A radar data simulation method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a radar data simulation method and device, electronic equipment and storage medium, which are applied to a receiving module of a radar model in a simulation scene. The radar model further includes a transmitting module, which is used to transmit a simulated transmitting ray. The method includes: obtaining a user-input custom parameter; the custom parameter includes a weather type and a weather degree; determining a number proportion of clutter according to the custom parameter; the number proportion is used to represent a proportion of target rays generating clutter in the transmitting rays; the number proportion is positively correlated with the weather degree; determining a probability function of a return distance of the clutter according to the custom parameter, and calculating a clutter power at the return distance; the probability function is used to represent a probability of the return distance of the clutter, and the probability is negatively correlated with the return distance; and the number proportion, the probability function and the clutter power simulate a clutter signal received by the receiving module.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of simulation, in particular to a radar data simulation method and device, an electronic device and a storage medium. BACKGROUND

[0002] With the continuous improvement of computer technology and performance, simulation has become an important and effective means to support the test of intelligent driving of vehicles. Intelligent driving technology of vehicles relies on various sensors on vehicles to sense environmental information around the vehicles. The sensing ability of various sensors such as radars on the environment has an important influence on intelligent driving. Testing the sensing ability of radars in a simulation scene can greatly ensure the safety of intelligent driving.

[0003] In a real scene, the transmission signal of a radar will return to the radar as a return signal, a clutter signal and noise after contacting an object. However, in a simulation scene, it is often difficult to simulate the returned clutter signal, especially the clutter signal generated by weather. This makes it difficult to test the sensing ability of radars in different weather in a simulation scene, and it is difficult to ensure the safety of intelligent driving in different weather. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a radar data simulation method and device, an electronic device and a storage medium, to achieve the technical effect of simulating the reception of returned clutter signals by radars in different weather.

[0005] The first aspect of the embodiments of the present application provides a radar data simulation method applied to a receiving module of a radar model in a simulation scene; the radar model further includes a transmitting module, and the transmitting module is configured to transmit a simulated transmission ray; the method comprises the following steps:

[0006] obtaining a user-input custom parameter; the custom parameter includes a weather type and a weather degree;

[0007] determining a quantity proportion of clutter according to the custom parameter; wherein the quantity proportion is used to represent the proportion of target rays generating the clutter in the transmission rays; the quantity proportion is positively correlated with the weather degree;

[0008] determining a probability function of a return distance of the clutter according to the custom parameter, and calculating the clutter power at the return distance; wherein the probability function is used to represent the probability of the return distance of the clutter, and the probability is negatively correlated with the return distance;

[0009] simulating the clutter signal received by the receiving module according to the quantity proportion, the probability function and the clutter power.

[0010] In the implementation process, the number proportion of the clutter, the probability function of the return distance, and the clutter power are determined according to the custom parameters input by the user, so that the clutter signal caused by the weather is simulated directly in the receiving module. Since the clutter signal is simulated directly from the return signal received by the receiving module, the transmission ray does not actually generate a clutter signal in the simulation scene due to the weather, so that a physical model related to the weather does not need to be established in the simulation scene, thereby avoiding the occupation of operation resources caused by modeling, and realizing the simulation of the clutter signal in different weather.

[0011] Further, the custom parameters further include a radar type; and the determining of the number proportion of the clutter according to the custom parameters includes:

[0012] determining a weather influence factor according to the weather type and the radar type;

[0013] determining a product of the weather influence factor and the weather degree, and a ratio of the product to a preset control parameter as the number proportion of the clutter.

[0014] In the implementation process, the weather influence factor, the weather degree, and the preset control parameter are used to evaluate the number proportion of the clutter, so that the technical effect of simulating the number of clutters without weather-related modeling in the simulation scene is realized.

[0015] Further, the calculating of the clutter power at the return distance includes:

[0016] determining a number of layers of the virtual weather layer within the return distance;

[0017] determining a virtual weather layer parameter according to the custom parameters, the virtual weather layer parameter including a reflection power retention rate and a penetration power retention rate of the virtual weather layer;

[0018] determining all virtual paths of the transmission ray in the virtual weather layer according to the number of layers, and calculating the clutter power at the return distance according to the transmission power of the transmission ray, the virtual paths, and the virtual weather layer parameter.

[0019] In the implementation process, the influence of the weather on the clutter power is considered by using a layered thinking, so that the size of the clutter power under different weather conditions is calculated, and the simulation calculation of the clutter power is completed.

[0020] Further, the virtual weather layer is used to assist in calculating the clutter power in the simulation scene, and the virtual weather layer is a non-rendering object and a non-model construction object of the simulation scene.

[0021] In the implementation process, the transmission path of the emission ray is fictitiously constructed through layered thinking, and the clutter power is calculated based on the transmission path. The calculated clutter power meets the judgment of the size of the clutter power in the real scene, and does not need to establish a weather-related physical model in the simulation scene, thereby avoiding the occupation of operation resources caused by modeling.

[0022] Further, the event of the emission ray penetrating each of the fictitious weather layers is independent and has the same probability.

[0023] The probability function conforms to a Poisson distribution, and the probability function is used to represent the probability of the return distance of the clutter under a Poisson coefficient. The Poisson coefficient is a function of the self-defined parameter, and the Poisson coefficient is negatively correlated with the weather degree.

[0024] In the implementation process, according to the characteristics of the calculation formula of the clutter power, the probability function is designed to conform to a Poisson distribution, so that the return distance of the clutter and the clutter power under the return distance conform to the same distribution. The simulated return distance is close to the return distance of the clutter in the real scene.

[0025] Further, the self-defined parameter further includes a radar parameter, and the Poisson coefficient is a function of the weather degree and the radar parameter.

[0026] In the implementation process, the Poisson coefficient is set as a function of the weather degree and the radar parameter. For radars produced by different manufacturers, the corresponding Poisson coefficients are different under different weather degrees, and the specific forms of the probability functions are also different. Under the same weather condition, the probabilities of the calculated return distances are also different. Thus, the differences between radars produced by different manufacturers are reflected. Then, different clutter signals can be simulated for radars produced by different manufacturers.

[0027] Further, the clutter signal received by the receiving module is simulated according to the number of clutters, the probability function, and the clutter power, including:

[0028] Randomly determining, from the emission rays, an emission ray satisfying the number ratio as a target ray for generating the clutter;

[0029] Simulating a clutter signal returned by the target ray under the probability function satisfying the return distance, and the power of the clutter signal is the clutter power.

