Passive positioning station site optimization calculation method and system for predicting target area
Patent Information
- Application Number
- CN202511466839.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-14
- Publication Date
- 2026-10-13
AI Technical Summary
[0003]本发明要解决的技术问题：针对现有技术的上述问题，提供一种针对预测目标区域的无源定位站址优化计算方法及系统，本发明旨在解决现有地基四站无源探测定位系统不能依据预测的目标区域自动计算最优站址的问题，提高无源探测定位系统的布站效率和探测效能
和现有技术相比，本发明主要可起到下述有益效果：（1）相比一般地面布站体制的四站无源定位系统而言，本发明方法可较好地解决地面布站选址受目标运动区域变化而无法实现高精度定位的困难。因本发明方法考虑了针对目标可能活动区域范围内平均GDOP的最小值约束，采用粒子群算法自动优化计算出站址坐标，由此，目标在预测区域范围内运动时，可确保定位的高精度。因此，对提高站址选择效率具有重要意义。（2）本发明方法在GIS地图上以目标可能活动的最大范围作对角线，生成四边形目标区域方法具有简单易于图上作业和实施的特点，对于后续布站站址坐标的优化计算发挥了重要作用。（3）本发明方法在GIS地图上确定主站位置和副站部署区域的方法，符合实际应用场景，并对减小站址坐标优化计算的计算量起到了重要作用。（4）本发明方法提出的通视计算与判定方法，为站址坐标优化计算提供了约束条件，确保了站址优化计算结果符合各个副站与主站之间及各个测站与目标之间通视无遮挡的要求。
Smart Images
Figure CN122901765A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of passive detection and positioning technology for multiple stations, specifically to a method and system for optimizing the calculation of passive positioning station sites for a predicted target area. Background Technology
[0002] Multi-station passive detection and localization technology is a technique that utilizes multiple spatially separated observation platforms to simultaneously receive and process radiation source signals, thereby determining the target's location. Currently known systems employing this technology include the Tamara and Vera-E systems. The Tamara system consists of three passive measurement stations, achieving passive localization by intercepting electromagnetic waves emitted by radar or other sources and measuring the time difference of arrival. Its effective range can reach several hundred kilometers. The two auxiliary measurement stations of the Tamara system are located on either side of the main measurement station, with a typical distance of tens of kilometers between them. The Vera-E system adds one auxiliary measurement station to the Tamara system. The ideal configuration for the Vera-E system is a star-shaped arrangement of the three auxiliary measurement stations, which ensures the system can intercept, detect, and passively localize radiation sources across the entire angular range. The Vera-E system can locate radiation sources over 200 kilometers away, but it cannot guarantee optimal localization accuracy in all directions. How to optimize the calculation of the station coordinates based on the optimal positioning accuracy of the key detection target area is a key technical problem that needs to be solved in the application and deployment of this type of passive detection and positioning system. Summary of the Invention
[0003] The technical problem to be solved by this invention is to provide a passive positioning station site optimization calculation method and system for a predicted target area, which addresses the above-mentioned problems of the prior art. This invention aims to solve the problem that the existing ground-based four-station passive detection and positioning system cannot automatically calculate the optimal station site based on the predicted target area, thereby improving the station deployment efficiency and detection performance of the passive detection and positioning system.
[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A passive positioning station site optimization calculation method for a predicted target area includes the following steps: S1, Determine the location of the main station; S2, establish a coordinate system based on the location of the main station; S3, determine the maximum range of the pre-selected station deployment area and the predicted target area, as well as the line-of-sight conditions between each station and the target point and between the main and auxiliary stations, and determine the positioning accuracy analysis model for the three-dimensional time difference positioning of the main and auxiliary stations; S4. Establish an overall positioning error model for the target area based on the positioning accuracy analysis model. The overall positioning error model for the target area uses the coordinates of the three substations as variables. S5 uses the overall positioning error model of the target area as the fitness function of the particle swarm optimization algorithm, and uses the particle swarm optimization algorithm to solve for the coordinates of the three substations with the smallest fitness function values.
[0005] Optionally, step S2 includes: taking the position of the lowest point of the main station P0 antenna in the determined main station location as the coordinate origin O, establishing a north-sky-east coordinate system O-XYZ with OX pointing north and OY pointing vertically to the sky, and the coordinates (x0, y0, z0) of the top position O' of the main station P0 antenna are (0, 0, h0), where h0 is the height of the main station P0 antenna.
