Quadrangular prism structure-based low-altitude three-dimensional sensing coverage enhancement method and system

Abstract

The invention discloses a quadrangular prism structure-based low-altitude three-dimensional sensing coverage enhancement method and a quadrangular prism structure-based low-altitude three-dimensional sensing coverage enhancement system, and mainly solves the problem that communication and sensing coverage cannot be enhanced at the same time in a low-altitude sensing integrated network in the prior art. According to the scheme, the method comprises the following steps: initializing network parameters, establishing a three-dimensional Cartesian coordinate system, and constructing a low-altitude sensing integrated network; performing triangulation on the base station according to the base station position parameter to obtain a base station triangular set corresponding to the triangular prism cell; calculating a global minimum sensing auxiliary base station number according to the base station triangular set, selecting an external sensing auxiliary base station for each triangular prism cell, and obtaining a quadrangular prism structure after the cells are combined; and calculating the communication coverage rate and the positioning precision in each quadrangular structure according to the parameters of the low-altitude sensing integrated network, and carrying out joint optimization on the communication coverage rate and the positioning precision to realize enhancement of low-altitude three-dimensional sensing coverage signals. According to the method, communication and sensing positioning requirements under the low-altitude communication and sensing integrated network can be effectively considered, the positioning precision is improved under the condition that the high communication coverage rate is guaranteed, and the method can be used for various flight activities such as unmanned aerial vehicle logistics.

Description

Technical Field

[0001] This invention belongs to the field of integrated sensing technology, and in particular relates to a method and system for enhancing low-altitude three-dimensional sensing coverage, which can be used for communication coverage and high-precision sensing and positioning assurance for various flight activities such as UAV logistics. Background Technology

[0002] The safe landing and orderly operation of various flight activities, such as drone logistics, rely heavily on seamless communication coverage and high-precision low-altitude sensing services across the entire airspace. The industry generally believes that building an integrated communication and sensing network based on existing 5G infrastructure is the core path to ensuring communication coverage and high-precision sensing. However, the existing 5G base station cell structure is primarily adapted to communication needs. Due to significant differences between this and the base station cell structure suitable for low-altitude sensing needs, directly utilizing the communication structure to provide sensing services can cause co-channel interference and weak sensing signal coverage, leading to large positioning errors. Conversely, directly utilizing the sensing structure to provide communication services can result in chaotic relationships and severe interference between communication and sensing cells, leading to low communication coverage. Therefore, designing an integrated sensing coverage enhancement method that addresses both communication and sensing performance in low-altitude three-dimensional areas is a problem that urgently needs to be solved.

[0003] Patent application CN202511071591.6 proposes a multi-base station joint localization method based on a self-attention mechanism. It constructs a joint channel matrix by collecting channel observation matrices from distributed base stations, normalizes and fuses features in each dimension, estimates the self-attention value of the fused feature matrix, and constructs a long short-term memory recurrent network to decode and obtain the spatial location of the sensed target. However, this method fails to specify the exact number of base stations required for joint localization, the selection rules for distributed base stations, and the specific sensing structure, thus failing to guarantee joint coverage for low-altitude communication and sensing.

[0004] Patent application CN202310947355.0 discloses a method for determining a cluster of sensing base stations. Its implementation steps for a sensing fusion scenario are: 1) obtaining a first sensing range based on the sensing task; 2) determining the communication base stations within the first sensing range; 3) selecting multiple first sensing base stations from multiple communication base stations; 4) determining a first sensing base station cluster from the multiple first sensing base stations. This method mainly selects sensing base stations based on the sensing task, lacking consideration of the impact of the sensing base station selection method on communication performance, thus making it difficult to guarantee low-altitude communication and sensing coverage.

[0005] In their paper "ISAC Network Planning: Sensing Coverage Analysis and 3-D BS Deployment Optimization," Kaitao Meng et al. proposed a base station deployment method for network-level integrated communication and sensing networks, which utilizes multi-base station collaboration to provide users with communication and sensing services. This method characterizes the scaling relationship between the lower bound of the multi-dimensional regional average positioning Cramero scale and the number of base stations deployed, and analyzes the differences in base station deployment locations under communication and sensing requirements. While this method offers novel insights into the relationship between the number and location of base stations for providing collaborative communication and sensing services, it fails to consider the increased communication and sensing interference caused by three-dimensional space in low-altitude scenarios and ignores the impact of the number of collaborative communication and sensing base stations and cell structure on communication coverage and positioning performance. Therefore, it is difficult to simultaneously guarantee communication and sensing coverage requirements. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of the prior art by proposing a low-altitude three-dimensional sensory coverage enhancement method and system based on a quadrangular prism structure, so as to take into account the communication and sensing positioning needs at low altitudes, and to obtain high-precision sensing positioning while ensuring communication coverage.

[0007] The technical approach to achieving the objective of this invention is as follows: by designing a low-altitude communication coverage structure with the minimum number of sensing auxiliary base stations, a low-altitude integrated sensing coverage structure based on a quadrangular prism is constructed to balance the communication and sensing / positioning requirements at low altitudes. By optimizing base station association and power resources constrained by communication coverage based on the quadrangular prism coverage structure, high-precision sensing and positioning are achieved while ensuring communication coverage.

