Unmanned aerial vehicle smoke bomb launching method based on adaptive optimization and space-time dynamic cooperation
By constructing motion models of missiles, drones, and smoke grenades, and combining them with an adaptive multi-resolution coordinate descent algorithm, the drone smoke grenade deployment strategy was optimized. This addressed the shortcomings of drone smoke grenade deployment methods in terms of mobility, adaptability, and coordination, achieving efficient masking effects and real-time performance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING UNIV OF TECH
- Filing Date
- 2026-05-11
- Publication Date
- 2026-07-31
AI Technical Summary
Existing drone smoke screen deployment methods are insufficient in terms of mobility, adaptability, and coordination, making it difficult to effectively deal with highly mobile, multi-batch threats.
We construct kinematic models for missiles, UAVs, and smoke grenades, combine them with an adaptive multi-resolution coordinate descent algorithm to optimize parameters, and optimize the UAV smoke grenade deployment strategy through adaptive optimization and spatiotemporal dynamic collaborative logic.
It improves parameter optimization efficiency by 3 to 5 times, increases masking duration by more than 235%, and significantly optimizes coverage balance, meeting the real-time requirements of high-speed missile interception.
Smart Images
Figure CN122490809A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of drone smoke grenade delivery, and particularly relates to a drone smoke grenade delivery method based on adaptive optimization and spatiotemporal dynamic coordination. Background Technology
[0002] Smoke jamming has become a cost-effective and fast-response soft defense method, and UAV platforms, with their flexible maneuverability and long-term loitering capabilities, have become an ideal carrier for implementing smoke jamming.
[0003] One existing technical solution involves releasing smoke screens at predetermined locations using fixed launchers or vehicle-mounted smoke grenade launchers in the direction of a threat. Advantages include low deployment costs, mature operational procedures, and suitability for static defense scenarios. Disadvantages include extremely poor mobility, making it impossible to adjust the smoke screen coverage area to follow moving targets; the deployment range is limited by the launcher's range, making it difficult to effectively conceal long-range, highly maneuverable targets (such as hypersonic missiles); and it is easily located, resulting in weak survivability.
[0004] One existing technical solution involves using a manned aircraft to carry smoke grenade launchers and manually deploying them in the airspace. The advantages are a large payload, enabling smoke coverage over a wide area; and greater decision-making flexibility due to manned operation. Disadvantages include susceptibility to airspace control and takeoff / landing site limitations, slow response time (typically several tens of minutes from mission command to deployment); deployment parameters (such as deployment time and detonation delay) rely on pilot experience, resulting in poor masking accuracy in complex environments.
[0005] Early methods of drone smoke screen deployment included manual operation. The advantage of this method is that it is intuitive to operate and suitable for low-complexity, small-scale interference tasks. The disadvantage is that it relies on the real-time judgment of the ground operator, is greatly affected by communication delays, and cannot cope with the needs of high-speed dynamic interception.
[0006] There is also fixed parameter preset deployment, which has the advantages of standardized process, small amount of calculation, and can quickly execute preset tasks; the disadvantage is that the parameters (such as heading angle and deployment time) are fixed in advance and cannot be adapted to dynamic variables (such as sudden wind direction changes and missile trajectory deviations), and the effective masking time is generally less than 2 seconds.
[0007] Existing intelligent algorithm-assisted drone smoke screen deployment methods employ intelligent algorithms such as genetic algorithms and particle swarm optimization (PSO) to optimize and solve deployment parameters.
[0008] The disadvantages are also obvious: low computational efficiency: the number of iterations is large under high-dimensional parameters (such as multi-machine and multi-missile scenarios), and the optimization of a single set of parameters takes 20 to 30 seconds, which cannot meet the real-time requirements of interception; It is prone to getting trapped in local optima: the algorithm's search process is highly blind, and the occlusion effect often fluctuates under complex conditions (such as high parameter coupling). Weak coordination capability: It only focuses on the optimization of parameters of a single machine and a single missile, lacks the spatiotemporal dynamic coordination logic of multiple machines and multiple missiles, and has poor coverage balance when multiple targets are covered.