[0006] Optionally, the maximum range of the pre-selected station area determined in step S3 is a quadrilateral S1S2S3S4 on the XOZ plane, with the center point of the quadrilateral S1S2S3S4 being the origin O of the reference spatial coordinate system. The coordinates of the four vertices of the quadrilateral S1S2S3S4 are S1(x1,y1,z1), S2(x2,y2,z2), S3(x3,y3,z3), and S4(x4,y4,z4), respectively. The length of the diagonal of the quadrilateral is usually greater than the preset threshold Dbz. The predicted target area determined in step S3 is a quadrilateral ABCD on the XOZ plane, with the coordinates of the four vertices of the quadrilateral ABCD being A(xa,ya,za), B(xb,yb,zb), C(xc,yc,zc), and D(xd,yd,zd), respectively, and ya=yb=yc=yd=ht, where ht is the height of the target area.
[0007] Optionally, when determining the line-of-sight conditions between each station and the target point, and between the main and auxiliary stations, in step S3, the coordinates of the three auxiliary stations are P1(xz1,yz1,zz1), P2(xz2,yz2,zz2), and P3(xz3,yz3,zz3), respectively, and y1=y2=y3=y4=0, yz1=h1, yz2=h2, yz3=h3, where h1 to h3 are the heights of the three auxiliary stations, respectively; the line-of-sight condition refers to the line-of-sight determination angle. Whether it is greater than 0, the view angle is determined. A value greater than 0 indicates unobstructed view; otherwise, it indicates obstruction and no view. The view angle is used to determine the viewing angle. The expression for the computation function is: , Let OAP be the angle between projection OAP and OA, where OAP is the line connecting the origin O and the projection point AP, and AP is the projection of point A onto the XOZ plane. Let OEP be the angle between projections OEP and OE. Projection OEP is the line connecting the origin O and the projection point EP. Projection point EP is the projection of the position E(xe,ye,ze) of the highest point of the obstacle onto the XOZ plane.
[0008] Optionally, the functional expression of the positioning accuracy analysis model for the three-dimensional time difference positioning of the primary and secondary stations in step S3 is: ; ; in, This is the geometric dilution factor for positioning accuracy. , and They are respectively The standard deviation of positioning in three directions, ~ Transformation coefficient matrix The first in The coefficients in rows 1-3 of the column, ~ Transformation coefficient matrix The coefficients in the j-th column, rows 1-3, for and The standard deviation between and The first , The standard deviation of the measurement error of the distance difference between each secondary station and the main station The standard deviation of each component of the site error is given. , , and These are the site errors. Components in three directions, for and The correlation coefficient between them and These are the points in the target area to the main station and the... , The distance difference between the secondary stations is as follows: ; in, This is a coefficient matrix related to the target and the site location. for transpose, Transformation coefficient matrix The first in OK The elements of the column.
[0009] Optionally, in step S4, the functional expression of the overall positioning error model of the target area established based on the positioning accuracy analysis model is: ; in, The overall positioning error model for the target area. Here are the coordinates of the three secondary stations. The total number of points after discretizing the target region ST. Coordinates of the three secondary stations The corresponding positioning accuracy geometric dilution factor is: ; Where (xz1,yz1,zz1), (xz2,yz2,zz2), and (xz3,yz3,zz3) are the coordinates of the three substations, respectively; the overall positioning error model of the target area established based on the positioning accuracy analysis model also includes the following constraints: ; ; in, and These represent the minimum and maximum distances between the main station and each of the secondary stations, respectively. and These are the preset minimum and maximum distance thresholds.
[0010] Optionally, when using the particle swarm optimization algorithm in step S5 to obtain the coordinates of the three substations with the smallest fitness function values, the functional expression for updating the position and velocity of the particles in the particle swarm optimization algorithm is: ; ; in, and The first The first particle The coordinate component in the first... Second and third Speed at the next iteration For the first Inertia weights in the next iteration As a self-learning factor for speed, For the group learning factor of speed, and A random number between 0 and 1 A global positioning error model for the target area of individual particles. The position of the historical minimum value. Global localization error model for the target area of the population The position of the historical minimum value. and The first The first particle The coordinate components of the particle are located at the positions of the (n+1)th and nth iterations, and the coordinate components of the particle are the coordinates of the three substations; and the update function expression for the inertial weight is: ; in, As the initial inertia weight, To reach the preset maximum number of iterations Inertial weight at time This represents the current iteration number. This represents the maximum number of iterations.
