanner; a transmission / reception setting unit that sets the position of a transmitter and the position of a receiver in the target environment; a parameter setting unit that sets parameters for a simulation in the target environment; a surface characteristic estimation unit that estimates surface characteristics of the target surface in the image based on the image captured by the camera and a learning model created by machine learning; and a surface characteristic reflection unit that reflects information on the surface characteristics estimated by the surface characteristic estimation unit in the 3D model created by the 3D model creation unit.

6. A radio wave propagation simulation method in a radio wave propagation simulation system having a camera that captures images, a 3D scanner that outputs 3D data based on image data captured by the camera, and a radio wave propagation simulation device that performs a radio wave propagation simulation, wherein the radio wave propagation simulation device creates a 3D model of a target surface in a target environment based on information obtained from the camera or the 3D scanner, sets the position of a transmitter and the position of a receiver in the target environment, sets simulation parameters for the target environment, estimates surface characteristics of the target surface in the image based on the image captured by the camera and a learning model created by machine learning, and reflects information on the estimated surface characteristics in the created 3D model.

7. A radio wave propagation simulation program for causing a computer to execute the processing of the radio wave propagation simulation method according to claim 6.

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