A method for optimizing the flight path of an aerial detection platform based on digital elevation model

Abstract

The present invention discloses a method for optimizing the route of an aerial detection platform based on a digital elevation model, and relates to the field of detection path optimization. The method for optimizing the route of an aerial detection platform based on a digital elevation model obtains digital elevation model data in a set route area; reads digital elevation model data in the set route area and performs route analysis to generate a plurality of route plans in the set route area; performs comprehensive analysis on each route plan to obtain the Doppler blind area optimization factor and coverage optimization factor of each route plan. The present invention optimizes the route plan through a particle swarm algorithm, and comprehensively considers the Doppler blind area optimization factor and the coverage optimization factor, thereby improving the task execution efficiency and detection effect, and thus effectively solving the limitations of the traditional route planning method, so that the final selected route is more in line with the actual task requirements, and the efficiency and accuracy of task execution are improved.

Description

Technical Field

[0001] The invention relates to the field of detection path optimization, and in particular to a method for optimizing the route of an aerial detection platform based on a digital elevation model. Background Art

[0002] Although ground radar and other sensor networks have a wide range of action and powerful detection capabilities, they are affected by terrain shielding and have blind spots in low-altitude areas, providing potential entry opportunities for detecting targets. At the same time, once a radar site is set, it is difficult to adjust quickly, resulting in poor maneuverability. In contrast, aerial detection platforms can effectively make up for these shortcomings. By placing radar systems on aircraft, aerial detection platforms overcome the limitations of terrain interference and the curvature of the earth, can effectively detect low-altitude targets, and provide better detection and search effects. The task area selection of aerial detection platforms is also relatively flexible, and can be flexibly deployed as needed, reducing the limitations of poor flexibility of ground radar networks.

[0003] In the prior art, traditional route planning methods usually rely on manual analysis or simple algorithms, which leads to low planning efficiency. In addition, it is difficult to effectively avoid problems such as Doppler blind spots during route planning. At the same time, some areas of the route are often difficult to be effectively covered due to terrain and other reasons, thus affecting the detection effect. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for optimizing the route of an aerial detection platform based on a digital elevation model, which solves the problem that traditional route planning methods usually rely on manual analysis or simple algorithms, which leads to low planning efficiency, and it is difficult to effectively avoid problems such as Doppler blind spots during route planning. At the same time, some areas of the route are often difficult to be effectively covered due to reasons such as terrain, thus affecting the detection effect.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for optimizing the route of an aerial detection platform based on a digital elevation model, comprising the following steps: obtaining digital elevation model data within a set route area; reading the digital elevation model data within the set route area and performing route analysis to generate a number of route plans within the set route area; performing a comprehensive analysis on each route plan to obtain a Doppler blind spot optimization factor and a coverage optimization factor for each route plan; reading the Doppler blind spot optimization factor and the coverage optimization factor of each route plan, and performing optimization analysis based on a particle swarm algorithm to obtain an optimal route plan; visualizing the optimal route plan, and outputting the processing result based on the aerial detection platform for display.

[0006] Furthermore, the digital elevation model data specifically includes the longitude value, latitude value, and elevation value of each coordinate point of the horizontal position in the set route area, and the digital elevation model data is stored in a grid form, and each grid unit contains the longitude value, latitude value, and corresponding elevation value of a coordinate point, i.e., the geographic location.

[0007] Furthermore, the specific calculation logic of the Doppler blind spot optimization factor of the route plan is as follows: obtain the Doppler threshold value, the angle between the speed direction of the route plan and the forward direction of the aerial detection platform; divide the route plan according to the same time interval to obtain the detection target track and the speed vector of the detection target at each time point of the route plan; based on the sign function, the Doppler threshold value, the angle between the speed direction of the route plan and the forward direction of the aerial detection platform, the detection target track and the speed direction vector of the detection target at each time point of the route plan, a comprehensive analysis is performed to obtain the Doppler blind spot optimization factor of the route plan.

