Regional coverage evaluation method for reconnaissance capability of unmanned aerial vehicle cluster system

Abstract

The invention belongs to the technical field of unmanned aerial vehicle cluster reconnaissance capability evaluation, and provides an unmanned aerial vehicle cluster system reconnaissance capability area coverage evaluation method, which comprises the following steps: constructing a single machine coverage probability model based on an unmanned aerial vehicle reconnaissance range and a target appearance position; calculating a single-machine coverage probability based on a single-machine coverage probability model; based on the Markov chain, evaluating the # imgabs0 # re-coverage probability of the unmanned aerial vehicle cluster system; based on the characteristics of the unmanned aerial vehicle reconnaissance assembly, constructing a single-machine reconnaissance assembly execution degree change rule And evaluating the area coverage of the unmanned aerial vehicle cluster system reconnaissance capability based on the execution degree change rule of the single-machine reconnaissance component. According to the method, for the given target area range and the unmanned aerial vehicle reconnaissance range, the minimum number of the unmanned aerial vehicles needing to be input is obtained, it is ensured that the cluster has the good area coverage capacity, the executability level of the reconnaissance capacity of the whole unmanned aerial vehicle cluster system can be dynamically evaluated in real time, the feasibility is higher, and the applicability is wider.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) swarm reconnaissance capability assessment, and in particular to a method for assessing the regional coverage of the reconnaissance capability of an UAV swarm system. Background Art

[0002] Reconnaissance and detection is one of the main functions of drones. It is generally believed that the detection area of ​​a drone is a radius of disc-shaped area.

[0003] The availability of the drone cluster is expressed as the number of drones that can perform reconnaissance and meet the requirements, that is, the executability. For a large target area, assuming that it is a radius of Circular area, and ≥ Reconnaissance of a target area using only one or a few drones would be time-consuming. However, by increasing the number of drones to form a swarm system, saturation reconnaissance of the target area can be achieved, giving the swarm system greater regional coverage. However, since the target's location within the entire area is random, the reconnaissance range of each drone varies, and drones dynamically change their positions during reconnaissance, some target areas may not be covered by the detection areas of sufficient drones, thus affecting the actual reconnaissance capability of the swarm.

[0004] Therefore, in order to ensure that the UAV swarm system has good regional coverage capabilities and reduce the possibility of missing targets, it is urgent and necessary to seek a regional coverage evaluation method for the reconnaissance capability of the UAV swarm system to achieve fast evaluation calculation speed and real-time dynamic evaluation of the feasibility level of the UAV swarm. Summary of the Invention

[0005] In view of the above-mentioned defects in the existing technology, the present invention proposes a method for evaluating the regional coverage of the reconnaissance capability of a drone swarm system. The method includes building a single-machine coverage probability model based on the drone reconnaissance range and the target appearance location; calculating the single-machine coverage probability based on the single-machine coverage probability model; and evaluating the coverage of the drone swarm system based on the Markov chain. The proposed method calculates the probability of re-coverage; based on the characteristics of drone reconnaissance components, a model for the variation of the execution rate of individual drone reconnaissance components is constructed; and based on the variation of the execution rate of individual drone reconnaissance components, the regional coverage of the drone swarm system's reconnaissance capability is evaluated. Given a given target area and drone reconnaissance range, this method determines the minimum number of drones required, ensuring the swarm has good regional coverage and reducing the likelihood of missed targets. It can assess the executability of the entire drone swarm system's reconnaissance capability, features fast calculation speed, enables real-time dynamic evaluation, and offers greater feasibility and applicability.

[0006] The present invention provides a method for evaluating the regional coverage of the reconnaissance capability of a UAV swarm system, which comprises the following steps:

[0007] S1. Build a single-machine coverage probability model based on the UAV reconnaissance range and target appearance location;

[0008] S11. Calculate the first The distance between the drone and the center of the target area :

[0009] (1)

[0010] in, Indicates the coordinates of the center of the target area; Indicates the The coordinates of the drone's location; Indicates the drone number and , Indicates the number of drones;

[0011] S12, based on the target location and the location of the UAV, calculate the The distance between the drone and the target's location :

[0012] (2)

[0013] in, Indicates the coordinates of the target's location;

[0014] S13. Construct a single-machine coverage probability model: Let the event ; Indicates the The detection area radius of the reconnaissance component of the drone; Indicates the radius of the target area;

[0015] S131, if , then a single-aircraft coverage probability model is constructed based on the circular flight area;

