CN113361196A - Missile killing probability evaluation method, system, equipment and readable medium - Google Patents
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Abstract
The invention provides a missile killing probability assessment method, which comprises the following steps: step S1: calculating the killing probability of different rendezvous conditions to different targets by using a missile single-shot killing probability calculation method; step S2: learning the killing probability results of different intersection conditions and different targets by using a feedforward neural network, and storing the learned neural network parameters; step S3: and the estimation of the missile killing probability can be completed by utilizing the learned feedforward neural network parameters. The invention also provides a missile killing probability evaluation system, equipment and a readable medium. According to the method, the missile single-shot killing probability is quickly calculated by using a simple model, so that the simulation time consumption can be greatly reduced, and the development efficiency is improved.
Description
Technical Field
The invention relates to the general technical field of missiles, in particular to a method, a system, equipment and a readable medium for missile killing probability evaluation.
Background
The design and parameter selection of the cooperation rule of the guided warfare of an important work event in the process of developing the guided warfare system, and the design and determination of the main technical indexes of the guided warfare and the warhead relate to a large amount of simulation calculation of the cooperation rule of the guided warfare under various meeting conditions. In addition, the missile single-shot killing probability under various intersection conditions is frequently required to be frequently calculated in the development process, such as the design and optimization of overall parameters in a scheme demonstration stage, the search of a killing area in an engineering development stage, the calculation of a full airspace killing probability, the determination of a flight test scheme and the like. Such simulations are computationally intensive and time consuming, but often do not require the acquisition of detailed intermediate data. The traditional missile single-shot killing probability calculation method is complex in model, long in calculation time and not suitable for the tasks.
Through retrieval, patent document CN112149277A discloses an anti-aircraft weapon target threat modeling method and device, which comprises the steps of obtaining an air-ground missile killer model and an antiaircraft gun killer model; according to the types of the ground-air missile and the antiaircraft gun, technical parameters and deployment position information corresponding to the ground-air missile and the antiaircraft gun are inquired from a weapon parameter database; correcting parameters of the air defense weapon target threat model according to the envelope ranges of the vertical and horizontal killing areas of the ground-air missile, the envelope ranges of the vertical and horizontal effective fire areas of the antiaircraft gun, the corresponding technical parameters of the ground-air missile and the antiaircraft gun and the deployment position information to generate an air defense weapon target threat model, and determining the threat probability of the air defense weapon on cruise navigation according to the output of the air defense weapon target threat model. The defects of the prior art are as follows: the ground-air missile killing model obtained by the technology is mainly parameters of a horizontal killing area and a vertical killing area, the parameters only describe whether the missile can kill the target at different coordinate points, and specific values of killing probability of the missile at different coordinate points to different targets are not included; therefore, the target threat model generated based on the technology only describes the result of whether the ground-air missile has the killing capability under different motion tracks and coordinates, and cannot give specific values of the killing probability of the missile to different targets under different coordinates.
Patent document CN112035780A discloses a missile terminal guidance stage killing effect calculation method, which includes: the missile and target geometry is fixedly connected to respective centroids according to direction cosine matrixes of the missile and the target, and the motion model of the respective three-dimensional geometry in the three-dimensional space is determined according to the direction cosine matrix of the missile and the target; after detecting the target, the fuze sends a detonation signal and reaches the warhead after signal delay; after the missile is detonated, fragments are uniformly distributed on a killing surface, a motion equation of each fragment is calculated, then the motion equation is substituted into a target geometric body motion equation, whether the fragments hit and the hit part are calculated, and the killing effect is evaluated according to the number of fragments hitting each part of the target and the weighting of the killing coefficient. The defects of the prior art are that a classical missile single shot killing probability calculation theory and a classical missile single shot killing probability calculation method are adopted, the motion process of each fragment, the hit of each fragment and each surface element in the target geometry and the damage condition of the target need to be solved and judged, and the simulation solution of the model needs to be carried out again on different missile meeting conditions. According to the number of fragments and the complexity of the target geometric model, the simulation calculation of the single bullet-and-eye interaction condition takes tens of minutes to hours, and the time consumption is long.
Therefore, it is necessary to develop and design a method and a system capable of rapidly evaluating the missile killing probability.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a missile killing probability evaluation method, a system, equipment and a readable medium, so as to achieve the capability of rapidly obtaining a missile killing probability evaluation result within dozens of milliseconds to several seconds.
The missile killing probability assessment method provided by the invention comprises the following steps:
step S1: calculating the killing probability of different rendezvous conditions to different targets by using a missile single-shot killing probability calculation method;
step S2: learning the killing probability results of different intersection conditions and different targets by using a feedforward neural network, and storing the learned neural network parameters;
step S3: and the estimation of the missile killing probability can be completed by utilizing the learned feedforward neural network parameters.
Preferably, the different meeting conditions in step S1 include the position vector, velocity vector and attitude angle of the missile to the target.