[0011] Furthermore, the present invention also provides a passive location site optimization calculation system for a predicted target area, including a microprocessor and a memory interconnected thereto, wherein the microprocessor is programmed or configured to execute the passive location site optimization calculation method for the predicted target area.
[0012] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program or instructions that are programmed or configured to execute the passive location site optimization calculation method for the predicted target area by a processor.
[0013] Furthermore, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the passive location site optimization calculation method for a predicted target area via a processor. Compared with the prior art, the present invention can mainly achieve the following beneficial effects: (1) Compared with the four-station passive positioning system of the general ground station system, the method of the present invention can better solve the difficulty of high-precision positioning due to the change of the target movement area when the ground station site selection is affected. Because the method of the present invention considers the minimum value constraint of the average GDOP within the range of the target's possible activity area, the particle swarm algorithm is used to automatically optimize and calculate the station coordinates. Thus, when the target moves within the predicted area, the high accuracy of positioning can be ensured. Therefore, it is of great significance to improve the efficiency of station selection. (2) The method of the present invention to generate a quadrilateral target area by taking the maximum range of the target's possible activity as the diagonal on the GIS map has the characteristics of being simple and easy to operate on the map and implement. It plays an important role in the subsequent optimization calculation of the station coordinates. (3) The method of the present invention to determine the location of the main station and the deployment area of the secondary station on the GIS map is in line with the actual application scenario and plays an important role in reducing the amount of calculation for the optimization calculation of the station coordinates. (4) The line-of-sight calculation and judgment method proposed in this invention provides constraints for the station coordinate optimization calculation, ensuring that the station optimization calculation results meet the requirements of unobstructed line-of-sight between each sub-station and the main station and between each measuring station and the target. Attached Figure Description
[0014] Figure 1 This is a schematic diagram of the basic process of the method in an embodiment of the present invention.
[0015] Figure 2 This is a schematic diagram of the North-East coordinate system and its related coordinates in an embodiment of the present invention.
[0016] Figure 3 This is the fitness function value curve during the iteration process in this embodiment of the invention. Detailed Implementation
[0017] This invention addresses the optimization calculation method for passive positioning stations in a predicted target area. Under constraints such as station baseline distance, line-of-sight, and station deployment area, it establishes a positioning error evaluation function based on the mean GDOP of the predicted target area. A particle swarm optimization algorithm is employed to automatically calculate the optimal station coordinates, ensuring optimal positioning accuracy in the target area. To enable those skilled in the art to better understand the technical solution of this invention, the following detailed description, in conjunction with the accompanying drawings of the embodiments, will further illustrate the technical solution of this invention.
[0018] like Figure 1 As shown, the passive positioning station site optimization calculation method for the predicted target area in this embodiment includes the following steps: S1, Determine the location of the main station; S2, establish a coordinate system based on the location of the main station; S3, determine the maximum range of the pre-selected station deployment area and the predicted target area, as well as the line-of-sight conditions between each station and the target point and between the main and auxiliary stations, and determine the positioning accuracy analysis model for the three-dimensional time difference positioning of the main and auxiliary stations; S4. Establish an overall positioning error model for the target area based on the positioning accuracy analysis model. The overall positioning error model for the target area uses the coordinates of the three substations as variables. S5 uses the overall positioning error model of the target area as the fitness function of the particle swarm optimization algorithm, and uses the particle swarm optimization algorithm to solve for the coordinates of the three substations with the smallest fitness function values.
[0019] In step S1 of this embodiment, the location of the main station is selected on the GIS map based on the approximate range of the deployment area of the passive detection and positioning system. The location is flat, open, unobstructed, and has a higher elevation in the middle that is easy for vehicles to pass through. The lowest point of the main station antenna is represented by the uppercase letter O.
[0020] See Figure 2In this embodiment, step S2 includes: taking the position of the bottom of the main station P0 antenna in the determined main station location as the coordinate origin O, establishing the North-Sky-East coordinate system O-XYZ with OX pointing north and OY pointing vertically to the sky, and the coordinates (x0, y0, z0) of the top position O' of the main station P0 antenna are (0, 0, h0), where h0 is the height (m) of the main station P0 antenna.