[0008] Furthermore, the specific calculation formula of the Doppler blind spot optimization factor of the route plan is as follows: Where DpL is the Doppler blind zone optimization factor of the route plan, sign(x) is the sign function, V T (i) is the velocity vector of the detected target at the i-th time point, V is the cosine value of the angle between the velocity direction of the route at the i-th time point and the forward direction of the aerial detection platform, MD is the Doppler threshold, i=1,2,3,…,N, and N is the number of time points of the divided route.

[0009] Furthermore, the specific process of obtaining the coverage optimization factor of the route is as follows: based on the route plan, analysis is performed to obtain the coverage coefficient and average weakness coefficient of the route plan; scalar processing is performed on the coverage coefficient and average weakness coefficient of the route plan; based on the scalar processing of the coverage coefficient and average weakness coefficient of the route plan, weighted analysis is performed to obtain the coverage optimization factor of the route plan.

[0010] Furthermore, the specific calculation formula for the coverage optimization factor of the route plan is as follows: FG=α1*Fgx+α2*Pjb; wherein, FG is the coverage optimization factor of the route plan, Fgx is the coverage range coefficient of the route plan after scalar processing, Pjb is the average weakness coefficient of the route plan after scalar processing, α1 is the proportional coefficient of the set coverage range coefficient, α2 is the proportional coefficient of the set average weakness coefficient, and α1+α2=1.

[0011] Furthermore, the specific calculation process of the coverage coefficient is as follows: obtain the maximum detection area value of the aerial detection platform; evenly divide the area covered by the route into several path points; for each path point, perform detection simulation based on the aerial detection platform to obtain the simulated detection area value of each path point; perform a comprehensive analysis based on the simulated detection area value of each path point and the maximum detection area value of the aerial detection platform to obtain the coverage coefficient.

[0012] Furthermore, the specific calculation process of the average weakness coefficient is as follows: obtain the number of areas within the detection range of the aerial detection platform and the weakness index of each area; arrange the weakness indexes of the areas in descending order to obtain the maximum weakness index within the detection range of the aerial detection platform; perform mean analysis based on the weakness index of each area and the maximum weakness index within the detection range of the aerial detection platform to obtain the average weakness coefficient.

[0013] Furthermore, the Doppler blind spot optimization factor and coverage optimization factor of each route plan are read, and optimization analysis is performed based on the particle swarm algorithm to obtain the optimal route plan. Specifically, several route plans generated in the set route area are read and marked as particle swarms; fitness analysis is performed on each particle in the particle swarm based on the Doppler blind spot optimization factor and coverage optimization factor of each route plan, that is, the Doppler blind spot optimization factor and coverage optimization factor of the route plan are normalized, and weighted analysis is performed based on the Doppler blind spot optimization factor and coverage optimization factor of the normalized route plan to obtain the fitness of the route plan; iterative processing is performed based on the update rule of the particle swarm algorithm; in each iteration process, the global optimal solution is updated; the fitness analysis, iterative processing, and global optimal solution update steps are repeated until the set stop condition is met; according to the convergence result of the particle swarm algorithm, the route plan with the best performance, that is, the global optimal solution, is obtained.

[0014] Furthermore, the optimal route plan is visualized, and the processing results are output and displayed based on the aerial detection platform. Specifically, the position, heading, and speed data of the aerial detection platform are obtained, and the data of the optimal route plan, including the route path and coverage information, which may be the coordinate points of the route, the route path segments, the polygons of the coverage, etc., and the trajectory of the aerial detection platform is drawn based on the visualization tool Matplotlib; the route path and coverage are drawn based on the optimal route plan; the trajectory of the aerial detection platform and the route path and coverage of the route plan are visually combined; and the visualization combination result is output to the aerial detection platform for display.

[0015] The present invention has the following beneficial effects:

[0016] (1) The route optimization method for the aerial detection platform based on the digital elevation model can optimize the route plan through the particle swarm algorithm. By comprehensively considering the Doppler blind area optimization factor and the coverage optimization factor, the task execution efficiency and detection effect are improved, which can effectively solve the limitations of the traditional route planning method, making the final selected route more in line with the actual task requirements and improving the efficiency and accuracy of task execution.