Preferably, the number of different meeting conditions selected in step S1 should cover the missile' S entire airspace of kill.
Preferably, the input parameters of the feedforward neural network are missile-target intersection condition parameters and target type parameters, and the output parameters are missile killing probability.
Preferably, a back propagation algorithm is selected to iteratively learn the feedforward neural network parameters repeatedly until the estimated mean square error MSE of the feedforward neural network is less than 0.0025.
Preferably, in step S3, the missile-target interaction condition and the target type to be evaluated are input according to the learned feedforward neural network model to realize the evaluation of the missile killing probability.
The missile killing probability evaluation system provided by the invention comprises:
module M1: calculating the killing probability of different rendezvous conditions to different targets by using a missile single-shot killing probability calculation method;
module M2: learning the killing probability results of different intersection conditions and different targets by using a feedforward neural network, and storing the learned neural network parameters;
module M3: and the estimation of the missile killing probability can be completed by utilizing the learned feedforward neural network parameters.
Preferably, the number of different selected rendezvous conditions covers the whole space-killing range of the missile, and the calculation number is more than 50000 times.
According to the present invention, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as described above.
According to the missile killing probability assessment device provided by the invention, the missile killing probability assessment system or the computer readable storage medium storing the computer program is included.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the missile single-shot killing probability is quickly calculated through the simple model, so that the simulation time consumption can be greatly reduced, and the development efficiency is improved.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a missile destruction probability assessment method of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in FIG. 1, the invention provides a missile killing probability assessment method, which comprises the following steps:
step S1: calculating the killing probability of different rendezvous conditions to different targets by using a missile single-shot killing probability calculation method; the different meeting conditions comprise the position vector, the speed vector and the attitude angle of the missile and the target. The number of different selected rendezvous conditions covers the whole restricted airspace of the missile, and the number of calculation is more than 50000 times. The missile single-shot killing probability calculation method comprises the steps of establishing a single-shot killing probability calculation simulation model (comprising a target characteristic model, a fuse starting model, a warhead killing field model, a missile cooperation model and a damage assessment model) by utilizing missile system design parameters, and calculating the single-shot killing probability of a missile on a target under a given missile-target interaction condition according to a typical trajectory interaction condition of the simulation model.
Step S2: learning the killing probability results of different intersection conditions and different targets by using a feedforward neural network, and storing the learned neural network parameters; the input parameters of the feedforward neural network are missile-target intersection condition parameters and target type parameters, and the output parameters are missile killing probability. And (4) learning the parameters of the feedforward neural network by selecting a back propagation algorithm until the estimated mean square error MSE of the feedforward neural network is less than 0.0025. MSE is the mean value of the square of the difference between the missile single-shot killing probability value estimated by the neural network under different intersection conditions and the killing probability true value calculated by the missile single-shot killing probability calculation method.
Step S3: and the estimation of the missile killing probability can be completed by utilizing the learned feedforward neural network parameters.
Wherein, the meeting condition parameters are as follows: velocity vector (V) of missilemx Vmy Vmz) Velocity vector (V) of targettx VtyVtz) Missile pitch angle (theta)m) Missile yaw angle (psi)m) Missile roll angle (xi)m) Target pitch angle (θ)t) Target yaw angle (psi)t) Target roll angle (xi)t) To reduce input parameters, missile and target position vectors are reduced to missile-target relative position vectors (R)mtx Rmty Rmtz)。
The target type input parameter T respectively takes values as follows according to different targets: 1 is a ammunition target; 2 is a conventional aircraft-like target; 3, a stealth aircraft target; the output parameter is the missile single shot killing probability P.
The number of layers of the feedforward neural network is 4-8, the number of neurons in each layer is 20-200, and the specific numerical value is adjusted according to the network learning effect. The middle layer activation function is chosen as the linear distillation function (ReLU) and the output layer activation function is chosen as the Sigmoid function. The training algorithm selects a gradient descent algorithm. If insufficient sample data results in overfitting, the Dropout method and regularization method may be added.
The learning stop condition is set to the feedforward neural network estimated mean square error MSE < 0.0025.
According to one embodiment of the invention, the velocity vector (V) of the missile in a new meeting condition is inputmx Vmy Vmz) Velocity vector (V) of targettx Vty Vtz) Missile pitch angle (theta)m) Missile yaw angle (psi)m) Missile roll angle (xi)m) Target pitch angle (θ)t) Target yaw angle (psi)t) Target roll angle (xi)t) To reduce input parameters, missile and target position vectors are reduced to missile-target relative position vectors (R)mtx Rmty Rmtz) And after 16 input parameters are combined with the target type parameter T, the missile single-shot killing probability can be quickly calculated by the learned feedforward neural network.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A missile killing probability assessment method is characterized by comprising the following steps:
step S1: calculating the killing probability of different rendezvous conditions to different targets by using a missile single-shot killing probability calculation method;
step S2: learning the killing probability results of different intersection conditions and different targets by using a feedforward neural network, and storing the learned neural network parameters;
step S3: and the estimation of the missile killing probability can be completed by utilizing the learned feedforward neural network parameters.