[0008] Based on the above ideas, the technical solution of the present invention includes:

[0009] 1. A method for enhancing low-altitude stereoscopic synesthetic coverage based on a quadrangular prism structure, characterized in that it includes:

[0010] S1) Initialize network parameters, establish a three-dimensional Cartesian coordinate system, and construct a low-altitude integrated sensing network;

[0011] S2) Perform Delaunay triangulation on the base station based on the base station location parameters to obtain the base station triangle set of the corresponding prism cell. ;

[0012] S3) Based on the base station triangle set Calculate the minimum number of global sensing auxiliary base stations For each triangular prism cell, an external sensing auxiliary base station is selected to obtain the quadrangular prism structure after cell merging.

[0013] S4) Based on the parameters of the low-altitude integrated sensing network, the communication coverage and positioning accuracy in each quadrangular prism structure are calculated;

[0014] S5) The obtained communication coverage and positioning accuracy are jointly optimized to achieve the effect of enhanced low-altitude three-dimensional sensing coverage;

[0015] Furthermore, in step S3), based on the base station triangulation set... Calculate the minimum number of global sensing auxiliary base stations For each triangular prism cell, an external sensing auxiliary base station is selected to obtain the quadrangular prism structure after cell merging. Its implementation includes:

[0016] S3a) will divide the scene plane area Divided into Each region constitutes a region set. ;

[0017] S3b) Calculate the minimum number of sensing auxiliary base stations in the scene area. A based on lower bound ;

[0018] S3c) will be adjacent to the base station trigonometric numbers The sorting is performed in descending order to obtain the sorted sequence. The minimum number of sensing auxiliary base stations required in the scene area is then calculated. based on lower bound ;

[0019] S3d) from the set of regions The number of consecutive decreases in the middle is randomly selected. In each region, calculate the minimum number of sensing auxiliary base stations required in the scenario region. based on lower bound ;

[0020] S3e) Based on the above, the minimum number of global sensing auxiliary base stations is obtained. The lower bound is used to obtain the global minimum number of sensing auxiliary base stations. Maximum lower bound and will As Starting size of an exact search ;

[0021] S3f) from the initial size Initially, the enumeration size is Sensing-assisted base station set ;

[0022] S3g) in the base station triangle set Select candidate pairs of triangular regions that can be merged. ,in accordance with and candidate pairing Determining the relationship between them Is it activated?

[0023] S3h) based on the activated candidate pairings Mapping determines the maximum matching set ;

[0024] S3i) Based on the set of base stations without sensing assistance base station set and Coverage set Based on the relationship between them, determine whether a feasible solution exists;

[0025] S3j) from the initial size Start traversing, repeating steps S3g)~S3i) until a feasible solution is found;

[0026] Based on the feasible solution obtained, S3k selects an external sensing auxiliary base station for each triangular prism cell to obtain the quadrangular prism structure after cell merging.

[0027] Furthermore, in step S5), the joint optimization of communication coverage and positioning accuracy is performed based on network constraints, which includes:

[0028] S5a) Based on the user's real-time three-dimensional location, the base station at the corresponding height layer within the triangular prism area is matched as the primary service base station, responsible for the core communication link; the other two base stations in the triangular area are used as auxiliary base stations to monitor the user's signal quality SINR in real time.

[0029] S5b) Establish a power resource optimization model based on the positioning accuracy of low-altitude UAV users;

[0030] S5c) The PSO algorithm is used to optimize the power resource optimization model in each quadrangular prism three-dimensional cell to obtain optimized high communication coverage and high positioning accuracy, thereby achieving the effect of low-altitude three-dimensional sensing coverage enhancement.

[0031] 2. A low-altitude stereoscopic synesthetic coverage enhancement system based on a quadrangular prism structure, characterized in that it comprises:

[0032] Network module: Used to initialize network parameters and generate ISAC base station node and low-altitude sensing integrated environment parameters;

[0033] Network planning module: used to extract the location parameters of ISAC base stations to divide the data into triangular prism structures, thus obtaining triangular prism cells;

[0034] Cell merging module: Used to merge triangular prism cells using sensing-assisted base stations to obtain a quadrangular prism structure partitioning that integrates sensing and communication.

[0035] Calculation module: used to calculate the communication coverage and positioning accuracy within each quadrangular prism solid area;

[0036] Decision module: Based on the obtained communication coverage and positioning accuracy, it uses the PSO algorithm to jointly optimize the two to achieve the effect of enhancing low-altitude stereoscopic sensing coverage.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] Firstly, this invention utilizes a quadrangular prism covering structure designed for low-altitude integrated sensing on the Delaunay triangulation, thus accommodating both low-altitude communication and sensing positioning requirements.

[0039] Secondly, because the present invention optimizes both communication coverage and positioning accuracy based on a quadrangular prism covering structure, it can achieve high-precision sensing and positioning while ensuring communication coverage. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the implementation of the low-altitude stereoscopic synesthetic coverage enhancement method based on a quadrangular prism structure according to the present invention.

