Method and device for generating air defense shell arrangement scheme

Abstract

The invention discloses a method and device for generating an air defense shell arrangement scheme, and the method comprises the steps: collecting the detection data of an incoming target in real time, carrying out the preprocessing, and generating the preprocessing data; pre-generating a trajectory of the incoming attack target according to the pre-processed data, and correcting the pre-generated trajectory of the incoming attack target to obtain a corrected trajectory of the incoming attack target; according to the corrected trajectory of the incoming target, performing air-defense shell arrangement planning in a square coverage area where the incoming target may appear to obtain an air-defense shell arrangement scheme; and optimizing the air-defense shell arrangement scheme by adopting a particle swarm optimization algorithm to obtain an optimized air-defense shell arrangement scheme for intercepting an attacking target. According to the invention, the response speed of a traditional air defense system in coping with dynamic threats is improved, and the interception precision is improved at the same time.

Description

Technical Field

[0001] The invention belongs to the technical field of anti-aircraft shell arrangement optimization, and in particular relates to a method and a device for generating an anti-aircraft shell arrangement scheme. Background Art

[0002] In modern warfare, the battlefield environment is complex and changeable, and the early warning system often faces the challenge of being unable to accurately track the trajectory of incoming targets. In this case, the early warning system can only provide the area where the target may appear, but cannot determine its specific location. This puts higher demands on the response capability of the air defense system, especially when a single air defense shell cannot cover all potential threat areas alone due to its limited mobility, air defense interception faces greater challenges. In order to effectively deal with the threat of incoming targets, it is necessary to adopt a coordinated interception strategy of multiple air defense shells. By carefully designing the launch positions, flight paths and attack timings of multiple air defense shells, they can form intersecting interception areas in space. This collaborative design strategy can expand the overall air defense coverage and ensure that the target can be intercepted in time no matter where it appears. To achieve this collaborative interception, it is necessary to consider a variety of factors, including the speed, mobility, ballistic characteristics of the air defense shells, and the expected movement pattern of the target. In addition, advanced command and control systems and communication networks are the key to the success of collaborative interception, which can ensure a high degree of coordination and information sharing between multiple air defense shells. Through this multi-level and multi-dimensional coordinated interception method, the air defense system can more effectively respond to complex threats and increase the probability of destroying incoming targets. This paper will discuss in detail the specific technical methods and tactical applications of multiple air defense shells to coordinately intercept a single incoming target, as well as the practice and development prospects in modern air defense operations.

[0003] In the prior art, regional coverage optimization mainly focuses on the location deployment of combat units. Specifically, each combat unit moves to the optimal position based on a specific algorithm and eventually stays at that position to achieve optimal perception and coverage of the mission area. In current research, the coverage capabilities of combat units are usually considered independent of each other, and the working sequence of a combat unit will not affect the coverage capabilities of other units. At the same time, the coverage capabilities of combat units are assumed to be constant over time. However, in some coverage tasks, the coverage capabilities of combat units or the covered mission areas may change over time. In addition, the coverage capabilities of some combat units for the region can be improved through information transmission. Therefore, when formulating work time scheduling, it is necessary to consider the mutual influence between combat units, which also prompts the optimization of the number of combat units to improve the overall coverage efficiency. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and device for generating an air defense shell deployment plan, which improves the response speed of a traditional air defense system when dealing with dynamic threats and improves the interception accuracy.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] In a first aspect, a method for generating an air defense artillery shell deployment plan is provided, comprising: collecting detection data of an incoming target in real time, and preprocessing it to generate preprocessed data; pregenerating a trajectory of the incoming target according to the preprocessed data, and correcting the pregenerated trajectory of the incoming target to obtain a corrected trajectory of the incoming target; planning the deployment of air defense artillery shells within a square coverage area where the incoming target may appear according to the corrected trajectory of the incoming target to obtain an air defense artillery shell deployment plan; optimizing the air defense artillery shell deployment plan using a particle swarm optimization algorithm to obtain an optimized air defense artillery shell deployment plan for intercepting the incoming target.

[0007] Furthermore, detection data of incoming targets are collected in real time and preprocessed, including: synchronizing and time-aligning different types of detection data through timestamp technology to obtain intermediate data; denoising, cleaning and data fusion of the intermediate data to obtain preprocessed data.

[0008] Furthermore, the trajectory of the incoming target is pre-generated based on the pre-processed data, including: obtaining a state estimate of the incoming target based on the pre-processed data, and establishing a motion model of the incoming target; according to the motion model of the incoming target, obtaining a mean and covariance matrix of the motion state of the incoming target, and then pre-generating the trajectory of the incoming target.

