Protection game method based on forward-looking situational awareness and regional triggered adaptive defense

Abstract

This invention relates to a protection game method based on forward-looking situational awareness and regionally triggered adaptive defense. Targeted at island and reef protection scenarios, this method prioritizes interception to maximize the safety of high-value fixed targets such as islands and reefs, with enhanced resistance to saturation strikes as a secondary task. The method first optimizes the rolling time domain algorithm to improve interceptor response efficiency. Furthermore, it smoothes enemy trajectories to simplify self-modeling and shorten the planning and deployment time of interception strategies. Furthermore, it combines forward-looking situational awareness with the ability to predict the position of enemy unmanned boats and utilizes a regionally triggered adaptive return defense interception mechanism based on terminal status to protect high-value, important targets. This method plays an important role in complex and harsh marine environments, such as coastal and island defense.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned boat control, and in particular to a protection game method based on forward-looking situational awareness and regional triggered adaptive defense. Background Art

[0002] For the application scenarios of unmanned boats on islands and reefs, the main task is to intercept targets to maximize the protection of the safety of high-value fixed targets such as islands and reefs, and the secondary task is to enhance the ability to resist saturation attacks.

[0003] The core of the linear quadratic rolling horizon algorithm is to dynamically calculate the optimal control time range and implement the optimal strategy. However, only considering the attacker's current location information while ignoring the attacker's possible future action path to calculate the optimization range will lack foresight, which may cause the interceptor's interception strategy to lag and reduce the chance of effective interception.

[0004] Existing pursuit and protection game solutions often overlook the possibility of sudden trajectory fluctuations caused by nonlinear or uncertain factors. When an attacker experiences drastic trajectory fluctuations, overly focusing on the attacker's drastic state transitions when modeling and planning interception strategies can make modeling and strategy development extremely difficult, resulting in the inability to execute a fast and effective interception, or even the inability to execute a successful interception. Therefore, a method is urgently needed to smooth out these drastic changes in the attacker's trajectory, thereby facilitating interception strategy planning and deployment.

[0005] In current pursuit and protection game problems, the conventional method is to simply use the control input obtained by merging the optimal solution of the linear quadratic game to form the interceptor's interception strategy. The game solution is more based on the current position information of the interceptor and the attacker, which is relatively passive. The interception foresight may be poor, making it difficult to formulate a more proactive interception strategy, thereby reducing the interception effect of the interceptor.

[0006] Existing protection game strategies, from the interceptor's perspective, focus primarily on the attacker's location, often overlooking the crucial influence of the target's location on the game when the attacker is at a dangerous distance from the target. This can expose the target to the attacker's threat range, leading to a less-concentrated interceptor's defensive area. If the attacker suddenly changes strategy at a dangerous distance, the interceptor, lacking a multi-layered defense mechanism, is unable to effectively recover and ultimately fail the interception.

[0007] Among the current protection game problems, for intelligent unmanned equipment with area killing capabilities such as unmanned boats, there is a lack of an indicator that takes into account the attack range and position at the terminal defense moment to distinguish the performance of the interceptor and the safety of the protected target.

[0008] In summary, the interceptor response time in the current protection game model needs to be optimized. There are problems such as the optimal control time range ignoring the future evolution trend of the system, difficulty in modeling the attacker's sudden maneuvers, inability of the interceptor to use the attacker's future predicted state information for interception, and inability to perform adaptive defensive actions when the attacker is at a dangerous distance from the protected target. Summary of the Invention

[0009] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a protection game method based on forward-looking situational awareness and regional triggered adaptive defense, so as to solve the current problem of lack of game methods for pursuit and protection.

[0010] The purpose of the present invention can be achieved by the following technical solutions:

[0011] The present invention provides a protection game method based on forward-looking situational awareness and regional triggered adaptive defense, comprising the following steps:

[0012] Step S1, for the protection game scenario, construct a dynamic equation including the interceptor and the attacker;

[0013] Step S2, setting the attacker's goal to avoid the interceptor and occupy the protected target, and setting the interceptor's goal to intercept the attacker before the attacker reaches the protected target, and constructing a protection game objective function based on soft constraints;

[0014] Step S3, dynamically calculating the optimal control time range using a linear quadratic rolling horizon algorithm based on the predicted attacker's position and the position of the protected target;

[0015] Step S4, calculating the control inputs of the interceptor and the attacker based on the dynamic equation, the protection game objective function and the optimal control time range;

[0016] Step S5, performing smoothing processing on the attacker's current sudden behavior and updating the attacker's control input;

[0017] Step S6, based on the distance between the interceptor and the attacker, adaptively adding a control input for the predicted position of the attacker to the control input of the interceptor to achieve forward situational awareness;

[0018] Step S7: If the distance between the attacker and the protected target is less than the preset danger distance, a control input is added to the interceptor's control input to move closer to the protected target, thereby realizing regional triggered adaptive defense.

