Space target laser radar scattering cross section simulation method, device, equipment and medium

Abstract

The application relates to the field of laser radar detection, in particular to a space target laser radar scattering cross section simulation method, device, equipment and medium. The method comprises the following steps: projecting a triangular facet to a pixel plane perpendicular to an incident wave to obtain a projected facet with a depth attribute; regarding a pixel located in the projected facet in a circumscribed rectangle of the projected facet as an area pixel, and calculating a weighted average value of depth values of three vertex pixels according to the relative position of the area pixel in the projected facet to obtain a depth value of the area pixel; selecting an area pixel with the minimum depth value from at least one area pixel corresponding to any pixel in the pixel plane as a visible pixel, and regarding a projected facet with a proportion of the visible pixel to the area pixel exceeding a preset visible threshold as a visible facet; and calculating a laser radar scattering cross section based on the visible facet. The visible pixel is identified by performing pixel-level depth testing on the facet to determine the visible facet, so that an accurate laser radar scattering cross section is obtained.

Description

Technical Field

[0001] This application relates to the field of lidar detection, specifically to a method, apparatus, equipment, and medium for simulating the lidar scattering cross section of a space target. Background Technology

[0002] The Laser Radar Cross Section (LRCS) comprehensively reflects the influence of various factors, such as the target's surface material and roughness, and its geometric shape, on the target's laser scattering characteristics. Its research is of great significance for space target detection, identification, tracking, and related technological applications. Because theoretical simulation is not limited by experimental conditions and is stable and reliable, it is the main method for studying the LRCS of complex targets. With the rise of visualization computing, graphical electromagnetic computation methods have been introduced into LRCS calculations. This method relies on surface element occlusion algorithms to achieve complex calculations such as occlusion removal and geometric transformations.

[0003] In existing technologies, occlusion judgment of surface elements of space targets is based on the depth of surface elements in order to eliminate hidden surfaces. However, this method of occlusion removal is difficult to apply to the surface of complex space targets, resulting in low accuracy of occlusion removal and thus poor performance of LRCS simulation. Summary of the Invention

[0004] In view of the above problems, embodiments of this application provide a method, apparatus, device, medium, and program product for simulating the scattering cross section of a space target lidar.

[0005] According to a first aspect of this application, a method for simulating the scattering cross section of a space target lidar is provided, comprising: projecting triangular facets of a space target surface element model onto a pixel plane perpendicular to the incident wave of the lidar, thereby obtaining projected facets with depth attributes, wherein the depth attributes of the projected facets include the depth values ​​of three vertex pixels; obtaining the bounding rectangle of any projected facet in the pixel plane, taking the pixels within the bounding rectangle that are located within the projected facet as area pixels, and calculating a weighted average of the depth values ​​of the three vertex pixels based on the relative positions of the area pixels in the projected facet, and using the weighted average as the depth value of the area pixels; selecting the area pixel with the smallest depth value from at least one area pixel with a depth value corresponding to any pixel in the pixel plane as a visible pixel, and taking the projected facets whose ratio of visible pixels to area pixels exceeds a preset visibility threshold as visible facets; calculating the scattering cross section of each visible facet based on the backscattering effect of the incident wave, and calculating the sum of the scattering cross sections of each facet to obtain the lidar scattering cross section.

[0006] According to an embodiment of this application, using a pixel located within a projection element in a bounding rectangle as an area pixel includes: for any pixel in the bounding rectangle, obtaining three relative triangles formed by connecting the pixel to any two vertex pixels of the projection element; calculating the directed area of ​​the relative triangles based on the pixel indices of the three vertex pixels of each relative triangle; and determining that the pixel is located within the projection element and using the pixel as the area pixel of the projection element if the directed areas of the three relative triangles have the same sign.

[0007] According to an embodiment of this application, calculating the weighted average of the depth values ​​of three vertex pixels based on the relative positions of area pixels in a projected surface element includes: calculating the directed area of ​​the projected surface element based on the three vertex pixels of the projected surface element to obtain a reference area; for any vertex pixel of the projected surface element, selecting a target relative surface element from the relative triangle elements whose vertices do not contain vertex pixels, calculating the ratio of the directed area of ​​the target relative surface element to the reference area to obtain the vertex depth weight of the vertex pixel; and performing a weighted summation of the depth values ​​of the corresponding vertex pixels based on the vertex depth weights of each vertex pixel to obtain a weighted average.

[0008] According to an embodiment of this application, calculating the directed area of ​​a relative triangle element based on the pixel indices of the three vertex pixels of each relative triangle element includes: for each relative triangle element, constructing two first edge vectors starting from the same vertex and ending at the other two vertices based on the pixel indices of the three vertex pixels of the relative triangle element; calculating half of the cross product of the two first edge vectors to obtain the directed area of ​​the relative triangle element.

[0009] According to an embodiment of this application, selecting the area pixel with the smallest depth value from at least one area pixel with a depth value corresponding to any pixel in the pixel plane as a visible pixel includes: sequentially comparing the depth of each area pixel included in each projection element and the pixel corresponding to the area pixel in the pixel plane to select the smaller depth value as an intermediate depth value, until the depth comparison of each area pixel of each projection element is completed, taking the last intermediate depth value of each pixel in the pixel plane as the lowest depth value, and taking the area pixel corresponding to the lowest depth value as a visible pixel; wherein, comparing the depth of any area pixel of any projection element and the pixel corresponding to the area pixel in the pixel plane to select the smaller depth value as an intermediate depth value includes: obtaining the pixel index of the area pixel, obtaining the pixel corresponding to the pixel index in the pixel plane, and the current visible depth value of the pixel; calculating the difference between the current visible depth value and a preset depth tolerance value to obtain the current depth threshold; in response to the depth value of the area pixel being less than the current depth threshold, taking the depth value of the area pixel as an intermediate depth value, and updating the visible depth value of the pixel to the intermediate depth value.

[0010] According to an embodiment of this application, projecting triangular facets of a spatial target surface model onto a pixel plane perpendicular to the incident wave of a lidar to obtain a projected surface with depth attributes includes: constructing a coordinate system using the incident direction of the lidar incident wave as the depth direction to obtain a view coordinate system, wherein the incident direction of the lidar incident wave is determined by spatial transformation based on the incident azimuth and elevation angles of the lidar incident wave; transforming the vertex coordinates of the triangular facets in the spatial target surface model to the view coordinate system to obtain the view vertex coordinates of the corresponding view surface; projecting the view surface onto a two-dimensional projection plane perpendicular to the depth direction based on the view vertex coordinates to obtain the planar coordinates of the view surface and the depth value in the depth direction, wherein the depth value represents the distance between the view surface and the lidar; performing rasterization processing on the two-dimensional projection plane based on a preset observation resolution to obtain a pixel plane; converting the view vertex coordinates into the pixel index of the corresponding vertex pixel in the pixel plane, and using the depth value as the depth value of the vertex pixel; and obtaining a projected surface with depth attributes based on the pixel index and depth value of the vertex pixel.

[0011] According to an embodiment of this application, the method for obtaining a spatial target surface model includes: transforming an initial model of the spatial target into a target coordinate system to obtain a first surface model, wherein the first surface model includes the coordinates of the first vertices of each first surface element, and the initial model is obtained by modeling the spatial target; rotating the first surface model according to the attitude angle of the spatial target in the target coordinate system to obtain a spatial target surface model, wherein the spatial target surface model is represented by the vertex coordinates of each triangular surface element in the target coordinate system, and the vertex coordinates are obtained by rotating the first vertex coordinates.

