A method, system, and device for target area identification in complex electromagnetic environments

Abstract

This invention belongs to the field of target detection technology, specifically relating to a method, system, and device for target area identification in complex electromagnetic environments. The method constructs a two-dimensional plane to form a planar coordinate system, using the center of the target area as the origin. The target area is discretized into a uniform grid, covering a square region centered at the origin. The inscribed circular region within the grid is considered the outer region, generating an outer region indicator matrix. This outer region indicator matrix is ​​then used to generate an arrival angle sector region indicator matrix, and further, an intersection region indicator matrix is ​​generated. Finally, the proportion of the arrival angle sector intersection region within the target area is calculated using the intersection region indicator matrix, and a judgment threshold is set. If the proportion is greater than or equal to the threshold, the target is determined to be within the target area; otherwise, the target is determined to be outside the target area. The system and device are used to implement this method. This invention significantly improves the accuracy, robustness, and computational efficiency of target area identification.

Description

Technical Field

[0001] This invention belongs to the field of target detection technology, specifically relating to a method, system, and device for target area identification in complex electromagnetic environments. Background Technology

[0002] Angle of Arrival (AOA) is a key method in passive positioning, determining the location of a transmitter by measuring the angle at which a signal arrives at a receiving station. AOA technology utilizes multiple measurement stations to obtain the angle of arrival of the target signal, then calculates the target's spatial position using triangulation principles. Compared to other positioning technologies, AOA offers advantages such as simple equipment structure, independence from time synchronization, and applicability to various signal types. However, AOA technology faces limitations in measurement accuracy in practical applications. Due to factors such as antenna array precision limitations, multipath effects, and environmental interference, AOA measurements often contain significant errors, especially in complex environments where such errors can reach tens of degrees, severely impacting positioning accuracy.

[0003] Donghua University proposed a keyless system positioning method based on Bluetooth RSSI (Received Signal Strength Indicator) in its patent application "A Positioning Method for Keyless Systems Based on Bluetooth RSSI" (Application No.: CN201710167473.4, Publication No.: CN107124696A). This invention aims to determine the location of a car's smart key using Bluetooth signal strength, applicable to both in-vehicle and external scenarios, and belongs to the field of wireless communication and intelligent control technology. This method utilizes Bluetooth RSSI for positioning, requiring no additional infrastructure, resulting in low cost, and applicability to various in-vehicle and external scenarios. However, this method relies on signal strength measurement and is susceptible to errors introduced by environmental noise, multipath effects, and obstacle attenuation, which may lead to inaccurate distance calculations.

[0004] The Fraunhofer Society for the Promotion of Applied Research in Germany proposed a path localization method based on radio signals in its patent application "Concept of Location on a Path" (application number: DE102006061650, publication number: DE102006061650A1). This path feature matching-based method provides a low-cost and easy-to-implement localization scheme for specific scenarios (such as navigation along repetitive paths). However, its dependence on pre-recorded paths, sensitivity to signal and path changes, and limited localization accuracy limit its application in situations requiring general and robust area identification.

[0005] Tianjin University proposed an indoor positioning algorithm based on AOA (Angle of Arrival) and PDOA (Phase Difference of Arrival) array antennas in its patent application "An Indoor Positioning Algorithm Based on AOA and PDOA" (Application No.: CN201710156453.7, Publication No.: CN106793087A). This invention aims to determine the location of objects indoors using the energy and phase information of multipath signals received by the array antenna, belonging to the field of wireless communication and positioning technology. The main steps of this method are: It utilizes the array antenna combined with AOA and PDOA estimation, effectively handling multipath and non-line-of-sight effects, and improving indoor positioning accuracy through virtual base stations and optimization algorithms. The drawbacks are that the method relies on the array antenna, increasing cost and complexity, and that AOA and PDOA estimation are susceptible to interference and environmental changes, potentially limiting its performance in complex environments.

[0006] The paper "An Improved RSSI-Based Positioning Method Using Sector Transmission Model and Distance Optimization Technique" proposes an RSSI-based positioning algorithm (Zhu, H., & Alsharari, T. (2015). This algorithm aims to determine the location of blind nodes using RSSI values. However, this method is highly dependent on the accuracy of environmental modeling; the granularity of sector partitioning and the fitting accuracy of the transmission model directly affect positioning performance. Furthermore, establishing an accurate sector transmission model requires a large amount of offline measurement data, which increases the cost and complexity of deployment and maintenance. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, the present invention aims to provide a target area identification method, system, and device for complex electromagnetic environments. This method first determines the possible sector-shaped area of ​​the target to be located for each positioning station based on the measured angle of arrival (AOA) of the target at each positioning station and a preset maximum error range. Then, it calculates the intersection area of ​​these sector-shaped areas within the outer circle. Finally, by analyzing the area distribution ratio of this intersection area under the boundary division of the inner and outer circles, it determines whether the target is located in the inner or outer region. This improves the accuracy and robustness of area identification even when there are errors in AOA measurements. It does not rely on array antennas, reducing cost and complexity, and avoids the problem of inaccurate distance calculation due to environmental complexity caused by RSSI. It also has low dependence on the accuracy of environmental modeling.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] A target area identification method for complex electromagnetic environments includes the following steps:

[0010] Step 1: Map the target area of ​​the positioning scene onto a two-dimensional plane to form a planar coordinate system through planar projection. Record the center of the target area as the calculation reference point. Move the target area under the planar coordinate system so that the calculation reference point coincides with the origin of the coordinate system.