[0021] See Figure 2 In this embodiment, the maximum range of the pre-selected station deployment area determined in step S3 is a quadrilateral S1S2S3S4 on the XOZ plane. The center point of the quadrilateral S1S2S3S4 is the origin O of the reference spatial coordinate system, and the coordinates of the four vertices of the quadrilateral S1S2S3S4 are S1(x1,y1,z1), S2(x2,y2,z2), S3(x3,y3,z3), and S4(x4,y4,z4), respectively. The length of the diagonal of the quadrilateral is usually greater than the preset threshold Dbz (m), and the value of Dbz can be set as needed; the predicted target area determined in step S3 is quadrilateral ABCD on the XOZ plane, and the coordinates of the four vertices of quadrilateral ABCD are A(xa,ya,za), B(xb,yb,zb), C(xc,yc,zc) and D(xd,yd,zd), respectively, and ya=yb=yc=yd=ht, where ht is the height of the target area. As an optional implementation, the coordinates of the four vertices of quadrilateral S1-S4 can be taken as: S1(x1,0,z1)=S1(-26000,0,-26000); S2(x2,0,z2)=S2(26000,0,-26000); S3(x3,0,z3)=S3(26000,0,26000); S4(x4,0,z4)=S4(-26000,0,26000).
[0022] As an optional implementation, in this embodiment, on the GIS map, assuming the maximum activity range of the detected and located target is used as the diagonal, a quadrilateral ABCD is generated to represent the predicted target area. If this quadrilateral is parallel to the XOZ plane in three-dimensional space, then the coordinates of the four vertices of this quadrilateral can be set as follows: A(xa,ya,za)=A(100000,8000,-100000); B(xb,yb,zb)=B(300000,8000,-100000); C(xc,yc,zc)=C(300000,8000,100000); D(xd,yd,zd)=D(100000,8000,100000); The predicted height of the target area is ht=ya=yb=yc=yd=8000(m).
[0023] In step S3 of this embodiment, when determining the line-of-sight conditions between each measuring station and the target point, and between the main and auxiliary stations, the coordinates of the three auxiliary stations are P1(xz1,yz1,zz1), P2(xz2,yz2,zz2), and P3(xz3,yz3,zz3), respectively, and y1=y2=y3=y4=0, yz1=h1, yz2=h2, yz3=h3, where h1 to h3 are the heights of the three auxiliary stations, respectively; the line-of-sight condition refers to the line-of-sight determination angle. Whether it is greater than 0, the view angle is determined. A value greater than 0 indicates unobstructed view; otherwise, it indicates obstruction and no view. The view angle is used to determine the viewing angle. The expression for the computation function is: , Let OAP be the angle between projection OAP and OA, where OAP is the line connecting the origin O and the projection point AP, and AP is the projection of point A onto the XOZ plane. Let OEP be the angle between projections OEP and OE. Projection OEP is the line connecting the origin O and the projection point EP. Projection point EP is the projection of the highest point of the obstacle, E(xe, ye, ze), onto the XOZ plane. In the previously defined North-East coordinate system, let AP be the projection of point A onto the XOZ plane, then the coordinates of AP are (xa, 0, za) = (100000, 0, -100000); point E is the position of the highest point of obstacles such as buildings and hills, with coordinates E(xe, ye, ze) = E(60000, 4000, -60000). The projection of this point onto the XOZ plane is EP, then the coordinates of EP are (xe, 0, ze). Let the angle between projections OAP and OA be... Then we have: ; in, The height of the main station antenna; If the projection OEP and projection OAP should coincide and the angle between them and OE is β, then: .
[0024] Therefore, the angle for determining the line of sight is calculated as follows: , It can be known The target point has unobstructed line of sight. Similarly, the methods for determining line of sight between other stations and the target point, and between the three auxiliary stations and the main station, can be implemented as described above. It should be noted that due to the curvature of the Earth, the maximum distance that each station can detect to the center point of the target area is limited. , in, The height of the antenna at a certain measuring station. The height of the target center point.
[0025] In four-station time difference positioning, the spatial location of the target can be determined using the solution of a system of nonlinear equations for three-dimensional positioning. Let the coordinates of a point in the target area be... It goes to the main station and three secondary stations , Distance difference for: ; in, For points in the target area to the main station and the first The distance difference between the secondary stations For points in the target region to the first The distance between the two secondary stations Let be the distance from a point in the target area to the main station, and we have: ; ; Will and Substituting the function expression into the distance difference We can obtain: ; in, Let be an intermediate variable, and we have: ; ; ; in, and The main site and the second site respectively The distance from each secondary station to the origin. The distance from each point in the target area to the main station and the first secondary station. Distance difference between the secondary stations The time difference measurement value is calculated by subtracting the fixed time difference between the secondary station and the primary station from the secondary station's time difference.
