A laser anti- drone system cooperative countermeasure decision method

Abstract

The application discloses a kind of laser anti-UAV system cooperative countermeasure decision methods, from multiple distributed deployment laser anti-UAV system according to the damage requirement decision of unmanned aerial vehicle best countermeasure combination. Main decision mechanism and process are: according to the unmanned aerial vehicle image information obtained by each laser anti-UAV system detection equipment, the unmanned aerial vehicle in the air is cooperatively positioned, and the flight trajectory data of unmanned aerial vehicle is obtained;Interception feasibility sequencing is carried out to the laser anti-UAV system satisfying accessibility;The launch energy of each accessible system is calculated in turn according to the distance and direction of target, and the energy of accessible system and the damage threshold of unmanned aerial vehicle are judged to choose, the decision method of the application can complete the countermeasure to unmanned aerial vehicle by the cooperation of multiple laser anti-UAV systems, overcome the defect that single laser anti-UAV system cannot realize countermeasure due to insufficient energy, reduce the requirement of energy, volume and the like of single laser anti-UAV system.

Description

Technical Field

[0001] This invention relates to the field of collaborative countermeasures technology for laser anti-drone systems, specifically a collaborative countermeasures decision-making method for laser anti-drone systems. Background Technology

[0002] The rapid development and increasing maturity of drone technology have led to its widespread application across various industries, including aerial photography, reconnaissance and surveillance, search and rescue, information reconnaissance, communication relay, and agricultural and forestry protection. While the lawful and regulated use of drones brings convenience, incidents of unauthorized and reckless flights by civilian drones, as well as offensive and defensive confrontations involving military drones, have resulted in a growing demand for drone countermeasures. For example, unauthorized drone flights near civilian airports pose a serious threat to the safe takeoff and landing of aircraft; drone-borne filming in sensitive areas such as key military and political sites, classified military industrial units, and military bases could also lead to the leakage of secrets. Therefore, researchers have explored various countermeasures against drones, including interference, interception and capture, deception and control seizure, and direct destruction. Interference and disruption primarily utilize electromagnetic waves, microwaves, and sound waves to disrupt the communication or positioning of drones, causing them to malfunction and crash. Interception and capture mainly involve using capture nets to capture drones. Deception and control exploitation use signal interference, signal deception, and signal intrusion to cause drones to deviate from their targets or be intercepted and controlled. Unlike the aforementioned soft-kill modes, direct destruction primarily uses traditional munitions firing, directed energy, and collisions to directly inflict hard damage on drones. Hard damage has advantages such as fast response speed, high accuracy, and good countermeasure effect. Among hard damage methods, laser countermeasures are currently the most researched approach, offering advantages such as minimal collateral damage, fast response speed, low cost, and high hit rate. However, high-power laser anti-drone systems suffer from drawbacks such as large size and inconsistency in use, and are currently mainly used in the military field. For civilian applications targeting "low, slow, and small" drones, low-power laser anti-drone systems or laser guns are currently the primary methods used. However, low-power countermeasure systems suffer from drawbacks such as short countermeasure range and limited energy and duration of a single laser beam. For protection over large areas like airports, ports, and bases, the capability of a single laser counter-drone system is limited. Therefore, deploying multiple laser counter-drone systems over large areas to form a distributed countermeasure system is considered. The collaboration of multiple laser counter-drone systems can solve the problems of a single countermeasure system. However, collaborative countermeasures involve various decision-making problems, such as determining the required number of systems, selecting available systems, and determining the countermeasure parameters of the cluster system. These decision-making activities vary depending on the diversity of drones, the randomness of their operation, and the complexity of the application environment. The decision-making technology is complex, and the decision-making method directly affects the countermeasure effectiveness. Currently, there is no mature technical solution for this. This invention proposes a complete decision-making scheme and method to address this decision-making challenge in laser collaborative counter-drone systems, thereby improving the effectiveness and capability of countering aerial drones. Summary of the Invention

[0003] One of the objectives of this invention is to provide a collaborative countermeasure decision-making method for laser anti-drone systems. Based on an established vulnerability database of various types of drones, after acquiring information such as the position, attitude, and shape of aerial drones from radar, photoelectric, and other detection equipment, the method performs vulnerability analysis on the drones and determines the required number of laser anti-drone systems, the azimuth and pitch positions of the laser anti-drone systems, and the required continuous irradiation time. This enables multiple laser anti-drone systems to collaboratively counter aerial drones, overcoming the deficiency that a single drone's insufficient energy cannot achieve countermeasures, and achieving the goal of distributed collaborative countermeasures over a large area.