[0041] Figure 2 This is a scene diagram of the low-altitude integrated sensing coverage network in the method of this invention;

[0042] Figure 3 This is a block diagram of the low-altitude stereoscopic sensory coverage enhancement system based on a quadrangular prism structure according to the present invention;

[0043] Figure 4 The plan view of the quadrangular prism community obtained by simulation using the method of this invention;

[0044] Figure 5 This is a comparison chart showing the average positioning error obtained by using the method of this invention and existing sensing coverage methods to perform sensing and positioning of drone users at different altitudes under communication coverage constraints. Detailed Implementation

[0045] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative effort should all fall within the protection scope of the present invention.

[0046] It should be noted that the step numbers in the specification and claims of this invention are only for the purpose of clearly describing the embodiments of this invention and facilitating understanding, and their order is not limited.

[0047] Example 1: A method for enhancing low-altitude stereoscopic synesthetic coverage based on a quadrangular prism structure

[0048] Reference Figure 1 The implementation steps of this example include the following:

[0049] Step 1: Initialize network parameters and build a low-altitude integrated sensing network.

[0050] To construct a low-altitude sensing integrated network, this step completed the definition of the network scenario scope, the initial definition of the ISAC (Integrated Communication and Sensing) base station and low-altitude UAV users, clarified the spatial coordinate rules, and established the topological association between the ISAC base station and low-altitude UAV users. Ultimately, it achieved network parameter initialization and the construction of the low-altitude sensing integrated network. The specific implementation is as follows:

[0051] 1.1) Initialize the scene area range The number of base stations for the integrated communication and sensing ISAC and base station set Low-altitude drone users ,in:

[0052] Indicates the first in the scene area One base station;

[0053] The scene area, such as Figure 2 As shown, its range is a three-dimensional rectangular region, specifically represented as , , ,in, These represent the length, width, and height of the scene area, respectively.

[0054] The aforementioned integrated communication and sensing ISAC base station refers to an integrated base station system with both communication and sensing functions. It can achieve simultaneous communication and sensing of targets under the same frequency band resources. Through an integrated hardware platform and frequency-sharing waveform design, it realizes the reuse of resources for communication links and sensing links.

[0055] 1.2) To simplify the calculation of the relative position between the ISAC base station and the low-altitude UAV user, the lower left corner of the scene area is selected as the origin, and a standard three-dimensional Cartesian coordinate system with x-axis, y-axis and z-axis is established. The x-axis, y-axis and z-axis represent the horizontal coordinate, vertical coordinate and height coordinate respectively. The unit of this coordinate system is "meter". The positive direction of the x-axis is set to east, the positive direction of the y-axis is set to north, and the positive direction of the z-axis is vertical upward from the ground.

[0056] 1.3) Initialize low-altitude channel environment parameters: path loss index Low-altitude channel factor Noise power spectral density Channel bandwidth ;

[0057] 1.4) Establish integrated communication and sensing ISAC base stations and low-altitude UAV users. The following topological relationships exist between them:

[0058] A single ISAC base station has multiple It provides communication and sensing services, adopts a frequency division multiplexing mechanism, and avoids interference between different low-altitude UAV users through orthogonal resource block allocation;

[0059] single Used to associate multiple ISAC base stations;

[0060] 1.5) By One integrated communication and sensing ISAC base station and multiple low-altitude drone users The topological relationships between them are formed within the scene area. A low-altitude integrated sensing network in space.

[0061] Step 2: Triangulate the base station.

[0062] There are four main existing triangulation methods: Delaunay, advancing wavefront, divide-and-conquer, and surface projection. Delaunay triangulation optimizes the mesh by "maximizing the minimum angle," making it suitable for unconstrained point sets. Advancing wavefront triangulation moves inward from the region boundary, allowing for precise control of the boundary mesh density. Divide-and-conquer recursively divides and merges point sets, making it highly efficient for processing large-scale data. Surface projection triangulation reduces 3D points to a planar triangulation and then maps them back to 3D, making it suitable for curved surface scenes.

[0063] This invention employs, but is not limited to, Delaunay triangulation of base stations to obtain Delaunay triangulated prism cells, providing a structural basis for subsequent merging of these prism cells to obtain a quadrangular prism structure. Its implementation includes the following:

[0064] 2.1) The transverse Mercator projection method is used to set the base station locations. The coordinates are projected from latitude and longitude to two-dimensional Cartesian coordinates, and the merged distance is less than the minimum merged distance threshold. Duplicate or nearly overlapping base stations, of which: Indicates the first The latitude and longitude coordinates of each base station;

[0065] The transverse Mercator projection method uses the WGS-84 ellipsoid as a reference, selects the corresponding UTM projection zone according to the longitude of the center of the scene area, converts the latitude and longitude coordinates of the base station into planar Cartesian coordinates, and the projection result is consistent with the three-dimensional coordinate system xy plane in step 1.2).

[0066] 2.2) Perform two-dimensional Delaunay triangulation on the projected Cartesian coordinates of the plane. That is, based on the projected base station plane coordinates, construct the convex polygon of the base station location with the core criterion of "maximizing the minimum interior angle", and divide it into... A set of non-overlapping, slender triangles without narrow angles forms a planar triangular set. ,in: Represents the first triangulation after Delaunay. A plane triangle;

[0067] 2.3) For any planar triangle Vertically extend the height of the scene area into three-dimensional space. To form a triangular prism cell, all base stations within the triangular prism cell area will form a triangular set of base stations: ,in:

[0068] Indicates the first The set of base stations contained in a planar triangular region;

[0069] Step 3, based on the base station triangulation set Calculate the minimum number of global sensing auxiliary base stations For each triangular prism cell, an external sensing auxiliary base station is selected to obtain the quadrangular prism structure after cell merging.