[0009] Furthermore, the pre-generated trajectory of the incoming target is corrected, including:

[0010] Error model construction:

[0011] The error model includes systematic error and measurement error. It is assumed that the error model is Gaussian distributed, and the systematic error and measurement error are uncorrelated and satisfy

[0012] , ,

[0013] in, represents the system noise, represents the measurement noise, represents the system noise covariance matrix, is the measurement noise covariance matrix, The mean is 0 and the variance is Gaussian distribution, The mean is 0 and the variance is Gaussian distribution, the system's motion state equation is expressed as

[0014] ,

[0015] in, represents the state vector of the system at time k, represents the state transfer matrix, express The control input at the moment, represents the input matrix, express System noise at each moment;

[0016] The measurement model equation is

[0017] ,

[0018] in, represents the measurement value of the sensor at time k, represents the observation matrix, represents the measurement noise at time k;

[0019] Incoming target status prediction:

[0020] According to the motion model of the incoming target, the state of the incoming target at time k Prediction

[0021] ,

[0022] in, represents the state prediction value at time k, is the estimated value of the state based on the previous moment k-1, It was the last moment Control input of

[0023] Forecast error covariance matrix

[0024] ,

[0025] in, Indicates at time The error covariance matrix of the predicted state indicates the magnitude of the prediction error, which is based on The estimated and observed data of the time; Indicates based on The observation data at time The error covariance matrix at time instant;

[0026] Measurement information update:

[0027] The measurement value collected by the sensor at time k With the predicted value The difference between Defined as

[0028] ,

[0029] Its covariance matrix is

[0030] ,

[0031] in, It reflects the error between actual measurement and prediction and is used to correct state estimation;

[0032] Kalman gain calculation:

[0033] Kalman Gain Determines the weight distribution of measured values ​​and predicted values ​​in state update, and its calculation formula is:

[0034] ,

[0035] The larger the Kalman gain, the more the system relies on the current measurement value; the smaller the Kalman gain, the more the system relies on the predicted value;

[0036] Status Update:

[0037] The predicted state is corrected using the Kalman gain, and the updated state estimate is

[0038] ,

[0039] At the same time, the covariance matrix is ​​updated:

[0040] ,

[0041] in, Indicates based on The observation data at time The error covariance matrix at time , Indicates based on The observation data at time The error covariance matrix at time instant;

[0042] Through this state update, the trajectory error of the incoming target is corrected, making the state estimation closer to the actual trajectory of the incoming target.

[0043] Further, according to the corrected trajectory of the incoming target, the anti-aircraft shells are arranged in the square coverage area where the incoming target may appear, including: defining the center coordinates of the square coverage area where the incoming target may appear as , with a side length of L; the square coverage area is divided into several grid cells, and the weight of each grid cell is calculated based on the probability that the incoming target may appear in a certain grid cell and the area of ​​the grid cell; the shells are evenly distributed in the square area, and the spacing of the shells is calculated to ensure that every grid cell in the square coverage area is covered. The launch position of each shell is determined by the following formula:

[0044] ,

[0045] in, Indicates the launch position of the shell, p is the index of the shell, M is the number of shells in each row, , The horizontal and vertical spacing of the shells, where the horizontal and vertical spacing refer to the horizontal and vertical distances between adjacent shells on the plane; the coverage effect of a grid unit is determined by calculating the distance from the center point of the grid unit to the shell launch position

[0046] ,

[0047] in, For the The center coordinates of the grid cells, is the launch position of the pth shell, represents the coverage effect of the grid unit, represents the radius of the shell detonation area. The coverage effect of the grid unit refers to whether each grid unit is completely or partially covered by the damage radius of the shell in the block grid unit. The target area is decomposed into multiple small areas through grid division to evaluate and optimize the coverage effect of the shell. Finally, the anti-aircraft shell layout plan is selected by calculating the total coverage rate of the entire square area.

[0048]

[0049] in, represents the total coverage of the entire square area, represents the weight of the grid cell, It represents the sum of the weights of all shells, and is used to measure whether the coverage area meets the requirements.

[0050] Furthermore, the weight of each grid cell is calculated by the following formula:

[0051] ,

[0052] in, represents the probability that the target is within the grid, Represents the area of ​​a grid cell.

[0053] Furthermore, the particle swarm optimization algorithm is used to optimize the anti-aircraft artillery shell deployment scheme, including: defining the basic parameters of the particle swarm and setting the objective function :

[0054] ,

[0055] ,

[0056] in, Indicates the total number of grids;

[0057] Based on the set objective function, particle swarm optimization is performed, including: initializing the particle swarm, randomly generating the position and speed of the initial particle, the position of the particle represents the launch position of the shell, and the speed represents the change in position, then updating the speed and position of each particle according to the formula of the particle swarm optimization algorithm; for each particle, checking whether the current position is better than its historical best position, if better, then updating the best position of particle i ,

[0058] ,

[0059] Update the global best position ,

[0060] ,

[0061] If the maximum number of iterations is reached Or if the change of the objective function is less than the predetermined threshold, the iteration is stopped; after the iteration, the position of the optimal particle This is the optimized anti-aircraft artillery shell layout plan.

[0062] Furthermore, define the triggering conditions for trajectory changes , Indicates at time The change in the incoming target position is used to trigger the dynamic optimization process.

[0063] ,

[0064] When the trajectory changes Exceeding the preset threshold When the dynamic optimization is triggered, that is, when , regenerates the shell layout plan.

[0065] Furthermore, when dynamic optimization is triggered, the objective function is first updated, assuming that the area of ​​the incoming target is represented by a rectangular grid. Indicates that the new optimization goal is to re-cover the area, and the objective function is expressed as:

[0066] ,

[0067] in, Represents particles In the new grid coverage situation, in order to speed up the optimization, the particle speed update formula is adjusted to:

[0068] ,

[0069] By adjusting the inertia weight , learning factor and , to achieve rapid convergence of the particle swarm, thus generating a new optimal layout plan in a shorter time;

[0070] After generating a new optimal layout plan, update the artillery launch instruction, where the artillery launch angle Corrected to,

[0071] ,

[0072] in, Indicates the coordinates of the incoming target, Indicates the coordinates of the launcher.