[0012] According to an embodiment of this application, the calculation of the surface element scattering cross section based on the backscattering effect of each visible surface element on the incident wave includes: for any visible surface element, determining the surface element incident angle of the incident wave hitting the visible surface element based on the angle between the incident direction of the lidar incident wave and the surface element normal vector of the visible surface element; determining the reflection angle dependence model of the visible surface element under the surface element incident angle based on the characteristics of the coating material of the visible surface element, the reflection angle dependence model characterizes the quantitative relationship between the incident angle of the incident wave hitting the surface element and the backscattering capability of the surface element; and calculating the surface element-level scattering cross section based on the reflection angle dependence model, the surface element incident angle, and the area of ​​the visible surface element to obtain the surface element scattering cross section.

[0013] According to a second aspect of this application, a space target lidar scattering cross section simulation device is provided, comprising: a projection surface element acquisition module: used to project the triangular surface elements of a space target surface element model onto a pixel plane perpendicular to the incident wave of the lidar, to obtain projection surface elements with depth attributes, wherein the depth attributes of the projection surface elements include the depth values ​​of three vertex pixels; an interpolation depth calculation module: used to acquire the bounding rectangle of any projection surface element in the pixel plane, take the pixels located within the projection surface element in the bounding rectangle as area pixels, and calculate the weighted average of the depth values ​​of the three vertex pixels based on the relative positions of the area pixels in the projection surface element, and use the weighted average as the depth value of the area pixels; a visible surface element recognition module: used to select the area pixel with the smallest depth value from at least one area pixel with a depth value corresponding to any pixel in the pixel plane as a visible pixel, and to take the projection surface element whose ratio of visible pixels to area pixels exceeds a preset visibility threshold as a visible surface element; and a lidar scattering cross section simulation module: used to calculate the surface element scattering cross section based on the backscattering effect of each visible surface element on the incident wave, and calculate the sum of the scattering cross sections of each surface element to obtain the lidar scattering cross section.

[0014] According to a third aspect of this application, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0015] According to a fourth aspect of this application, a computer-readable storage medium is also provided, on which a computer program or instructions are stored, wherein the computer program or instructions, when executed by a processor, implement the steps of the above-described method.

[0016] According to a fifth aspect of this application, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0017] According to the embodiments of this application, by rasterizing each surface element into multiple pixels, performing depth testing based on each pixel to obtain visible pixels, and determining the visible surface element based on the proportion of visible pixels, compared with the depth testing based on the entire surface element in the prior art, it can more accurately reflect the occlusion relationship between surface elements on the outer surface of a space target, especially the outer surface of a complex space target, thereby obtaining more accurate surface element occlusion results and further obtaining more accurate lidar scattering cross section. Attached Figure Description

[0018] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0019] Figure 1 The illustration schematically depicts an application scenario of a space target lidar cross-section simulation method, apparatus, device, medium, and program product according to embodiments of this application;

[0020] Figure 2 A flowchart illustrating a method for simulating the scattering cross section of a space target lidar according to an embodiment of this application is shown schematically.

[0021] Figure 3 The flowchart illustrates an embodiment of a space target lidar scattering cross section simulation method according to an embodiment of this application;

[0022] Figure 4 The flowchart illustrating the visible element determination step in one embodiment of the space target lidar scattering cross section simulation method according to the present application is shown in the schematic diagram.

[0023] Figure 5 This schematically illustrates a flowchart of the main steps of one embodiment of the space target lidar scattering cross section simulation method according to embodiments of this application;

[0024] Figure 6 This schematic diagram illustrates the structural block diagram of a space target lidar scattering cross section simulation device according to an embodiment of this application;

[0025] Figure 7 A block diagram schematically illustrates an electronic device suitable for implementing a method for simulating the scattering cross section of a space target using lidar, according to an embodiment of this application. Detailed Implementation

[0026] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0029] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0030] Figure 1 The diagram illustrates an application scenario of the space target lidar scattering cross section simulation method according to an embodiment of this application.

[0031] like Figure 1 As shown, application scenario 100 according to an embodiment of this application may include a first terminal device 101, a second terminal device 102, a database 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the database 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables. For example, a user can use the first terminal device 101 and the second terminal device 102 to interact with the server 105 and / or the database 103 through the network 104 to receive or send information, etc.

[0032] The first terminal device 101 and the second terminal device 102 can be electronic devices such as smartphones, wearable devices, personal computers, intelligent voice interaction devices, smart home appliances, intelligent vehicles, in-vehicle terminals, aircraft, unmanned vending terminals, and extended reality devices. Extended reality devices can include virtual reality devices, augmented reality devices, and mixed reality devices. A client application for the target application can be installed and run on the terminal device. This target application can include, but is not limited to, web browser applications, search applications, instant messaging tools, email clients, and social media platform software (only examples). Furthermore, this application embodiment does not limit the form of the target application, including but not limited to applications, mini-programs, etc., installed on the terminal device, and can also be in the form of a webpage.

[0033] Server 105 can be a server providing various services, such as a backend management server supporting websites browsed by users using the first terminal device 101 and the second terminal device 102 (this is just an example). The backend management server can analyze and process received user requests and other data, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services such as cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data. The server can be a backend server for the aforementioned target application, used to provide backend services to the clients of the target application.

[0034] Database 103 is a professional storage system for storing and managing data. It can store various types of data related to the target application, such as spatial target model data, temporary data, and intermediate data. It supports structured, semi-structured, or unstructured data storage and has management capabilities such as adding, deleting, modifying, querying, backing up, and restoring data. In this application scenario, database 103 can be connected to server 105 via a communication link. Server 104 can retrieve the required data from database 103 for processing based on requests from the first terminal device 101 and the second terminal device 102. It can also synchronously store new data generated by the operations of the first terminal device 101 and the second terminal device 102 into database 103, thereby achieving data persistence and efficient retrieval.

[0035] It should be noted that the space target lidar cross-section simulation method provided in this application embodiment can generally be executed by server 105 and / or terminal devices 101-102. Accordingly, the space target lidar cross-section simulation device provided in this application embodiment can generally be set in server 105 and / or terminal devices 101-102.

[0036] It should be understood that Figure 1 The number of terminal devices, networks, databases, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, databases, and servers can be included.

[0037] Figure 2 A flowchart illustrating a method for simulating the scattering cross section of a space target using lidar according to an embodiment of this application is shown. Figure 2 As shown, the space target lidar scattering cross section simulation method 200 according to the embodiment of this application may include steps S210 to S240.

[0038] In step S210, the triangular facets of the spatial target facet model are projected onto a pixel plane perpendicular to the incident wave of the lidar, resulting in a projected facet with depth attributes, wherein the depth attributes of the projected facet include the depth values ​​of the three vertex pixels. In step S220, the bounding rectangle of any projected facet in the pixel plane is obtained, and the pixels within the bounding rectangle that are located within the projected facet are taken as area pixels. The weighted average of the depth values ​​of the three vertex pixels is calculated based on the relative positions of the area pixels in the projected facet, and the weighted average is taken as the depth value of the area pixel. In step S230, the area pixel with the smallest depth value is selected from at least one area pixel with a depth value corresponding to any pixel in the pixel plane as a visible pixel, and the projected facet with a ratio of visible pixels to area pixels exceeding a preset visibility threshold is taken as a visible facet. In step S240, based on the backscattering effect of each visible facet on the incident wave, the facet scattering cross section is calculated, and the sum of the scattering cross sections of each facet is calculated to obtain the lidar scattering cross section.

[0039] In S210, the space target surface element model is a three-dimensional model constructed for the outer surface of the space target. The model includes multiple triangular surface elements obtained by dividing the outer surface. Each triangular surface element is determined by the coordinates of its three vertices, where the vertex coordinates represent its spatial position in the target coordinate system. The coordinate system in which the space target itself is located is defined as the target coordinate system, and the directions and angles of the incident and scattered waves are defined in this target coordinate system. This coordinate system measures the position and relative position of the space target and the lidar in space. The target coordinate system can be a coordinate system with the geometric center of the space target as its origin.