[0030] In the implementation process, after determining the number proportion of the clutter, the probability function of the return distance, and the clutter power according to the custom parameters, the target ray satisfying the number proportion is randomly selected in the transmitted rays, the return distance of each clutter is calculated according to the probability function of the return distance, the clutter power corresponding to each clutter at the respective return distance is calculated, and thus the corresponding clutter signal can be simulated according to the return distance and the power of each clutter. The technical effect of simulating the clutter signal without establishing a weather-related physical model in the simulation scene is achieved.

[0031] Further, the method further comprises:

[0032] According to the clutter power, a target clutter signal is screened out, the clutter signal is subjected to signal processing, and simulation point cloud data is obtained.

[0033] In the implementation process, after the clutter signal is simulated, the simulation signal is continuously processed, so that the simulation point cloud data with weather influence is calculated in the radar model, the simulation effect is visualized, and the influence of weather on radar perception performance can be intuitively observed.

[0034] Further, the custom parameters further include a radar type; and the method further comprises:

[0035] Simulating an initial echo power of an echo of a target object based on a ray tracing algorithm;

[0036] Obtaining a target distance of the target object from a radar in the simulation scene;

[0037] According to the custom parameters input by the user, the target distance obtained, and a preset attenuation function, an attenuation coefficient is calculated; the attenuation function is a function of the weather degree, a weather influence factor, and the object distance; the attenuation coefficient is positively correlated with the weather degree and the weather influence factor, and is negatively correlated with the object distance; the weather influence factor is related to the weather type and the radar type;

[0038] The attenuation coefficient is applied to the initial echo power to obtain an attenuation power of the echo.

[0039] In the implementation process, an attenuation function about weather conditions and radar types is designed, the attenuation function has strong universality and can be applied to modeling of echo attenuation in various physical level radars, and the attenuation function can be directly called according to different radars and weather conditions. The simulation of echo power attenuation is more consistent with the real situation.

[0040] Further, the types of the radar include a millimeter wave radar and a laser radar.

[0041] In the implementation process, the clutter signals are simulated by using the same modeling logic as the laser radar for the millimeter wave radar, and the clutter simulation of different types of radars is realized.

[0042] The second aspect of the embodiment of the application provides a radar data simulation device, which is applied to a receiving module of a radar model in a simulation scene; the radar model further comprises a transmitting module, and the transmitting module is used for transmitting a simulated transmitting ray; the device comprises:

[0043] An acquisition module is configured to acquire a custom parameter input by a user; the custom parameter comprises a weather type and a weather degree;

[0044] A proportion determination module is configured to determine a quantity proportion of the clutter according to the custom parameter; the quantity proportion is used to represent a proportion of a target ray generating the clutter in the transmitting ray; the quantity proportion is positively correlated with the weather degree;

[0045] A distance and power calculation module is configured to determine a probability function of a return distance of the clutter according to the custom parameter, and to calculate a clutter power at the return distance; the probability function is used to represent a probability of the return distance of the clutter, and the probability is negatively correlated with the return distance;

[0046] A simulation module is configured to simulate a clutter signal received by the receiving module according to the quantity of the clutter, the probability function and the clutter power.

[0047] The third aspect of the embodiment of the application provides an electronic device, which comprises:

[0048] A processor;

[0049] A memory for storing processor-executable instructions;

[0050] When the processor invokes the executable instructions, the operations of the method in any of the first aspects are implemented.

[0051] The fourth aspect of the embodiment of the application provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are executed by a processor to implement the steps of the method in any of the first aspects. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments of the application. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0053] Figure 1 A flowchart of a radar data simulation method provided by an embodiment of the present application is shown in FIG. 1.

[0054] Figure 2 A flowchart of another radar data simulation method provided by an embodiment of the present application is shown in FIG. 2.

[0055] Figure 3 A schematic diagram of a fictitious weather layer provided by an embodiment of the present application is shown in FIG. 3.

[0056] Figure 4 A flowchart of another radar data simulation method provided by an embodiment of the present application is shown in FIG. 4.

[0057] Figure 5 A schematic diagram of a fictitious path of a transmitted ray provided by an embodiment of the present application is shown in FIG. 5.

[0058] Figure 6 A flowchart of another radar data simulation method provided by an embodiment of the present application is shown in FIG. 6.

[0059] Figure 7 A simulated point cloud diagram provided by an embodiment of the present application is shown in FIG. 7.

[0060] Figure 8 A flowchart of another radar data simulation method provided by an embodiment of the present application is shown in FIG. 8.

[0061] Figure 9 A comparison diagram of a measured and simulated signal-to-noise ratio provided by an embodiment of the present application is shown in FIG. 9.

[0062] Figure 10 A structural block diagram of a radar data simulation device provided by an embodiment of the present application is shown in FIG. 10.

[0063] Figure 11 A hardware structural diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 11. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings.

[0065] It should be noted that similar reference numerals and letters refer to like items in the accompanying drawings, and thus, once an item is defined in one drawing, it need not be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms “first”, “second”, and the like are merely used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0066] With the continuous improvement of computer technology and performance, simulation has become an important and effective means to support intelligent driving test of vehicles. Intelligent driving technology of vehicles relies on various sensors on vehicles to sense environmental information around the vehicles. The sensing ability of various sensors such as radars has an important influence on intelligent driving. Testing the sensing ability of radars in a simulation scene can ensure the safety of intelligent driving to a great extent.

[0067] In a real scene, the transmitted signal of the radar will produce a return wave after colliding with an object in its propagation direction, and the clutter refers to the radar return wave of other objects except the target of interest, which is an unwanted return wave. The clutter can include return waves from the ground, sea, weather, bird and insect swarms, etc. The definition of the clutter depends largely on the desired target. In the intelligent driving scene, the clutter is usually from the weather. Especially in rainy, snowy and foggy weather, the weather particles (water particles, snow particles, fog particles) in the environment will reflect the transmitted signal to produce clutter. Different degrees of interference are caused by rainy, snowy and foggy weather, so that the target object that can be detected cannot be detected. The sensing ability of the radar decreases in rainy, snowy and foggy weather. Therefore, it is necessary to test the sensing performance of the radar in rainy, snowy and foggy weather by using simulation technology, to restore the physical properties caused by the weather as much as possible, so that the simulation result is closer to the output of the real radar.

[0068] However, in the related art, the simulation of the vehicle-mounted radar does not consider the influence of the weather, resulting in that the intelligent driving algorithm can normally run on the simulation platform, but in the real vehicle test, the output of many clutters is caused due to the influence of the weather. Since there is no output for the influence of the weather on the simulation platform, problems are caused in the design or function test of the intelligent driving algorithm.