[0019] As a preferred technical solution, in step S1, the dynamic equation is:

[0020]

[0021] in, and are the attacker's speed changes on the x-axis and y-axis, v a is the attacker's speed, and u ay are the attacker’s control inputs on the x-axis and y-axis respectively, and are the speed changes of the interceptor on the x-axis and y-axis, v i is the interceptor's speed, and u iy are the control inputs of the interceptor on the x-axis and y-axis, u x 、u y are the control inputs of the attacker or interceptor on the x-axis and y-axis respectively, and u is u x and u y The combined control input, Use a or i instead to indicate different roles.

[0022] As a preferred technical solution, in step S2, the protection game objective function is:

[0023]

[0024] Among them, γ a ,γ i represent the feedback strategies of the attacker and the interceptor respectively, γ a (x,t)∈u a , γ i (x,t)∈u i ,u a and u i are the control inputs of the attacker and the interceptor respectively, T is the terminal state, P(x a )、P(x i )、P(x t ) are the location information of the attacker, interceptor and protected target respectively, is the distance penalty between the attacker and the protected target during the game, is the distance penalty between the interceptor and the attacker during the game, w a is the distance penalty between the terminal state attacker and the protected target, w i is the distance penalty between the terminal state interceptor and the attacker.

[0025] As a preferred technical solution, in step S3, the process of calculating the optimal control time range includes the following steps:

[0026] Step S301, based on the predicted position of the attacker and the position of the protected target, the optimal control time is obtained using the optimal control time calculation formula;

[0027] Step S302: cyclically perform optimal control. During the cyclic process, when the time exceeds the optimal control time, the optimal control time is updated.

[0028] As a preferred technical solution, the optimal control time calculation formula is:

[0029]

[0030] Among them, norm is the Euclidean distance, P(x t ) represent the predicted attacker's location information and the protected target's location information, v a Indicates the attacker's speed.

[0031] As a preferred technical solution, in step S4, the control inputs of the interceptor and the attacker are obtained by dynamically solving the state feedback matrix using the Riccati equation within the optimal control time range.

[0032] As a preferred technical solution, in step S5, the attacker's control input is updated by adding the attacker's additional control input toward the protected target to the attacker's control input.

[0033] As a preferred technical solution, in step S6, when the distance between the interceptor and the attacker is less than a preset value, the control input for the attacker's predicted position and the control input for the attacker's current position are weighted based on a learnable adaptive weight to obtain an updated control input for the interceptor.

[0034] As a preferred technical solution, the attacker and interceptor are different unmanned boats.

[0035] As a preferred technical solution, it also includes:

[0036] Step S8, evaluating the security of the protected target by calculating the intersection area of ​​a circle formed by a circle double the attack range and the interceptor's attack range with the protected target as the center.

[0037] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0038] (1) Improving the response time of the interceptor: Based on the predicted position of the attacker and the position of the protected target, the present invention uses a linear quadratic rolling time domain algorithm to dynamically calculate the optimal control time range, thereby improving the response time of the interceptor.

[0039] (2) Avoiding interception failures caused by excessive focus on the attacker's maneuvering details: The present invention smoothes the current sudden behavior in the attacker's control input, allowing the attacker to have a stronger target. On the basis of the interceptor's attack range, the interceptor can quickly build models and quickly plan and deploy interception strategies, avoiding interception failures caused by excessive focus on the attacker's maneuvering details.

[0040] (3) Forward situational awareness: This invention is based on the distance between the interceptor and the attacker. In the interceptor's control input, the control input for the attacker's predicted position is adaptively added. The attacker's current state and future state are comprehensively considered to help the interceptor make a better interception strategy. At the same time, adaptive weights and learning rates are introduced. By judging the distance between the interceptor and the attacker, it is decided whether to move towards the predicted position for interception, thereby achieving forward situational awareness.