[0040] In S210, when performing Laser Radar Cross Section (LRCS) analysis, the incident laser wave is defined in the target coordinate system. However, the origin of the modeling coordinate system used for modeling may not be located at the geometric center of the spatial target. Therefore, it is necessary to transform the initial model obtained from the modeling software, i.e., transform it from the modeling coordinate system to the target coordinate system. This ensures that the vertex coordinates of the surface elements and the normal vectors calculated based on the vertex coordinates are consistent with the laser incident wave defined in the target coordinate system. For example, the modeling coordinate system can be the reference coordinate system in the modeling software.

[0041] In S210, after obtaining the vertex coordinates of the triangular facets in the target coordinate system, the triangular facets are projected onto a pixel plane perpendicular to the incident wave of the lidar based on the observation direction. This can be achieved by first performing view coordinate transformation and viewport mapping, and then, after viewport mapping, rasterizing the observation plane to convert it into a pixel plane. Here, the viewport refers to the lidar's observation viewport, which is also the computer display interface during graphic electromagnetic simulation calculations. The view coordinate system is a coordinate system with the viewport as the observation plane and the direction perpendicular to the viewport as the depth direction. Transforming the target coordinate system to the view coordinate system is to determine which facets are occluded in the view coordinate system for hidden surface removal. The observation direction, the lidar's incident direction, and the viewport direction are all consistent; the viewport plane is also the lidar's observation plane. The lidar's incident direction is determined by the lidar's azimuth angle. Pitch angle Confirmed. The pixel plane is perpendicular to the incident direction of the LiDAR incident wave.

[0042] In S210, after projection, the precise 3D coordinates in the spatial target surface element model are discretized, and the precise coordinates are converted into pixel indices and corresponding depth values ​​in a 2D plane. Correspondingly, the triangular surface element determined by the coordinates of its three vertices is projected into a projected surface element determined by the pixel indices of its three vertices. This projected surface element has a depth attribute, and the depth values ​​of its three vertex pixels represent the depth attribute of the projected surface element. Therefore, occlusion removal judgment can be performed on the projected surface elements based on the depth attribute, that is, occlusion removal judgment can be performed on the outer surface elements of the spatial target corresponding to the projected surface element. The depth attribute of the projected surface element represents the distance between the triangular surface element on the outer surface of the spatial target corresponding to the projected surface element and the lidar; the smaller the depth value, the closer the surface element is to the lidar, and the larger the depth value, the farther away it is from the lidar.

[0043] In S220, since the projection surface element is also a triangular surface element, the bounding rectangle of each projection surface element is obtained in the pixel plane, that is, the minimum bounding rectangle. For each pixel in the bounding rectangle, it is determined whether the pixel is located in the projection surface element by the edge function method. If the pixel is located in the projection surface element, the depth attribute of the pixel can be calculated according to the depth attribute of the projection surface element based on the relative position of the pixel in the projection surface element, that is, the depth value of the pixel. The depth value represents the distance between the pixel and the LiDAR.

[0044] In S230, a depth test is performed on each pixel based on its depth value, thereby determining the area pixel with the lowest depth value corresponding to each pixel in the pixel plane. This area pixel is then designated as a visible pixel, while other pixels with depth values ​​greater than this area pixel are considered invisible due to occlusion. After completing the depth test on all pixels in the pixel plane, the visible pixels corresponding to each pixel are determined. For each projection element, the ratio of visible pixels to area pixels is calculated. If the ratio exceeds a preset visibility threshold, the projection element is considered visible; otherwise, the projection element is considered invisible.

[0045] For example, suppose The total number of area pixels obtained by counting the k-th projected triangle element. This represents the number of visible pixels for that surface element. Given the percentage of visible pixels, the rules for determining visible pixels are as follows:

[0046] ;

[0047] In the above formula, =1 indicates that the element is a visible element; otherwise, the element is an invisible element. , This is a sensitivity parameter (i.e., a preset visibility threshold). By setting different preset visibility thresholds, the visibility ratio of overlapping pixels can be freely adjusted, optimizing the visibility judgment effect of scattered pixels and tiny pixels at the edges. For example, the range of the preset visibility threshold can be 0.5 to 1.

[0048] In S240, after determining the visible elements, the surface scattering interface of each surface element is calculated based on the material characteristics of each surface element, the incident angle of the incident wave on the surface element, and the backscattering effect of the visible elements on the incident wave. Finally, the surface scattering interfaces of all visible elements are summed to obtain the lidar scattering cross section of the space target.

[0049] In S240, backscattering refers to the physical phenomenon where scattered light propagates in the opposite or quasi-opposite direction of the incident light after it strikes a target medium. It is a core effect in fields such as lidar, remote sensing, and photoelectric detection, and is the only effective scattering effect that allows lidar to receive target echoes and calculate the backscattering cross section (LRCS). For a lidar target element i, the backscattering intensity varies with the incident angle of the element. When a laser beam illuminates a target, specular reflection, diffuse reflection, and scattering occur simultaneously. Backscattering is the component of scattering that travels in the opposite direction to the incident light. For example, the backscattering capability of a surface element can be measured using an angle-dependent quantization model of the backscattering effect in lidar. This model could be a BRDF (Bidirectional Reflectance Distribution Function) model, where the specular scattering term corresponds to the directional component of backscattering, and the diffuse reflection term corresponds to the random component. The sum of these two terms represents the total backscattering capability of the surface element.

[0050] According to the embodiments of this application, by dividing each surface element into multiple pixels, performing depth testing based on each pixel to obtain visible pixels, and determining the visible surface element based on the proportion of visible pixels, compared with the depth testing based on the entire surface element in the prior art, the occlusion relationship between surface elements on the outer surface of a space target (especially a complex space target) can be more accurately reflected, thereby obtaining more accurate surface element occlusion results and further obtaining a more accurate lidar scattering cross section.

[0051] According to an embodiment of this application, using a pixel located within a projection element in a bounding rectangle as an area pixel includes: for any pixel in the bounding rectangle, obtaining three relative triangles formed by connecting the pixel to any two vertex pixels of the projection element; calculating the directed area of ​​the relative triangles based on the pixel indices of the three vertex pixels of each relative triangle; and determining that the pixel is located within the projection element and using the pixel as the area pixel of the projection element if the directed areas of the three relative triangles have the same sign.

[0052] For example, the edge function method is used to determine whether pixel P is located within a projected surface element. The specific method for the edge function includes: rasterizing any triangular surface element in the target 3D model, calculating the pixel index mapping, and defining... This is the pixel index box of the bounding matrix of the triangular facets, i.e., the minimum bounding rectangle. This represents the minimum horizontal value of the smallest bounding rectangle in the pixel plane. This represents the maximum horizontal axis value of the smallest bounding rectangle in the pixel plane. This represents the minimum value of the vertical axis of the smallest bounding rectangle in the pixel plane. This represents the maximum vertical axis value of the minimum bounding rectangle on the pixel plane. For each pixel in the minimum bounding rectangle, the centroid coordinate components of the corresponding vertex can be calculated using the edge function method to determine whether the pixel is within the polygon, thus avoiding vertex entanglement during model construction. The centroid coordinate components represent the relative position of the pixel in the projected polygon.

[0053] For example, assuming the three vertex pixels of the projection surface element are A, B, and C, for any pixel P in the circumscribed rectangle, the three relative triangle elements can be represented as PAB, PBC, and PCA respectively. Knowing the pixel index of the vertex pixel of the relative triangle element, the directed area of ​​the relative triangle element can be calculated. If the directed areas of the three relative triangle elements have the same sign, pixel P is taken as the area pixel of the projection surface element.

