Radar detection range calculation method and device

Abstract

The embodiment of the invention aims to provide a radar detection range calculation method and device, a computer program product and a computer program storage medium. Obtaining an initial detection grid set of a radar detection range of at least one radar according to a predetermined three-dimensional grid coding mode; according to the three-dimensional grid coding mode, based on topographic obstacle data corresponding to the radar detection range, obtaining a topographic obstacle grid set of the topographic obstacle data; a shielding grid set composed of the terrain obstacle grids which are not detected is obtained; and removing the shielding grid set from the initial detection grid set to obtain a target detection grid set. According to the embodiment of the invention, based on the subdivision grid technology, the three-dimensional grid visualization model is constructed for the multi-radar detection range, for example, the grid calculation is carried out on the radar detection range by using Beidou grid coding, and the radar detection range calculation under the terrain influence is supported.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of radar simulation technology, and in particular to a method for calculating a radar detection range. Background Art

[0002] Multi-base radar systems have been widely studied and applied in recent years due to their anti-stealth, anti-interference and strong survivability. The method of calculating the target detection range of multi-base radar based on grid division has made significant progress. Many units have successively developed actual systems and carried out different types of target detection tests, achieving good test results. However, some problems have also been exposed in these practical applications. Radar relies on electromagnetic waves for target detection, and the propagation distance of electromagnetic waves is affected by many environmental factors, especially the obstruction of terrain. Therefore, how to accurately calculate the detection range of radar in a real environment is still an important issue that needs to be solved. Summary of the invention

[0003] The purpose of each embodiment of the present disclosure is to provide a method, device, computer program product and computer program storage medium for calculating radar detection range.

[0004] According to one aspect of the present disclosure, a method for calculating a radar detection range is provided, wherein the method comprises the following steps:

[0005] Acquire an initial detection grid set of a radar detection range of at least one radar according to a predetermined three-dimensional grid encoding method;

[0006] According to the three-dimensional grid coding method, based on the terrain obstacle data corresponding to the radar detection range, obtaining a terrain obstacle grid set of the terrain obstacle data;

[0007] For each terrain obstacle grid in the terrain obstacle grid set, along the detection direction of one of the at least one radar relative to the current terrain obstacle grid, obtaining undetected terrain obstacle grids with the current grid as the starting point within the maximum detection distance of the corresponding radar, thereby obtaining an obstruction grid set consisting of the undetected terrain obstacle grids;

[0008] The occlusion grid set is removed from the initial detection grid set to obtain a target detection grid set.

[0009] According to one aspect of the present disclosure, a radar detection range calculation device is further provided, wherein the device includes a memory and a processor, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the device is configured to perform the following operations:

[0010] Obtain an initial detection grid set of the radar detection range of at least one radar according to a predetermined three-dimensional grid coding method;

[0011] According to the three-dimensional grid coding method, based on the terrain obstacle data corresponding to the radar detection range, obtain a terrain obstacle grid set of the terrain data;

[0012] For each terrain obstacle grid in the terrain obstacle grid set, along the detection direction of one of the at least one radar relative to the current terrain obstacle grid, obtain the terrain obstacle grids that cannot be detected within the maximum detection distance of the corresponding radar starting from the current grid, so as to obtain an occlusion grid set composed of the terrain obstacle grids that cannot be detected;

[0013] Remove the occlusion grid set from the initial detection grid set to obtain a target detection grid set.

[0014] According to one aspect of the present disclosure, there is also provided a computer program product, including computer program instructions, wherein when the computer program instructions are executed by a computer device, the computer device is configured to execute a radar detection range calculation method, and the method includes the following steps:

[0015] Obtain an initial detection grid set of the radar detection range of at least one radar according to a predetermined three-dimensional grid coding method;

[0016] According to the three-dimensional grid coding method, based on the terrain obstacle data corresponding to the radar detection range, obtain a terrain obstacle grid set of the terrain data;

[0017] For each terrain obstacle grid in the terrain obstacle grid set, along the detection direction of one of the at least one radar relative to the current terrain obstacle grid, obtain the terrain obstacle grids that cannot be detected within the maximum detection distance of the corresponding radar starting from the current grid, so as to obtain an occlusion grid set composed of the terrain obstacle grids that cannot be detected;

[0018] Remove the occlusion grid set from the initial detection grid set to obtain a target detection grid set.

[0019] According to one aspect of the present disclosure, there is also provided a computer program storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a computer device, the computer device is configured to execute a radar detection range calculation method, and the method includes the following steps:

[0020] Obtain an initial detection grid set of the radar detection range of at least one radar according to a predetermined three-dimensional grid coding method;

[0021] According to the three-dimensional grid encoding method, based on the terrain obstacle data corresponding to the radar detection range, obtain the terrain obstacle grid set of the terrain obstacle data;

[0022] For each terrain obstacle grid in the terrain obstacle grid set, along the detection direction of one of the at least one radar relative to the current terrain obstacle grid, obtain the terrain obstacle grids that cannot be detected starting from the current grid within the maximum detection distance of the corresponding radar, so as to obtain an occlusion grid set composed of the terrain obstacle grids that cannot be detected;

[0023] Remove the occlusion grid set from the initial detection grid set to obtain a target detection grid set.