[0073] In a second aspect, a device for generating an air defense artillery shell deployment plan is provided, comprising: a data acquisition module, for real-time acquisition of detection data of an incoming target, and preprocessing to generate preprocessed data; a trajectory generation module, for pregenerating a trajectory of the incoming target according to the preprocessed data, and correcting the pregenerated trajectory of the incoming target to obtain a corrected trajectory of the incoming target; a plan generation module, for planning the deployment of air defense artillery shells within a square coverage area where the incoming target may appear according to the corrected trajectory of the incoming target, and obtaining an air defense artillery shell deployment plan; a plan optimization module, for optimizing the air defense artillery shell deployment plan by using a particle swarm optimization algorithm, and obtaining an optimized air defense artillery shell deployment plan for intercepting the incoming target.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] (1) The fixed deployment scheme in the prior art is usually unable to effectively cope with the maneuvering or trajectory changes of the target, while the present invention can adjust the shell deployment scheme in real time according to the dynamic changes of the target trajectory, and continuously optimize the deployment through the particle swarm algorithm to ensure the interception success rate. Even if the target changes during flight, the coverage area of ​​the shell can be quickly adjusted;

[0076] (2) Compared with the traditional shell placement algorithm, the present invention adopts the particle swarm algorithm (PSO) for optimization; the particle swarm algorithm can quickly approach the optimal solution in complex, multi-objective optimization problems with its efficient global search capability; therefore, the present invention can generate the optimal shell placement scheme more quickly and efficiently, maximize the coverage of the target area, and improve the response speed and interception accuracy of the system;

[0077] (3) Traditional air defense systems find it difficult to respond efficiently when faced with ever-changing air threats. The present invention can not only respond to changes in a single target, but can also reasonably plan the deployment of artillery shells through rapid iteration of algorithms in a multi-target, multi-threat environment, so that the system can maintain efficient defense capabilities in a complex combat environment and adapt to a complex and ever-changing threat environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 It is a schematic diagram of the implementation steps of a method for generating an air defense shell arrangement scheme provided by an embodiment of the present invention;

[0079] Figure 2 It is the artillery shell layout plan diagram within the square coverage area before the anti-aircraft artillery shell layout plan is optimized;

[0080] Figure 3 It is the artillery shell layout plan within the square coverage area after the anti-aircraft artillery shell layout plan is optimized. DETAILED DESCRIPTION

[0081] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0082] Embodiment 1

[0083] like Figure 1 As shown, a method for generating an air defense artillery shell arrangement plan includes: collecting detection data of an incoming target in real time, and performing preprocessing to generate preprocessed data; pregenerating a trajectory of the incoming target according to the preprocessed data, and correcting the pregenerated trajectory of the incoming target to obtain a corrected trajectory of the incoming target; performing air defense artillery shell arrangement planning in a square coverage area where the incoming target may appear according to the corrected trajectory of the incoming target to obtain an air defense artillery shell arrangement plan; and optimizing the air defense artillery shell arrangement plan using a particle swarm optimization algorithm to obtain an optimized air defense artillery shell arrangement plan for intercepting the incoming target.

[0084] S1. Collect detection data of incoming targets in real time, perform preprocessing, and generate preprocessed data.

[0085] S11. Detection data collection

[0086] In actual air defense systems, a variety of sensor devices, such as radar, infrared detectors, optoelectronic detection equipment, satellite monitoring systems or drone reconnaissance devices, are used to monitor aerial targets in real time. Different devices have different characteristics. For example, radar can provide accurate distance and speed information, infrared equipment can capture the target's thermal signal, optoelectronic equipment can provide high-resolution visual images, and satellite monitoring and drone reconnaissance can cover a larger range. These multi-source devices work together to effectively reduce the limitations of a single sensor and ensure the integrity and accuracy of incoming target data.

[0087] In order to better realize data collection, multi-source sensor fusion technology can be used. In this technology, multiple sensors work simultaneously and transmit their respective data to the central processing system. Through fusion technology, the data information of multiple sensors can be integrated to obtain a more comprehensive target image and information.

[0088] S12. Data synchronization and time alignment

[0089] Due to the different types of sensors, their data refresh frequency, delay time and acquisition cycle may vary. Therefore, before processing these data, all data need to be synchronized and time-aligned. The present invention uses timestamp technology to add accurate time information to each set of data to ensure that the data of each sensor is consistent at the same time point.

[0090] The key to time synchronization is to ensure that all sensor systems use a unified clock signal. For example, using the Global Positioning System (GPS) to synchronize the time of all devices can avoid data deviations caused by time differences. In this way, the air defense system can obtain the complete situation of the target at the same time point, ensuring the accuracy of subsequent analysis.

[0091] S13. Data denoising and cleaning

[0092] Due to the complex detection environment, sensor equipment may be affected by meteorological conditions, electromagnetic interference or other external noise, resulting in noise or outliers in the data. Therefore, the collected raw data must be denoised and cleaned. Common denoising techniques include low-pass filters, Kalman filters and other methods.