[0009] Current smokescreen deployment methods have not yet solved the balance problem of "mobility-adaptability-coordination": traditional methods lack mobility, early drone methods have poor adaptability, and existing intelligent algorithm methods lack efficiency and coordination capabilities, making it difficult to cope with highly mobile and multi-batch threats. Summary of the Invention
[0010] To address the aforementioned shortcomings in existing technologies, this invention provides a drone smoke grenade delivery method based on adaptive optimization and spatiotemporal dynamic coordination, which solves the problem of insufficient efficiency in existing methods.
[0011] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a method for deploying drone smoke bombs based on adaptive optimization and spatiotemporal dynamic coordination, comprising: Construct a differential equation model of missile kinematics, a motion model of unmanned aerial vehicles, and a motion and cloud model of smoke grenade; Geometric shielding criteria are set based on the missile kinematic differential equation model, the UAV motion model, and the smoke grenade motion and cloud model; The system determines the operational conditions for drone smoke grenade deployment. Based on the missile kinematic differential equation model, drone motion model, smoke grenade motion and cloud model, and geometric obscuration criteria, it uses an adaptive multi-resolution coordinate descent algorithm to optimize parameters. Based on the optimized parameters, it obtains the corresponding control commands for the drone and missile, thus arriving at the optimal smoke grenade deployment strategy.
[0012] The beneficial effects of this invention are as follows: it is compatible with all working conditions of single-machine single-missile, single-machine multiple-missile, multi-machine collaborative, and multi-machine multi-target scenarios, solving the problem of narrow adaptability of existing methods; by combining adaptive optimization algorithms with spatiotemporal dynamic collaborative logic, the parameter optimization efficiency is improved by 3 to 5 times, meeting the real-time requirements of high-speed missile interception; the multi-missile multi-machine collaborative strategy can achieve continuous connection of shielding intervals, and the effective shielding time is increased by more than 235% compared with existing methods, with significantly optimized coverage balance.
[0013] Furthermore, the expression for the missile kinematic differential equation model is as follows:
[0014] in, for The missile's spatial position at all times; This is the initial position of the missile; This is the unit vector representing the missile's flight direction. For time; The missile's flight speed; It is an L2 norm.
[0015] The beneficial effects of the above-mentioned further scheme are as follows: the missile flight direction unit vector is accurately defined based on the L2 norm, ensuring the accuracy of missile trajectory calculation; the model expression is concise and has a clear physical meaning, and can quickly iterate to calculate the missile's spatial position at any time, providing reliable data support for the real-time verification of subsequent masking criteria.
[0016] Furthermore, the expression for the UAV motion model is:
[0017] in, for Real-time drone spatial location; This is the initial position of the drone; The flight speed of the drone; For time; This is the unit vector representing the UAV's flight direction. This refers to the heading angle of the drone.
[0018] The beneficial effects of the above-mentioned further scheme are: by associating the heading angle with the unit vector of the flight direction using trigonometric functions, the uniform linear motion state of the UAV on the horizontal plane can be accurately characterized; the model parameters (heading angle, velocity) can be directly used as variables to be optimized without additional conversion, thus reducing the complexity of algorithm optimization.
[0019] Furthermore, the expression for the smoke grenade motion and cloud model is as follows:
[0020] in, for Location of unexploded smoke grenades; for Real-time drone spatial location; The timing for deploying smoke grenades; The flight speed of the drone; For time; This is the unit vector representing the UAV's flight direction. It is the acceleration due to gravity; for The center of the smoke cloud at all times; The center of the smoke cloud detonation; The descending speed of the smoke cloud; This is the moment the smoke grenade detonates.
