CN116911004A - Trajectory drop point correction method based on neural network - Google Patents
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Abstract
The invention relates to the technical field of gun trajectory, in particular to a trajectory drop point correction method based on a neural network. The invention utilizes the characteristic of infinite approximation of the neural network to the nonlinear function, realizes the correction of the drop point of the trajectory, greatly eliminates errors generated by the limitation of a mathematical model under the influence of various factors such as different trajectory conditions, meteorological conditions, geographic conditions and the like of the self-propelled gun, improves the drop point precision of the calculation trajectory, and increases the reliability of simulation.
Description
Technical Field
The invention relates to the technical field of gun trajectory, in particular to a trajectory drop point correction method based on a neural network.
Background
The accurate prediction of the falling point of the cannon is an important problem to be solved in the modern military field, and the prediction of the falling point of the self-propelled cannon becomes very complex under the action of multiple random factors such as different ballistic conditions, meteorological conditions, geographical conditions and the like. In the prior art, the traditional external trajectory calculation is equivalent to the particle calculation, and although a plurality of disturbance factors are added to carry out numerical calculation, uncertain or difficult disturbance factors such as movement of the projectile around the mass center exist, so that the traditional external trajectory calculation has a certain limitation.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a ballistic drop point correction method based on a neural network, which improves the drop point precision of a solution trajectory and increases the reliability of simulation by adding drop point correction.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a ballistic drop point correction method based on a neural network comprises the following steps:
step 1, according to ballistic conditions, meteorological conditions and geographical conditions, a ballistic differential equation set is listed, and calculation of the outside-particle ballistic drop points is carried out through Matlab;
step 2, making a difference between the falling point coordinate obtained through calculation and the falling point coordinate of the shell launching experimental data to obtain an error sample;
step 3, determining the number of samples and parameters of the neural network;
step 4, training a model by using Matlab;
and 5, saving the trained neural network model, performing off-line simulation by applying the trained neural network, and correcting the calculated falling point coordinates by using the obtained error value.
Further, in step 1, the system of ballistic differential equations is:
in the formula ,the force of gravity is applied to the acceleration vector,Cas a function of the coefficient of trajectory,Has a function of the density of the air,Gas a function of the resistance force,v x 、 v y 、v z respectively isx、y、zThe velocity of the projectile in the direction is,ω x 、ω z respectively isx、zThe wind speed in the direction of the wind,vfor the theoretical projectile velocity,v r in order to achieve the actual velocity of the projectile,αin a ground coordinate systemxThe included angle between the axis and the north direction,Rfor the radius of the earth,Λis latitude, omega is the earth rotation angular velocity vector, and the initial parameters of the selected shell aretWhen the value of the sum is =0,θthe angle of inclination of the projectilev x =v 0 cosθ 0 ，v y =v 0 cosθ 0 ，v z =0，x=y=z=0, and the falling point coordinates of the outside-particle trajectory were calculated by Matlab using the range-Kutta method and cubic spline interpolation.
Advancing oneIn step 2, the coordinates of the falling point are calculatedxValue sumzA value; the error sample is the difference value.
Further, in step 3, the neural network parameters include input layer, output layer, hidden layer, algorithm, data set division and error optimization parameters.
Further, the data of the nodes of the input layer corresponds to the data of the initial parameters of the projectile launching experiment in the step 1, and is the initial projectile velocityv 0 Angle of emissionθ 0 Two parameters are used as independent variables.
Further, the output layer is the error sample in step 2, and is used as a dependent variable.
Further, the hidden layer setting method comprises the following steps: in the field function in Matlab, hiddenaersize is set as a 3-layer hidden layer, and the number of hidden layer nodes is selectedM：
，
wherein ,nandmthe number of the input and output layers is respectively,ataking a constant between 0 and 10, thereforeMFor constants between 2 and 12, selecting the number of hidden layer nodes to be 2 to 12, training, and selecting a constant capable of obtaining a minimum mean square error value as the constantMValues.
Further, the algorithm uses a tranfcn function.
Further, the data set division uses a divideFcn function to divide the sample into a training data sample, a verification data sample and a test data sample; the error optimization parameter is performFcn, the training step number epoch is 10000 steps, the training precision gold is 0.01, namely, the training is completed when the training reaches 10000 steps or less than the training precision 0.01.
Further, the specific steps of the step 5 are as follows:
using a function RFModelSavePath to store the trained model under a computer directory;
inputting initial parameters in shell launching experimental data into a neural network model to obtain corresponding errors, and inputting the same initial parameters into Matlab to calculate out the outside trajectory drop points of particles;
and the calculated trajectory coordinates and the error value are summed to obtain a more accurate trajectory drop point.