[0093] Among them, the low-pass filter can effectively remove high-frequency noise, filter out sudden abnormal information in the detection data, and retain the smooth trajectory of the target. As a classic data processing technology, the Kalman filter can optimize the estimation of the target's position, speed and other information in multiple measurements, reducing the impact of random errors on the data.

[0094] Through denoising and cleaning processing, more accurate and stable target data can be obtained, thus providing high-quality data input for subsequent predictions and decisions.

[0095] S14. Data fusion and model building

[0096] After data cleaning and normalization, the next step is data fusion. Using data fusion algorithms (such as weighted averaging or Bayesian estimation), the system can combine the data from each sensor to generate a more accurate and reliable target state estimate. For example, radar data provides accurate distance information, and infrared data provides thermal signal characteristics. By fusing these data, the system can generate complete information about the target, including position, speed, direction, and characteristic attributes.

[0097] The radar and other sensor equipment obtained in the above steps are used to obtain the real-time position, velocity and acceleration data of the target. Then, the target's motion model is established by analyzing the target's historical motion trajectory and known characteristics (such as speed, acceleration, and maneuverability). Assume that the target has the following motion model

[0098]

[0099] in, Indicates the motion state of the incoming target. represents its derivative, represents the control input of the incoming target, Represents the measurement information of the detection system, Represents a nonlinear function.

[0100] When the motion model of the incoming target exhibits nonlinear characteristics, its motion equation may not be solved by analytical methods. In this case, numerical calculation methods can be used to estimate the position of the target. Specifically, the motion model of the target can be discretized and the discrete time step can be set to , from which the discrete motion model of the incoming target can be obtained:

[0101]

[0102] in, , Respectively represent the target at discrete moments and The state of A dynamic function representing the target, used to describe the change of state over time. Represents the control input of the target at time.

[0103] S15. Obtain the mean and covariance matrix of the current incoming target motion state

[0104] Assume that the input of the incoming target and the motion state at the initial moment obey Gaussian distribution, where the mean of the control input is , the variance is , the mean motion state at the initial moment is , the covariance matrix is In this setting, let the control input be , through iterative calculation of the discrete motion model, the predicted hit time can be obtained The mean of the target motion state at each moment . The covariance matrix of the target motion state at the moment It can be calculated iteratively through the following equation

[0105]

[0106] in, , They represent the state transfer matrix and the input control matrix, respectively, reflecting the dynamic changes of the system in discrete time steps. Through this iterative approach, the covariance matrix of the target can be continuously updated and predicted, thus providing an accurate state estimate at the moment of impact.

[0107] S2. Pre-generate a trajectory of the incoming target according to the pre-processed data, and correct the pre-generated trajectory of the incoming target to obtain a corrected trajectory of the incoming target.

[0108] S21. Error model construction

[0109] The construction of the error model is the basis for trajectory correction. The following two types of errors are usually used:

[0110] (1) System error: caused by the error of the system itself (such as detection equipment), usually expressed as the system noise covariance matrix express;

[0111] (2) Measurement error: random error generated during sensor measurement, expressed as the measurement noise covariance matrix express.

[0112] The error model usually assumes that the noise is Gaussian distributed and the system noise and measurement noise are uncorrelated, satisfying:

[0113] ,

[0114] in, represents the system noise, represents the measurement noise, The mean is 0 and the variance is Gaussian distribution, The mean is 0 and the variance is Gaussian distribution.

[0115] The system's state equation of motion is usually expressed as:

[0116]

[0117] in, represents the state vector (such as speed, position) of the system at time k, represents the state transfer matrix, express The control input at the moment, represents the input matrix, express System noise at all times.

[0118] The measurement model equation is:

[0119]

[0120] in, yes The sensor's measurement value at that moment, is the observation matrix, yes The measurement noise at the moment.

[0121] S22. Incoming target status prediction

[0122] According to the motion model, the state of the target at time k The prediction is:

[0123]

[0124] in, represents the state prediction value at time k, is the estimated value of the state based on the previous time k-1, It was the last moment control input.

[0125] Forecast error covariance matrix:

[0126]

[0127] in, Indicates at time The error covariance matrix of the predicted state indicates the magnitude of the prediction error, which is based on The estimated time and observed data are obtained, Indicates based on The observation data at time The error covariance matrix at time .

[0128] S23, measurement information update

[0129] The measurement value collected by the sensor at time k With the predicted value The difference between is defined as:

[0130]

[0131] Its covariance matrix is:

[0132]

[0133] in, It reflects the error between actual measurement and prediction and is used to correct state estimation.

[0134] S24, Kalman gain calculation

[0135] Kalman Gain Determines the weight distribution of measured values ​​and predicted values ​​in state update, and its calculation formula is:

[0136]

[0137] The larger the Kalman gain, the more the system relies on the current measurement value; the smaller the gain, the more the system relies on the predicted value.

[0138] S25. Status Update

[0139] The predicted state is corrected using the Kalman gain, and the updated state estimate is

[0140]

[0141] At the same time, the covariance matrix is ​​updated:

[0142]

[0143] in, Indicates based on The observation data at time The error covariance matrix at time , Indicates based on The observation data at time The error covariance matrix at time instant;

[0144] Through this state update, the error of the incoming target trajectory can be effectively corrected, making the state estimation closer to the actual trajectory of the incoming target.