[0004] To achieve the above objectives, the present invention provides a collaborative countermeasure decision-making method for laser-based anti-drone systems, comprising the following steps:

[0005] Step 1: Based on the drone image information obtained by each laser anti-drone system detection equipment, perform collaborative positioning of the aerial drones to obtain the drone flight trajectory data;

[0006] Step 2: Predict the flight path of the UAV based on its flight data;

[0007] Step 3: Perform reachability calculations for the laser anti-drone system;

[0008] Step 4: Determine the reachability of the laser anti-drone system. If the number is 0, abandon the countermeasures; otherwise, proceed to the next step.

[0009] Step 5: Determine the required damage level according to the damage level determination criteria defined in GJB1301-91.

[0010] Step 6: Search the drone vulnerability database to determine the part of the drone that the laser anti-drone system should irradiate;

[0011] Step 7: Perform parallelepiped equivalent modeling for vulnerable parts;

[0012] Step 8: Calculate the material threshold of the selected irradiation site;

[0013] Step 9: Sort the reachability of the reachable laser anti-drone systems;

[0014] Step 10: Select the laser anti-drone system in sequence and calculate its irradiation energy and irradiation parameters;

[0015] Step 11, calculate the illumination angle parameters;

[0016] Step 12: Calculate the area of ​​the laser spot formed by the laser anti-drone system illuminating the drone.

[0017] Step 13: Calculate the irradiance of the laser aiming point to the corresponding spot after the laser travels a distance through the atmosphere;

[0018] Step 14: Compare the obtained irradiation energy with the material threshold calculated in Step 8 to determine whether it is greater than the material threshold. If it is greater, execute the countermeasure decision; otherwise, proceed to the next step.

[0019] Step 15: Determine if there are any uncalculated reachable systems. If so, proceed to step 10 to continue the calculation; otherwise, determine to abandon the countermeasure.

[0020] In one or more embodiments of the present invention, the above-mentioned UAV cooperative positioning method: when performing cooperative positioning of UAVs in the air using UAV image information obtained by each laser anti-UAV system detection device, the detection information of multiple detection devices is weighted and averaged.

[0021] In one or more embodiments of the present invention, the above-mentioned target reachability determination method comprehensively judges the distance judgment criteria between the predetermined interception point in the air and the laser anti-drone system, as well as the upper and lower bounds of azimuth and pitch.

[0022] In one or more embodiments of the present invention, the above-mentioned drone damage capability threshold is compared with the irradiation energy of multiple laser anti-drone systems by calculating the irradiation energy of the laser anti-drone systems in sequence, and stopping the calculation when the cumulative energy of the laser anti-drone system is greater than the drone damage capability threshold.

[0023] In one or more embodiments of the present invention, the method for calculating the irradiation energy of the laser anti-drone system includes the continuous light emission time of the laser anti-drone system, the initial power, the natural environment, and the area of ​​the light spot formed by the irradiation angle.

[0024] In one or more embodiments of the present invention, when predicting the flight path of a UAV based on UAV flight data, the Singer model and the adaptive Kalman filter prediction method are used to calculate the flight path of the UAV.

[0025] Beneficial effects

[0026] This invention provides a collaborative countermeasure decision-making method for laser-based anti-drone systems. Compared with existing technologies, it has the following advantages:

[0027] 1. The decision-making method of the present invention can counter drones through the coordination of multiple laser anti-drone systems, overcoming the defect that a single laser anti-drone system cannot achieve countermeasures due to insufficient energy, and reducing the requirements for energy, size and other aspects of a single laser anti-drone system.