[0016] S132, if , then a single-aircraft coverage probability model is constructed based on the circular flight area;

[0017] S2. Calculate the single-machine coverage probability based on the single-machine coverage probability model;

[0018] S3. Evaluate the UAV swarm system based on Markov chain Recover probability;

[0019] S4. Based on the characteristics of the UAV reconnaissance component, a model of the execution degree change of the single-machine reconnaissance component is constructed; let the random process Represents the time-varying trends of the characteristics of the UAV reconnaissance components; Indicates the minimum acceptance level for the characteristic;

[0020] S41, for the A drone with a reconnaissance component in Probability of being available at all times for:

[0021] (13);

[0022] S42, when a target appears in the area, it can be The probability of being discovered by a drone Expressed as:

[0023] (14)

[0024] in, represents the constant impact factor; Representing an event Probability of occurrence;

[0025] S5. Evaluate the regional coverage of the UAV swarm reconnaissance capability based on the model of the change in the execution rate of the single-machine reconnaissance component;

[0026] S51. For a The executability of a drone swarm is defined as the number of remaining drones capable of performing optoelectronic reconnaissance meeting the requirement of The minimum number of drones required for re-coverage is expressed as:

[0027] (15)

[0028] in, Indicates the minimum value; express Recover probability; express Recover probability threshold;

[0029] S52, Order Indicates whether the drone's reconnaissance function is executed or not. If it cannot be executed, then ,otherwise ;

[0030] S53, use Replace the expression (11) , obtain regional coverage of drone swarm system reconnaissance capabilities :

[0031] (16)

[0032] in, It means "before the first drone joins the drone swarm system, the target is covered by 0 drones"; Indicates " After a drone joins the drone swarm system, the target is at least More than 100 drones are covered"; Indicates a continuous multiplication operation; Represents the probability transition matrix of the Markov chain.

[0033] Furthermore, the step S131 specifically includes the following steps:

[0034] S1311, Order Event , then the event The probability of occurrence is :

[0035] (3);

[0036] S1312, Event Next The probability that a drone can cover the target Equivalent to the drone reconnaissance range area Target range area The ratio is:

[0037] (4);

[0038] S1313, based on expressions (3) and (4), event Single machine coverage probability for:

[0039] (5)

[0040] in, Indicates coverage angle; represents differential;

[0041] The step S132 specifically includes the following steps:

[0042] S1321, Order Event , then the event The probability of occurrence is :

[0043] (6);

[0044] S1322, Event Next The probability that a drone can cover the target Equivalent to the fan-shaped area of ​​the drone's reconnaissance range Target range area The ratio is:

[0045] (7);

[0046] S1323. Based on expressions (6) and (7), the event Single machine coverage probability for:

[0047] (8).

[0048] Preferably, the step S2 specifically includes the following steps:

[0049] S21. Considering the event and events Mutually exclusive, when a target appears, it can be The probability of being covered by a drone, that is, the event Probability of occurrence for:

[0050] (9);

[0051] S22, based on The detection area radius of the reconnaissance component of the drone and the radius of the target area , and obtain the numerical solution of expression (9).

[0052] Preferably, the step S3 specifically includes the following steps:

[0053] S31. Let the Markov chain Indicates " The state space of the Markov chain is a pseudo random process of "the number of times any target is covered by a drone when a drone joins the drone cluster". for:

[0054] (10)

[0055] in, Indicates that the target is at least According to the status covered by the above drones, is a positive integer;

[0056] S32. Considering the probability of event B , then the probability transfer matrix of the Markov chain is for:

[0057] (11);

[0058] S33, Computing UAV Cluster System Recover probability :

[0059] (12).

[0060] Preferably, when the performance change rules of different drones in step S41 are the same, different Under the value of The value of is the same; when the performance change rules of different drones are different, different Under the value of The values ​​are different.

[0061] Preferably, in step S53 ; ; Represents a vector transpose operation.

[0062] Compared with the prior art, the technical effects of the present invention are:

[0063] 1. The present invention designs a method for evaluating the regional coverage of the reconnaissance capability of a drone swarm system. For a given target area range and drone reconnaissance range, the method obtains the minimum number of drones required to "re-cover" the target area. The method can plan the number of drones to be dispatched before the drone swarm system performs a mission, ensuring that the drone swarm system has good regional coverage capabilities and reducing the possibility of missing targets.