[0145] S26. Obtain the area where the incoming target may appear

[0146] By updating the mean and covariance matrix mentioned in S1 according to the updating method in S21-S25, the predicted hit area at the predicted hit time can be obtained.

[0147]

[0148] in, The mean is , the variance is Gaussian distribution.

[0149] S3. According to the corrected trajectory of the incoming target, the air defense shells are deployed in the square coverage area where the incoming target may appear, and an air defense shells deployment plan is obtained.

[0150] S31, defining a square coverage area;

[0151] First determine the parameters of the square area: center position is the center coordinate of the square area, the side length L is the side length of the square area, and the corner point coordinates of the square area can be calculated as:

[0152]

[0153] The square coverage area is then divided into grid cells so that the coverage performance of each grid cell can be evaluated. The gridding steps are as follows:

[0154] Select the grid size: Select the appropriate grid size d, which is determined based on the effective coverage radius of the shell.

[0155] Generate a grid: Generate several grids of size The coordinate range of the grid cell is

[0156]

[0157] Among them, i and j are the row and column indices of the grid cells.

[0158] Finally, the weight of the grid cell is calculated. The weight is calculated for each grid cell. The weight can be based on factors such as the probability of the target appearing in the grid and the area of ​​the grid cell:

[0159]

[0160] in For the The weight of the grid cells, is the probability that the target is within the grid, is the area of ​​the grid cell.

[0161] S32. Develop a shell deployment plan

[0162] First, set the layout constraints. The shell range G represents the effective range of each shell. The layout quantity It represents the number of available shells, and the overlapping effect represents the overlapping effect of the detonation coverage area of ​​the shells.

[0163] Then generate the placement plan. Generate the shell placement plan based on the data of the gridded coverage area. The following methods can be used:

[0164] (1) Uniform distribution: The shells are evenly distributed in the square area. The spacing of the shells is calculated to ensure that every grid cell in the coverage area is covered. The launch position of each shell can be determined by the following formula:

[0165]

[0166] Among them, p is the index of the shell, M is the number of shells in each row, , is the transverse and longitudinal spacing of the shells, where the transverse spacing and longitudinal spacing refer to the horizontal and vertical distances between adjacent shells on a plane;

[0167] (2) Coverage evaluation: Evaluate the coverage effect of each layout scheme. The coverage effect can be evaluated by calculating the coverage degree of each grid cell. Assuming that the detonation area of ​​the shell is a circle with a radius of R, then a grid cell The coverage effect can be judged by calculating the distance from the center point of the grid unit to the launch position of the shell:

[0168]

[0169] in, For the The center coordinates of the grid cells, is the launch position of the pth shell, represents the coverage effect of the grid unit, Represents the radius of the shell detonation area. The coverage effect of a grid cell usually refers to whether each cell in the block grid is fully or partially covered by the shell coverage area (i.e., damage radius). Grid division is to decompose a larger target area into multiple small areas, so as to facilitate the evaluation and optimization of the shell coverage effect.

[0170] Finally, the anti-aircraft shells layout plan is selected by calculating the total coverage of the entire square area.

[0171]

[0172] in, represents the total coverage of the entire square area, represents the weight of the grid cell, It represents the sum of the weights of all shells, which can be used to measure whether the effect on the coverage area meets the requirements.

[0173] Finally, the selected optimal solution is verified and adjusted to ensure its effectiveness in practical applications. The effectiveness of the solution can be verified through simulation tests or actual exercises.

[0174] S4. The particle swarm optimization algorithm is used to optimize the air defense artillery shell layout plan to obtain the optimized air defense artillery shell layout plan for intercepting incoming targets.

[0175] S41. Define the parameters of the particle swarm algorithm

[0176] The basic parameters of the particle swarm are as follows:

[0177] Number of particles : The number of particles in the particle swarm. The number of particles affects the comprehensiveness and computational complexity of the search. Maximum number of iterations : Maximum number of iterations of the particle swarm algorithm.

[0178] Inertia Weight : Controls the degree to which the particle's current velocity affects its position.

[0179] Learning Factor : They are the learning factors of particle self-cognition and global cognition, respectively, used to adjust the update of particle position.

[0180] Then define the position and velocity of the particle:

[0181] Location : The current position of the ith particle, where d is the dimension (here the launch position of the cannonball).

[0182] speed : The velocity of the ith particle.

[0183] S42. Setting the objective function

[0184] First, define the objective function, which is used to evaluate the quality of each particle placement scheme. For the shell placement, the objective function can be defined as the sum of the coverage effects or the minimization of the error.

[0185] The coverage effect is the total coverage effect of the shells in the area, and the calculation formula is as follows:

[0186]

[0187] in, is the coverage effect of the i-th particle arrangement scheme on the j-th grid unit (1 means coverage, 0 means no coverage), Indicates the total number of grids.

[0188] The objective function f is defined as follows

[0189]

[0190] The goal is to maximize coverage.

[0191] S43. Execute particle swarm optimization

[0192] The first step is to initialize the particle swarm and randomly generate the position and velocity of the initial particles. Indicates the launch position and speed of the projectile Indicates the change in position.

