CN116151105A - Air-ground collaborative detection networking deployment optimization method - Google Patents

Air-ground collaborative detection networking deployment optimization method Download PDF

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CN116151105A
CN116151105A CN202310038646.8A CN202310038646A CN116151105A CN 116151105 A CN116151105 A CN 116151105A CN 202310038646 A CN202310038646 A CN 202310038646A CN 116151105 A CN116151105 A CN 116151105A
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郭剑辉
郭冬雷
陶叔银
李伦波
濮存来
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Abstract

The invention discloses a deployment optimization method of an air-ground collaborative detection networking, which comprises the steps of firstly, establishing a mathematical model of ground radar detection sensor networking deployment; secondly, carrying out rasterization treatment on the detection area, and carrying out optimized deployment on a ground radar detection sensor network by utilizing a genetic algorithm to obtain Pareto solution; calculating the weak index of each grid of the detection area again, and further obtaining a weak matrix; then, the weak matrix is processed by utilizing breadth-first traversal ideas, and weak areas are extracted and ordered; and finally, formulating an air-based radar deployment task according to the weak area, and finally generating an air-ground collaborative detection networking deployment scheme. The invention can make up the defects of poor detection performance and scheduling maneuverability of the ground radar networking on low-altitude targets such as unmanned aerial vehicles, improves the coverage capability and electromagnetic interference resistance and anti-stealth capability of the ground radar networking in the space frequency domain, and ensures that the patrol route assignment of the space-based radar detector for the heavy-point task is more professional and strict.

Description

Air-ground collaborative detection networking deployment optimization method
Technical Field
The invention belongs to the technical field of radar sensor networking detection, and particularly relates to a deployment optimization method for air-ground collaborative detection networking.
Background
The networking radar can form an omnibearing, all-weather, three-dimensional and layering detection system through the arrangement of the positions of the radars, and has more excellent detection capability and electromagnetic interference resistance than the traditional radars. The space-based radar has excellent low-altitude detection capability and scheduling maneuverability, and has excellent performance when detecting low-small slow targets such as unmanned aerial vehicles.
The playing of the radar networking function depends on the distribution relation of each networking radar in space and frequency, and the optimized deployment is the premise and the foundation for realizing the efficiency multiplication of the radar networking detection task. At present, the research on the optimal deployment of the ground radar networking is mainly focused on the deployment of the radar network when the global is unknown, and the adopted method is mainly an enumeration method, an expert reasoning method, a heuristic algorithm and the like. The research of space-based radar deployment is mainly focused on the problem of patrol route planning of the space-based radar, and the current commonly used patrol route is mainly runway-shaped, 8-shaped, circular and the like.
Because the enumeration method and the expert reasoning method have the defects of combined explosion, low execution speed and the like, when the networking radar number is more, an optimal deployment scheme is almost impossible to obtain, so that heuristic algorithms are mostly adopted in the current field practice. Patent (application number: CN202010561560.X, application date: 2020-06-18) discloses a radar networking optimization deployment method based on an artificial bee colony algorithm, which constructs a radar deployment optimization objective function more conforming to a system, adopts the artificial bee colony algorithm, improves convergence and convergence speed of iterative optimization, but does not fully consider requirements of electromagnetic interference resistance, stealth and the like in an actual detection task. The paper (Dai Yu, wang Xianchao, shang Ziyue, zhang Yuanpeng. Early warning machine route planning [ J ] for important task route guarantee, fire and command control, 2018,43 (04): 62-65+70.) provides an early warning machine route planning method for sectional detection, which adopts runway-shaped patrol routes and utilizes particle swarm optimization algorithm to solve, so that the optimal early warning machine route planning for important task guarantee is obtained, but the patrol routes of important tasks in the method are required to be manually specified, and the method has randomness.
Disclosure of Invention
The invention aims to make up the defect of ground radar networking in low-altitude detection and scheduling mobility, solve the problems of coverage of a ground radar networking space frequency domain and low electromagnetic interference resistance and stealth resistance, and overcome the defect of randomness in patrol route assignment of important tasks of an air-borne radar.