[0064] 2. The present invention designs a method for evaluating the regional coverage of the reconnaissance capability of a drone swarm system. This method can evaluate the feasibility of the reconnaissance capability of the entire drone swarm system based on the fluctuations in the key performance characteristics of the optoelectronic reconnaissance components of each drone and the minimum number of drones required to meet "heavy coverage".

[0065] 3. The regional coverage assessment method for the reconnaissance capability of a drone cluster system designed by the present invention has a fast calculation speed and can be embedded into a drone cluster cluster head decision algorithm to dynamically evaluate the executability level of the drone cluster in real time, thereby providing a basis for autonomous decision-making on whether the cluster should continue to perform subsequent reconnaissance missions. It has stronger feasibility and wider applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Other features, objects and advantages of the present application will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings.

[0067] Figure 1 This is a flow chart of the regional coverage evaluation method of the UAV swarm system reconnaissance capability of the present invention;

[0068] Figure 2 This is a schematic diagram of the regional coverage of a drone cluster system in a specific embodiment of the present invention;

[0069] Figure 3 In a specific embodiment of the present invention Schematic diagram of heavy coverage;

[0070] Figure 4 In a specific embodiment of the present invention, the event Schematic diagram of;

[0071] Figure 5 In a specific embodiment of the present invention, the event Schematic diagram of;

[0072] Figure 6 In a specific embodiment of the present invention Curve graph showing how the re-coverage probability changes with the number of drones;

[0073] Figure 7 In a specific embodiment of the present invention Simulation verification result diagram of re-coverage probability;

[0074] Figure 8 It is a diagram showing the dynamic evaluation results of the feasibility of the reconnaissance capability of the UAV cluster system in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0075] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0076] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0077] Figure 1 The present invention shows a method for evaluating the regional coverage of the reconnaissance capability of a drone swarm system, which includes the following steps:

[0078] S1. Based on the UAV reconnaissance range and target appearance location, a single-machine coverage probability model is constructed.

[0079] S11. Calculate the first The distance between the drone and the center of the target area :

[0080] (1)

[0081] in, Indicates the coordinates of the center of the target area; Indicates the The coordinates of the drone's location; Indicates the drone number and , Indicates the number of drones.

[0082] S12, based on the target location and the location of the UAV, calculate the The distance between the drone and the target's location :

[0083] (2)

[0084] in, Indicates the coordinates of the target's appearance location.

[0085] S13. Construct a single-machine coverage probability model.

[0086] like Figure 2 As shown, let the event ; Indicates the The detection area radius of the reconnaissance component of the drone; Indicates the radius of the target area.

[0087] S131, such as Figure 3 As shown, if , a single-aircraft coverage probability model is constructed based on the circular flight area.

[0088] S1311, Order Event , then the event The probability of occurrence is :

[0089] (3).

[0090] S1312, Event Next The probability that a drone can cover the target Equivalent to the drone reconnaissance range area Target range area The ratio is:

[0091] (4).

[0092] S1313, based on expressions (3) and (4), event Single machine coverage probability for:

[0093] (5)

[0094] in, Indicates coverage angle; Represents differential.

[0095] S132, such as Figure 4 As shown, if , a single-aircraft coverage probability model is constructed based on the circular flight area.

[0096] S1321, Order Event , then the event The probability of occurrence is :

[0097] (6).

[0098] S1322, Event Next The probability that a drone can cover the target Equivalent to the fan-shaped area of ​​the drone's reconnaissance range Target range area The ratio is:

[0099] (7).

[0100] S1323. Based on expressions (6) and (7), the event Single machine coverage probability for:

[0101] (8).

[0102] S2. Calculate the single-machine coverage probability based on the single-machine coverage probability model.

[0103] S21. Considering the event and events Mutually exclusive, when a target appears, it can be The probability of being covered by a drone, that is, the event Probability of occurrence for:

[0104] (9).

[0105] S22, based on The detection area radius of the reconnaissance component of the drone and the radius of the target area , and obtain the numerical solution of expression (9).

[0106] S3. Evaluate the UAV swarm system based on Markov chain Recover probability.

[0107] S31. Let the Markov chain Indicates " The state space of the Markov chain is a pseudo random process of "the number of times any target is covered by a drone when a drone joins the drone cluster". for:

[0108] (10)

[0109] in, Indicates that the target is at least According to the status covered by the above drones, Is a positive integer.

[0110] S32. Considering the probability of event B , then the probability transfer matrix of the Markov chain is for:

[0111] (11).

[0112] S33, Computing UAV Cluster System Recover probability :

[0113] (12).