[0041] (4) Regional triggered adaptive defense: When the Euclidean distance between the interceptor and the attacker satisfies both situational awareness and adaptive defense mechanisms, the present invention adds a control input to the interceptor's control input that moves closer to the protected target. By comprehensively considering the positions of the three parties, the interceptor's next moving direction and distance are determined to ensure that the interceptor can be effectively returned to defense in an emergency, thereby improving the defect of insufficient attention to the protected target position information in the terminal defense state.

[0042] (5) Realizing the evaluation of the security of the protected target: The present invention realizes the evaluation of the security of the protected target by calculating the intersection area of ​​the circle formed by one times the attack range and the interceptor's attack range with the protected target as the center, and combines the indicators of attack range and position information to measure the security of the protected target and the ability to resist saturation attack. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Flowchart of a protection game method based on forward-looking situational awareness and regional triggered adaptive defense in an embodiment;

[0044] Figure 2 Module relationship diagram in the embodiment;

[0045] Figure 3 T before improvement in the embodiment o Schematic diagram of the calculation process;

[0046] Figure 4 is the improved T in the embodiment o Schematic diagram of the calculation process;

[0047] Figure 5 This is a schematic diagram of the interception strategy before optimization in the embodiment;

[0048] Figure 6Schematic diagram of the optimized interception strategy in the embodiment;

[0049] Figure 7 A schematic diagram of an interception strategy that adds forward-looking situational awareness to the embodiment;

[0050] Figure 8 Schematic diagram of an ideal protection state in the embodiment;

[0051] Figure 9 Schematic diagram showing the comparison of saturation attack resistance in the embodiment;

[0052] Figure 10 This is a schematic diagram of an interception strategy that adds trend-triggered adaptive defense to the embodiment;

[0053] Figure 11 This is a MATLAB simulation diagram. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0055] Example 1

[0056] This embodiment targets the pursuit and protection game scenario of unmanned boats on islands and reefs. With the primary task of intercepting targets to maximize the security of high-value fixed targets such as islands and reefs, and the secondary task of enhancing the ability to resist saturation strikes, it provides a pursuit and protection game method based on forward-looking situational awareness and a regionally triggered adaptive defense mechanism. First, the rolling time domain algorithm is optimized to improve the interceptor's response efficiency. On this basis, the enemy's trajectory is smoothed to simplify the own modeling and shorten the planning and deployment time of the interception strategy. The forward-looking situational awareness module is combined with the prediction of the enemy's unmanned boat's position and the use of a regionally triggered adaptive return defense interception mechanism based on the terminal state to protect high-value and important targets. This method plays an important role in complex and harsh marine environments such as border and coastal defense of islands and reefs.

[0057] The protection game in the island and reef unmanned boat defense scenario is a special form of pursuit and escape game, which is modeled as a linear quadratic game problem. In order to optimize the interception performance of the interceptor in the game scenario, the relevant algorithms are optimized and new functional modules are added to improve the interception capability. For details, see Figure 1 and Figure 2 , this method comprises the following steps:

[0058] Step S1: Establish the dynamic equation in the protection game problem.

[0059] use Represents the attacker's state change rate at a certain moment, x a (t) represents the attacker’s state variable at a certain moment, A a represents the attacker's state transition matrix, which represents the attacker's inherent evolution when there is no control input, B a represents the attacker's control input matrix, which determines how the control input acts on the attacker to change the attacker's state, u a represents the attacker's control input; the corresponding Represents the rate of change of the interceptor's state at a certain moment, x i (t) represents the state variable of the interceptor at a certain moment, A i Represents the state transition matrix of the interceptor, B i represents the interceptor's control input matrix, u i represents the control input of the interceptor. Assuming that the protected target is a high-value fixed target, the dynamic equation is as follows:

[0060]

[0061] Combining the dynamic equations of the attacker and the interceptor, we get the joint dynamic equation as follows:

[0062]

[0063] Here x(t) is the attacker x a (t) and interceptor x i (t), the A matrix is ​​the joint state transfer matrix, and the other parameters are introduced as above. For the joint dynamic equation, x(t), A, B a 、B i The matrix descriptions are shown below.

