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Hypersonic aircraft trajectory online prediction method based on parameter extrapolation

A hypersonic and prediction method technology, applied in prediction, neural learning methods, instruments, etc., can solve problems such as difficult to accurately characterize nonlinear laws, prediction model interference, and affect prediction performance, so as to improve prediction accuracy and good prediction performance , the effect of improving accuracy

Pending Publication Date: 2022-06-03
AIR FORCE EARLY WARNING ACADEMY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, due to the high maneuverability of hypersonic vehicles, the predicted parameters obtained after tracking are often nonlinear and contain unknown noise, which creates two problems: one is to use the least square method or linear prediction model to predict the parameters It is difficult to accurately characterize nonlinear laws in modeling; the second is to directly use these noise-containing prediction parameters for prediction, which will interfere with the prediction model and affect the prediction performance

Method used

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  • Hypersonic aircraft trajectory online prediction method based on parameter extrapolation
  • Hypersonic aircraft trajectory online prediction method based on parameter extrapolation
  • Hypersonic aircraft trajectory online prediction method based on parameter extrapolation

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Embodiment 1

[0054] figure 1 A preferred embodiment of the present application is shown ( figure 1 A schematic flowchart of a method for online prediction of a hypersonic aircraft trajectory based on parameter extrapolation provided by the first embodiment of the present application) is shown. For the convenience of description, only the parts relevant to this embodiment are shown, and the details are as follows:

[0055] Step 1: Select hypersonic vehicle trajectory prediction parameters;

[0056] Step 2: estimating the trajectory prediction parameters of the hypersonic vehicle based on the dynamic tracking model;

[0057] Step 3: denoising the hypersonic vehicle trajectory prediction parameters based on the set empirical mode decomposition, constructing a data set, and training a pre-built attention time series neural network model according to the data of the data set;

[0058] Step 4: Input the data set into the trained attention time series neural network model to obtain the predicte...

Embodiment 2

[0125] In this embodiment, the above method is verified in combination with a specific calculation example, and the process is as follows:

[0126] The present invention designs the following simulation experiment scenario: a hypersonic aircraft is boosted to a height of 50km by launching a rocket, then continues to rise to the highest point by inertia, and then re-enters the atmosphere, enters the jumping and gliding stage, and maneuvers according to a preset mode. Assume that the geographic coordinates of the radar site are [12°, 0.5°, 1km], the sampling interval is 0.5s, the azimuth and pitch errors are 0.15°, and the distance error is 200m. The tilt angle of the hypersonic vehicle is always 5°, and the angle of attack is set as a continuous function related to the speed according to formula (20), such as image 3 shown. Unscented Kalman (UKF) is used for filtering in the tracking process. Target maneuver trajectories, observation trajectories such as Figure 4 shown. I...

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Abstract

The invention relates to the technical field of aircraft trajectory online prediction, in particular to a hypersonic aircraft trajectory online prediction method based on parameter extrapolation. Selecting trajectory prediction parameters of the hypersonic flight vehicle; estimating the hypersonic flight vehicle trajectory prediction parameters based on a dynamic tracking model; denoising the hypersonic aircraft trajectory prediction parameters based on ensemble empirical mode decomposition, constructing a data set, and training a pre-constructed attention time sequence neural network model according to data of the data set; and inputting the data set into a trained attention time sequence neural network model to obtain a prediction trajectory of the hypersonic flight vehicle. On the basis of ensemble empirical mode decomposition and an attention time sequence neural network, aircraft trajectory online prediction is carried out, the law of prediction parameters is modeled by means of a deep learning technology, the prediction parameters are denoised before prediction, and the prediction accuracy is effectively improved.

Description

technical field [0001] The invention relates to the technical field of on-line prediction of the trajectory of an aircraft, in particular to an on-line prediction method of the trajectory of a hypersonic aircraft based on parameter extrapolation. Background technique [0002] A hypersonic vehicle usually refers to an aircraft with a speed of more than 5Ma and a flight airspace between 20km and 100km. With the characteristics of high maneuverability, high speed and high precision, this type of aircraft has become an important means of strategic checks and balances between countries, and will play an important role in the future battlefield situation. The world's military powers are engaged in a fierce arms race around the research and development of hypersonic vehicles. The continuous development of hypersonic vehicles has brought new severe challenges to the aerospace safety of various countries. Therefore, the study of hypersonic vehicle trajectory prediction is of great ...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08G06Q10/04G06F111/08G06F119/14
CPCG06F30/27G06N3/08G06Q10/04G06F2111/08G06F2119/14G06N3/044Y02T90/00
Inventor 张君彪熊家军沈延安兰旭辉席秋实
Owner AIR FORCE EARLY WARNING ACADEMY