The beneficial effects of the invention are as follows:
1. according to the trajectory drop point prediction method based on the neural network, aiming at the limitation of the existing numerical value solution of the trajectory outside the particles, the influence of disturbance parameters which are not considered by a numerical value solution on the solution result is greatly eliminated, and the accuracy of the method exceeds that of the traditional numerical value solution and is more approximate to real experimental data;
2. the method combines classical numerical value calculation and intelligent algorithm, has innovative thought, and uses a large number of samples, thereby increasing the accuracy of the neural network model and ensuring that the obtained error has higher universality;
3. the method is simple and convenient, can optimize the drop point coordinates of the numerical settlement by only storing the offline neural network model, and has the characteristics of high calculation speed, small occupied space, high calculation precision and high reliability.
Drawings
FIG. 1 is a schematic flow chart of a method for correcting a ballistic drop point based on a neural network;
fig. 2 is a schematic diagram of a Matlab neural network used in the present invention.
Detailed Description
The invention will now be described in detail by way of example with reference to the accompanying drawings.
Examples:
as shown in fig. 1, the embodiment relates to a method for predicting a drop point of a trajectory based on a neural network, which adds drop point correction to the existing calculation accuracy, improves the drop point accuracy of the calculated trajectory, and increases the reliability of simulation, and comprises the following specific steps:
step 1, according to ballistic conditions, meteorological conditions and geographical conditions, a ballistic differential equation set is listed, and the calculation of the outside-particle ballistic drop point is carried out through Matlab, wherein the method comprises the following specific steps:
meanwhile, the change of the earth curvature, the change of the gravity acceleration along with the change of the altitude, the Coriolis force generated by the rotation of the earth, the air resistance of the crosswind and the longitudinal wind to the shell and the change of the air resistance acceleration are considered, and the external trajectory equation set of the particles is obtained as follows:
in the formula ,for the initial gravitational acceleration vector,Cas a function of the coefficient of trajectory,Has a function of the density of the air,Gas a function of the resistance force,v x 、v y 、v z respectively isx、y、zThe velocity of the projectile in the direction is,ω x 、ω z is thatx、zThe wind speed in the direction of the wind,vfor the theoretical projectile velocity,v r in order to achieve the actual velocity of the projectile,αin a ground coordinate systemxThe included angle between the axis and the north direction,Rfor the radius of the earth,Λis latitude, omega is the earth rotation angular velocity vector, and the initial parameters of the selected shell aretWhen the value of the sum is =0,θthe angle of inclination of the projectilev x =v 0 cosθ 0 ，v y =v 0 cosθ 0 ，v z = 0，x=y=z=0, using the range-Kutta method and cubic spline interpolation, calculating the falling point coordinates of the outside trajectory of the particle by Matlab;
step 2, making a difference between the falling point coordinate obtained through calculation and the falling point coordinate of the shell launching experimental data to obtain an error sample; wherein the coordinates of the drop point are calculatedxValue sumzA value; the error sample is a value obtained by making a difference;
step 3, determining the number of samples and parameters of the neural network; wherein the number of the samples is 500, namely the initial parameters of 500 groups of projectile firing experimental data and the errors obtained in the step 2A sample; the neural network parameters comprise input layer, output layer, hidden layer, algorithm, data set division and error optimization parameters, the data of the nodes of the input layer correspond to the data of the initial parameters of the projectile launching experiment in the step 1, and the data is the initial speed of the projectilev 0 Angle of emissionθ 0 Two parameters as independent variables; the output layer is an error sample in the step 2 and is used as a dependent variable; the hidden layer setting method comprises the following steps: in the fitnet function in Matlab, hiddenaersize is set to [M MM]I.e. 3 hidden layers, the invention selects the number of hidden layer nodes according to an empirical formulaM：
，
wherein ,nandmthe number of the input and output layers is respectively,ataking a constant between 0 and 10, thereforeMThe constant is between 2 and 12, the number of hidden layer nodes is selected to be between 2 and 12, training is carried out, and the constant which can obtain the minimum mean square error value is selected asMA value; in this embodiment, through trial calculation, the number of the finally selected hidden layer nodes is 10, the mean square error is minimum, and the neural network structure schematic diagram is shown in fig. 2; the algorithm uses a tranFcn function, and inputs a tranlm' namely a Levenberg-Marquardt algorithm; the data set division uses a divideFcn function, 350 groups of samples are selected as training data samples, 75 groups of samples are selected as verification data samples, and 75 groups of samples are selected as test data samples; the error optimization parameter is performFcn, the training step number epoch is 10000 steps, and the training precision gold is 0.01, namely the training is completed when the training reaches 10000 steps or less than the training precision of 0.01; in this embodiment, the correlation of regression analysis, which is the regression analysis obtained after training is completed, is 1, which indicates that the training result has reliability;
step 4, training a model by using Matlab;
and 5, saving a trained neural network model, performing off-line simulation by applying the trained neural network, and correcting the calculated falling point coordinates by using the obtained error value, wherein the method comprises the following specific steps of:
the function RFModelSavePath is used for storing a trained model under a computer directory, the model is input into a neural network model according to initial parameters in shell emission experimental data, corresponding errors can be obtained, the same initial parameters are input into Matlab for resolving out-of-particle trajectory drop points, and the resolved trajectory coordinates and error values are summed to obtain more accurate trajectory drop points.
