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Missile flight path prediction method based on deep learning

A deep learning and flight trajectory technology, applied in neural learning methods, prediction, complex mathematical operations, etc., can solve problems such as low speed and large computing resources, and achieve the effect of low computing consumption and large-scale parallel computing

Pending Publication Date: 2021-02-09
HARBIN INST OF TECH
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Problems solved by technology

[0007] The present invention aims to solve the problems of low speed and large calculation resources when calculating the flight trajectory of the existing numerical integration method, and now provides a missile flight trajectory prediction method based on deep learning

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  • Missile flight path prediction method based on deep learning
  • Missile flight path prediction method based on deep learning
  • Missile flight path prediction method based on deep learning

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specific Embodiment approach 1

[0051] Specific implementation mode one: refer to Figures 1 to 7 Specifically illustrate this embodiment, a kind of missile flight track prediction method based on deep learning described in this embodiment, comprises the following steps:

[0052] Step 1: First, extract the feature vector.

[0053]In the training phase, the dynamics and kinematics of known missile flight trajectories are learned. For this, a large amount of different flight data needs to be obtained. In order to collect these data, a three-degree-of-freedom missile dynamics and kinematics model is used to generate the historical flight trajectory database of the missile through numerical integration with 0.1s as the integral step. In the historical flight trajectory database of the missile, the flight trajectory is sampled at an interval of 1s to obtain the flight data of the missile when it moves along the X-axis, Y-axis and Z-axis in the launch coordinate system, and the flight data includes different tim...

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Abstract

The invention discloses a missile flight path prediction method based on deep learning, and relates to the technical field of state prediction. The method aims to solve the problems that an existing numerical integration method is low in speed and large in occupied calculation resource during flight path calculation. The missile flight path prediction method based on deep learning is composed of two sub-modules, namely offline learning training and online path prediction, is high in prediction precision and high in calculation speed, does not depend on a numerical integration method needing alarge amount of repeated calculation, has online real-time calculation capacity, and the feasibility and accuracy of simultaneously calculating a large amount of flight path data are improved. Meanwhile, the prediction of the flight path of the missile can be accurately realized in different missile prediction initial states, the calculation consumption of the algorithm is low, the online realization can be realized, and besides, the method has the capability of large-scale parallel calculation.

Description

technical field [0001] The invention belongs to the technical field of state prediction, in particular to the prediction of missile flight trajectory. Background technique [0002] The missile dynamics and kinematics model is a differential equation model used to calculate the flight state of the missile at each moment in flight. It calculates the flight state of the missile by calculating the comprehensive force of the missile based on the current flight time of the missile and the force and moment it receives during the flight. The basic idea of ​​missile dynamics and kinematics model is: [0003] Firstly, the parameters in the missile dynamics and kinematics model that do not change with the flight state of the missile are given; [0004] Then, the initial state of the missile is given. For each calculation, the forces and moments experienced by the missile are solved through the model equations; [0005] Finally, by means of numerical integration, the state informati...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/04G06N3/08G06F17/16
CPCG06Q10/04G06N3/084G06F17/16G06N3/045
Inventor 郭继峰白成超郑红星王子健
Owner HARBIN INST OF TECH
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