[0021] The beneficial effects of the above-mentioned further scheme are: it depicts the smoke screen movement process in stages (flat throwing before detonation and uniform descent after detonation), which conforms to the actual physical laws and avoids the deviation of the occlusion position caused by the existing model ignoring the movement stage; it clearly associates the UAV movement parameters with the smoke screen position, ensuring a strong coupling and matching between the deployment parameter optimization and the smoke screen movement state.
[0022] Furthermore, the geometric occlusion criterion is specifically: when At that time, it was determined that the smoke screen was in The missile is effectively shielded at all times; otherwise, it is ineffectively shielded. for From the center of the smoke screen and The shortest distance between the line segments; for The missile's spatial position at all times; The coordinates of the center of the lower surface of the true target are given. The radius of the smoke cloud.
[0023] The beneficial effects of the above-mentioned further scheme are: transforming the vague concept of "smoke screen obscuring missile line of sight" into a quantitative criterion of "distance ≤ radius", which is logically rigorous and highly operable; focusing on the line segment connecting the missile and the real target (not an infinite straight line), avoiding invalid obscuring judgments, and improving the calculation accuracy of obscuring effectiveness.
[0024] Furthermore, the aforementioned The formula for calculation is:
[0025] in, for The center of the smoke cloud at all times; For time; The coordinates of the closest point on the line segment from the center of the smoke screen to the missile-real target; for The missile's spatial position at all times; The parameter represents the closest point on the line segment from the center of the smoke screen to the missile-real target. for arrive The connecting vector; It is the maximum value; It is the minimum value; The parameters for the straight-line projection from the center of the smoke screen to the missile-real target; for arrive ; The coordinates of the center of the lower plane of the true target are given.
[0026] The advantages of the above-mentioned further solutions are as follows: by using vector calculation and projection parameter clipping, the solution process for the shortest distance from the center of the smoke screen to the target line segment is standardized, and the results are highly reproducible; the calculation process does not require complex iterations, balancing accuracy and efficiency, and is suitable for real-time occlusion determination scenarios.
[0027] Furthermore, the adaptive multi-resolution coordinate descent algorithm is specifically as follows: S1. Determine the parameter vector to be optimized and the optimization objective; S2. For each parameter in the parameter vector to be optimized, perform the following operations to determine the high-quality value range of each parameter: Fix the remaining parameters to be optimized (excluding the current parameter) as the corresponding current optimal solution; for the current parameter to be optimized, traverse the corresponding feasible region with a step size of 'a', and evaluate the objective function value for each parameter value based on the optimization objective; fit the parameter value-objective function value curve based on each parameter value and the corresponding objective function value; extract the parameter value range that meets the engineering constraints from the parameter value-objective function value curve based on a preset objective function threshold, and use it as the high-quality value range of the current parameter to be optimized; the engineering constraints include the value range of each parameter to be optimized. S3. Based on the high-quality value range of each parameter to be optimized, perform the following operations to determine the optimal solution for each parameter to be optimized: fix the other parameters to be optimized except the current parameter to be optimized as the corresponding current optimal solution; for the current parameter to be optimized, traverse within the corresponding high-quality value range with a step size of b; and based on the optimization objective, take the value of the current parameter to be optimized that maximizes the objective function value as the current optimal solution for the current parameter to be optimized; b <a; S4. After sequentially traversing and optimizing all parameters to be optimized, determine whether the difference between the current objective function value and the objective function value completed in the previous round of traversal is less than the difference threshold. If so, obtain the optimal solution for each parameter to be optimized; otherwise, return to S3 to continue traversing each parameter to be optimized in the high-quality value range.
[0028] The beneficial effects of the above-mentioned further scheme are as follows: the adoption of the "coarse scan + fine scan" two-stage strategy avoids the local optimum trap of traditional algorithms and greatly reduces the search space, balancing global exploration and local refinement; the adaptive step size (a>b) design can dynamically adjust the search accuracy according to the parameter distribution, improving the optimization efficiency by more than 40% compared with the fixed step size algorithm; the iterative convergence threshold determination mechanism ensures that the algorithm terminates in time when the accuracy requirements are met, avoiding invalid calculations.