[0114] in, means "before the first drone joins the drone swarm system, the target is covered by 0 drones", and ; Indicates " After a drone joins the drone swarm system, the target is at least More than 100 drones are covered", and ; Represents vector transpose; Represents a continuous multiplication operation.

[0115] S4. Based on the characteristics of the UAV reconnaissance component, a model of the execution change of the single-machine reconnaissance component is constructed; for different objects and different usage scenarios, it is used to characterize the change of the characteristics of the UAV reconnaissance component over time. are different.

[0116] Let the random process Represents the time-varying trends of the characteristics of the UAV reconnaissance components; Indicates the minimum acceptance level for this characteristic.

[0117] S41. Since the performance of each drone reconnaissance component degrades over time during the reconnaissance process, some drone detection functions may be unavailable during use. A drone with a reconnaissance component in Probability of being available at all times for:

[0118] (13).

[0119] When the performance change rules of different drones are the same, Under the value of The value of is the same; when the performance change rules of different drones are different, different Under the value of The values ​​are different.

[0120] S42, when a target appears in the area, it can be The probability of being discovered by a drone Expressed as:

[0121] (14)

[0122] in, Represents a constant impact factor.

[0123] S5. Based on the variation pattern of the execution degree of the single-machine reconnaissance component, evaluate the regional coverage of the UAV swarm system's reconnaissance capability.

[0124] S51. For a The executability of a drone swarm is defined as the number of remaining drones capable of performing optoelectronic reconnaissance meeting the requirement of The minimum number of drones required for re-coverage is expressed as:

[0125] (15)

[0126] in, Indicates the minimum value; express Recover probability; express Overcoverage probability threshold.

[0127] S52, Order Indicates whether the drone's reconnaissance function is executed or not. If it cannot be executed, then ,otherwise .

[0128] S53, use Replace the expression (11) , obtain regional coverage of drone swarm system reconnaissance capabilities :

[0129] (16).

[0130] An important point of the present invention is to propose " The "recovery probability" indicator is denoted as: The connotation of this indicator is: a The unmanned swarm system consisting of 100 drones can ensure that every point in the target area is at least The probability of being covered by the reconnaissance range of more than one UAV. Figure 5 As shown, when a point is When the imaging range of the drones is covered at the same time, it is called "double coverage / triple coverage / quadruple coverage". When the acceptable value of the "recovery probability" is set, it is recorded as: , then , determine the minimum number of deployed nodes required to ensure the regional coverage of the drone cluster system meets the requirements .

[0131] In one embodiment, an unmanned cluster includes UAVs, the target area radius is (km), assuming that all drones have the same reconnaissance components and the same reconnaissance range. (km); When a target appears in the When a drone is within its reconnaissance range, its detectable constant influence factor is If the performance of the drone's reconnaissance component changes in accordance with the Poisson process, it is advisable to make its arrival rate a constant (min), then the probability of reconnaissance component availability can be expressed as Assuming that the requirements are met The re-coverage probability threshold is , solve: ①When When you want to satisfy Recover probability threshold , at least the number of drone clusters that need to be dispatched ,②When and The variation law of the executability of the unmanned swarm system's reconnaissance capability over time is given.

[0132] S1. Based on the UAV detection area and target location, a single-machine coverage probability model is constructed. (km) and (km), we can get: , .about The numerical calculation problem can be solved by using Matlab based on the expression, which will not be described here.

[0133] S2. Calculate the single-machine coverage probability based on the single-machine coverage probability model. In a specific embodiment, and For other values, the single-machine coverage probability The values ​​of are shown in Table 1.

[0134]

[0135] Table 1

[0136] S3. Evaluate the UAV swarm system based on Markov chain Recover probability. (km) and (km) , according to the Markov chain From the definition of , we can see that:

[0137] when When , its probability transfer matrix is:

[0138] (17).

[0139] when When , its probability transfer matrix is:

[0140] (18).

[0141] when When , its probability transfer matrix is:

[0142] (19).

[0143] when When , its probability transfer matrix is:

[0144] (20).

[0145] make ,Right now , can be satisfied Minimum number of drones required for heavy coverage .like Figure 6As shown, the following conclusions can be drawn:

[0146] When put into When a drone is deployed, it can ensure that 95% of the target area can be covered by at least one drone;

[0147] When put into When two drones are deployed, it can ensure that 95% of the target area can be covered by at least two drones;

[0148] When put into When deploying drones, it can ensure that 95% of the target area can be covered by at least 3 drones;

[0149] When put into When 4 drones are deployed, it can be guaranteed that 95% of the target area can be covered by at least 4 drones.