[0064]

[0065] To further simplify the dynamic equation, only the effects of control input on the attacker and interceptor are considered. In addition, since the game participants in the undefended island reef scenario are all assumed to be unmanned boats, the dynamics are only reflected on the x-axis and y-axis. The dynamic equation can be further refined as follows:

[0066]

[0067] in and Represents the attacker's speed change on the x-axis and y-axis, v a represents the attacker's speed, and u ayRepresent the attacker’s control input on the x-axis and y-axis respectively; and Represents the interceptor's speed change on the x-axis and y-axis, v i Represents the speed of the interceptor, and u iy Represents the interceptor's control input on the x-axis and y-axis respectively.

[0068] In order to ensure the constraints on the control input, the To constrain the angle, in order to further ensure boundedness, the following nonlinear function is used to further constrain the control input:

[0069]

[0070] It can be seen that the control input can be better constrained by introducing the normalization restriction operation, where u x 、u y are the control inputs of the attacker or interceptor on the x-axis and y-axis respectively, and u is u x and u y The combined control input, It can be replaced by a or i to indicate different roles.

[0071] Step S2: Construct the objective function of the protection game.

[0072] Considering that strict compliance with hard constraints may lead to an unsolvable optimization problem, soft constraints are introduced as penalty terms in the objective function. Soft constraints allow the system to violate constraints to a certain extent. This flexibility gives the system a high probability of finding a feasible solution even when faced with difficult-to-meet conditions. The constructed objective function is shown below:

[0073]

[0074] The main purpose of the objective function is to balance the control input and the game objective based on position deviation to find the optimal solution. In the above formula, γ a ,γ i Represent the feedback strategies of the attacker and the interceptor respectively, specifically γ a (x,t)∈u a and γ i (x,t)∈u i ,u a and u i are the control inputs of the attacker and the interceptor respectively. In the above formula, T represents the terminal state, P(x a )、P(x i )、P(x t ) represent the location information of the attacker, interceptor and protected target respectively, Reflects the distance penalty between the attacker and the protected target during the game; represents the distance penalty between the interceptor and the attacker during the game; w a represents the distance penalty between the terminal state attacker and the protected target; w i The objective function can be used to determine the goals of the attacker and interceptor UAVs during the game: the enemy UAVs hope to avoid attacking islands and reefs by their own UAVs, while their own UAVs hope to effectively intercept the enemy UAVs before they reach the islands and reefs.

[0075] By combining the penalty weights into the process penalty matrix Q and the terminal penalty matrix Q f , in order to simplify the objective function, the simplified objective function is as follows:

[0076]

[0077] Where Q represents the penalty matrix in the game process, Q f Represents the penalty for the terminal state.

[0078] Step S3: Use the improved linear quadratic rolling horizon algorithm to dynamically solve the optimal control time range T o .

[0079] Considering that the soft constraints provide penalty terms for the optimization control process within a certain time range, the improved linear quadratic rolling horizon algorithm is used to dynamically solve the optimal control time range T o The core of the linear quadratic rolling time domain algorithm is to derive the most appropriate T o The traditional linear quadratic rolling horizon algorithm, when used in the context of a protection game, requires the use of both the attacker's and the target's location information. While this can speed up the interceptor's response time, in the ever-changing ocean, the defender needs to ensure that their unmanned boat has a faster response time.

[0080] This embodiment is based on the idea of ​​improving the interception performance through prediction, and dynamically calculates T using the predicted attacker's position and the protected target's position. o , which can effectively shorten the interceptor's response time. The specific formula is as follows.

[0081]

[0082] The formula mentioned is the attacker's state variable after prediction, in order to better show T o Calculation of T before and after improvemento The calculation process is visualized as follows Figure 3 and Figure 4 shown.

[0083] from Figure 3 It can be seen that the algorithm before improvement has a significant effect on T o The calculation process, where the red one is the attacker, the circle is the attack range, T1 is the shortest time for the attacker to attack the protected target in the initial state, and T1 is recorded as the initial T o After T1, the attacker reaches the second position. Then the shortest time for the attacker to attack the protected target is calculated again, T2, which is recorded as the new T o By continuously adjusting T o To ensure the normal introduction of soft constraints.

[0084] Figure 4 It is the improved T o Calculation process, compared with the previous time T1, the predicted attacker's location information is used to calculate the shortest time T to the protected target 1' As the new T o , which can shorten the interceptor's reaction time and encourage the interceptor to improve his performance.