[0070] To obtain a coverage structure suitable for integrated sensing and communication, this step, based on the triangular prism cells obtained in step 2, selects a sensing auxiliary base station outside each triangular prism cell and merges adjacent triangular prism cells to obtain a merged quadrangular prism structure. This structure can meet the requirements of low-altitude communication and sensing positioning, wherein:

[0071] The sensing auxiliary base station is selected from the integrated communication sensing ISAC base stations in the scene area. It is an auxiliary base station specifically designed to compensate for the sensing blind spots of the triangular prism cell and improve the positioning accuracy of the integrated communication sensing. Its position must be located outside the triangular prism cell and in a non-collinear spatial layout with the ISAC base station of the triangular prism cell.

[0072] The specific implementation of this step includes the following:

[0073] 3.1) Delineate the scene's planar area Divided into These regions constitute a set of regions: And the first each region and the each region Must meet , The two conditions indicate that the partitioned regions do not overlap and cover the entire base station triangle set. ;

[0074] 3.2) For any base station will be adjacent to the base station The trigonometric number is denoted as Maximum adjacent to base station The trigonometric number is denoted as ,in accordance with and the number of regions If you want to cover The region must meet the following requirements. Therefore, the minimum number of global sensing auxiliary base stations is obtained. A based on The lower bound is: ;

[0075] 3.3) Take the steps from step 3.2) The sorted sequence is obtained by performing a descending sort: Choose any size Base station set ,according to Take before The largest can cover up to Base stations in each region, if This indicates that any size is The vertex set is insufficient to cover all Region, based on , The sequence after descending order and the number of regions divided The minimum number of global sensing auxiliary base stations is obtained. A based on The lower bound is: ;

[0076] 3.4) From the set of regions in 3.1) above The number of consecutive decreases in the middle is randomly selected. Each region makes Each region satisfies the condition that all included base stations are unique. In order to achieve this... All areas were hit, at least... Since there are several different base stations, the minimum number of global sensing auxiliary base stations is obtained. A based on The lower bound is: ;

[0077] 3.5) The minimum number of global sensing auxiliary base stations obtained from 3.2), 3.3), and 3.4) The lower bound is set based on lower bound ,based on lower bound ,based on lower bound ,in accordance with The minimum number of global sensing auxiliary base stations is obtained. Maximum lower bound and will As Starting size of an exact search and order ;

[0078] 3.6) Enumeration size is All Sensing Assist Base Station Set ;

[0079] 3.7) In the base station triangulation set Select candidate pairs of triangular regions that can be merged. The candidate pairing It contains four base stations, which together form a set. And only if Time determination Activated;

[0080] 3.8) Base station triangulation For the node set, all activated candidate pairings Mapped to undirected edges, forming a graph. In this graph, select a set of lines that do not share endpoints, and make the set as large as possible. This set of lines is the maximum matching set. ;

[0081] 3.9) Note For sets without sensing-assisted base stations Let the set of base stations be denoted by the maximum matching set. The coverage set is If and only if Time determination It is feasible, and the number of pairs is recorded. If a feasible solution is found, then a feasible solution is considered to have been found; otherwise, no feasible solution is considered to have been found.

[0082] 3.10) The number of sensing auxiliary base stations is determined based on whether a feasible solution exists. Perform a search:

[0083] If a feasible solution is found, stop searching for larger solutions. The search yields the global minimum number of sensing auxiliary base stations. All sensing-assisted base stations set and the corresponding covering set ;

[0084] If no feasible solution is found, then let (Return to step 3.6) to continue the search;

[0085] 3.11) All sets of sensing-assisted base stations Choose the set that maximizes the number of pairs. ,in accordance with The corresponding covering set For each triangular prism cell, an external sensing auxiliary base station is selected to obtain the quadrangular prism structure after cell merging.

[0086] Step 4: Based on the parameters of the low-altitude integrated sensing network, calculate the communication coverage and positioning accuracy in each quadrangular prism structure.

[0087] To measure the communication and sensing / positioning performance of the low-altitude sensing integrated network, the communication coverage and positioning accuracy of each quadrangular prism structure are calculated based on the network parameters. The communication coverage and positioning accuracy directly reflect the sensing and collaborative service capabilities of the quadrangular prism structure, providing a basis for subsequent low-altitude UAV user and base station association strategies and power resource allocation strategies. The implementation includes the following:

[0088] 4.1) Obtain parameters of the low-altitude sensing integrated network:

[0089] Based on the location of the low-altitude UAV user and the location of the communication base station, the three-dimensional distance between the low-altitude UAV user and the communication base station is calculated. Based on the location of the low-altitude UAV user in the quadrangular prism structure and the first The location of the base station was calculated, and the low-altitude drone user and the first base station in the quadrangular prism structure were obtained. Three-dimensional distance between base stations ,in, ;

[0090] The transmit power of the base station providing communication services in the low-altitude sensing integrated network is denoted as […]. The first quadrangular prism structure The sensing power of each base station is denoted as The low-altitude communication channel factor is denoted as The first quadrangular prism structure The low-altitude sensing channel factor of each base station is denoted as . The path loss factor is denoted as The noise power spectral density is denoted as The channel bandwidth is denoted as ,in, ;

[0091] 4.2) Based on the low-altitude sensing network parameters obtained in step 4.1), the signal-to-interference-plus-noise ratio (SINR) of the low-altitude UAV user is calculated:

[0092] ,

[0093] The signal-to-interference-plus-noise ratio (SINR) is the ratio of the useful signal power to the sum of the interference signal power and the background noise power. Its value directly affects the transmission quality of the communication link.