[0029] Furthermore, when iterating through the parameters to be optimized, multi-threading technology is used to compute the objective function value under different parameter combinations in parallel.
[0030] The beneficial effects of the above-mentioned further solutions are: multi-threaded parallel evaluation of the objective function value of different parameter combinations reduces the parameter traversal calculation time by more than 60%, further enhancing the real-time performance of the algorithm; parallel computing does not change the parameter optimization logic, is compatible with parameter dimensions under different working conditions, and has strong scalability.
[0031] Furthermore, when the drone smoke grenade deployment condition is a single target and a single drone with a single grenade, the parameter vector to be optimized is: The optimization objective is to ;in, To optimize the parameter vector; This refers to the heading angle of the drone; The flight speed of the drone; The timing for deploying smoke grenades; Delay time for smoke grenade detonation; Total effective masking duration; To effectively mask the computation function; When the drone smoke grenade deployment scenario is a single target with multiple grenades per drone, the parameters to be optimized include: , And various smoke bombs and The optimization objective is to maximize the union duration of the multi-projectile obscuring intervals. When the drone smoke grenade deployment is a single-target and multi-drone coordinated operation, each drone is optimized for a single-drone single-grenade operation. When the scenario of drone smoke grenade deployment involves multiple drones and multiple targets, targets are assigned to each drone based on distance weights. For drone groups assigned to the same target, optimization methods are applied to scenarios involving single target and multiple drones launching multiple grenades, or single target and multiple drones coordinating.
[0032] The beneficial effects of the above-mentioned further solutions are as follows: Optimization parameters and objective functions are designed differently for different working conditions, achieving "one method for multiple uses" and solving the problem of poor scenario adaptability of existing methods; single-aircraft multi-missile focusing timing coordination, multi-aircraft multi-target focusing target allocation and intra-group coordination, and the progressive optimization logic ensures that the masking effect is maximized in complex scenarios; target allocation combined with distance weights ensures that UAV resources are tilted towards higher priority interception tasks, improving overall defense effectiveness. Attached Figure Description
[0033] Figure 1 This is a flowchart of the method of the present invention.
[0034] Figure 2 This is a two-dimensional visualization model of the basic model in the embodiments of the present invention.
[0035] Figure 3 This is a schematic diagram illustrating the effective shielding range for verifying fixed parameters in an embodiment of the present invention.
[0036] Figure 4 This is a historical graph of the adaptive coordinate descent optimization process in an embodiment of the present invention.
[0037] Figure 5 This is a schematic diagram of the shielding interval under the optimal single-unit single-missile strategy in an embodiment of the present invention.
[0038] Figure 6 This is a schematic diagram of the shielding interval under the optimal strategy of single-machine three-missile in an embodiment of the present invention.
[0039] Figure 7 This is a schematic diagram of the timeline of three-machine collaborative occlusion in an embodiment of the present invention.
[0040] Figure 8 This is a comparison diagram of the five-machine collaborative occlusion effect in an embodiment of the present invention. Detailed Implementation
[0041] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.
[0042] like Figure 1 As shown, in one embodiment of the present invention, a method for delivering drone smoke bombs based on adaptive optimization and spatiotemporal dynamic coordination includes: Construct a differential equation model of missile kinematics, a motion model of unmanned aerial vehicles, and a motion and cloud model of smoke grenade; Geometric shielding criteria are set based on the missile kinematic differential equation model, the UAV motion model, and the smoke grenade motion and cloud model; The system determines the operational conditions for drone smoke grenade deployment. Based on the missile kinematic differential equation model, drone motion model, smoke grenade motion and cloud model, and geometric obscuration criteria, it uses an adaptive multi-resolution coordinate descent algorithm to optimize parameters. Based on the optimized parameters, it obtains the corresponding control commands for the drone and missile, thus arriving at the optimal smoke grenade deployment strategy.