[0054] Currently, before determining whether a surface element is occluded, the dot product of the surface element's normal vector and the incident vector is typically used. If the dot product is greater than 0, the surface element is considered to be facing the incident wave, and further hidden surface removal is performed on the surface element facing the incident wave. However, this method has a problem: when calculating the normal vector based on the coordinates of the three vertices of the triangular surface element using the cross product of spatial vectors combined with the right-hand screw rule, if there is vertex entanglement, it may lead to misjudging a surface element facing the incident wave as being away from the incident wave, or vice versa, thus affecting the accuracy of visible surface element recognition.

[0055] According to the embodiments of this application, by determining whether a pixel is within a surface element based on the directed area sign of three relative triangular surface elements, the vertex entanglement phenomenon that occurs during model construction is avoided, which leads to incorrect selection of surface elements. Furthermore, by judging the area pixels one by one and selecting visible surface elements based on the proportion of visible pixels in the area pixels in subsequent steps, a higher accuracy can be obtained than first identifying and screening surface elements facing the incident wave based on the surface element normal vector and then performing hidden surface removal.

[0056] According to an embodiment of this application, calculating the weighted average of the depth values ​​of three vertex pixels based on the relative positions of area pixels in a projected surface element includes: calculating the directed area of ​​the projected surface element based on the three vertex pixels of the projected surface element to obtain a reference area; for any vertex pixel of the projected surface element, selecting a target relative surface element from the relative triangle elements whose vertices do not contain vertex pixels, calculating the ratio of the directed area of ​​the target relative surface element to the reference area to obtain the vertex depth weight of the vertex pixel; and performing a weighted summation of the depth values ​​of the corresponding vertex pixels based on the vertex depth weights of each vertex pixel to obtain a weighted average.

[0057] For example, after determining the directed area of ​​each relative triangular element based on the side function method, the depth value of the vertex pixel of the projected element can be calculated by weighted averaging of these directed areas to obtain the depth value of each area pixel.

[0058] In one specific embodiment, the target relative triangle element corresponding to vertex pixel A is PBC, the target relative triangle element corresponding to vertex pixel B is PCA, and the target relative triangle element corresponding to vertex pixel C is PAB. Therefore, the vertex depth weight of each vertex pixel is represented as follows:

[0059] ; ; ;

[0060] in, This represents the directed area of ​​the relative triangular element PAB. Represents the directed area of ​​the relative triangular element PBC. This represents the directed area of ​​the relative triangular element PCA. This represents the reference area of ​​triangle ABC. This represents the vertex depth weight of vertex pixel A. This represents the vertex depth weight of vertex pixel B. This represents the vertex depth weight of vertex pixel C. This refers to the centroid coordinate components of the vertex corresponding to pixel P.

[0061] Assumption The depth value of pixel P, also known as the interpolation depth, is calculated using the following formula:

[0062] ;

[0063] in, This represents the vertex depth value of vertex pixel A. This represents the vertex depth value of vertex pixel B. This represents the vertex depth value of the projected vertex pixel C.

[0064] For example, It represents the distance between pixel P and the lidar.

[0065] According to embodiments of this application, the depth value corresponding to each area pixel in the projection surface element is calculated to facilitate subsequent depth testing based on the depth value of the area pixel. This improves the existing surface element-level depth testing to pixel-level depth testing. After the depth test is completed, visible surfaces are determined based on the proportion of visible pixels in the projection surface element. Compared with the existing technology of eliminating surfaces facing away from the incident wave based on the product of the normal vector and the incident vector, or surface element-level depth testing, the visibility judgment effect of edge sporadic pixels and small surfaces can be optimized, thereby making the final judgment of visible surfaces more accurate.

[0066] According to an embodiment of this application, calculating the directed area of ​​a relative triangle element based on the pixel indices of the three vertex pixels of each relative triangle element includes: for each relative triangle element, constructing two first edge vectors starting from the same vertex and ending at the other two vertices based on the pixel indices of the three vertex pixels of the relative triangle element; calculating half of the cross product of the two first edge vectors to obtain the directed area of ​​the relative triangle element.

[0067] For example, using a pixel within a projection element of a bounding rectangle as the area pixel of the projection element includes: for any pixel in the bounding rectangle, obtaining three relative triangles formed by connecting the pixel to any two vertex pixels of the projection element; for each relative triangle, obtaining the pixel indices of the three vertex pixels of the relative triangle, constructing two first edge vectors based on the pixel indices, starting from the same vertex and ending at the other two vertices, and obtaining the directed area of ​​the relative triangle based on half of the cross product of the two first edge vectors; if the directed areas of the three relative triangles have the same sign, determining that the pixel is within the projection element, and using the pixel as the area pixel of the projection element.

[0068] For example, assuming a pixel to be measured in the bounding rectangle is P, the directed area of ​​its relative triangle element is calculated using the following formula:

[0069] ;

[0070] ;

[0071] ;

[0072] Where A, B, and C represent the three vertex pixels of the projected surface element. This represents the index of vertex pixel A, also known as the pixel index. This represents the index of vertex pixel B. This represents the index of vertex pixel C. This represents the index of the pixel P to be measured. The area calculation formula uses the determinant rule to calculate the cross product of the vectors.

[0073] If there is a directional area , and If the signs are the same, then pixel P is determined to be within the triangle element, and pixel P is the area pixel of the triangle element. This process continues until each pixel in the outer rectangle is determined, and the number of area pixels in the triangle element can be obtained. This is the number of area pixels corresponding to the projected element.

[0074] According to an embodiment of this application, by substituting the pixel index into the formula for calculating the area of ​​a triangle based on the vertex coordinates of the triangle, the area of ​​a relative triangular element is quickly calculated. This area represents the number of pixels included in the relative triangular element, so as to determine the relative position of the pixel to be tested P based on the area ratio of each relative triangular element. Based on this relative position, the depth value of the pixel is calculated, that is, the distance between the pixel and the LiDAR, which facilitates more accurate identification of visible pixels in the future based on the depth value.

[0075] According to an embodiment of this application, selecting the area pixel with the smallest depth value from at least one area pixel with a depth value corresponding to any pixel in the pixel plane as a visible pixel includes: sequentially comparing the depth of each area pixel included in each projection element and the pixel corresponding to the area pixel in the pixel plane to select the smaller depth value as an intermediate depth value, until the depth comparison of each area pixel of each projection element is completed, taking the last intermediate depth value of each pixel in the pixel plane as the lowest depth value, and taking the area pixel corresponding to the lowest depth value as a visible pixel; wherein, comparing the depth of any area pixel of any projection element and the pixel corresponding to the area pixel in the pixel plane to select the smaller depth value as an intermediate depth value includes: obtaining the pixel index of the area pixel, obtaining the pixel corresponding to the pixel index in the pixel plane, and the current visible depth value of the pixel; calculating the difference between the current visible depth value and a preset depth tolerance value to obtain the current depth threshold; in response to the depth value of the area pixel being less than the current depth threshold, taking the depth value of the area pixel as an intermediate depth value, and updating the visible depth value of the pixel to the intermediate depth value.

[0076] For example, after obtaining the depth values ​​(i.e., interpolated depths) of each pixel area, depth testing is performed on all pixels based on the z-buffer concept, and a depth tolerance mechanism is introduced. A tolerance value is added during depth testing to avoid the "flickering" phenomenon that occurs when the same pixel is extremely close. The depth of the nearest pixel is continuously updated, and the final pixel number to which each pixel belongs is marked. This depth tolerance value can be determined based on the computer's precision to overcome the flickering phenomenon; for example, the tolerance value can be set to 0.001.

[0077] In one specific embodiment, for each area pixel of a projection surface element, the depth value is calculated. Using this depth value as the first depth value, the currently stored depth value of the pixel corresponding to the index of that area pixel is obtained, and the currently stored depth value is used as the second depth value. The smaller of the first and second depth values ​​is taken as the intermediate depth value, and the number of the projection surface element to which the area pixel corresponding to the intermediate depth value belongs is marked. Initially, the initial depth value of all pixels is set to a maximum value, such as infinity, to represent that all pixels are extremely far from the LiDAR in the initial state. After performing depth testing on each area pixel of each projection surface element, the visible pixels corresponding to each pixel in the pixel space are finally obtained. Pixels with a depth value of the initial value represent blank pixels, i.e., no surface element is projected onto that pixel. When performing visible pixel statistics, these blank pixels are not visible pixels of the target and need to be removed.