[0011] Step 2: Discretize the moved target area into a uniform grid, with the grid covering the area centered at the origin and containing an inscribed circle with a radius of 1. A square region;

[0012] Step 3, using the origin as the center, A circular region with a radius of is taken as the outer region, i.e. the total detection range, and an outer region indication matrix is ​​generated;

[0013] Step 4: Generate the arrival angle sector region indication matrix based on the outside region indication matrix;

[0014] Step 5: Generate the intersection region indication matrix based on the arrival angle sector region indication matrix;

[0015] Step 6: Calculate the proportion of the intersection region of the arrival corner sector within the target area based on the intersection region indicator matrix, and set a judgment threshold. If the proportion is greater than or equal to the judgment threshold, the target is determined to be within the target area; otherwise, the target is determined to be outside the target area.

[0016] The positioning scenario includes Location stations and A target to be located, a location station Coordinates are , Target to be located Coordinates are , Define two concentric circular regions, with the center being the calculation reference point and the inner circle being the target region with a radius of [missing information]. The outer radius is ;

[0017] Location site The target to be located is evenly distributed on the inner circle boundary. Randomly distributed throughout the scene;

[0018] Each location station The received data from the target to be located The signal is used to measure the angle of arrival and locate the station. To locate the target The true angle of arrival is defined as the angle from the location station. Point to the target to be located The angle between the vector and the reference direction is denoted as... ;

[0019] Define measurement error as This measurement error Follow the mean Standard deviation is Gaussian distribution, or If the locations are evenly distributed within the area, then the positioning stations Treatment of positioning target Angle of arrival measurement Represented as:

[0020]

[0021] in, The measurement error has a maximum error value of [value missing]. Spend.

[0022] In step 1, the latitude and longitude of the reference point are calculated as follows: The coordinates after the plane projection are For latitude and longitude The coordinates of any point after translation are:

[0023]

[0024] in, This is the Earth's semi-major axis.

[0025] In step 2, the mesh coverage is centered at the origin and has an inscribed circle radius of [missing information]. A square region, the coordinates of each point in the grid. Determined by the following formula:

[0026]

[0027] in, , The radius is ; The grid resolution represents the number of grid points in the horizontal and vertical directions of the planar coordinate system; these two parameters are used to generate... × A uniform grid, covering from arrive The range of the horizontal and vertical coordinates.

[0028] Step 3, extra-domain region indication matrix The definition is as follows:

[0029]

[0030] in, Represents the planar coordinates of any point in the grid. It is the preset inscribed circle radius, in the function. This is an indicator function, defined as follows: the function value is 1 when the condition within the parentheses is met; otherwise, the function value is 0.

[0031] Step 4 specifically includes the following steps:

[0032] Calculate the direction from any grid point to the current location station. The relative vector, the x-coordinate component of the relative vector. and ordinate components :

[0033]

[0034] in, Represents the coordinates of any grid point. Indicates the current location station Plane coordinates;

[0035] Using x-axis components and ordinate components Calculate any grid point relative to the current location station The theoretical angle of arrival is denoted as :

[0036]

[0037] The theoretical angle of arrival is defined as the angle from the positive vertical axis to the direction of the relative vector, ranging from 0° to 360°. The angle between the relative vector and the positive direction of the horizontal axis;

[0038] Calculate the current location station Measured angle of arrival Relative to the current location station and grid points Theoretical arrival angle value The angle difference between .

[0039]

[0040] Based on the calculation of each location station angular difference and the preset robustness range of the angle of arrival Generate the current location station Arrival angle sector area indication matrix If the external region indicator matrix The distance to the origin is less than or equal to the preset radius. ,but The corresponding element is 1, indicating that the point is within the total detection range; otherwise, it is 0, indicating that the point is outside the total detection range. The formula for calculating the arrival sector region indication matrix is:

[0041]

[0042] in, It is an indicator function.

[0043] Step 5 specifically includes the following steps: Current location station The intersection region of the reached corner sector regions is the current intersection region, and the current intersection region is used to indicate the matrix. This means, firstly, the current intersection region indicator matrix... Initialized as an out-of-domain region indicator matrix ;

[0044] Then, iterate through all the location sites. The current intersection region indicator matrix With the arrival angle sector area indication matrix of each positioning station Perform logical AND operation;

[0045] By all location sites Arrival angle sector area indication matrix Perform intersection operations sequentially to obtain the final intersection region indicator matrix. This indicates all location stations. The common area of ​​the arrival corner sector region, that is, satisfying all stations The set of grid points within the range of the angle of arrival measurement error.