[0043] In this embodiment, obtaining the final delivery strategy based on these optimized parameter values involves four steps: Step 1: Obtain the optimized values of each parameter output by the adaptive multi-resolution coordinate descent algorithm (e.g., under single-unit, single-missile conditions). , , , ); Step 2: Combining the UAV's flight control logic, the "heading angle" is... Flight speed "Transformed into control commands for the drone; Step 3: Based on the triggering logic of the smoke grenade launcher, set the "deployment time" Detonation delay "Transformed into timing trigger commands for the transmitter; Step 4: In the case of multiple aircraft / multiple missiles, additionally combine "timing coordination strategies (such as multiple missile release intervals)" and "space allocation strategies (such as multi-aircraft trajectory collision avoidance)" to integrate the optimized parameter values of each aircraft / missile into a coordinated execution command. The expression for the missile kinematic differential equation model is as follows:
[0044] in, for The missile's spatial position at all times; This is the initial position of the missile; This is the unit vector representing the missile's flight direction. For time; The missile's flight speed; It is an L2 norm.
[0045] The expression for the UAV motion model is:
[0046] in, for Real-time drone spatial location; This is the initial position of the drone; The flight speed of the drone; For time; This is the unit vector representing the UAV's flight direction. This refers to the heading angle of the drone.
[0047] The expression for the motion of the smoke grenade and the cloud model is as follows:
[0048] in, for Location of unexploded smoke grenades; for Real-time drone spatial location; The timing for deploying smoke grenades; The flight speed of the drone; For time; This is the unit vector representing the UAV's flight direction. It is the acceleration due to gravity; for The center of the smoke cloud at all times; The center of the smoke cloud detonation; The descending speed of the smoke cloud; This is the moment the smoke grenade detonates.
[0049] The geometric occlusion criterion is specifically: when At that time, it was determined that the smoke screen was in The missile is effectively shielded at all times; otherwise, it is ineffectively shielded. for From the center of the smoke screen and The shortest distance between the line segments; for The missile's spatial position at all times; The coordinates of the center of the lower surface of the true target are given. The radius of the smoke cloud.
[0050] The The formula for calculation is:
[0051] in, for The center of the smoke cloud at all times; For time; The coordinates of the closest point on the line segment from the center of the smoke screen to the missile-real target; for The missile's spatial position at all times; The parameter represents the closest point on the line segment from the center of the smoke screen to the missile-real target. for arrive The connecting vector; It is the maximum value; It is the minimum value; The parameters for the straight-line projection from the center of the smoke screen to the missile-real target; for arrive ; The coordinates of the center of the lower plane of the true target are given.
[0052] The adaptive multi-resolution coordinate descent algorithm is specifically as follows: S1. Determine the parameter vector to be optimized and the optimization objective; S2. For each parameter in the parameter vector to be optimized, perform the following operations to determine the high-quality value range of each parameter: Fix the remaining parameters to be optimized (excluding the current parameter) as the corresponding current optimal solution; for the current parameter to be optimized, traverse the corresponding feasible region with a step size of 'a', and evaluate the objective function value for each parameter value based on the optimization objective; fit the parameter value-objective function value curve based on each parameter value and the corresponding objective function value; extract the parameter value range that meets the engineering constraints from the parameter value-objective function value curve based on a preset objective function threshold, and use it as the high-quality value range of the current parameter to be optimized; the engineering constraints include the value range of each parameter to be optimized. S3. Based on the high-quality value range of each parameter to be optimized, perform the following operations to determine the optimal solution for each parameter to be optimized: fix the other parameters to be optimized except the current parameter to be optimized as the corresponding current optimal solution; for the current parameter to be optimized, traverse within the corresponding high-quality value range with a step size of b; and based on the optimization objective, take the value of the current parameter to be optimized that maximizes the objective function value as the current optimal solution for the current parameter to be optimized; b <a; S4. After sequentially traversing and optimizing all parameters to be optimized, determine whether the difference between the current objective function value and the objective function value completed in the previous round of traversal is less than the difference threshold. If so, obtain the optimal solution for each parameter to be optimized; otherwise, return to S3 to continue traversing each parameter to be optimized in the high-quality value range.