[0024] Embodiments of the present disclosure are based on the dissection grid technology to construct a three-dimensional grid visualization model for the multi-radar detection range. For example, the Beidou grid encoding is used to perform grid calculation on the radar detection range, supporting the calculation of the radar detection range under the influence of terrain. Among them, embodiments of the present disclosure obtain the occluded grid set through the method of connecting the radar center and the occlusion grid; apply it to all occlusion grids and take the union to obtain the total occlusion grid set; remove the total occlusion grid set from the radar detection range grid set to obtain the non-occluded grid set, thereby significantly reducing the calculation amount and improving the calculation efficiency.

[0025] Embodiments of the present disclosure make full use of the speed advantage of the binary bit operation mechanism, do not involve complex floating-point calculations, and significantly improve the calculation efficiency.

[0026] Furthermore, embodiments of the present disclosure support aggregating processing of multi-level grids as needed, reducing grid redundancy, reducing the three-dimensional grid data volume, and improving the calculation performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objectives, and advantages of the present disclosure will become more apparent:

[0028] Figure 1 Show a flowchart of a method for calculating the radar detection range according to an embodiment of the present disclosure;

[0029] Figures 2(a) and (b) respectively show schematic diagrams of the hierarchical dissection process of the radar detection range along the vertical and horizontal directions according to an exemplary embodiment of the present disclosure;

[0030] Figures 3(a) and (b) respectively show schematic diagrams of an exemplary original terrain obstacle model diagram and a dissected terrain obstacle grid model diagram according to the present disclosure;

[0031] Figure 4Schematic diagram showing an exemplary multi-level grid upward aggregation according to the present disclosure;

[0032] Figure 5 Visualization effect diagram of radar detection range under terrain influence according to an exemplary of the present disclosure;

[0033] Figure 6 Visualization effect diagram showing the final calculation of the detection range of multi-radar superposition according to an exemplary of the present disclosure.

[0034] The same or similar reference numerals in the drawings represent the same or similar components. Detailed implementation manners

[0035] To make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some but not all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0036] In a typical configuration of the present disclosure, the terminal and the devices of the service network both include one or more processors (CPUs), input / output interfaces, network interfaces and memories.

[0037] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0038] The computer-readable medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information may be computer program instructions, data structures, program modules or other data. Examples of the computer storage medium include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information accessible by a computing device.

[0039] For example, a computer program for performing the functions and processes of various embodiments of the present disclosure is stored in a memory. When a processor executes the corresponding computer program, various embodiments of the present disclosure are implemented.

[0040] Refer to Figure 1 , which shows a radar detection range calculation process according to an embodiment of the present disclosure.

[0041] As Figure 1 shown, in step S1, the computer device obtains an initial detection grid set of the radar detection range of at least one radar according to a predetermined three-dimensional grid encoding method; in step S2, the computer device obtains a terrain obstacle grid set of the terrain obstacle data based on the three-dimensional grid encoding method according to the terrain obstacle data corresponding to the radar detection range; in step S3, for each terrain obstacle grid in the terrain obstacle grid set, the computer device obtains, along the detection direction of one of the at least one radar relative to the current terrain obstacle grid, the terrain obstacle grids that cannot be detected within the maximum detection distance of the corresponding radar starting from the current grid, so as to obtain an occlusion grid set composed of the terrain obstacle grids that cannot be detected; in step S4, the computer device removes the occlusion grid set from the initial detection grid set to obtain a target detection grid set.

[0042] Herein, the "computer device" generally refers to various types of computing resources. For example, it can be a user device, or a device formed by integrating a user device and a network device through a network, or it can also be an application program running on the above devices. The user device includes but is not limited to various terminal devices such as computers, mobile phones, and tablet computers. The network device includes but is not limited to being implemented such as a network host, a single network server, a set of multiple network servers, or a computer set based on cloud computing, and can be used to implement some processing functions when setting an alarm clock. Herein, the cloud is composed of a large number of hosts or network servers based on cloud computing (Cloud Computing). Among them, cloud computing is a type of distributed computing and consists of a virtual computer formed by a group of loosely coupled computer sets.

[0043] Specifically, in step S1, the computer device obtains an initial detection grid set of the radar detection range of at least one radar according to a predetermined three-dimensional grid encoding method.

[0044] Herein, the three-dimensional grid encoding method divides the three-dimensional space into regular grid cells and assigns a unique code to each grid to accurately identify its position and range. Common three-dimensional grid encoding methods are usually implemented based on a coordinate system (such as longitude, latitude, and altitude) and a dissection rule (such as a regular division at a fixed interval). This encoding method is widely used in fields such as geographic information systems (GIS), radar detection, and airspace management.