[0046] Step 6 specifically includes the following steps:

[0047] First, generate an inner circular region indicator matrix. , representing the coordinates of grid points Whether it is located inside the inner circle or on its boundary;

[0048]

[0049] in, It is the preset inner circle radius. It is still an indicator function;

[0050] Next, calculate the proportion of the intersection region of the reaching sector within the inner circle, denoted as . The calculation formula is:

[0051]

[0052] In the formula, the numerator This represents the number of grid points that simultaneously reside within the intersection of the arrival corner sector and the inner circle; the denominator is... This indicates the total number of grid points reaching the intersection area of ​​the corner sectors;

[0053] Finally, based on the calculated proportions Compared with the preset judgment threshold The comparisons are made to determine the final region discrimination result, and the judgment rules are as follows:

[0054]

[0055] If the proportion of the intersection area of ​​the corner sectors within the inner circle is... Greater than or equal to If the target is within the target area, then the target is determined to be located within the target area; otherwise, the target is determined to be located outside the target area.

[0056] A target area identification system for complex electromagnetic environments includes:

[0057] The preprocessing module maps the target area on the Earth's surface onto a two-dimensional plane to form a planar coordinate system through planar projection. The center of the target area is recorded as the calculation reference point. The target area under the planar coordinate system is moved so that the calculation reference point coincides with the origin of the coordinate system.

[0058] The mesh discretization module discretizes the moved target area into a uniform mesh, with the mesh coverage centered at the origin and containing an inscribed circle of radius [missing information]. A square region;

[0059] The matrix generation module will use the origin as the center of the circle. A circular region with a radius of is defined as the outer region, i.e., the total detection range, and an outer region indicator matrix is ​​generated; an arrival corner sector region indicator matrix is ​​generated based on the outer region indicator matrix; and an intersection region indicator matrix is ​​generated based on the arrival corner sector region indicator matrix.

[0060] The region determination module calculates the proportion of the intersection region of the arrival corner sector within the target region based on the intersection region indicator matrix, and sets a determination threshold. If the proportion is greater than or equal to the determination threshold, the target is determined to be within the target region; otherwise, the target is determined to be outside the target region.

[0061] A target area identification device for complex electromagnetic environments includes:

[0062] Memory: Used to store computer programs that implement target area identification methods for complex electromagnetic environments;

[0063] Processor: Used to implement a target area identification method for complex electromagnetic environments when executing the computer program.

[0064] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0065] 1. Step 2 of this invention uses a high-resolution grid to discretize the target region, which enables refined modeling and quantification of the target region and the intersection region of AOA sectors of multiple sites, significantly improving the accuracy of region area calculation and providing a reliable basis for the final region attribution judgment.

[0066] 2. Step 6 of this invention employs a dynamic site combination selection mechanism, which enables the intelligent search and utilization of effective site combinations to form an intersection region even when the arrival angle measurement of some sites is severely biased, resulting in the loss of intersection of all sites. This significantly enhances the adaptability and robustness of the algorithm in complex electromagnetic environments.

[0067] 3. Step 6 of this invention uses the area distribution ratio of the intersection region under the division of the inner and outer circular boundaries to make a judgment, thereby converting the arrival angle error into a quantifiable area coverage ratio, and using this as the basis for determining the region's affiliation, avoiding dependence on a single intersection point, and improving the accuracy and reliability of region identification.

[0068] 4. Step 4 of this invention uses the approach of reaching a cone-shaped error range for region identification, which directly addresses the large errors in angle of arrival measurement under complex electromagnetic environments. It transforms single-point measurement into a sector-shaped region with tolerable errors, and determines the possible target region through the intersection of multiple station sector regions, significantly improving the accuracy and robustness of region identification and effectively solving the problem of traditional precise point positioning methods failing when the error is too large.

[0069] 5. Step 1 of this invention converts the input latitude and longitude coordinates into two-dimensional plane coordinates with the reference point as the origin through planar projection, thereby simplifying the complex geometric problem of the Earth's surface into a standard two-dimensional Cartesian coordinate system. This greatly simplifies the complexity of subsequent mesh generation, angle of arrival calculation, and region intersection analysis, and improves the versatility and computational efficiency of the algorithm.

[0070] In summary, this invention effectively solves the key problem of traditional positioning methods failing due to excessive angle of arrival measurement errors in complex electromagnetic environments by combining high-resolution mesh modeling, dynamic site combination selection, area ratio-based region judgment, and region identification within the range of arrival cone error, and simplifies latitude and longitude coordinates into two-dimensional plane calculations. It significantly improves the accuracy, robustness, and computational efficiency of target area identification. Attached Figure Description

[0071] Figure 1 This is a schematic diagram illustrating the hypothetical scenario for area identification in this invention.

[0072] Figure 2 This is a simulation diagram of the region identification scene of the present invention.

[0073] Figure 3(a) is a performance comparison curve of the present invention and the AOA positioning method when the target to be located is distributed within the target area.

[0074] Figure 3(b) is a performance comparison curve of the present invention and the AOA positioning method when the target to be located is distributed outside the target area.

[0075] Figure 4 The graph shows a performance comparison between the present invention and the AOA positioning method when the target to be located is evenly distributed inside and outside the target area. Detailed Implementation

[0076] The present invention will now be described in detail with reference to the accompanying drawings.