[0028] 2. The distributed deployment of the multi-laser anti-drone system was achieved through the decision-making method, which can protect a larger area. Moreover, the distributed countermeasure method enables the coordinated tracking, positioning, prediction and countermeasure against drones, which improves the accuracy and efficiency of countermeasure. The decision-making method, as a countermeasure decision-making algorithm, has a wide range of applications, and can be applied to both fixed-wing drone countermeasures and rotary-wing drone countermeasures.

[0029] 3. The decision-making algorithm comprehensively considers the deployment, motion characteristics, and energy characteristics of the laser anti-drone system, enabling it to complete the countermeasure mission with minimal cost and resources. It has a better cost-effectiveness ratio, a simple process, low computational load, and strong timeliness. Attached Figure Description

[0030] Figure 1 This is a flowchart of the collaborative countermeasure decision-making method for laser anti-drone systems of the present invention;

[0031] Figure 2 This is a schematic diagram of the cooperative positioning of the laser anti-drone system of the present invention;

[0032] Figure 3 This is a flowchart of the reachability determination process for the laser anti-drone system of the present invention;

[0033] Figure 4 This is a schematic diagram of the equivalent hexahedron model of the UAV wing of the present invention;

[0034] Figure 5 This is a flowchart illustrating the calculation of irradiation energy for the laser anti-drone system of the present invention. Detailed Implementation

[0035] The following describes several embodiments of the present invention with reference to the accompanying drawings. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. Furthermore, for the sake of simplicity, some conventional structures and elements will be shown in the drawings in a simple schematic manner, and in all drawings, the same reference numerals will be used to denote the same or similar elements. And, where feasible, features of different embodiments can be interchanged.

[0036] Unless otherwise defined, all terms used herein (including technical and scientific terms) have their ordinary meanings, which are understandable to those skilled in the art. Furthermore, the definitions of the foregoing terms in commonly used dictionaries should be interpreted in the context of this specification as having the meaning consistent with the relevant field of this invention. Unless specifically defined, these terms will not be construed as having idealized or overly formal meanings.

[0037] Please see Figure 1-5 As shown, this invention provides a collaborative countermeasure decision-making method for laser anti-drone systems. Based on an established vulnerability database of various types of drones, after acquiring information such as the position, attitude, and shape of aerial drones from radar, photoelectric, and other detection equipment, the method performs vulnerability analysis on the drones and determines the required number of laser anti-drone systems, the azimuth and pitch positions of the laser anti-drone systems, and the required continuous irradiation time. This enables multiple laser anti-drone systems to collaboratively counter aerial drones, overcoming the deficiency that a single drone's insufficient energy cannot achieve countermeasures, and achieving the goal of distributed collaborative countermeasures over a large area.

[0038] Decision-making process as follows Figure 1 As shown, it includes the following steps:

[0039] S1: If the detection equipment of the laser anti-drone system detects a target, it first performs collaborative positioning of the drone in the air based on the drone image information obtained by each laser anti-drone system detection equipment, and obtains the drone's flight trajectory data.

[0040] S2: Predict the UAV's flight path based on its flight data. To ensure accuracy, assuming the UAV uses the Singer model, the theoretically and practically mature adaptive Kalman filter prediction method is employed to calculate the UAV's flight path.

[0041] S3: As Figure 3 As shown, the reachability calculation of a laser-based anti-drone system is performed. For each laser-based anti-drone system, based on its location, performance parameters, and the predicted drone flight path, the distance and angle between the laser-based anti-drone system and the drone are first calculated. Then, it is determined whether the illumination distance is feasible. If the distance is feasible, the illumination angle is then determined. If both the distance and angle are feasible, the anti-drone system is considered feasible. satisfy Reachability: Counts the number of all reachable systems.

[0042] S4: If the number of laser anti-drone systems that meet the accessibility requirement is not zero, continue to the next step; otherwise, if it is determined that the drone cannot be activated to implement countermeasures at this moment, execute step 17 to abandon the countermeasures at this moment and continue to track and detect the target drone until the target drone is intercepted or escapes.

