A vehicle micro-motion signal extraction and classification method based on eemd

A classification method and signal extraction technology, which can be used in instruments, computing, character and pattern recognition, etc., and can solve problems such as non-adaptive

Active Publication Date: 2019-01-18
徐州新南湖科技有限公司
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

Wavelet transform is a mechanical decomposition of various types of time-frequency plane, without self-adaptation

Method used

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  • A vehicle micro-motion signal extraction and classification method based on eemd
  • A vehicle micro-motion signal extraction and classification method based on eemd
  • A vehicle micro-motion signal extraction and classification method based on eemd

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Embodiment Construction

[0055] The present invention is further analyzed below in conjunction with specific examples.

[0056] In this embodiment, the simulation data is used as the training sample, and a large amount of data is generated as the training sample set by continuously changing the direction angle range (0-60 degrees) and the vehicle speed (0-20m / s), and then the training sample set is divided into There are 5 subsets, and the data of each subset is obtained from the training sample set at equal intervals. The purpose of this is to ensure that each subset contains each pose of the target sample and ensure the stability of the training sample set. Other simulation parameters used in the experiment are shown in the table below:

[0057] Table 1 Simulation radar parameters

[0058]

[0059] There are mainly two types of targets in the test samples: wheeled vehicles and tracked vehicles. In this embodiment, multiple groups of test data are generated by randomly changing the speed and dir...

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Abstract

The invention discloses a vehicle micro-motion signal extraction and classification method based on EEMD. Traditional pattern recognition methods cannot meet the requirements of precise classification in complex environments and complex motion patterns. The present invention first utilizes EEMD to decompose the original signal. Since the micro-Doppler modulation of the wheeled vehicle and the tracked vehicle have obvious differences, in order to further determine which part of the signal each decomposed eigenmode function corresponds to, the Its correlation analysis further verifies the effectiveness of EEMD decomposition. By extracting four features, they are the signal strength of the high frequency band of IMF1, the dispersion between each IMF, the fluctuation degree of the high frequency band of IMF1, and the maximum amplitude of the main part of the IMF2 body. Finally, the support vector machine is used for object classification and recognition. The algorithm provided by the invention improves the recognition rate of the vehicle and is robust to different motion states.

Description

technical field [0001] The invention belongs to the field of SAR radar micro-Doppler signal processing method and application technology, and relates to a method for extracting and classifying micro-motion signals of SAR / GMTI wheeled / tracked vehicles based on EEMD. Background technique [0002] It is difficult to find a target with a single motion mode in actual military and civilian use, and the traditional single speed and distance recognition can no longer meet the needs of applications. The identification method based on micro-Doppler proposed by the present invention is based on the difference of micro-Doppler produced by the complex motion of different targets as a breakthrough. [0003] Fretting refers to the tiny movement of the target or its components other than the translation of the center of mass, such as vibration, rotation, etc. With the development of target stealth technology, the requirements for radar detection technology are getting higher and higher, an...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/08G06F18/2411
Inventor 陈华杰林萍林封笑郭宝峰
Owner 徐州新南湖科技有限公司
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