[0085] Specifically, the linear quadratic rolling horizon algorithm process of this step is shown in Table 1.

[0086] Table 1 Improved linear quadratic rolling horizon algorithm

[0087]

[0088] The improved linear quadratic rolling time domain algorithm provided in this step calculates the optimal control range by jointly predicting the attacker's position information and the position information of the protected target. This allows the interceptor to have better preemptive judgment to improve the accuracy of interception. Compared with the algorithm before the improvement, it can help the interceptor respond more promptly and facilitate the interceptor to find the appropriate optimization range in a dynamic environment. It avoids the disadvantages of over-planning caused by an excessively large optimal control range and the disadvantages of relatively short-sightedness caused by a too small optimal control range.

[0089] Step S4: Based on the dynamic equations and objective functions in steps S1-S2, the control range T is dynamically calculated using the improved linear quadratic rolling horizon algorithm. o Under the premise of dynamic in different T o The Riccati equation is used to preliminarily solve the state feedback matrix, and the formula is as follows.

[0090]

[0091] Among them, A and B a 、Bi The matrix introduction is shown in step S1. The Z matrix reflects the feedback gain, Q f The present invention uses the reverse integration method to solve the optimal control input within the range, and the final control inputs of the attacker and interceptor are:

[0092]

[0093] Step S5: The attacker’s targeting is enhanced by strengthening the influence of the target location information on the attacker’s control input, making it easier for the interceptor to model and plan strategies.

[0094] In the past, the control input was usually to calculate the Riccati equation to obtain the solution matrix and then obtain the optimal control input u through state feedback. a and u i However, in some scenarios, the attacker may experience sudden fluctuations or deviations in his trajectory due to some uncontrollable external factors such as environmental disturbances and sensor noise. At this time, if the interceptor pursues too much refinement, it may cause the interceptor to over-respond to these uncertain trajectories, resulting in interception failure. To address this problem, this embodiment adopts a method to enhance the attacker's control input of the protected target location information to simplify the attacker's trajectory to form an effective fuzzy prediction trajectory. This trajectory is not a prediction of the future, but an effective smoothing of the current attacker's sudden behavior, so as to facilitate the interceptor to formulate interception strategies more quickly. In addition, on the basis of the interceptor's attack range, the interceptor's active strike capability against the attacker can be improved. The modified attacker control input is: in Refers to additional control input from the attacker towards the protected target. Figure 5 、 Figure 6 By increasing the attacker's control input on the protected target location information to simplify the interceptor modeling and strategy planning deployment, the before-and-after comparison of the interceptor's implementation of the interception strategy is demonstrated.

[0095] Figure 5 This is the interceptor's interception strategy before optimization, without adding new control inputs to the attacker. Red dots represent the attacker's trajectory, blue dots represent the interceptor's trajectory, red and blue circles represent the attack range, blue crosses indicate mission failure due to interception anomalies, and green represents fixed protection targets. When an attacker performs a sudden turn maneuver due to uncontrollable factors, excessively refined modeling can lead to inability to properly implement the interception strategy or even cause abnormal behavior and inaction.

[0096] Figure 6The figure below shows the optimized interception strategy. The yellow dot represents the smoothed attacker trajectory, the yellow arrow indicates the initial smoothed prediction phase, and the yellow circle represents the attack range. Considering that the attacker's unusual maneuvers can complicate the trajectory and lead to interception failures, this method provides a method to enhance the control input of the protected target's position information to obtain a smoothed attacker trajectory, preventing the interceptor from falling into a local optimum and failing to intercept properly. This improved trajectory allows the interceptor to quickly respond and plan its strategy. While some controllable errors will occur during this process, these errors do not affect the interceptor's ability to effectively intercept the enemy target.

[0097] In summary, the method of smoothing the attacker's trajectory provided in this step addresses the problem in the past protection game problem that the attacker may undergo sudden state transitions due to some external factors, making it difficult for the interceptor to model. By introducing more information about the protection target into the attacker's attack strategy, the attacker's targeting is enhanced, and a smooth fuzzy prediction trajectory is generated. This trajectory can simplify the interceptor's modeling process and facilitate the interceptor to activate the interception mechanism. Even if there are slight errors in the attacker's interception strategy, the enemy unmanned boat can be intercepted well on the basis of having an attack range.

