Ground Surveillance Radar Target Classification Based On Fuzzy Logic Approach

Andrić, Milenko; Đurović, Željko; Zrnic, B.

doi:10.1109/eurcon.2005.1630220

Cited by 20 publications

(15 citation statements)

References 3 publications

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“…[19] extracted bispectra as features; ref. [23] used the fuselage Doppler frequency and the covariance around it; ref. [24] used periodicity of the time domain echoes; ref.…”

Section: Feature Extraction Of Jem Characteristics With Low-resolutiomentioning

confidence: 99%

Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra

Chen

Liu

et al. 2010

Sci. China Inf. Sci.

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Most low-resolution radar systems, especially ground surveillance radar systems, work at relatively low pulse repeat frequency (PRF) and with short time-on-target (TOT) (duration in scanning). Low PRF leads to Doppler ambiguity and short TOT results in low Doppler resolution, which poses a problem to target classification with low-resolution radar based on the jet engine modulation (JEM) characteristic of radar echo. From the pattern classification viewpoint, we propose a method of using dispersion situations of JEM eigenvalue spectra to categorize aeroplanes into three kinds, namely turbojet aircraft, prop aircraft and helicopter. We analyze the mathematical model of JEM echoes consisting of a series of line spectra and regard them as a sum of several series of harmonious waves. Classification features can be extracted based on the harmonious wave sum model. Some schemes for extracting features from echoes within or between pulses are proposed. Low-dimensional features are extracted to reduce computation burden. Our methods do not compensate for the fuselage echoes and are insensitive to the variation of fuselage Doppler. The feasibility of our methods is demonstrated by simulation experiment.

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“…[19] extracted bispectra as features; ref. [23] used the fuselage Doppler frequency and the covariance around it; ref. [24] used periodicity of the time domain echoes; ref.…”

Section: Feature Extraction Of Jem Characteristics With Low-resolutiomentioning

confidence: 99%

Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra

Chen

Liu

et al. 2010

Sci. China Inf. Sci.

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“…One of the most used is spectrogram, but some new time-frequency distributions are proposed, such as S-method [5], multiwindow S-method [6], modified B-distributions [7], etc. In [8][9][10][11][12][13], the features extracted from the spectrogram are used for the classification of various human movement or various radar targets. Authors, in [8], have proposed using six features extracted from the spectrogram to classify different human movements of one person (walking, running, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, sitting still).…”

Section: Introductionmentioning

confidence: 99%

“…Authors, in [8], have proposed using six features extracted from the spectrogram to classify different human movements of one person (walking, running, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, sitting still). In [9], a fuzzy system for classification of vehicles and persons who have moved in the ground surveillance radar line-of-sight is projected. Inputs in this system were central Doppler frequency and bandwidth around it.…”

Section: Introductionmentioning

confidence: 99%

Parameter order selection of autoregressive model for classification of ground surveillance radar targets

Bujaković¹,

Andric²,

Mikluc³

et al. 2016

Sci Tech Rev

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In this research the order selection of autoregressive model parameters for classification of signals from the ground surveillance radar audio-output is considered. For this purpose, a measure based on maximal separability between used radar classes (clutter, person walking, person running, group of persons walking, group of persons running, vehicle) is suggested. Determined order of the autoregressive model is compared to the information criterion proposed by Akaike for different used windows. After the feature reduction of used real radar Doppler echo signals, it is showed that the proposed measure determines as optimal significantly lower order with a higher separability between used classes of radar targets

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“…On basis of analyzing the performance of methods using some typical low-resolution radar target classification features [15,[24][25][26][27][28][29][30][31][32][33][34], [16] indicates that the classification method based on dispersion situations of eigenvalue spectra (CMDSES) outgoes other methods remarkably.…”

Section: Fuzzy-fractal-feature-based Classification Experimentsmentioning

confidence: 99%

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

Li¹,

Xie²

2013

PIER M

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Abstract-The fuzzy fractal characteristics of return signals from aircraft targets in conventional radars offer a description of dynamic features which induce the echo structure of targets, therefore they can provide a new way for aircraft target classification and recognition with low-resolution surveillance radars. On basis of introducing fuzzy fractal theory, the paper analyzes the fuzzy fractal characteristics of return signals from aircraft targets in a VHF-band surveillance radar by means of the fuzzy fractal analysis, and puts forward a fuzzyfractal-feature-based classification method for aircraft targets with a low-resolution radar from the viewpoint of pattern recognition. The analysis shows that the fuzzy fractal characteristic parameters such as the local fuzzy fractal dimension (LFFD) and local degree of fractality (LGF) can be used as effective features for aircraft target classification and recognition. The results of classification experiments validate the proposed method.

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Ground Surveillance Radar Target Classification Based On Fuzzy Logic Approach

Cited by 20 publications

References 3 publications

Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra

Target classification with low-resolution radar based on dispersion situations of eigenvalue spectra

Parameter order selection of autoregressive model for classification of ground surveillance radar targets

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

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