In order to achieve the aim of the invention, the invention discloses a space-ground collaborative detection networking deployment optimization method which is characterized by comprising the following steps:
step 1, determining a ground radar networking deployment evaluation system, and establishing a mathematical model of ground radar networking deployment according to various performance requirements and constraint conditions of the evaluation system;
step 2, performing rasterization processing on the detection area, and performing optimized deployment on the ground radar networking by utilizing a genetic algorithm to obtain Pareto solution of the ground radar networking deployment;
step 3, calculating the weak index of each grid of the detection area on the basis of obtaining Pareto solution of ground radar networking deployment, and further obtaining a weak matrix of the whole detection area;
step 4, processing the weak matrix by utilizing breadth-first traversal ideas, extracting weak areas and sorting according to the area size and the weak degree;
and 5, formulating space-based radar deployment tasks according to the number of the space-based radars and the sequencing condition of the weak areas, searching the longest diagonal line of the weak areas, forming patrol routes of the space-based radars, and finally generating a space-ground collaborative detection networking deployment scheme.
Further, each performance requirement of the evaluation system is a spatial coverage coefficient, a spatial overlap coverage coefficient and a frequency interference coefficient respectively.
The airspace coverage coefficient characterizes the effective coverage capability of the ground radar to the airspace of the detection area and the key detection area, and the airspace coverage coefficient alpha is as follows:
Figure BDA0004050406020000021
wherein n is the number of ground networking radars; s is S i The detection range of the ith ground radar is the range of the detection area, S is the range of the detection area key The size of the range of the key detection area; alpha has a value of [0,1]]。
The spatial overlapping coverage coefficient characterizes the overlapping coverage rate of two adjacent radars in a detection area, the improvement of the double coverage rate of radar networking is an advantageous means for improving the electromagnetic interference resistance of the radar networking, and the spatial overlapping coverage coefficient beta is as follows:
Figure BDA0004050406020000022
wherein, the value range of beta is [0,1].
The frequency interference coefficient characterizes the interference degree of two adjacent radars on the frequency domain, the adjacent frequency band radars can have mutual interference, and the frequency interference coefficient gamma is as follows:
Figure BDA0004050406020000031
wherein f i And f j The working frequency ranges of the ith radar and the jth radar are respectively; the value range of gamma is [0,1]]。
Constraint conditions in the mathematical model are engagement coefficients, which represent the tightness of adjacent radar overlapping, the value of the engagement coefficients is limited in a certain range to ensure reasonable utilization of radar resources, and the engagement coefficients delta are shown in the following formula:
δ＝S cHR /S rHR
wherein S is cHR Overlapping detection ranges of two adjacent radars; s is S rHR For a smaller radar detection range therein; delta is in the range of 0,1]。
According to the performance requirements and constraint conditions, a mathematical model of ground radar networking deployment can be established as follows:
Figure BDA0004050406020000032
wherein w is i The weight coefficients of different height layers can be set according to the air condition quantity, the task importance and the like of the different height layers; k (k) 1 、k 2 、k 3 The weight coefficients of the performance indexes can be set according to task requirements.
Furthermore, the detection area is subjected to rasterization, whether the center of each grid falls into the radar detection range is taken as a standard, and polygonal area calculation generated by the radar range intersection set can be converted into grid center point intersection set calculation, so that the calculation amount is reduced, and the accuracy is ensured. And inputting parameters such as radar information, detection area size, key detection area size and the like into a genetic algorithm, and obtaining Pareto solution of ground radar networking deployment through iteration.
Further, the weakness index W is an index specially designed and proposed for air-ground collaborative detection networking deployment optimization, and aims to find defects of ground radar networking deployment aiming at low-speed targets (including unmanned aerial vehicles) so as to guide deployment planning of an air-based radar; calculating the weak index W of each grid of the detection area by using the Pareto solution obtained in the step 2, wherein the weak index W is formed by the airspace weak index W ky And a frequency domain weakness index W py Composition is prepared.