[0069] In addition, in the related art, the simulation scene is usually rendered based on a light tracing algorithm. In the intelligent driving related simulation scene, only important participants in traffic behaviors such as vehicles, pedestrians, obstacles, road signs, lane lines, etc. are usually modeled. The light tracing ray will react to these modeled objects. Although various weather can be rendered in the simulation scene. However, these weather are only rendering effects, and the related model of the weather particles is not really generated in the simulation scene, so the light tracing ray will not react to the rendering effect.

[0070] If various meteorological particles are modeled in the simulation scene, on the one hand, due to the sharp increase in the number of modeling objects, the operation speed will be seriously affected and a large amount of operation resources of the simulation platform will be occupied. On the other hand, in a real scene, the radar transmission signal can penetrate raindrops, snow and fog, and only the transmission signal power will be attenuated after penetration, but the attenuated transmission signal can still continue to detect the object behind. But in the simulation scene, the light tracing ray acts on the meteorological particle and is reflected back to the coordinate of the water particle, and does not continue to go forward. That is, the penetration effect of the transmission signal on the meteorological particle cannot be simulated in the simulation scene. It can be understood that if the rain is large in the simulation scene, that is, there are more water particles, most of the light tracing rays will be reflected by the water particles, so that the object behind cannot be detected. But in a real scene, even in a heavy rain, the radar can still retain some detection function. It can be seen that such a simulation scene cannot truly simulate the real situation. For the above two reasons, it is often difficult to simulate the clutter signal in the related technology, especially the clutter signal generated by the weather. It is difficult to test the perception ability of the radar in different weather in the simulation scene, and it is difficult to ensure the safety of intelligent driving in different weather.

[0071] Therefore, the present application proposes a radar data simulation method applied to a simulation scene. For example, a simulation scene for intelligent driving. The simulation scene is equipped with a radar model. The radar model includes a transmission module and a receiving module. Optionally, the radar model also includes a processing module. Wherein the transmission module is used to simulate the transmission of the simulated transmission ray in the simulation scene. The receiving module is used to simulate the echo, clutter and noise returned by the transmission ray after contacting the object in the simulation scene. That is, the signal returned by the receiving module includes the echo signal, clutter signal and noise after the transmission ray contacts the object.

[0072] In some embodiments, the radar simulated by the present application is a physical level radar, including millimeter wave radar and laser radar. Accordingly, the radar model includes a millimeter wave radar model and a laser radar model. Wherein the transmission ray of the millimeter wave radar model is a simulated electromagnetic wave, and the transmission ray of the laser radar model is a simulated pulse wave.

[0073] Although the return of laser radar is different from that of millimeter wave radar, laser radar uses TOF (Time Of Flight) at the peak to measure the distance, and has no requirement for angle measurement. Millimeter wave radar uses FFT (Fast Fourier Transform) to measure the distance and angle. Therefore, for these two different physical level sensors, the clutter needed to be constructed is different, and millimeter wave sensors have strong anti-interference ability to weather, and the clutter of laser and millimeter wave needs to be modeled differently. But the logic of modeling is similar, and the aspects of weather affecting clutter include the number of clutters, return distance, and clutter power. Therefore, the modeling of clutter can be carried out in terms of the number of clutters, return distance, and power.

[0074] As shown in Figure 1 The radar data simulation method provided by the application comprises:

[0075] Step 110: obtaining a user input custom parameter;

[0076] The custom parameter comprises a weather type and a weather degree.

[0077] Step 120: determining a number proportion of clutter according to the custom parameter;

[0078] The number proportion is used to represent the proportion of target rays generating the clutter in the transmitting rays; and the number proportion is positively correlated with the weather degree.

[0079] Step 130: determining a probability function of return distance of the clutter according to the custom parameter, and calculating a clutter power at the return distance;

[0080] The probability function is used to represent the probability of the return distance of the clutter, and the probability is negatively correlated with the return distance.

[0081] Step 140: simulating the clutter signal returned by the radar according to the number proportion, the probability function, and the clutter power.

[0082] The simulation platform can provide a parameter input interface for the user to input the custom parameter, for example, a human-computer interaction interface can be provided, so that the user can input the custom parameter through the human-computer interaction interface.

[0083] The custom parameters include weather type and weather degree. Exemplarily, the weather type can include, but is not limited to, one or more of rainy day, snowy day, and foggy day. The weather degree can take the form of a numerical value. The numerical value can be equivalently converted with the number, density, concentration, etc. of meteorological particles. For example, the numerical value of the weather degree can be equivalently converted with the rainfall.

[0084] Taking the rainy weather type as an example, when the value of the weather degree is the smallest, it represents no rainfall, which is the default value under no weather setting. When the value of the weather degree is the largest, it represents the maximum rainfall allowed in the simulation scene. For example, the value range of the weather degree can be [0, 10].

[0085] After obtaining the custom parameters input by the user, the number, return distance, and power of the clutter can be determined according to the custom parameters, respectively.

[0086] The number of the clutter is represented by a number ratio. The number ratio is used to represent the proportion of the target rays producing the clutter in the transmitted rays, and the number ratio is positively correlated with the weather degree.

[0087] As described above, the transmitted rays return in the form of echoes, clutters, and noises after contacting the object. Therefore, in the simulation scene, for all the transmitted rays simulated within a certain time period, it can be considered that part of the transmitted rays return in the form of echoes, and part of the rays return in the form of clutters. The transmitted rays producing the clutters are the target rays. By calculating the proportion of the target rays in all the transmitted rays, the number of the clutters can be represented. It is not difficult to understand that the value range of the number ratio is [0, 1]. In addition, the greater the numerical value of the weather degree, the more meteorological particles, and the greater the probability that the meteorological particles interact with the transmitted rays, the more target rays, and the greater the number ratio. Therefore, the number ratio is positively correlated with the weather degree.

[0088] Of course, since the meteorological particles are not actually modeled in the simulation scene, the target rays do not actually return in the form of clutters. Instead, the clutter signals are obtained by simulating the returned data in the receiving module of the radar model.

[0089] Further, the return distance of the clutter is represented by a probability function. This is because the distance between the meteorological particles and the radar cannot be represented by the distance between two points, and therefore the distance that can be returned in the direction of the target rays returning the clutter can be estimated by considering a probability model. In this way, the probability function is used to represent the probability of the return distance of the clutter.

[0090] Taking the rainy weather as an example, in a real scene, the rainfall can be considered as uniformly distributed in space. With the increase of the distance, the probability of the return clutter will gradually decrease. In other words, for the return clutter, the probability of returning from a far distance is less than that of returning from a near distance. Therefore, the probability of the return distance of the clutter is negatively correlated with the return distance.