[0026] Will and Substituting the function expression into the distance difference The resulting matrix form of the function expression is: ; , ,
[0027] The above formula can be considered as having parameters. of A system of linear equations.
[0028] In four-station time difference positioning, ranging error is inevitably introduced. To describe the relationship between positioning error and geometric position, the geometric dilution factor (GDOP) is used to describe the three-dimensional geometric distribution of positioning error, which is defined as follows: ; in, This is the geometric dilution factor for positioning accuracy. , and They are respectively The standard deviation of positioning in three directions. In a four-station positioning system, let the coefficient matrix relating the target and station locations be... At the same time, let: ; in, This is a coefficient matrix related to the target and the site location. for transpose, Transformation coefficient matrix The first in OK The elements of the column. Assume the measurement error, after system correction, has zero mean, and that the elements of the station error and the errors at different stations are uncorrelated. By analyzing the equation... Taking the differential, since each time difference measurement includes the error of the arrival time measured by the main station, the errors of each time difference measurement result are correlated. Let... for and The correlation coefficient between them. And it is assumed that the standard deviations of each component of the site error are the same, that is... Furthermore, the functional expression of the positioning accuracy analysis model for determining the three-dimensional time difference positioning of the main and secondary stations in step S3 of this embodiment can be derived as follows: ; ; in, This is the geometric dilution factor for positioning accuracy. , and They are respectively The standard deviation of positioning in three directions, ~ Transformation coefficient matrix The coefficients in the first to third rows of the i-th column, ~ Transformation coefficient matrix The coefficients in the j-th column, rows 1-3, for and The standard deviation between and The first , The standard deviation of the measurement error of the distance difference between each secondary station and the main station The standard deviation of each component of the site error is given. , , and These are the site errors. Components in three directions, for and The correlation coefficient between them and These are the points in the target area to the main station and the... , The distance difference between the secondary stations is as follows: ; in, This is a coefficient matrix related to the target and the site location. for transpose, Transformation coefficient matrix The first in OK The elements of the column.
[0029] In the selected passive detection and positioning system deployment area, the specific locations of the three auxiliary stations are unknown. To ensure the overall positioning accuracy of the target area, the target area ST is discretized using an equal probability criterion. Let the total number of points after discretization be... Then we have: ; in, This indicates rounding down. This represents the step size for discretization, which is taken here. The mean of the geometric dilution factor of positioning accuracy within the region is used to measure the overall positioning error of the target area. Therefore, in step S4 of this embodiment, the functional expression of the overall positioning error model of the target area established based on the positioning accuracy analysis model is: ; in, The overall positioning error model for the target area. Here are the coordinates of the three secondary stations. The total number of points after discretizing the target region ST. Coordinates of the three secondary stations The corresponding positioning accuracy geometric dilution factor is: ; Where (xz1,yz1,zz1), (xz2,yz2,zz2), and (xz3,yz3,zz3) are the coordinates of the three substations; the location of each passive detection and positioning system substation is within the possible deployment area on the ground, i.e. SZ represents the ground area where a passive radar station could be deployed, such as... Figure 2 As shown. Similar to the Czech Vera passive positioning system, the overall positioning error model of the target area established based on the positioning accuracy analysis model in this embodiment also includes the following constraints: ; ; in, and These represent the minimum and maximum distances between the main station and each of the secondary stations, respectively. and These are the preset minimum and maximum distance thresholds.
[0030] To minimize the overall positioning error within the target area, the coordinates of three auxiliary stations are optimized using a Particle Swarm Optimization (PSO) algorithm. Based on the study of bird flocks hunting in nature, PSO simulates the behavior of birds searching for prey, adjusting their paths based on their own experience and that of other individuals in the population, ultimately reaching the location with the most food. Compared to traditional analytical methods, PSO is simpler in principle, faster in computation, and has stronger global search capabilities. Its basic principle is as follows: a swarm of particles is initialized in the feasible solution space, each with its own position and velocity. Different particles possess individual fitness corresponding to a specified objective function. Each particle adjusts its flight trajectory based on its own optimal fitness and the population's optimal fitness, continuously moving towards the optimal point. After multiple iterations, the position corresponding to the population's historical optimal fitness (minimum fitness function value) is the optimal solution. The overall positioning error model of the target area is then calculated using the PSO algorithm. Iterative optimization enables The variable with the smallest value This is the optimal location for the passive substation.