[0017] (2) The route optimization method for the aerial detection platform based on the digital elevation model can increase the coverage of the route by optimizing the coverage optimization factor of the route plan, thereby improving the detection efficiency. The addition of the coverage optimization factor makes the route planning more focused on the comprehensive coverage of the target area, effectively improving the detection efficiency of the detection platform, and enabling the aerial detection mission to be better completed.

[0018] (3) The route optimization method for the aerial detection platform based on the digital elevation model realizes real-time optimization and adaptive adjustment of the route by adopting the particle swarm algorithm, and makes flexible adjustments according to the real-time status and mission requirements. This real-time optimization capability makes route planning more flexible and can quickly respond to changes in mission requirements, thereby improving the adaptability and response speed of the aerial detection platform.

[0019] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a flow chart of a method for optimizing a route of an aerial detection platform based on a digital elevation model.

[0021] Figure 2 The present invention is a flowchart of specific calculation steps of the Doppler blind spot optimization factor of the route plan in the method for optimizing the route of an aerial detection platform based on a digital elevation model.

[0022] Figure 3 The present invention is a flowchart of the specific steps of obtaining the coverage optimization factor of the route in the method for optimizing the route of an aerial detection platform based on a digital elevation model. DETAILED DESCRIPTION

[0023] The embodiment of the present application uses a route optimization method for an aerial detection platform based on a digital elevation model to solve the problem that traditional route planning methods usually rely on manual analysis or simple algorithms, which leads to low planning efficiency, and it is difficult to effectively avoid problems such as Doppler blind spots during route planning. At the same time, some areas of the route are often difficult to be effectively covered due to reasons such as terrain, thus affecting the detection effect.

[0024] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0025] First, the digital elevation model data in the set route area is obtained, and it is read and converted into a usable format for subsequent route analysis and optimization processing. Then, the route analysis is performed on the digital elevation model data in the set route area to generate several route plans. For each route plan, a comprehensive analysis is performed to calculate the Doppler blind area optimization factor and coverage optimization factor. The Doppler blind area optimization factor and coverage optimization factor of each route plan are read, and optimization analysis is performed based on the particle swarm algorithm to obtain the optimal route plan. Finally, the optimal route plan is visualized to obtain the position, heading, and speed data of the aerial detection platform, and the trajectory, route path, and coverage range of the aerial detection platform are drawn in combination with the data of the optimal route plan. The visualization results are output to the aerial detection platform for display.

[0026] See also Figure 1 The embodiment of the present invention provides a technical solution: a method for optimizing the route of an aerial detection platform based on a digital elevation model, comprising the following steps: obtaining digital elevation model data in a set route area based on laser radar LIDAR measurement technology; reading the digital elevation model data in the set route area and performing route analysis to generate a plurality of route plans in the set route area; performing a comprehensive analysis on each route plan to obtain a Doppler blind spot optimization factor and a coverage optimization factor of each route plan; reading the Doppler blind spot optimization factor and the coverage optimization factor of each route plan, and performing optimization analysis based on a particle swarm algorithm to obtain an optimal route plan; visualizing the optimal route plan, and outputting the processing result based on the aerial detection platform for display.

[0027] The digital elevation model data specifically refers to the longitude, latitude, and elevation values ​​of each coordinate point in the set route area with the ground as the horizontal position, and the digital elevation model data is usually stored in the form of a grid, and each grid cell contains the longitude, latitude, and corresponding elevation value of a coordinate point, i.e., the geographic location.

[0028] Specifically, Figure 2 As shown in the figure, the specific calculation logic of the Doppler blind spot optimization factor of the route plan is as follows: obtain the Doppler threshold value, the angle between the speed direction of the route plan and the forward direction of the aerial detection platform; divide the route plan according to the same time interval, and obtain the detection target track and the speed vector of the detection target at each time point of the route plan. Then, the vector of the detection target in the line of sight direction of the aerial detection platform at the i-th time point can be expressed as: in, is the vector of the detection target in the line of sight of the aerial detection platform at the i-th time point, x R(i) is the longitude value of the detection target in the digital elevation model data at the i-th time point, x r (i) is the longitude value of the aerial detection platform in the digital elevation model data at the i-th time point, y R (i) is the latitude value of the detection target at the i-th time point in the digital elevation model data, y r (i) is the latitude value of the aerial detection platform in the digital elevation model data at the i-th time point, H is the elevation value set for the aerial detection platform, and z r (i) The elevation value of the aerial detection platform at the i-th time point in the digital elevation model data, r represents the aerial detection platform, and R represents the detection target.