[0091] In addition, as mentioned above, the power of the transmitted signal of the radar will be attenuated after penetrating the meteorological particles, so the clutter power is also related to the weather type and the weather degree. It is not difficult to understand that the greater the numerical value of the weather degree, the more meteorological particles, and the greater the attenuation effect of the meteorological particles on the transmitted rays, so the smaller the clutter power. Therefore, the clutter power is negatively correlated with the weather degree. The power of the clutter can be simulated and calculated through the self-defined parameters.

[0092] Finally, according to the number proportion of the clutter, the probability function of the return distance, and the clutter power, the clutter signal received by the receiving module can be simulated.

[0093] It can be seen that the radar data simulation method provided by the present application is applied in the receiving module of the radar model, and the number proportion of the clutter, the probability function of the return distance, and the clutter power are determined and calculated through the self-defined parameters input by the user, so that the clutter signal caused by the weather can be directly simulated in the receiving module. Since the return signal received by the receiving module is directly simulated into the clutter signal, the transmitted rays actually do not generate the clutter signal in the simulation scene due to the weather, so it is not necessary to establish a physical model related to the weather in the simulation scene, which not only avoids the occupation of the operation resources caused by modeling, but also realizes the simulation of the clutter signal in different weather.

[0094] As mentioned above, compared with the laser radar, the millimeter wave sensor has strong anti-interference ability to the weather. One of the performances of the anti-interference ability is the number of clutters. That is, under the same weather conditions (including the weather type and the weather degree), the laser radar and the millimeter wave radar will generate different numbers of clutters, and then the number proportions of the clutters are different.

[0095] In order to distinguish the difference in the number of clutters generated by the laser radar and the millimeter wave radar, in some embodiments, the self-defined parameters further include the radar type.

[0096] The self-defined parameters include the weather type, the weather degree, and the radar type. In this way, the calculation process of the number proportion in step 130 above includes the steps as shown in the following table: Figure 2

[0097] Step 1311: determining a weather influence factor according to the weather type and the radar type;

[0098] ​Step 1312: determining that the ratio of the product of the weather influence factor and the weather degree to the preset control parameter is the number proportion of the clutter.

[0099] The weather influence factor refers to an influence factor of a certain radar in a certain type of weather, which can be in the form of a numerical value. According to the weather type and the radar type, the weather influence factor can be determined. Exemplarily, the value of the weather influence factor corresponding to each type of radar in different weather types can be pre-set and stored. In this way, after obtaining the user-input custom parameters, the value of the weather influence factor corresponding to the weather type and the radar type in the custom parameters can be directly queried.

[0100] The greater the numerical value of the weather influence factor, the greater the influence of the weather of this type on the radar. For example, for a millimeter wave radar, since fog has almost no influence on the millimeter wave radar, the weather influence factor of the millimeter wave radar in fog can be set to 0.

[0101] Subsequently, the product of the weather influence factor and the weather degree can be obtained, and the ratio of the product to the preset control parameter is determined as the number proportion of the clutter. The control parameter is used to control the size of the number proportion, which needs to be determined by actual measurement, that is, the real number proportion of the clutter of different types of radars in different weather conditions (including weather type and weather degree) is tested in real scenes, and the value of the control parameter is back calculated according to the weather type and the weather degree corresponding to the weather conditions during the actual measurement. In addition, since the value range of the number proportion is [0, 1], the control parameter is greater than the maximum value of the product of the weather influence factor and the weather degree.

[0102] Exemplarily, the calculation formula of the number proportion of the clutter is:

[0103]

[0104] Wherein, Ratio is the number proportion of the clutter; a is the weather influence factor; x is the weather degree; N is the control parameter.

[0105] For example, the weather influence factor of the millimeter wave radar in fog is 0, so the clutter proportion of the millimeter wave radar is always 0, and the transmission ray does not generate clutter.

[0106] In this way, after obtaining the user-input custom parameters, the number proportion of the clutter can be simulated according to the above calculation formula of the number proportion. In addition, if the total number of the transmission rays simulated in the simulation scene is obtained, the number of the target rays generating the clutter, that is, the number of the clutter, can be calculated according to the total number and the number proportion.

[0107] It can be seen that in the embodiment, the weather influencing factor, the weather degree, and the preset control parameter are used to evaluate the proportion of the number of clutter. Thus, the technical effect that the number of clutter can be simulated without weather-related modeling in the simulation scene is achieved. Meanwhile, since the control parameter is a value determined by actual measurement, the simulated proportion is consistent with the evaluation of the proportion of clutter in the real scene.

[0108] Regarding the calculation process of the clutter power, in some embodiments, a layered thinking can be used to consider the influence of weather on the clutter power. As shown in Figure 3 , it can be assumed that the meteorological particles 311 are distributed in layers in the propagation direction of the transmitted ray. The space range filled with meteorological particles 311 is the imaginary weather layer 310. Different types of meteorological particles are included in the imaginary weather layer for different weather types. The greater the value of the weather degree, the more meteorological particles 311 in the imaginary weather layer 310. The imaginary weather layers 310 are arranged at intervals. It can be assumed that the transmitted ray penetrates several layers of imaginary weather layers 310 in the propagation process until it is reflected by a layer of imaginary weather layer 310 to form clutter. At the same time, it can be assumed that the power of the transmitted ray will be attenuated when it penetrates each layer of imaginary weather layer 310, and the power will also be attenuated when it is reflected by the imaginary weather layer 310. Thus, in the step 130, the calculation process of the clutter power can include the steps as shown in Figure 4 .

[0109] Step 1321: determining the number of layers of the imaginary weather layer within the return distance;

[0110] Step 1322: determining the imaginary weather layer parameters according to the self-defined parameters;

[0111] The imaginary weather layer parameters include the reflection power retention rate and the penetration power retention rate of the imaginary weather layer.

[0112] Step 1323: determining all the imaginary paths of the transmitted ray in the imaginary weather layer according to the number of layers, and calculating the clutter power at the return distance according to the transmission power of the transmitted ray, the imaginary paths, and the imaginary weather layer parameters.

[0113] First, after determining the probability function of the clutter return distance, the probabilities corresponding to different clutter return distances can be obtained. For example, the probability of a clutter return distance of 5 meters is 80%, the probability of a return distance of 6 meters is 15%, and the probability of a return distance of 7 meters is 5%. Assuming there are 100 target rays generating clutter in the simulation scenario, 80 target rays generate clutter at a distance of 5 meters from the radar, meaning there are 80 clutter rays with a return distance of 5 meters; 15 target rays generate clutter at a distance of 6 meters from the radar, meaning there are 15 clutter rays with a return distance of 6 meters; and 5 target rays generate clutter at a distance of 7 meters from the radar, meaning there are 5 clutter rays with a return distance of 7 meters. Thus, for each of the 100 clutter rays, the clutter power at the corresponding return distance can be calculated using steps 1321-1323 above.