[0098] Step S6: Based on steps S3-S5, in order to avoid the drawback of only considering the attacker's current state information and lacking foresight, this method adds attention to the attacker's future state information in addition to the interceptor's control input to avoid local optimality. When the interceptor reaches a certain distance from the attacker, the interceptor activates this forward-looking situational awareness module, and the control input becomes: in and They are the interceptor's control input to the attacker's current position and the control input to the attacker's predicted position, respectively. k1 and k2 are learnable weights. The learning goal of the weights is that the greater the difference between the attacker's current state and the predicted state, the greater the weight of the interceptor's control input to the attacker's predicted position. This method optimizes decision-making based on the attacker's current and future state information. In addition, adaptive weights and learning rates are introduced in this module. By calculating the Euclidean distance between the attacker's current position information and the predicted position information, the interceptor can optimize the decision-making based on the attacker's current and future state information. When the distance is too large, the interceptor will be more inclined to move closer to the attacker's predicted position to ensure successful interception. The specific interception diagram is as follows Figure 7 shown.

[0099] from Figure 7As can be seen from the figure, after adding the situational awareness module, the interceptor will respond sensitively to the attacker by comprehensively judging the attacker's current position and future predicted position. This method enables the interceptor to better adapt to environmental changes and uncertainties, and avoid being misled by the attacker's short-term sudden behavior. At the same time, the attacker can use its own attack range combined with situational awareness to better destroy the enemy target.

[0100] This step provides an interception strategy that adds a forward-looking situational awareness module. It relies on adaptive weights to balance the interceptor's reliance on the attacker's current state information and the attacker's predicted state information. A learning rate is introduced to dynamically adjust the adaptive weights. The Euclidean distance between the attacker's current position and the predicted position is used to determine whether the interceptor is more concerned with the attacker's predicted position. This module allows the interceptor to balance its attention to the attacker's current position information and the attacker's predicted position information when they reach a certain distance. Compared with interception strategies without the prediction module, this significantly increases dynamic adjustment and interception capabilities.

[0101] Step S7: In the unmanned defense scenario of islands and reefs based on the protection game, the core task is to ensure that high-value fixed targets such as islands and reefs are not attacked and occupied by enemy unmanned boats. Considering that the closer the enemy unmanned boat is to the protected target, the more attention the enemy unmanned boat should pay to the enemy unmanned boat, and the stronger the confrontation with the enemy unmanned boat. However, at the end of the confrontation game, the position information of the protected target is usually not taken seriously. In some cases, the position of the interceptor cannot successfully intercept the attacker. At this time, if the attacker is continuously tracked, it will deviate from the key position of defense, resulting in defense failure. To address this problem, this embodiment allows the interceptor to move closer to the protected target based on the current and predicted position information of the attacker, and intercepts the attacker in a "shortcut" way by means of returning to defense.

[0102] For the protected target, when the interceptor is at the center of the protected target, the protected target is the safest and has the strongest ability to resist saturation attacks. See the schematic diagram. Figure 8 shown.

[0103] See also Figure 8 For an extremely ideal protection state, unmanned boats usually need to perform dynamic patrol missions and are not suitable for long-term stationing at the protected target, which would waste military resources. In some scenarios, being close to the protected target will also enhance the target's ability to resist saturation attacks, such as Figure 9 shown.

[0104] according to Figure 9 ,It can be seen that after the interceptor is appropriately close to the protected target, it can better protect the high-value fixed target when facing multiple attackers.,This shows that returning to defense close to the protected target is an effective defense mechanism.

[0105] This step introduces a regional triggering adaptive defense mechanism. The idea of ​​this mechanism is as follows: once the distance norm(P(x a )-P(x t If the distance is less than or equal to the danger distance, the adaptive defense mechanism will be triggered. Based on the control input optimized in step S6, some control inputs are added to guide the interceptor to move closer to the protected target to produce a return defense effect. The optimized control input is: This improvement can maximize the safety of the target, make the interception strategy more flexible, and avoid the target being destroyed by the enemy due to continuous over-tracking. It satisfies both the forward situational awareness module and the adaptive defense mechanism. Figure 10 shown.

[0106] Figure 10 In the figure, the red dot represents the attacker, the purple dot is the predicted attacker's trajectory, and the blue dot represents the interceptor. Under the premise of meeting situational awareness, when the attacker is at a dangerous distance from the protected target, the interceptor will increase its attention to the protected target. The position of the protected target will also become part of the guide for the interceptor to make position changes, thereby increasing the interceptor's interception probability.