[0031] In step S5 of this embodiment, when using the particle swarm optimization algorithm to obtain the coordinates of the three substations with the smallest fitness function values, the functional expression for updating the position and velocity of the particles in the particle swarm optimization algorithm is as follows: ; ; in, and The first The first particle The coordinate component in the first... Second and third Speed at the next iteration For the first Inertia weights in the next iteration As a self-learning factor for speed, For the group learning factor of speed, and A random number between 0 and 1 A global positioning error model for the target area of individual particles. The position of the historical minimum value. Global localization error model for the target area of the population The position of the historical minimum value. and The first The first particle The coordinate components of the particle are located at the positions of the (n+1)th and nth iterations, and the coordinate components of the particle are the coordinates of the three substations; and the update function expression for the inertial weight is: ; in, As the initial inertia weight, To reach the preset maximum number of iterations Inertial weight at time This represents the current iteration number. This represents the maximum number of iterations.
[0032] In this embodiment, step S5, which uses the Particle Swarm Optimization (PSO) algorithm to solve the problem, includes the following steps: S5.1, Population Initialization: Randomly generate a population of size... A population of individual particles Set the number of iterations for the total number of points after discretizing the target region ST, and initialize the horizontal coordinate position and coordinate movement speed of each substation in the particle item within the possible station deployment area; S5.2, based on the overall positioning error model of the target area Calculate the positioning error of each particle to obtain the historical best position of each individual particle (overall positioning error model of the target area). The historical minimum position of the value), the historical best position of the population (the overall positioning error model of the target area). The values are: the position with the lowest historical value, the individual's optimal fitness, and the population's optimal fitness. The smaller the particle's positioning error, the smaller its fitness, and the better its placement.
[0033] S5.3, update its own position and velocity according to the particle update formula of the particle swarm algorithm: ; ; Since the inertia weight reflects the magnitude of particle inertia, in the early stages of algorithm iteration, its global search capability should be enhanced to quickly find the region where the optimal solution is located. In the later stages of algorithm iteration, its local search capability should be enhanced to perform a more refined search for the optimal solution region. Therefore, to improve the algorithm's search capability, this embodiment establishes the following linearly decreasing inertia weight LDIW: ; in, As the initial inertia weight, To reach the preset maximum number of iterations Inertial weight at time This represents the current iteration number. This is the preset maximum number of iterations. Thus, as the number of iterations increases, the inertia weight decreases. In the early stages of iteration, the PSO algorithm has a stronger global search capability, while in the later stages, its local search capability is stronger. Here, the initial iteration value... Take 0.8 as the final iteration value. Taking 0.3, the PSO algorithm has good search capabilities.
[0034] S5.4, proceed to step S5.2 to update particle position and velocity, until the preset maximum number of iterations is reached. N (Can be configured as needed, for example in this embodiment) N =1000). In this embodiment, during the iteration process, the overall positioning error model of the target area is... As the fitness function of the particle swarm optimization algorithm, the obtained N The fitness function value curve during the 1000 iterations is shown below. Figure 3As shown, the particle with the smallest fitness function value obtained after iteration is the optimal station location. For example, the optimal station locations obtained in this embodiment are: main station O (0,0,0), secondary station P1 (-8.432,0,-14.627), secondary station P2 (-12.975,0,5.593), and secondary station P3 (14.555,0,4.330), in km. The corresponding overall positioning error model of the target area is shown. =3.422 km.