[0077] A target area identification method for complex electromagnetic environments includes the following scenario assumptions:

[0078] Assuming the location scenario includes Location stations and Individual target to be located , Indicates location station coordinate, Indicates the target to be located Coordinates are defined, with two concentric circular regions. The inner circle represents the target region, and its radius is [radius value missing]. (meters), outer radius is (rice);

[0079] Location site The target to be located is evenly distributed on the inner circle boundary. Randomly distributed in the scene, in order to simulate the positioning situation at different distances, some targets are located in the inner circular area, and other targets are located in the annular area between the inner and outer circles;

[0080] Each location station The received data from the target to be located The signal is used to measure the Angle of Arrival (AOA) to locate the station. To locate the target The true definition of AOA is from the location site Point to the target to be located The angle between the vector and the reference direction (e.g., true north) is denoted as... ;

[0081] In actual measurements, AOA measurements are affected by random errors, which are defined as follows: This measurement error Follow the mean Standard deviation is Gaussian distribution, or If the locations are evenly distributed within the area, then the positioning stations Treatment of positioning target AOA measurement value Represented as:

[0082]

[0083] in, The measurement error has a maximum error value of [value missing]. Spend;

[0084] This scenario aims to simulate the positioning of a target using AOA measurement data with inherent errors under different spatial distributions. Research on the challenges and methods of location services. Typical location scenarios include... Figure 1 As shown.

[0085] The specific steps of the target region identification method are as follows:

[0086] Step 1: Boundary Preprocessing

[0087] For region identification algorithms with unknown positioning results, to improve the computational efficiency and accuracy of region discrimination algorithms based on Angle of Arrival (AOA), the input boundary latitude and longitude coordinates are preprocessed and converted into coordinates in a planar coordinate system. Furthermore, the calculation reference point (the center of the target area, i.e., the center of the concentric circles) is moved to the origin. Since direct distance and angle calculations in a spherical coordinate system introduce significant errors due to the Earth's curvature, especially in large areas or near poles, a planar projection is used to map the target area of ​​the positioning scene onto a two-dimensional plane, making the calculation more accurate. The center of the target area in the planar coordinate system is designated as the calculation reference point. Moving the target area in the planar coordinate system to the origin effectively simplifies subsequent geometric calculations. In a Cartesian coordinate system, the distance between two points is calculated using the square root of the sum of squared coordinate differences. Placing the calculation reference point at the origin makes vector calculations related to that point (such as calculating the vector of a grid point relative to a station) intuitive and easy to handle. For example, when calculating the AOA, the arctangent operation is directly performed using the relative coordinate difference. This coordinate translation not only simplifies the calculation formula and reduces the calculation steps, but also helps to control possible numerical errors during the calculation process, thereby further improving the efficiency and stability of the algorithm.

[0088] Let the latitude and longitude of the center of the target area be... The coordinates after the plane projection are latitude and longitude are The coordinates of any point after translation are:

[0089]

[0090]

[0091] in, This is the Earth's semi-major axis.

[0092] Step 2: Planar coordinate system and mesh discretization

[0093] When performing region discrimination, the target region is set on a two-dimensional plane, with the center of the monitored area as the origin. To perform effective analysis and calculation of this region, the entire target region is discretized into a uniform grid. The grid covers a region centered at the origin with an inscribed circle of radius [missing information]. A square region. The coordinates of each point in the grid. Determined by the following formula:

[0094]

[0095] in, It defines the extent of the grid coverage; The resolution of the grid is represented by the horizontal and vertical axes in the planar coordinate system. How many grid points are there in each direction (in this embodiment, Set to 200). A generator is produced using these two parameters. × A uniform grid, covering from arrive x and y coordinates scope.

[0096] Step 3: Delineation of the total monitoring area and generation of the indicator matrix for the outer region

[0097] After discretizing the target area into a uniform grid, the primary task is to define an overall monitoring range. This step aims to ensure that all subsequent calculations and region determinations are strictly confined to a circle centered at the origin with a preset radius. Within the circular region. To accurately represent this range, an out-of-domain region indicator matrix is ​​defined.

[0098] The extra-area region indication matrix is ​​essentially a two-dimensional Boolean matrix with dimensions identical to the generated uniform grid. The value of each element in the extra-area region indication matrix (corresponding to a point in the grid) explicitly indicates whether that point is located inside or above the boundary of the defined total monitoring range (i.e., the extra-area region).

[0099] Define the out-of-domain region indicator matrix as follows:

[0100]

[0101] in, Represents the planar coordinates of any point in the grid. It is the preset inscribed circle radius. (In the function...) This is an indicator function, defined as follows: the function value is 1 when the condition within the parentheses is met; otherwise, the function value is 0.