[0053] When iterating through the parameters to be optimized, multi-threading technology is used to calculate the objective function value under different parameter combinations in parallel.
[0054] When the drone smoke grenade deployment condition is single target and single drone with single grenade, the parameter vector to be optimized is: The optimization objective is to ;in, To optimize the parameter vector; This refers to the heading angle of the drone; The flight speed of the drone; The timing for deploying smoke grenades; Delay time for smoke grenade detonation; Total effective masking duration; To effectively mask the computation function; When the drone smoke grenade deployment scenario is a single target with multiple grenades per drone, the parameters to be optimized include: , And various smoke bombs and The optimization objective is to maximize the union duration of the multi-projectile obscuring intervals. When the drone smoke grenade deployment is a single-target and multi-drone coordinated operation, each drone is optimized for a single-drone single-grenade operation. When the scenario of drone smoke grenade deployment involves multiple drones and multiple targets, targets are assigned to each drone based on distance weights. For drone groups assigned to the same target, optimization methods are applied to scenarios involving single target and multiple drones launching multiple grenades, or single target and multiple drones coordinating.
[0055] In this embodiment, the core method proposed in this invention improves computational efficiency by 3 to 5 times compared to traditional intelligent iterative algorithms such as particle swarm optimization and genetic algorithms. It exhibits robustness under parameter perturbations, and the model combines computational efficiency with engineering practicality.
[0056] To address the strategy optimization problem of using drone smoke screens to interfere with multiple incoming air-to-ground missiles, a spatiotemporal dynamic collaborative model integrating physical mechanisms and data-driven approaches was constructed. By accurately characterizing the motion patterns of missiles, drones, and smoke screens, an innovative adaptive coordinate descent algorithm was proposed, and a spatiotemporal dynamic collaborative strategy model was built to solve the deployment strategy problem of drone swarms facing incoming missile swarms.
[0057] For condition one (fixed parameter verification), based on the geometric obscuration criterion of line segment-sphere intersection detection, differential equation models of the precise trajectories of the missile's uniform linear motion, the smoke grenade's projectile motion, and the cloud's uniform descent were established (e.g., Figure 2 As shown in the figure, this problem presents the calculation of the interference duration of an FY1 missile carrying a single smoke grenade against an M1 missile under fixed parameters (origin and destination, velocity, path, release time, and detonation time). Working condition one aims to verify the correctness of the basic model. Based on the given parameters (…), =120m / s, =-180°, =1.5s, =3.6s), which was substituted into the model for calculation. Through high-precision time scanning (step size 0.01s) combined with the precise boundary conditions of the bisection method, all conditions satisfying the... ≤ The time points were calculated and merged to form a continuous interval. The effective shielding interval was calculated to be [8.013, 9.448] seconds (starting from when the missile was detected), and the total shielding duration was... =1.435 seconds. Figure 3 It shows the relative positions and changes in the obscuring status of the missile, smoke cloud, and real target during this period.
[0058] For scenario two (single-unit, single-missile optimization), an adaptive coordinate descent method is applied, combined with a two-stage strategy of coarse and fine scans, to globally optimize four parameters: UAV heading, speed, release time, and detonation delay. For the four decision variables... Optimization was performed. The algorithm converged after two rounds of coarse scanning and two rounds of fine scanning. The optimization process history is as follows: Figure 4 As shown. Heading angle =9.0°, speed =74.0m / s, release time =0.0s (deployed immediately after receiving the mission), detonation delay =1.0s, under this optimal strategy, the effective occlusion duration reaches =4.811 seconds, the occlusion interval is [1.023, 5.834] seconds, such as Figure 5 As shown.