[0043] S5: Determine the damage level to the UAV. For the obtained UAV type, determine the required damage level according to the damage level assessment criteria defined in GJB1301-91;

[0044] S6: The laser anti-drone system checks the vulnerability data table of its command and control system to determine the part of the drone to be irradiated by the laser anti-drone system;

[0045] S7: As Figure 4 As shown, a parallelepiped equivalent model is performed on the vulnerable parts, and the center position and size of each equivalent model are determined based on the target size obtained by the photoelectric detection equipment.

[0046] S8: Based on the material properties of the selected irradiation site, and according to the equivalence model, calculate the material threshold of the equivalent hexahedral model of the selected irradiation site;

[0047] S9: For all reachable laser counter-drone systems, determine the order of candidate laser counter-drone systems in ascending order of distance;

[0048] S10: Select each laser anti-drone system in sequence and calculate the irradiation energy and irradiation parameters respectively.

[0049] S11: Calculate the illumination angle parameters. Since the laser beam exits at a high speed, the illumination lead can be ignored, and it can be regarded as a straight line illumination, thus obtaining the illumination azimuth angle α and elevation angle β of the irradiated part;

[0050] S12: Calculate the area of ​​the circular spot formed by the laser anti-drone system at the aiming point. First, calculate the divergence angle σ of the laser transmission process based on the laser wavelength λ and the diameter D of the transmitting mirror; then, calculate the spot radius deviation based on the deviation γ between the actual laser beam pointing angle (composed of the elevation angle and the direction angle) and the theoretical laser beam pointing angle.

[0051] Finally, the area of ​​the laser spot formed by the laser beam on the surface illuminated by the UAV is calculated:

[0052]

[0053] S13: As Figure 5 As shown, the irradiance of the laser beam reaching the corresponding spot after traveling a distance R through the atmosphere from the laser aiming point is calculated. First, the energy attenuation rate η of the laser beam due to atmospheric refraction and absorption is calculated. Then, based on the laser beam power P0, the divergence coefficient κ, and the irradiance I of the laser beam reaching the spot, the calculation formula is as follows:

[0054]

[0055] Finally, for "low, slow, and small" drones, considering the short illumination time and slow drone speed, the distance changes very little with time during the continuous illumination period. Let's assume the duration of a single laser emission from the anti-drone system is t. f Then the energy that the i-th laser anti-drone system can irradiate onto the drone can be simplified as:

[0056] E (i) =I·t f

[0057] S14: Through

[0058]

[0059] Determine whether the total energy of the selected laser anti-drone system is greater than the energy required to illuminate the drone's target area as calculated in step 8. If it is greater, it means that the currently selected laser anti-drone system meets the countermeasure requirements, and step 16 is executed to implement countermeasures; otherwise, proceed to the next judgment step.

[0060] S15: Determine if there are any unselected laser anti-drone systems. If so, return to step 10 for a loop calculation; otherwise, determine that countermeasures cannot be implemented at this moment and execute step 17 to abandon countermeasures at this moment. Continue tracking and detecting the target drone until it is intercepted or escapes.

[0061] System Implementation Case:

[0062] Suppose multiple laser-based anti-drone systems are deployed near an airport to monitor the airspace above it in real time. At a certain moment, the photoelectric detection equipment of one of the laser-based anti-drone systems detects an unidentified fixed-wing drone over the airport. After continuous tracking and identification, it decides to activate the laser-based anti-drone system to counter it. Figure 1 The countermeasures shown

[0063] The policy process, and its implementation, are as follows:

[0064] S1: If the detection equipment of the laser anti-drone system detects a target, it first performs collaborative localization of the aerial drone based on the drone image information obtained by each laser anti-drone system detection equipment, and obtains the drone's flight trajectory data, such as... Figure 2 As shown, assume there are q laser anti-drone systems that detect the target, and the drone's position information measured by each system is (r k , ε k θ k The flight position data of the UAV, k = 1, 2, ..., q, can be calculated using the following formula:

[0065]

[0066] S2 predicts the UAV's flight path based on its flight data. To ensure accuracy, it assumes the UAV uses a Singer model and employs the theoretically and practically mature adaptive Kalman filter prediction method to calculate the UAV's flight path. Assuming the reaction time of the laser anti-UAV system is t, then at time t in the future, the UAV's aerial position will be (x... T (t), y T (t), z T (t)).