[0094] 4.3) Based on the signal-to-interference-plus-noise ratio (SINR) obtained in 4.2), define the communication coverage. for Greater than or equal to the receiver demodulation threshold The probability of:

[0095] ;

[0096] 4.4) Calculate the Fisher information matrix of user location parameters :

[0097] Based on the first quadrangular prism structure The location of the base station and the location of the low-altitude drone user are used to calculate the location of the first base station. Unit direction vector of each base station to low-altitude drone users The first quadrangular prism structure The sensing measurement noise of each base station is denoted as . ,in, ;

[0098] in accordance with and Calculate the location of the low-altitude drone user Fisher Information Matrix for:

[0099] ,

[0100] in, Represents the transpose of a matrix;

[0101] 4.5) Based on the Fisher information matrix in 4.4) The positioning accuracy of low-altitude UAV users was calculated as follows:

[0102] ,

[0103] in, For positioning errors of low-altitude drone users, Describes the 2-norm. Represents the trace of a matrix. This represents the inverse of a matrix.

[0104] Step 5: Perform joint optimization of communication coverage and positioning accuracy.

[0105] To simultaneously ensure communication coverage and positioning accuracy, it is necessary to determine the association strategy between low-altitude UAV users and base stations and optimize power resources using the PSO algorithm. This results in high positioning accuracy while maintaining high communication coverage, achieving enhanced low-altitude stereoscopic sensing coverage. The implementation includes the following:

[0106] 5.1) Determine the association strategy between low-altitude drone users and base stations:

[0107] The triangular prism area is divided into three height layers—low, medium, and high—based on average height. (The last part, "using...", appears to be a fragment and doesn't translate directly. It's left as is.) Representing the low, medium, and high altitude layers, the set of three base stations within the triangular prism region is denoted as... , These represent the first, second, and third base stations in the triangular prism region, respectively.

[0108] Based on the real-time 3D position of the low-altitude drone user, determine the altitude layer where the user is located. Low-altitude drone users and To associate, by Responsible for the core communication link, located in Low-altitude drone users and To associate, by Responsible for the core communication link, located in Low-altitude drone users and To associate, by Responsible for core communication links;

[0109] For low-altitude drone users, the two base stations in the triangular prism area that are not responsible for the core communication link are designated as auxiliary base stations. They monitor the signal-to-interference-plus-noise ratio (SINR) of low-altitude drone users in real time. When the SINR of low-altitude drone users under the core communication link... In such cases, an auxiliary base station with a better signal can quickly take over and be responsible for the core communication link;

[0110] 5.2) Establish a power resource optimization model:

[0111] Let the quadrangular prism region be denoted as , in the quadrangular prism region the first The communication transmission power of each base station is denoted as , in the quadrangular prism region the first The sensing transmit power of each base station is denoted as... ,in, ;

[0112] Set the maximum transmit power of a single base station to Set the communication coverage threshold to Based on the communication coverage obtained in step 4.3) The low-altitude UAV user positioning error obtained in step 4.5) The power resource optimization model is established as follows:

[0113] ,

[0114] in, The average positioning error in the quadrangular prism region;

[0115] 5.3) The PSO algorithm is used to optimize the power resource optimization model within each quadrangular prism three-dimensional cell:

[0116] 5.3.1) Particle Encoding and Population Initialization:

[0117] For the parameters to be optimized in the power resource model, namely communication power, Sensing power Transform them into particles that can be handled by the PSO algorithm, generate an initial set of feasible solutions, and then process each particle... The position vector directly maps to a set of power allocation schemes, expressed as: Its dimension is 8;

[0118] Set the population size as Randomly generated The position and velocity of each particle are determined to ensure that the power components of all particles satisfy... , ;

[0119] 5.3.2) Definition of fitness function:

[0120] To quantify the merits of each power allocation scheme, an evaluation criterion strongly correlated with the model's objectives and constraints needs to be established, namely, a fitness function. The function aims to minimize the average positioning error of the quadrangular prism region. With "as the core objective, and incorporating constraint penalty terms to enforce compliance with model requirements, the specific expression is as follows:

[0121] ,

[0122] in, This is the penalty coefficient for communication coverage constraints. This is the penalty coefficient for the total power of the base station;

[0123] 5.3.3) Optimal solution selection:

[0124] First, after obtaining the initial population according to step 5.3.1) and the fitness function according to step 5.3.2), for each particle... Calculate its fitness value Compare it with the particle's historical best fitness:

[0125] If the current fitness is better, then update the individual's optimal position. for: ;

[0126] Otherwise, the optimal position of the individual is not updated;

[0127] Then, the individual optimal positions of all particles are traversed, and the position with the minimum fitness is selected as the global optimal position. That is, the optimal power allocation scheme for the current stage of the population;

[0128] 5.3.4) Particle Iterative Update:

[0129] Adjust the particle velocity based on the selected individual optimal position and the global optimal position. for:

[0130] ,

[0131] in Inertial weights are used to balance global exploration and local development. These are the learning factor weights, used to control the particle's learning towards the individual optimum and the global optimum, respectively. for Random numbers within, Indicates the first The next iteration;

[0132] The particle positions are adjusted based on the updated velocity as follows: ;

[0133] After updating the velocity and location, the model constraints are verified to ensure that the power component is non-negative and the total power of a single base station does not exceed the limit. Violating parameters are truncated to the boundary, and finally a new generation of better population is generated, completing one iteration of optimization.