Further, the weakness index W is as follows:
W＝W ky +W py
airspace weakness index W ky Determined by the coverage weight of the current grid, W ky The value range of (2) is [0,1]]The following table:
number of cover weights Uncovered by One-to-one coverage Double covering Triple or above coverage
Airspace weakness index 0 0.7 0.9 1
Frequency domain weakness index W py Electromagnetic interference resistance F of frequency band kgr And frequency band anti-stealth capability F fys The composition is as follows:
W py ＝F kgr +F fys
frequency band anti-electromagnetic interference capability F kgr The formula is as follows:
Figure BDA0004050406020000041
in the method, in the process of the invention,
Figure BDA0004050406020000042
for covering the union of the k radar operating bands of the current grid, < >>
Figure BDA0004050406020000043
F for deploying the union of all the ground radar working frequency bands kgr The value range of (2) is [0,1]]；
Frequency band anti-stealth capability F fys The formula is as follows:
Figure BDA0004050406020000044
in the method, in the process of the invention,
Figure BDA0004050406020000045
to cover the anti-stealth capability score of the k radar working frequency bands of the current grid, F fys The value range of (2) is [0,1]]。
Shan Bulei anti-stealth ability scores are as follows:
operating frequency band HF VHF UHF L S C X Others
Score of 1 1 0.8 0.5 0.5 0.5 0.5 0.6
The weak indexes W of all grids are calculated to obtain a weak matrix of the whole detection area
Figure BDA0004050406020000046
Further, setting a threshold value W according to the detection task requirement min If for weak matrix
Figure BDA0004050406020000047
Any element W of (2) i Satisfy W i <W min W is then i Known as weak points; each weak point of the weak matrix is found by utilizing breadth-first traversal ideas, and adjacent weak points are combined into weak areas.
Further, each weak area is ordered in a descending order, wherein the first standard of the ordering is the area size, namely the number of weak points, and the second standard is the weak degree, namely the weak index mean value of each weak point.
Further, according to the quantity of the space-based radars and the weak area sequencing condition, a runway shape is selected by a patrol route of the space-based radars according to the principle that one space-based radar guarantees a weak area, and a space-based radar deployment task is formulated.
Further, the weak area is mapped back to the detection area grid, a patrol route of the space-based radar is formed by finding the longest diagonal line of the weak area grid, and finally, the space-ground collaborative detection networking deployment scheme is generated.
Compared with the prior art, the invention has the remarkable progress that: 1) The invention combines the ground radar networking with the space-based radar by utilizing the high-altitude detection advantage of the space-based radar, thereby making up the defect of the ground radar in low-altitude detection and scheduling mobility and improving the coverage capacity and electromagnetic interference resistance and anti-stealth capacity of the ground radar networking space-frequency domain; 2) According to the invention, the ground radar networking area obtained by using the heuristic algorithm is subjected to weakness evaluation, and key information such as a weak area is extracted to plan patrol route tasks of the space-based radar, so that patrol route assignment of the space-based radar key tasks is more professional and strict.
In order to more clearly describe the functional characteristics and structural parameters of the present invention, the following description is made with reference to the accompanying drawings and detailed description.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
fig. 1 is an overall framework diagram of a space-ground collaborative detection networking deployment optimization method.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the air-ground collaborative detection networking deployment optimization method comprises the following steps:
1) Determining a ground radar networking deployment evaluation system, and establishing a mathematical model of ground radar networking deployment according to various performance requirements and constraint conditions of the evaluation system;
2) Performing rasterization treatment on the detection area, and performing optimized deployment on the ground radar networking by utilizing a genetic algorithm to obtain Pareto solution of the ground radar networking deployment;
3) On the basis of obtaining a Pareto solution of ground radar networking deployment, calculating a weak index of each grid of a detection area, and further obtaining a weak matrix of the whole detection area;
4) Processing the weak matrix by utilizing breadth-first traversal thought, extracting weak areas and sorting according to the area size and the weak degree;
5) And formulating an air-based radar deployment task according to the number of the air-based radars and the ordering condition of the weak areas, searching the longest diagonal line of the weak areas, forming a patrol route of the air-based radars, and finally generating an air-ground collaborative detection networking deployment scheme.
In step 1, each performance requirement of the evaluation system is a spatial coverage coefficient, a spatial overlap coverage coefficient and a frequency interference coefficient respectively.