[0067] S3: As Figure 3 As shown, the reachability calculation of the laser anti-drone system is performed.

[0068] Assume there are n laser anti-drone systems, and the deployment location of each system is (x... i y i ), i = 1, 2, ..., n, the illumination range of the laser anti-drone system is R. max The irradiation boundary is R min The azimuth range is [θ] min θ max The pitch angle operating range is...

[0069]

[0070] First, calculate the distance and angle between the laser anti-drone system and the drone at time t:

[0071]

[0072]

[0073]

[0074] Then determine whether the irradiation distance is feasible, that is:

[0075] R min ≤R(t)≤R max

[0076] Given the required distance, determine whether the illumination angle is feasible using the following formula.

[0077]

[0078] If both distance and angle are feasible, the countermeasure system is considered to satisfy reachability, and the number m of all reachable systems is counted.

[0079] S4: Determine the reachability of the laser anti-drone system. If m ≥ 1, proceed to the next step; otherwise, determine that the drone cannot be activated to implement countermeasures at this moment, execute step 17 to abandon countermeasures at this moment, and continue.

[0080] Track and detect the target drone until it is intercepted or escapes.

[0081] S5: Determine the damage level to the UAV. For the obtained UAV type, determine the required damage level according to the damage level determination criteria defined in GJB1301-91.

[0082] S6: The laser anti-drone system checks the vulnerability data table of its command and control system to determine the part of the drone to be irradiated by the laser anti-drone system;

[0083] S7: As Figure 4 As shown, a parallelepiped equivalent model is performed on the vulnerable parts, and the center position and size of each equivalent model are determined based on the target size obtained by the photoelectric detection equipment.

[0084] S8: Calculate the material threshold of the equivalent hexahedral model of the selected irradiated area. The approximate calculation formula for the surface material threshold of the UAV is as follows (unit: kJ / cm). 2 )

[0085] E=ρd(C(T m -T0)+H m +C(T V -T M )+H V ) / 1000

[0086] Where: ρ is the material density; d is the material thickness; C is the material heat capacity; T m T is the melting temperature of the material; T0 is the ambient temperature; T v H is the vaporization temperature of the material. m Potential heat of fusion; H v It is the potential heat of vaporization.

[0087] S9: For M reachable laser anti-drone systems, sort them by distance to obtain the order s of candidate laser anti-drone systems. (1) s (2) ,…,m.

[0088] S10: Select s in sequence (1) , i = 1, 2, ..., m, calculate the irradiable energy and irradiation parameters.

[0089] S11: Calculation of illumination angle parameters. Due to the high laser emission speed, the illumination lead can be disregarded, and the illumination can be treated as a straight line. This allows us to obtain the azimuth angle α and elevation angle β of the illuminated area.

[0090]

[0091] S12: Calculation of laser-based anti-drone system (1) The area S of the circular light spot formed by i = 1, 2, ..., m at the aiming point (1) The calculation process is as follows:

[0092] First, calculate the divergence angle σ during laser transmission based on the laser wavelength λ and the diameter D of the transmitting mirror.

[0093]

[0094] Then, based on the deviation γ between the actual laser beam pointing angle (composed of the elevation angle and the direction angle) and the theoretical laser beam pointing angle, the spot radius deviation is calculated:

[0095] Δr=R·γ

[0096] Finally, the area of ​​the laser spot formed by the laser beam on the surface illuminated by the UAV is calculated:

[0097]

[0098] S13: As Figure 5 As shown, the irradiance of the laser aiming point reaching the corresponding spot after the laser travels a distance R through the atmosphere is calculated. The calculation process is as follows:

[0099] First, calculate the energy attenuation rate of the laser light in the atmosphere due to atmospheric refraction and absorption.