[0102] Step 4: Calculation of the AOA sector region indicator matrix

[0103] To use angle of arrival (AOA) information for region discrimination, it is necessary to calculate the distance of each point in the grid relative to each positioning station. Theoretical AOA. Assume the current process is the first... Location stations Its plane coordinates are For any point in the grid, first calculate the distance from that grid point to the current location station. The relative vector. The x-coordinate component of this relative vector. and ordinate components They are respectively:

[0104]

[0105] Using x-axis components and ordinate components Calculate grid points relative to the current location station The theoretical AOA, denoted as The theoretical AOA is defined as the angle from the positive vertical axis to the direction of the relative vector, rotating clockwise, and ranges from 0° to 360°. The calculation formula is:

[0106]

[0107] in, The calculation measures the angle (in radians) between the relative vector and the positive x-axis, by multiplying by... Convert to degrees. Since AOA is calculated starting from the positive side of the vertical axis and in a clockwise direction, it is necessary to subtract this included angle from 90° and take the modulus of the result as 360° to ensure that the angle is within the range of 0° to 360°.

[0108] Calculate the current location station Measurement of AOA value Relative to the current location station and grid points Theoretical AOA value The angle difference between .

[0109]

[0110] It uses the difference between two angles Adding 180° and then taking the modulo of 360° normalizes the difference to the range [0°, 360°). Then, subtracting 180° from the result maps it to (...). Within the interval [180°, 180°], obtain the signed shortest angle difference. Finally, take the absolute value to ensure... It is a non-negative value, ranging from 0° to 180°, thus effectively handling the periodicity of angles.

[0111] Based on the calculation of each location station angular difference and the default AOA robustness range (In this example, the angle is set to 50°), generate the current location station. AOA sector region indicator matrix If the external region indicator matrix The distance to the origin is less than or equal to the preset radius. ,but The corresponding element is 1 (true), indicating that the point is within the total detection range; otherwise, it is 0 (false), indicating that the point is outside the total detection range. The formula for calculating the AOA sector region indicator matrix is:

[0112]

[0113] in, It is an indicator function, when the angle difference Within the allowable error range When the region is within the domain, its value is 1; otherwise, it is 0. The result of this indicator function is compared with the out-of-domain indicator matrix. Multiplication ensures that only grid points that simultaneously meet the AOA error condition and are located outside the domain are at the current positioning station. The value is 1 only in the AOA sector region indicator matrix.

[0114] Step 5: Generation of the common area indication matrix for multiple sites

[0115] After calculating the AOA sector area indication matrix for each site The next step is to determine the intersection region of the AOA sector areas. The intersection region represents the set of grid points that simultaneously satisfy the AOA measurement error range for all stations. An intersection region indicator matrix is ​​used. Indicates the intersection region.

[0116] First, the current intersection region indicator matrix Initialized as an out-of-domain region indicator matrix This means that the initial intersection area is the total monitoring range.

[0117] Then, iterate through all the location sites. The current intersection region indicator matrix AOA sector area indication matrix for each location site Perform a logical AND operation (i.e., find the intersection). The logical AND operation ensures that only grid points that are simultaneously within the current intersection region and the AOA sector of the current site are retained in the updated intersection region.

[0118] By all location sites AOA sector region indicator matrix Perform intersection operations sequentially to obtain the final intersection region indicator matrix. This indicates all location stations. The common area of ​​the AOA sector region, that is, satisfying all sites The set of grid points within the AOA measurement error range.

[0119] Step Six: Region Determination Rules

[0120] After calculating all sites Intersection region indicator matrix Then, based on the relationship between this intersection area and the preset internal area, it is determined whether the target is located in the "internal area" or the "external area". The internal area is the defined inner circular area, and the external area is the defined annular area between the inner and outer circles.

[0121] First, generate an inner circular region indicator matrix. , representing the coordinates of grid points Whether it is located inside the inner circle or on its boundary;

[0122] For cases where the positioning result is unknown, the target location is determined by forming a cone-shaped area using the angle of arrival (AOA). Each positioning station... The AOA (Area of ​​Interest) is considered as an extent, which forms a sector region in space. The vertices of the sector region are located at the positioning station. The location and sector opening angle are determined by the preset AOA robustness range. Decision made. Thus, each location station... The measured values ​​form a possible target location area.

[0123]

[0124] in, It is the preset inner circle radius. It is still an indicator function.

[0125] Next, calculate the proportion of the intersection region of the AOA sectors within the inner circle, denoted as . The calculation formula is:

[0126]

[0127] In the formula, the numerator This represents the number of grid points that simultaneously lie within the intersection region of the AOA sectors and inside the inner circle. (Denominator) This indicates the total number of grid points in the intersection region of AOA sectors.

[0128] Finally, based on the calculated proportions Compared with the preset judgment threshold The comparison is performed to determine the final region discrimination result. In this embodiment... The judgment rules are as follows:

[0129]

[0130] If the proportion of the intersection area of ​​AOA sectors within the inner circle If the value is greater than or equal to 0.55 (this threshold can be adjusted according to actual needs), the target is determined to be within the target area; otherwise, the target is determined to be outside the target area.

[0131] A target area identification system for complex electromagnetic environments includes:

[0132] The preprocessing module maps the target area on the Earth's surface onto a two-dimensional plane to form a planar coordinate system through planar projection. The center of the target area is recorded as the calculation reference point. The target area under the planar coordinate system is moved so that the calculation reference point coincides with the origin of the coordinate system. This is used to implement step 1 of the method described in this invention.