[0114] Specifically, for a given clutter, the number of fictitious weather layers within the clutter's return distance can be determined. For example, the interval between fictitious weather layers can be preset, so that the number of fictitious weather layers can be determined based on the return distance and the interval.

[0115] Subsequently, the parameters of the fictional weather layer can be determined based on the custom parameters, including the reflection power retention rate and penetration power retention rate of the fictional weather layer.

[0116] As described above, it is assumed that the power of the emitted ray decreases each time it penetrates or is reflected by a fictional weather layer. The degree to which the power is retained after attenuation is called the reflection power retention rate and the penetration power retention rate. For example, if the emitted ray experiences a 10% power attenuation after penetrating a fictional weather layer, the penetration power retention rate is 90%. If the emitted ray experiences a 60% power attenuation after being reflected by a fictional weather layer, the reflection power retention rate is 40%.

[0117] Clearly, the parameters of the fictional weather layer are related to both weather type and weather severity. For example, both the reflection power retention rate and the penetration power retention rate are negatively correlated with weather severity. Thus, the parameters of the fictional weather layer can be determined based on the weather type and weather severity.

[0118] Furthermore, as mentioned above, millimeter-wave sensors possess stronger anti-interference capabilities compared to lidar. One manifestation of this anti-interference capability is power attenuation. That is, under the same weather conditions (including weather type and severity), the reflection power retention rate and penetration power retention rate of lidar and millimeter-wave radar are different.

[0119] Thus, to distinguish the difference in clutter power between the laser radar and the millimeter wave radar, the self-defined parameters can optionally further include a radar type. Then, determining the fictitious weather layer parameters according to the self-defined parameters in step 1322 can include determining the reflection power retention rate and the penetration power retention rate of the fictitious weather layer according to the weather type, the weather degree, and the radar type.

[0120] After the number of layers of the fictitious weather layer is determined, all the fictitious paths of the transmitted ray in the fictitious weather layer can be determined according to the number of layers. And the clutter power at the return distance can be calculated according to the transmission power of the transmitted ray, the fictitious path, and the fictitious weather layer parameters.

[0121] Exemplarily, the calculation formula of the clutter power is:

[0122]

[0123] wherein, I k is the clutter power returned by the kth layer; I0 is the transmission power; P i is the path occurrence frequency; p is the reflection power retention rate; t is the penetration power retention rate.

[0124] As shown in Figure 5 , taking the example of 7 layers of fictitious weather layer included in the return distance, then k = 7 in the above calculation formula. The total number of times of penetration and reflection of the transmitted ray in the 7 layers of fictitious weather layer is 7 times, including 1 time of reflection and 6 times of penetration (as shown in Figure 5 Fig. a), 3 times of reflection and 4 times of penetration (as shown in Figure 5 Figs. b-d), and 5 times of reflection and 2 times of penetration (as shown in Figure 5 Fig. e). Thus, for the fictitious path of 1 time of reflection and 6 times of penetration, the path occurrence frequency P1 = 1, and the corresponding decayed power is p 1 t 6 . For the fictitious path of 3 times of reflection and 4 times of penetration, since there are three fictitious paths in total, the path occurrence frequency P2 = 3, and the decayed power corresponding to each fictitious path is p 3 t 4 . For the fictitious path of 5 times of reflection and 2 times of penetration, the path occurrence frequency P3 = 1, and the corresponding decayed power is p 5 t 2 . Thus, the clutter power returned by the 7 layers of fictitious weather layer can be calculated by using the above calculation formula of the clutter power.

[0125] In a real scene, the clutter power is usually a very small value. In the above calculation formula, the reflection power retention rate and the penetration power retention rate are very small after the power calculation, which is consistent with the judgment of the size of the clutter power in the real scene.

[0126] It can be seen that in the embodiment, the influence of the weather on the clutter power is considered by using the layered thinking, so that the size of the clutter power under different weather conditions is calculated, and the simulation calculation of the clutter power is completed.

[0127] In addition, the virtual weather layer is used to assist in calculating the clutter power in the simulation scene. By virtual, it is meant that the virtual weather layer is not really rendered or physically modeled in the simulation scene. That is, the emitted rays do not really interact with the virtual weather layer in the simulation scene. Similarly, the above-mentioned virtual path means that the emitted rays do not really propagate according to the virtual path in the simulation scene, but are assumed transmission paths in the layered thinking. Therefore, the virtual weather layer is a non-rendering object and a non-modeling object in the simulation scene.

[0128] It can be seen that the embodiment virtually constructs the transmission path of the emitted rays by using the layered thinking, and calculates the clutter power based on the transmission path. The calculated clutter power not only conforms to the judgment of the size of the clutter power in the real scene, but also does not need to establish a physical model related to the weather in the simulation scene, avoiding the occupation of operation resources caused by modeling.

[0129] Regarding the probability function of the return distance of the clutter, the probability function can conform to different distributions, for example, including but not limited to binomial distribution, exponential distribution, normal distribution, etc.

[0130] In some embodiments, according to the calculation formula of the clutter power, it can be known that the calculation formula conforms to the multinomial distribution. When the multinomial term is infinite, it can be approximated to the Poisson distribution. At the same time, the events of the emitted rays penetrating each layer of the virtual weather layer are independent, and the event probability of penetrating each layer of the virtual weather is the same. Therefore, it meets the physical meaning of multiple independent events in the Poisson distribution. In this way, the probability function of the return distance conforms to the Poisson distribution. The probability function is used to represent the probability of the return distance of the clutter under the Poisson coefficient. The probability can be understood as the proportion of the clutter returned at the return distance to all clutters. Or in other words, the proportion of the target rays producing clutter at the return distance to all target rays. For example, according to the total number of target rays and the probability of the return distance, the number of target rays at different return distances can be calculated.

[0131] Wherein, the Poisson coefficient is a function of the self-defined parameter, and the Poisson coefficient is negatively related to the weather degree. In this way, according to the weather degree input by the user, the Poisson coefficient can be obtained, so that the probability function conforming to the Poisson distribution is obtained.

[0132] In the embodiment, according to the characteristics of the calculation formula of the clutter power, the probability function is designed to conform to the Poisson distribution, so that the return distance of the clutter and the clutter power at the return distance conform to the same distribution. The simulated return distance is close to the return distance of the clutter in the real scene.