The airspace coverage coefficient characterizes the effective coverage capability of the ground radar to the airspace of the detection area and the key detection area, and the airspace coverage coefficient alpha is as follows:
Figure BDA0004050406020000061
wherein n is the number of ground networking radars; s is S i The detection range of the ith ground radar is the range of the detection area, S is the range of the detection area key The size of the range of the key detection area; alpha has a value of [0,1]]。
The spatial overlapping coverage coefficient characterizes the overlapping coverage rate of two adjacent radars in a detection area, the improvement of the double coverage rate of radar networking is an advantageous means for improving the electromagnetic interference resistance of the radar networking, and the spatial overlapping coverage coefficient beta is as follows:
Figure BDA0004050406020000062
wherein, the value range of beta is [0,1].
The frequency interference coefficient characterizes the interference degree of two adjacent radars on the frequency domain, the adjacent frequency band radars can have mutual interference, and the frequency interference coefficient gamma is as follows:
Figure BDA0004050406020000063
/>
wherein f i And f j The working frequency ranges of the ith radar and the jth radar are respectively; value range of gammaIs [0,1]。
Constraint conditions in the mathematical model are engagement coefficients, which represent the tightness of adjacent radar overlapping, the value of the engagement coefficients is limited in a certain range to ensure reasonable utilization of radar resources, and the engagement coefficients delta are shown in the following formula:
δ＝S cHR /δ rHR
wherein S is cHR Overlapping detection ranges of two adjacent radars; s is S rHR For a smaller radar detection range therein; delta is in the range of 0,1]。
According to the performance requirements and constraint conditions, a mathematical model of ground radar networking deployment can be established as follows:
Figure BDA0004050406020000071
wherein w is i The weight coefficients of different height layers can be set according to the air condition quantity, the task importance and the like of the different height layers; k (k) 1 、k 2 、k 3 The weight coefficients of the performance indexes can be set according to task requirements.
Step 2, detection zone Using [ X ] min ,X max ]×[Y min ,Y max ]Expressed in terms of (a), for example, the detection region may be defined as [0,1000 ]]×[0,600]I.e. the detection area is rectangular with a transverse dimension of 1000km and a longitudinal dimension of 600 km. The key detection area can be expressed as such, and will not be described in detail.
The detection area is rasterized, i.e. divided into a plurality of grids Deltax Deltay, the x-axis being divided into N x In parts, the y-axis is divided into N y The coordinates of the center point of any grid can be expressed as
(X min +i x Δx+Δx/2,Y min +i y Δy+Δy/2)
Wherein i is 0.ltoreq.i x <N x ；0≤i y <N y . The size of the grid can be coarsely adjusted by changing the size of deltax and deltay according to the actual task requirements.
And taking whether the center of each grid falls into the radar detection range as a standard, the polygonal area calculation generated by the radar range intersection set can be converted into the grid center point intersection set calculation, so that the calculation amount is reduced, and the accuracy is ensured.
And inputting parameters such as radar information, detection area size, key detection area size and the like into a genetic algorithm, and obtaining Pareto solution of ground radar networking deployment through iteration.