[0134] 5.3.5) Iteration Termination Judgment:

[0135] Set the maximum number of iterations to The current iteration number With maximum number of iterations contrast:

[0136] If the current iteration number Reaching the maximum number of iterations When the iteration is complete, the global optimal solution is locked.

[0137] Otherwise, return to step 5.3.3), recalculate the fitness of the new generation population and update the individual optimum and global optimum, and enter the next round of iterative optimization to form a closed-loop control process.

[0138] 5.3.6) Output of the optimal solution

[0139] After the iteration terminates, extract the global optimal position. This is analyzed into the final power allocation scheme for the four base stations. for: To ensure the effectiveness of the solution, constraint verification needs to be completed to confirm that the solution meets the communication coverage requirements. and total power of a single base station The core requirement is to output the optimal power resource allocation scheme that fully meets the model requirements, obtain the high positioning accuracy optimization result under the guarantee of high communication coverage, and realize the low-altitude three-dimensional sensory coverage enhancement effect based on the quadrangular prism structure.

[0140] Example 2: Low-altitude stereoscopic sensory coverage enhancement system based on a quadrangular prism structure.

[0141] Reference Figure 3 This example includes: network initialization module 1, network planning module 2, cell merging module 3, calculation module 4, and decision-making module 5. Cell merging module 3 includes: a base station number lower bound calculation submodule 31, a base station set calculation submodule 32, and a triangular prism merging submodule 33. The working principle of the entire system is as follows:

[0142] The network initialization module 1 is used to initialize network parameters, generate ISAC base station node and low-altitude sensing integrated network environment parameters, and transmit the network parameters and ISAC base station location parameters to the network planning module 2, and transmit the low-altitude sensing integrated network environment parameters to the calculation module 4.

[0143] The network planning module 2 is used to extract the location parameters of the ISAC base station, divide the data into triangular prism structures, obtain triangular prism cells, and transmit the triangular prism cells to the cell merging module 3.

[0144] The cell merging module 3 is used to merge triangular prism cells using sensing-assisted base stations to obtain a quadrangular prism structure partitioning that integrates sensing and communication. The base station number lower bound calculation submodule 31 calculates three lower bounds for the number of sensing-assisted base stations that satisfy the minimum number of globally available sensing-assisted base stations and the maximum number of merged triangular prism cells, based on the triangular prism structure partitioning, and transmits these three lower bounds to the base station set calculation submodule 32. The base station set calculation submodule 32 calculates all sets of sensing-assisted base stations based on the maximum value of the three lower bounds, and transmits all sets of sensing-assisted base stations to the triangular prism merging submodule 33. The triangular prism merging submodule 33 selects the set of sensing-assisted base stations that satisfies the minimum number of remaining triangular prism cells, merges triangular prism cells based on the selected set, and transmits the merged quadrangular prism structure to the calculation module 4.

[0145] The calculation module 4 is used to calculate the communication coverage and positioning accuracy within each quadrangular prism solid area, and transmit the communication coverage and positioning accuracy to the decision module 5.

[0146] The decision module 5 is used to jointly optimize the obtained communication coverage and positioning accuracy using the PSO algorithm to obtain high positioning accuracy under the guarantee of high communication coverage, thereby enhancing the coverage of low-altitude stereoscopic sensing signals.

[0147] It should be noted that the above functional modules can be implemented, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, they can be implemented, in whole or in part, as program instruction products. A program instruction product includes one or a set of program instructions. When the program instructions are loaded and executed on a computer, the described process or function is generated, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The program instructions can be stored in a computer-readable and writable storage medium, or transferred from one computer's readable and writable storage medium to another.

[0148] In this embodiment, the direct coupling or communication connection between the modules can be achieved through indirect coupling or communication connection via interfaces, devices, or modules. The functional modules and sub-modules in this embodiment can dynamically reside within a single processing unit, or each module can exist physically independently, or two or more modules can dynamically reside within a single processing unit. When these dynamic components are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable and writable storage medium. This storage medium can be a memory, disk, or optical disc, etc.

[0149] The effects of this invention can be further illustrated by the following simulation results:

[0150] I. Simulation Conditions

[0151] The simulation software is PyCharm, and the software environment is Python 3.10.

[0152] The hardware specifications are: 12th Gen Intel(R) Core(TM) i5-12400, 2.50 GHz, 32GB RAM.