2. The missile killing probability evaluation method of claim 1, wherein the different meeting conditions in the step S1 comprise a position vector, a velocity vector and an attitude angle of the missile and the target.
3. The missile killing probability assessment method of claim 1, wherein the number of different meeting conditions selected in step S1 covers the entire missile killing airspace.
4. The missile killing probability assessment method according to claim 1, wherein the feedforward neural network input parameters are missile encounter condition parameters and target type parameters, and the output parameters are missile killing probability.
5. The missile killing probability assessment method of claim 1, wherein a back propagation algorithm is selected to iteratively learn the feedforward neural network parameters until the estimated mean square error MSE of the feedforward neural network is less than 0.0025.
6. The missile killing probability assessment method according to claim 1, wherein in step S3, the missile killing probability assessment is realized by inputting the missile-target interaction condition to be assessed and the target type according to the learned feedforward neural network model.
7. A missile destruction probability assessment system, comprising:
module M1: calculating the killing probability of different rendezvous conditions to different targets by using a missile single-shot killing probability calculation method;
module M2: learning the killing probability results of different intersection conditions and different targets by using a feedforward neural network, and storing the learned neural network parameters;
module M3: and the estimation of the missile killing probability can be completed by utilizing the learned feedforward neural network parameters.
8. The missile killing probability evaluation system of claim 7, wherein the number of different selected rendezvous conditions covers the entire missile killing airspace, and the number of calculations is greater than 50000 times.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
10. A missile destruction probability evaluation apparatus comprising the missile destruction probability evaluation system according to any one of claims 7 to 8 or the computer-readable storage medium storing the computer program according to claim 9.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109241591A (en) * | 2018-08-21 | 2019-01-18 | 哈尔滨工业大学 | Anti-ship Missile Operational Effectiveness assessment and aid decision-making method |
| KR102014514B1 (en) * | 2019-04-03 | 2019-08-26 | 국방과학연구소 | Weapon effect analysis method and apparatus including crater as damage mechanism |
| CN110414140A (en) * | 2019-07-30 | 2019-11-05 | 同济大学 | The model training and test method of wood damage, device, equipment and storage medium |
| CN110966898A (en) * | 2019-12-06 | 2020-04-07 | 上海机电工程研究所 | Missile projectile recovery system after examination flight test |
| CN111191359A (en) * | 2019-12-24 | 2020-05-22 | 中国人民解放军海军工程大学 | Method for constructing air-defense missile space killer area model and calculating shooting data based on small amount of actual data |
| CN112100822A (en) * | 2020-08-26 | 2020-12-18 | 中国人民解放军63856部队 | System for evaluating power of typical fragment killing warhead |
| CN112182501A (en) * | 2020-08-21 | 2021-01-05 | 中国人民解放军93114部队 | Method and device for calculating penetration probability of cruise missile |
| CN112861257A (en) * | 2021-01-10 | 2021-05-28 | 西北工业大学 | Aircraft fire control system precision sensitivity analysis method based on neural network |
-
2021
- 2021-06-04 CN CN202110626673.8A patent/CN113361196A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109241591A (en) * | 2018-08-21 | 2019-01-18 | 哈尔滨工业大学 | Anti-ship Missile Operational Effectiveness assessment and aid decision-making method |
| KR102014514B1 (en) * | 2019-04-03 | 2019-08-26 | 국방과학연구소 | Weapon effect analysis method and apparatus including crater as damage mechanism |
| CN110414140A (en) * | 2019-07-30 | 2019-11-05 | 同济大学 | The model training and test method of wood damage, device, equipment and storage medium |
| CN110966898A (en) * | 2019-12-06 | 2020-04-07 | 上海机电工程研究所 | Missile projectile recovery system after examination flight test |
| CN111191359A (en) * | 2019-12-24 | 2020-05-22 | 中国人民解放军海军工程大学 | Method for constructing air-defense missile space killer area model and calculating shooting data based on small amount of actual data |
| CN112182501A (en) * | 2020-08-21 | 2021-01-05 | 中国人民解放军93114部队 | Method and device for calculating penetration probability of cruise missile |
| CN112100822A (en) * | 2020-08-26 | 2020-12-18 | 中国人民解放军63856部队 | System for evaluating power of typical fragment killing warhead |
| CN112861257A (en) * | 2021-01-10 | 2021-05-28 | 西北工业大学 | Aircraft fire control system precision sensitivity analysis method based on neural network |
Non-Patent Citations (2)
| Title |
|---|
| 张新伟: "空空导弹战斗部毁伤效能评估技术研究", 《航空兵器》 * |
| 杨新颖: "巡航导弹突防概率的神经网络算法", 《第六届全国仿真器学术会议》 * |
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