[0029] Then the velocity vector of the detected target at the i-th time point can be expressed as: Among them, V T (i) is the velocity vector of the detected target at the i-th time point, x T ′(i) is the derivative value of the target detected at the i-th time point in the longitude direction, y T ′(i) is the derivative value of the target detected at the i-th time point in the latitude direction, z T ′(i) is the derivative value of the detected target in the height direction at the i-th time point.

[0030] Then the cosine value of the angle between the velocity direction of the detection target and the forward direction of the aerial detection platform at the i-th time point can be expressed as: The Doppler blind spot optimization factor of the route plan is obtained through a comprehensive analysis based on the sign function, the Doppler threshold, the angle between the velocity direction of the route plan and the forward direction of the aerial detection platform, the track of the detected target at each time point of the route plan, and the velocity direction vector of the detected target.

[0031] The specific calculation formula of the Doppler blind area optimization factor of the route plan is as follows: Where DpL is the Doppler blind zone optimization factor of the route plan, sign(x) is the sign function, V T (i) is the velocity vector of the detected target at the i-th time point, V is the cosine value of the angle between the velocity direction of the route plan at the i-th time point and the forward direction of the aerial detection platform, MD is the Doppler threshold, i=1,2,3,…,N, N is the number of time points of the divided route plan; the specific sign function is:

[0032] In this implementation scheme, the Doppler threshold value and the angle between the speed direction of the route plan and the forward direction of the aerial detection platform are first obtained, and then the route plan is divided according to the same time interval to obtain the detection target track and speed vector at each time point, and then the vector of the detection target in the line of sight direction of the aerial detection platform at each time point is calculated, and then the speed vector of the detection target at each time point is calculated, and then the cosine value of the angle between the speed direction of the detection target and the forward direction of the aerial detection platform at each time point is calculated, and then a comprehensive analysis is performed based on the above data to obtain the Doppler blind spot optimization factor of the route plan, and finally, according to the value of the Doppler blind spot optimization factor and the speed direction of the route plan, the calculation formula of the coverage optimization factor is used to obtain the coverage optimization factor. This step helps to solve two key problems in the route planning of the aerial detection platform: Doppler blind spot and coverage range. First, by calculating the Doppler blind spot optimization factor, the detection blind spot caused by the radar Doppler effect can be avoided, and the detection efficiency and accuracy can be improved. Secondly, by calculating the coverage optimization factor, the route plan can be optimized to have a wider coverage range, ensuring comprehensive monitoring of the target area, which helps to improve the task execution efficiency and detection effect of the aerial detection platform.

[0033] Specifically, Figure 3 As shown, the specific process of obtaining the coverage optimization factor of the route plan is as follows: based on the route plan, analyze to obtain the coverage coefficient and average weakness coefficient of the route plan; perform scalar processing on the coverage coefficient and average weakness coefficient of the route plan; based on the scalar processed coverage coefficient and average weakness coefficient of the route plan, perform weighted analysis to obtain the coverage optimization factor of the route plan.

[0034] The specific calculation formula for the coverage optimization factor of the route plan is as follows: FG=α1*Fgx+α2*Pjb; wherein, FG is the coverage optimization factor of the route plan, Fgx is the coverage range coefficient of the route plan after scalar processing, Pjb is the average weakness coefficient of the route plan after scalar processing, α1 is the proportional coefficient of the set coverage range coefficient, α2 is the proportional coefficient of the set average weakness coefficient, and α1+α2=1.