[0045] According to an example, the three-dimensional grid encoding method can be an encoding based on longitude, latitude, and altitude. This encoding method divides the Earth's surface into longitude and latitude grids (horizontal grids), and performs layering at fixed intervals in the altitude direction (vertical grids), and assigns a unique encoding to each three-dimensional grid cell. This encoding usually consists of longitude, latitude, and layer index. The encoding of a three-dimensional grid cell can be expressed as Code(x, y, z), where x is the longitude index, y is the latitude index, and z is the altitude layer index.

[0046] According to an example, the three-dimensional grid encoding method can also be Morton encoding (also known as Z-Order Curve). This is an encoding method that maps three-dimensional coordinates (x, y, z) to a one-dimensional integer. Through bit interleaving, the binary representations of the three-dimensional coordinates are alternately arranged to form a unique encoding value. For example, for a three-dimensional coordinate (3, 5, 2), the corresponding binary is: x = 011, y = 101, z = 010, and the Morton encoding is 010110101 after interleaving.

[0047] According to an example, the three-dimensional grid encoding method can also be GeoSOT (Geographical coordinate global Subdivision based on One-dimension-integer and Two to n th power, 2 n encoding of the global longitude and latitude subdivision grid of a one-dimensional integer array). GeoSOT encoding is a three-dimensional grid encoding method based on the global geospatial organization. It recursively divides the global three-dimensional space in the longitude, latitude, and altitude directions to form a multi-level three-dimensional grid structure. Among them, the high-level grid encoding contains the prefix of the low-level grid encoding, and the size and quantity of the grid cells decrease as the level increases.

[0048] Furthermore, the Beidou grid encoding is a three-dimensional grid encoding method based on the global geospatial subdivision theory (GeoSOT). It recursively divides the Earth's space, accurately maps the geographical coordinates (longitude, latitude, altitude) to discrete grid cells, and generates a unique encoding for each grid cell. The Beidou grid encoding system has the characteristics of multi-level and multi-resolution, and is widely used in fields such as geographic information systems (GIS), navigation and positioning, radar detection, and airspace management.

[0049] Specifically, the Beidou grid coding is based on a recursive partitioning method of the global geospatial space. The Earth's surface is divided into regular two-dimensional grids in the longitude and latitude directions, and then combined with the stratification in the height direction to form a complete three-dimensional grid coding system. In this process, the longitude range of the Earth's surface is from -180° to 180°, and the latitude range is from -90° to 90°. Each direction continuously refines the grid in a binary recursive partitioning manner. By adjusting the level of subdivision, the size and resolution of the grid can be flexibly controlled.

[0050] In three-dimensional space, the Beidou grid coding further adds the division in the height direction. The height range is recursively subdivided at fixed intervals and combined with the longitude and latitude grids to generate a unique three-dimensional grid code. Each code consists of the indexes of longitude, latitude, and height, represented in binary form, and has a multi-level prefix relationship, that is, the high-level grid code is the prefix of the low-level grid code, which is convenient for multi-scale spatial analysis. This coding method precisely associates each grid with its position in the Earth's three-dimensional space.

[0051] In this article, for the convenience of illustration, the Beidou grid coding is used to describe the subdivision and coding of the three-dimensional grid below. Those skilled in the art should understand that other three-dimensional grid coding methods, such as those that can be used in the radar detection range calculation scheme in the present disclosure, are hereby incorporated by reference and are included in the patent protection scope of the present disclosure. For example, any other spatial subdivision grid coding that includes multi-scale division and grid aggregation characteristics can be used to replace the Beidou grid coding.

[0052] Radar usually detects targets by sending electromagnetic waves and receiving echo signals. When detecting, the detection range of the radar is one of the key factors. To accurately model the radar detection range, the present disclosure uses a grid method to divide the radar detection range and obtain the corresponding initial detection grid set.

[0053] One or more radars can be deployed according to detection requirements and regional characteristics. These radars cover the target area by sending detection signals to obtain potential target information within the area. The radars can be deployed individually or in multi-point cooperation to improve detection efficiency, coverage, and target positioning accuracy.

[0054] For each radar, its detection range is determined according to the radar's transmission power, antenna directivity, terrain conditions, and the influence of the detection environment. The detection range is usually represented in the form of three-dimensional space, including both the horizontal coverage range and the vertical detection height. The detection range of the radar can usually be modeled as a three-dimensional region, such as a fan-shaped, conical, or spherical region. This three-dimensional region will be the basis for the Beidou grid subdivision.

[0055] The three-dimensional detection space of the radar is finely dissected to generate regular three-dimensional grids, and a unique Beidou three-dimensional grid position code is assigned to each grid cell. This process combines the rules of Beidou grid coding, divides the three-dimensional space of the earth into multiple ordered grid cells, and finally constructs a three-dimensional grid model that can completely represent the radar detection range.

[0056] Refer to Figure 2(a), which shows the hierarchical dissection process of the radar detection range in the vertical direction. The radar is centered at point O, and the detection range is a hemisphere with a radius of R.

[0057] The vertical direction is divided into multiple concentric spheres S 1 , S 2 , … S i , S i+1 , S N , and each sphere represents the detection boundary of the radar at different vertical heights. This division method can be used to distinguish different ranges of detection distances.