[0133] The mesh discretization module discretizes the moved target area into a uniform mesh, with the mesh coverage centered at the origin and containing an inscribed circle of radius [missing information]. A square region is used to implement step 2 of the method described in this invention;

[0134] The matrix generation module will use the origin as the center of the circle. A circular region with a radius of is taken as the outer region, i.e. the total detection range, and an outer region indicator matrix is ​​generated; an arrival corner sector region indicator matrix is ​​generated based on the outer region indicator matrix; an intersection region indicator matrix is ​​generated based on the arrival corner sector region indicator matrix, which is used to implement steps 3-5 of the method described in this invention;

[0135] The region determination module calculates the proportion of the intersection region of the reachable corner sector within the target region based on the intersection region indicator matrix, and sets a determination threshold. If the proportion is greater than or equal to the determination threshold, the target is determined to be within the target region; otherwise, the target is determined to be outside the target region. This is used to implement step 6 of the method described in this invention.

[0136] A target area identification device for complex electromagnetic environments includes:

[0137] Memory: Used to store computer programs that implement target area identification methods for complex electromagnetic environments;

[0138] Processor: Used to implement a target area identification method for complex electromagnetic environments when executing the computer program.

[0139] Experimental Analysis

[0140] To comprehensively verify the performance of the target area identification method proposed in this invention for complex electromagnetic environments, this invention compares and analyzes it with the AOA localization BRPLE algorithm. The AOA localization BRPLE algorithm is a mature bias-elimination pseudo-linear estimation (BRPLE) method, primarily used for accurate 3D AOA point localization. Although its core objective is to provide high-precision position estimation, its localization results can be transformed into region determination (i.e., determining whether the estimated point falls within a specific region), thus serving as an effective benchmark for this region identification task.

[0141] The target area identification method for complex electromagnetic environments proposed in this invention directly addresses the task of area determination, by identifying each location station... Set AOA error range Each location station The AOA measurement range is expanded to include positive and negative values ​​centered on the measurement of AOA. A cone-shaped region was analyzed, along with multiple positioning stations. By analyzing the intersection of the cone-shaped regions and the overlap with the target region, a direct determination of the target region can be made.

[0142] In target region classification under a single measurement, algorithm performance is mainly reflected in accuracy. This invention mainly compares and analyzes its own method with the AOA localization BRPLE algorithm from the perspective of influencing factors such as AOA measurement error.

[0143] The feasibility, effectiveness, and performance of this invention are verified through simulation experiments.

[0144] Assume the localization scene is constructed in a two-dimensional Cartesian coordinate system, centered at the origin (0,0). The scene contains two concentric circular regions, with an inner circle radius of 400 (meters) and an outer circle radius of 1000 (meters).

[0145] Two positioning stations are evenly distributed along the inner circle boundary. The target to be positioned... Randomly distributed throughout the scene. To simulate and verify the region recognition performance of this invention under different spatial distributions, we set up 2000 targets in this experiment. These targets to be located... The targets are uniformly distributed within a predefined region to ensure the statistical realism and universality of the simulation results, thereby enabling a comprehensive evaluation of the region identification accuracy of this invention under different target location conditions. Specifically, the following three target distribution strategies are considered: Strategy 1, some targets are located inside the inner circular region, while the remaining targets are distributed within the annular region between the inner and outer circles; Strategy 2, all targets are located inside the inner circular region; Strategy 3, outer field concentration: all targets are distributed within the annular region between the inner and outer circles.

[0146] Each location station The received data from the target to be located The signal is used to measure the Angle of Arrival (AOA). (Site) To the target The true AOA is defined as the angle between the vector pointing from the site to the target and the reference direction (0 degrees with true north as the reference, increasing clockwise), denoted as . In the simulation, the AOA measurement value will be affected by random errors. These errors are set to a positive and negative symmetrical distribution centered on the true AOA. Specifically, the AOA measurement error range is set to start from 0 degrees and increase in increments of 5 degrees to 30 degrees.

[0147] This scenario aims to simulate the challenge of region identification of targets under varying spatial distributions and measurement uncertainties. Typical scenarios include... Figure 2 As shown. For clarity, Figure 2 Only 20 targets to be located were depicted. To avoid 2000 targets to be located Visually too dense, it affects observation and identification.