[0133] Further, as described above, different types of radars have different anti-interference abilities to weather. In addition, for the same type of radar, if the specific parameters of the radar are different, the sensing effect of the radar is also different. For example, radars produced by different manufacturers have different sensing effects, and then the probability functions are also different. In order to reflect the differences of radars produced by different manufacturers in the clutter, in some embodiments, the custom parameters include radar parameters.

[0134] The radar parameters can include but are not limited to radar type, divergence angle, transmitted signal strength, bandwidth, wavelength, etc. There are many types of radar parameters, and it is difficult for users to input each radar parameter one by one. Therefore, the simulation platform can pre-input the respective parameters of multiple radars. For example, the radar parameters corresponding to radars produced by different manufacturers can be saved in the simulation platform for user selection. Thus, the radars produced by different manufacturers can be simulated and tested under different weather conditions.

[0135] In this way, the custom parameters include weather type, weather degree, and radar parameters. The Poisson coefficient is a function of the weather degree and the radar parameters in the custom parameters.

[0136] Exemplarily, the probability function of the return distance is:

[0137]

[0138] Wherein, P(λ(x),R) is the probability function of the return distance under the Poisson coefficient λ(x); R is the return distance.

[0139] It can be seen that in the above probability function, the probability of the return distance is negatively related to the return distance, which represents that the farther away from the radar, the smaller the probability of returning clutter. The probability of the return distance is also positively related to the Poisson coefficient, and the value of the Poisson coefficient λ(x) is negatively related to the weather degree, which represents that for the same return distance, if the value of the weather degree is larger, the more meteorological particles, then the probability of returning clutter at the return distance is smaller.

[0140] It can be understood that before the present embodiment is proposed, the Poisson coefficient is a coefficient artificially set according to an empirical value. In the present embodiment, the Poisson coefficient is set as a function of the weather degree and the radar parameter. So that for radars produced by different manufacturers, the corresponding Poisson coefficients are different under different weather degrees, and then the specific forms of the probability functions are also different. Under the same weather conditions, the probabilities of the calculated return distances are also different. Thus, the differences of radars of different manufacturers in clutter are embodied. Then different clutter signals can be simulated for radars produced by different manufacturers.

[0141] Regarding the simulation process of the clutter signal, in some embodiments, the above step 140 includes the steps as shown in Figure 6 .

[0142] Step 141: randomly determining a transmission ray satisfying the number ratio from the transmission rays as a target ray for generating the clutter;

[0143] Step 142: simulating a clutter signal returned by the target ray under the probability function satisfying the return distance and having a power of the clutter power.

[0144] The simulation platform can set the total number of transmission rays of the radar transmission module within a period of time. In this way, the number of target rays for generating the clutter, that is, the number of clutters, can be calculated through the total number of transmission rays and the simulated number ratio.

[0145] Subsequently, the transmission ray for generating the clutter can be randomly selected from all the transmission rays as the target ray. And the selected target ray is simulated to return a clutter signal having a power of the clutter power under the probability function satisfying the return distance.

[0146] It can be seen that in the present embodiment, after the number ratio for calculating the clutter, the probability function of the return distance, and the clutter power are determined according to the self-defined parameters, the target ray satisfying the number ratio is randomly selected from the transmission rays, then the return distance of each clutter is calculated according to the probability function of the return distance, and the corresponding clutter power of each clutter under the respective return distance is calculated, so that the corresponding clutter signal can be simulated according to the return distance and the power of each clutter. The technical effect that the clutter signal can be simulated without establishing a physical model related to the weather in the simulation scene is realized.

[0147] In some embodiments, after the clutter signal received by the receiving module is simulated, the target clutter signal can be further selected according to the clutter power, and the signal processing of the clutter signal is performed to obtain the simulation point cloud data.

[0148] Exemplarily, the receiving module also receives echoes returned after the transmitted rays contact the target object. In this way, the simulated clutter signals can be mixed into the echo signals for signal processing.

[0149] Exemplarily, a power threshold can be set to screen out clutter signals with a clutter power higher than the power threshold as target clutter signals, and to eliminate clutter signals with a too low clutter power. For example, Figure 7 As shown, they are simulated point cloud diagrams obtained under different weather conditions. It can be seen that as the weather degree increases, the number of point clouds corresponding to the clutter gradually increases. And as the return distance increases, the number of point clouds corresponding to the clutter decreases, which conforms to the above-mentioned physical law. In addition, as mentioned above, since the clutter power is generally not high, the number of point clouds corresponding to the clutter observed in the simulated point cloud diagram will be lower than the number of clutters calculated according to the proportion.

[0150] It can be seen that in this embodiment, after the clutter signals are simulated, the simulated point cloud data with weather influence is calculated in the radar model by continuing to process the simulated signals, so that the simulation effect is visualized, and the influence of weather on radar perception performance can be observed intuitively.

[0151] As mentioned above, the signal returned by the receiving module includes three parts of echo signal after the transmitted rays contact the object, clutter signal and noise. In rainy, snowy and foggy weather, in addition to causing the transmitted rays to produce clutter, the echo power of the target object will also be attenuated. Therefore, the attenuation of the echo power will also be simulated.

[0152] However, in the related art, when simulating the radar, only a fixed attenuation coefficient with a value of 0 to 1 is simply set to act on the echo power, similar to the atmospheric attenuation coefficient, so that the attenuation model of the echo power in the simulation scene is too simple. However, in the real scene, the attenuation degree of the echo power is related to the weather type, the weather degree, and even the sensitivity of the radar to the weather. Simply setting a fixed attenuation coefficient is difficult to make the simulated attenuation result fit the real situation.

[0153] Therefore, in some embodiments, the custom parameters further include the radar type. In this way, the custom parameters include the weather type, the weather degree, and the radar type. The above method further includes the steps as shown in Figure 8

[0154] Step 151: Simulate the initial echo power of the echo of the target object based on the ray tracing algorithm;

[0155] Step 152: Obtain the target distance of the target object from the radar in the simulation scene;

[0156] ​Step 153: calculating an attenuation coefficient according to the user-inputted custom parameter, the obtained target distance, and a preset attenuation function;

[0157] wherein the attenuation function is a function of the weather degree, a weather influence factor, and an object distance; the attenuation coefficient is positively correlated with the weather degree and the weather influence factor, and is negatively correlated with the object distance; the weather influence factor is related to the weather type and the radar type;

[0158] Step 154: applying the attenuation coefficient to the initial echo power to obtain the attenuation power of the echo.

[0159] Different from the simulation process of the clutter, for the echo, since the vehicle, the pedestrian, the obstacle, the road sign, the lane line and other target objects are modeled in the simulation scene, the echo is generated after the transmitted ray contacts the target object in the simulation scene. Based on the commonly used ray tracing algorithm in the simulation scene, the initial echo power of the echo can be simulated. The ray tracing algorithm can refer to the description of the related art, which will not be expanded herein.