[0058] Refer to Figure 2(b), which shows the hierarchical dissection process of the radar detection range in the horizontal direction. A circle S i in the vertical plane, centered at point O i , and this circle S i is divided into several horizontal bands K 1 , K 2 , … K j , K j+1 , K M , and the boundaries of these band regions are determined by a fixed height interval.

[0059] Each horizontal band represents the radar detection range within different height intervals, and this division method can describe the coverage of the radar in the vertical plane.

[0060] During the dissection process, the radar detection range is divided into multiple equally spaced grid cells in the horizontal and vertical directions, forming a three-dimensional grid structure. The size of each grid cell is determined by a preset dissection interval, and the fineness of the dissection depends on the task requirements and the resolution of the radar detection. This dissection not only covers the theoretical detection range of the radar but also can reflect the detection ability boundary and spatial distribution characteristics.

[0061] Each dissected grid cell will be assigned a unique Beidou three-dimensional grid position code. This position code is generated through recursive coding of longitude, latitude, and altitude based on the dissection rules of the three-dimensional space of the earth and has global uniqueness. The Beidou grid position code can be output through a Beidou terminal, which can clearly identify the position of each grid cell, enabling all grid cells within the entire detection range to have the ability to be accurately located.

[0062] According to an example, select the appropriate Beidou grid coding level L according to the accuracy requirements x 。

[0063] The coding level L of the Beidou grid x is an important parameter that determines the grid division accuracy and size. It represents the recursive depth of the grid division. For each additional level, the side length of the grid cell is reduced by half, thereby improving the fineness of the spatial division. The determination of the coding level is usually related to application requirements and positioning accuracy. Especially in the modeling of the radar detection range, it should be adapted to the positioning accuracy of the Beidou satellite navigation system (including the augmentation system), and the grid side length should be less than or equal to the positioning accuracy to ensure that the grid division can meet the detection requirements.

[0064] The Beidou grid data model of the radar detection range can be represented by the following Beidou grid coding set A:

[0065]

[0066] wherein, represents the Beidou three-dimensional grid position code of a grid cell i at the coding level L x , and M is the total number of grid cells within the radar detection range. By summarizing the codes of each grid, the grid-based spatial structure of the radar detection range can be comprehensively described.

[0067] In step S2, the computer device obtains the terrain obstacle grid set of the terrain obstacle data based on the terrain obstacle data corresponding to the radar detection range according to a predetermined three-dimensional grid coding method.

[0068] Here, the terrain obstacle data corresponding to the radar detection range can be, for example, the data of the area covered by the radar detection range or the data of the target area where the radar conducts detection.

[0069] The terrain obstacle data is not limited to terrain data, but also includes factors such as buildings, adverse weather conditions, vehicles, and pedestrians that affect the radar detection effect. Terrain data usually refers to natural geographical features, including the undulations and forms of the earth's surface, such as mountains, hills, plains, river valleys, etc. These terrain obstacles may block or reflect radar signals, thereby affecting its detection effect. Terrain obstacle data usually comes from digital elevation models (DEM) or topographic mapping data, for example, and is a basic factor in radar detection analysis.

[0070] For the terrain obstacle data corresponding to the radar detection range, obtain the terrain obstacle grid set of this terrain obstacle data according to the same subdivision and coding method as the radar detection range, such as Beidou grid coding. Through the grid processing of the terrain obstacle data, each grid cell within the terrain obstacle area is assigned a unique Beidou grid code, thereby obtaining a complete terrain Beidou grid data model.

[0071] Terrain obstacle data usually includes the longitude and latitude coordinate range, elevation information, and other possible terrain attributes of the target area, as well as stereoscopic dissection attributes, such as parameters like the coordinates of the area range, the interval of dissection grids, the number of rows and columns, etc. These terrain attribute data can be sourced from digital elevation models (DEMs), satellite remote sensing, aerial photography, etc. The terrain obstacle data needs to be preprocessed before modeling, including unifying the coordinate system, filling in missing elevation values, denoising, and adjusting the resolution to adapt to the dissection rules of the radar detection range.

[0072] During the grid processing, first, determine the grid division scheme of the terrain data according to the dissection interval and the number of rows and columns. On the horizontal plane, based on the longitude and latitude coordinate range of the terrain obstacle area and in combination with the dissection interval, generate regular two-dimensional grids; in the vertical direction, based on the height range of the terrain obstacle area, layer the height according to the same layering rules as the radar detection range. Through this process, the terrain obstacle area is divided into a series of three-dimensional grid cells. The spatial range of each grid cell is determined by its geographical coordinates (longitude and latitude range) and height range, and the encoding of these grid cells is generated by the Beidou grid encoding rules.

[0073] The terrain obstacle grid set of the terrain obstacle data can be represented by the following Beidou grid encoding set B:

[0074]

[0075] Among them, represents the Beidou three-dimensional grid position code of a grid cell j at the encoding level L x and N is the total number of grid cells in the target terrain obstacle area. By summarizing the encoding of each grid, the grid-based spatial structure of the target terrain obstacle area can be comprehensively described.