[0100] η=(1-ε) R

[0101]

[0102] Where V represents atmospheric visibility, and the relationship between the value of q and V is as follows:

[0103]

[0104] Then, the irradiance of the laser reaching the spot is calculated using the following formula:

[0105]

[0106] Where: P0 is the power of the laser beam; κ is the divergence coefficient, ranging from 84% to 98%, depending on the beam characteristics and the laser beam.

[0107] It depends on the optical system of the optical device.

[0108] Finally, for "low, slow, and small" drones, considering the short illumination time and slow drone speed, the distance changes very little with time during the continuous illumination period. Assuming the duration of a single laser emission from the laser anti-drone system is tf, the energy that the i-th laser anti-drone system can illuminate onto the drone can be simplified as:

[0109] E (i) =I·t f

[0110] S14: Determine whether the total energy of the selected laser anti-drone system is greater than the energy required for the drone irradiation area calculated in step 8, according to the following formula.

[0111]

[0112] If the above formula is true, it means that the selected laser anti-drone system has met the countermeasure requirements, and step 16 is executed to implement countermeasures; otherwise, the next judgment is executed.

[0113] S15: Determine if there are any unselected laser anti-drone systems. If so, return to step 10 for a loop calculation; otherwise, determine that countermeasures cannot be implemented at this moment and execute step 17 to abandon countermeasures at this moment. Continue tracking and detecting the target drone until it is intercepted or escapes.

[0114] Although the present invention has been disclosed in conjunction with the above embodiments, it is not intended to limit the present invention. Any person skilled in the art can make various modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A collaborative countermeasure decision-making method for laser-based anti-drone systems, characterized in that, Includes the following steps: Step 1: Based on the drone image information obtained by each laser anti-drone system detection equipment, perform collaborative positioning of the aerial drones to obtain the drone's flight trajectory data; Step 2: Predict the flight path of the UAV based on its flight data; Step 3: Perform reachability calculations for the laser anti-drone system; Step 4: Determine the reachability of the laser anti-drone system. If the number is 0, abandon the countermeasures; otherwise, proceed to the next step. Step 5: Determine the required damage level according to the damage level determination criteria defined in GJB1301-91. Step 6: Search the drone vulnerability database to determine the part of the drone that the laser anti-drone system should irradiate; Step 7: Perform parallelepiped equivalent modeling for vulnerable parts; Step 8: Calculate the material threshold of the selected irradiation site; Step 9: Sort the reachability of the reachable laser anti-drone systems; Step 10: Select the laser anti-drone system in sequence and calculate its irradiation energy and irradiation parameters; Step 11, calculate the illumination angle parameters; Step 12: Calculate the area of ​​the laser spot formed by the laser anti-drone system illuminating the drone. Step 13: Calculate the irradiance of the laser aiming point to the corresponding spot after the laser travels a distance through the atmosphere; Step 14: Compare the obtained irradiation energy with the material threshold calculated in Step 8 to determine whether it is greater than the material threshold. If it is greater, execute the countermeasure decision; otherwise, proceed to the next step. Step 15: Determine if there are any uncalculated reachable systems. If so, proceed to step 10 to continue the calculation; otherwise, determine to abandon the countermeasure.

2. The collaborative countermeasure decision-making method for a laser-based anti-drone system according to claim 1, characterized in that, UAV cooperative positioning method: When performing cooperative positioning of aerial UAVs using UAV image information obtained from various laser anti-UAV system detection devices, the detection information from multiple detection devices is weighted and averaged.

3. The collaborative countermeasure decision-making method for a laser-based anti-drone system according to claim 1, characterized in that, Target reachability calculation method: For each laser counter-drone system, based on its location and performance parameters and the predicted drone flight path, the distance and angle of the laser counter-drone system relative to the drone are first calculated; then, it is determined whether the illumination distance is feasible. If the distance is satisfied, the illumination angle is then determined. If both the distance and angle are feasible, the countermeasure system is considered to meet the reachability requirement.

4. The collaborative countermeasure decision-making method for a laser-based anti-drone system according to claim 1, characterized in that, When predicting the flight path of a UAV based on its flight data, the Singer model and an adaptive Kalman filter prediction method are used to calculate the UAV's flight path.

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