[0035] In this implementation plan, first, the route plan is analyzed using digital elevation model data to determine the route coverage and target density distribution. This step can help us understand the basic situation of route planning, including the terrain characteristics of the coverage area, possible blind spots, and target distribution density, and provide basic data for subsequent optimization. After obtaining the coverage coefficient and average weakness coefficient of the route plan, they need to be converted into numerical form, that is, scalar processing. The purpose of this step is to quantify the coverage and weakness of the route plan for subsequent calculations and analysis. Based on the data after scalar processing, the coverage coefficient and average weakness coefficient are weighted and analyzed using a pre-set proportional coefficient to obtain the coverage optimization factor of the route plan. By comprehensively considering the coverage of the route and weakness, we can get a comprehensive evaluation index to evaluate the quality of the route plan. These steps fully consider the coverage and weakness of the route. Through quantitative analysis and weighted calculation, a comprehensive optimization factor is obtained, which can objectively evaluate the performance of the route plan, which helps to guide the route planning of the aerial detection platform and improve the execution efficiency and success rate of the detection mission. In addition, this method has a positive contribution to solving technical problems. By optimizing the route plan, we can maximize the coverage of the detection mission and reduce the existence of blind spots, thereby improving the detection efficiency and accuracy of the target. At the same time, evaluating and optimizing the weakness of the route will help improve the safety and stability of the detection mission and reduce the risk and uncertainty during the mission execution.

[0036] Specifically, the specific calculation process of the coverage coefficient is as follows: obtain the maximum detection area value of the aerial detection platform; evenly divide the area covered by the route plan into several path points; for each path point, perform detection simulation based on the aerial detection platform to obtain the simulated detection area value of each path point; perform comprehensive analysis based on the simulated detection area value of each path point and the maximum detection area value of the aerial detection platform to obtain the coverage coefficient; its calculation formula is as follows: Where Fgx is the coverage factor, Tc d is the simulated detection area value of the dth path point based on the detection simulation of the aerial detection platform, Tc Max is the maximum detection area value of the aerial detection platform, d = 1, 2, 3, …, U, and U is the number of path points in the area covered by the divided route plan.

[0037] In this implementation scheme, first, it is necessary to obtain the maximum detection area value that the aerial detection platform can cover. This value can be set according to the performance and specifications of the detection platform or obtained through field testing for subsequent analysis and calculation. The area covered by the route plan is evenly divided into several path points. The purpose of this step is to refine the coverage area of ​​the route plan into multiple points for simulated detection and calculation, so as to more accurately evaluate the coverage range. For each path point, detection simulation is performed based on the aerial detection platform to obtain the simulated detection area value of each path point. Through simulated detection, the coverage of each path point in actual detection can be determined. In order to provide data support for subsequent analysis, a comprehensive analysis is conducted based on the simulated detection area value of each path point and the maximum detection area value of the aerial detection platform to obtain the coverage coefficient. This coefficient reflects the proportional relationship between the coverage of the route plan and the maximum detection capability, and can be used to evaluate the coverage effect of the route plan. Through detailed analysis and simulated detection of the coverage of the route plan, the coverage effect of the route can be objectively and accurately evaluated, which helps to optimize route planning and improve the execution efficiency and success rate of detection tasks. In addition, this method can also help identify and solve problems such as insufficient route coverage or blind spots, and provide technical support and guarantee for the smooth execution of detection tasks.

[0038] Specifically, the specific calculation process of the average weakness coefficient is as follows: obtain the number of areas within the detection range of the aerial detection platform and the weakness index of each area; arrange the weakness indexes of the areas in descending order to obtain the maximum weakness index within the detection range of the aerial detection platform; perform mean analysis based on the weakness index of each area and the maximum weakness index within the detection range of the aerial detection platform to obtain the average weakness coefficient; its calculation formula is as follows: Among them, Pjb is the average weakness coefficient, Br q is the weakness index of the qth area within the detection range of the aerial detection platform, Br Max is the maximum weakness index within the detection range of the aerial detection platform, q=1,2,3,…,S, S is the number of areas within the detection range of the aerial detection platform.