[0078] According to the embodiments of this application, for complex spatial target geometry and dense component overlap, depth testing is performed based on the z-buffer (depth buffer) concept. During depth testing, a depth tolerance mechanism is introduced, that is, the cached depth value and the corresponding area pixels are only updated when the depth difference between the two depth comparisons is greater than the tolerance value. This optimizes the visibility determination of overlapping facets that are very close together, avoids flickering caused by the close proximity, and obtains stable visible facet determination results.

[0079] According to an embodiment of this application, projecting triangular facets of a spatial target surface model onto a pixel plane perpendicular to the incident wave of a lidar to obtain a projected surface with depth attributes includes: constructing a coordinate system using the incident direction of the lidar incident wave as the depth direction to obtain a view coordinate system, wherein the incident direction of the lidar incident wave is determined by spatial transformation based on the incident azimuth and elevation angles of the lidar incident wave; transforming the vertex coordinates of the triangular facets in the spatial target surface model to the view coordinate system to obtain the view vertex coordinates of the corresponding view surface; projecting the view surface onto a two-dimensional projection plane perpendicular to the depth direction based on the view vertex coordinates to obtain the planar coordinates of the view surface and the depth value in the depth direction, wherein the depth value represents the distance between the view surface and the lidar; performing rasterization processing on the two-dimensional projection plane based on a preset observation resolution to obtain a pixel plane; converting the view vertex coordinates into the pixel index of the corresponding vertex pixel in the pixel plane, and using the depth value as the depth value of the vertex pixel; and obtaining a projected surface with depth attributes based on the pixel index and depth value of the vertex pixel.

[0080] For example, the observation direction is represented as The observation direction is the incident direction of the lidar wave in the target coordinate system. Assuming the initial upward direction is... Constructing a view coordinate system with the observation direction as the Z-axis, the right direction can be calculated as: In order to avoid degradation, if ,make ,otherwise The upper vector is recalculated as follows: Normalizing the above vector yields Then the view transformation matrix The formula for transforming the target coordinate system to the view coordinate system is shown below:

[0081] ;

[0082] In the above formula, These represent the coordinates of any vertex of any triangular element in the view coordinate system; they are also known as view vertex coordinates or viewpoint coordinates. This represents the vertex coordinate vector of any triangular facet element in the target coordinate system after attitude transformation.

[0083] Decompose the viewpoint coordinates into planar coordinates and depth values ​​in the depth direction. Assuming the resolution is R, calculate the viewpoint coordinates. Two-dimensional bounding box in the observation plane ,in This represents the minimum x-coordinate of each triangular element in the spatial target model when transformed to the view coordinate system. This represents the maximum value of the x-coordinate. This represents the minimum sum of the ordinates of each triangular element in the spatial target model when transformed to the view coordinate system. This represents the maximum value of the ordinate.

[0084] The mapping ratio between the view vertex coordinates and the various indices in pixel space is as follows:

[0085] ;

[0086] in, Indicates the horizontal mapping ratio. This indicates the vertical mapping ratio, where R is an integer representing the resolution.

[0087] Then for any view coordinate Its pixel index It can be mapped as follows:

[0088] ;

[0089] Let Vz represent the planar coordinates obtained by projecting the view vertex coordinates of any given vertex pixel, where (i, j) represents the corresponding pixel index after projection. Based on this mapping, by rasterizing any triangular facet in the target 3D model, the pixel index mapping of each triangular facet can be calculated. Then, Vz in the coordinates represents the depth value corresponding to the pixel.

[0090] According to the embodiments of this application, by transforming the vertex coordinates in the spatial target surface model to the view coordinate system with the observation direction as the depth direction, pixel-level depth testing can be performed based on the pixel index obtained by the planar coordinate transformation according to the depth value in the observation direction, thus realizing convenient, fast and accurate depth testing in the view coordinate system.

[0091] According to an embodiment of this application, the method for obtaining a spatial target surface model includes: transforming an initial model of the spatial target into a target coordinate system to obtain a first surface model, wherein the first surface model includes the coordinates of the first vertices of each first surface element, and the initial model is obtained by modeling the spatial target; rotating the first surface model according to the attitude angle of the spatial target in the target coordinate system to obtain a spatial target surface model, wherein the spatial target surface model is represented by the vertex coordinates of each triangular surface element in the target coordinate system, and the vertex coordinates are obtained by rotating the first vertex coordinates.

[0092] For example, the initial model of a spatial target surface element model can include a construction model and a general geometric model. The model can be saved in formats such as .obj or .stl. The construction model can be created using modeling software for spatial modeling. Based on the actual geometric shape and size of the target, the target's outline wireframe is constructed in the reference coordinate system of the modeling software. Then, the target surface is divided into triangular surface elements using the surface element method. Finally, the target file is saved in a general geometric model format, such as .obj or .stl. The model file stores the vertex numbers of the surface elements. The vertex coordinates can be obtained from the vertex numbers. Based on the coordinates of the three vertices of a surface element, its position can be determined, and the normal vector and area of ​​the surface element can be calculated from these coordinates.

[0093] For example, during model file parsing, the vertex numbers of the face elements are read first. Based on these vertex numbers, the position coordinates of the vertices are obtained, from which the normal vector and area of ​​the face element can be calculated. For instance, the vertex coordinates obtained from modeling can be spatially transformed to transform the initial model into a target coordinate system. The target coordinate system has its origin at the geometric center of the target space, and the coordinate axes follow a preset direction. After the transformation, the first face element model in the target coordinate system is obtained. Assuming A, B, and C are the three vertices of a triangular face element in the first face element model, their corresponding vertex coordinates, or vertex coordinate vectors, are expressed as follows: , , For example, the relationship between vertices and vertex pixels is as follows: vertex A is projected onto the pixel plane to obtain vertex pixel A, vertex B is projected onto the pixel plane to obtain vertex pixel B, and vertex C is projected onto the pixel plane to obtain vertex pixel C.

[0094] To simulate the scattering characteristics of a space target at any attitude angle, it is assumed that... The attitude angles representing the target's rotation around the Z-axis, Y-axis, and X-axis, namely azimuth, pitch, and roll, are transformed using the following formulas:

[0095] ;

[0096] ;

[0097] ;

[0098] in, Represents the rotation transformation matrix for rotating around the Z-axis by an azimuth angle α; The rotation transformation matrix represents the pitch angle β around the Y-axis; This represents the rotation transformation matrix that represents the roll angle γ around the X-axis.

[0099] make For rotation matrix, and Let represent the three-dimensional coordinate vectors before and after the attitude transformation, respectively. The calculation formula is as follows:

[0100] ;

[0101] in, This represents the vertex coordinate vector of the triangular facet element in the target coordinate system before the attitude transformation. This represents the vertex coordinate vector of the triangular facet element in the target coordinate system after the attitude transformation.

[0102] The vertex coordinate vectors of the triangular facets in the target coordinate system before attitude transformation are obtained by performing a preliminary spatial transformation on the coordinates in the modeling coordinate system. The preliminary spatial transformation may include moving the origin of the modeling coordinate system to the geometric center of the spatial target and transforming the coordinate axis directions of the modeling coordinate system to a preset coordinate axis direction.

[0103] According to the embodiments of this application, by identifying the attitude angle of a spatial target, performing attitude transformation on the vertex coordinates, and then projecting and identifying visible elements, omnidirectional simulation and analysis of the lidar scattering cross section of a spatial target under any attitude angle can be achieved.