And 3, calculating the weakness index W of each grid of the detection area by using the obtained Pareto solution. The weakness index W is defined by the airspace weakness index W ky And a frequency domain weakness index W py The weak index W is an index specially designed for air-ground collaborative detection networking deployment optimization, and aims to find defects of ground radar networking deployment aiming at low and slow targets (including unmanned aerial vehicles) so as to guide deployment planning of an air-based radar. The purpose of calculating the airspace weak index is to evaluate the tightness degree of the radar networking in the airspace, and the purpose of calculating the frequency domain weak index is to evaluate the strength of the anti-electromagnetic interference and anti-stealth capability of the radar networking. The weakness index W of the individual grids is as follows:
W＝W ky +W py
in which W is ky Is the airspace weak index, W py Is a frequency domain weakness index; w has a value of [0,3 ]]。
Airspace weakness index W ky Determined by the coverage weight of the current grid, W ky The value range of (2) is [0,1]]The following table:
TABLE 1 airspace weakness index table
Number of cover weights Uncovered by One-to-one coverage Double covering Triple or above coverage
Airspace weakness index 0 0.7 0.9 1
Frequency domain weakness index W py Electromagnetic interference resistance F of frequency band kgr And frequency band anti-stealth capability F fys The composition is as follows:
W py ＝F kgr +F fys
frequency band anti-electromagnetic interference capability F kgr The formula is as follows:
Figure BDA0004050406020000081
in the method, in the process of the invention,
Figure BDA0004050406020000082
for covering the union of the k radar operating bands of the current grid, < >>
Figure BDA0004050406020000083
The method is a union set of all the working frequency bands of the ground radar; f (F) kgr The value range of (2) is [0,1]]。
Frequency band anti-stealth capability F fys The formula is as follows:
Figure BDA0004050406020000084
in the method, in the process of the invention,
Figure BDA0004050406020000085
to cover whenAnti-stealth capability scores of k radar working frequency bands of the front grid; f (F) kgr The value range of (2) is [0,1]]。
Shan Bulei anti-stealth ability scores are as follows:
table 2 single radar anti-stealth capability score table
Operating frequency band HF VHF UHF L S C X Others
Score of 1 1 0.8 0.5 0.5 0.5 0.5 0.6
Mapping the calculated weak indexes of all grids into a matrix to obtain a weak matrix of the whole detection area
Figure BDA0004050406020000086
Step 4, setting a threshold W according to task requirements min If for weak matrix
Figure BDA0004050406020000091
Any element W of (2) i Satisfy W i <W min W is then i Known as weak points. Traversing the whole weak matrix by utilizing the breadth-first traversing thought to find each weak point, combining adjacent weak points which are upper, lower, left and right into the same weak area, and finally obtaining a plurality of weak areas.
And then, sequencing each weak area in a descending order, so as to conveniently plan patrol route tasks of the space-based radar. The first criterion of the ordering is the area size, namely the number of weak points, and the second criterion is the weakness degree, namely the average of the weakness indexes of the weak points.
And 5, according to the number of the space-based radars and the weak area sequencing condition, formulating a space-based radar deployment task according to the principle that one space-based radar guarantees one weak area. This is because the space-based radar is limited in the actual mission and only the weakest area can be deployed preferentially. A runway-shaped patrol route of the space-based radar is selected, and the aim is to ensure the detection performance of the space-based radar.
The most important of runway-shaped patrol route planning is to calculate the rotation angle of the patrol route and the patrol route center, the space-based radar range can be simplified into a circle, and in order to cover the weak area to the greatest extent, the patrol route can be planned by only finding the longest diagonal line in the weak area. And mapping the weak area back to the detection area grid, connecting the centers of the edge grids of the weak area in pairs, and obtaining the longest diagonal line and forming a patrol route of the space-based radar to finally generate a space-ground collaborative detection networking deployment scheme.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (9)

1. The air-ground collaborative detection networking deployment optimization method is characterized by comprising the following steps of:
step 1, determining a ground radar networking deployment evaluation system, and establishing a mathematical model of ground radar networking deployment according to various performance requirements and constraint conditions of the evaluation system;
step 2, performing rasterization processing on the detection area, and performing optimized deployment on the ground radar networking by utilizing a genetic algorithm to obtain Pareto solution of the ground radar networking deployment;
step 3, calculating the weak index of each grid of the detection area on the basis of obtaining Pareto solution of ground radar networking deployment, and further obtaining a weak matrix of the whole detection area;
step 4, processing the weak matrix by utilizing breadth-first traversal ideas, extracting weak areas and sorting according to the area size and the weak degree;
and 5, formulating space-based radar deployment tasks according to the number of the space-based radars and the sequencing condition of the weak areas, searching the longest diagonal line of the weak areas, forming patrol routes of the space-based radars, and finally generating a space-ground collaborative detection networking deployment scheme.
2. The deployment optimization method of the air-ground cooperative detection networking according to claim 1, wherein in the step 1, each performance requirement of the evaluation system is a space domain coverage coefficient, a space domain overlapping coverage coefficient and a frequency interference coefficient, and the constraint condition of the evaluation system is a linking coefficient.