[0035] In summary, the main station location in this embodiment is selected from a flat, open area with a higher elevation in the center on a GIS map, based on the deployment requirements of the passive detection and positioning system. The North-Eastern (O-XYZ) coordinate system is established with the main station location as the origin, OX pointing north and OY pointing vertically to the sky. The pre-selected deployment area is a quadrilateral centered at point O in the XOZ plane, with a diagonal length typically greater than 50 km. The predicted target area is a quadrilateral region parallel to the XOZ plane that can be marked on a GIS map, and its corresponding altitude value can be determined based on the target's typical cruising altitude. The method for determining the line of sight between the main station and the secondary station, as well as between each station and the target, is the angle between the line connecting the highest points of the antennas of the main and secondary stations and the projection of that line through the highest point of a station's antenna parallel to the XOZ plane, which is greater than the angle between the highest point of the station's antenna and an obstacle. The angle between the line connecting the highest points and the line parallel to the XOZ plane projection at the highest point of the antenna at the station, or the angle between the line connecting the highest point of one antenna and the target point and the line parallel to the XOZ plane projection at the highest point of one antenna at the station, is greater than the angle between the line connecting the highest point of the antenna at the station and the highest point of the obstacle and the line parallel to the XOZ plane projection at the highest point of the antenna at the station. The passive positioning site optimization calculation method, under the above constraints, uses the mean GDOP of discrete points within the predicted target area as the positioning error evaluation function, and employs a particle swarm optimization algorithm to automatically optimize and calculate the optimal site coordinates within the selected deployment area to ensure optimal positioning accuracy within the predicted target area. This invention can achieve passive positioning site coordinate optimization calculation for the predicted target area, ensuring that the potential target area is within a high-precision positioning range to support the detection and positioning of high-value targets with electromagnetic radiation information.
[0036] Furthermore, this embodiment also provides a passive positioning station site optimization calculation system for a predicted target area, including a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to execute the passive positioning station site optimization calculation method for the predicted target area. Additionally, this embodiment provides a computer-readable storage medium storing a computer program or instructions programmed or configured to execute the passive positioning station site optimization calculation method for the predicted target area via a processor. Furthermore, this embodiment provides a computer program product, including a computer program or instructions programmed or configured to execute the passive positioning station site optimization calculation method for the predicted target area via a processor. The passive positioning station site optimization calculation system, storage medium, and product refer to a hardware platform system loaded with the aforementioned station coordinate optimization calculation software, as well as other electronic products integrating and encapsulating the algorithm software, to realize the passive positioning station coordinate optimization calculation function for a predicted target area, which can play an important role in improving the deployment efficiency of passive detection and positioning systems.
[0037] Those skilled in the art will understand that the technical solutions provided by this invention may take the form of a method, system, or computer program product. Therefore, this invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this invention may take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce an implementation of the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0038] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.
Claims
1. A passive positioning station site optimization calculation method for a predicted target area, characterized in that, Includes the following steps: S1, Determine the location of the main station; S2, establish a coordinate system based on the location of the main station; S3, determine the maximum range of the pre-selected station deployment area and the predicted target area, as well as the line-of-sight conditions between each station and the target point and between the main and auxiliary stations, and determine the positioning accuracy analysis model for the three-dimensional time difference positioning of the main and auxiliary stations; S4. Establish an overall positioning error model for the target area based on the positioning accuracy analysis model. The overall positioning error model for the target area uses the coordinates of the three substations as variables. S5 uses the overall positioning error model of the target area as the fitness function of the particle swarm optimization algorithm, and uses the particle swarm optimization algorithm to solve for the coordinates of the three substations with the smallest fitness function values.
2. The passive positioning station site optimization calculation method for the predicted target area according to claim 1, characterized in that, Step S2 includes: taking the position of the lowest point of the main station P0 antenna in the determined main station location as the coordinate origin O, establishing a north-sky-east coordinate system O-XYZ with OX pointing north and OY pointing vertically to the sky, and the coordinates (x0, y0, z0) of the top position O' of the main station P0 antenna are (0, 0, h0), where h0 is the height of the main station P0 antenna.
3. The passive positioning station site optimization calculation method for the predicted target area according to claim 2, characterized in that, The maximum range of the pre-selected station deployment area determined in step S3 is quadrilateral S1S2S3S4 on the XOZ plane. The center point of quadrilateral S1S2S3S4 is the origin O of the reference spatial coordinate system. The coordinates of the four vertices of quadrilateral S1S2S3S4 are S1(x1,y1,z1), S2(x2,y2,z2), S3(x3,y3,z3) and S4(x4,y4,z4), respectively. The length of the diagonal of this quadrilateral is usually greater than the preset threshold Dbz. The predicted target area determined in step S3 is quadrilateral ABCD on the XOZ plane. The coordinates of the four vertices of quadrilateral ABCD are A(xa,ya,za), B(xb,yb,zb), C(xc,yc,zc) and D(xd,yd,zd), respectively, and ya=yb=yc=yd=ht, where ht is the height of the target area.