[0160] In the simulation scene, the target distance of the target object from the radar, i.e., the return distance of the echo. The radar refers to the simulation radar in the simulation scene. Since the coordinates of the target object and the radar in the simulation scene are pre-set by the simulation platform, the coordinate data of the target object and the radar can be obtained from the simulation platform, and then the target distance is determined according to the coordinate data.

[0161] The embodiment is pre-provided with an attenuation function. The attenuation function is a function of the weather degree, the weather influence factor, and the object distance. The attenuation coefficient calculated according to the attenuation function is positively correlated with the weather degree and the weather influence factor, and is negatively correlated with the object distance. The weather influence factor is related to the weather type and the radar type. The value range of the attenuation function is [0, 1].

[0162] Exemplarily, the attenuation function is:

[0163]

[0164] wherein W(x, R, a) is a function of the weather degree x, the weather influence factor a, and the object distance R; R min is the minimum detection distance of the radar. For the distance smaller than the minimum detection distance R min , the radar can certainly detect the object and is not affected by the weather. M is the maximum value of the weather degree x. For example, if the value range of the weather degree can be [0, 10], then M = 10. The introduction of the weather influence factor can refer to the above, which will not be repeated herein.

[0165] From the expression of the above attenuation function, when the specific value of the target distance of the target object and the radar is obtained and is brought into the above attenuation function, the attenuation function is with as the base, the exponential function about the weather degree and the weather influence factor, indicates that under the same target distance, the attenuation coefficients corresponding to different weather conditions and different radar types.

[0166] In this way, according to the user input custom parameters (including weather type, weather degree and radar type), the obtained target distance, and the preset attenuation function, the attenuation coefficient can be calculated. Finally, the attenuation coefficient is applied to the initial echo power, and the attenuation power of the echo can be obtained.

[0167] It can be seen that under different target distances, weather conditions and radar types, the calculated attenuation coefficients are not the same. As described above, fog has little effect on millimeter wave radar. The weather influence factor of millimeter wave radar in fog can be set to 0, so the attenuation coefficient of millimeter wave radar in fog is always 1, and the echo does not attenuate.

[0168] As shown in Figure 9 , the left graph is the signal-to-noise ratio diagram of a certain laser radar in fog, and it can be observed that when the visibility is still relatively high, the change of signal-to-noise ratio with distance is very consistent with the case in weather without fog, and as the visibility decreases, the signal-to-noise ratio decreases rapidly with the increase of distance, but it can also be observed that the attenuation of signal-to-noise ratio is not obvious when the distance is relatively short. The right graph is the calculated signal-to-noise ratio under the condition that R min = 10, a = 4, M = 10, and it can be observed that the attenuation trend is consistent with the actual measurement.

[0169] It can be seen that in the embodiment, an attenuation function about weather conditions and radar types is designed, which has strong universality and can be used in various physical level radar modeling of echo attenuation. The attenuation function can be directly called according to different radars and weather conditions. The simulation of echo power attenuation is more consistent with the actual situation.

[0170] Based on the radar data simulation method provided in any of the above embodiments, the application further provides a radar data simulation device applied to a receiving module of a radar model in a simulation scene; the radar model further includes a transmitting module, and the transmitting module is used to transmit a simulated transmitting ray. As shown in Figure 10 , the radar data simulation device 1000 includes:

[0171] The acquisition module 1010 is configured to acquire user input custom parameters; the custom parameters include weather type and weather degree;

[0172] The proportion determination module 1020 is used to determine the proportion of clutter quantity based on the custom parameters; wherein, the proportion of quantity is used to characterize the proportion of the target ray that generates the clutter in the emitted ray; the proportion of quantity is positively correlated with the weather severity.

[0173] The distance and power calculation module 1030 is used to determine the probability function of the return distance of the clutter based on the custom parameters, and to calculate the clutter power at the return distance; wherein, the probability function is used to represent the probability of the return distance of the clutter, and the probability is negatively correlated with the return distance;

[0174] The simulation module 1040 is used to simulate the clutter signal received by the receiving module based on the quantity ratio, the probability function, and the clutter power.

[0175] In some embodiments, the custom parameters further include radar type; the proportion determination module 1020 is specifically used for:

[0176] Determine the weather influencing factors based on the weather type and the radar type;

[0177] The product of the weather influence factor and the weather severity is determined, and the ratio of this product to a preset control parameter is the proportion of the clutter quantity.

[0178] In some embodiments, the distance and power calculation module 1030 is specifically used for:

[0179] Determine the number of layers of the fictitious weather layer within the stated return distance;

[0180] The parameters of the fictional weather layer are determined based on the custom parameters, and the parameters of the fictional weather layer include the reflection power retention rate and the penetration power retention rate of the fictional weather layer.

[0181] The number of layers determines all the fictitious paths of the emitted ray in the fictitious weather layer, and the clutter power at the return distance is calculated based on the emission power of the emitted ray, the fictitious paths, and the parameters of the fictitious weather layer.

[0182] In some embodiments, the fictional weather layer is used to assist in calculating the clutter power in the simulation scenario, and the fictional weather layer is a non-rendered object and a non-model building object of the simulation scenario.

[0183] In some embodiments, the events in which the emitted rays penetrate each of the fictional weather layers are independent and have the same probability;

[0184] The probability function conforms to a Poisson distribution, and the probability function is used to represent a probability of a return distance of the clutter under a Poisson coefficient; the Poisson coefficient is a function of the self-defined parameter, and the Poisson coefficient is negatively correlated with the weather degree.

[0185] In some embodiments, the self-defined parameter further includes a radar parameter, and the Poisson coefficient is a function of the weather degree and the radar parameter.

[0186] In some embodiments, the simulation module 1040 is specifically configured to:

[0187] randomly determine, from the transmission rays, a transmission ray satisfying the quantity proportion as a target ray generating the clutter;

[0188] simulate a clutter signal returned by the target ray under the probability function satisfying the return distance and having a power of the clutter power.

[0189] In some embodiments, the radar data simulation apparatus 1000 further includes:

[0190] a point cloud module configured to filter out a target clutter signal according to the clutter power, and perform signal processing on the clutter signal to obtain simulation point cloud data.