[0193]

[0194]

[0195] in, To generate random positions or parameter values ​​within a specified range, To generate a random speed value within a given speed range

[0196] Then, update the position and velocity of the particle. Update the velocity and position of each particle according to the formula of the particle swarm optimization algorithm. The velocity update formula is:

[0197]

[0198] in, is a random number, is the best historical position of particle i, is the global best position.

[0199] The position update formula is:

[0200]

[0201] in, represents the position of the i-th particle at time t+1, represents the velocity of the ith particle at time t+1, represents the position of the i-th particle at time t.

[0202] Next, update the optimal position of the particle. For each particle, check whether the current position is better than its historical best position. If it is better, update the optimal position of particle i:

[0203]

[0204] Update the global best position

[0205]

[0206] in, represents the position of the ith particle at time t+1, and f is an objective function used to calculate the value of the particle with respect to a specific variable (e.g. ) related values ​​to evaluate the pros and cons of a certain solution

[0207] If the maximum number of iterations is reached Or if the change of the objective function is less than a predetermined threshold, the iteration is stopped.

[0208] S44, Generate the optimal shell placement plan

[0209] First, the optimal arrangement is obtained. After the iteration, the position of the optimal particle This is the optimal shell placement scheme, which can achieve the best coverage effect in the target area.

[0210] Then formulate an implementation plan and, based on the optimal deployment plan, formulate specific artillery firing instructions, including the launch position, launch time and direction of each artillery shell.

[0211] Finally, the layout plan is executed and verified in the actual environment to ensure that the generated layout plan can achieve the expected coverage effect.

[0212] S45. Set dynamic optimization trigger conditions

[0213] When the target trajectory deviates significantly, the system needs to make timely adjustments. In order to trigger the dynamic optimization process, define the trigger conditions for trajectory changes. , indicating that in a short time Change in internal target position:

[0214]

[0215] When the trajectory changes Exceeding the preset threshold , the system triggers dynamic optimization, namely:

[0216]

[0217] This trigger condition is used to determine whether it is necessary to regenerate the shell deployment plan.

[0218] In order to improve the response speed, dynamic optimization is performed in a fixed time window. The target trajectory is monitored internally, and whenever the trajectory change exceeds the threshold, the system will immediately start the optimization process.

[0219] S46, Real-time re-optimization based on particle swarm algorithm

[0220] When the target trajectory changes, the particle swarm algorithm is called again to adjust the layout. Each particle represents a possible shell layout, and the optimization goal is to maximize the coverage of all shells and overlap with the target area.

[0221] First, update the objective function. During the dynamic optimization process, the objective function needs to be adjusted according to the latest position and motion state of the incoming target. Assume that the area of ​​the incoming target is a rectangular grid. Indicates that the new optimization goal is to re-cover the area. The objective function can be expressed as:

[0222]

[0223] in, Represents particles Performance under new grid coverage conditions. During each iteration, the algorithm gradually optimizes the placement of the shells based on the coverage of the particles in the grid.

[0224] Then, adjust the particle speed. In the dynamic optimization process, in order to speed up the optimization, you can adjust the particle speed update formula. The particle speed update formula can be adjusted as follows:

[0225]

[0226] By adjusting the parameters (Inertia weight), (learning factor) can achieve rapid convergence of the particle swarm, thereby generating a new optimal layout solution in a shorter time.

[0227] S47, Real-time adjustment of shell placement

[0228] First, update the firing instructions. After obtaining the optimized shell layout plan, it is necessary to immediately update the firing instructions of each launcher, including parameters such as firing angle, time and position. The firing of shells must be synchronized to ensure the maximum coverage area.

[0229] Coordination and execution are then carried out. Coordination between multiple launchers is very important, especially in dynamic optimization. It is necessary to make synchronous adjustments according to the positions of different launchers and changes in targets to avoid repeated coverage or missing areas.

[0230] Finally, the parameters are adjusted. The key parameters of artillery launch include the launch angle , initial speed and explosion time According to the optimized layout, these parameters will be updated in real time during each adjustment. The calculation of the emission angle can be corrected by the following formula:

[0231]

[0232] Correct the firing angle according to the trajectory of the incoming target to ensure that the shell explodes accurately in the target area. Indicates the coordinates of the incoming target, Indicates the coordinates of the launcher.

[0233] The present invention firstly integrates the real-time detection data from multiple sensors to pre-process the target trajectory to improve the accuracy and reliability of the data. Data pre-processing includes noise filtering and error correction of the original detection data to obtain the accurate target motion trajectory. Then, the trajectory of the incoming target is corrected in real time based on the target error model to ensure the accuracy of the target motion data. Secondly, according to the real-time position and motion state of the target, the possible threat area is generated, and the layout of the shells is planned in the area. The layout of the shells is optimized by using the particle swarm algorithm, and the optimal shell layout configuration is found by simulating the movement and iterative update of multiple particles to ensure the coverage of the maximum threat area and improve the interception success rate. In this process, the system fully considers the changes in the target's motion trajectory and the threat area to provide an accurate shell layout plan. Finally, it has real-time monitoring and dynamic optimization capabilities, and can quickly adjust the shell layout plan when the target trajectory changes. Through continuous monitoring of real-time data, the changes in the target position can be detected in time, and the shell layout can be dynamically optimized according to the new data, thereby improving the system's response speed and interception accuracy. Simulation experiments show that the method designed by the present invention can effectively improve the combat effectiveness of the air defense system in a complex threat environment, and provide a more flexible and accurate solution for the modern air defense system.