[0150] In order to verify the correctness of the proposed method, a simulation was conducted based on the Monte Carlo method in a specific embodiment. The basic idea is: first, Randomly put into the circle The points represent drones, followed by the points with a radius of Randomly select from the circle The points represent the location where the target appears, and then for each target point , calculate the distance between it and the drone point ,in , and finally compare and The size of the target point Whether it was a drone The simulation results are as follows: Figure 7 As shown, among which, The simulation calculation idea of ​​"recovery probability" is:

[0151] If for all , If both are true, it means that the target has not been covered and is marked as “black” in the figure;

[0152] If for all , there is exactly one point that can make If both are true, it means that the target is covered by exactly one drone, and it is marked as “white gray” in the figure;

[0153] If for all , there are exactly 2 points that can make If both are true, it means that the target is covered by two drones and is marked as “gray” in the figure;

[0154] If for all , there are exactly 3 points that can make If all of them are true, it means that the target is covered by exactly three drones and is marked as “dark gray” in the figure;

[0155] If for all , there are more than 4 points to make If all of the above are true, it means that the target can be covered by at least 4 drones, and it is marked as “light gray” in the figure.

[0156] by Figure 7 Taking the upper right corner of the sub-graph as an example, it is calculated that in order to ensure that each target point in the target area is covered by at least 2 drones, at least 70 drones should be deployed to form a cluster system. In the simulation verification, the points that do not meet the "double coverage" requirement are the points in the "black" and "white-gray" areas. According to statistics, they account for 10% of the total The proportion of points is about 4.1%, indicating that about 95.9% of the area can meet the "double coverage" requirement. Similar explanations can be given for other verification results, which will not be repeated here. The simulation results show that the present invention gives the calculation The method is basically accurate.

[0157] S4. Based on the characteristics of the UAV reconnaissance component, a model of the execution degree change of the single-machine reconnaissance component is constructed. The "recovery probability" only solves the problem of the coverage of the target area by the drone cluster. To achieve target reconnaissance, it is also necessary to ensure that the drone reconnaissance component is available. In a specific embodiment, it is assumed that the probability of a single drone reconnaissance component being available is expressed as .

[0158] S5. Based on the change of execution degree of single-machine reconnaissance components, evaluate the regional coverage of the UAV swarm system reconnaissance capability. Heavy coverage, a drone cluster contains drones (greater than the minimum requirement The constant influencing factor of the target being able to be found after it appears is , so the probability that a drone can find any randomly appearing target is:

[0159] (twenty one).

[0160] Therefore, according to , we can obtain the change law of the executability of the UAV cluster reconnaissance capability over time, such as Figure 8 shown.

[0161] At the initial moment, due to the existence of the discovery constant influencing factor, the drone cluster has about a 95% probability of being able to detect targets appearing in the area. As time goes by, the availability probability of a single drone reconnaissance component gradually decreases, causing the executability of the entire cluster to degrade. (min), if two drone reconnaissance components fail, the feasibility of the drone swarm system will drop instantly. (min), due to the destruction of three drones, the feasibility of the entire cluster system has been significantly reduced. After that, assuming that no drones are damaged, (min), the feasibility of the drone swarm reconnaissance capability can still be maintained at around 55%.

[0162] The present invention designs a method for evaluating the regional coverage of the reconnaissance capability of a drone swarm system. For a given reconnaissance area range and drone photoelectric detection imaging range, the method obtains the minimum number of drones required to re-cover the target area. The method can plan the number of drones to be dispatched before the drone swarm performs a mission, ensuring that the drone swarm has good regional coverage capability and reducing the possibility of missing targets. The method can evaluate the executability level of the reconnaissance capability of the entire drone swarm based on the fluctuations of the key performance characteristics of each drone photoelectric reconnaissance group and the minimum number of drones required to meet the re-coverage requirement. The method has a fast calculation speed and can be embedded into a drone cluster head decision algorithm to dynamically evaluate the executability level of the drone cluster in real time, thereby autonomously deciding whether the cluster should continue to perform subsequent reconnaissance missions. The method has stronger feasibility and wider applicability.