[0059] For operational condition three (single-unit three-missile coordination), based on fixed optimal motion parameters (operations one and two), and with the goal of maximizing the effective shielding duration, multi-missile timing coordination optimization is performed. By controlling the deployment and detonation sequence of the three decoy munitions, a joint shielding range with a wider coverage area is formed, achieving a total effective shielding duration of 6.180s. Figure 6 As shown, the problem of coordinating the three smoke grenades is solved, and the effect is improved by 28.5% compared with the best single-grenade.
[0060] For scenario four (three-drone coordination), based on the initial positions of the UAVs in FY1, FY2, and FY3, a phased interception strategy was adopted. Optimal window periods were allocated to each drone for different time periods (FY1 → initial phase, FY2 → middle phase, FY3 → final phase), and their motion and ballistic parameters were independently optimized. Ultimately, the three shielding intervals did not overlap, and the total duration reached 11.285 seconds. Figure 7 As shown.
[0061] For scenario five (five UAVs and three missiles working together), facing the global resource allocation problem, a mathematical target allocation model based on distance weight and time window matching degree was first established. This model dynamically allocates five UAVs to three missiles: FY1→M1, FY2→M2, FY3→M3, FY4→M2, and FY5→M3. Then, the timing of multiple missile deployments within each group is optimized collaboratively. Ultimately, a total effective masking duration of 10.863s was achieved for the three missiles (M1: 4.148s, M2: 3.907s, M3: 2.808s). Figure 8 As shown, the coverage is balanced.
Claims
1. A method for launching a UAV smoke bomb based on adaptive optimization and spatiotemporal dynamic coordination, characterized in that, include: Construct a differential equation model of missile kinematics, a motion model of unmanned aerial vehicles, and a motion and cloud model of smoke grenade; Geometric shielding criteria are set based on the missile kinematic differential equation model, the UAV motion model, and the smoke grenade motion and cloud model; The system determines the operational conditions for drone smoke grenade deployment. Based on the missile kinematic differential equation model, drone motion model, smoke grenade motion and cloud model, and geometric obscuration criteria, it uses an adaptive multi-resolution coordinate descent algorithm to optimize parameters. Based on the optimized parameters, it obtains the corresponding control commands for the drone and missile, thus arriving at the optimal smoke grenade deployment strategy.
2. The method of claim 1, wherein, The expression for the missile kinematic differential equation model is as follows: wherein, is the momentary missile space position; is the initial missile position; is the missile flight direction unit vector; is the time; is the missile flight speed; is the L2 norm.
3. The method of claim 1, wherein, The expression for the UAV motion model is: in, for Real-time drone spatial location; This is the initial position of the drone; The flight speed of the drone; For time; This is the unit vector representing the UAV's flight direction. This refers to the heading angle of the drone.
4. The UAV smoke grenade delivery method based on adaptive optimization and spatiotemporal dynamic coordination according to claim 1, characterized in that, The expression for the motion of the smoke grenade and the cloud model is as follows: in, for Location of unexploded smoke grenades; for Real-time drone spatial location; The timing for deploying smoke grenades; The flight speed of the drone; For time; This is the unit vector representing the UAV's flight direction. It is the acceleration due to gravity; for The center of the smoke cloud at all times; The location of the smoke cloud detonation center; The descending speed of the smoke cloud; This is the moment the smoke grenade detonates.
5. The UAV smoke grenade delivery method based on adaptive optimization and spatiotemporal dynamic coordination according to claim 1, characterized in that, The geometric occlusion criterion is specifically: when At that time, it was determined that the smoke screen was in The missile is effectively shielded at all times; otherwise, it is ineffectively shielded. for From the center of the smoke screen and The shortest distance between the line segments; for The missile's spatial position at all times; The coordinates of the center of the lower surface of the true target are given. The radius of the smoke cloud.