[0076] Refer to FIGS. 3(a) and 3(b) for reference, which respectively show the original terrain obstacle model diagram and the dissected terrain obstacle grid model diagram according to an example of the present disclosure.

[0077] In step S3, for each terrain obstacle grid in the terrain obstacle grid set, the computer device obtains, along the detection direction of the radar relative to the current grid, the terrain obstacle grids that cannot be detected within the maximum detection distance of the radar starting from the current grid, thereby obtaining an occlusion grid set composed of these undetected terrain obstacles.

[0078] For each terrain obstacle grid in the terrain obstacle grid set, along the direction from the radar center to the current terrain obstacle grid, that is, the detection direction of the radar relative to the current terrain obstacle grid, a new line segment (occlusion line segment) is formed with the maximum detection distance of the radar as the end point and the terrain obstacle grid as the starting point. That is, the starting point of the occlusion line segment is the current terrain obstacle grid, and the end point is at the maximum detection distance from the radar center along the detection direction of the radar relative to the current terrain obstacle grid. The terrain obstacle grids on the occlusion line segment are all occluders that cannot be detected by the radar. The level of the grids on the occlusion line segment is the same as that of the starting terrain obstacle grid.

[0079] According to an example, based on the grids on the occlusion line segment, an occlusion grid subset is obtained for each terrain obstacle grid. The occlusion grid subsets of all the terrain obstacle grids in the terrain obstacle grid set constitute the occlusion grid set C of the radar.

[0080] According to another example, an occlusion grid set C is created with an initial value of empty. Each time an occlusion grid or a batch of occlusion grids is obtained from the occlusion line segment of a terrain obstacle grid, it is added to the occlusion grid set C until all the occlusion grids of the terrain obstacle grids in the terrain obstacle grid set are added to the occlusion grid set C.

[0081] According to an embodiment, 3D grids can be aggregated to be expressed as a multi-scale grid encoding set for spatial solid objects, such as Figure 4 as shown, so as to reduce the number of 3D grids and significantly improve the calculation efficiency.

[0082] In this embodiment, the initial detection grid set A within the radar detection range in step S1 can be represented as a set A' of multiple hierarchical grid encoding subsets after being aggregated upward according to different levels:

[0083]

[0084] At the encoding level L 1 , there are a total of M 1 grids, and the encoding of each grid i is Code L 1i ;

[0085] At the encoding level L 2 , there are a total of M 2 grids, and the encoding of each grid j is Code L 2j ;

[0086] …

[0087] At the encoding level L x , there are a total of Mx grids, and the encoding of each grid k is Code L xk .

[0088] The set A′ consists of grid - coded subsets at multiple different levels. Different coding levels correspond to different grid division precisions. For example, a lower level represents a coarser grid (large cells), and a higher level represents a finer grid (small cells).

[0089] In radar detection range modeling, different regions may require different resolutions. The boundary regions or target - sparse regions may be represented by lower - level grids to reduce the computational burden. The target - dense regions or important regions may be represented by higher - level grids to provide more detailed information. The set of grids at different levels can dynamically adapt to target changes, detection range expansion, or task requirements. For example, in a specific region, it may be necessary to temporarily increase the level to improve detection accuracy. By aggregating grid subsets at different levels, the set A′ can completely represent the coverage of the radar detection range while taking into account the accuracy requirements of different regions.

[0090] The terrain obstacle grid set B in step S2 can also be represented as a set B′ of grid - coded subsets at multiple levels after aggregation according to different levels:

[0091]

[0092] At coding level L 1 , there are N 1 grids, and the code of each grid p is Code L 1p ;

[0093] At coding level L 2 , there are N 2 grids, and the code of each grid q is Code L 2q ;

[0094] …

[0095] At coding level L x , there are Nx grids, and the code of each grid r is Code L xr .

[0096] By aggregating grid subsets at different levels, the set B′ can completely represent the grid model of the target terrain area while reducing the amount of calculation.

[0097] Thus, in step S3, according to the aggregated terrain obstacle grid set B′, at the corresponding level, for each terrain obstacle grid, a new line segment is formed with the starting point being the terrain obstacle grid and the end point being the maximum detection distance of the radar along the direction from the radar center to the current terrain obstacle grid, that is, the detection direction of the radar relative to the current terrain obstacle grid. A set of grids on the new line segment is generated, and the level is the same as that of the current starting grid. The occluded grid subset C′ of the terrain obstacle grid at each level is recorded i, finally take the union of all occluded grid subsets C′ i to obtain the grid set C′ that cannot be detected due to occlusion.

[0098]

[0099] At coding level L 1 , there are a total of O 1 grids, and the code of each grid u is Code L 1u ;

[0100] At coding level L 2 , there are a total of O 2 grids, and the code of each grid v is Code L 2v ;

[0101] …

[0102] At coding level L x , there are a total of Ox grids, and the code of each grid w is Code L xw .

