In this article, a novel bicepstrum-based approach is suggested for ground moving radar target classification. Distinctive classification features were extracted from short-time backscattering bispectrum estimates of the micro-Doppler signature. Real radar data were obtained using surveillance Doppler microwave radar operating at 34 GHz. Classifier performance was studied in detail using the Ga...

one of the problems in detecting radar targets is the variation of target cross section due to aspect angle; which is named as target fluctuation. this perfectly depends on target geometry and transmitted wavelength and can cause significant loss of detection. one of the solutions to this is improving the radar range resolution. stepped frequency is one of the signals that can improve the range...

Radar target identification using decision-theoretic distance based methods have long been used for classifying unknown non-cooperative radar targets using their Radar Cross Section (RCS). This study revisits this subj ect using the recently developed Large Margin Nearest Neighbor (LMNN) technique in addition to other traditional nearest neighbor methods. Radar target recognition has been defin...

Stochastic resonance has received significant attention recently in the signal processing community with emphasis on signal detection. The basic notion is that the performance of some suboptimal detectors can be improved by adding independent noise to the measured (and already noise contaminated) observation. The notion of adding noise makes sense if the observation is the result of nonlinear p...

The passive bistatic radar (PBR) system can utilize the illuminator of opportunity to enhance radar capability. By utilizing the forward scattering technique and procedure into the specific mode of PBR can provide an improvement in target detection and classification. The system is known as passive Forward Scattering Radar (FSR). The passive FSR system can exploit the peculiar advantage of the ...

Among the applications of a radar system, target classification for ground surveillance is one of the most widely used. This paper deals with micro-Doppler Signature (μ-DS) based radar Automatic Target Recognition (ATR). The main goal for performing μ-DS classification using speech processing tools was to investigate whether automatic speech recognition (ASR) techniques are suitable methods for...

In this paper, radar target classification based on Synthetic Aperture Radar (SAR) images is investigated. Different criteria for extracting features from MSTAR data are presented, and classification rates shown, emphasizing where the useful information in terms of recognition resides. The combination of different features is also examined, linking the classification accuracy of the system to t...

Target motions, other than the main bulk translation of the target, induce Doppler modulations around the main Doppler shift that form what is commonly called a target micro-Doppler signature. Radar micro-Doppler signatures are generally both target and action specific and hence can be used to classify and recognise targets as well as to identify possible threats. In recent years, research into...