[0039] In this implementation scheme, first, it is necessary to determine how many areas are within the detection range of the aerial detection platform and the weakness index of each area. This step can be determined by analyzing the performance of the detection platform and the detection target, and quantifying the weakness index of each area, and arranging the weakness index of the area in descending order to find the maximum weakness index within the detection range. Through this step, the most challenging area within the detection range can be determined, providing an important reference for subsequent analysis. Based on the weakness index of each area and the maximum weakness index within the detection range, a mean analysis is performed to obtain an average weakness coefficient. This coefficient reflects the average level of regional weakness within the entire detection range and can be used to evaluate the difficulty and risk of the detection task. Through the analysis and mean calculation of the regional weakness index within the detection range, the difficulty and risk of the detection task can be objectively evaluated, providing an important reference for route planning and task execution. In addition, this method can also help identify and focus on weak areas within the detection range, so as to take targeted measures to strengthen detection and improve the execution efficiency and success rate of the task.

[0040] Specifically, the Doppler blind spot optimization factor and coverage optimization factor of each route plan are read, and optimization analysis is performed based on the particle swarm algorithm to obtain the optimal route plan. Specifically, several route plans generated in the set route area are read and marked as particle swarms, and each particle corresponds to a route plan; the fitness of each particle in the particle swarm is analyzed based on the Doppler blind spot optimization factor and coverage optimization factor of each route plan, that is, the Doppler blind spot optimization factor and coverage optimization factor of the route plan are normalized, and the Doppler blind spot optimization factor and coverage optimization factor of the route plan after normalization are weighted analysis to obtain the fitness of the route plan; the calculation formula is as follows: Syd=θ1*DpL+θ2*FG; wherein Syd is the fitness of the route plan, and DpL is the weight of the route plan after normalization. Doppler blind area optimization factor, FG is the coverage optimization factor after normalization of the route plan, θ1 is the proportional coefficient of the set Doppler blind area optimization factor, θ2 is the proportional coefficient of the set coverage optimization factor, θ1+θ2=1; iterative processing is performed based on the update rule of the particle swarm algorithm, specifically, according to the update rule of the particle swarm algorithm, the speed and position of each particle are updated, and the inertia weight, individual experience and group experience are usually used to adjust the speed and position in order to find a better solution; in each iteration process, the global optimal solution is updated, and the route plan with the best fitness is recorded; the fitness analysis, iterative processing, and updating of the global optimal solution steps are repeated until the set stop condition is met, such as reaching the maximum number of iterations or reaching the convergence condition; according to the convergence result of the particle swarm algorithm, the route plan with the best performance, that is, the global optimal solution, is obtained.

[0041] In this implementation scheme, first, several route plans generated in the set route area are used as the initial solution of the particle swarm. Each particle represents a route plan. In this way, the optimal solution can be searched in the solution space. The fitness analysis is performed on each particle, that is, the route plan is evaluated according to the Doppler blind area optimization factor and the coverage optimization factor. The fitness of each particle is obtained through normalization and weighted analysis. This step helps to evaluate the quality of each solution and guide the next search direction. According to the update rules of the particle swarm algorithm, the speed and position of each particle in the particle swarm are updated. Commonly used update rules include considering inertia weight, individual experience and group experience to adjust the speed and position. In the hope of finding a better solution, the global optimal solution is updated in each iteration, that is, the route plan with the best fitness is recorded. This helps to track the best solution in the search process and provide the global optimal route plan after the algorithm converges. The steps of fitness analysis, iterative update, and updating the global optimal solution are repeated until the set stop condition is met, such as reaching the maximum number of iterations or meeting the convergence condition. Through this process, the particle swarm algorithm can be used to optimize the route plan and obtain the global optimal solution with the best performance. This method helps to find the optimal solution in complex route planning problems and improves the mission execution efficiency and success rate of the aerial detection platform.

[0042] Specifically, the optimal route plan is visualized, and the processing results are output and displayed based on the aerial detection platform. Specifically, the position, heading, and speed data of the aerial detection platform are obtained through the GPS device, and the data of the optimal route plan, including the route path, coverage range and other information, which may be the coordinate points of the route, the route path segments, the polygons of the coverage range, etc., and the trajectory of the aerial detection platform is drawn based on the visualization tool Matplotlib, which can be a moving point, line or arrow, indicating the position, heading and speed of the aerial detection platform; the route path and coverage range are drawn based on the optimal route plan, which can be a straight line, curve or polygon, indicating the route path and the covered area; the trajectory of the aerial detection platform and the route path and coverage range of the route plan are visualized and combined, the trajectory of the aerial detection platform can be drawn on the same graph, and the route path and coverage range can be displayed around it to intuitively show the effect of the optimal route plan; the visualization combination result is output to the aerial detection platform for display.