6. The UAV smoke grenade delivery method based on adaptive optimization and spatiotemporal dynamic coordination according to claim 5, characterized in that, The The formula for calculation is: in, for The center of the smoke cloud at all times; For time; The coordinates of the closest point on the line segment from the center of the smoke screen to the missile-real target; for The missile's spatial position at all times; The parameter represents the closest point on the line segment from the center of the smoke screen to the missile-real target. for arrive The connecting vector; It is the maximum value; It is the minimum value; The parameters for the straight-line projection from the center of the smoke screen to the missile-real target; for arrive ; The coordinates of the center of the lower surface of the true target are given.
7. The UAV smoke grenade delivery method based on adaptive optimization and spatiotemporal dynamic coordination according to claim 1, characterized in that, The adaptive multi-resolution coordinate descent algorithm is specifically as follows: S1. Determine the parameter vector to be optimized and the optimization objective; S2. For each parameter in the parameter vector to be optimized, perform the following operations to determine the high-quality value range of each parameter: Fix the remaining parameters to be optimized (excluding the current parameter) as the corresponding current optimal solution; for the current parameter to be optimized, traverse the corresponding feasible region with a step size of 'a', and evaluate the objective function value for each parameter value based on the optimization objective; fit the parameter value-objective function value curve based on each parameter value and the corresponding objective function value; extract the parameter value range that meets the engineering constraints from the parameter value-objective function value curve based on a preset objective function threshold, and use it as the high-quality value range of the current parameter to be optimized; the engineering constraints include the value range of each parameter to be optimized. S3. Based on the high-quality value range of each parameter to be optimized, perform the following operations to determine the optimal solution for each parameter to be optimized: fix the other parameters to be optimized except the current parameter to be optimized as the corresponding current optimal solution; for the current parameter to be optimized, traverse within the corresponding high-quality value range with a step size of b; and based on the optimization objective, take the value of the current parameter to be optimized that maximizes the objective function value as the current optimal solution for the current parameter to be optimized; b <a; S4. After sequentially traversing and optimizing all parameters to be optimized, determine whether the difference between the current objective function value and the objective function value completed in the previous round of traversal is less than the difference threshold. If so, obtain the optimal solution for each parameter to be optimized; otherwise, return to S3 to continue traversing each parameter to be optimized in the high-quality value range.
8. The UAV smoke grenade delivery method based on adaptive optimization and spatiotemporal dynamic coordination according to claim 7, characterized in that, When iterating through the parameters to be optimized, multi-threading technology is used to calculate the objective function value under different parameter combinations in parallel.
9. The UAV smoke grenade delivery method based on adaptive optimization and spatiotemporal dynamic coordination according to claim 7, characterized in that, When the drone smoke grenade deployment condition is single target and single drone with single grenade, the parameter vector to be optimized is: The optimization objective is to ;in, To optimize the parameter vector; This refers to the heading angle of the drone; The flight speed of the drone; The timing for deploying smoke grenades; Delay time for smoke grenade detonation; Total effective masking duration; To effectively mask the computation function; When the drone smoke grenade deployment scenario is a single target with multiple grenades per drone, the parameters to be optimized include: , And various smoke bombs and The optimization objective is to maximize the union duration of the multi-projectile obscuring intervals. When the drone smoke grenade deployment is a single-target and multi-drone coordinated operation, each drone is optimized for a single-drone single-grenade operation. When the scenario of drone smoke grenade deployment involves multiple drones and multiple targets, targets are assigned to each drone based on distance weights. For drone groups assigned to the same target, optimization methods are applied to scenarios involving single target and multiple drones launching multiple grenades, or single target and multiple drones coordinating.