[0107] Step S8: After the game is complete, an indicator is used to assess the safety of the protected target after the interception. Previous indicators used only distance to judge the effectiveness of interception strategies, making them unsuitable for unprotected island and reef scenarios. This method provides an indicator that combines distance and attack range: the protected area. This indicator is calculated as the intersection area between a circle centered on the protected target, twice the attack range, and the interceptor's attack range. A larger intersection area indicates a safer target and a greater degree of control over the interceptor's protection.

[0108] This step introduces an indicator that can measure the security of the protected target. By comprehensively considering the location information and the attack range of both the attacker and the interceptor, it is based on the location of the protected target. This indicator can be used to measure whether the target's defense domain is fully covered, and can also be used to roughly determine the protected target's ability to resist saturation attacks.

[0109] To validate the effectiveness of this method, a simulation was conducted using MATLAB software. The application scenario was set up for the interception of high-value fixed targets, such as islands and reefs, in border waters. Assuming both sides are unmanned aerial vehicles (UAVs), each with its own attack range. The enemy UAV's goal is to quickly destroy the UAV's protected target while maintaining a safe distance from it. The UAV's goal is to quickly intercept the enemy UAV and maximize the distance between them. During the initial interception, an improved algorithm is used to improve the UAV's response speed. Furthermore, the trajectory of the attacking UAV is smoothed by more control inputs related to the protected target, facilitating rapid modeling and interception strategy planning. Subsequently, the UAV conducts a normal intercept. Upon reaching a certain distance from the enemy unit, it activates its situational awareness module to predict the enemy's position. The Euclidean distance between the predicted position and the current position determines whether to prioritize the predicted trajectory. If the enemy unit is at a dangerous distance from the protected target, the UAV activates a region-triggered adaptive defense mechanism and effectively intercepts the attacker before it reaches the protected target. MATLAB simulation diagram as follows Figure 11 As shown, green represents the protected target, red is the attacking unmanned boat, and blue is the defending unmanned boat. It can be seen that the scheme successfully intercepts the enemy unmanned boat at the end.

[0110] In summary, this embodiment addresses the following issues in current protection game models: the interceptor's response time needs to be optimized, the optimal control time range ignores the system's future evolution trends, the difficulty in modeling the attacker's sudden maneuvers, the interceptor's inability to use the attacker's predicted future state information for interception, and the inability to perform adaptive defensive actions when the attacker is at a dangerous distance from the protected target. Based on an improved linear quadratic rolling time domain algorithm framework, an interception strategy that introduces a forward-looking situational awareness module and a regionally triggered adaptive defense module improves the interception effectiveness of enemy unmanned boats in unmanned island and reef defense scenarios. At the same time, an indicator for measuring interception effectiveness and the safety of the protected target based on the terminal defense state is proposed.

[0111] This method has the following beneficial effects:

[0112] (1) By calculation To improve the optimal control time range T in the linear quadratic rolling horizon algorithm o Calculation to improve the interceptor's response time

[0113] (2) Set the attacker's control input to This allows attackers to have stronger targeting, smooths the attacker's trajectory, and enables interceptors to quickly build models and quickly plan and deploy interception strategies based on the interceptor's attack range, avoiding interception failures caused by excessive focus on the attacker's maneuvering details.

[0114] (3) By introducing a forward-looking situational awareness module, the optimized control input is Comprehensively consider the attacker's current state and future state to help the interceptor make a better interception strategy, while introducing adaptive weights and learning rates. The size judgment determines whether it is more inclined to move toward the predicted position for interception.

[0115] (4) By introducing a regional triggering adaptive defense mechanism, the defect of insufficient attention to the target location information in the terminal defense state is improved. If the Euclidean distance between the interceptor and the attacker satisfies both situational awareness and adaptive defense mechanism, the control input is optimized to By comprehensively considering the positions of the three parties, we can determine the interceptor's next movement direction and distance, ensuring that we can effectively return to defense and block the interceptor in an emergency.

[0116] (5) By introducing an indicator that combines attack range and location information to measure the security of the protected target and its ability to resist saturation attacks.