[0104] According to an embodiment of this application, the calculation of the surface element scattering cross section based on the backscattering effect of each visible surface element on the incident wave includes: for any visible surface element, determining the surface element incident angle of the incident wave hitting the visible surface element based on the angle between the incident direction of the lidar incident wave and the surface element normal vector of the visible surface element; determining the reflection angle dependence model of the visible surface element under the surface element incident angle based on the characteristics of the coating material of the visible surface element, the reflection angle dependence model characterizes the quantitative relationship between the incident angle of the incident wave hitting the surface element and the backscattering capability of the surface element; and calculating the surface element-level scattering cross section based on the reflection angle dependence model, the surface element incident angle, and the area of ​​the visible surface element to obtain the surface element scattering cross section.

[0105] For example, when parsing the surface model file of a spatial target, the vertex number of the surface element is read first, and the position coordinates of the vertex are obtained according to the vertex number. From this, the normal vector and surface area of ​​the surface element can be calculated.

[0106] Assuming A, B, and C are the vertices of a triangular element in the target coordinate system, their corresponding vertex coordinates are expressed as follows: , , ,vector and The definition is as follows:

[0107] ;

[0108] According to the above formula, the area of ​​the triangular element is S. The normal vector of the surface element is The normalized normal vector is And set the vector direction from the surface of the target entity outward to the positive direction of the surface element.

[0109] For complex spatial targets, the coordinate system of the 3D model target itself is defined as the target coordinate system, that is, a coordinate system with the geometric center of the spatial target as the origin. The directions and angles of the incident and scattered waves are defined in this target coordinate system. Assuming the azimuth angle is... The pitch angle is If the distance is D, then the incident vector The definition is as follows:

[0110] ;

[0111] Normalizing it, we get ,in, This represents the normalized incident vector, which is also the observation direction mentioned earlier. This observation direction is also the incident direction of the lidar. When calculating the LRCS value of a target, it is necessary to calculate the LRCS value of each surface element. All calculated parameters are defined in the coordinate system of the surface element. Therefore, by transforming the incident vector to the surface element coordinate system, the scattering characteristics of the surface element can be calculated, and thus the scattering characteristics of the target can be calculated. and Different meanings This indicates the incident azimuth angle of the lidar. Let represent the angle of incidence of the i-th face element.

[0112] Let represent the incident angle of the i-th surface element, i.e., the surface element incident angle. The incident angle characterizes the angle between the incident vector of the incident wave of the lidar and the normal vector of the surface element. Its calculation formula is as follows:

[0113] ;

[0114] In the formula, That is, the normalized incident vector calculated above, which is also the observation direction. That is, the normalized normal vector of the surface element calculated above, or the normalized normal vector. This represents the inverse cosine function, also written as arccos().

[0115] The incident angle of a surface element is calculated using the incident angle of the target and the normal vector of each surface element. By using the incident angle of the surface element and constructing a fitted BRDF (Bidirectional Reflectance Distribution Function) statistical model, the BRDF value at any angle can be simulated, thus obtaining the final LRCS.

[0116] For example, the surface coating materials of complex space targets may include a variety. For models with different surface materials, a material table is constructed after parsing the model elements. Different component IDs are combined with element numbers to associate the elements with the BRDF model materials, thereby selecting appropriate materials with similar surface coating reflective properties to give the elements different material characteristics.

[0117] For example, the single-face element scattering cross section is calculated for each visible element, taking into account the type, characteristics, and optical properties of the coating on the space target surface. For example, the BRDF statistical model is used to obtain the scattering cross section of different materials under different detection conditions. The BRDF model of its single-station lidar, i.e., the reflection angle dependent model, is constructed as follows:

[0118] ;

[0119] In the above formula, Let the incident angle of the i-th face element be denoted as . This represents the bidirectional reflection distribution function of the i-th surface element, also known as the reflection angle-dependent model. Let represent the incident angle of the i-th surface element. A, B, s, and m are undetermined coefficients, the magnitude of which is related to factors such as laser wavelength and material type. Among them, s is the specular reflection coefficient, m is the diffuse reflection coefficient, A and B are the specular reflection and amplitude components, respectively, and B is the diffuse reflection amplitude component. The undetermined coefficient values ​​in this model can be determined by fitting measured data or by selecting approximate values ​​from published data based on coating material information.

[0120] In determining Afterwards, the surface element LRCS can be derived from... The calculated LRCS of the target can be obtained by summing up all visible facets, and the formula is as follows:

[0121] ;

[0122] Let S represent the scattering cross section of the i-th surface element, and let S represent the area of ​​the surface element.

[0123] The final laser scattering cross section (LRCS) is expressed as follows:

[0124] ;

[0125] Where n represents the total number of visible elements in the space target model at a specific incident angle and attitude angle.

[0126] In summary, the LRCS of complex space targets can be calculated based on their different attitudes, incident azimuth angles, and elevation angles, thus analyzing the dynamic characteristics of space targets.

[0127] Figure 3 The flowchart illustrates an embodiment of a space target lidar scattering cross section simulation method according to an embodiment of this application.

[0128] like Figure 3 As shown, in one embodiment, the method for simulating the scattering cross section of a space target lidar includes steps S310 to S340.

[0129] In S310, the 3D geometric mesh model is analyzed, the vertex coordinates of the facet elements are calculated, and the normal vector and area of ​​the facet elements are calculated. A facet material table is constructed, assigning material information to different facet elements. In S320, the target coordinate system is defined, the incident vector is calculated and normalized, and the target coordinate system is converted into the facet coordinate system. The target azimuth, pitch, and roll angles are defined to complete the target attitude angle transformation. In S330, the view coordinate transformation and viewport mapping are constructed, arbitrary triangular facet elements are rasterized and depth tests are performed to achieve visible facet identification. In S340, based on the characteristics of the space target, the material properties of different materials under different detection conditions are obtained, a single-station lidar BRDF statistical model is constructed, the model parameters are fitted, the LRCS and BRDF conversion is realized, the LRCS of visible facet elements is calculated, and the total LRCS of the target is obtained by summing them.

[0130] Specifically, Figure 4 The flowchart illustrating the visible element determination step is shown in one embodiment of the space target lidar scattering cross section simulation method according to an embodiment of the present application.

[0131] like Figure 4 As shown, the visible element determination steps include S331~S333.

[0132] In S331, a view coordinate system is constructed, the target coordinate system is transformed to the view coordinate system, the two-dimensional bounding box of the viewpoint coordinates is calculated, the scaling is completed according to the resolution, and the pixel index mapping of any point coordinate is calculated.

[0133] In S332, the triangular facets of the target 3D model are rasterized, the pixel index mapping is calculated, the bounding box of the triangular facets is defined, the directed area of ​​the triangular facets is calculated, and it is determined whether the pixel is inside the triangular facet. If so, the centroid coordinate components of the vertex are calculated to obtain the interpolation depth of the point in the facet.

[0134] In S333, depth testing is performed on all pixels, and a tolerance mechanism is introduced to continuously update the depth of the nearest pixel, mark the cell number to which each pixel belongs, count the total number of pixels in the cell and the number of pixels after depth testing, calculate the pixel ratio, and use the sensitivity parameter to freely adjust the visibility judgment ratio of overlapping cells to optimize the visibility judgment effect.

[0135] Figure 5 The illustration schematically shows the main steps of one embodiment of the space target lidar scattering cross section simulation method according to embodiments of this application. For example... Figure 5As shown, one embodiment of the simulation method for the scattering cross section of a space target using lidar includes steps S510 to S540. In step S510, a three-dimensional model of the space target is constructed and analyzed. In step S520, a coordinate system is constructed and the attitude is transformed. In step S530, an optimization algorithm for hidden object occlusion judgment is performed. In step S540, the target's LRCS characteristics are calculated.