[0163] Finally, it should be noted that the above embodiments are only intended to illustrate rather than limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating the regional coverage of the reconnaissance capability of a drone swarm system, characterized in that: It includes the following steps: S1. Build a single-machine coverage probability model based on the drone reconnaissance range and target appearance location; S11. Calculate the first The distance between the drone and the center of the target area : （1） in, Indicates the coordinates of the center of the target area; Indicates The coordinates of the drone’s location; Indicates the drone number and , Indicates the number of drones; S12. Calculate the first The distance between the drone and the target location : （2） in, Indicates the coordinates of the target's location; S13. Construct a single-machine coverage probability model: Let the event ; Indicates The detection area radius of the reconnaissance component of the drone; Indicates the radius of the target area; S131, if , then a single-aircraft coverage probability model is constructed based on the circular flight area; S132, if , then a single-aircraft coverage probability model is constructed based on the circular flight area; S2. Calculate the single-machine coverage probability based on the single-machine coverage probability model; S3. Evaluate the UAV swarm system based on Markov chain Recover probability; S4. Based on the characteristics of the UAV reconnaissance component, a model of the execution degree change of the single-machine reconnaissance component is constructed; let the random process Represents the time-varying trends of the characteristics of the drone reconnaissance components; Indicates the minimum acceptance level for the characteristic; S41, for A drone with a reconnaissance component Probability of being available at any time for: （13）； S42, when a target appears in the area, it can be The probability of being spotted by a drone It is expressed as: （14） in, represents the constant impact factor; Indicates an event Probability of occurrence; S5. Based on the model of the execution rate change of the single-machine reconnaissance component, the regional coverage of the reconnaissance capability of the drone cluster system is evaluated; S51. For a The execution degree of a drone swarm is defined as the number of remaining drones capable of performing optoelectronic reconnaissance that meets the requirement of The minimum number of drones required for re-coverage is expressed as: （15） in, Indicates the minimum value; express Recover probability; express Recover probability threshold; S52, Order Indicates whether the drone's reconnaissance function is executed or not. If it cannot be executed, then ,otherwise ; S53. Obtain regional coverage of drone swarm system reconnaissance capabilities : （16） in, It means that before the first drone joins the drone swarm system, the target is covered by 0 drones; Indicates After a drone joins the drone swarm system, the target is at least Covered by more than 100 drones; Indicates a continuous multiplication operation; Represents the probability transition matrix of the Markov chain.

2. The regional coverage evaluation method of the UAV swarm system reconnaissance capability according to claim 1 is characterized in that: The step S131 specifically includes the following steps: S1311, Order Event , then the event The probability of occurrence is : （3）； S1312, Event Next The probability that a drone can cover the target Equivalent to the drone reconnaissance range area Target area The ratio is expressed as: （4）； S1313. Based on expressions (3) and (4), the event Single machine coverage probability for: （5） in, Indicates the coverage angle; Represents differential.

3. The regional coverage evaluation method of the UAV swarm system reconnaissance capability according to claim 1 is characterized in that: The step S132 specifically includes the following steps: S1321, Order Event , then the event The probability of occurrence is : （6）； S1322, Event Next The probability that a drone can cover the target Equivalent to the fan-shaped area of ​​the drone's reconnaissance range Target area The ratio is: （7）； S1323. Based on expressions (6) and (7), the event Single machine coverage probability for: （8）。 4. The regional coverage evaluation method of the reconnaissance capability of the drone swarm system according to claim 1 or 2 is characterized in that: The step S2 specifically includes the following steps: S21. Considering the event and events Mutually exclusive, when a target appears, it can be The probability of being covered by a drone, that is, the event Probability of occurrence for: （9）； S22, based on The detection area radius of the drone's reconnaissance component and the radius of the target area , and obtain the numerical solution of expression (9).

5. The regional coverage evaluation method of the UAV swarm system reconnaissance capability according to claim 1 is characterized in that: The step S3 specifically comprises the following steps: S31. Let the Markov chain Indicates When a drone joins a drone cluster, the number of times any target is covered by a drone is a pseudo random process. Then the state space of the Markov chain is for: （10） in, Indicates that the target is at least According to the status covered by the above drones, is a positive integer; S32. Considering the probability of event B , then the probability transfer matrix of the Markov chain is for: （11）； S33, Computation of UAV Cluster System Recover probability : （12）。 6. The regional coverage evaluation method of the UAV swarm system reconnaissance capability according to claim 1 is characterized in that: When the performance change rules of different drones in step S41 are the same, different Under the value of The value of is the same; when the performance change rules of different drones are different, different Under the value of The values ​​of are different.

7. The regional coverage evaluation method of the UAV swarm system reconnaissance capability according to claim 1 is characterized in that: In step S53 ; ; Represents a vector transpose operation.

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