[0148] Figure 3(a) shows the scenario where the target is only distributed in the inner circular area (Strategy 2), while Figure 3(b) shows the scenario where the target is only distributed in the outer area (Strategy 3). The horizontal axis of both figures represents the range of AOA measurement error (positive and negative degrees), and the vertical axis represents the accuracy of area identification (%). The blue solid line represents the present invention (the area identification technique within the AOA cone error range is referred to here as the area method because it uses the intersection area to determine the area's location), and the orange dashed line represents the "AOA Localization Method (BRPLE)". As the AOA measurement error gradually increases from 0 degrees to 30 degrees, as shown in Figure 3(a), the accuracy of both algorithms decreases with the increase of the AOA measurement error in this scenario. At zero error (0 degrees), the accuracy of the "AOA Localization Method (BRPLE)" is close to 100%, slightly higher than the present invention (approximately 92%). However, as the measurement error increases, the present invention exhibits significant robustness, and its accuracy decreases more gradually. When the measurement error reaches 30 degrees, the accuracy of this invention remains above 80%, while the accuracy of the "AOA positioning method (BRPLE)" drops sharply to around 60%, demonstrating sensitivity to larger measurement errors. This indicates that when the target is concentrated in the inner region, although the "AOA positioning method (BRPLE)" performs excellently under ideal conditions, its performance is significantly affected by measurement errors. This invention exhibits significantly higher accuracy and robustness under different measurement error conditions. Specifically, even when the AOA measurement error range reaches ±30 degrees... ±30 Even in extreme cases, the present invention can still maintain an accuracy of about 81%, which is much higher than the approximately 60% accuracy of the traditional AOA positioning method.

[0149] Figure 4 For comparing the algorithm performance of targets evenly distributed within and outside the area, under ideal error-free conditions, the "AOA localization method (BRPLE)" exhibits an accuracy rate close to 100%, indicating that under precise measurement, the AOA-based localization method can identify the target area very accurately. The accuracy rate of this invention under error-free conditions is also close to 95%, which, although slightly lower than the "AOA localization method (BRPLE)," is still at a high level.

[0150] As the AOA measurement error increases, the region identification accuracy of both algorithms shows a downward trend, which is expected, as measurement uncertainty reduces the reliability of identification. Robustness of the present invention: The blue solid line represents the present invention, which exhibits stronger robustness. Its accuracy decline trend is very gradual; even when the AOA measurement error reaches a large value of 30 degrees, its accuracy remains above 85%. This indicates that the present invention has low sensitivity to measurement errors and maintains good stability in practical applications facing inaccurate measurements. Sensitivity of the "AOA Positioning Method (BRPLE)": The orange dashed line represents the "AOA Positioning Method (BRPLE)," which is more sensitive to measurement errors. Although it performs excellently when there are no errors, its accuracy decreases more significantly with increasing errors. When the measurement error reaches 30 degrees, its accuracy has significantly decreased to around 70%. The present invention (blue solid line in the figure) also exhibits excellent robustness and consistently high accuracy in scenarios where the target is uniformly distributed inside and outside. Although its accuracy is slightly lower than that of the AOA positioning method (approximately 96%) when there is no error, its performance declines much more slowly with increasing AOA measurement error. Even when the measurement error reaches ±30... ±30 Even at this time, the accuracy of the present invention can still be maintained at about 87%, which is significantly higher than the about 70% of the "AOA positioning method (BRPLE)". This fully demonstrates that the present invention can provide a more stable and reliable area identification capability in complex electromagnetic environments and in the face of unavoidable AOA measurement errors.

Claims

1. A target area identification method for a complex electromagnetic environment, characterized by, The method comprises the following steps: Step 1, mapping a target region of a positioning scene to a two-dimensional plane by plane projection to form a plane coordinate system, taking the center of the target region as a calculation reference point, and moving the target region in the plane coordinate system to make the calculation reference point coincide with the coordinate origin; Step 2, discretize the moved target region into a uniform grid, the grid covers a square region with the origin as the center and the radius of the inscribed circle as ​ Step 3, a circular region with the origin as the center, as the radius as the out-of-domain region, i.e., the total detection range, and generate an out-of-domain region indication matrix; Step 4, generating an angle-of-arrival sector region indication matrix based on the out-of-domain region indication matrix; Step 5, generating an intersection region indication matrix based on the angle-of-arrival sector region indication matrix; Step 6, calculating the proportion of the intersection region of the angle-of-arrival sector in the target region based on the intersection region indication matrix, and setting a determination threshold, if the proportion is greater than or equal to the determination threshold, determining that the target is located in the target region, otherwise, determining that the target is located out of the target region.

2. The method of claim 1, wherein, The positioning scenario includes one positioning site and one target to be positioned , the positioning site has coordinates , , the target to be positioned has coordinates , , two concentric circular areas are defined, the center of the circle is the calculation reference point, the inner circle is the target area, the radius is , and the outer circle radius is ; Positioning sites Uniformly distributed on the inner circle border, target to be positioned Randomly distributed in the scene; Each location station The received data from the target to be located The signal is used to measure the angle of arrival and locate the station. To locate the target The true angle of arrival is defined as the angle from the location station. Point to the target to be located The angle between the vector and the reference direction is denoted as... ; The measurement error is defined as The measurement error obeys a Gaussian distribution with a mean value of and a standard deviation of or is uniformly distributed in the range The positioning station measures the angle of arrival of the signal from the target to be positioned as ​ wherein is the measurement error, with a maximum error value of degrees.

3. The method of claim 1, wherein, The longitude and latitude of the reference point calculated in step 1 are , the coordinates after planar projection are , and the coordinates of any point with longitude and latitude after translation are wherein is the Earth's semi-major axis.

