Empirical mode decomposition based moving vehicle target classification method

Abstract

The invention discloses an empirical mode decomposition based moving vehicle target classification method which is used for mainly solving the problems that the conventional similar methods are sensitive to variation in translational velocities of targets, extra clutter suppression is required and special structural information of the target cannot be utilized. A realization process of the methodcomprises the following steps of: performing empirical mode decomposition on a Doppler echo signal; finishing clutter suppression by rejecting a remainder; defining a Doppler spectrum of a first intrinsic modular function and Doppler spectrums of remaining intrinsic modular functions by using a decomposition result; judging whether doubling translation micro-Doppler components exist according to the defined spectrums and preliminarily discriminating a track-laying vehicle; if the discrimination fails, extracting characteristics of the intrinsic modular functions and the defined spectrums; andclassifying the extracted characteristics by using a classifier. By adopting the method, influence of variation in the translational velocities of the targets on positions and widths of the Doppler spectrums of the targets can be eliminated, the clutter suppression is automatically performed, and the method can be used for classifying moving vehicle targets with maneuvering parts by using the special structural information of a track.

Description

technical field [0001] The invention belongs to the technical field of radar and relates to a target classification method, which can be used to classify moving vehicle targets with motor parts. Background technique [0002] In the field of radar target classification and recognition, the environment of moving vehicle targets is more complex than that of air targets, and its radar echo contains a lot of ground clutter, so it is difficult to obtain accurate information that is beneficial to target classification and recognition from the time domain signal of the target. , since most of the ground objects are stationary, when the target moves, based on the Doppler effect, the target will appear at a position deviated from zero frequency in the Doppler domain. Using this feature, the moving target and the stationary ground object can be realized Separation of clutter. At the same time, the Doppler spectrum of the target provides the motion information of the target itself, whi...

AI Technical Summary

Problems solved by technology

In the case of directly calculating the entropy value of the target Doppler spectrum without certain preprocessing to eliminate the influence of these factors, even for the same target, the entropy value will have a large difference, and the classification result obtained in this way is not ideal.

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