[0234] The present invention adopts MATLAB 2022b as simulation software to simulate how to ensure coverage of possible target areas with a minimum number of artillery shells for air defense systems or ground artillery systems. The method proposed in the present invention can achieve coverage of areas where incoming targets may appear with multiple artillery shells, thereby achieving precise strikes on incoming targets.

[0235] In the simulation environment, it is assumed that the target area is a 100m*100m square area. The boundary of the square area is displayed by a black rectangular frame. The effective damage radius of each shell is 10m. The coverage area of ​​each shell is drawn in the form of a circular contour line. The radius of the circular coverage area is equal to the effective coverage range of the shell.

[0236] The code is used to calculate and show the layout plan of using the least number of circular shells to cover the area in a square area. The shells are added gradually by using the shell layout planning method based on the square coverage area of ​​the possible area, ensuring that the shell position selected at each step can maximize the coverage of the area that has not been covered. Figure 2 This is the layout planning diagram (before optimization) obtained by using the artillery shell layout planning method based on the square coverage area of ​​the possible area. It can be concluded that 80 shells are needed to achieve complete coverage of the square area using this method.

[0237] In the same context, Figure 3 The layout planning diagram (after optimization) obtained by using the artillery shell layout plan generation method based on particle swarm algorithm can be concluded that 56 artillery shells are needed to fully cover the square area using this method, which can effectively reduce the number of artillery shells covered compared with the above method.

[0238] It can be clearly seen from the above simulation results that the present invention can achieve the goal of multiple shells covering the area where the incoming target may appear, thereby achieving a precise strike on the incoming target.

[0239] Embodiment 2

[0240] Based on the method for generating an air defense shell arrangement scheme described in the first embodiment, this embodiment provides a device for generating an air defense shell arrangement scheme, including:

[0241] The data acquisition module is used to collect the detection data of the incoming target in real time, and perform preprocessing to generate preprocessed data;

[0242] A trajectory generation module, used to pre-generate the trajectory of the incoming target according to the pre-processed data, and to correct the pre-generated trajectory of the incoming target to obtain a corrected trajectory of the incoming target;

[0243] A scheme generation module is used to plan the deployment of air defense shells in a square coverage area where the incoming target may appear according to the corrected trajectory of the incoming target, and obtain an air defense shell deployment scheme;

[0244] The scheme optimization module is used to optimize the anti-aircraft artillery shell deployment scheme using the particle swarm optimization algorithm to obtain the optimized anti-aircraft artillery shell deployment scheme for intercepting incoming targets.

[0245] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for generating an air defense shell arrangement scheme, characterized in that: include: Collect detection data of incoming targets in real time, perform preprocessing, and generate preprocessed data; Pre-generating a trajectory of the incoming target according to the pre-processed data, and correcting the pre-generated trajectory of the incoming target to obtain a corrected trajectory of the incoming target; According to the corrected trajectory of the incoming target, the air defense shells are deployed in the square coverage area where the incoming target may appear, and the air defense shells deployment plan is obtained; The particle swarm optimization algorithm is used to optimize the air defense artillery shell layout plan, and the optimized air defense artillery shell layout plan is obtained to intercept incoming targets.

2. The method for generating an air defense shell arrangement scheme according to claim 1, characterized in that: Collect detection data of incoming targets in real time and perform preprocessing, including: Through the time stamp technology, different types of detection data are synchronized and time-aligned to obtain intermediate data; The intermediate data is denoised, cleaned and fused to obtain preprocessed data.

3. The method for generating an air defense shell arrangement plan according to claim 1, characterized in that: Pre-generate the trajectory of the incoming target based on the pre-processed data, including: Obtaining a state estimate of the incoming target based on the preprocessed data and establishing a motion model of the incoming target; According to the motion model of the incoming target, the mean and covariance matrix of the motion state of the incoming target are obtained, and then the trajectory of the incoming target is pre-generated.

4. The method for generating an air defense shell arrangement plan according to claim 3, characterized in that: Correct the pre-generated trajectory of incoming targets, including: Error model construction: The error model includes systematic error and measurement error. It is assumed that the error model is Gaussian distributed, and the systematic error and measurement error are uncorrelated and satisfy ， ， in, represents the system noise, represents the measurement noise, represents the system noise covariance matrix, is the measurement noise covariance matrix, The mean is 0 and the variance is Gaussian distribution, The mean is 0 and the variance is Gaussian distribution, the system's motion state equation is expressed as ， in, represents the state vector of the system at time k, represents the state transfer matrix, express The control input at the moment, represents the input matrix, express System noise at each moment; The measurement model equation is ， in, represents the measurement value of the sensor at time k, represents the observation matrix, represents the measurement noise at time k; Incoming target status prediction: According to the motion model of the incoming target, the state of the incoming target at time k Prediction ， in, represents the state prediction value at time k, is the estimated value of the state based on the previous moment k-1, It was the last moment Control input of Forecast error covariance matrix ， in, Indicates at time The error covariance matrix of the predicted state indicates the magnitude of the prediction error, which is based on The estimated and observed data of the time; Indicates based on The observation data at time The error covariance matrix at time; Measurement information update: The measurement value collected by the sensor at time k With the predicted value The difference between Defined as ， Its covariance matrix is ， in, It reflects the error between actual measurement and prediction and is used to correct state estimation; Kalman gain calculation: Kalman Gain Determines the weight distribution of measured values ​​and predicted values ​​in state update, and its calculation formula is: ， The larger the Kalman gain, the more the system relies on the current measurement value; the smaller the Kalman gain, the more the system relies on the predicted value; Status Update: The predicted state is corrected using the Kalman gain, and the updated state estimate is ， At the same time, the covariance matrix is ​​updated: ， in, Indicates based on The observation data at time The error covariance matrix at time , Indicates based on The observation data at time The error covariance matrix at time; Through this state update, the trajectory error of the incoming target is corrected, making the state estimation closer to the actual trajectory of the incoming target.