[0117] Example 2

[0118] This embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, and the one or more programs include instructions for executing the pursuit and protection game method based on forward-looking situational awareness and area-triggered adaptive defense mechanism as described in Example 1.

[0119] Example 3

[0120] This embodiment provides a computer-readable storage medium, including one or more programs for execution by one or more processors of an electronic device, wherein the one or more programs include instructions for executing the pursuit and protection game method based on forward-looking situational awareness and area-triggered adaptive defense mechanism as described in Example 1.

[0121] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0123] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0124] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0125] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0126] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A protection game method based on forward-looking situational awareness and regional triggered adaptive defense, characterized in that: The steps include: Step S1, for the protection game scenario, construct a dynamic equation including the interceptor and the attacker; Step S2, setting the attacker's goal to avoid the interceptor and occupy the protected target, and setting the interceptor's goal to intercept the attacker before the attacker reaches the protected target, and constructing a protection game objective function based on soft constraints; Step S3, dynamically calculating the optimal control time range using a linear quadratic rolling horizon algorithm based on the predicted attacker's position and the position of the protected target; Step S4, calculating the control inputs of the interceptor and the attacker based on the dynamic equation, the protection game objective function and the optimal control time range; Step S5, performing smoothing processing on the attacker's current sudden behavior and updating the attacker's control input; Step S6, based on the distance between the interceptor and the attacker, adaptively adding a control input for the predicted position of the attacker to the control input of the interceptor to achieve forward situational awareness; Step S7: If the distance between the attacker and the protected target is less than the preset danger distance, a control input is added to the interceptor's control input to move closer to the protected target, thereby realizing regional triggered adaptive defense. In the step S1, the dynamic equation is: in, and The attackers are Axis and The speed of the axis changes, is the attacker's speed, and The attackers are Axis and Axis control input, and The interceptors are Axis and The speed of the axis changes, is the interceptor's speed, and The interceptors are Axis and Axis control input, 、 The attacker or interceptor is Axis and Axis control input, for and The combined control input, use or to represent different roles, The protection game objective function is: in, Represent the feedback strategies of the attacker and the interceptor respectively, , , and are the control inputs of the attacker and interceptor respectively, is the terminal state, 、 、 are the location information of attacker, interceptor and protected target respectively, is the distance penalty between the attacker and the protected target during the game, is the distance penalty between the interceptor and the attacker during the game, is the distance penalty between the terminal state attacker and the protected target, is the distance penalty between the terminal state interceptor and the attacker.

2. A protection game method based on forward-looking situational awareness and regional triggered adaptive defense according to claim 1, characterized in that: In step S3, the process of calculating the optimal control time range includes the following steps: Step S301, based on the predicted position of the attacker and the position of the protected target, the optimal control time is obtained using the optimal control time calculation formula; Step S302: cyclically perform optimal control. During the cyclic process, when the time exceeds the optimal control time, the optimal control time is updated.

3. A protection game method based on forward-looking situational awareness and regional triggered adaptive defense according to claim 2, characterized in that: The optimal control time calculation formula is: in, is the Euclidean distance, 、 represent the predicted attacker's location information and the protected target's location information respectively, Indicates the attacker's speed.

4. The protection game method based on forward-looking situational awareness and regional triggered adaptive defense according to claim 1, characterized in that: In step S4, by dynamically utilizing The equation is solved for the state feedback matrix to obtain the control inputs of the interceptor and attacker.

5. The protection game method based on forward-looking situational awareness and regional triggered adaptive defense according to claim 1, characterized in that: In step S5, the attacker's control input is updated by adding the attacker's additional control input toward the protected target to the attacker's control input.

6. The protection game method based on forward-looking situational awareness and regional triggered adaptive defense according to claim 1, characterized in that: In step S6, when the distance between the interceptor and the attacker is less than a preset value, the control input for the attacker's predicted position and the control input for the attacker's current position are weighted based on a learnable adaptive weight to obtain an updated control input for the interceptor.

7. The protection game method based on forward-looking situational awareness and regional triggered adaptive defense according to claim 1, characterized in that: The attacker and interceptor are different unmanned boats.

8. The protection game method based on forward-looking situational awareness and regional triggered adaptive defense according to claim 1, characterized in that: Also includes: Step S8, evaluating the security of the protected target by calculating the intersection area of ​​a circle formed by a circle double the attack range and the interceptor's attack range with the protected target as the center.

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