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EEMD-based vehicle micro-tremor signal extraction and classification method

A classification method and signal extraction technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problem of no self-adaptation

Active Publication Date: 2016-06-29
徐州新南湖科技有限公司
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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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  • EEMD-based vehicle micro-tremor signal extraction and classification method
  • EEMD-based vehicle micro-tremor signal extraction and classification method
  • EEMD-based vehicle micro-tremor signal extraction and classification method

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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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PUM

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Abstract

The invention discloses an EEMD-based vehicle micro-tremor signal extraction and classification method. A conventional mode identification method cannot satisfy accurate classification under the conditions of complex environments and complex motion modes. First of all, original signals are decomposed by use of EEMD, since an obvious difference exists in micro Doppler modulation between a wheeled vehicle and a tracked vehicle, for the purpose of further determining signals corresponding to each intrinsic mode function after decomposition, correlation analysis is carried out, and the effectiveness of the EEMD is also further verified. Four features, which are respectively signal intensity of the high frequency band of IMF1, discretivity between the IMFs, a fluctuation degree of the high frequency band of the IMF1, and an amplitude maximum value of the main body part of IMF2, are extracted, and finally, object classification identification is carried out by use of a support vector machine. The algorithm provided by the invention improves the vehicle identification rate and has robustness for 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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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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