4. The method of claim 1, wherein, The grid in step 2 covers a square region centered at the origin with an inscribed circle radius of The coordinates of each point in the grid are determined by the following equations: x = 2 * i - 1, y = 2 * j - 1, i, j = 1, 2, 3,... wherein, , is the radius; is the resolution of the grid, indicating how many grid points there are in the horizontal and vertical directions of the plane coordinate system; through these two parameters, a uniform grid of × is generated, covering the horizontal and vertical coordinate ranges from to .

5. The method of claim 1, wherein, The out-of-domain region indication matrix in step 3 Is defined as follows: wherein, represents the plane coordinates of any point in the grid, is a preset radius of the inscribed circle, and the function in is an indicator function, which is defined as: when the condition in the parentheses is satisfied, the function value is 1; otherwise, the function value is 0.

6. The method of claim 1, wherein, The step 4 specifically comprises the following steps: Computing the relative vector of any grid point pointing to the current positioning site with its horizontal coordinate component and its vertical coordinate component : wherein, represents the coordinates of any grid point, represents the planar coordinates of the current positioning site ; Using the horizontal component of the cross coordinate and the vertical component of the cross coordinate to calculate the theoretical angle of arrival of any grid point relative to the current fix site , denoted as : wherein the theoretical angle of arrival is defined as the angle from the positive longitudinal axis clockwise to the direction of the relative vector, ranging between 0° and 360°; is the angle between the relative vector and the positive transverse axis. calculating a current positioning site a measured angle of arrival value a theoretical angle of arrival value between the grid point and the current positioning site an angle difference : According to the calculated angle difference of each positioning station and the preset angle of arrival robustness range , the angle of arrival sector area indication matrix of the current positioning station is generated If the distance from the out-of-domain area indication matrix to the origin is less than or equal to the preset radius , then the corresponding element is 1, indicating that the point is within the total detection range; otherwise, it is 0, indicating that the point is outside the total detection range; the calculation formula of the angle of arrival sector area indication matrix is: wherein is an indicator function.

7. The method of claim 1, wherein, The step 5 specifically includes the following steps: a current positioning site The intersection region of the angle sector regions is a current intersection region, and the current intersection region is indicated by a current intersection region indication matrix It is indicated that the current intersection region indication matrix Is initialized as an out-of-domain region indication matrix ; Then, all the positioning sites are traversed The current intersection region indication matrix is logically AND operated with the angle of arrival sector region indication matrix of each positioning site is logically AND operated with the angle of arrival sector region indication matrix of each positioning site The intersection region indication matrix of all positioning sites The intersection region indication matrix of all positioning sites The intersection region indication matrix of all positioning sites The intersection region indication matrix of all positioning sites The intersection region indication matrix of all positioning sites The intersection region indication matrix of all positioning sites 8. The method of claim 1, wherein, The step 6 specifically comprises the following steps: First, an inner circle region indication matrix is generated , indicating whether the coordinates of the grid points are inside the inner circle or on the boundary ; wherein is a predetermined inner circle radius, is still an indicator function; Then, the proportion of the intersection area of the angle sector to the inner circle is calculated, denoted as The calculation formula is as follows: In the formula, the numerator represents the number of grid points located in the intersection region of the angle-of-arrival sector and the inner circle at the same time; the denominator represents the total number of grid points in the intersection region of the angle-of-arrival sector. Finally, the calculated ratio is compared with a preset decision threshold and the final region discrimination result is determined according to the following decision rule: If the proportion of the intersection area of the angle sector of arrival within the inner circle is greater than or equal to a predetermined threshold value , it is determined that the target is located within the target region; otherwise, it is determined that the target is located outside the target region.

9. A target area discrimination system for complex electromagnetic environments based on the method of any of claims 1 to 8, characterized by The method comprises: a preprocessing module, which maps a target region on the earth surface to a two-dimensional plane by plane projection to form a plane coordinate system, takes the center of the target region as a calculation reference point, and moves the target region in the plane coordinate system to make the calculation reference point coincide with the coordinate origin; The grid discretization module discretizes the moved target region into uniform grids, and the grid covers a square region with the origin as the center and a radius of the inscribed circle. The matrix generation module will use the origin as the center of the circle. A circular region with a radius of is defined as the outer region, i.e., the total detection range, and an outer region indicator matrix is ​​generated; an arrival corner sector region indicator matrix is ​​generated based on the outer region indicator matrix; and an intersection region indicator matrix is ​​generated based on the arrival corner sector region indicator matrix. a region determination module, which calculates the proportion of the intersection region of the angle-of-arrival sector in the target region based on the intersection region indication matrix, and sets a determination threshold, if the proportion is greater than or equal to the determination threshold, determining that the target is located in the target region, otherwise, determining that the target is located out of the target region.

10. A target area discrimination device for complex electromagnetic environments, characterized by The method comprises: a memory, which is used to store a computer program for implementing the target region identification method for a complex electromagnetic environment according to any one of claims 1-8; a processor, which is used to execute the computer program to implement the target region identification method for a complex electromagnetic environment according to any one of claims 1-8.

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