4. The passive positioning station site optimization calculation method for the predicted target area according to claim 2, characterized in that, When determining the line-of-sight conditions between each station and the target point, and between the main and auxiliary stations, in step S3, the coordinates of the three auxiliary stations are P1(xz1,yz1,zz1), P2(xz2,yz2,zz2), and P3(xz3,yz3,zz3), respectively, and y1=y2=y3=y4=0, yz1=h1, yz2=h2, yz3=h3, where h1 to h3 are the heights of the three auxiliary stations, respectively; the line-of-sight condition refers to the line-of-sight determination angle. Whether it is greater than 0, the line-of-sight determination angle A value greater than 0 indicates unobstructed view; otherwise, it indicates obstruction and no view. The view determination angle is... The expression for the computation function is: , Let OAP be the angle between projection OAP and OA, where OAP is the line connecting the origin O and the projection point AP, and AP is the projection of point A onto the XOZ plane. Let OEP be the angle between projections OEP and OE. Projection OEP is the line connecting the origin O and the projection point EP. Projection point EP is the projection of the position E(xe,ye,ze) of the highest point of the obstacle onto the XOZ plane.
5. The passive positioning station site optimization calculation method for the predicted target area according to claim 1, characterized in that, In step S3, the functional expression for determining the positioning accuracy analysis model of the three-dimensional time difference positioning of the primary and secondary stations is: ； ； in, This is the geometric dilution factor for positioning accuracy. , and They are respectively The standard deviation of positioning in three directions, ~ Transformation coefficient matrix The first in The coefficients in rows 1-3 of the column, ~ Transformation coefficient matrix The coefficients in the j-th column, rows 1-3, for and The standard deviation between and The first , The standard deviation of the measurement error of the distance difference between each secondary station and the main station The standard deviation of each component of the site error is given. , , and These are the site errors. Components in three directions, for and The correlation coefficient between them and These are the points in the target area to the main station and the... , The distance difference between the secondary stations is as follows: ； in, This is a coefficient matrix related to the target and the site location. for transpose, Transformation coefficient matrix The first in OK The elements of the column.
6. The passive positioning station site optimization calculation method for the predicted target area according to claim 5, characterized in that, In step S4, the functional expression of the overall positioning error model of the target area established based on the positioning accuracy analysis model is as follows: ； in, The overall positioning error model for the target area. Here are the coordinates of the three secondary stations. The total number of points after discretizing the target region ST. Coordinates of the three secondary stations The corresponding positioning accuracy geometric dilution factor is: ； Where (xz1,yz1,zz1), (xz2,yz2,zz2), and (xz3,yz3,zz3) are the coordinates of the three substations, respectively; the overall positioning error model of the target area established based on the positioning accuracy analysis model also includes the following constraints: ； ； in, and These represent the minimum and maximum distances between the main station and each of the secondary stations, respectively. and These are the preset minimum and maximum distance thresholds.
7. The passive positioning station site optimization calculation method for the predicted target area according to claim 1, characterized in that, In step S5, when using the particle swarm optimization algorithm to obtain the coordinates of the three substations with the smallest fitness function values, the functional expression for updating the position and velocity of a particle in the particle swarm optimization algorithm is: ； ； in, and The first The first particle The coordinate component in the first... Second and third Speed at the next iteration For the first Inertia weights in the next iteration As a self-learning factor for speed, For the group learning factor of speed, and A random number between 0 and 1 A global positioning error model for the target area of individual particles. The position of the historical minimum value. Global localization error model for the target area of the population The position of the historical minimum value. and The first The first particle The coordinate components of the particle are located at the positions of the (n+1)th and nth iterations, and the coordinate components of the particle are the coordinates of the three substations; and the update function expression for the inertial weight is: ； in, As the initial inertia weight, To reach the preset maximum number of iterations Inertial weight at time This represents the current iteration number. This represents the maximum number of iterations.
8. A passive location site optimization calculation system for predicting target areas, comprising a microprocessor and a memory interconnected, characterized in that, The microprocessor is programmed or configured to execute the passive location site optimization calculation method for the predicted target area as described in any one of claims 1 to 7.
9. A computer-readable storage medium storing a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the passive location site optimization calculation method for the predicted target area as described in any one of claims 1 to 7.
10. A computer program product, comprising a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the passive location site optimization calculation method for the predicted target area as described in any one of claims 1 to 7.