[0103] In step S4, the computer device removes the occluded grid set from the initial detection grid set of the radar to obtain the target detection grid set.

[0104] According to one example, the occluded grid set C that cannot be detected due to occlusion is removed from the initial detection range grid set A of the radar to obtain the final target detection grid set D, that is, D = A - C.

[0105] According to another example, for the grid aggregation scenario, the occluded grid set C′ that cannot be detected due to occlusion is removed from the radar detection range grid set A′, and the difference sets are taken respectively on the grids at each corresponding level to obtain the final target detection grid set D′.

[0106]

[0107] Refer to Figure 5 , which shows the visualization effect diagram of the radar detection range under the influence of terrain according to one example.

[0108] The above calculations are all based on a single radar. In a multi-radar scenario, due to the different parameters of different radars, the coverage areas of their detection ranges are also different. Using traditional methods to find the intersection of the coverage areas detected by multiple radars is relatively complex and involves complex floating-point equation-solving operations. However, using Beidou grid coding to perform grid calculations on the radar detection range is a binary bit-matching operation, which can be achieved by comparing grid codes. For example, performing a bitwise exclusive OR operation on two grid codes. If the code of grid 1 is 100101000111 and the code of grid 2 is 1001010001110001, the calculation result can determine that grid 2 is included in grid 1. Thus, the calculation complexity of the superimposed detection range of multiple radars can be significantly reduced.

[0109] The final visualization effect of the calculation of the superimposed detection range of multiple radars is as Figure 6 shown, where the set of white grids represents the detection range of radar A, the set of yellow grids represents the detection range of radar B, and the set of red grids represents the intersection of the detection ranges of the two radars.

[0110] Therefore, according to an example, in a multi-radar scenario, based on the above steps S1 - S4, the target detection grid set D of each radar n is obtained respectively n After that, take the union of the results to obtain the target detection grid set D of the superimposed detection range of all radars total .

[0111]

[0112] At coding level L 1 , there are a total of P 1 grids, and the code of each grid i is Code L 1i ;

[0113] At coding level L 2 , there are a total of P 2 grids, and the code of each grid j is Code L 2j ;

[0114] …

[0115] At coding level L x , there are a total of Px grids, and the code of each grid k is Code L xk .

[0116] According to another example, in step S1, for the initial detection grid set A of the detection range of a single radar, when multiple radars are arranged, the initial detection grid sets of the detection ranges of these radars are union-processed, and the set after union is the initial detection grid set A of the radar detection range totalSubsequently, through steps S2 - S3, an occlusion grid set C of the target area is obtained. Accordingly, in step S4, the occlusion grid set C is removed from the initial detection grid sets A of multiple radars total to obtain the final target detection grid sets D of these radars total .

[0117] Based on this example, when the grids are aggregated upward, an initial detection grid set A' total and an occlusion grid set C' are obtained. The occlusion grid set C' is removed from the initial detection grid sets A' of multiple radars total to obtain the final target detection grid sets D' of these radars total .

[0118] Supports the superposition calculation of the detection ranges of multiple radars to form a three - dimensional space electromagnetic distribution based on Beidou grid coding. The Beidou grid coding can be applied to the output of various system terminals of the Beidou satellite navigation system and is designed to be adaptable to the positioning accuracy of the Beidou satellite navigation system (including the augmentation system).

[0119] According to an embodiment of the present disclosure, after determining the target detection grid sets of the radars, target tracking can be performed within the radar detection area.

[0120] Based on the same three - dimensional grid coding method, such as Beidou grid coding, the position of the tracked target is encoded to obtain the Beidou grid position code T of the target. The intersection calculation is performed between the position grid T of the tracked target and the target detection grid sets D of the radars. If the result is not empty, it proves that the target is within the detection range; if the result is empty, it proves that the target is not within the detection range.

[0121] According to an example, when using the aggregated grid set D' of the superposed detection ranges of the radars total , since the number of aggregated grids is significantly reduced compared to before aggregation, the number of calculations during dynamic target real - time tracking is also significantly reduced, thereby improving the tracking efficiency.

[0122] Perform dynamic Beidou grid coding on the target (such as an aircraft) and perform spatial calculations with the aggregated radar detection range grid set under a unified spatial reference system, effectively improving the calculation efficiency for judging whether a flying target enters the radar detection range.

[0123] The following shows an exemplary embodiment.

[0124] Assume that the positioning accuracy of the Beidou satellite navigation system (including the augmentation system) is 1 meter, and the maximum detection radius of the radar can be simplified to 100 meters. Select the level with a grid size of 0.5 meters as the coding level, which is level 27 for GeoSOT coding.

[0125] First, establish a three-dimensional grid model of the theoretical detection range of the radar to obtain the initial detection grid set A. Aggregate the initial detection grid set A of the radar's theoretical detection range upward to obtain the radar detection range grid set A'.