5. The method for generating an air defense shell arrangement plan according to claim 4, characterized in that: According to the corrected trajectory of the incoming target, the anti-aircraft artillery shells are deployed in the square coverage area where the incoming target may appear, including: Define the center coordinates of the square coverage area where the incoming target may appear as , the side length is L; Divide the square coverage area into several grid cells, and calculate the weight of each grid cell based on the probability that the incoming target may appear in a certain grid cell and the area of ​​the grid cell; The shells are evenly distributed in the square area, and the spacing of the shells is calculated to ensure that every grid cell in the square coverage area is covered. The launch position of each shell is determined by the following formula: ， in, Indicates the launch position of the shell, p is the index of the shell, M is the number of shells in each row, , is the transverse and longitudinal spacing of the shells, where the transverse spacing and longitudinal spacing refer to the horizontal and vertical distances between adjacent shells on a plane; The coverage effect of a grid cell is determined by calculating the distance from the center point of the grid cell to the launch position of the shell. ， in, For the The center coordinates of the grid cells, is the launch position of the pth shell, represents the coverage effect of the grid unit, Indicates the radius of the shell detonation area. The coverage effect of the grid unit refers to whether each grid unit in the block is completely or partially covered by the shell damage radius. The target area is decomposed into multiple small areas through grid division to evaluate and optimize the coverage effect of the shell. Finally, the anti-aircraft shells layout plan is selected by calculating the total coverage of the entire square area. , in, represents the total coverage of the entire square area, represents the weight of the grid cell, It represents the sum of the weights of all shells, and is used to measure whether the coverage area meets the requirements.

6. The method for generating an air defense shell arrangement plan according to claim 5, characterized in that: The weight of each grid cell is calculated by the following formula: ， in, represents the probability that the target is within the grid, Represents the area of ​​a grid cell.

7. The method for generating an air defense shell arrangement plan according to claim 5, characterized in that: The particle swarm optimization algorithm is used to optimize the anti-aircraft artillery shell layout scheme, including: Define the basic parameters of the particle swarm and set the objective function : ， ， in, Indicates the total number of grids; Based on the set objective function, particle swarm optimization is performed, including: Initialize the particle swarm and randomly generate the position and velocity of the initial particle. The position of the particle represents the launch position of the shell, and the velocity represents the change in position. Then, the speed and position of each particle are updated according to the formula of the particle swarm optimization algorithm; For each particle, check whether the current position is better than its historical best position. If so, update the best position of particle i. , ， Update the global best position , ， If the maximum number of iterations is reached Or if the change of the objective function is less than a predetermined threshold, the iteration is stopped; After the iteration, the position of the optimal particle This is the optimized anti-aircraft artillery shell layout plan.

8. The method for generating an air defense shell arrangement plan according to claim 7, characterized in that: Define the trigger conditions for trajectory changes , Indicates at time The change in the incoming target position is used to trigger the dynamic optimization process. ， When the trajectory changes Exceeding the preset threshold When the dynamic optimization is triggered, that is, when , regenerates the shell layout plan.

9. The method for generating an air defense shell arrangement plan according to claim 8, characterized in that: When dynamic optimization is triggered, the objective function is updated first, assuming that the target area is represented by a rectangular grid. Indicates that the new optimization goal is to re-cover the area, and the objective function is expressed as: ， in, Represents particles In the new grid coverage situation, in order to speed up the optimization, the particle speed update formula is adjusted to: ， By adjusting the inertia weight , learning factor and , to achieve rapid convergence of particle swarm, thus generating a new optimal layout plan in a shorter time; After generating a new optimal layout plan, update the artillery launch instruction, where the artillery launch angle Corrected to, ， in, Indicates the coordinates of the incoming target, Indicates the coordinates of the launcher.

10. A device for generating an air defense shell arrangement plan, characterized in that: include: The data acquisition module is used to collect the detection data of the incoming target in real time, and perform preprocessing to generate preprocessed data; A trajectory generation module, used to pre-generate the trajectory of the incoming target according to the pre-processed data, and to correct the pre-generated trajectory of the incoming target to obtain a corrected trajectory of the incoming target; A scheme generation module is used to plan the deployment of air defense shells in a square coverage area where the incoming target may appear according to the corrected trajectory of the incoming target, and obtain an air defense shell deployment scheme; The scheme optimization module is used to optimize the anti-aircraft artillery shell deployment scheme using the particle swarm optimization algorithm to obtain the optimized anti-aircraft artillery shell deployment scheme for intercepting incoming targets.

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