[0043] In this implementation scheme, the position, heading and speed data of the aerial detection platform are obtained through the GPS device, and the data of the optimal route plan, including information such as the route path and coverage, are read, and the trajectory of the aerial detection platform is drawn using the visualization tool Matplotlib. This can be a moving point, line or arrow, which intuitively displays the position, heading and speed of the aerial detection platform, provides real-time location information for operators, and draws the route path and coverage based on the optimal route plan. The route path can be a straight line, a curve or a polygon, and the coverage represents the area covered by the route, intuitively showing the flight path and coverage, visually combining the trajectory of the aerial detection platform with the route path and coverage, and displaying the position and route plan during the flight in the same graphic. This comprehensive visualization can intuitively show the effect of route planning, improve the visualization and real-time performance of task execution, and output the visualization results to the aerial detection platform for display. Operators can monitor the position of the aerial detection platform and the execution of the route plan in real time through the display interface, adjust the flight strategy in time, and evaluate the execution effect.

[0044] In summary, this application has at least the following effects:

[0045] Through the particle swarm algorithm, the route plan can be optimized. By comprehensively considering the Doppler blind area optimization factor and the coverage optimization factor, the task execution efficiency and detection effect are improved, which can effectively solve the limitations of the traditional route planning method, making the final selected route more in line with the actual task requirements and improving the efficiency and accuracy of task execution.

[0046] By optimizing the coverage optimization factor of the route plan, this method can increase the coverage of the route, thereby improving the detection efficiency. The addition of the coverage optimization factor makes the route planning pay more attention to the comprehensive coverage of the target area, effectively improving the detection efficiency of the detection platform, and enabling the aerial detection mission to be better completed.

[0047] By adopting the particle swarm algorithm to achieve real-time optimization and adaptive adjustment of the route, flexible adjustments can be made according to the real-time status and mission requirements. This real-time optimization capability makes route planning more flexible, able to quickly respond to changes in mission requirements, and improves the adaptability and response speed of the aerial detection platform.

[0048] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0049] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the route of an aerial detection platform based on a digital elevation model, characterized in that: The following steps are involved: Obtain digital elevation model data within the set route area; Read the digital elevation model data in the set route area and perform route analysis to generate several route plans in the set route area; Comprehensively analyze each route plan to obtain the Doppler blind area optimization factor and coverage optimization factor of each route plan; Read the Doppler blind area optimization factor and coverage optimization factor of each route plan, and perform optimization analysis based on the particle swarm algorithm to obtain the optimal route plan; Visualize the optimal route plan and output the processing results based on the aerial detection platform; The specific calculation logic of the Doppler blind area optimization factor of the route plan is as follows: Obtain the Doppler threshold value, the velocity direction of the route plan and the angle between the forward direction of the aerial detection platform; The route plan is divided into equal time intervals to obtain the track of the detected target and the velocity vector of the detected target at each time point of the route plan; Based on the sign function, Doppler threshold, the angle between the speed direction of the route plan and the forward direction of the aerial detection platform, the track of the detected target at each time point of the route plan, and the speed direction vector of the detected target, the Doppler blind area optimization factor of the route plan is obtained; The specific process of obtaining the coverage optimization factor of the route is as follows: Based on the analysis of the route plan, the coverage coefficient and average weakness coefficient of the route plan are obtained; Perform scalar processing on the coverage coefficient and average weakness coefficient of the route plan; Based on the coverage coefficient and average weakness coefficient of the route plan after scalar processing, a weighted analysis is performed to obtain the coverage optimization factor of the route plan.