[0153] The parameters used in the simulation scenario are shown in Table 1:

[0154] Table 1 Simulation test parameter settings

[0155]

[0156] II. Simulation Content

[0157] Simulation 1: Under the above simulation conditions, the method of this invention is used to simulate the generation of a quadrangular prism structure for the integrated communication and sensing ISAC base station in the scene area. The results are as follows: Figure 4 As shown in the diagram. Here, BSi represents ISAC base station i, triangles represent sensing-assisted base stations, T represents triangular prism regions, and Q represents quadrangular prism regions.

[0158] from Figure 4 As can be seen, the quadrangular prism structure obtained by this invention generates a set that satisfies the minimum number of global sensing auxiliary base stations and the maximum merging set of triangular prisms. However, there are two triangular prism regions that cannot be merged. This is because, under the constraints of satisfying the minimum number of global sensing auxiliary base stations and the maximum merging set, neither of these two triangular prism regions can find unmerged adjacent triangular prisms for non-overlapping merging.

[0159] Simulation 2: Under the above simulation conditions, the average regional positioning error at different heights in the scene was simulated using the method of this invention and existing methods. The results are as follows: Figure 5 As shown.

[0160] from Figure 5 As can be seen, compared with existing methods, the method of the present invention can significantly reduce the average positioning error in the area while ensuring the same communication coverage threshold of 95%, and the average positioning error decreases even more with increasing altitude than existing methods.

Claims

1. A method for enhancing low-altitude stereoscopic synesthetic coverage based on a quadrangular prism structure, characterized in that it includes: S1) Initialize network parameters, establish a three-dimensional Cartesian coordinate system, and construct a low-altitude integrated sensing network; S2) Perform Delaunay triangulation on the base station based on the base station location parameters to obtain the base station triangle set of the corresponding prism cell. ; S3) Based on the base station triangle set Calculate the minimum number of global sensing auxiliary base stations For each triangular prism cell, an external sensing auxiliary base station is selected to obtain the quadrangular prism structure after cell merging. S4) Based on the parameters of the low-altitude integrated sensing network, the communication coverage and positioning accuracy in each quadrangular prism structure are calculated; S5) The obtained communication coverage and positioning accuracy are jointly optimized to achieve the effect of enhanced low-altitude three-dimensional sensing coverage.

2. The method as described in claim 1, characterized in that, In step S1), the network parameters are initialized, a Cartesian three-dimensional coordinate system is established, and a low-altitude integrated sensing network is constructed. The implementation of this includes: S1a) Initialize the scene area range The number of base stations for the integrated communication and sensing ISAC and base station set Low-altitude drone users ; S1b) Establish a standard three-dimensional Cartesian coordinate system with the x-axis, y-axis, and z-axis as the origin, with the lower left corner of the scene area as the origin; S1c) Establish integrated communication and sensing ISAC base stations and low-altitude drone users The following topological relationships exist between them: A single ISAC base station has multiple Provide communication and sensing services; single Used to associate multiple ISAC base stations; S1d) by One integrated communication and sensing ISAC base station and multiple low-altitude drone users The topological relationships between them are formed within the scene area. A low-altitude integrated sensing network in space.

3. The method as described in claim 1, characterized in that, In step S2), the base station is delaunay triangulated based on its location parameters to obtain the base station triangulation set of the corresponding triangular prism cell. Its implementation includes: S2a) Project the set of base station locations from latitude and longitude to planar Cartesian coordinates, and merge the distances that are less than the minimum merging distance threshold. The points of repetition or near overlap; S2b) Perform two-dimensional Delaunay triangulation on the projected planar coordinates, that is, divide the convex polygon of the base into... There are 3 triangles, and the number of vertices of their convex hulls is . This yields a planar triangular set. ; S2c) for any planar triangle Vertically extend the height of the scene area into three-dimensional space. A triangular prism cell is formed, and all base stations within the triangular prism cell area form a base station triangle set. .

4. The method as described in claim 1, characterized in that, In step S3), based on the base station triangulation set Calculate the minimum number of global sensing auxiliary base stations For each triangular prism cell, an external sensing auxiliary base station is selected to obtain the quadrangular prism structure after cell merging, as follows: S3a) will divide the scene plane area Divided into These regions constitute a set of regions: And the first each region and the each region Must meet as well as These two conditions indicate that the partitioned regions do not overlap and cover the entire base station triangle set. ; S3b) For any base station will be adjacent to the base station The trigonometric number is denoted as Maximum adjacent to base station The trigonometric number is denoted as ,in accordance with and the number of regions The minimum number of global sensing auxiliary base stations was calculated. A based on The lower bound is: ; S3c) will be in S3b) The sorted sequence is obtained by performing a descending sort: Choose any size Base station set ,according to Take before The largest can cover up to Base stations in each region, based on , The sequence after descending order and the number of regions divided The minimum number of global sensing auxiliary base stations was calculated. A based on The lower bound is: ; S3d) From the region set of S3a) above In successive decreasing random selection Each region makes Given a region where all included base stations are unique, we obtain the global minimum number of sensing auxiliary base stations. A based on The lower bound is: ; S3e) The minimum number of global sensing auxiliary base stations obtained from S3b), S3c), and S3d) The lower bound is set based on lower bound ,based on lower bound ,based on lower bound ,in accordance with The minimum number of global sensing auxiliary base stations is obtained. Maximum lower bound and will As Starting size of an exact search ; S3f) from the initial size Initially, the enumeration size is Sensing-assisted base station set ; S3g) in the base station triangle set Select candidate pairs of triangular regions that can be merged. The candidate pairing It contains four base stations, which together form a set. And only if Time determination Activated; S3h) based on base station triangle set For the node set, all activated candidate pairings Mapped to undirected edges, forming a graph. In this graph, select a set of lines that do not share endpoints, and make the set as large as possible. This set of lines is the maximum matching set. ; S3i) For sets without sensing-assisted base stations Let the set of base stations be denoted by the maximum matching set. The coverage set is If and only if Time determination It is feasible, and the number of pairs is recorded. At this point, a feasible solution is considered to have been found; S3j) from the initial size Begin by repeating steps S3g) to S3i). If no feasible solution is found at present, then let... Continue the search until a feasible solution is found, thus obtaining the global minimum number of sensing auxiliary base stations. All sensing-assisted base stations set and the corresponding covering set ; S3k) in all sensing-assisted base station sets Choose the set that maximizes the number of pairs. ,in accordance with The corresponding covering set For each triangular prism cell, an external sensing auxiliary base station is selected to obtain the quadrangular prism structure after cell merging.