[0136] According to the embodiments of this application, the visible surface elements are determined by improving the occlusion elimination algorithm and the material information of the visible surface elements is identified. Based on the material characteristics, a BRDF model is selected or fitted to calculate the surface element scattering cross section of each visible surface element, thereby realizing the three-dimensional model and material data based on any target, and realizing the omnidirectional simulation and analysis of the scattering cross section of the space target lidar under any attitude angle.

[0137] Based on the above-described method for simulating the cross-section of a space target's lidar, embodiments of this application also provide a device for simulating the cross-section of a space target's lidar. The following will combine... Figure 6 The device is described in detail.

[0138] Figure 6 A schematic diagram of a space target lidar scattering cross section simulation device according to an embodiment of this application is shown.

[0139] like Figure 6 As shown, the space target lidar scattering cross section simulation device 600 of this embodiment includes a projection surface element acquisition module 610, an interpolation depth calculation module 620, a visible surface element recognition module 630, and a lidar scattering cross section simulation module 640.

[0140] The projection surface element acquisition module 610 is used to project the triangular surface elements of the spatial target surface element model onto a pixel plane perpendicular to the incident wave of the lidar, thereby obtaining projection surface elements with depth attributes. The depth attributes of the projection surface elements include the depth values ​​of the three vertex pixels. In one embodiment, the projection surface element acquisition module 610 can be used to execute step S210 as described above, which will not be repeated here.

[0141] The interpolation depth calculation module 620 is used to obtain the bounding rectangle of any projection element in the pixel plane, take the pixels within the bounding rectangle that are located within the projection element as area pixels, and calculate the weighted average of the depth values ​​of the three vertex pixels based on the relative positions of the area pixels in the projection element, using the weighted average as the depth value of the area pixel. In one embodiment, the interpolation depth calculation module 620 can be used to perform step S220 described above, which will not be repeated here.

[0142] The visible surface element recognition module 630 is used to select the area pixel with the smallest depth value from at least one area pixel with a depth value corresponding to any pixel in the pixel plane as a visible pixel, and to select the projected surface element whose ratio of visible pixel to area pixel exceeds a preset visibility threshold as a visible surface element. In one embodiment, the visible surface element recognition module 630 can be used to perform step S230 described above, which will not be repeated here.

[0143] The lidar scattering cross section simulation module 640 is used to calculate the scattering cross section of each visible surface element based on the backscattering effect of the incident wave, and to calculate the sum of the scattering cross sections of each surface element to obtain the lidar scattering cross section. In one embodiment, the lidar scattering cross section simulation module 640 can be used to perform step S240 described above, which will not be repeated here.

[0144] According to an embodiment of this application, the interpolation depth calculation module 620 is further configured to: for any pixel in the circumscribed rectangle, obtain three relative triangles obtained by connecting the pixel with any two vertex pixels of the projection surface element; calculate the directed area of ​​the relative triangle based on the pixel index of the three vertex pixels of each relative triangle; and determine that the pixel is located within the projection surface element if the directed areas of the three relative triangles have the same sign, and use the pixel as the area pixel of the projection surface element.

[0145] According to an embodiment of this application, the interpolation depth calculation module 620 is further configured to: calculate the directed area of ​​the projected surface element based on the three vertex pixels of the projected surface element to obtain a reference area; for any vertex pixel of the projected surface element, select a target relative surface element from the relative triangle elements whose vertices do not contain vertex pixels, calculate the ratio of the directed area of ​​the target relative surface element to the reference area to obtain the vertex depth weight of the vertex pixel; and perform a weighted summation of the depth values ​​of the corresponding vertex pixels based on the vertex depth weights of each vertex pixel to obtain a weighted average value.

[0146] According to an embodiment of this application, the interpolation depth calculation module 620 is further configured to: for each relative triangle element, construct two first edge vectors based on the pixel indices of the three vertex pixels of the relative triangle element, starting from the same vertex and ending at the other two vertices; calculate half of the cross product of the two first edge vectors to obtain the directed area of ​​the relative triangle element.

[0147] According to an embodiment of this application, the visible surface element recognition module 630 is further configured to: sequentially perform depth comparisons on each area pixel included in each projected surface element and the corresponding pixel in the pixel plane to select the smaller depth value as the intermediate depth value, until the depth comparison of each area pixel of each projected surface element is completed, and the last intermediate depth value of each pixel in the pixel plane is taken as the lowest depth value, and the area pixel corresponding to the lowest depth value is taken as the visible pixel; wherein, performing depth comparisons on any area pixel of any projected surface element and the corresponding pixel in the pixel plane to select the smaller depth value as the intermediate depth value includes: obtaining the pixel index of the area pixel, obtaining the pixel corresponding to the pixel index in the pixel plane, and the current visible depth value of the pixel; calculating the difference between the current visible depth value and a preset depth tolerance value to obtain the current depth threshold; in response to the depth value of the area pixel being less than the current depth threshold, taking the depth value of the area pixel as the intermediate depth value, and updating the visible depth value of the pixel to the intermediate depth value.

[0148] According to an embodiment of this application, the projection surface element acquisition module 610 is further configured to: construct a coordinate system using the incident direction of the laser radar incident wave as the depth direction to obtain a view coordinate system, wherein the incident direction of the laser radar incident wave is determined by spatial transformation based on the incident azimuth and elevation angles of the laser radar incident wave; transform the vertex coordinates of the triangular surface elements in the spatial target surface element model to the view coordinate system to obtain the view vertex coordinates of the corresponding view surface element; project the view surface element onto a two-dimensional projection plane perpendicular to the depth direction based on the view vertex coordinates to obtain the planar coordinates of the view surface element and the depth value in the depth direction, wherein the depth value represents the distance between the view surface element and the laser radar; perform rasterization processing on the two-dimensional projection plane based on a preset observation resolution to obtain a pixel plane; convert the view vertex coordinates into the pixel index of the corresponding vertex pixel in the pixel plane, and use the depth value as the depth value of the vertex pixel; and obtain a projection surface element with depth attributes based on the pixel index of the vertex pixel and the depth value of the vertex pixel.

[0149] According to an embodiment of this application, the projection surface element acquisition module 610 is further configured to: transform the initial model of the spatial target into the target coordinate system to obtain a first surface element model, the first surface element model including the first vertex coordinates of each first surface element, the initial model being obtained by modeling the spatial target; and perform a rotation transformation on the first surface element model based on the attitude angle of the spatial target in the target coordinate system to obtain a spatial target surface element model, wherein the spatial target surface element model is represented by the vertex coordinates of each triangular surface element in the target coordinate system, the vertex coordinates being obtained by rotating the first vertex coordinates.

[0150] According to an embodiment of this application, the lidar scattering cross section calculation module 640 is further configured to: for any visible surface element, determine the incident angle of the incident wave hitting the visible surface element based on the angle between the incident direction of the lidar incident wave and the surface element normal vector of the visible surface element; determine the reflection angle dependence model of the visible surface element under the surface element incident angle based on the characteristics of the coating material of the visible surface element, wherein the reflection angle dependence model characterizes the quantitative relationship between the incident angle of the incident wave hitting the surface element and the backscattering capability of the surface element; and calculate the surface element-level scattering cross section based on the reflection angle dependence model, the surface element incident angle, and the area of ​​the visible surface element to obtain the surface element scattering cross section.

[0151] According to embodiments of this application, any multiple modules among the projection surface element acquisition module 610, interpolation depth calculation module 620, visible surface element recognition module 630, and lidar cross-section simulation module 640 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the projection surface element acquisition module 610, interpolation depth calculation module 620, visible surface element recognition module 630, and lidar cross-section simulation module 640 can be at least partially implemented as hardware circuits, such as field-programmable gate arrays, programmable logic arrays, systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits, or any other reasonable means of integrating or packaging circuits, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the projection element acquisition module 610, interpolation depth calculation module 620, visible element recognition module 630, and lidar scattering cross section simulation module 640 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0152] Figure 7 A block diagram schematically illustrates an electronic device suitable for implementing a method for simulating the scattering cross section of a space target using lidar, according to an embodiment of this application.