2. The method for optimizing the route of an aerial detection platform based on a digital elevation model according to claim 1, characterized in that: The digital elevation model data specifically includes the longitude value, latitude value, and elevation value of each coordinate point of the horizontal position in the set route area, and the digital elevation model data is stored in a grid form, and each grid unit contains the longitude value, latitude value, and corresponding elevation value of a coordinate point, i.e., the geographic location.

3. The method for optimizing the route of an aerial detection platform based on a digital elevation model according to claim 2, characterized in that: The specific calculation formula of the Doppler blind area optimization factor of the route plan is as follows: Where DpL is the Doppler blind zone optimization factor of the route plan, sign(x) is the sign function, V T (i) is the velocity vector of the detected target at the i-th time point, V is the cosine value of the angle between the velocity direction of the route at the i-th time point and the forward direction of the aerial detection platform, MD is the Doppler threshold, i=1,2,3,…,N, and N is the number of time points of the divided route.

4. The method for optimizing the route of an aerial detection platform based on a digital elevation model according to claim 3, characterized in that: The specific calculation formula of the coverage optimization factor of the route plan is as follows: FG=α1*Fgx+α2*Pjb; Among them, FG is the coverage optimization factor of the route plan, Fgx is the coverage range coefficient of the route plan after scalar processing, Pjb is the average weakness coefficient of the route plan after scalar processing, α1 is the proportional coefficient of the set coverage range coefficient, α2 is the proportional coefficient of the set average weakness coefficient, α1+α2=1.

5. The method for optimizing the route of an aerial detection platform based on a digital elevation model according to claim 4, characterized in that: The specific calculation process of the coverage coefficient is as follows: Get the maximum detection area value of the aerial detection platform; Divide the area covered by the route evenly into several waypoints; For each path point, detection simulation is performed based on the aerial detection platform to obtain the simulated detection area value of each path point; The coverage coefficient is obtained by comprehensive analysis based on the simulated detection area value of each path point and the maximum detection area value of the aerial detection platform.

6. The method for optimizing the route of an aerial detection platform based on a digital elevation model according to claim 4, characterized in that: The specific calculation process of the average weakness coefficient is as follows: Obtain the number of areas within the detection range of the aerial detection platform and the weakness index of each area; Arrange the weakness indexes of the regions in descending order to obtain the maximum weakness index within the detection range of the aerial detection platform; The average weakness coefficient is obtained by performing a mean analysis based on the weakness index of each area and the maximum weakness index within the detection range of the aerial detection platform.

7. The method for optimizing the route of an aerial detection platform based on a digital elevation model according to claim 4, characterized in that: The Doppler blind area optimization factor and coverage optimization factor of each route plan are read, and optimization analysis is performed based on the particle swarm algorithm to obtain the optimal route plan: Read several route plans generated in the set route area and mark them as particle swarms; Based on the Doppler blind spot optimization factor and coverage optimization factor of each route plan, the fitness of each particle in the particle swarm is analyzed, that is, the Doppler blind spot optimization factor and coverage optimization factor of the route plan are normalized, and the Doppler blind spot optimization factor and coverage optimization factor of the route plan after normalization are weighted analyzed to obtain the fitness of the route plan; Iterative processing is performed based on the update rules of the particle swarm algorithm; In each iteration, the global optimal solution is updated; Repeat the steps of fitness analysis, iterative processing, and updating the global optimal solution until the set stop condition is met; According to the convergence results of the particle swarm algorithm, the route plan with the best performance, that is, the global optimal solution, is obtained.

8. The method for optimizing the route of an aerial detection platform based on a digital elevation model according to claim 1, characterized in that: The optimal route plan is visualized and the processing results are output based on the aerial detection platform as follows: Obtain the position, heading, and speed data of the aerial detection platform, and read the data for obtaining the optimal route plan, including the route path and coverage information. These data may be the coordinate points of the route, the route path segments, the polygons of the coverage, etc., and draw the trajectory of the aerial detection platform based on the visualization tool Matplotlib; Draw route paths and coverage based on the optimal route plan; The trajectory of the aerial detection platform and the route path and coverage of the route plan are visually combined; the visualization combination result is output to the aerial detection platform for display.

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