5. The method as described in claim 1, characterized in that, In step S4), the communication coverage rate in each quadrangular prism structure is obtained based on the parameters of the low-altitude integrated sensing network, which includes: S4a) Calculate the signal-to-interference-plus-noise ratio (SINR) for low-altitude drone users based on the parameters of the low-altitude integrated sensing network: ， in, Indicates the transmission power of the base station providing communication. For the first Sensing power of each base station, Indicates the low-altitude communication channel factor. Indicates the first Low-altitude sensing channel factor of each base station, This represents the three-dimensional distance between the low-altitude drone user and the communication base station. Indicates low-altitude drone users and the The three-dimensional distance between base stations Indicates the path loss factor. Represents the noise power spectral density. Indicates channel bandwidth; S4b) Based on the signal-to-interference-plus-noise ratio (SINR) obtained in S4a), the communication coverage is calculated. : ， in, The SINR threshold at which the receiver can successfully demodulate, and the communication coverage. express Greater than or equal to the receiver demodulation threshold The probability of.

6. The method as described in claim 1, characterized in that, In step S4), the positioning accuracy in each quadrangular prism structure is calculated based on the parameters of the low-altitude integrated sensing network, including: S4c) Based on the parameters of the low-altitude integrated sensing network, the Fisher information matrix of user location parameters is obtained: ， in, For the first Sensing measurement noise of each base station, For the first The unit direction vector of each base station relative to the user's location. Represents the transpose of a matrix; S4d) Based on the Fisher information matrix in S4c) The positioning accuracy of low-altitude UAV users was calculated as follows: ， in, For positioning errors of low-altitude drone users, It represents the 2-norm.

7. The method as described in claim 1, characterized in that, Step S5) involves jointly optimizing communication coverage and positioning accuracy, which includes: S5a) Based on the user's real-time 3D location, the base station at the corresponding height level within the triangular prism area is selected as the primary serving base station, responsible for the core communication link; the other two base stations in the triangular area are used as auxiliary base stations to monitor the user's signal quality SINR in real time. When the primary base station link... In such cases, an auxiliary base station with a better signal can quickly take over. S5b) Establish a power resource optimization model based on the positioning accuracy of low-altitude UAV users: ； in, For scene area Average positioning error For communication coverage threshold, This represents the maximum transmit power of a single base station. For communication transmission power, To sense the transmission power; S5c) The PSO algorithm is used to optimize the power resource optimization model in each quadrangular prism three-dimensional cell to obtain high positioning accuracy under the guarantee of high communication coverage, thereby achieving the effect of low-altitude three-dimensional sensing coverage enhancement.

8. A low-altitude stereoscopic synesthetic coverage enhancement system based on a quadrangular prism structure, characterized in that it comprises: Network initialization module: Used to initialize network parameters and generate ISAC base station node and low-altitude sensing integrated environment parameters; Network planning module: used to extract the location parameters of ISAC base stations to divide the data into triangular prism structures, thus obtaining triangular prism cells; Cell merging module: Used to merge triangular prism cells using sensing-assisted base stations to obtain a quadrangular prism structure partitioning that integrates sensing and communication. Calculation module: used to calculate the communication coverage and positioning accuracy within each quadrangular prism solid area; Decision module: Based on the obtained communication coverage and positioning accuracy, it uses the PSO algorithm to jointly optimize the two to achieve the effect of enhancing low-altitude stereoscopic sensing coverage.

9. The system according to claim 8, characterized in that the cell merging module comprises: The base station number lower bound calculation submodule is used to calculate three lower bounds of the number of sensing auxiliary base stations that satisfy the global minimum sensing auxiliary base station and the maximum triangular prism cell merging set based on the triangular prism structure partitioning. The base station set calculation submodule is used to calculate the set of all sensing auxiliary base stations based on the maximum value of the three lower bounds of the number of sensing auxiliary base stations; The triangular prism merging submodule is used to select the set of sensing auxiliary base stations that satisfies the minimum number of remaining triangular prism cells based on all sensing auxiliary base station sets, and to merge triangular prism cells based on the selected set of sensing auxiliary base stations.

Images