Pulse doppler radar target recognition using a two-stage SVM procedure
IEEE Transactions on Aerospace and Electronic Systems
1450 - 1457
Item Usage Stats
It is possible to detect and classify moving and stationary targets using ground surveillance pulse-Doppler radars (PDRs). A two-stage support vector machine (SVM) based target classification scheme is described here. The first stage tries to estimate the most descriptive temporal segment of the radar echo signal and the target signal is classified using the selected temporal segment in the second stage. Mel-frequency cepstral coefficients of radar echo signals are used as feature vectors in both stages. The proposed system is compared with the covariance and Gaussian mixture model (GMM) based classifiers. The effects of the window duration and number of feature parameters over classification performance are also investigated. Experimental results are presented.
Gaussian mixture model
Mel-frequency cepstral coefficients
Radar target recognition
Published Version (Please cite this version)http://dx.doi.org/10.1109/TAES.2011.5751269
Showing items related by title, author, creator and subject.
Duman, Kaan; Eryıldırım, Abdulkadir; Çetin, A. Enis (SPIE, 2009-04)In this paper, a novel descriptive feature parameter extraction method from synthetic aperture radar (SAR) images is proposed. The new approach is based on region covariance (RC) method which involves the computation of a ...
Duman, Kaan; Çetin, A. Enis (SPIE, 2010)Target detection in SAR images using region covariance (RC) and codifference methods is shown to be accurate despite the high computational cost. The proposed method uses directional filters in order to decrease the search ...
Sevimli, R. Akın; Tofighi, Mohammad; Çetin, A. Enis (IEEE, 2014-09)Compressive sensing (CS) idea enables the reconstruction of a sparse signal from a small set of measurements. CS approach has applications in many practical areas. One of the areas is radar systems. In this article, the ...