[0126] Subsequently, based on the terrain, establish three-dimensional grid models of obstacles such as buildings, pedestrians, and vehicles, and take the union of all the three-dimensional grid sets of the obstacles to obtain the entire terrain obstacle grid set B. Aggregate the terrain obstacle grid set B of all the obstacles upward to obtain the terrain obstacle grid set B'. At this time, the number of grids is significantly reduced, and the levels are, for example, 25, 26, and 27.

[0127] Calculate the occluded grid set C' that cannot be detected due to obstacle occlusion. According to the terrain obstacle grids of each level, obtain the occluded grid subsets at that level respectively, and then take the union of the occluded grid subsets at each level to obtain the occluded grid set C' of the radar.

[0128] 1) Iterate over all the 25-level obstacle grids

[0129] Taking the direction from the radar center to the current grid and the maximum detection distance of the radar as the end point, use the current grid as the starting point to form a new line segment;

[0130] Generate a grid set of the new line segment with a level of 25;

[0131] Record the new grid set C' 1 .

[0132] 2) Iterate over all the 26-level obstacle grids

[0133] Taking the direction from the radar center to the current grid and the maximum detection distance of the radar as the end point, use the current grid as the starting point to form a new line segment;

[0134] Generate a grid set of the new line segment with a level of 26;

[0135] Record the new grid set C' 2 .

[0136] 3) Iterate over all the 27-level obstacle grids

[0137] Taking the direction from the radar center to the current grid and the maximum detection distance of the radar as the end point, use the current grid as the starting point to form a new line segment;

[0138] Generate a grid set of the new line segment with a level of 27;

[0139] Record the new grid set C' 3 .

[0140] 4) Take C' 1 , C' 2 , C'3 Take the union to obtain the occluded grid set C'.

[0141] Remove the occluded grid set C' from the radar detection range grid set A' to obtain the final target detection grid set D'.

[0142] Perform Beidou grid encoding on the target aircraft 1 to obtain the position grid T of the tracked target 1 ; Take the intersection of the target detection grid set D' and the grid T of the target aircraft 1 1 If the result is not empty, it proves that the target aircraft 1 is within the radar detection range.

[0143] Perform Beidou grid encoding on the target aircraft 2 to obtain the position grid T of the tracked target 2 ; Take the intersection of the target detection grid set D' and the grid T of the target aircraft 2 2 If the result is empty, it proves that the target aircraft 2 is not within the radar detection range.

[0144] The present disclosure proposes to use the Beidou grid encoding calculation model to improve the existing radar detection range calculation method, perform grid-based calculation on the radar detection range, support multi-radar superposition analysis and radar detection range calculation under the influence of terrain, and support aggregation processing of multiple grid levels, reducing the number of dissection grids of the radar detection range and terrain, and significantly improving the calculation efficiency.

[0145] The present disclosure proposes a multi-radar superposition detection range calculation method based on Beidou grid, constructs a Beidou grid dissection model of the radar detection range and terrain, performs Beidou grid encoding on dynamic targets, and solves the problems that the radar detection area modeling under the existing three-dimensional grid conditions cannot consider terrain occlusion, there is redundancy in grid data in multi-radar target detection calculation, and the real-time tracking of high-speed dynamic targets is not efficient enough through multi-radar detection range superposition calculation. By aggregating the grids of the radar detection range and terrain, the number of grids is reduced and the calculation efficiency is improved.

[0146] Based on the same inventive concept, an embodiment of the present disclosure also provides a radar detection range calculation device. The method corresponding to the radar detection range calculation device may be the radar detection range calculation method in the foregoing embodiments, and the principle of solving problems is similar to that of this method. The radar detection range calculation device provided by the embodiment of the present disclosure includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the speech synthesis method and / or technical solution of multiple embodiments of the present disclosure described above.

[0147] The electronic device may be a user device, or a device formed by integrating the user device and a network device through a network, or may also be an application program running on the above device. The user device includes, but is not limited to, various terminal devices such as computers, mobile phones, tablet computers, smart watches, and bracelets. The network device includes, but is not limited to, being implemented by a network host, a single network server, a set of multiple network servers, or a computer cluster based on cloud computing, etc., and can be used to implement some processing functions when setting an alarm. Here, the cloud is composed of a large number of hosts or network servers based on cloud computing. Among them, cloud computing is a type of distributed computing and consists of a virtual computer formed by a group of loosely coupled computers.

[0148] Specifically, the method and / or embodiment in the embodiments of the present disclosure can be implemented as a computer software program. For example, the embodiments disclosed in the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium. The computer program contains program codes for executing the method shown in the flowchart. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present disclosure are executed.

[0149] Another embodiment of the present disclosure also provides a computer-readable storage medium, on which computer program instructions are stored. The computer program instructions can be executed by a processor to implement the method and / or technical solution of any one or more of the foregoing embodiments of the present disclosure.

[0150] Specifically, this embodiment can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.

[0151] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take many forms, including - but not limited to - electromagnetic signals, optical signals, or any suitable combination of the foregoing. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0152] The program code contained on a computer-readable medium may be transmitted using any appropriate medium, including - but not limited to - wireless, wireline, optical fiber cable, RF, and the like, or any suitable combination of the foregoing.