[0153] like Figure 7As shown, an electronic device 700 according to an embodiment of this application includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory 702 or a program loaded from a storage portion 708 into a random access memory 703. The processor 701 may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a dedicated microprocessor. The processor 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for executing different steps of the method flow according to an embodiment of this application.

[0154] Random access memory 703 stores various programs and data required for the operation of electronic device 700. Processor 701, read-only memory 702, and random access memory 703 are interconnected via bus 704. Processor 701 executes various steps of the method flow according to embodiments of this application by executing programs in read-only memory 702 and / or random access memory 703. It should be noted that the programs may also be stored in one or more memories other than read-only memory 702 and random access memory 703. Processor 701 may also execute various steps of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0155] According to embodiments of this application, the electronic device 700 may further include an input / output interface 705, which is also connected to a bus 704. The electronic device 700 may also include one or more of the following components connected to the input / output interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube, liquid crystal display, etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card, such as a local area network card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0156] Embodiments of this application also provide a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0157] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include the read-only memory 702, and / or random access memory 703, and / or one or more memories other than read-only memory 702 and random access memory 703 described above.

[0158] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods provided in the embodiments of this application.

[0159] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 709, and / or installed from a removable medium 711. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0160] In embodiments of this application, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by processor 701, it performs the functions defined in the system of embodiments of this application. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0161] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0163] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A method for simulating the scattering cross section of a space target using lidar, characterized in that, The method includes: The triangular facets of the spatial target facet model are projected onto a pixel plane perpendicular to the incident wave of the lidar to obtain a projected facet with depth attributes, wherein the depth attributes of the projected facet include the depth values ​​of the three vertex pixels. Obtain the bounding rectangle of any projection surface element in the pixel plane, take the pixels located within the projection surface element in the bounding rectangle as area pixels, and calculate the weighted average of the depth values ​​of the three vertex pixels based on the relative positions of the area pixels in the projection surface element, and use the weighted average as the depth value of the area pixel. From any pixel in the pixel plane, select the area pixel with the smallest depth value as the visible pixel, and take the projection element whose ratio of visible pixel to area pixel exceeds a preset visible threshold as the visible element. Based on the backscattering effect of each visible surface element on the incident wave, the scattering cross section of the surface element is calculated, and the sum of the scattering cross sections of each surface element is calculated to obtain the scattering cross section of the lidar. Specifically, projecting the triangular facets of the spatial target surface model onto a pixel plane perpendicular to the incident laser radar wave yields projection surfacets with depth attributes, including: A coordinate system is constructed using the incident direction of the laser radar incident wave as the depth direction to obtain the view coordinate system. The incident direction of the laser radar incident wave is determined by spatial transformation based on the incident azimuth and elevation angles of the laser radar incident wave. Transform the vertex coordinates of the triangular facets in the spatial target facet model to the view coordinate system to obtain the view vertex coordinates of the corresponding view facets. Based on the view vertex coordinates, the view element is projected onto a two-dimensional projection plane perpendicular to the depth direction to obtain the planar coordinates of the view element and the depth value in the depth direction. The depth value represents the distance between the view element and the lidar. Based on a preset observation resolution, the two-dimensional projection plane is rasterized to obtain a pixel plane. The coordinates of the view vertex are converted into the pixel index of the corresponding vertex pixel in the pixel plane, and the depth value is used as the depth value of the vertex pixel. Based on the pixel index of the vertex pixel and the depth value of the vertex pixel, a projection element with depth attribute is obtained.

2. The method according to claim 1, characterized in that, The step of using pixels within the projection plane element of the bounding rectangle as area pixels includes: For any pixel in the bounding rectangle, obtain three opposing triangular elements by connecting the pixel with any two vertex pixels of the projected surface element; The directed area of ​​each relative triangle is calculated based on the pixel index of the three vertex pixels of each relative triangle. If the signs of the directed areas of the three opposing triangular facets are the same, it is determined that the pixel is located within the projected facet, and the pixel is taken as the area pixel of the projected facet.

3. The method according to claim 2, characterized in that, The step of calculating the weighted average of the depth values ​​of the three vertex pixels based on the relative positions of the area pixels in the projected surface element includes: The directed area of ​​the projected surface element is calculated based on the three vertex pixels of the projected surface element to obtain the reference area; For any vertex pixel of the projected surface element, select a target relative surface element from the relative triangle elements whose vertices do not contain the vertex pixel, calculate the ratio of the directed area of ​​the target relative surface element to the reference area, and obtain the vertex depth weight of the vertex pixel. The depth values ​​of the corresponding vertex pixels are weighted and summed based on the vertex depth weight of each vertex pixel to obtain a weighted average value.

4. The method according to claim 2, characterized in that, The calculation of the directed area of ​​the relative triangle element based on the pixel indices of the three vertex pixels of each relative triangle element includes: For each of the relative triangular facets, two first edge vectors are constructed based on the pixel indices of the three vertex pixels of the relative triangular facet, starting from the same vertex and terminating at the other two vertices. The directed area of ​​the opposing triangular element is obtained by calculating half of the cross product of the two first edge vectors.

5. The method according to claim 1, characterized in that, The step of selecting the area pixel with the smallest depth value as the visible pixel from at least one area pixel with a depth value corresponding to any pixel in the pixel plane includes: Each area pixel included in each projection surface element and the pixel corresponding to the area pixel in the pixel plane are compared in depth sequentially to select the smaller depth value as the intermediate depth value until the depth comparison of each area pixel of each projection surface element is completed. The last intermediate depth value of each pixel in the pixel plane is taken as the minimum depth value, and the area pixel corresponding to the minimum depth value is taken as the visible pixel. The process of comparing the depth of any area pixel of any projected surface element with the corresponding pixel in the pixel plane to select the smaller depth value as the intermediate depth value includes: Obtain the pixel index of the area pixel, obtain the pixel corresponding to the pixel index in the pixel plane, and the current visible depth value of the pixel; The difference between the current visible depth value and the preset depth tolerance value is calculated to obtain the current depth threshold; In response to the fact that the depth value of the area pixel is less than the current depth threshold, the depth value of the area pixel is used as an intermediate depth value, and the visible depth value of the pixel is updated to the intermediate depth value.

6. The method according to claim 1, characterized in that, The method for obtaining the spatial target surface model includes: The initial model of the spatial target is transformed into the target coordinate system to obtain the first surface model. The first surface model includes the coordinates of the first vertex of each first surface element. The initial model is obtained by modeling the spatial target. The first surface model is rotated and transformed according to the attitude angle of the spatial target in the target coordinate system to obtain the spatial target surface model. The spatial target surface model is represented by the vertex coordinates of each triangular surface element in the target coordinate system. The vertex coordinates are obtained by rotating and transforming the first vertex coordinates.

7. The method according to claim 1, characterized in that, The calculation of the scattering cross section of the surface element based on the backscattering effect of each visible surface element on the incident wave includes: For any visible surface element, the incident angle of the incident wave illuminating the visible surface element is determined based on the angle between the incident direction of the incident wave of the lidar and the surface element normal vector of the visible surface element. Based on the characteristics of the coating material of the visible surface element, a reflection angle dependence model of the visible surface element under the incident angle of the surface element is determined. The reflection angle dependence model characterizes the quantitative relationship between the incident angle of the incident wave hitting the surface element and the backscattering capability of the surface element. The surface element scattering cross section is calculated based on the reflection angle dependence model, the incident angle of the surface element, and the area of ​​the visible surface element to obtain the surface element scattering cross section.

8. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 7.

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