The invention utilizes the characteristic of infinite approximation of the neural network to the nonlinear function, realizes the correction of the ballistic drop point, and greatly eliminates the errors of the self-propelled gun due to the limitation of the mathematical model under the influence of various factors such as different ballistic conditions, meteorological conditions, geographical conditions and the like.
Alterations, modifications, substitutions and variations of the embodiments herein will be apparent to those of ordinary skill in the art in light of the teachings of the present invention without departing from the spirit and principles of the invention.
Claims (10)
1. The ballistic drop point correction method based on the neural network is characterized by comprising the following steps of:
step 1, according to ballistic conditions, meteorological conditions and geographical conditions, a ballistic differential equation set is listed, and calculation of the outside-particle ballistic drop points is carried out through Matlab;
step 2, making a difference between the falling point coordinate obtained through calculation and the falling point coordinate of the shell launching experimental data to obtain an error sample;
step 3, determining the number of samples and parameters of the neural network;
step 4, training a model by using Matlab;
and 5, saving the trained neural network model, performing off-line simulation by applying the trained neural network, and correcting the calculated falling point coordinates by using the obtained error value.
2. The method for correcting a trajectory drop point based on a neural network according to claim 1, wherein in step 1, the system of trajectory differential equations is:
in the formula ,the force of gravity is applied to the acceleration vector,Cas a function of the coefficient of trajectory,Has a function of the density of the air,Gas a function of the resistance force,v x 、v y 、v z respectively isx、y、zThe velocity of the projectile in the direction is,ω x 、ω z respectively isx、zThe wind speed in the direction of the wind,vfor the theoretical projectile velocity,v r in order to achieve the actual velocity of the projectile,αin a ground coordinate systemxThe included angle between the axis and the north direction,Rfor the radius of the earth,Λis latitude, omega is the earth rotation angular velocity vector, and the initial parameters of the selected shell aretWhen the value of the sum is =0,θthe angle of inclination of the projectilev x =v 0 cosθ 0 ，v y =v 0 cosθ 0 ，v z =0，x=y =z=0, and the falling point coordinates of the outside-particle trajectory were calculated by Matlab using the range-Kutta method and cubic spline interpolation.
3. The method for correcting a ballistic drop point based on a neural network according to claim 1, wherein in step 2, the drop point coordinates are calculatedxValue sumzA value; the error sample is the difference value.
4. A method of correcting ballistic drop points based on neural networks according to claim 1, 2 or 3, wherein in step 3, the neural network parameters include input layer, output layer, hidden layer, algorithm, data set partitioning and error optimization parameters.
5. The neural network-based trajectory landing point correction method of claim 4, wherein the data of the nodes of the input layer corresponds to the data of the initial parameters of the projectile firing experiment in step 1, and is the initial projectile velocityv 0 Angle of emissionθ 0 Two parameters are used as independent variables.
6. The neural network-based trajectory drop correction method of claim 4, wherein the output layer is the error sample in step 2 as a dependent variable.
7. The method for correcting the trajectory drop point based on the neural network according to claim 4, wherein the hidden layer setting method is as follows: in the field function in Matlab, hiddenaersize is set as a 3-layer hidden layer, and the number of hidden layer nodes is selectedM：
，
wherein ,nandmthe number of the input and output layers is respectively,ataking a constant between 0 and 10, thereforeMFor constants between 2 and 12, selecting the number of hidden layer nodes to be 2 to 12, training, and selecting a constant capable of obtaining a minimum mean square error value as the constantMValues.
8. The neural network-based trajectory drop correction method of claim 4, wherein the algorithm uses a tranfcn function.
9. The neural network-based ballistic drop point correction method of claim 4, wherein the data set partitioning uses a divideFcn function to partition the samples into training data samples, validation data samples, and test data samples; the error optimization parameter is performFcn, the training step number epoch is 10000 steps, the training precision gold is 0.01, namely, the training is completed when the training reaches 10000 steps or less than the training precision 0.01.
10. The method for correcting the trajectory drop point based on the neural network according to claim 1, wherein the specific steps of the step 5 are as follows:
using a function RFModelSavePath to store the trained model under a computer directory;
inputting initial parameters in shell launching experimental data into a neural network model to obtain corresponding errors, and inputting the same initial parameters into Matlab to calculate out the outside trajectory drop points of particles;
and the calculated trajectory coordinates and the error value are summed to obtain a more accurate trajectory drop point.
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