[0191] In some embodiments, the self-defined parameter further includes a radar type; and the radar data simulation apparatus 1000 further includes:

[0192] an echo simulation module configured to simulate an initial echo power of an echo of a target object based on a ray tracing algorithm;

[0193] a distance acquisition module configured to acquire a target distance of the target object from a radar in the simulation scene;

[0194] an attenuation module configured to calculate an attenuation coefficient according to the self-defined parameter input by the user, the acquired target distance, and a preset attenuation function; the attenuation function is a function of the weather degree, a weather influence factor, and an object distance; the attenuation coefficient is positively correlated with the weather degree and the weather influence factor, and is negatively correlated with the object distance; the weather influence factor is related to the weather type and the radar type;

[0195] an action module configured to act the attenuation coefficient on the initial echo power to obtain an attenuation power of the echo.

[0196] In some embodiments, the radar type includes a millimeter wave radar and a laser radar.

[0197] The implementation process of the functions and roles of each module in the above device is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0198] Based on the radar data simulation method described in any of the above embodiments, the present application also provides an electronic device as shown in Figure 11 , a structural schematic diagram of an electronic device. As shown in Figure 11 , at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and of course can also include other hardware required by the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to implement the radar data simulation method described in any of the above embodiments.

[0199] The present application also provides a computer storage medium, and the storage medium stores a computer program, which can be executed by a processor to execute the radar data simulation method described in any of the above embodiments.

[0200] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are only schematic, for example, the flowchart and block diagram in the drawings show the possible implementation architecture, function and operation of the device, method and computer program product according to the embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for executing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0201] In addition, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0202] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts of the prior art that make contributions or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0203] The above merely provides an example of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0204] The above merely provides an example of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0205] It should be noted that, in this document, the terms such as first and second are used merely to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

Claims

1. A method of radar data simulation, characterized in that, A receiving module of a radar model applied to a simulation scene; The radar model further comprises a transmitting module for transmitting simulated transmitting rays; the method comprises: obtaining a user-input custom parameter; the custom parameter comprises a weather type and a weather degree; determining a quantity ratio of clutter according to the custom parameter; wherein the quantity ratio represents a ratio of target rays generating the clutter in the transmitting rays; the quantity ratio is positively correlated with the weather degree; determining a probability function of a return distance of the clutter according to the custom parameter; wherein the probability function represents a probability of the return distance of the clutter, and the probability is negatively correlated with the return distance; determining a number of layers of a fictitious weather layer in the return distance, determining a fictitious weather layer parameter according to the custom parameter, determining all fictitious paths of the transmitting rays in the fictitious weather layer according to the number of layers, and calculating a clutter power in the return distance according to a transmitting power of the transmitting rays, the fictitious paths, and the fictitious weather layer parameter; the fictitious weather layer parameter comprises a reflection power retention rate and a penetration power retention rate of the fictitious weather layer; simulating a clutter signal received by the receiving module according to the quantity ratio, the probability function, and the clutter power.

2. The method of claim 1, wherein, The custom parameter further comprises a radar type; the determination of the quantity ratio of clutter according to the custom parameter comprises: determining a weather influence factor according to the weather type and the radar type; determining a product of the weather influence factor and the weather degree, and determining the quantity ratio of clutter as a ratio of the product to a preset control parameter.

3. The method of claim 1, wherein, The fictitious weather layer is used to assist in calculating the clutter power in the simulation scene, and the fictitious weather layer is a non-rendering object and a non-model construction object of the simulation scene.

4. The method of claim 1, wherein, Events of the transmitting rays penetrating each layer of the fictitious weather layer are independent and have the same probability; The probability function conforms to a Poisson distribution, and the probability function is used to represent a probability of the return distance of the clutter under a Poisson coefficient; the Poisson coefficient is a function of the custom parameter, and the Poisson coefficient is negatively correlated with the weather degree.

5. The method of claim 4, wherein, The custom parameter further comprises a radar parameter, and the Poisson coefficient is a function of the weather degree and the radar parameter.

6. The method of claim 1, wherein, The simulation of the clutter signal received by the receiving module according to the quantity ratio, the probability function, and the clutter power comprises: randomly determining, from the transmitting rays, a transmitting ray satisfying the quantity ratio as a target ray generating the clutter; simulating a clutter signal returned by the target ray under the probability function satisfying the return distance, and the clutter signal has a power of the clutter power.

7. The method of claim 1, wherein, The method further comprises: screening a target clutter signal according to the clutter power, performing signal processing on the clutter signal, and obtaining simulation point cloud data.

8. The method of claim 1, wherein, The custom parameter further comprises a radar type; the method further comprises: simulating an initial echo power of an echo of a target object based on a ray tracing algorithm; obtaining a target distance of the target object from a radar in the simulation scene; According to the user input custom parameter, the target distance obtained, and a preset attenuation function, an attenuation coefficient is calculated; wherein the attenuation function is a function of the weather degree, a weather influence factor, and a distance to an object; the attenuation coefficient is positively correlated with the weather degree and the weather influence factor, and is negatively correlated with the distance to the object; the weather influence factor is related to the weather type and the radar type; The attenuation coefficient is applied to the initial echo power to obtain an attenuation power of the echo.

9. The method according to any of claims 1 to 8, characterized in that, The radar type includes a millimeter wave radar and a laser radar.

10. A radar data simulation apparatus, characterized by comprising: The receiving module of the radar model in the simulation scenario is applied; The radar model further includes a transmitting module, which is configured to transmit a simulated transmitting ray; the device includes: An obtaining module configured to obtain a user input custom parameter; the custom parameter includes a weather type and a weather degree; A proportion determining module configured to determine a number proportion of clutter according to the custom parameter; wherein the number proportion is used to represent a proportion of target rays generating the clutter in the transmitting ray; the number proportion is positively correlated with the weather degree; A distance and power calculating module configured to determine a probability function of a return distance of the clutter according to the custom parameter, determine a number of layers of a virtual weather layer in the return distance, determine a virtual weather layer parameter according to the custom parameter, determine all virtual paths of the transmitting ray in the virtual weather layer according to the number of layers, and calculate a clutter power at the return distance according to a transmitting power of the transmitting ray, the virtual paths, and the virtual weather layer parameter; wherein the probability function is used to represent a probability of the return distance of the clutter, and the probability is negatively correlated with the return distance; the virtual weather layer parameter includes a reflection power retention rate and a penetration power retention rate of the virtual weather layer; A simulation module configured to simulate a clutter signal received by the receiving module according to the number proportion, the probability function, and the clutter power.

11. An electronic device, comprising: The electronic device includes: A processor; A memory for storing processor executable instructions; When the processor invokes the executable instructions, the operations of the method in any one of claims 1-9 are implemented.

12. A computer-readable storage medium, characterized in that, A computer instruction is stored thereon, and the computer instruction is executed by a processor to implement the steps of the method in any one of claims 1-9.

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