Classification of Aircraft Targets With Surveillance Radars Based on Fuzzy Fractal Features

Li, Qiusheng; Xie, Weixin

doi:10.2528/pierm12121601

Cited by 6 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, some nonlinear analysis methods, such as mono-fractal, fuzzy fractal, and multifractal, have been introduced into the characteristic analysis of aircraft echoes from low-resolution radars. The proposed nonlinear features are used for target classification and recognition, and better classification and recognition results are obtained under specific experimental conditions [13][14][15][16][17][18]. However, in a strong clutter background, due to relatively weak target echo, the echo nonlinear characteristics will be more about the characteristics of clutter.…”

Section: Introductionmentioning

confidence: 99%

Research on Analysis of Aircraft Echo Characteristics and Classification of Targets in Low-Resolution Radars Based on Eemd

Li¹,

Zhang²,

Lu³

et al. 2018

PIER M

Self Cite

View full text Add to dashboard Cite

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 BW 0 , and normalized frequency interval between the adjacent maximum spectral peaks on both sides of the spectrum center Δf 0. The analysis results show that EEMD can be used to separate the body translational component and micro-motion component of an aircraft echo effectively, and the proposed features (E mc , BW 0 and Δf 0) can be used as effective features for aircraft target classification and recognition.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on Analysis of Aircraft Echo Characteristics and Classification of Targets in Low-Resolution Radars Based on Eemd

Li¹,

Zhang²,

Lu³

et al. 2018

PIER M

Self Cite

View full text Add to dashboard Cite

show abstract

Generalized dimension spectrum features based classification method for aircraft

Xie

2016

2016 CIE International Conference on Radar (RADAR)

View full text Add to dashboard Cite

Multifractal Modelling of Aircraft Echoes from Low-resolution Radars Based on Structural Functions

Xie

2013

DSJ

View full text Add to dashboard Cite

As a kind of complex targets, the nonrigid vibration and attitude change of an aircraft as well as the rotation of its rotating parts will induce complex nonlinear modulation on its echo from low-resolution radars. If one performs the multifractal analysis of measures on an aircraft echo, it may offer a fine description of the dynamic characteristics which induce the echo structure. On basis of introducing multifractal theory based on structural functions, the paper models real recorded aircraft echo data from a low-resolution radar by using the random walk process and the incremental process respectively, and investigates the application of echo multifractal characteristics in aircraft target classification with low-resolution radars. The analysis shows that aircraft echoes from low-resolution radars have clear multifractal characteristics, and one should take an aircraft echo series as a random walk process to perform the multifractal analysis. The experimental results validate the classification method based on multifractal signatures.

show abstract

Classification of Aircraft Targets With Surveillance Radars Based on Fuzzy Fractal Features

Cited by 6 publications

References 30 publications

Research on Analysis of Aircraft Echo Characteristics and Classification of Targets in Low-Resolution Radars Based on Eemd

Research on Analysis of Aircraft Echo Characteristics and Classification of Targets in Low-Resolution Radars Based on Eemd

Generalized dimension spectrum features based classification method for aircraft

Multifractal Modelling of Aircraft Echoes from Low-resolution Radars Based on Structural Functions

Contact Info

Product

Resources

About