volume | PIER Journals

Abstract

As a kind of complex targets, the non-rigid vibration of an aircraft as well as its attitude change and the rotation of its rotating parts will induce complex nonlinear modulation on its echo from low-resolution radars. If these nonlinear modulation features which reflect the physical characteristics of an aircraft target can be extracted effectively, then they are helpful to target classification and recognition. However, the echo translational component and background clutter have a very adverse effect on the extraction of such features. On basis of introducing the ensemble empirical mode decomposition (EEMD) algorithm, the paper firstly performs the decomposition of real recorded aircraft echo data from a low-resolution radar by EEMD and distinguishes the false component, body translational component and micro-motion component by calculating waveform entropy in the Doppler domain. Secondly, it carries out characteristic analysis and feature extraction further on the echo micro-motion component separated and extracts three features of the micro-motion component, including Doppler domain waveform entropy E_mc, normalized equivalent Doppler spectrum width BW₀, and normalized frequency interval between the adjacent maximum spectral peaks on both sides of the spectrum center Δf₀. The analysis results show that EEMD can be used to separate the body translational component and micromotion component of an aircraft echo effectively, and the proposed features (E_mc, BW₀ and Δf₀) can be used as effective features for aircraft target classification and recognition.

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Dr. Lehui Wei