Method for identifying maneuvering state of hypersonic aircraft

A hypersonic and state recognition technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as difficult feature extraction, fast flight speed, and wide space range

Pending Publication Date: 2022-06-24
AIR FORCE EARLY WARNING ACADEMY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that in the prior art, hypersonic aircraft in near space fly fast, the space range required for maneuver state transition is wide, and feature extraction is difficult. The problem of maneuvering state provides a hypersonic vehicle maneuvering state identification method, using deep learning theory to study the hypersonic vehicle maneuvering state identification, it first establishes a hypersonic vehicle maneuvering model, analyzes the maneuvering control parameters, according to the high The horizontal and vertical maneuvering characteristics of supersonic aircraft are divided into maneuver categories. Then, the conversion steps from radar observations to feature recognition parameters are deduced to realize the extraction of feature parameters. Finally, the convolution length and short-term Online identification of hypersonic vehicle maneuvering state by memory network

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  • Method for identifying maneuvering state of hypersonic aircraft
  • Method for identifying maneuvering state of hypersonic aircraft
  • Method for identifying maneuvering state of hypersonic aircraft

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Embodiment 1

[0077] like figure 1 As mentioned above, this embodiment provides a recognition process of a method for recognizing the maneuvering state of a hypersonic aircraft, and the specific processing process of the recognition method is as follows:

[0078] Step 1: Maneuver modeling of hypersonic vehicle;

[0079] Step 2: Divide the maneuvering categories of the hypersonic aircraft, construct a feature parameter set, and screen the feature parameters through information gain and information gain rate, and use the different controls obtained by the hypersonic aircraft maneuvering modeling in step 1 under different initial variable conditions. A parameter model, generating a trajectory library containing the maneuvering trajectories of the hypersonic aircraft in different maneuvering states, and using the feature identification parameter information contained in the trajectory library as training data;

[0080] Step 3: establish a hypersonic aircraft maneuvering state recognition model...

Embodiment 2

[0189] In this embodiment, the above method is verified in combination with a specific calculation example, and the process is as follows:

[0190] Firstly, it is verified on the typical hypersonic vehicle trajectory, and two aircraft maneuvering trajectories are designed. Among them, the maneuvering state of trajectory 1 is the right turn of balance gliding, and the maneuvering state of trajectory 2 is jumping and gliding without maneuvering, such as image 3 shown. The parameters of the radar are set as follows: the distance error is 200m, the azimuth error is 0.1°, the pitch angle error is 0.1°, and the sampling rate is 0.5s.

[0191] Then, it is verified on the multi-sample aircraft trajectory, and 24 hypersonic aircraft trajectories (three for each maneuvering state) observed in real time by radar are selected, with a total of 43,200 trajectory points.

[0192] The method described in step 4 converts the state information of the hypersonic aircraft observed by the radar...

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Abstract

The invention relates to the technical field of aircraft maneuvering state recognition, in particular to a hypersonic aircraft maneuvering state recognition method. Carrying out maneuvering modeling on the hypersonic flight vehicle; the method comprises the following steps: dividing maneuvering categories of a hypersonic aircraft, constructing a feature parameter set, generating a trajectory library under different initial variable conditions, and taking feature recognition parameter information contained in the trajectory library as training data; establishing a hypersonic aircraft maneuvering state recognition model based on a multi-channel attention mechanism convolution long-short term memory network, and training the model; and collecting feature recognition parameter information, inputting the feature recognition parameter information into the trained model, and outputting a maneuvering state recognition result of the hypersonic aircraft. The problems that a near space hypersonic flight vehicle is high in flight speed, the space range needed by maneuvering state conversion is wide, feature extraction is difficult, and a conventional maneuvering state recognition method is difficult to be directly applied to the hypersonic flight vehicle and recognize the maneuvering state of the hypersonic flight vehicle in a high-precision mode are solved.

Description

technical field [0001] The invention relates to the technical field of aircraft maneuvering state identification, in particular to a method for identifying a maneuvering state of a hypersonic aircraft. Background technique [0002] Hypersonic aircraft refers to weapons with a speed of Mach 5 or more and a flight airspace between 20 and 100 kilometers. With its high maneuverability and large range, it has broken the traditional balance of offense and defense, and has caused subversive effects on the existing air defense defense system. Influence, opened up new fields and new forms of military struggle. 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 poses a serious threat to the aerospace security of various countries, and the tracking, prediction and defense of hypersonic vehicles has become a research hotspot. The research on the recognition of the m...

Claims

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Application Information

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IPC IPC(8): G05D1/08G06K9/62G06N3/04G06N3/08
CPCG05D1/0833G06N3/08G06N3/045G06N3/044G06F18/2415
Inventor 张君彪熊家军兰旭辉沈延安陈新
Owner AIR FORCE EARLY WARNING ACADEMY
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