Abstract
The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI) platforms are mainly composed of two parts, the micro-Doppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles.
Highlights
The need to gain better and highly accurate battlefield awareness has boosted a number of technological advancements in the field of military surveillance
By analyzing the frequency of the modulated echoes, it has been found that significant differences between different targets exist, providing an indication that a micro-Doppler signature can be used as the basis for target classification or recognition (Chen et al, 2006)
Based on the Ensemble Empirical Mode Decomposition (EEMD) micro-Doppler decomposition described in the previous sections, we present a vehicle classification approach that constitutes of two main stages: (1) feature extraction and (2) classifier training
Summary
The need to gain better and highly accurate battlefield awareness has boosted a number of technological advancements in the field of military surveillance. Recent evolution in battlefield operations has seen the combined use of Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI) sensors that provide high resolution and near-real time information for valuable moving targets such as battlefield vehicles. The movement of many targets can generally be decomposed into the global movement of the main body, and the local movement, called micro-motion, of one of its components. The micro-motion of a moving target is unique and stable, becomes a valuable cue to identify the target. Micro-motion produces modulation of radar echo of a moving target, and this phenomenon is called micro-Doppler effect. By analyzing the frequency of the modulated echoes, it has been found that significant differences between different targets exist, providing an indication that a micro-Doppler signature can be used as the basis for target classification or recognition (Chen et al, 2006)
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