[0153] The computer program code for carrying out operations of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0154] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of code, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in an order different from that noted in the figures. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0155] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0156] In several embodiments provided by the present disclosure, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or page components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0157] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, in each embodiment of the present disclosure, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional units.

[0159] The above-mentioned integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional units stored in a storage medium include several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods described in each embodiment of the present disclosure. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0160] It should be noted that the embodiments of the present disclosure can be implemented in software and / or a combination of software and hardware. For example, application-specific integrated circuits (ASICs), general-purpose computers, or any other similar hardware devices can be used to implement them. In one embodiment, the software programs of the embodiments of the present disclosure can be executed by a processor to implement the steps or functions described above. Similarly, the software programs (including related data structures) of the embodiments of the present disclosure can be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive, or a floppy disk and similar devices. Additionally, some steps or functions of the embodiments of the present disclosure can be implemented using hardware, for example, as a circuit that cooperates with a processor to execute each step or function.

[0161] Furthermore, at least a part of the embodiments of the present disclosure can be applied as a computer program product, such as computer program instructions. When executed by a computing device, through the operation of the computing device, the methods and / or technical solutions according to the embodiments of the present disclosure can be invoked or provided. The program instructions for invoking / providing the methods of the embodiments of the present disclosure may be stored in a fixed or removable recording medium, and / or transmitted through a data stream in a broadcast or other signal-bearing medium, and / or stored in the working memory of a computing device that runs according to the program instructions.

[0162] For those skilled in the art, it is obvious that the embodiments of the present disclosure are not limited to the details of the above-described exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the embodiments of the present disclosure. Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the embodiments of the present disclosure is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the embodiments of the present disclosure. Any reference signs in the claims should not be construed as limiting the claimed rights. Additionally, it is obvious that the word "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or devices stated in the system claims can also be implemented by one unit or device through software or hardware. The terms first, second, etc. are used to denote names and do not indicate any particular order.

Claims

1. A method for calculating radar detection range, wherein: The method comprises the following steps: Acquire an initial detection grid set of a radar detection range of at least one radar according to a predetermined three-dimensional grid encoding method; According to the three-dimensional grid coding method, based on the terrain obstacle data corresponding to the radar detection range, obtaining a terrain obstacle grid set of the terrain obstacle data; For each terrain obstacle grid in the terrain obstacle grid set, along the detection direction of one of the at least one radar relative to the current terrain obstacle grid, obtaining undetected terrain obstacle grids with the current grid as the starting point within the maximum detection distance of the corresponding radar, thereby obtaining an obstruction grid set consisting of the undetected terrain obstacle grids; The occlusion grid set is removed from the initial detection grid set to obtain a target detection grid set.

2. The method according to claim 1, wherein: The grids in the initial detection grid set and the grids in the terrain obstacle grid set are divided according to the same coding level.

3. The method according to claim 2, wherein: The initial detection grid set is aggregated upward according to different levels to include a set of grid code subsets at multiple levels; The terrain obstacle grid set is aggregated upward according to different levels to include a set of grid code subsets at multiple levels; According to the terrain obstacle grids at each level, the terrain obstacle grids that cannot be detected at the level are respectively obtained, thereby obtaining the blocking grid set.

4. The method according to claim 1, wherein: There are multiple radars, and the target detection grid set is obtained for each radar respectively; The method further comprises: According to the target detection grid set of each radar, a target detection grid set after all the radars are superimposed is obtained.

5. The method according to claim 1, wherein: There are multiple radars, and the initial detection grid set includes an initial detection grid set of the radar detection range of each radar.

6. The method according to claim 1, wherein: The method further comprises the following steps: Target tracking is performed based on the target detection grid set.

7. The method according to claim 6, wherein: The tracked target is position-encoded using the three-dimensional grid encoding method; By comparing the position code of the tracked target and the target detection grid set, it is determined whether the tracked target is within the radar detection range.

8. A radar detection range calculation device, wherein: The device comprises a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the device is configured to perform the following operations: Acquire an initial detection grid set of a radar detection range of at least one radar according to a predetermined three-dimensional grid encoding method; According to the three-dimensional grid coding method, based on the terrain obstacle data corresponding to the radar detection range, a terrain obstacle grid set of the terrain data is obtained; For each terrain obstacle grid in the terrain obstacle grid set, along the detection direction of one of the at least one radar relative to the current terrain obstacle grid, obtaining undetected terrain obstacle grids with the current grid as the starting point within the maximum detection distance of the corresponding radar, thereby obtaining an obstruction grid set consisting of the undetected terrain obstacle grids; The occlusion grid set is removed from the initial detection grid set to obtain a target detection grid set.

9. A computer program product comprising computer program instructions, wherein: When the computer program instructions are executed by a computer device, the computer device is configured to perform the method according to any one of claims 1 to 7.

10. A computer program storage medium having computer executable instructions stored therein, wherein when the computer executable instructions are executed by a computer device, the computer device is configured to perform the method according to any one of claims 1 to 7.

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