3. The air-ground collaborative detection networking deployment optimization method according to claim 1, wherein in the step 2, the detection area is subjected to rasterization, and polygonal area calculation generated by radar range intersection sets is converted into grid center point intersection set calculation by taking whether each grid center falls into a radar detection range as a standard, so that the calculation amount is reduced and the accuracy is ensured; and then inputting radar information, the size of a detection area and the size parameters of a key detection area into a genetic algorithm, and obtaining Pareto solution of ground radar networking deployment through iteration.
4. The air-ground collaborative detection networking deployment optimization method according to claim 1, wherein in the step 3, the weak index W is an index designed and proposed for air-ground collaborative detection networking deployment optimization, and aims at finding defects of ground radar networking deployment aiming at low and slow targets so as to guide deployment planning of an air-based radar; calculating the weak index W of each grid of the detection area by using the Pareto solution obtained in the step 2, wherein the weak index W is formed by the airspace weak index W ky And a frequency domain weakness index W py Composition is prepared.
5. The air-ground collaborative detection networking deployment optimization method according to claim 4, wherein the weakness index W is as follows:
W＝W ky +W py
the airspace weakness index W ky Determined by the coverage weight of the current grid, W ky The value range of (2) is [0,1]]；
The frequency domain weakness index W py Electromagnetic interference resistance by frequency bandCapacity F kgr And frequency band anti-stealth capability F fys The composition is as follows:
W py ＝F kgr +F fys
the electromagnetic interference resistance capability F of the frequency band kgr The formula is as follows:
Figure FDA0004050406010000021
in the method, in the process of the invention,
Figure FDA0004050406010000022
for covering the union of the k radar operating bands of the current grid, < >>
Figure FDA0004050406010000023
F for deploying the union of all the ground radar working frequency bands kgr The value range of (2) is [0,1]]；
The frequency band anti-stealth capability F fys The formula is as follows:
Figure FDA0004050406010000024
in the method, in the process of the invention,
Figure FDA0004050406010000025
to cover the anti-stealth capability score of the k radar working frequency bands of the current grid, F fys The value range of (2) is [0,1]]；/>
The weak indexes W of all grids are calculated to obtain a weak matrix of the whole detection area
Figure FDA0004050406010000026
6. The air-ground collaborative detection networking deployment optimization method according to claim 3, wherein in step 4, a threshold is set according to detection task requirementsValue W min If for the weak matrix
Figure FDA0004050406010000027
Any element W of (2) i Satisfy W i ＜W min W is then i Known as weak points; each weak point of the weak matrix is found by utilizing breadth-first traversal ideas, and adjacent weak points are combined into weak areas.
7. The method for deployment optimization of air-ground collaborative detection networking according to claim 1, wherein in step 4, each weak area is ordered in descending order, the first criterion of the ordering is the area size, namely the number of weak points, and the second criterion is the weak degree, namely the weak index mean value of each weak point.
8. The space-ground collaborative detection networking deployment optimization method according to claim 1 is characterized in that in step 5, a runway shape is selected for a patrol route of the space-based radar according to the number of the space-based radars and the weak area sequencing condition and the principle that one weak area is guaranteed by one space-based radar, and a space-based radar deployment task is formulated.
9. The air-ground collaborative detection networking deployment optimization method according to claim 1 is characterized in that in step 5, a weak area is mapped back to a detection area grid, a patrol route of an air-based radar is formed by finding the longest diagonal of the weak area grid, and finally an air-ground collaborative detection networking deployment scheme is generated.
CN202310038646.8A 2023-01-12 2023-01-12 Air-ground collaborative detection networking deployment optimization method Pending CN116151105A (en)

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Publication number Priority date Publication date Assignee Title
CN117310679A (en) * 2023-11-28 2023-12-29 中国人民解放军空军工程大学 Gridding sensing system for detecting low-low aircraft

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117310679A (en) * 2023-11-28 2023-12-29 中国人民解放军空军工程大学 Gridding sensing system for detecting low-low aircraft
CN117310679B (en) * 2023-11-28 2024-02-20 中国人民解放军空军工程大学 Gridding sensing system and method for detecting low-low aircraft

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